diff --git a/cookbooks/8-organizations/1-GRID-preview.ipynb b/cookbooks/8-organizations/1-Organization-data-preview.ipynb similarity index 81% rename from cookbooks/8-organizations/1-GRID-preview.ipynb rename to cookbooks/8-organizations/1-Organization-data-preview.ipynb index 7cdc0d17..ee0a14f7 100644 --- a/cookbooks/8-organizations/1-GRID-preview.ipynb +++ b/cookbooks/8-organizations/1-Organization-data-preview.ipynb @@ -12,11 +12,9 @@ "\n", "This tutorial provides an overview of the [Organizations data source](https://docs.dimensions.ai/dsl/datasource-organizations.html) available via the [Dimensions Analytics API](https://docs.dimensions.ai/dsl/). \n", "\n", - "Organizations data in Dimensions is based on [GRID](https://grid.ac/) - the Global Research Identifiers Database. \n", - "\n", "The topics covered in this notebook are:\n", "\n", - "* How to align your affiliation data with GRID/Dimensions using the API [disambiguation service](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations) \n", + "* How to align your affiliation data with Dimensions using the API [disambiguation service](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations) \n", "* How to retrieve organizations metadata using the [search fields](https://docs.dimensions.ai/dsl/datasource-organizations.html) available\n", "* How to use the [schema API](https://docs.dimensions.ai/dsl/data-sources.html#metadata-api) to obtain some statistics about the Organizations data available \n", " \n" @@ -33,7 +31,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Jan 25, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -68,19 +66,9 @@ "text/html": [ " \n", + " \n", " " ] }, @@ -100,8 +88,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -154,7 +142,7 @@ "id": "8zxcg9gPgZAv" }, "source": [ - "## 1. Matching affiliation data to GRID IDs using `extract_affiliations`\n", + "## 1. Matching affiliation data to Dimensions Organization IDs using `extract_affiliations`\n", "\n", "The API function `extract_affiliations` ([docs](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations)) can be used to enrich private datasets including non-disambiguated organizations data with Dimensions IDs, so to then take advantage of the wealth of linked data available in Dimensions.\n", "\n", @@ -194,7 +182,7 @@ "id": "AcAypP1agx3M" }, "source": [ - "We want to look up GRID identifiers for those affiliations using the **structured** affiliation matching. " + "We want to look up Dimensions Organization identifiers for those affiliations using the **structured** affiliation matching. " ] }, { @@ -239,7 +227,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c54ddb7d16ce461ea6ac85ef57b69d7c", + "model_id": "8549169e2ba046c29ab3adeb6a09c465", "version_major": 2, "version_minor": 0 }, @@ -263,7 +251,7 @@ "{'results': [{'geo': {'cities': [], 'countries': [], 'states': []}, 'input': {'city': '', 'country': '', 'name': 'P.G. Department of Zoology and Research Centre, Shri Shiv Chhatrapati College of Arts, Commerce and Science, Junnar 410502, Pune, India.', 'state': ''}, 'institutes': []}]}\n", "{'results': [{'geo': {'cities': [{'geonames_id': 1835848, 'name': 'Seoul'}], 'countries': [{'code': 'KR', 'geonames_id': 1835841, 'name': 'South Korea'}], 'states': [{'code': None, 'geonames_id': 1835847, 'name': 'Seoul'}]}, 'input': {'city': 'Seoul', 'country': 'South Korea', 'name': 'Sungkyunkwan University', 'state': ''}, 'institutes': [{'institute': {'city': 'Seoul', 'country': 'South Korea', 'id': 'grid.264381.a', 'name': 'Sungkyunkwan University', 'state': None}, 'metadata': {'requires_manual_review': False}}]}]}\n", "{'results': [{'geo': {'cities': [{'geonames_id': 1259229, 'name': 'Pune'}], 'countries': [{'code': 'IN', 'geonames_id': 1269750, 'name': 'India'}], 'states': [{'code': None, 'geonames_id': 1264418, 'name': 'Maharashtra'}]}, 'input': {'city': 'Pune', 'country': 'India', 'name': 'Centre for Materials for Electronics Technology', 'state': ''}, 'institutes': [{'institute': {'city': 'Pune', 'country': 'India', 'id': 'grid.494569.3', 'name': 'Centre for Materials for Electronics Technology', 'state': None}, 'metadata': {'requires_manual_review': False}}]}]}\n", - "{'results': [{'geo': {'cities': [{'geonames_id': 2988507, 'name': 'Paris'}], 'countries': [{'code': 'FR', 'geonames_id': 3017382, 'name': 'France'}], 'states': [{'code': None, 'geonames_id': 3012874, 'name': 'Ile-de-France'}]}, 'input': {'city': '', 'country': '', 'name': 'Institut Necker-Enfants Malades (INEM), INSERM U1151-CNRS UMR8253, Université de Paris, Faculté de Médecine, 156 rue de Vaugirard, 75730 Paris Cedex 15, France', 'state': ''}, 'institutes': [{'institute': {'city': 'Paris', 'country': 'France', 'id': 'grid.508487.6', 'name': 'University of Paris', 'state': None}, 'metadata': {'requires_manual_review': False}}]}]}\n" + "{'results': [{'geo': {'cities': [{'geonames_id': 2988507, 'name': 'Paris'}], 'countries': [{'code': 'FR', 'geonames_id': 3017382, 'name': 'France'}], 'states': [{'code': None, 'geonames_id': 3012874, 'name': 'Ile-de-France'}]}, 'input': {'city': '', 'country': '', 'name': 'Institut Necker-Enfants Malades (INEM), INSERM U1151-CNRS UMR8253, Université de Paris, Faculté de Médecine, 156 rue de Vaugirard, 75730 Paris Cedex 15, France', 'state': ''}, 'institutes': [{'institute': {'city': 'Paris', 'country': 'France', 'id': 'grid.508487.6', 'name': 'Université Paris Cité', 'state': None}, 'metadata': {'requires_manual_review': False}}]}]}\n" ] } ], @@ -327,7 +315,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f58062b2b4a244ba9df163a9a325f17b", + "model_id": "225073d7b7cb409b82a7cd252c729cc9", "version_major": 2, "version_minor": 0 }, @@ -351,7 +339,7 @@ "{'results': [{'input': {'affiliation': 'P.G. Department of Zoology and Research Centre, Shri Shiv Chhatrapati College of Arts, Commerce and Science, Junnar 410502, Pune, India. '}, 'matches': [{'affiliation_part': 'P.G. Department of Zoology and Research Centre, Shri Shiv Chhatrapati College of Arts, Commerce and Science, Junnar 410502, Pune, India', 'geo': {'cities': [{'geonames_id': 1259229, 'name': 'Pune'}], 'countries': [{'code': 'IN', 'geonames_id': 1269750, 'name': 'India'}], 'states': [{'code': None, 'geonames_id': 1264418, 'name': 'Maharashtra'}]}, 'institutes': []}]}]}\n", "{'results': [{'input': {'affiliation': 'Sungkyunkwan University Seoul South Korea'}, 'matches': [{'affiliation_part': 'Sungkyunkwan University Seoul South Korea', 'geo': {'cities': [{'geonames_id': 1835848, 'name': 'Seoul'}], 'countries': [{'code': 'KR', 'geonames_id': 1835841, 'name': 'South Korea'}], 'states': [{'code': None, 'geonames_id': 1835847, 'name': 'Seoul'}]}, 'institutes': [{'institute': {'city': 'Seoul', 'country': 'South Korea', 'id': 'grid.264381.a', 'name': 'Sungkyunkwan University', 'state': None}, 'metadata': {'requires_manual_review': False}}]}]}]}\n", "{'results': [{'input': {'affiliation': 'Centre for Materials for Electronics Technology Pune India'}, 'matches': [{'affiliation_part': 'Centre for Materials for Electronics Technology Pune India', 'geo': {'cities': [{'geonames_id': 1259229, 'name': 'Pune'}], 'countries': [{'code': 'IN', 'geonames_id': 1269750, 'name': 'India'}], 'states': [{'code': None, 'geonames_id': 1264418, 'name': 'Maharashtra'}]}, 'institutes': [{'institute': {'city': 'Pune', 'country': 'India', 'id': 'grid.494569.3', 'name': 'Centre for Materials for Electronics Technology', 'state': None}, 'metadata': {'requires_manual_review': False}}]}]}]}\n", - "{'results': [{'input': {'affiliation': 'Institut Necker-Enfants Malades (INEM), INSERM U1151-CNRS UMR8253, Université de Paris, Faculté de Médecine, 156 rue de Vaugirard, 75730 Paris Cedex 15, France '}, 'matches': [{'affiliation_part': 'Institut Necker-Enfants Malades (INEM), INSERM U1151-CNRS UMR8253, Université de Paris, Faculté de Médecine, 156 rue de Vaugirard, 75730 Paris Cedex 15, France', 'geo': {'cities': [{'geonames_id': 2988507, 'name': 'Paris'}], 'countries': [{'code': 'FR', 'geonames_id': 3017382, 'name': 'France'}], 'states': [{'code': None, 'geonames_id': 3012874, 'name': 'Ile-de-France'}]}, 'institutes': [{'institute': {'city': 'Paris', 'country': 'France', 'id': 'grid.508487.6', 'name': 'University of Paris', 'state': None}, 'metadata': {'requires_manual_review': False}}]}]}]}\n" + "{'results': [{'input': {'affiliation': 'Institut Necker-Enfants Malades (INEM), INSERM U1151-CNRS UMR8253, Université de Paris, Faculté de Médecine, 156 rue de Vaugirard, 75730 Paris Cedex 15, France '}, 'matches': [{'affiliation_part': 'Institut Necker-Enfants Malades (INEM), INSERM U1151-CNRS UMR8253, Université de Paris, Faculté de Médecine, 156 rue de Vaugirard, 75730 Paris Cedex 15, France', 'geo': {'cities': [{'geonames_id': 2988507, 'name': 'Paris'}], 'countries': [{'code': 'FR', 'geonames_id': 3017382, 'name': 'France'}], 'states': [{'code': None, 'geonames_id': 3012874, 'name': 'Ile-de-France'}]}, 'institutes': [{'institute': {'city': 'Paris', 'country': 'France', 'id': 'grid.508487.6', 'name': 'Université Paris Cité', 'state': None}, 'metadata': {'requires_manual_review': False}}]}]}]}\n" ] } ], @@ -384,7 +372,7 @@ "toc-hr-collapsed": false }, "source": [ - "## 2. Searching GRID organizations \n", + "## 2. Searching the API for organizations \n", "\n", "This can be done using full text search and/or fielded search. \n" ] @@ -429,8 +417,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Organizations: 10 (total = 247)\n", - "\u001b[2mTime: 0.56s\u001b[0m\n" + "Returned Organizations: 10 (total = 352)\n", + "\u001b[2mTime: 5.56s\u001b[0m\n" ] }, { @@ -454,189 +442,144 @@ " \n", " \n", " \n", - " acronym\n", - " city_name\n", - " country_name\n", " id\n", - " latitude\n", - " linkout\n", - " longitude\n", " name\n", - " state_name\n", + " country_code\n", + " country_name\n", " types\n", + " city_name\n", + " state_name\n", " \n", " \n", " \n", " \n", " 0\n", - " MSK\n", - " New York\n", + " grid.798367.4\n", + " Bank of New York\n", + " US\n", " United States\n", - " grid.51462.34\n", - " 40.764194\n", - " [https://www.mskcc.org/]\n", - " -73.956100\n", - " Memorial Sloan Kettering Cancer Center\n", - " New York\n", - " [Healthcare]\n", + " [Company]\n", + " NaN\n", + " NaN\n", " \n", " \n", " 1\n", - " NaN\n", - " Brooklyn\n", + " grid.798343.2\n", + " Research Foundation of University of New York\n", + " US\n", " United States\n", - " grid.511519.8\n", - " 40.691113\n", - " [https://www.vascularnyc.com/]\n", - " -73.963890\n", - " Vascular Institute of New York\n", - " New York\n", - " [Healthcare]\n", + " [Education]\n", + " NaN\n", + " NaN\n", " \n", " \n", " 2\n", - " NYPC\n", - " New York\n", + " grid.797561.b\n", + " New York Hospital-Cornell Medical Center\n", + " US\n", " United States\n", - " grid.511327.3\n", - " 40.804780\n", - " [https://www.nyproton.com/]\n", - " -73.934070\n", - " New York Proton Center\n", + " [Healthcare]\n", + " New York\n", " New York\n", - " [Facility]\n", " \n", " \n", " 3\n", - " NaN\n", - " New York\n", + " grid.796770.8\n", + " Research Foundation of City University of New ...\n", + " US\n", " United States\n", - " grid.511090.c\n", - " 40.755230\n", - " [https://www.journalism.cuny.edu/]\n", - " -73.988830\n", - " Craig Newmark Graduate School of Journalism at...\n", - " New York\n", - " [Education]\n", + " [Other]\n", + " NaN\n", + " NaN\n", " \n", " \n", " 4\n", - " CMS\n", - " New York\n", + " grid.796173.d\n", + " Bank of New York Mellon Trust Co NA\n", + " US\n", " United States\n", - " grid.510787.c\n", - " 40.761470\n", - " [https://cmsny.org/]\n", - " -73.965450\n", - " Center for Migration Studies of New York\n", - " New York\n", - " [Education]\n", + " [Company]\n", + " NaN\n", + " NaN\n", " \n", " \n", " 5\n", - " NYSCC\n", - " Alfred\n", + " grid.795276.8\n", + " New York University Medical Center\n", + " US\n", " United States\n", - " grid.507867.b\n", - " 42.253372\n", - " [https://www.alfred.edu/academics/colleges-sch...\n", - " -77.787575\n", - " New York State College of Ceramics\n", - " New York\n", " [Education]\n", + " New York\n", + " New York\n", " \n", " \n", " 6\n", - " ILR\n", - " New York\n", + " grid.794869.d\n", + " International General Electric Company of New ...\n", + " US\n", " United States\n", - " grid.507863.f\n", - " 42.448510\n", - " [https://www.ilr.cornell.edu/]\n", - " -76.478620\n", - " New York State School of Industrial and Labor ...\n", - " New York\n", - " [Education]\n", + " [Other]\n", + " NaN\n", + " NaN\n", " \n", " \n", " 7\n", - " MVCC\n", - " Utica\n", + " grid.782261.8\n", + " New York Digital Investment Group LLC\n", + " US\n", " United States\n", - " grid.507861.d\n", - " 43.076850\n", - " [https://www.mvcc.edu/]\n", - " -75.220120\n", - " Mohawk Valley Community College\n", - " New York\n", - " [Education]\n", + " [Other]\n", + " NaN\n", + " NaN\n", " \n", " \n", " 8\n", - " CALS\n", - " New York\n", + " grid.778414.9\n", + " China CITIC Bank International Ltd New York Br...\n", + " US\n", " United States\n", - " grid.507860.c\n", - " 42.448290\n", - " [https://cals.cornell.edu/#]\n", - " -76.479390\n", - " New York State College of Agriculture & Life S...\n", - " New York\n", - " [Education]\n", + " [Government]\n", + " NaN\n", + " NaN\n", " \n", " \n", " 9\n", - " NaN\n", - " New York\n", + " grid.777726.4\n", + " Morgan Guaranty Trust Company of New York\n", + " US\n", " United States\n", - " grid.507859.6\n", - " 42.447483\n", - " [https://www.vet.cornell.edu/]\n", - " -76.464905\n", - " New York State College of Veterinary Medicine\n", - " New York\n", - " [Education]\n", + " [Company]\n", + " NaN\n", + " NaN\n", " \n", " \n", "\n", "" ], "text/plain": [ - " acronym city_name country_name id latitude \\\n", - "0 MSK New York United States grid.51462.34 40.764194 \n", - "1 NaN Brooklyn United States grid.511519.8 40.691113 \n", - "2 NYPC New York United States grid.511327.3 40.804780 \n", - "3 NaN New York United States grid.511090.c 40.755230 \n", - "4 CMS New York United States grid.510787.c 40.761470 \n", - "5 NYSCC Alfred United States grid.507867.b 42.253372 \n", - "6 ILR New York United States grid.507863.f 42.448510 \n", - "7 MVCC Utica United States grid.507861.d 43.076850 \n", - "8 CALS New York United States grid.507860.c 42.448290 \n", - "9 NaN New York United States grid.507859.6 42.447483 \n", - "\n", - " linkout longitude \\\n", - "0 [https://www.mskcc.org/] -73.956100 \n", - "1 [https://www.vascularnyc.com/] -73.963890 \n", - "2 [https://www.nyproton.com/] -73.934070 \n", - "3 [https://www.journalism.cuny.edu/] -73.988830 \n", - "4 [https://cmsny.org/] -73.965450 \n", - "5 [https://www.alfred.edu/academics/colleges-sch... -77.787575 \n", - "6 [https://www.ilr.cornell.edu/] -76.478620 \n", - "7 [https://www.mvcc.edu/] -75.220120 \n", - "8 [https://cals.cornell.edu/#] -76.479390 \n", - "9 [https://www.vet.cornell.edu/] -76.464905 \n", + " id name \\\n", + "0 grid.798367.4 Bank of New York \n", + "1 grid.798343.2 Research Foundation of University of New York \n", + "2 grid.797561.b New York Hospital-Cornell Medical Center \n", + "3 grid.796770.8 Research Foundation of City University of New ... \n", + "4 grid.796173.d Bank of New York Mellon Trust Co NA \n", + "5 grid.795276.8 New York University Medical Center \n", + "6 grid.794869.d International General Electric Company of New ... \n", + "7 grid.782261.8 New York Digital Investment Group LLC \n", + "8 grid.778414.9 China CITIC Bank International Ltd New York Br... \n", + "9 grid.777726.4 Morgan Guaranty Trust Company of New York \n", "\n", - " name state_name types \n", - "0 Memorial Sloan Kettering Cancer Center New York [Healthcare] \n", - "1 Vascular Institute of New York New York [Healthcare] \n", - "2 New York Proton Center New York [Facility] \n", - "3 Craig Newmark Graduate School of Journalism at... New York [Education] \n", - "4 Center for Migration Studies of New York New York [Education] \n", - "5 New York State College of Ceramics New York [Education] \n", - "6 New York State School of Industrial and Labor ... New York [Education] \n", - "7 Mohawk Valley Community College New York [Education] \n", - "8 New York State College of Agriculture & Life S... New York [Education] \n", - "9 New York State College of Veterinary Medicine New York [Education] " + " country_code country_name types city_name state_name \n", + "0 US United States [Company] NaN NaN \n", + "1 US United States [Education] NaN NaN \n", + "2 US United States [Healthcare] New York New York \n", + "3 US United States [Other] NaN NaN \n", + "4 US United States [Company] NaN NaN \n", + "5 US United States [Education] New York New York \n", + "6 US United States [Other] NaN NaN \n", + "7 US United States [Other] NaN NaN \n", + "8 US United States [Government] NaN NaN \n", + "9 US United States [Company] NaN NaN " ] }, "execution_count": 6, @@ -680,8 +623,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Organizations: 8 (total = 8)\n", - "\u001b[2mTime: 0.56s\u001b[0m\n" + "Returned Organizations: 9 (total = 9)\n", + "\u001b[2mTime: 0.62s\u001b[0m\n" ] }, { @@ -705,157 +648,194 @@ " \n", " \n", " \n", + " id\n", + " name\n", + " country_code\n", + " country_name\n", + " types\n", " acronym\n", " city_name\n", - " country_name\n", - " id\n", " latitude\n", " linkout\n", " longitude\n", - " name\n", " state_name\n", - " types\n", " \n", " \n", " \n", " \n", " 0\n", - " MVCC\n", - " Utica\n", + " grid.757191.c\n", + " New York Community Bank\n", + " US\n", " United States\n", + " [Company]\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 1\n", " grid.507861.d\n", + " Mohawk Valley Community College\n", + " US\n", + " United States\n", + " [Education]\n", + " MVCC\n", + " Utica\n", " 43.076850\n", " [https://www.mvcc.edu/]\n", " -75.220120\n", - " Mohawk Valley Community College\n", " New York\n", - " [Education]\n", " \n", " \n", - " 1\n", + " 2\n", + " grid.490742.c\n", + " Health Foundation for Western & Central New York\n", + " US\n", + " United States\n", + " [Nonprofit]\n", " NaN\n", " Buffalo\n", - " United States\n", - " grid.490742.c\n", " 42.874810\n", " [https://hfwcny.org/]\n", " -78.849690\n", - " Health Foundation for Western & Central New York\n", " New York\n", - " [Other]\n", " \n", " \n", - " 2\n", + " 3\n", + " grid.480917.3\n", + " New York Community Trust\n", + " US\n", + " United States\n", + " [Nonprofit]\n", " NaN\n", " New York\n", - " United States\n", - " grid.480917.3\n", " 40.758870\n", " [http://www.nycommunitytrust.org/]\n", " -73.968185\n", - " New York Community Trust\n", " New York\n", - " [Nonprofit]\n", " \n", " \n", - " 3\n", + " 4\n", + " grid.478715.8\n", + " Central New York Community Foundation\n", + " US\n", + " United States\n", + " [Nonprofit]\n", " CNYCF\n", " Syracuse\n", - " United States\n", - " grid.478715.8\n", " 43.056038\n", " [https://www.cnycf.org/]\n", " -76.148210\n", - " Central New York Community Foundation\n", " New York\n", - " [Nonprofit]\n", " \n", " \n", - " 4\n", + " 5\n", + " grid.475804.a\n", + " Community Service Society of New York\n", + " US\n", + " United States\n", + " [Other]\n", " CSS\n", " New York\n", - " United States\n", - " grid.475804.a\n", " 40.749622\n", " [http://www.cssny.org/]\n", " -73.974620\n", - " Community Service Society of New York\n", " New York\n", - " [Other]\n", " \n", " \n", - " 5\n", + " 6\n", + " grid.475783.a\n", + " Long Term Care Community Coalition\n", + " US\n", + " United States\n", + " [Other]\n", " LTCCC\n", " New York\n", - " United States\n", - " grid.475783.a\n", " 40.751163\n", " [http://www.ltccc.org/]\n", " -73.992470\n", - " Long Term Care Community Coalition\n", - " New York\n", - " [Other]\n", - " \n", - " \n", - " 6\n", - " ECC\n", - " Williamsville\n", - " United States\n", - " grid.468887.d\n", - " 42.960820\n", - " [https://www.ecc.edu/]\n", - " -78.721660\n", - " Erie Community College\n", " New York\n", - " [Education]\n", " \n", " \n", " 7\n", + " grid.429257.f\n", + " Korean Community Services of Metropolitan New ...\n", + " US\n", + " United States\n", + " [Nonprofit]\n", " KCS\n", " New York\n", - " United States\n", - " grid.429257.f\n", " 40.770954\n", " [https://www.kcsny.org/]\n", " -73.786670\n", - " Korean Community Services of Metropolitan New ...\n", " New York\n", - " [Nonprofit]\n", + " \n", + " \n", + " 8\n", + " funder.196228\n", + " Community Health Foundation of Western and Cen...\n", + " NaN\n", + " United States\n", + " NaN\n", + " Community Health Foundation of Western and Centra\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", "\n", "" ], "text/plain": [ - " acronym city_name country_name id latitude \\\n", - "0 MVCC Utica United States grid.507861.d 43.076850 \n", - "1 NaN Buffalo United States grid.490742.c 42.874810 \n", - "2 NaN New York United States grid.480917.3 40.758870 \n", - "3 CNYCF Syracuse United States grid.478715.8 43.056038 \n", - "4 CSS New York United States grid.475804.a 40.749622 \n", - "5 LTCCC New York United States grid.475783.a 40.751163 \n", - "6 ECC Williamsville United States grid.468887.d 42.960820 \n", - "7 KCS New York United States grid.429257.f 40.770954 \n", + " id name \\\n", + "0 grid.757191.c New York Community Bank \n", + "1 grid.507861.d Mohawk Valley Community College \n", + "2 grid.490742.c Health Foundation for Western & Central New York \n", + "3 grid.480917.3 New York Community Trust \n", + "4 grid.478715.8 Central New York Community Foundation \n", + "5 grid.475804.a Community Service Society of New York \n", + "6 grid.475783.a Long Term Care Community Coalition \n", + "7 grid.429257.f Korean Community Services of Metropolitan New ... \n", + "8 funder.196228 Community Health Foundation of Western and Cen... \n", + "\n", + " country_code country_name types \\\n", + "0 US United States [Company] \n", + "1 US United States [Education] \n", + "2 US United States [Nonprofit] \n", + "3 US United States [Nonprofit] \n", + "4 US United States [Nonprofit] \n", + "5 US United States [Other] \n", + "6 US United States [Other] \n", + "7 US United States [Nonprofit] \n", + "8 NaN United States NaN \n", "\n", - " linkout longitude \\\n", - "0 [https://www.mvcc.edu/] -75.220120 \n", - "1 [https://hfwcny.org/] -78.849690 \n", - "2 [http://www.nycommunitytrust.org/] -73.968185 \n", - "3 [https://www.cnycf.org/] -76.148210 \n", - "4 [http://www.cssny.org/] -73.974620 \n", - "5 [http://www.ltccc.org/] -73.992470 \n", - "6 [https://www.ecc.edu/] -78.721660 \n", - "7 [https://www.kcsny.org/] -73.786670 \n", + " acronym city_name latitude \\\n", + "0 NaN NaN NaN \n", + "1 MVCC Utica 43.076850 \n", + "2 NaN Buffalo 42.874810 \n", + "3 NaN New York 40.758870 \n", + "4 CNYCF Syracuse 43.056038 \n", + "5 CSS New York 40.749622 \n", + "6 LTCCC New York 40.751163 \n", + "7 KCS New York 40.770954 \n", + "8 Community Health Foundation of Western and Centra NaN NaN \n", "\n", - " name state_name types \n", - "0 Mohawk Valley Community College New York [Education] \n", - "1 Health Foundation for Western & Central New York New York [Other] \n", - "2 New York Community Trust New York [Nonprofit] \n", - "3 Central New York Community Foundation New York [Nonprofit] \n", - "4 Community Service Society of New York New York [Other] \n", - "5 Long Term Care Community Coalition New York [Other] \n", - "6 Erie Community College New York [Education] \n", - "7 Korean Community Services of Metropolitan New ... New York [Nonprofit] " + " linkout longitude state_name \n", + "0 NaN NaN NaN \n", + "1 [https://www.mvcc.edu/] -75.220120 New York \n", + "2 [https://hfwcny.org/] -78.849690 New York \n", + "3 [http://www.nycommunitytrust.org/] -73.968185 New York \n", + "4 [https://www.cnycf.org/] -76.148210 New York \n", + "5 [http://www.cssny.org/] -73.974620 New York \n", + "6 [http://www.ltccc.org/] -73.992470 New York \n", + "7 [https://www.kcsny.org/] -73.786670 New York \n", + "8 NaN NaN NaN " ] }, "execution_count": 7, @@ -880,7 +860,7 @@ "source": [ "### Fielded search \n", "\n", - "We can easily look up an organization using its grid ID, eg [grid.468887.d](https://grid.ac/institutes/grid.468887.d). " + "We can easily look up an organization using its ID, e.g." ] }, { @@ -913,10 +893,10 @@ "output_type": "stream", "text": [ "Returned Errors: 1\n", - "\u001b[2mTime: 0.47s\u001b[0m\n", - "1 QueryError found\n", + "\u001b[2mTime: 5.84s\u001b[0m\n", + "Query Error\n", "Semantic errors found:\n", - "\tField / Fieldset 'all' is not present in Source 'organizations'. Available fields: acronym,city_name,cnrs_ids,country_name,dimensions_url,established,external_ids_fundref,hesa_ids,id,isni_ids,latitude,linkout,longitude,name,nuts_level1_code,nuts_level1_name,nuts_level2_code,nuts_level2_name,nuts_level3_code,nuts_level3_name,organization_child_ids,organization_parent_ids,organization_related_ids,orgref_ids,redirect,ror_ids,state_name,status,types,ucas_ids,ukprn_ids,wikidata_ids,wikipedia_url and available fieldsets: basics,nuts\n" + "\tField / Fieldset 'all' is not present in Source 'organizations'. Available fields: acronym,city_name,cnrs_ids,country_code,country_name,dimensions_url,established,external_ids_fundref,hesa_ids,id,isni_ids,latitude,linkout,longitude,name,nuts_level1_code,nuts_level1_name,nuts_level2_code,nuts_level2_name,nuts_level3_code,nuts_level3_name,organization_child_ids,organization_parent_ids,organization_related_ids,orgref_ids,redirect,ror_ids,score,state_name,status,types,ucas_ids,ukprn_ids,wikidata_ids,wikipedia_url and available fieldsets: basics,nuts\n" ] } ], @@ -956,8 +936,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Organizations: 10 (total = 80)\n", - "\u001b[2mTime: 0.60s\u001b[0m\n" + "Returned Organizations: 10 (total = 93)\n", + "\u001b[2mTime: 0.64s\u001b[0m\n" ] }, { @@ -981,201 +961,200 @@ " \n", " \n", " \n", - " city_name\n", - " country_name\n", " id\n", + " name\n", + " country_code\n", + " country_name\n", + " types\n", + " city_name\n", + " state_name\n", " latitude\n", " linkout\n", " longitude\n", - " name\n", - " state_name\n", - " types\n", " acronym\n", " \n", " \n", " \n", " \n", " 0\n", - " New York\n", + " grid.798343.2\n", + " Research Foundation of University of New York\n", + " US\n", " United States\n", - " grid.511090.c\n", - " 40.755230\n", - " [https://www.journalism.cuny.edu/]\n", - " -73.988830\n", - " Craig Newmark Graduate School of Journalism at...\n", - " New York\n", " [Education]\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " 1\n", - " New York\n", + " grid.795276.8\n", + " New York University Medical Center\n", + " US\n", " United States\n", - " grid.510787.c\n", - " 40.761470\n", - " [https://cmsny.org/]\n", - " -73.965450\n", - " Center for Migration Studies of New York\n", - " New York\n", " [Education]\n", - " CMS\n", + " New York\n", + " New York\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " 2\n", - " Alfred\n", - " United States\n", - " grid.507867.b\n", - " 42.253372\n", - " [https://www.alfred.edu/academics/colleges-sch...\n", - " -77.787575\n", - " New York State College of Ceramics\n", - " New York\n", + " grid.512545.2\n", + " State University of New York, Korea\n", + " KR\n", + " South Korea\n", " [Education]\n", - " NYSCC\n", + " Incheon\n", + " NaN\n", + " 37.376694\n", + " [http://www.sunykorea.ac.kr/]\n", + " 126.667170\n", + " NaN\n", " \n", " \n", " 3\n", - " New York\n", + " grid.511090.c\n", + " Craig Newmark Graduate School of Journalism at...\n", + " US\n", " United States\n", - " grid.507863.f\n", - " 42.448510\n", - " [https://www.ilr.cornell.edu/]\n", - " -76.478620\n", - " New York State School of Industrial and Labor ...\n", - " New York\n", " [Education]\n", - " ILR\n", + " New York\n", + " New York\n", + " 40.755230\n", + " [https://www.journalism.cuny.edu/]\n", + " -73.988830\n", + " NaN\n", " \n", " \n", " 4\n", - " Utica\n", + " grid.510787.c\n", + " Center for Migration Studies of New York\n", + " US\n", " United States\n", - " grid.507861.d\n", - " 43.076850\n", - " [https://www.mvcc.edu/]\n", - " -75.220120\n", - " Mohawk Valley Community College\n", - " New York\n", " [Education]\n", - " MVCC\n", + " New York\n", + " New York\n", + " 40.761470\n", + " [https://cmsny.org/]\n", + " -73.965450\n", + " CMS\n", " \n", " \n", " 5\n", - " New York\n", + " grid.507867.b\n", + " New York State College of Ceramics\n", + " US\n", " United States\n", - " grid.507860.c\n", - " 42.448290\n", - " [https://cals.cornell.edu/#]\n", - " -76.479390\n", - " New York State College of Agriculture & Life S...\n", - " New York\n", " [Education]\n", - " CALS\n", + " Alfred\n", + " New York\n", + " 42.253372\n", + " [https://www.alfred.edu/academics/colleges-sch...\n", + " -77.787575\n", + " NaN\n", " \n", " \n", " 6\n", - " New York\n", + " grid.507863.f\n", + " New York State School of Industrial and Labor ...\n", + " US\n", " United States\n", - " grid.507859.6\n", - " 42.447483\n", - " [https://www.vet.cornell.edu/]\n", - " -76.464905\n", - " New York State College of Veterinary Medicine\n", - " New York\n", " [Education]\n", - " NaN\n", + " Ithaca\n", + " New York\n", + " 42.439213\n", + " [https://www.ilr.cornell.edu/]\n", + " -76.493380\n", + " ILR\n", " \n", " \n", " 7\n", - " Ithaca\n", + " grid.507861.d\n", + " Mohawk Valley Community College\n", + " US\n", " United States\n", - " grid.507858.7\n", - " 42.449740\n", - " [https://www.human.cornell.edu/]\n", - " -76.479065\n", - " New York State University College of Human Eco...\n", - " New York\n", " [Education]\n", - " HumEc\n", + " Utica\n", + " New York\n", + " 43.076850\n", + " [https://www.mvcc.edu/]\n", + " -75.220120\n", + " MVCC\n", " \n", " \n", " 8\n", - " New York\n", + " grid.507860.c\n", + " New York State College of Agriculture and Life...\n", + " US\n", " United States\n", - " grid.493073.a\n", - " 40.752880\n", - " [http://website.aub.edu.lb/nyo/Pages/index.aspx]\n", - " -73.969040\n", - " American University of Beirut New York Office\n", - " New York\n", " [Education]\n", - " AUB\n", + " Ithaca\n", + " New York\n", + " 42.448290\n", + " [https://cals.cornell.edu/#]\n", + " -76.479390\n", + " CALS\n", " \n", " \n", " 9\n", - " New York\n", + " grid.507859.6\n", + " New York State College of Veterinary Medicine ...\n", + " US\n", " United States\n", - " grid.487836.6\n", - " 40.740430\n", - " [https://www.csnyc.org/]\n", - " -73.995630\n", - " New York City Foundation for Computer Science ...\n", - " New York\n", " [Education]\n", - " CSNYC\n", + " Ithaca\n", + " New York\n", + " 42.447483\n", + " [https://www.vet.cornell.edu/]\n", + " -76.464905\n", + " NaN\n", " \n", " \n", "\n", "" ], "text/plain": [ - " city_name country_name id latitude \\\n", - "0 New York United States grid.511090.c 40.755230 \n", - "1 New York United States grid.510787.c 40.761470 \n", - "2 Alfred United States grid.507867.b 42.253372 \n", - "3 New York United States grid.507863.f 42.448510 \n", - "4 Utica United States grid.507861.d 43.076850 \n", - "5 New York United States grid.507860.c 42.448290 \n", - "6 New York United States grid.507859.6 42.447483 \n", - "7 Ithaca United States grid.507858.7 42.449740 \n", - "8 New York United States grid.493073.a 40.752880 \n", - "9 New York United States grid.487836.6 40.740430 \n", - "\n", - " linkout longitude \\\n", - "0 [https://www.journalism.cuny.edu/] -73.988830 \n", - "1 [https://cmsny.org/] -73.965450 \n", - "2 [https://www.alfred.edu/academics/colleges-sch... -77.787575 \n", - "3 [https://www.ilr.cornell.edu/] -76.478620 \n", - "4 [https://www.mvcc.edu/] -75.220120 \n", - "5 [https://cals.cornell.edu/#] -76.479390 \n", - "6 [https://www.vet.cornell.edu/] -76.464905 \n", - "7 [https://www.human.cornell.edu/] -76.479065 \n", - "8 [http://website.aub.edu.lb/nyo/Pages/index.aspx] -73.969040 \n", - "9 [https://www.csnyc.org/] -73.995630 \n", + " id name \\\n", + "0 grid.798343.2 Research Foundation of University of New York \n", + "1 grid.795276.8 New York University Medical Center \n", + "2 grid.512545.2 State University of New York, Korea \n", + "3 grid.511090.c Craig Newmark Graduate School of Journalism at... \n", + "4 grid.510787.c Center for Migration Studies of New York \n", + "5 grid.507867.b New York State College of Ceramics \n", + "6 grid.507863.f New York State School of Industrial and Labor ... \n", + "7 grid.507861.d Mohawk Valley Community College \n", + "8 grid.507860.c New York State College of Agriculture and Life... \n", + "9 grid.507859.6 New York State College of Veterinary Medicine ... \n", "\n", - " name state_name types \\\n", - "0 Craig Newmark Graduate School of Journalism at... New York [Education] \n", - "1 Center for Migration Studies of New York New York [Education] \n", - "2 New York State College of Ceramics New York [Education] \n", - "3 New York State School of Industrial and Labor ... New York [Education] \n", - "4 Mohawk Valley Community College New York [Education] \n", - "5 New York State College of Agriculture & Life S... New York [Education] \n", - "6 New York State College of Veterinary Medicine New York [Education] \n", - "7 New York State University College of Human Eco... New York [Education] \n", - "8 American University of Beirut New York Office New York [Education] \n", - "9 New York City Foundation for Computer Science ... New York [Education] \n", + " country_code country_name types city_name state_name latitude \\\n", + "0 US United States [Education] NaN NaN NaN \n", + "1 US United States [Education] New York New York NaN \n", + "2 KR South Korea [Education] Incheon NaN 37.376694 \n", + "3 US United States [Education] New York New York 40.755230 \n", + "4 US United States [Education] New York New York 40.761470 \n", + "5 US United States [Education] Alfred New York 42.253372 \n", + "6 US United States [Education] Ithaca New York 42.439213 \n", + "7 US United States [Education] Utica New York 43.076850 \n", + "8 US United States [Education] Ithaca New York 42.448290 \n", + "9 US United States [Education] Ithaca New York 42.447483 \n", "\n", - " acronym \n", - "0 NaN \n", - "1 CMS \n", - "2 NYSCC \n", - "3 ILR \n", - "4 MVCC \n", - "5 CALS \n", - "6 NaN \n", - "7 HumEc \n", - "8 AUB \n", - "9 CSNYC " + " linkout longitude acronym \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 [http://www.sunykorea.ac.kr/] 126.667170 NaN \n", + "3 [https://www.journalism.cuny.edu/] -73.988830 NaN \n", + "4 [https://cmsny.org/] -73.965450 CMS \n", + "5 [https://www.alfred.edu/academics/colleges-sch... -77.787575 NaN \n", + "6 [https://www.ilr.cornell.edu/] -76.493380 ILR \n", + "7 [https://www.mvcc.edu/] -75.220120 MVCC \n", + "8 [https://cals.cornell.edu/#] -76.479390 CALS \n", + "9 [https://www.vet.cornell.edu/] -76.464905 NaN " ] }, "execution_count": 9, @@ -1220,8 +1199,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Organizations: 8 (total = 8)\n", - "\u001b[2mTime: 0.58s\u001b[0m\n" + "Returned Organizations: 9 (total = 9)\n", + "\u001b[2mTime: 5.97s\u001b[0m\n" ] }, { @@ -1245,13 +1224,14 @@ " \n", " \n", " \n", + " id\n", + " name\n", " city_name\n", + " country_code\n", " country_name\n", - " id\n", " latitude\n", " linkout\n", " longitude\n", - " name\n", " types\n", " acronym\n", " state_name\n", @@ -1260,104 +1240,126 @@ " \n", " \n", " 0\n", + " grid.512545.2\n", + " State University of New York, Korea\n", + " Incheon\n", + " KR\n", + " South Korea\n", + " 37.376694\n", + " [http://www.sunykorea.ac.kr/]\n", + " 126.667170\n", + " [Education]\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 1\n", + " grid.479986.d\n", + " New York University Paris\n", " Paris\n", + " FR\n", " France\n", - " grid.479986.d\n", " 48.869614\n", " [http://www.nyu.edu/paris.html]\n", " 2.346863\n", - " New York University Paris\n", " [Education]\n", " NaN\n", " NaN\n", " \n", " \n", - " 1\n", + " 2\n", + " grid.473731.5\n", + " New York University Florence\n", " Florence\n", + " IT\n", " Italy\n", - " grid.473731.5\n", " 43.795910\n", " [http://www.nyu.edu/florence.html]\n", " 11.265850\n", - " New York University Florence\n", " [Education]\n", " NYU\n", " NaN\n", " \n", " \n", - " 2\n", + " 3\n", + " grid.473728.d\n", + " New York Institute of Technology\n", " Vancouver\n", + " CA\n", " Canada\n", - " grid.473728.d\n", " 49.284374\n", " [http://nyit.edu/vancouver]\n", " -123.116480\n", - " New York Institute of Technology\n", " [Education]\n", " NYIT\n", " British Columbia\n", " \n", " \n", - " 3\n", + " 4\n", + " grid.449989.1\n", + " University of New York in Prague\n", " Prague\n", + " CZ\n", " Czechia\n", - " grid.449989.1\n", " 50.074043\n", " [https://www.unyp.cz/]\n", " 14.433994\n", - " University of New York in Prague\n", " [Education]\n", " UNYP\n", " NaN\n", " \n", " \n", - " 4\n", + " 5\n", + " grid.449457.f\n", + " New York University Shanghai\n", " Shanghai\n", + " CN\n", " China\n", - " grid.449457.f\n", " 31.225506\n", " [https://shanghai.nyu.edu/]\n", " 121.533510\n", - " New York University Shanghai\n", " [Education]\n", " NaN\n", " NaN\n", " \n", " \n", - " 5\n", + " 6\n", + " grid.444973.9\n", + " University of New York Tirana\n", " Tirana\n", + " AL\n", " Albania\n", - " grid.444973.9\n", " 41.311060\n", " [http://unyt.edu.al/]\n", " 19.801466\n", - " University of New York Tirana\n", " [Education]\n", " UNYT\n", " NaN\n", " \n", " \n", - " 6\n", + " 7\n", + " grid.440573.1\n", + " New York University Abu Dhabi\n", " Abu Dhabi\n", + " AE\n", " United Arab Emirates\n", - " grid.440573.1\n", " 24.485000\n", " [https://nyuad.nyu.edu/]\n", " 54.353000\n", - " New York University Abu Dhabi\n", " [Education]\n", " NaN\n", " NaN\n", " \n", " \n", - " 7\n", + " 8\n", + " grid.410685.e\n", + " SUNY Korea\n", " Seoul\n", + " KR\n", " South Korea\n", - " grid.410685.e\n", " 37.377018\n", " [http://www.sunykorea.ac.kr/]\n", " 126.666770\n", - " SUNY Korea\n", " [Education]\n", " NaN\n", " NaN\n", @@ -1367,35 +1369,38 @@ "" ], "text/plain": [ - " city_name country_name id latitude \\\n", - "0 Paris France grid.479986.d 48.869614 \n", - "1 Florence Italy grid.473731.5 43.795910 \n", - "2 Vancouver Canada grid.473728.d 49.284374 \n", - "3 Prague Czechia grid.449989.1 50.074043 \n", - "4 Shanghai China grid.449457.f 31.225506 \n", - "5 Tirana Albania grid.444973.9 41.311060 \n", - "6 Abu Dhabi United Arab Emirates grid.440573.1 24.485000 \n", - "7 Seoul South Korea grid.410685.e 37.377018 \n", + " id name city_name country_code \\\n", + "0 grid.512545.2 State University of New York, Korea Incheon KR \n", + "1 grid.479986.d New York University Paris Paris FR \n", + "2 grid.473731.5 New York University Florence Florence IT \n", + "3 grid.473728.d New York Institute of Technology Vancouver CA \n", + "4 grid.449989.1 University of New York in Prague Prague CZ \n", + "5 grid.449457.f New York University Shanghai Shanghai CN \n", + "6 grid.444973.9 University of New York Tirana Tirana AL \n", + "7 grid.440573.1 New York University Abu Dhabi Abu Dhabi AE \n", + "8 grid.410685.e SUNY Korea Seoul KR \n", "\n", - " linkout longitude \\\n", - "0 [http://www.nyu.edu/paris.html] 2.346863 \n", - "1 [http://www.nyu.edu/florence.html] 11.265850 \n", - "2 [http://nyit.edu/vancouver] -123.116480 \n", - "3 [https://www.unyp.cz/] 14.433994 \n", - "4 [https://shanghai.nyu.edu/] 121.533510 \n", - "5 [http://unyt.edu.al/] 19.801466 \n", - "6 [https://nyuad.nyu.edu/] 54.353000 \n", - "7 [http://www.sunykorea.ac.kr/] 126.666770 \n", + " country_name latitude linkout \\\n", + "0 South Korea 37.376694 [http://www.sunykorea.ac.kr/] \n", + "1 France 48.869614 [http://www.nyu.edu/paris.html] \n", + "2 Italy 43.795910 [http://www.nyu.edu/florence.html] \n", + "3 Canada 49.284374 [http://nyit.edu/vancouver] \n", + "4 Czechia 50.074043 [https://www.unyp.cz/] \n", + "5 China 31.225506 [https://shanghai.nyu.edu/] \n", + "6 Albania 41.311060 [http://unyt.edu.al/] \n", + "7 United Arab Emirates 24.485000 [https://nyuad.nyu.edu/] \n", + "8 South Korea 37.377018 [http://www.sunykorea.ac.kr/] \n", "\n", - " name types acronym state_name \n", - "0 New York University Paris [Education] NaN NaN \n", - "1 New York University Florence [Education] NYU NaN \n", - "2 New York Institute of Technology [Education] NYIT British Columbia \n", - "3 University of New York in Prague [Education] UNYP NaN \n", - "4 New York University Shanghai [Education] NaN NaN \n", - "5 University of New York Tirana [Education] UNYT NaN \n", - "6 New York University Abu Dhabi [Education] NaN NaN \n", - "7 SUNY Korea [Education] NaN NaN " + " longitude types acronym state_name \n", + "0 126.667170 [Education] NaN NaN \n", + "1 2.346863 [Education] NaN NaN \n", + "2 11.265850 [Education] NYU NaN \n", + "3 -123.116480 [Education] NYIT British Columbia \n", + "4 14.433994 [Education] UNYP NaN \n", + "5 121.533510 [Education] NaN NaN \n", + "6 19.801466 [Education] UNYT NaN \n", + "7 54.353000 [Education] NaN NaN \n", + "8 126.666770 [Education] NaN NaN " ] }, "execution_count": 10, @@ -1452,8 +1457,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Country_name: 10\n", - "\u001b[2mTime: 0.52s\u001b[0m\n" + "Returned Country_name: 11\n", + "\u001b[2mTime: 0.50s\u001b[0m\n" ] }, { @@ -1477,77 +1482,83 @@ " \n", " \n", " \n", - " count\n", " id\n", + " count\n", " \n", " \n", " \n", " \n", " 0\n", - " 238\n", " United States\n", + " 341\n", " \n", " \n", " 1\n", - " 1\n", - " Albania\n", + " South Korea\n", + " 2\n", " \n", " \n", " 2\n", + " Albania\n", " 1\n", - " Canada\n", " \n", " \n", " 3\n", + " Canada\n", " 1\n", - " China\n", " \n", " \n", " 4\n", + " China\n", " 1\n", - " Czechia\n", " \n", " \n", " 5\n", + " Czechia\n", " 1\n", - " France\n", " \n", " \n", " 6\n", + " France\n", " 1\n", - " Italy\n", " \n", " \n", " 7\n", + " Italy\n", " 1\n", - " South Korea\n", " \n", " \n", " 8\n", + " Panama\n", " 1\n", - " United Arab Emirates\n", " \n", " \n", " 9\n", + " United Arab Emirates\n", " 1\n", + " \n", + " \n", + " 10\n", " United Kingdom\n", + " 1\n", " \n", " \n", "\n", "" ], "text/plain": [ - " count id\n", - "0 238 United States\n", - "1 1 Albania\n", - "2 1 Canada\n", - "3 1 China\n", - "4 1 Czechia\n", - "5 1 France\n", - "6 1 Italy\n", - "7 1 South Korea\n", - "8 1 United Arab Emirates\n", - "9 1 United Kingdom" + " id count\n", + "0 United States 341\n", + "1 South Korea 2\n", + "2 Albania 1\n", + "3 Canada 1\n", + "4 China 1\n", + "5 Czechia 1\n", + "6 France 1\n", + "7 Italy 1\n", + "8 Panama 1\n", + "9 United Arab Emirates 1\n", + "10 United Kingdom 1" ] }, "execution_count": 11, @@ -1592,7 +1603,7 @@ "output_type": "stream", "text": [ "Returned Types: 8\n", - "\u001b[2mTime: 0.61s\u001b[0m\n" + "\u001b[2mTime: 5.47s\u001b[0m\n" ] }, { @@ -1616,65 +1627,65 @@ " \n", " \n", " \n", - " count\n", " id\n", + " count\n", " \n", " \n", " \n", " \n", " 0\n", - " 72\n", " Education\n", + " 84\n", " \n", " \n", " 1\n", - " 59\n", " Nonprofit\n", + " 75\n", " \n", " \n", " 2\n", - " 39\n", - " Government\n", + " Company\n", + " 57\n", " \n", " \n", " 3\n", - " 29\n", - " Other\n", + " Government\n", + " 46\n", " \n", " \n", " 4\n", - " 22\n", - " Healthcare\n", + " Other\n", + " 34\n", " \n", " \n", " 5\n", - " 9\n", - " Archive\n", + " Healthcare\n", + " 28\n", " \n", " \n", " 6\n", - " 5\n", - " Facility\n", + " Archive\n", + " 9\n", " \n", " \n", " 7\n", - " 3\n", - " Company\n", + " Facility\n", + " 7\n", " \n", " \n", "\n", "" ], "text/plain": [ - " count id\n", - "0 72 Education\n", - "1 59 Nonprofit\n", - "2 39 Government\n", - "3 29 Other\n", - "4 22 Healthcare\n", - "5 9 Archive\n", - "6 5 Facility\n", - "7 3 Company" + " id count\n", + "0 Education 84\n", + "1 Nonprofit 75\n", + "2 Company 57\n", + "3 Government 46\n", + "4 Other 34\n", + "5 Healthcare 28\n", + "6 Archive 9\n", + "7 Facility 7" ] }, "execution_count": 12, @@ -1700,9 +1711,9 @@ "source": [ "### Returning organizations facets from publications\n", "\n", - "The GRID organization data is used thoughout Dimensions. \n", + "Organization data is used thoughout Dimensions. \n", "\n", - "So, for example, one can do a publications search and return organizations as a facet. This allows to take advantage of GRID metadata - e.g. latiture and longitude - in order to quickly build a geograpical visualization. \n" + "So, for example, one can do a publications search and return organizations as a facet. This allows to take advantage of organization metadata - e.g. latiture and longitude - in order to quickly build a geograpical visualization. \n" ] }, { @@ -1735,7 +1746,7 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 50\n", - "\u001b[2mTime: 1.26s\u001b[0m\n" + "\u001b[2mTime: 1.16s\u001b[0m\n" ] }, { @@ -1759,14 +1770,15 @@ " \n", " \n", " \n", + " id\n", + " name\n", " city_name\n", " count\n", + " country_code\n", " country_name\n", - " id\n", " latitude\n", " linkout\n", " longitude\n", - " name\n", " state_name\n", " types\n", " acronym\n", @@ -1775,70 +1787,75 @@ " \n", " \n", " 0\n", + " grid.38142.3c\n", + " Harvard University\n", " Cambridge\n", - " 9846\n", + " 33545\n", + " US\n", " United States\n", - " grid.38142.3c\n", " 42.377052\n", " [http://www.harvard.edu/]\n", " -71.116650\n", - " Harvard University\n", " Massachusetts\n", " [Education]\n", " NaN\n", " \n", " \n", " 1\n", - " Oxford\n", - " 5900\n", - " United Kingdom\n", - " grid.4991.5\n", - " 51.753437\n", - " [http://www.ox.ac.uk/]\n", - " -1.254010\n", - " University of Oxford\n", - " Oxfordshire\n", - " [Education]\n", - " NaN\n", - " \n", - " \n", - " 2\n", + " grid.17063.33\n", + " University of Toronto\n", " Toronto\n", - " 5519\n", + " 21731\n", + " CA\n", " Canada\n", - " grid.17063.33\n", " 43.661667\n", " [http://www.utoronto.ca/]\n", " -79.395000\n", - " University of Toronto\n", " Ontario\n", " [Education]\n", " NaN\n", " \n", " \n", - " 3\n", + " 2\n", + " grid.21107.35\n", + " Johns Hopkins University\n", " Baltimore\n", - " 5289\n", + " 19419\n", + " US\n", " United States\n", - " grid.21107.35\n", " 39.328888\n", " [https://www.jhu.edu/]\n", " -76.620280\n", - " Johns Hopkins University\n", " Maryland\n", " [Education]\n", " JHU\n", " \n", " \n", + " 3\n", + " grid.4991.5\n", + " University of Oxford\n", + " Oxford\n", + " 19345\n", + " GB\n", + " United Kingdom\n", + " 51.753437\n", + " [http://www.ox.ac.uk/]\n", + " -1.254010\n", + " Oxfordshire\n", + " [Education]\n", + " NaN\n", + " \n", + " \n", " 4\n", + " grid.83440.3b\n", + " University College London\n", " London\n", - " 5232\n", + " 19047\n", + " GB\n", " United Kingdom\n", - " grid.83440.3b\n", " 51.524470\n", " [http://www.ucl.ac.uk/]\n", " -0.133982\n", - " University College London\n", " NaN\n", " [Education]\n", " UCL\n", @@ -1848,25 +1865,25 @@ "" ], "text/plain": [ - " city_name count country_name id latitude \\\n", - "0 Cambridge 9846 United States grid.38142.3c 42.377052 \n", - "1 Oxford 5900 United Kingdom grid.4991.5 51.753437 \n", - "2 Toronto 5519 Canada grid.17063.33 43.661667 \n", - "3 Baltimore 5289 United States grid.21107.35 39.328888 \n", - "4 London 5232 United Kingdom grid.83440.3b 51.524470 \n", + " id name city_name count country_code \\\n", + "0 grid.38142.3c Harvard University Cambridge 33545 US \n", + "1 grid.17063.33 University of Toronto Toronto 21731 CA \n", + "2 grid.21107.35 Johns Hopkins University Baltimore 19419 US \n", + "3 grid.4991.5 University of Oxford Oxford 19345 GB \n", + "4 grid.83440.3b University College London London 19047 GB \n", "\n", - " linkout longitude name \\\n", - "0 [http://www.harvard.edu/] -71.116650 Harvard University \n", - "1 [http://www.ox.ac.uk/] -1.254010 University of Oxford \n", - "2 [http://www.utoronto.ca/] -79.395000 University of Toronto \n", - "3 [https://www.jhu.edu/] -76.620280 Johns Hopkins University \n", - "4 [http://www.ucl.ac.uk/] -0.133982 University College London \n", + " country_name latitude linkout longitude \\\n", + "0 United States 42.377052 [http://www.harvard.edu/] -71.116650 \n", + "1 Canada 43.661667 [http://www.utoronto.ca/] -79.395000 \n", + "2 United States 39.328888 [https://www.jhu.edu/] -76.620280 \n", + "3 United Kingdom 51.753437 [http://www.ox.ac.uk/] -1.254010 \n", + "4 United Kingdom 51.524470 [http://www.ucl.ac.uk/] -0.133982 \n", "\n", " state_name types acronym \n", "0 Massachusetts [Education] NaN \n", - "1 Oxfordshire [Education] NaN \n", - "2 Ontario [Education] NaN \n", - "3 Maryland [Education] JHU \n", + "1 Ontario [Education] NaN \n", + "2 Maryland [Education] JHU \n", + "3 Oxfordshire [Education] NaN \n", "4 NaN [Education] UCL " ] }, @@ -1916,6 +1933,13 @@ "Education" ] ], + [ + "Seattle", + "grid.34477.33", + [ + "Education" + ] + ], [ "Stanford", "grid.168010.e", @@ -1924,8 +1948,8 @@ ] ], [ - "Seattle", - "grid.34477.33", + "Ann Arbor", + "grid.214458.e", [ "Education" ] @@ -1938,8 +1962,8 @@ ] ], [ - "Ann Arbor", - "grid.214458.e", + "New Haven", + "grid.47100.32", [ "Education" ] @@ -1952,8 +1976,8 @@ ] ], [ - "New Haven", - "grid.47100.32", + "Los Angeles", + "grid.19006.3e", [ "Education" ] @@ -1966,29 +1990,29 @@ ] ], [ - "New York", - "grid.21729.3f", + "Chapel Hill", + "grid.10698.36", [ "Education" ] ], [ - "Los Angeles", - "grid.19006.3e", + "Atlanta", + "grid.189967.8", [ "Education" ] ], [ - "Ithaca", - "grid.5386.8", + "New York", + "grid.137628.9", [ "Education" ] ], [ - "Atlanta", - "grid.189967.8", + "New York", + "grid.21729.3f", [ "Education" ] @@ -2000,13 +2024,6 @@ "Healthcare" ] ], - [ - "New York", - "grid.59734.3c", - [ - "Education" - ] - ], [ "San Diego", "grid.266100.3", @@ -2015,8 +2032,8 @@ ] ], [ - "New York", - "grid.137628.9", + "Durham", + "grid.26009.3d", [ "Education" ] @@ -2029,15 +2046,15 @@ ] ], [ - "Durham", - "grid.26009.3d", + "Minneapolis", + "grid.17635.36", [ "Education" ] ], [ - "Chapel Hill", - "grid.10698.36", + "Pittsburgh", + "grid.21925.3d", [ "Education" ] @@ -2057,15 +2074,22 @@ ] ], [ - "Pittsburgh", - "grid.21925.3d", + "New York", + "grid.59734.3c", [ "Education" ] ], [ - "Minneapolis", - "grid.17635.36", + "Ithaca", + "grid.5386.8", + [ + "Education" + ] + ], + [ + "St Louis", + "grid.4367.6", [ "Education" ] @@ -2083,116 +2107,48 @@ "hovertext": [ "Harvard University", "Johns Hopkins University", - "Stanford University", "University of Washington", + "Stanford University", + "University of Michigan-Ann Arbor", "University of Pennsylvania", - "University of Michigan", - "University of California, San Francisco", "Yale University", - "Massachusetts General Hospital", - "Columbia University", + "University of California, San Francisco", "University of California, Los Angeles", - "Cornell University", + "Massachusetts General Hospital", + "University of North Carolina at Chapel Hill", "Emory University", - "Brigham and Women's Hospital", - "Icahn School of Medicine at Mount Sinai", - "University of California, San Diego", "New York University", - "Mayo Clinic", + "Columbia University", + "Brigham and Womens Hospital Inc", + "University of California, San Diego", "Duke University", - "University of North Carolina at Chapel Hill", + "Mayo Clinic", + "University of Minnesota Twin Cities", + "University of Pittsburgh", "University of Florida", "Northwestern University", - "University of Pittsburgh", - "University of Minnesota", + "Icahn School of Medicine at Mount Sinai", + "Cornell University", + "Washington University in St. Louis", "Boston University" ], - "lat": [ - 42.377052, - 39.328888, - 37.43, - 47.655537, - 39.952457, - 42.278305, - 37.7628, - 41.302094, - 42.362804, - 40.8076, - 34.072224, - 42.44851, - 33.792786, - 42.3356, - 40.789085, - 32.881, - 40.73, - 44.02407, - 36.003147, - 35.905163, - 29.643902, - 42.05485, - 40.443504, - 44.974194, - 42.3496 - ], + "lat": { + "bdata": "GXJsPUMwRUD/BYIAGapDQLMo7KLo00dA16NwPQq3QkAaqIx/nyNFQFPsaBzq+UNAriglBKumREDEsS5uo+FCQCkF3V7SCEFAAAAAAAAA+H9ORpVh3PNBQANd+wJ65UBAPQrXo3BdREDzH9JvX2dEQAAAAAAAAPh/+yMMA5ZwQEBOCvMeZwBCQEloy7kUA0ZAzvqUY7J8RkCV0jO9xDhEQDnU78LWpD1AwhcmUwUHRUAAAAAAAAD4f+j2ksZoOUVALINqgxNTQ0A/V1uxvyxFQA==", + "dtype": "f8" + }, "legendgroup": "United States", - "lon": [ - -71.11665, - -76.62028, - -122.17, - -122.30353, - -75.19322, - -83.73822, - -122.45767, - -72.93066, - -71.068634, - -73.96239, - -118.4441, - -76.47862, - -84.32401, - -71.106415, - -73.95311, - -117.238, - -73.995, - -92.46631, - -78.926895, - -79.04694, - -82.35495, - -87.67394, - -79.961525, - -93.22776, - -71.0997 - ], + "lon": { + "bdata": "BcWPMXfHUcA5l+KqsidTwLbWFwltk17AexSuR+GKXsAzUBn/Pu9UwLg7a7ddzFLAgez17o87UsAPlxx3Sp1ewIrNx7WhnF3AAAAAAAAA+H/0N6EQAcNTwO1kcJS8FFXASOF6FK5/UsDW/znMl31SwAAAAAAAAPh/pn1zf/VOXcDc9Gc/UrtTwA7z5QXYHVfAkdWtnpNOV8BUUiegif1TwMZtNIC3llTA19081SHrVcAAAAAAAAD4f4rNx7WhHlPAi/1l9+STVsBR2ht8YcZRwA==", + "dtype": "f8" + }, "marker": { "color": "#636efa", - "size": [ - 9846, - 5289, - 4157, - 4125, - 3923, - 3781, - 3644, - 3629, - 3554, - 3522, - 3038, - 2985, - 2966, - 2925, - 2736, - 2614, - 2422, - 2380, - 2360, - 2173, - 2141, - 2139, - 2120, - 2027, - 2019 - ], + "size": { + "bdata": "CYMAANtLAAC4PgAAXTsAAEA4AAAcNwAANTQAALAzAABzLQAAySwAAEYrAABmKgAA3ykAAM0oAAAmJgAAviMAAJgjAABrIwAARiMAACAjAADlIQAAdCEAAFghAAAxIQAARB8AAAcfAAA=", + "dtype": "i4" + }, "sizemode": "area", - "sizeref": 24.615, + "sizeref": 83.8625, "symbol": "circle" }, "mode": "markers", @@ -2200,6 +2156,69 @@ "showlegend": true, "type": "scattergeo" }, + { + "customdata": [ + [ + "Toronto", + "grid.17063.33", + [ + "Education" + ] + ], + [ + "Vancouver", + "grid.17091.3e", + [ + "Education" + ] + ], + [ + "Montreal", + "grid.14709.3b", + [ + "Education" + ] + ], + [ + "Hamilton", + "grid.25073.33", + [ + "Education" + ] + ] + ], + "geo": "geo", + "hovertemplate": "%{hovertext}

country_name=Canada
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", + "hovertext": [ + "University of Toronto", + "University of British Columbia", + "McGill University", + "McMaster University" + ], + "lat": { + "bdata": "1esWgbHURUBxdQDEXaFIQKjg8IKIwEZAwf7r3LShRUA=", + "dtype": "f8" + }, + "legendgroup": "Canada", + "lon": { + "bdata": "4XoUrkfZU8DIDFTGv89ewNsWZTbIZFLAxqcAGM/6U8A=", + "dtype": "f8" + }, + "marker": { + "color": "#EF553B", + "size": { + "bdata": "41TjMF4hzR8=", + "dtype": "i2" + }, + "sizemode": "area", + "sizeref": 83.8625, + "symbol": "circle" + }, + "mode": "markers", + "name": "Canada", + "showlegend": true, + "type": "scattergeo" + }, { "customdata": [ [ @@ -2218,21 +2237,28 @@ ], [ "London", - "grid.7445.2", + "grid.13097.3c", [ "Education" ] ], [ "London", - "grid.13097.3c", + "grid.7445.2", + [ + "Education" + ] + ], + [ + "Cambridge", + "grid.5335.0", [ "Education" ] ], [ - "Cambridge", - "grid.5335.0", + "Manchester", + "grid.5379.8", [ "Education" ] @@ -2250,13 +2276,6 @@ [ "Education" ] - ], - [ - "Manchester", - "grid.5379.8", - [ - "Education" - ] ] ], "geo": "geo", @@ -2264,110 +2283,34 @@ "hovertext": [ "University of Oxford", "University College London", - "Imperial College London", "King's College London", + "Imperial College London", "University of Cambridge", + "University of Manchester", "London School of Hygiene & Tropical Medicine", - "University of Edinburgh", - "University of Manchester" - ], - "lat": [ - 51.753437, - 51.52447, - 51.4986, - 51.511417, - 52.204453, - 51.5209, - 55.944897, - 53.46705 + "University of Edinburgh" ], + "lat": { + "bdata": "VUyln3DgSUDX3TzVIcNJQCnPvBx2wUlAj+TyH9K/SUDYSBKEKxpKQGTMXUvIu0pAQj7o2azCSUBUi4hi8vhLQA==", + "dtype": "f8" + }, "legendgroup": "United Kingdom", - "lon": [ - -1.2540097, - -0.1339817, - -0.175478, - -0.116727, - 0.114908, - -0.1307, - -3.189284, - -2.233884 - ], - "marker": { - "color": "#EF553B", - "size": [ - 5900, - 5232, - 4402, - 3615, - 3056, - 2692, - 2371, - 2270 - ], - "sizemode": "area", - "sizeref": 24.615, - "symbol": "circle" + "lon": { + "bdata": "S96leWwQ9L8NmeH1TybBv75ojxfS4b2/P3CVJxB2xr8PfAxWnGq9Pxl2GJP+3gHAXynLEMe6wL+eP21Up4MJwA==", + "dtype": "f8" }, - "mode": "markers", - "name": "United Kingdom", - "showlegend": true, - "type": "scattergeo" - }, - { - "customdata": [ - [ - "Toronto", - "grid.17063.33", - [ - "Education" - ] - ], - [ - "Vancouver", - "grid.17091.3e", - [ - "Education" - ] - ], - [ - "Montreal", - "grid.14709.3b", - [ - "Education" - ] - ] - ], - "geo": "geo", - "hovertemplate": "%{hovertext}

country_name=Canada
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", - "hovertext": [ - "University of Toronto", - "University of British Columbia", - "McGill University" - ], - "lat": [ - 43.661667, - 49.260674, - 45.504166 - ], - "legendgroup": "Canada", - "lon": [ - -79.395, - -123.24608, - -73.57472 - ], "marker": { "color": "#00cc96", - "size": [ - 5519, - 3069, - 2098 - ], + "size": { + "bdata": "kUtnSlk0dDDoKDgj9iKZIQ==", + "dtype": "i2" + }, "sizemode": "area", - "sizeref": 24.615, + "sizeref": 83.8625, "symbol": "circle" }, "mode": "markers", - "name": "Canada", + "name": "United Kingdom", "showlegend": true, "type": "scattergeo" }, @@ -2384,22 +2327,25 @@ "geo": "geo", "hovertemplate": "%{hovertext}

country_name=Brazil
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", "hovertext": [ - "University of São Paulo" - ], - "lat": [ - -23.563051 + "Universidade de São Paulo" ], + "lat": { + "bdata": "6Po+HCSQN8A=", + "dtype": "f8" + }, "legendgroup": "Brazil", - "lon": [ - -46.730103 - ], + "lon": { + "bdata": "EtvdA3RdR8A=", + "dtype": "f8" + }, "marker": { "color": "#ab63fa", - "size": [ - 3727 - ], + "size": { + "bdata": "Bzw=", + "dtype": "i2" + }, "sizemode": "area", - "sizeref": 24.615, + "sizeref": 83.8625, "symbol": "circle" }, "mode": "markers", @@ -2449,37 +2395,28 @@ "hovertemplate": "%{hovertext}

country_name=Australia
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", "hovertext": [ "University of Melbourne", - "University of Sydney", + "The University of Sydney", "Monash University", "UNSW Sydney", "University of Queensland" ], - "lat": [ - -37.797115, - -33.88858, - -37.9083, - -33.917732, - -27.495964 - ], + "lat": { + "bdata": "VRNE3QfmQsCZ8Ev9vPFAwHh6pSxD9ELAED//PXj1QMBM/id/9347wA==", + "dtype": "f8" + }, "legendgroup": "Australia", - "lon": [ - 144.95998, - 151.1873, - 145.138, - 151.23096, - 153.00963 - ], + "lon": { + "bdata": "DRr6J7geYkBO0ZFc/uViQCPb+X5qJGJA5dU5BmTnYkDv4ZLjTiBjQA==", + "dtype": "f8" + }, "marker": { "color": "#FFA15A", - "size": [ - 3275, - 2985, - 2889, - 2489, - 2172 - ], + "size": { + "bdata": "0TmBMmkytim/JQ==", + "dtype": "i2" + }, "sizemode": "area", - "sizeref": 24.615, + "sizeref": 83.8625, "symbol": "circle" }, "mode": "markers", @@ -2490,141 +2427,94 @@ { "customdata": [ [ - "Wuhan", - "grid.33199.31", - [ - "Education" - ] - ], - [ - "Hong Kong", - "grid.194645.b", - [ - "Education" - ] - ], - [ - "Hangzhou", - "grid.13402.34", + "Rome", + "grid.7841.a", [ "Education" ] ] ], "geo": "geo", - "hovertemplate": "%{hovertext}

country_name=China
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", + "hovertemplate": "%{hovertext}

country_name=Italy
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", "hovertext": [ - "Huazhong University of Science and Technology", - "University of Hong Kong", - "Zhejiang University" - ], - "lat": [ - 30.508183, - 22.283287, - 30.263878 - ], - "legendgroup": "China", - "lon": [ - 114.41474, - 114.13708, - 120.12342 + "Sapienza University of Rome" ], + "lat": { + "bdata": "iGh0B7HzREA=", + "dtype": "f8" + }, + "legendgroup": "Italy", + "lon": { + "bdata": "qaPjamQHKUA=", + "dtype": "f8" + }, "marker": { "color": "#19d3f3", - "size": [ - 3058, - 2227, - 2095 - ], + "size": { + "bdata": "hSQ=", + "dtype": "i2" + }, "sizemode": "area", - "sizeref": 24.615, + "sizeref": 83.8625, "symbol": "circle" }, "mode": "markers", - "name": "China", + "name": "Italy", "showlegend": true, "type": "scattergeo" }, { "customdata": [ [ - "Milan", - "grid.4708.b", + "Hangzhou", + "grid.13402.34", [ "Education" ] ], [ - "Rome", - "grid.7841.a", + "Shanghai", + "grid.16821.3c", [ "Education" ] - ] - ], - "geo": "geo", - "hovertemplate": "%{hovertext}

country_name=Italy
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", - "hovertext": [ - "University of Milan", - "Sapienza University of Rome" - ], - "lat": [ - 45.46099, - 41.90384 - ], - "legendgroup": "Italy", - "lon": [ - 9.194592, - 12.514438 - ], - "marker": { - "color": "#FF6692", - "size": [ - 2749, - 2709 ], - "sizemode": "area", - "sizeref": 24.615, - "symbol": "circle" - }, - "mode": "markers", - "name": "Italy", - "showlegend": true, - "type": "scattergeo" - }, - { - "customdata": [ [ - "New Delhi", - "grid.413618.9", + "Hong Kong", + "grid.194645.b", [ "Education" ] ] ], "geo": "geo", - "hovertemplate": "%{hovertext}

country_name=India
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", + "hovertemplate": "%{hovertext}

country_name=China
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", "hovertext": [ - "All India Institute of Medical Sciences" - ], - "lat": [ - 28.585018 - ], - "legendgroup": "India", - "lon": [ - 77.20922 + "Zhejiang University", + "Shanghai Jiao Tong University", + "University of Hong Kong" ], + "lat": { + "bdata": "RiI0go1DPkCYw+47hjM/QMhhMH+FSDZA", + "dtype": "f8" + }, + "legendgroup": "China", + "lon": { + "bdata": "B+v/HOYHXkA5l+KqslteQOI7MevFiFxA", + "dtype": "f8" + }, "marker": { - "color": "#B6E880", - "size": [ - 2463 - ], + "color": "#FF6692", + "size": { + "bdata": "ZSQuIyYi", + "dtype": "i2" + }, "sizemode": "area", - "sizeref": 24.615, + "sizeref": 83.8625, "symbol": "circle" }, "mode": "markers", - "name": "India", + "name": "China", "showlegend": true, "type": "scattergeo" }, @@ -2643,20 +2533,23 @@ "hovertext": [ "National University of Singapore" ], - "lat": [ - 1.295556 - ], + "lat": { + "bdata": "ai+i7Zi69D8=", + "dtype": "f8" + }, "legendgroup": "Singapore", - "lon": [ - 103.776665 - ], + "lon": { + "bdata": "qbwd4bTxWUA=", + "dtype": "f8" + }, "marker": { - "color": "#FF97FF", - "size": [ - 2401 - ], + "color": "#B6E880", + "size": { + "bdata": "5yM=", + "dtype": "i2" + }, "sizemode": "area", - "sizeref": 24.615, + "sizeref": 83.8625, "symbol": "circle" }, "mode": "markers", @@ -2667,42 +2560,44 @@ { "customdata": [ [ - "Tehran", - "grid.411705.6", + "Stockholm", + "grid.4714.6", [ "Education" ] ] ], "geo": "geo", - "hovertemplate": "%{hovertext}

country_name=Iran
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", + "hovertemplate": "%{hovertext}

country_name=Sweden
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", "hovertext": [ - "Tehran University of Medical Sciences" - ], - "lat": [ - 35.748272 - ], - "legendgroup": "Iran", - "lon": [ - 51.38122 + "Karolinska Institutet" ], + "lat": { + "bdata": "TS1b64usTUA=", + "dtype": "f8" + }, + "legendgroup": "Sweden", + "lon": { + "bdata": "/TBCeLQFMkA=", + "dtype": "f8" + }, "marker": { - "color": "#FECB52", - "size": [ - 2063 - ], + "color": "#FF97FF", + "size": { + "bdata": "ASA=", + "dtype": "i2" + }, "sizemode": "area", - "sizeref": 24.615, + "sizeref": 83.8625, "symbol": "circle" }, "mode": "markers", - "name": "Iran", + "name": "Sweden", "showlegend": true, "type": "scattergeo" } ], "layout": { - "autosize": true, "geo": { "center": {}, "domain": { @@ -2908,57 +2803,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], "histogram": [ { "marker": { @@ -3101,11 +2945,10 @@ ], "scatter": [ { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -3160,6 +3003,17 @@ "type": "scattergl" } ], + "scattermap": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermap" + } + ], "scattermapbox": [ { "marker": { @@ -3549,9 +3403,10 @@ } }, "text/html": [ - "
\n", + "
" + " }) }; " ] }, "metadata": {}, @@ -3676,6 +3531,16 @@ " \n", " 3\n", " organizations\n", + " country_code\n", + " string\n", + " Country of the organisation, identified using ...\n", + " True\n", + " False\n", + " True\n", + " \n", + " \n", + " 4\n", + " organizations\n", " country_name\n", " string\n", " GRID name of the organization country. E.g., \"...\n", @@ -3684,7 +3549,7 @@ " True\n", " \n", " \n", - " 4\n", + " 5\n", " organizations\n", " dimensions_url\n", " string\n", @@ -3694,7 +3559,7 @@ " False\n", " \n", " \n", - " 5\n", + " 6\n", " organizations\n", " established\n", " integer\n", @@ -3704,7 +3569,7 @@ " False\n", " \n", " \n", - " 6\n", + " 7\n", " organizations\n", " external_ids_fundref\n", " string\n", @@ -3714,7 +3579,7 @@ " False\n", " \n", " \n", - " 7\n", + " 8\n", " organizations\n", " hesa_ids\n", " string\n", @@ -3724,7 +3589,7 @@ " False\n", " \n", " \n", - " 8\n", + " 9\n", " organizations\n", " id\n", " string\n", @@ -3734,7 +3599,7 @@ " False\n", " \n", " \n", - " 9\n", + " 10\n", " organizations\n", " isni_ids\n", " string\n", @@ -3744,7 +3609,7 @@ " False\n", " \n", " \n", - " 10\n", + " 11\n", " organizations\n", " latitude\n", " float\n", @@ -3754,7 +3619,7 @@ " False\n", " \n", " \n", - " 11\n", + " 12\n", " organizations\n", " linkout\n", " string\n", @@ -3764,7 +3629,7 @@ " False\n", " \n", " \n", - " 12\n", + " 13\n", " organizations\n", " longitude\n", " float\n", @@ -3774,7 +3639,7 @@ " False\n", " \n", " \n", - " 13\n", + " 14\n", " organizations\n", " name\n", " string\n", @@ -3784,7 +3649,7 @@ " False\n", " \n", " \n", - " 14\n", + " 15\n", " organizations\n", " nuts_level1_code\n", " string\n", @@ -3794,7 +3659,7 @@ " True\n", " \n", " \n", - " 15\n", + " 16\n", " organizations\n", " nuts_level1_name\n", " string\n", @@ -3804,7 +3669,7 @@ " True\n", " \n", " \n", - " 16\n", + " 17\n", " organizations\n", " nuts_level2_code\n", " string\n", @@ -3814,7 +3679,7 @@ " True\n", " \n", " \n", - " 17\n", + " 18\n", " organizations\n", " nuts_level2_name\n", " string\n", @@ -3824,7 +3689,7 @@ " True\n", " \n", " \n", - " 18\n", + " 19\n", " organizations\n", " nuts_level3_code\n", " string\n", @@ -3834,7 +3699,7 @@ " True\n", " \n", " \n", - " 19\n", + " 20\n", " organizations\n", " nuts_level3_name\n", " string\n", @@ -3844,7 +3709,7 @@ " True\n", " \n", " \n", - " 20\n", + " 21\n", " organizations\n", " organization_child_ids\n", " string\n", @@ -3854,7 +3719,7 @@ " False\n", " \n", " \n", - " 21\n", + " 22\n", " organizations\n", " organization_parent_ids\n", " string\n", @@ -3864,7 +3729,7 @@ " False\n", " \n", " \n", - " 22\n", + " 23\n", " organizations\n", " organization_related_ids\n", " string\n", @@ -3874,7 +3739,7 @@ " False\n", " \n", " \n", - " 23\n", + " 24\n", " organizations\n", " orgref_ids\n", " string\n", @@ -3884,7 +3749,7 @@ " False\n", " \n", " \n", - " 24\n", + " 25\n", " organizations\n", " redirect\n", " string\n", @@ -3894,7 +3759,7 @@ " False\n", " \n", " \n", - " 25\n", + " 26\n", " organizations\n", " ror_ids\n", " string\n", @@ -3904,7 +3769,17 @@ " False\n", " \n", " \n", - " 26\n", + " 27\n", + " organizations\n", + " score\n", + " float\n", + " For full-text queries, the relevance score is ...\n", + " True\n", + " False\n", + " False\n", + " \n", + " \n", + " 28\n", " organizations\n", " state_name\n", " string\n", @@ -3914,7 +3789,7 @@ " True\n", " \n", " \n", - " 27\n", + " 29\n", " organizations\n", " status\n", " string\n", @@ -3924,7 +3799,7 @@ " True\n", " \n", " \n", - " 28\n", + " 30\n", " organizations\n", " types\n", " string\n", @@ -3934,7 +3809,7 @@ " True\n", " \n", " \n", - " 29\n", + " 31\n", " organizations\n", " ucas_ids\n", " string\n", @@ -3944,7 +3819,7 @@ " False\n", " \n", " \n", - " 30\n", + " 32\n", " organizations\n", " ukprn_ids\n", " string\n", @@ -3954,7 +3829,7 @@ " False\n", " \n", " \n", - " 31\n", + " 33\n", " organizations\n", " wikidata_ids\n", " string\n", @@ -3964,7 +3839,7 @@ " False\n", " \n", " \n", - " 32\n", + " 34\n", " organizations\n", " wikipedia_url\n", " string\n", @@ -3982,78 +3857,82 @@ "0 organizations acronym string \n", "1 organizations city_name string \n", "2 organizations cnrs_ids string \n", - "3 organizations country_name string \n", - "4 organizations dimensions_url string \n", - "5 organizations established integer \n", - "6 organizations external_ids_fundref string \n", - "7 organizations hesa_ids string \n", - "8 organizations id string \n", - "9 organizations isni_ids string \n", - "10 organizations latitude float \n", - "11 organizations linkout string \n", - "12 organizations longitude float \n", - "13 organizations name string \n", - "14 organizations nuts_level1_code string \n", - "15 organizations nuts_level1_name string \n", - "16 organizations nuts_level2_code string \n", - "17 organizations nuts_level2_name string \n", - "18 organizations nuts_level3_code string \n", - "19 organizations nuts_level3_name string \n", - "20 organizations organization_child_ids string \n", - "21 organizations organization_parent_ids string \n", - "22 organizations organization_related_ids string \n", - "23 organizations orgref_ids string \n", - "24 organizations redirect string \n", - "25 organizations ror_ids string \n", - "26 organizations state_name string \n", - "27 organizations status string \n", - "28 organizations types string \n", - "29 organizations ucas_ids string \n", - "30 organizations ukprn_ids string \n", - "31 organizations wikidata_ids string \n", - "32 organizations wikipedia_url string \n", + "3 organizations country_code string \n", + "4 organizations country_name string \n", + "5 organizations dimensions_url string \n", + "6 organizations established integer \n", + "7 organizations external_ids_fundref string \n", + "8 organizations hesa_ids string \n", + "9 organizations id string \n", + "10 organizations isni_ids string \n", + "11 organizations latitude float \n", + "12 organizations linkout string \n", + "13 organizations longitude float \n", + "14 organizations name string \n", + "15 organizations nuts_level1_code string \n", + "16 organizations nuts_level1_name string \n", + "17 organizations nuts_level2_code string \n", + "18 organizations nuts_level2_name string \n", + "19 organizations nuts_level3_code string \n", + "20 organizations nuts_level3_name string \n", + "21 organizations organization_child_ids string \n", + "22 organizations organization_parent_ids string \n", + "23 organizations organization_related_ids string \n", + "24 organizations orgref_ids string \n", + "25 organizations redirect string \n", + "26 organizations ror_ids string \n", + "27 organizations score float \n", + "28 organizations state_name string \n", + "29 organizations status string \n", + "30 organizations types string \n", + "31 organizations ucas_ids string \n", + "32 organizations ukprn_ids string \n", + "33 organizations wikidata_ids string \n", + "34 organizations wikipedia_url string \n", "\n", " description is_filter is_entity \\\n", "0 GRID acronym of the organization. E.g., \"UT\" f... True False \n", "1 GRID name of the organization country. E.g., \"... True False \n", "2 CNRS IDs for this organization True False \n", - "3 GRID name of the organization country. E.g., \"... True False \n", - "4 Link pointing to the Dimensions web application False False \n", - "5 Year when the organization was estabilished True False \n", - "6 Fundref IDs for this organization True False \n", - "7 HESA IDs for this organization True False \n", - "8 GRID ID of the organization. E.g., \"grid.26999... True False \n", - "9 ISNI IDs for this organization True False \n", - "10 None False False \n", + "3 Country of the organisation, identified using ... True False \n", + "4 GRID name of the organization country. E.g., \"... True False \n", + "5 Link pointing to the Dimensions web application False False \n", + "6 Year when the organization was estabilished True False \n", + "7 Fundref IDs for this organization True False \n", + "8 HESA IDs for this organization True False \n", + "9 GRID ID of the organization. E.g., \"grid.26999... True False \n", + "10 ISNI IDs for this organization True False \n", "11 None False False \n", "12 None False False \n", - "13 GRID name of the organization. E.g., \"Universi... True False \n", - "14 Level 1 code for this organization, based on `... True False \n", - "15 Level 1 name for this organization, based on `... True False \n", - "16 Level 2 code for this organization, based on `... True False \n", - "17 Level 2 name for this organization, based on `... True False \n", - "18 Level 3 code for this organization, based on `... True False \n", - "19 Level 3 name for this organization, based on `... True False \n", - "20 Child organization IDs True False \n", - "21 Parent organization IDs True False \n", - "22 Related organization IDs True False \n", - "23 OrgRef IDs for this organization True False \n", - "24 GRID ID of an organization this one was redire... True False \n", - "25 ROR IDs for this organization True False \n", - "26 GRID name of the organization country. E.g., \"... True False \n", - "27 Status of an organization. May be be one of:\\n... True False \n", - "28 Type of an organization. Available types inclu... True False \n", - "29 UCAS IDs for this organization True False \n", - "30 UKPRN IDs for this organization True False \n", - "31 WikiData IDs for this organization True False \n", - "32 Wikipedia URL False False \n", + "13 None False False \n", + "14 GRID name of the organization. E.g., \"Universi... True False \n", + "15 Level 1 code for this organization, based on `... True False \n", + "16 Level 1 name for this organization, based on `... True False \n", + "17 Level 2 code for this organization, based on `... True False \n", + "18 Level 2 name for this organization, based on `... True False \n", + "19 Level 3 code for this organization, based on `... True False \n", + "20 Level 3 name for this organization, based on `... True False \n", + "21 Child organization IDs True False \n", + "22 Parent organization IDs True False \n", + "23 Related organization IDs True False \n", + "24 OrgRef IDs for this organization True False \n", + "25 GRID ID of an organization this one was redire... True False \n", + "26 ROR IDs for this organization True False \n", + "27 For full-text queries, the relevance score is ... True False \n", + "28 GRID name of the organization country. E.g., \"... True False \n", + "29 Status of an organization. May be be one of:\\n... True False \n", + "30 Type of an organization. Available types inclu... True False \n", + "31 UCAS IDs for this organization True False \n", + "32 UKPRN IDs for this organization True False \n", + "33 WikiData IDs for this organization True False \n", + "34 Wikipedia URL False False \n", "\n", " is_facet \n", "0 False \n", "1 True \n", "2 False \n", "3 True \n", - "4 False \n", + "4 True \n", "5 False \n", "6 False \n", "7 False \n", @@ -4063,25 +3942,27 @@ "11 False \n", "12 False \n", "13 False \n", - "14 True \n", + "14 False \n", "15 True \n", "16 True \n", "17 True \n", "18 True \n", "19 True \n", - "20 False \n", + "20 True \n", "21 False \n", "22 False \n", "23 False \n", "24 False \n", "25 False \n", - "26 True \n", - "27 True \n", + "26 False \n", + "27 False \n", "28 True \n", - "29 False \n", - "30 False \n", + "29 True \n", + "30 True \n", "31 False \n", - "32 False " + "32 False \n", + "33 False \n", + "34 False " ] }, "execution_count": 15, @@ -4148,17 +4029,29 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "79f6b38840974891aa943514642fe463", + "model_id": "a689d15c56d44fcbb5076b72f75f8ae6", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/33 [00:00\n", " 0\n", " All Organizations (no filter)\n", - " 107202\n", + " 406172.0\n", " \n", " \n", - " 5\n", + " 6\n", " dimensions_url\n", - " 107202\n", + " 406172.0\n", " \n", " \n", - " 14\n", + " 30\n", + " status\n", + " 406172.0\n", + " \n", + " \n", + " 15\n", " name\n", - " 107202\n", + " 406172.0\n", " \n", " \n", - " 9\n", - " id\n", - " 107202\n", + " 31\n", + " types\n", + " 406087.0\n", " \n", " \n", - " 28\n", - " status\n", - " 107202\n", + " 5\n", + " country_name\n", + " 406058.0\n", " \n", " \n", " 4\n", - " country_name\n", - " 104670\n", + " country_code\n", + " 406012.0\n", " \n", " \n", " 2\n", " city_name\n", - " 104598\n", + " 166300.0\n", " \n", " \n", - " 29\n", - " types\n", - " 104428\n", + " 14\n", + " longitude\n", + " 131622.0\n", " \n", " \n", - " 11\n", + " 12\n", " latitude\n", - " 103357\n", + " 131622.0\n", " \n", " \n", - " 13\n", - " longitude\n", - " 103357\n", + " 23\n", + " organization_parent_ids\n", + " 119442.0\n", " \n", " \n", - " 26\n", - " ror_ids\n", - " 103263\n", + " 13\n", + " linkout\n", + " 119290.0\n", " \n", " \n", - " 12\n", - " linkout\n", - " 102383\n", + " 27\n", + " ror_ids\n", + " 94038.0\n", " \n", " \n", - " 6\n", + " 7\n", " established\n", - " 90925\n", + " 91959.0\n", " \n", " \n", - " 10\n", - " isni_ids\n", - " 50018\n", + " 29\n", + " state_name\n", + " 66188.0\n", " \n", " \n", - " 32\n", - " wikidata_ids\n", - " 47950\n", + " 16\n", + " nuts_level1_code\n", + " 51585.0\n", " \n", " \n", - " 1\n", - " acronym\n", - " 41954\n", + " 18\n", + " nuts_level2_code\n", + " 51585.0\n", " \n", " \n", - " 27\n", - " state_name\n", - " 38081\n", + " 17\n", + " nuts_level1_name\n", + " 51585.0\n", " \n", " \n", - " 33\n", - " wikipedia_url\n", - " 36158\n", + " 21\n", + " nuts_level3_name\n", + " 51585.0\n", " \n", " \n", - " 18\n", + " 19\n", " nuts_level2_name\n", - " 34031\n", + " 51585.0\n", " \n", " \n", - " 19\n", + " 20\n", " nuts_level3_code\n", - " 34031\n", + " 51585.0\n", " \n", " \n", - 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" 839\n", + " 920.0\n", " \n", " \n", - " 31\n", + " 33\n", " ukprn_ids\n", - " 173\n", + " 172.0\n", " \n", " \n", - " 8\n", + " 9\n", " hesa_ids\n", - " 172\n", + " 171.0\n", " \n", " \n", - " 30\n", + " 32\n", " ucas_ids\n", - " 153\n", + " 152.0\n", + " \n", + " \n", + " 10\n", + " id\n", + " NaN\n", + " \n", + " \n", + " 28\n", + " score\n", + " NaN\n", " \n", " \n", "\n", "" ], "text/plain": [ - " filter_by results\n", - "0 All Organizations (no filter) 107202\n", - "5 dimensions_url 107202\n", - "14 name 107202\n", - "9 id 107202\n", - "28 status 107202\n", - "4 country_name 104670\n", - "2 city_name 104598\n", - "29 types 104428\n", - "11 latitude 103357\n", - "13 longitude 103357\n", - "26 ror_ids 103263\n", - "12 linkout 102383\n", - "6 established 90925\n", - "10 isni_ids 50018\n", - "32 wikidata_ids 47950\n", - "1 acronym 41954\n", - "27 state_name 38081\n", - "33 wikipedia_url 36158\n", - "18 nuts_level2_name 34031\n", - "19 nuts_level3_code 34031\n", - "20 nuts_level3_name 34031\n", - "16 nuts_level1_name 34031\n", - "15 nuts_level1_code 34031\n", - "17 nuts_level2_code 34031\n", - "24 orgref_ids 14997\n", - "22 organization_parent_ids 14525\n", - "7 external_ids_fundref 10199\n", - "25 redirect 4369\n", - "21 organization_child_ids 4262\n", - "23 organization_related_ids 4255\n", - "3 cnrs_ids 839\n", - "31 ukprn_ids 173\n", - "8 hesa_ids 172\n", - "30 ucas_ids 153" + " filter_by results\n", + "0 All Organizations (no filter) 406172.0\n", + "6 dimensions_url 406172.0\n", + "30 status 406172.0\n", + "15 name 406172.0\n", + "31 types 406087.0\n", + "5 country_name 406058.0\n", + "4 country_code 406012.0\n", + "2 city_name 166300.0\n", + "14 longitude 131622.0\n", + "12 latitude 131622.0\n", + "23 organization_parent_ids 119442.0\n", + "13 linkout 119290.0\n", + "27 ror_ids 94038.0\n", + "7 established 91959.0\n", + "29 state_name 66188.0\n", + "16 nuts_level1_code 51585.0\n", + "18 nuts_level2_code 51585.0\n", + "17 nuts_level1_name 51585.0\n", + "21 nuts_level3_name 51585.0\n", + "19 nuts_level2_name 51585.0\n", + "20 nuts_level3_code 51585.0\n", + "34 wikidata_ids 51499.0\n", + "11 isni_ids 49885.0\n", + "1 acronym 45701.0\n", + "35 wikipedia_url 33440.0\n", + "22 organization_child_ids 21945.0\n", + "25 orgref_ids 14577.0\n", + "8 external_ids_fundref 9406.0\n", + "26 redirect 5669.0\n", + "24 organization_related_ids 4751.0\n", + "3 cnrs_ids 920.0\n", + "33 ukprn_ids 172.0\n", + "9 hesa_ids 171.0\n", + "32 ucas_ids 152.0\n", + "10 id NaN\n", + "28 score NaN" ] }, "execution_count": 16, @@ -4452,7 +4357,6 @@ }, "data": [ { - "alignmentgroup": "True", "hovertemplate": "filter_by=%{x}
results=%{y}", "legendgroup": "", "marker": { @@ -4462,7 +4366,6 @@ } }, "name": "", - "offsetgroup": "", "orientation": "v", "showlegend": false, "textposition": "auto", @@ -4470,81 +4373,50 @@ "x": [ "All Organizations (no filter)", "dimensions_url", - "name", - "id", "status", + "name", + "types", "country_name", + "country_code", "city_name", - "types", - "latitude", "longitude", - "ror_ids", + "latitude", + "organization_parent_ids", "linkout", + "ror_ids", "established", - "isni_ids", - "wikidata_ids", - "acronym", "state_name", - "wikipedia_url", - "nuts_level2_name", - "nuts_level3_code", - "nuts_level3_name", - "nuts_level1_name", "nuts_level1_code", "nuts_level2_code", + "nuts_level1_name", + "nuts_level3_name", + "nuts_level2_name", + "nuts_level3_code", + "wikidata_ids", + "isni_ids", + "acronym", + "wikipedia_url", + "organization_child_ids", "orgref_ids", - "organization_parent_ids", "external_ids_fundref", "redirect", - "organization_child_ids", "organization_related_ids", "cnrs_ids", "ukprn_ids", "hesa_ids", - "ucas_ids" + "ucas_ids", + "id", + "score" ], "xaxis": "x", - "y": [ - 107202, - 107202, - 107202, - 107202, - 107202, - 104670, - 104598, - 104428, - 103357, - 103357, - 103263, - 102383, - 90925, - 50018, - 47950, - 41954, - 38081, - 36158, - 34031, - 34031, - 34031, - 34031, - 34031, - 34031, - 14997, - 14525, - 10199, - 4369, - 4262, - 4255, - 839, - 173, - 172, - 153 - ], + "y": { + "bdata": "AAAAAHDKGEEAAAAAcMoYQQAAAABwyhhBAAAAAHDKGEEAAAAAHMkYQQAAAACoyBhBAAAAAPDHGEEAAAAA4EwEQQAAAAAwEQBBAAAAADARAEEAAAAAICn9QAAAAACgH/1AAAAAAGD19kAAAAAAcHP2QAAAAADAKPBAAAAAACAw6UAAAAAAIDDpQAAAAAAgMOlAAAAAACAw6UAAAAAAIDDpQAAAAAAgMOlAAAAAAGAl6UAAAAAAoFvoQAAAAACgUOZAAAAAAABU4EAAAAAAQG7VQAAAAACAeMxAAAAAAABfwkAAAAAAACW2QAAAAAAAj7JAAAAAAADAjEAAAAAAAIBlQAAAAAAAYGVAAAAAAAAAY0AAAAAAAAD4fwAAAAAAAPh/", + "dtype": "f8" + }, "yaxis": "y" } ], "layout": { - "autosize": true, "barmode": "relative", "legend": { "tracegroupgap": 0 @@ -4728,57 +4600,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "heatmapgl" - } - ], "histogram": [ { "marker": { @@ -4921,11 +4742,10 @@ ], "scatter": [ { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 }, "type": "scatter" } @@ -4980,6 +4800,17 @@ "type": "scattergl" } ], + "scattermap": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermap" + } + ], "scattermapbox": [ { "marker": { @@ -5371,43 +5202,31 @@ }, "xaxis": { "anchor": "y", - "autorange": true, "domain": [ 0, 1 ], - "range": [ - -0.5, - 33.5 - ], "title": { "text": "filter_by" - }, - "type": "category" + } }, "yaxis": { "anchor": "x", - "autorange": true, "domain": [ 0, 1 ], - "range": [ - 0, - 112844.21052631579 - ], "title": { "text": "results" - }, - "type": "linear" + } } } }, - "image/png": 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", "text/html": [ - "
\n", + "
" + " }) }; " ] }, "metadata": {}, @@ -5451,7 +5270,7 @@ "source": [ "## Where to find out more\n", "\n", - "Please have a look at the [official documentation](https://docs.dimensions.ai/dsl/data-sources.html) for more information on the GRID source." + "Please have a look at the [official documentation](https://docs.dimensions.ai/dsl/data-sources.html) for more information on the organizations data source." ] } ], @@ -5481,7 +5300,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/cookbooks/8-organizations/2-Industry-Collaboration.ipynb b/cookbooks/8-organizations/2-Industry-Collaboration.ipynb index 310851ec..55967f0e 100644 --- a/cookbooks/8-organizations/2-Industry-Collaboration.ipynb +++ b/cookbooks/8-organizations/2-Industry-Collaboration.ipynb @@ -11,7 +11,7 @@ "source": [ "# Identifying the Industry Collaborators of an Academic Institution\n", "\n", - "Dimensions uses [GRID](https://grid.ac/) identifiers for institutions, hence you can take advantage of the GRID metadata with Dimensions queries. \n", + "Dimensions has an enormous amount of data about organizations and you can query this data with the Dimensions Analytics API.\n", "\n", "In this tutorial we identify all organizations that have an `industry` type. \n", "\n", @@ -29,7 +29,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Jan 25, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -64,19 +64,9 @@ "text/html": [ " \n", + " \n", " " ] }, @@ -96,8 +86,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -150,8 +140,8 @@ "id": "L6uIjSVnGRQV" }, "source": [ - "For the purpose of this exercise, we will use [University of Trento, Italy (grid.11696.39)](https://grid.ac/institutes/grid.11696.39) as a starting point. \n", - "You can pick any other GRID organization of course. Just use a [DSL query](https://digital-science.github.io/dimensions-api-lab/cookbooks/8-organizations/1-GRID-preview.html) or the [GRID website](https://grid.ac/institutes) to discover the ID of an organization that interests you. " + "For the purpose of this exercise, we will use University of Trento, Italy (organization ID `grid.11696.39`) as a starting point. \n", + "You can pick any other organization of course. Just use a [DSL query](https://digital-science.github.io/dimensions-api-lab/cookbooks/8-organizations/1-GRID-preview.html) to discover the ID of an organization that interests you. " ] }, { @@ -182,7 +172,7 @@ { "data": { "text/html": [ - "GRID: grid.11696.39 - University of Trento ⧉" + "Organization: grid.11696.39 - University of Trento ⧉" ], "text/plain": [ "" @@ -206,7 +196,7 @@ ], "source": [ "#@markdown The main organization we are interested in:\n", - "GRIDID = \"grid.11696.39\" #@param {type:\"string\"}\n", + "ORGID = \"grid.11696.39\" #@param {type:\"string\"}\n", " \n", "#@markdown The start/end year of publications used to extract industry collaborations:\n", "YEAR_START = 2000 #@param {type: \"slider\", min: 1950, max: 2020}\n", @@ -219,11 +209,11 @@ "# gen link to Dimensions\n", "#\n", "try:\n", - " gridname = dsl.query(f\"\"\"search organizations where id=\"{GRIDID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", + " orgname = dsl.query(f\"\"\"search organizations where id=\"{ORGID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", "except:\n", - " gridname = \"\"\n", - "from IPython.core.display import display, HTML\n", - "display(HTML('GRID: {} - {} ⧉'.format(dimensions_url(GRIDID), GRIDID, gridname)))\n", + " orgname = \"\"\n", + "from IPython.display import display, HTML\n", + "display(HTML('Organization: {} - {} ⧉'.format(dimensions_url(ORGID), ORGID, orgname)))\n", "display(HTML('Time period: {} to {}

'.format(YEAR_START, YEAR_END)))\n" ] }, @@ -273,39 +263,58 @@ "output_type": "stream", "text": [ "Starting iteration with limit=1000 skip=0 ...\u001b[0m\n", - "0-1000 / 30088 (0.63s)\u001b[0m\n", - "1000-2000 / 30088 (0.57s)\u001b[0m\n", - "2000-3000 / 30088 (0.69s)\u001b[0m\n", - "3000-4000 / 30088 (0.52s)\u001b[0m\n", - "4000-5000 / 30088 (0.51s)\u001b[0m\n", - "5000-6000 / 30088 (0.61s)\u001b[0m\n", - "6000-7000 / 30088 (0.52s)\u001b[0m\n", - "7000-8000 / 30088 (0.56s)\u001b[0m\n", - "8000-9000 / 30088 (2.24s)\u001b[0m\n", - "9000-10000 / 30088 (0.56s)\u001b[0m\n", - "10000-11000 / 30088 (0.57s)\u001b[0m\n", - "11000-12000 / 30088 (0.58s)\u001b[0m\n", - "12000-13000 / 30088 (0.62s)\u001b[0m\n", - "13000-14000 / 30088 (1.74s)\u001b[0m\n", - "14000-15000 / 30088 (0.58s)\u001b[0m\n", - "15000-16000 / 30088 (0.49s)\u001b[0m\n", - "16000-17000 / 30088 (0.58s)\u001b[0m\n", - "17000-18000 / 30088 (0.53s)\u001b[0m\n", - "18000-19000 / 30088 (0.57s)\u001b[0m\n", - "19000-20000 / 30088 (0.50s)\u001b[0m\n", - "20000-21000 / 30088 (0.51s)\u001b[0m\n", - "21000-22000 / 30088 (0.51s)\u001b[0m\n", - "22000-23000 / 30088 (0.54s)\u001b[0m\n", - "23000-24000 / 30088 (0.50s)\u001b[0m\n", - "24000-25000 / 30088 (0.53s)\u001b[0m\n", - "25000-26000 / 30088 (0.62s)\u001b[0m\n", - "26000-27000 / 30088 (0.49s)\u001b[0m\n", - "27000-28000 / 30088 (0.48s)\u001b[0m\n", - "28000-29000 / 30088 (0.56s)\u001b[0m\n", - "29000-30000 / 30088 (0.90s)\u001b[0m\n", - "30000-30088 / 30088 (0.61s)\u001b[0m\n", + "0-1000 / 158994 (0.60s)\u001b[0m\n", + "1000-2000 / 158994 (0.59s)\u001b[0m\n", + "2000-3000 / 158994 (4.55s)\u001b[0m\n", + "3000-4000 / 158994 (0.58s)\u001b[0m\n", + "4000-5000 / 158994 (0.62s)\u001b[0m\n", + "5000-6000 / 158994 (2.44s)\u001b[0m\n", + "6000-7000 / 158994 (2.06s)\u001b[0m\n", + "7000-8000 / 158994 (4.03s)\u001b[0m\n", + "8000-9000 / 158994 (1.97s)\u001b[0m\n", + "9000-10000 / 158994 (2.47s)\u001b[0m\n", + "10000-11000 / 158994 (0.63s)\u001b[0m\n", + "11000-12000 / 158994 (0.57s)\u001b[0m\n", + "12000-13000 / 158994 (1.93s)\u001b[0m\n", + "13000-14000 / 158994 (0.65s)\u001b[0m\n", + "14000-15000 / 158994 (2.35s)\u001b[0m\n", + "15000-16000 / 158994 (2.11s)\u001b[0m\n", + "16000-17000 / 158994 (3.86s)\u001b[0m\n", + "17000-18000 / 158994 (6.32s)\u001b[0m\n", + "18000-19000 / 158994 (0.71s)\u001b[0m\n", + "19000-20000 / 158994 (3.59s)\u001b[0m\n", + "20000-21000 / 158994 (0.72s)\u001b[0m\n", + "21000-22000 / 158994 (3.72s)\u001b[0m\n", + "22000-23000 / 158994 (5.98s)\u001b[0m\n", + "23000-24000 / 158994 (0.61s)\u001b[0m\n", + "24000-25000 / 158994 (0.71s)\u001b[0m\n", + "25000-26000 / 158994 (1.70s)\u001b[0m\n", + "26000-27000 / 158994 (1.34s)\u001b[0m\n", + "27000-28000 / 158994 (4.45s)\u001b[0m\n", + "28000-29000 / 158994 (0.70s)\u001b[0m\n", + "29000-30000 / 158994 (0.56s)\u001b[0m\n", + "30000-31000 / 158994 (1.76s)\u001b[0m\n", + "31000-32000 / 158994 (0.64s)\u001b[0m\n", + "32000-33000 / 158994 (0.58s)\u001b[0m\n", + "33000-34000 / 158994 (0.70s)\u001b[0m\n", + "34000-35000 / 158994 (2.38s)\u001b[0m\n", + "35000-36000 / 158994 (2.06s)\u001b[0m\n", + "36000-37000 / 158994 (0.71s)\u001b[0m\n", + "37000-38000 / 158994 (3.82s)\u001b[0m\n", + "38000-39000 / 158994 (0.65s)\u001b[0m\n", + "39000-40000 / 158994 (4.50s)\u001b[0m\n", + "40000-41000 / 158994 (5.95s)\u001b[0m\n", + "41000-42000 / 158994 (0.81s)\u001b[0m\n", + "42000-43000 / 158994 (0.65s)\u001b[0m\n", + "43000-44000 / 158994 (4.53s)\u001b[0m\n", + "44000-45000 / 158994 (0.61s)\u001b[0m\n", + "45000-46000 / 158994 (4.54s)\u001b[0m\n", + "46000-47000 / 158994 (0.60s)\u001b[0m\n", + "47000-48000 / 158994 (0.78s)\u001b[0m\n", + "48000-49000 / 158994 (0.74s)\u001b[0m\n", + "49000-50000 / 158994 (0.68s)\u001b[0m\n", "===\n", - "Records extracted: 30088\u001b[0m\n" + "Records extracted: 50000\u001b[0m\n" ] } ], @@ -350,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "Collapsed": "false", "colab": { @@ -390,18 +399,18 @@ "===\n", "Extracting grid.11696.39 publications with industry collaborators ...\n", "Records per query : 1000\n", - "GRID IDs per query: 200\n" + "Organization IDs per query: 200\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f9ce542da620433da388b9c568a55130", + "model_id": "ec0375ac4b8f455893d5d882ad2543a1", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/151 [00:00\n", " \n", " 0\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/0264-9381/33/23/235015\n", - " pub.1059063534\n", - " 7\n", - " article\n", + " [{'affiliations': [{'city': 'Dublin', 'city_id...\n", + " 10.1109/eucnc.2016.7561056\n", + " pub.1094950798\n", + " 28\n", + " proceeding\n", " 2016\n", " \n", " \n", " 1\n", " [{'affiliations': [{'city': 'Dublin', 'city_id...\n", - " 10.1145/2984356.2984363\n", - " pub.1001653422\n", - " 14\n", + " 10.1109/noms.2016.7503003\n", + " pub.1094654631\n", + " 35\n", " proceeding\n", " 2016\n", " \n", " \n", " 2\n", - " [{'affiliations': [{'city': 'Stuttgart', 'city...\n", - " 10.1016/j.apnum.2016.02.001\n", - " pub.1038596770\n", - " 12\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1002/adem.201400134\n", + " pub.1049335111\n", + " 50\n", " article\n", - " 2016\n", + " 2014\n", " \n", " \n", " 3\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1103/physrevlett.116.231101\n", - " pub.1001053038\n", - " 313\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1111/j.1551-2916.2005.00043.x\n", + " pub.1042663343\n", + " 64\n", " article\n", - " 2016\n", + " 2005\n", " \n", " \n", " 4\n", - " [{'affiliations': [{'city': 'Dublin', 'city_id...\n", - " 10.1109/eucnc.2016.7561056\n", - " pub.1094950798\n", - " 22\n", - " proceeding\n", - " 2016\n", + " [{'affiliations': [{'city': 'Legnaro', 'city_i...\n", + " 10.1016/s0168-583x(03)01322-3\n", + " pub.1041242454\n", + " 14\n", + " article\n", + " 2003\n", " \n", " \n", " 5\n", - " [{'affiliations': [{'city': 'Trento', 'city_id...\n", - " 10.1140/epjds/s13688-016-0064-6\n", - " pub.1033140941\n", - " 15\n", + " [{'affiliations': [{'city': 'MENLO PARK', 'cit...\n", + " 10.1111/jace.12485\n", + " pub.1033867339\n", + " 48\n", " article\n", - " 2016\n", + " 2013\n", " \n", " \n", " 6\n", " [{'affiliations': [{'city': 'Trento', 'city_id...\n", - " 10.1089/big.2014.0054\n", - " pub.1018945654\n", - " 48\n", - " article\n", - " 2015\n", + " 10.1145/2663204.2663254\n", + " pub.1033777395\n", + " 225\n", + " proceeding\n", + " 2014\n", " \n", " \n", " 7\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/1742-6596/610/1/012027\n", - " pub.1031150191\n", - " 1\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1140/epjds/s13688-016-0064-6\n", + " pub.1033140941\n", + " 25\n", " article\n", - " 2015\n", + " 2016\n", " \n", " \n", " 8\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/1742-6596/610/1/012005\n", - " pub.1052522882\n", - " 17\n", - " article\n", - " 2015\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1145/2063518.2063544\n", + " pub.1028019246\n", + " 1\n", + " proceeding\n", + " 2011\n", " \n", " \n", " 9\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/1742-6596/610/1/012026\n", - " pub.1033837350\n", - " 2\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1089/big.2014.0054\n", + " pub.1018945654\n", + " 68\n", " article\n", " 2015\n", " \n", @@ -542,64 +551,64 @@ ], "text/plain": [ " authors \\\n", - "0 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", + "0 [{'affiliations': [{'city': 'Dublin', 'city_id... \n", "1 [{'affiliations': [{'city': 'Dublin', 'city_id... \n", - "2 [{'affiliations': [{'city': 'Stuttgart', 'city... \n", - "3 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", - "4 [{'affiliations': [{'city': 'Dublin', 'city_id... \n", - "5 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "2 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "3 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "4 [{'affiliations': [{'city': 'Legnaro', 'city_i... \n", + "5 [{'affiliations': [{'city': 'MENLO PARK', 'cit... \n", "6 [{'affiliations': [{'city': 'Trento', 'city_id... \n", - "7 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", - "8 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", - "9 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", + "7 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "8 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "9 [{'affiliations': [{'city': 'Trento', 'city_id... \n", "\n", - " doi id times_cited type \\\n", - "0 10.1088/0264-9381/33/23/235015 pub.1059063534 7 article \n", - "1 10.1145/2984356.2984363 pub.1001653422 14 proceeding \n", - "2 10.1016/j.apnum.2016.02.001 pub.1038596770 12 article \n", - "3 10.1103/physrevlett.116.231101 pub.1001053038 313 article \n", - "4 10.1109/eucnc.2016.7561056 pub.1094950798 22 proceeding \n", - "5 10.1140/epjds/s13688-016-0064-6 pub.1033140941 15 article \n", - "6 10.1089/big.2014.0054 pub.1018945654 48 article \n", - "7 10.1088/1742-6596/610/1/012027 pub.1031150191 1 article \n", - "8 10.1088/1742-6596/610/1/012005 pub.1052522882 17 article \n", - "9 10.1088/1742-6596/610/1/012026 pub.1033837350 2 article \n", + " doi id times_cited type \\\n", + "0 10.1109/eucnc.2016.7561056 pub.1094950798 28 proceeding \n", + "1 10.1109/noms.2016.7503003 pub.1094654631 35 proceeding \n", + "2 10.1002/adem.201400134 pub.1049335111 50 article \n", + "3 10.1111/j.1551-2916.2005.00043.x pub.1042663343 64 article \n", + "4 10.1016/s0168-583x(03)01322-3 pub.1041242454 14 article \n", + "5 10.1111/jace.12485 pub.1033867339 48 article \n", + "6 10.1145/2663204.2663254 pub.1033777395 225 proceeding \n", + "7 10.1140/epjds/s13688-016-0064-6 pub.1033140941 25 article \n", + "8 10.1145/2063518.2063544 pub.1028019246 1 proceeding \n", + "9 10.1089/big.2014.0054 pub.1018945654 68 article \n", "\n", " year \n", "0 2016 \n", "1 2016 \n", - "2 2016 \n", - "3 2016 \n", - "4 2016 \n", - "5 2016 \n", - "6 2015 \n", - "7 2015 \n", - "8 2015 \n", + "2 2014 \n", + "3 2005 \n", + "4 2003 \n", + "5 2013 \n", + "6 2014 \n", + "7 2016 \n", + "8 2011 \n", "9 2015 " ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "gridis = list(company_grids.as_dataframe()['id'])\n", + "orgids = list(company_grids.as_dataframe()['id'])\n", "\n", "#\n", - "# loop through all grids\n", + "# loop through all organizations\n", "\n", "ITERATION_RECORDS = 1000 # Publication records per query iteration\n", - "GRID_RECORDS = 200 # grid IDs per query\n", + "ORG_RECORDS = 200 # organization IDs per query\n", "VERBOSE = False # set to True to view full extraction logs\n", - "print(f\"===\\nExtracting {GRIDID} publications with industry collaborators ...\")\n", + "print(f\"===\\nExtracting {ORGID} publications with industry collaborators ...\")\n", "print(\"Records per query : \", ITERATION_RECORDS)\n", - "print(\"GRID IDs per query: \", GRID_RECORDS)\n", + "print(\"Organization IDs per query: \", ORG_RECORDS)\n", "results = []\n", "\n", "\n", - "for chunk in progress(list(chunks_of(gridis, GRID_RECORDS))):\n", - " query = query_template.format(GRIDID, json.dumps(chunk), YEAR_START, YEAR_END)\n", + "for chunk in progress(list(chunks_of(orgids, ORG_RECORDS))):\n", + " query = query_template.format(ORGID, json.dumps(chunk), YEAR_START, YEAR_END)\n", "# print(query)\n", " data = dsl.query_iterative(query, verbose=VERBOSE, limit=ITERATION_RECORDS)\n", " if data.errors:\n", @@ -641,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": { "Collapsed": "false", "colab": { @@ -672,382 +681,48 @@ }, "data": [ { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "type=article
year=%{x}
count=%{y}", - "legendgroup": "article", + "hovertemplate": "type=proceeding
year=%{x}
count=%{y}", + "legendgroup": "proceeding", "marker": { "color": "#636efa", "pattern": { "shape": "" } }, - "name": "article", - "offsetgroup": "article", + "name": "proceeding", "orientation": "v", "showlegend": true, "type": "histogram", - "x": [ - 2016, - 2016, - 2016, - 2016, - 2015, - 2015, - 2015, - 2015, - 2015, - 2015, - 2015, - 2015, - 2015, - 2014, - 2013, - 2013, - 2012, - 2012, - 2011, - 2011, - 2011, - 2011, - 2010, - 2009, - 2009, - 2008, - 2008, - 2008, - 2007, - 2005, - 2005, - 2005, - 2005, - 2004, - 2016, - 2003, - 2015, - 2009, - 2013, - 2012, - 2015, - 2016, - 2016, - 2015, - 2004, - 2014, - 2016, - 2014, - 2013, - 2016, - 2016, - 2016, - 2016, - 2016, - 2015, - 2015, - 2005, - 2016, - 2016, - 2015, - 2014, - 2012, - 2010, - 2008, - 2008, - 2008, - 2016, - 2015, - 2013, - 2014, - 2013, - 2009, - 2016, - 2016, - 2016, - 2015, - 2010, - 2016, - 2016, - 2011, - 2009, - 2008, - 2015, - 2014, - 2013, - 2013, - 2011, - 2011, - 2011, - 2009, - 2008, - 2016, - 2014, - 2011, - 2015, - 2009, - 2011, - 2015, - 2014, - 2016, - 2011, - 2015, - 2005, - 2004, - 2015, - 2015, - 2015, - 2006, - 2006, - 2012, - 2011, - 2015, - 2010, - 2012, - 2016, - 2015, - 2015, - 2012, - 2011, - 2004, - 2002, - 2016, - 2015, - 2014, - 2011, - 2016, - 2008, - 2002, - 2016, - 2015, - 2015, - 2015, - 2014, - 2014, - 2014, - 2013, - 2004, - 2003, - 2003, - 2005, - 2016, - 2014, - 2014, - 2012, - 2012, - 2003, - 2016, - 2015, - 2014, - 2014, - 2011, - 2007, - 2004, - 2003, - 2006, - 2006, - 2014, - 2012, - 2011, - 2008, - 2016, - 2016, - 2016, - 2016, - 2016, - 2014, - 2014, - 2014, - 2014, - 2014, - 2014, - 2014, - 2013, - 2013, - 2013, - 2013, - 2012, - 2011, - 2011, - 2010, - 2010, - 2009, - 2008, - 2008, - 2007, - 2007, - 2007, - 2006, - 2005, - 2016, - 2016, - 2016, - 2016, - 2016, - 2016, - 2015, - 2015, - 2011, - 2011, - 2011, - 2010, - 2010, - 2005, - 2015, - 2014, - 2014, - 2012, - 2009, - 2009, - 2009, - 2009, - 2009, - 2006, - 2006, - 2006, - 2006, - 2006, - 2005, - 2004, - 2016, - 2016, - 2011, - 2007, - 2007, - 2006, - 2016, - 2014, - 2011, - 2007, - 2006, - 2000 - ], + "x": { + "bdata": "4AfgB94H2wfeB98H4AfdB9wH3gfXB98H3QfWB9YH1gfWB9cH3wfYBw==", + "dtype": "i2" + }, "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "type=proceeding
year=%{x}
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" + " }) }; " ] }, "metadata": {}, @@ -2030,7 +1611,7 @@ "px.histogram(pubs, \n", " x=\"year\", \n", " color=\"type\",\n", - " title=f\"Publications per year with industry collaborations for {GRIDID}\")" + " title=f\"Publications per year with industry collaborations for {ORGID}\")" ] }, { @@ -2046,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "Collapsed": "false", "colab": { @@ -2077,7 +1658,6 @@ }, "data": [ { - "alignmentgroup": "True", "hovertemplate": "year=%{x}
times_cited=%{y}", "legendgroup": "", "marker": { @@ -2087,53 +1667,23 @@ } }, "name": "", - "offsetgroup": "", "orientation": "v", "showlegend": false, "textposition": "auto", "type": "bar", - "x": [ - 2000, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, - 2014, - 2015, - 2016 - ], + "x": { + "bdata": "0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH", + "dtype": "i2" + }, "xaxis": "x", - "y": [ - 37, - 106, - 146, - 249, - 449, - 839, - 278, - 592, - 1940, - 550, - 729, - 499, - 634, - 1135, - 3539, - 6701 - ], + "y": { + "bdata": "UwEhAAUAGgEXAhYARACSAJEAyQCuAOwASgRgAZ0f", + "dtype": "i2" + }, "yaxis": "y" } ], "layout": { - "autosize": true, "barmode": "relative", "legend": { "tracegroupgap": 0 @@ -2317,57 +1867,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - 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true, "domain": [ 0, 1 ], - "range": [ - 0, - 7053.684210526316 - ], "title": { "text": "times_cited" - }, - "type": "linear" + } } } }, - "image/png": 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" + " }) }; " ] }, "metadata": {}, @@ -3030,7 +2527,7 @@ "px.bar(pubs_grouped, \n", " x=\"year\", \n", " y=\"times_cited\",\n", - " title=f\"Tot Citations per year for publications with industry collaborations for {GRIDID}\")" + " title=f\"Tot Citations per year for publications with industry collaborations for {ORGID}\")" ] }, { @@ -3120,7 +2617,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": { "Collapsed": "false", "colab": { @@ -3143,6 +2640,16 @@ "outputId": "a997bc97-e4b6-4b32-f63f-1739e921a1df" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/apilab/lib/python3.12/site-packages/dimcli/core/dataframe_factory.py:195: FutureWarning:\n", + "\n", + "Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + "\n" + ] + }, { "data": { "text/html": [ @@ -3181,120 +2688,120 @@ " \n", " \n", " \n", - " 7\n", - " Hamburg\n", - " 2911298.0\n", - " Germany\n", - " DE\n", - " grid.410308.e\n", - " Airbus (Germany)\n", - " Airbus Defence and Space, Claude-Dornier-Stras...\n", - " \n", + " 2\n", + " Paris\n", + " 2988507.0\n", + " France\n", + " FR\n", + " grid.89485.38\n", + " Orange SA\n", + " Orange S.A., France\n", " \n", - " pub.1059063534\n", " \n", - " N\n", - " Brandt\n", + " pub.1094950798\n", + " ur.014561075723.99\n", + " Imen Grida Ben\n", + " Yahia\n", " \n", " \n", - " 8\n", - " Milan\n", - " 3173435.0\n", - " Italy\n", - " IT\n", - " grid.424032.3\n", - " OHB (Italy)\n", - " CGS S.p.A, Compagnia Generale per lo Spazio, V...\n", + " 7\n", + " Madrid\n", + " 3117735.0\n", + " Spain\n", + " ES\n", + " grid.99308.3b\n", + " Telefonica Investigacion y Desarrollo SA\n", + " Telefonica I+D, Spain\n", " \n", " \n", - " pub.1059063534\n", - " ur.014542047336.90\n", - " A\n", - " Bursi\n", + " pub.1094950798\n", + " ur.016452362717.24\n", + " Antonio\n", + " Pastor\n", " \n", " \n", - " 15\n", - " Milan\n", - " 3173435.0\n", - " Italy\n", - " IT\n", - " grid.424032.3\n", - " OHB (Italy)\n", - " CGS S.p.A, Compagnia Generale per lo Spazio, V...\n", - " \n", + " 8\n", + " Madrid\n", + " 3117735.0\n", + " Spain\n", + " ES\n", + " grid.99308.3b\n", + " Telefonica Investigacion y Desarrollo SA\n", + " Telefonica I+D, Spain\n", " \n", - " pub.1059063534\n", " \n", - " D\n", - " Desiderio\n", + " pub.1094950798\n", + " ur.014322160107.95\n", + " Pedro A.\n", + " Aranda\n", " \n", " \n", - " 16\n", - " Milan\n", - " 3173435.0\n", - " Italy\n", - " IT\n", - " grid.424032.3\n", - " OHB (Italy)\n", - " CGS S.p.A, Compagnia Generale per lo Spazio, V...\n", + " 24\n", + " Madrid\n", + " 3117735.0\n", + " Spain\n", + " ES\n", + " grid.99308.3b\n", + " Telefonica Investigacion y Desarrollo SA\n", + " Telefonica I+d\n", " \n", " \n", - " pub.1059063534\n", - " \n", - " E\n", - " Piersanti\n", + " pub.1094654631\n", + " ur.014574231073.91\n", + " Diego R.\n", + " Lopez\n", " \n", " \n", - " 19\n", - " Bristol\n", - " 2654675.0\n", - " United Kingdom\n", - " GB\n", - " grid.7546.0\n", - " Airbus (United Kingdom)\n", - " Airbus Defence and Space, Gunnels Wood Road, S...\n", + " 34\n", + " Paris\n", + " 2988507.0\n", + " France\n", + " FR\n", + " grid.89485.38\n", + " Orange SA\n", + " Orange, France\n", " \n", " \n", - " pub.1059063534\n", - " ur.010504106037.54\n", - " N\n", - " Dunbar\n", + " pub.1094654631\n", + " ur.014561075723.99\n", + " Imen Grida Ben\n", + " Yahia\n", " \n", " \n", "\n", "" ], "text/plain": [ - " aff_city aff_city_id aff_country aff_country_code aff_id \\\n", - "7 Hamburg 2911298.0 Germany DE grid.410308.e \n", - "8 Milan 3173435.0 Italy IT grid.424032.3 \n", - "15 Milan 3173435.0 Italy IT grid.424032.3 \n", - "16 Milan 3173435.0 Italy IT grid.424032.3 \n", - "19 Bristol 2654675.0 United Kingdom GB grid.7546.0 \n", + " aff_city aff_city_id aff_country aff_country_code aff_id \\\n", + "2 Paris 2988507.0 France FR grid.89485.38 \n", + "7 Madrid 3117735.0 Spain ES grid.99308.3b \n", + "8 Madrid 3117735.0 Spain ES grid.99308.3b \n", + "24 Madrid 3117735.0 Spain ES grid.99308.3b \n", + "34 Paris 2988507.0 France FR grid.89485.38 \n", "\n", - " aff_name \\\n", - "7 Airbus (Germany) \n", - "8 OHB (Italy) \n", - "15 OHB (Italy) \n", - "16 OHB (Italy) \n", - "19 Airbus (United Kingdom) \n", + " aff_name aff_raw_affiliation aff_state \\\n", + "2 Orange SA Orange S.A., France \n", + "7 Telefonica Investigacion y Desarrollo SA Telefonica I+D, Spain \n", + "8 Telefonica Investigacion y Desarrollo SA Telefonica I+D, Spain \n", + "24 Telefonica Investigacion y Desarrollo SA Telefonica I+d \n", + "34 Orange SA Orange, France \n", "\n", - " aff_raw_affiliation aff_state \\\n", - "7 Airbus Defence and Space, Claude-Dornier-Stras... \n", - "8 CGS S.p.A, Compagnia Generale per lo Spazio, V... \n", - "15 CGS S.p.A, Compagnia Generale per lo Spazio, V... \n", - "16 CGS S.p.A, Compagnia Generale per lo Spazio, V... \n", - "19 Airbus Defence and Space, Gunnels Wood Road, S... \n", + " aff_state_code pub_id researcher_id first_name \\\n", + "2 pub.1094950798 ur.014561075723.99 Imen Grida Ben \n", + "7 pub.1094950798 ur.016452362717.24 Antonio \n", + "8 pub.1094950798 ur.014322160107.95 Pedro A. \n", + "24 pub.1094654631 ur.014574231073.91 Diego R. \n", + "34 pub.1094654631 ur.014561075723.99 Imen Grida Ben \n", "\n", - " aff_state_code pub_id researcher_id first_name last_name \n", - "7 pub.1059063534 N Brandt \n", - "8 pub.1059063534 ur.014542047336.90 A Bursi \n", - "15 pub.1059063534 D Desiderio \n", - "16 pub.1059063534 E Piersanti \n", - "19 pub.1059063534 ur.010504106037.54 N Dunbar " + " last_name \n", + "2 Yahia \n", + "7 Pastor \n", + "8 Aranda \n", + "24 Lopez \n", + "34 Yahia " ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -3307,7 +2814,7 @@ "# extract affiliations as a dataframe\n", "affiliations = pubsnew.as_dataframe_authors_affiliations()\n", "# focus only on affiliations including a grid from the industry set created above\n", - "affiliations = affiliations[affiliations['aff_id' ].isin(gridis)]\n", + "affiliations = affiliations[affiliations['aff_id' ].isin(orgids)]\n", "# preview the data\n", "affiliations.head(5)" ] @@ -3327,7 +2834,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": { "Collapsed": "false", "colab": { @@ -3358,7 +2865,6 @@ }, "data": [ { - "alignmentgroup": "True", "bingroup": "x", "hovertemplate": "aff_name=%{x}
count=%{y}", "legendgroup": "", @@ -3369,801 +2875,218 @@ } }, "name": "", - "offsetgroup": "", "orientation": "v", "showlegend": false, "type": "histogram", "x": [ - "Airbus (Germany)", - "OHB (Italy)", - "OHB (Italy)", - "OHB (Italy)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "Airbus (Germany)", - "Airbus (Germany)", - "OHB (Italy)", - "OHB (Italy)", - "Airbus (Germany)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "Telefonica Research and Development", - "Robert Bosch (Germany)", - "Robert Bosch (Germany)", - "Robert Bosch (Germany)", - "Airbus (Germany)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "Airbus (Germany)", - "Airbus (Germany)", - "OHB (Italy)", - "Airbus (Germany)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "IBM (Ireland)", - "IBM (Ireland)", - "Orange (France)", - "IBM (Ireland)", - 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"Sweden", + "Sweden", "Italy", - "United Kingdom", - "Germany", - "Germany", - "Germany", "Italy", "Italy", - "Germany", - "United Kingdom", - "United Kingdom", - "United Kingdom", - "United Kingdom", - "Germany", - "Spain", - "Germany", - "Germany", - "Germany", - "Germany", - "United Kingdom", - "Germany", - "Germany", - "Germany", "Italy", - "Germany", - "United Kingdom", - "United Kingdom", + "Italy", + "Italy", + "Italy", + "Italy", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", "United Kingdom", + "Japan", + "Japan", + "Japan", + "Japan", + "Japan", + "Japan", + "Italy", + "Italy", + "United States", + "Italy", + "Italy", + "Sweden", + "Italy", + "Italy", + "Finland", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "Switzerland", + "Switzerland", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", "Germany", - "Ireland", - "Ireland", + "United States", "France", - "Ireland", - "Spain", - "Spain", - "Ireland", "Italy", "Italy", "Italy", - "Spain", "Italy", - "Spain", - "Germany", + "France", "Italy", - "United Kingdom", - "Germany", - "Germany", "Italy", "Italy", + "United States", + "United States", + "Italy", + "Italy", + "Italy", + "Italy", + "United States", + "United States", + "Switzerland", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", 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aff_name=%{x}
count=%{y}", - "legendgroup": "India", + "hovertemplate": "aff_country=Japan
aff_name=%{x}
count=%{y}", + "legendgroup": "Japan", "marker": { "color": "rgb(102,194,165)", "pattern": { "shape": "" } }, - "name": "India", - "offsetgroup": "India", + "name": "Japan", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Venus Remedies (India)", - "Tata Elxsi (India)", - "Samsung (India)", - "IBM (India)" + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=South Korea
aff_name=%{x}
count=%{y}", - "legendgroup": "South Korea", + "hovertemplate": "aff_country=Finland
aff_name=%{x}
count=%{y}", + "legendgroup": "Finland", "marker": { "color": "rgb(50,136,189)", "pattern": { "shape": "" } }, - "name": "South Korea", - "offsetgroup": "South Korea", + "name": "Finland", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Amorepacific (South Korea)" + "Nokia Research Center" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=Slovenia
aff_name=%{x}
count=%{y}", - "legendgroup": "Slovenia", + "hovertemplate": "aff_country=Switzerland
aff_name=%{x}
count=%{y}", + "legendgroup": "Switzerland", "marker": { "color": "rgb(94,79,162)", "pattern": { "shape": "" } }, - "name": "Slovenia", - "offsetgroup": "Slovenia", + "name": "Switzerland", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "EN-FIST Centre of Excellence (Slovenia)" + "Novartis Forschungsstiftung Zweigniederlassung Friedrich Miescher Institute for Biomedical Research", + "Novartis Forschungsstiftung Zweigniederlassung Friedrich Miescher Institute for Biomedical Research", + "Novartis Forschungsstiftung Zweigniederlassung Friedrich Miescher Institute for Biomedical Research" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=Russia
aff_name=%{x}
count=%{y}", - "legendgroup": "Russia", + "hovertemplate": "aff_country=Germany
aff_name=%{x}
count=%{y}", + "legendgroup": "Germany", "marker": { "color": "rgb(158,1,66)", "pattern": { "shape": "" } }, - "name": "Russia", - "offsetgroup": "Russia", + "name": "Germany", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Surface Phenomena Researches Group (Russia)", - "Surface Phenomena Researches Group (Russia)", - "Surface Phenomena Researches Group (Russia)", - "Surface Phenomena Researches Group (Russia)" + "Nokia Solutions and Networks GmbH and Co KG", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "MacDermid Enthone GmbH" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=Greece
aff_name=%{x}
count=%{y}", - "legendgroup": "Greece", + "hovertemplate": "aff_country=South Korea
aff_name=%{x}
count=%{y}", + "legendgroup": "South Korea", "marker": { "color": "rgb(213,62,79)", "pattern": { "shape": "" } }, - "name": "Greece", - "offsetgroup": "Greece", - "orientation": "v", - "showlegend": true, - "type": "histogram", - "x": [ - "Advanced Microwave Systems (Greece)", - "Advanced Microwave Systems (Greece)", - "Advanced Microwave Systems (Greece)", - "Athens Technology Center (Greece)", - "Athens Technology Center (Greece)", - "Athens Technology Center (Greece)" - ], - "xaxis": "x", - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "bingroup": "x", - "hovertemplate": "aff_country=Austria
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count=%{y}", - "legendgroup": "Austria", - "marker": { - "color": "rgb(244,109,67)", - "pattern": { - "shape": "" - } - }, - "name": "Austria", - "offsetgroup": "Austria", + "name": "South Korea", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Vienna Consulting Engineers (Austria)", - "Siemens (Austria)", - "Siemens (Austria)", - "Böhler Edelstahl (Austria)", - "Böhler Edelstahl (Austria)" + "Korea Hydro and Nuclear Power Co Ltd" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", "hovertemplate": "aff_country=Belgium
aff_name=%{x}
count=%{y}", "legendgroup": "Belgium", "marker": { - "color": "rgb(253,174,97)", + "color": "rgb(244,109,67)", "pattern": { "shape": "" } }, "name": "Belgium", - "offsetgroup": "Belgium", - "orientation": "v", - "showlegend": true, - "type": "histogram", - "x": [ - "Aquaplus (Belgium)", - "Aquaplus (Belgium)", - "Aquaplus (Belgium)", - "Aquaplus (Belgium)" - ], - "xaxis": "x", - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "bingroup": "x", - "hovertemplate": "aff_country=Liechtenstein
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count=%{y}", - "legendgroup": "Liechtenstein", - "marker": { - "color": "rgb(254,224,139)", - "pattern": { - "shape": "" - } - }, - "name": "Liechtenstein", - "offsetgroup": "Liechtenstein", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Ivoclar Vivadent (Liechtenstein)", - "Ivoclar Vivadent (Liechtenstein)" + "Vesuvius Group SA" ], "xaxis": "x", "yaxis": "y" } ], "layout": { - "autosize": true, "barmode": "relative", + "height": 900, "legend": { "title": { "text": "aff_country" @@ -8358,57 +5723,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 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"text/html": [ - "
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" + " }) }; " ] }, "metadata": {}, @@ -9070,7 +6383,7 @@ " x=\"aff_name\", \n", " height=900, \n", " color=\"aff_country\",\n", - " title=f\"Top Countries and Industry collaborators for {gridname}-{GRIDID}\",\n", + " title=f\"Top Countries and Industry collaborators for {orgname}-{ORGID}\",\n", " color_discrete_sequence=px.colors.diverging.Spectral)" ] } @@ -9099,7 +6412,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" }, "nteract": { "version": "0.15.0" diff --git a/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb b/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb index 9dc9cbcf..ce8f4f27 100644 --- a/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb +++ b/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb @@ -28,7 +28,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Aug 22, 2023\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -58,33 +58,14 @@ "Collapsed": "false" }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" - ] - }, { "data": { "text/html": [ " \n", + " \n", " " ] }, @@ -104,8 +85,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v1.1)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.7\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -159,9 +140,9 @@ "id": "L6uIjSVnGRQV" }, "source": [ - "For the purpose of this exercise, we will use [grid.412125.1](https://grid.ac/institutes/grid.412125.1) (King Abdulaziz University, Saudi Arabia). \n", + "For the purpose of this exercise, we will use King Abdulaziz University, Saudi Arabia (grid.412125.1). \n", "\n", - "> You can try using a different GRID ID to see how results change, e.g. by [browsing for another GRID organization](https://grid.ac/institutes).\n" + "> You can try using a different organization ID to see how results change." ] }, { @@ -174,7 +155,7 @@ { "data": { "text/html": [ - "GRID: grid.412125.1 - King Abdulaziz University ⧉" + "Organization: grid.412125.1 - King Abdulaziz University ⧉" ], "text/plain": [ "" @@ -209,7 +190,7 @@ } ], "source": [ - "GRIDID = \"grid.412125.1\" #@param {type:\"string\"}\n", + "ORGID = \"grid.412125.1\" #@param {type:\"string\"}\n", " \n", "#@markdown The start/end year of publications used to extract patents\n", "YEAR_START = 2000 #@param {type: \"slider\", min: 1950, max: 2020}\n", @@ -226,11 +207,11 @@ "# gen link to Dimensions\n", "#\n", "try:\n", - " gridname = dsl.query(f\"\"\"search organizations where id=\"{GRIDID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", + " orgname = dsl.query(f\"\"\"search organizations where id=\"{ORGID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", "except:\n", - " gridname = \"\"\n", + " orgname = \"\"\n", "from IPython.display import display, HTML\n", - "display(HTML('GRID: {} - {} ⧉'.format(dimensions_url(GRIDID), GRIDID, gridname)))\n", + "display(HTML('Organization: {} - {} ⧉'.format(dimensions_url(ORGID), ORGID, orgname)))\n", "display(HTML('Time period: {} to {}'.format(YEAR_START, YEAR_END)))\n", "display(HTML('Topic: \"{}\"

'.format(TOPIC)))\n" ] @@ -292,10 +273,10 @@ "Note: \n", "\n", "* **Extra columns**. The resulting dataframe contains two extra columns: a) `id_from`, which is the 'seed' institution we start from; b) `level`, an optional parameter representing the network depth of the query (we'll see later how it is used with recursive querying).\n", - "* **Self-collaboration**. The query returns 11 records - that's because the first one is normally the seed GRID (due to internal collaborations) which we will omit from the results.\n", + "* **Self-collaboration**. The query returns 11 records - that's because the first one is normally the seed organization (due to internal collaborations) which we will omit from the results.\n", "* **Custom changes**. Lastly, it's important to remember that this step can be easily customised by changing the `query_template` sttructure. For example, we could focus on specific research areas (using FOR codes), or set a threshold based on citation counts. The possibilities are endless! \n", "\n", - "For example, let's try it out with our GRID ID:" + "For example, let's try it out with our organization ID:" ] }, { @@ -341,6 +322,7 @@ " acronym\n", " city_name\n", " count\n", + " country_code\n", " country_name\n", " latitude\n", " linkout\n", @@ -358,7 +340,8 @@ " King Abdulaziz University\n", " KAU\n", " Jeddah\n", - " 1444\n", + " 1435\n", + " SA\n", " Saudi Arabia\n", " 21.493889\n", " [http://www.kau.edu.sa/home_english.aspx]\n", @@ -375,6 +358,7 @@ " NU\n", " Boston\n", " 106\n", + " US\n", " United States\n", " 42.339830\n", " [http://www.northeastern.edu/]\n", @@ -391,6 +375,7 @@ " NaN\n", " Cambridge\n", " 98\n", + " US\n", " United States\n", " 42.377052\n", " [http://www.harvard.edu/]\n", @@ -407,6 +392,7 @@ " MIT\n", " Cambridge\n", " 73\n", + " US\n", " United States\n", " 42.359820\n", " [http://web.mit.edu/]\n", @@ -423,6 +409,7 @@ " NU\n", " Evanston\n", " 59\n", + " US\n", " United States\n", " 42.054850\n", " [http://www.northwestern.edu/]\n", @@ -434,27 +421,12 @@ " \n", " \n", " 5\n", - " grid.413735.7\n", - " Harvard–MIT Division of Health Sciences and Te...\n", - " HST\n", - " Cambridge\n", - " 58\n", - " United States\n", - " 42.361780\n", - " [http://hst.mit.edu/]\n", - " -71.086914\n", - " [Education]\n", - " Massachusetts\n", - " grid.412125.1\n", - " 1\n", - " \n", - " \n", - " 6\n", " grid.411340.3\n", " Aligarh Muslim University\n", " AMU\n", " Aligarh\n", - " 47\n", + " 46\n", + " IN\n", " India\n", " 27.917370\n", " [http://www.amu.ac.in/]\n", @@ -465,12 +437,13 @@ " 1\n", " \n", " \n", - " 7\n", + " 6\n", " grid.412621.2\n", " Quaid-i-Azam University\n", " QAU\n", " Islamabad\n", - " 47\n", + " 46\n", + " PK\n", " Pakistan\n", " 33.747223\n", " [http://www.qau.edu.pk/]\n", @@ -481,12 +454,47 @@ " 1\n", " \n", " \n", + " 7\n", + " grid.411818.5\n", + " Jamia Millia Islamia\n", + " JMI\n", + " New Delhi\n", + " 40\n", + " IN\n", + " India\n", + " 28.561607\n", + " [http://jmi.ac.in/]\n", + " 77.280150\n", + " [Education]\n", + " NaN\n", + " grid.412125.1\n", + " 1\n", + " \n", + " \n", " 8\n", + " grid.62560.37\n", + " Brigham and Womens Hospital Inc\n", + " BWH\n", + " Boston\n", + " 40\n", + " US\n", + " United States\n", + " NaN\n", + " [http://www.brighamandwomens.org/]\n", + " NaN\n", + " [Healthcare]\n", + " Massachusetts\n", + " grid.412125.1\n", + " 1\n", + " \n", + " \n", + " 9\n", " grid.33003.33\n", " Suez Canal University\n", " NaN\n", " Ismailia\n", - " 42\n", + " 39\n", + " EG\n", " Egypt\n", " 30.622778\n", " [http://scuegypt.edu.eg/ar/]\n", @@ -497,28 +505,13 @@ " 1\n", " \n", " \n", - " 9\n", - " grid.411818.5\n", - " Jamia Millia Islamia\n", - " JMI\n", - " New Delhi\n", - " 42\n", - " India\n", - " 28.561607\n", - " [http://jmi.ac.in/]\n", - " 77.280150\n", - " [Education]\n", - " NaN\n", - " grid.412125.1\n", - " 1\n", - " \n", - " \n", " 10\n", " grid.56302.32\n", " King Saud University\n", " KSU\n", " Riyadh\n", - " 42\n", + " 39\n", + " SA\n", " Saudi Arabia\n", " 24.723982\n", " [http://ksu.edu.sa/en/]\n", @@ -533,44 +526,44 @@ "" ], "text/plain": [ - " id name acronym \\\n", - "0 grid.412125.1 King Abdulaziz University KAU \n", - "1 grid.261112.7 Northeastern University NU \n", - "2 grid.38142.3c Harvard University NaN \n", - "3 grid.116068.8 Massachusetts Institute of Technology MIT \n", - "4 grid.16753.36 Northwestern University NU \n", - "5 grid.413735.7 Harvard–MIT Division of Health Sciences and Te... HST \n", - "6 grid.411340.3 Aligarh Muslim University AMU \n", - "7 grid.412621.2 Quaid-i-Azam University QAU \n", - "8 grid.33003.33 Suez Canal University NaN \n", - "9 grid.411818.5 Jamia Millia Islamia JMI \n", - "10 grid.56302.32 King Saud University KSU \n", + " id name acronym city_name \\\n", + "0 grid.412125.1 King Abdulaziz University KAU Jeddah \n", + "1 grid.261112.7 Northeastern University NU Boston \n", + "2 grid.38142.3c Harvard University NaN Cambridge \n", + "3 grid.116068.8 Massachusetts Institute of Technology MIT Cambridge \n", + "4 grid.16753.36 Northwestern University NU Evanston \n", + "5 grid.411340.3 Aligarh Muslim University AMU Aligarh \n", + "6 grid.412621.2 Quaid-i-Azam University QAU Islamabad \n", + "7 grid.411818.5 Jamia Millia Islamia JMI New Delhi \n", + "8 grid.62560.37 Brigham and Womens Hospital Inc BWH Boston \n", + "9 grid.33003.33 Suez Canal University NaN Ismailia \n", + "10 grid.56302.32 King Saud University KSU Riyadh \n", "\n", - " city_name count country_name latitude \\\n", - "0 Jeddah 1444 Saudi Arabia 21.493889 \n", - "1 Boston 106 United States 42.339830 \n", - "2 Cambridge 98 United States 42.377052 \n", - "3 Cambridge 73 United States 42.359820 \n", - "4 Evanston 59 United States 42.054850 \n", - "5 Cambridge 58 United States 42.361780 \n", - "6 Aligarh 47 India 27.917370 \n", - "7 Islamabad 47 Pakistan 33.747223 \n", - "8 Ismailia 42 Egypt 30.622778 \n", - "9 New Delhi 42 India 28.561607 \n", - "10 Riyadh 42 Saudi Arabia 24.723982 \n", + " count country_code country_name latitude \\\n", + "0 1435 SA Saudi Arabia 21.493889 \n", + "1 106 US United States 42.339830 \n", + "2 98 US United States 42.377052 \n", + "3 73 US United States 42.359820 \n", + "4 59 US United States 42.054850 \n", + "5 46 IN India 27.917370 \n", + "6 46 PK Pakistan 33.747223 \n", + "7 40 IN India 28.561607 \n", + "8 40 US United States NaN \n", + "9 39 EG Egypt 30.622778 \n", + "10 39 SA Saudi Arabia 24.723982 \n", "\n", - " linkout longitude types \\\n", - "0 [http://www.kau.edu.sa/home_english.aspx] 39.250280 [Education] \n", - "1 [http://www.northeastern.edu/] -71.089180 [Education] \n", - "2 [http://www.harvard.edu/] -71.116650 [Education] \n", - "3 [http://web.mit.edu/] -71.092110 [Education] \n", - "4 [http://www.northwestern.edu/] -87.673940 [Education] \n", - "5 [http://hst.mit.edu/] -71.086914 [Education] \n", - "6 [http://www.amu.ac.in/] 78.077850 [Education] \n", - "7 [http://www.qau.edu.pk/] 73.138885 [Education] \n", - "8 [http://scuegypt.edu.eg/ar/] 32.275000 [Education] \n", - "9 [http://jmi.ac.in/] 77.280150 [Education] \n", - "10 [http://ksu.edu.sa/en/] 46.645840 [Education] \n", + " linkout longitude types \\\n", + "0 [http://www.kau.edu.sa/home_english.aspx] 39.250280 [Education] \n", + "1 [http://www.northeastern.edu/] -71.089180 [Education] \n", + "2 [http://www.harvard.edu/] -71.116650 [Education] \n", + "3 [http://web.mit.edu/] -71.092110 [Education] \n", + "4 [http://www.northwestern.edu/] -87.673940 [Education] \n", + "5 [http://www.amu.ac.in/] 78.077850 [Education] \n", + "6 [http://www.qau.edu.pk/] 73.138885 [Education] \n", + "7 [http://jmi.ac.in/] 77.280150 [Education] \n", + "8 [http://www.brighamandwomens.org/] NaN [Healthcare] \n", + "9 [http://scuegypt.edu.eg/ar/] 32.275000 [Education] \n", + "10 [http://ksu.edu.sa/en/] 46.645840 [Education] \n", "\n", " state_name id_from level \n", "0 NaN grid.412125.1 1 \n", @@ -578,10 +571,10 @@ "2 Massachusetts grid.412125.1 1 \n", "3 Massachusetts grid.412125.1 1 \n", "4 Illinois grid.412125.1 1 \n", - "5 Massachusetts grid.412125.1 1 \n", - "6 Uttar Pradesh grid.412125.1 1 \n", + "5 Uttar Pradesh grid.412125.1 1 \n", + "6 NaN grid.412125.1 1 \n", "7 NaN grid.412125.1 1 \n", - "8 NaN grid.412125.1 1 \n", + "8 Massachusetts grid.412125.1 1 \n", "9 NaN grid.412125.1 1 \n", "10 NaN grid.412125.1 1 " ] @@ -592,7 +585,7 @@ } ], "source": [ - "get_collaborators(GRIDID, printquery=True)" + "get_collaborators(ORGID, printquery=True)" ] }, { @@ -605,9 +598,9 @@ "\n", "What if we want to retrieve the collaborators of the collaborators? In other words, what if we want to generate a larger network?\n", "\n", - "If we think of our collaboration data as a [graph structure](https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)) with nodes and edges, we can see that the `get_collaborators` function defined above is limited. That's because it allows to obtain only the objects *directly* linked to the 'seed' GRID organization. \n", + "If we think of our collaboration data as a [graph structure](https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)) with nodes and edges, we can see that the `get_collaborators` function defined above is limited. That's because it allows to obtain only the objects *directly* linked to the 'seed' organization. \n", "\n", - "We would like to run the same collaborators-extraction step **iteratively** for any GRID ID in our results, so to generate an N-degrees network where N is chosen by us. \n", + "We would like to run the same collaborators-extraction step **iteratively** for any ID in our results, so to generate an N-degrees network where N is chosen by us. \n", "\n", "To this purpose, we can set up a [recursive](https://en.wikipedia.org/wiki/Recursion_(computer_science)) function. This function essentially repeats the `get_collaborators` function as many times as needed. Here's what it looks like:" ] @@ -627,8 +620,8 @@ " print(\"--\" * thislevel, seed, \" :: level =\", thislevel)\n", " if thislevel < maxlevel:\n", " # remove the originating grid-id\n", - " gridslist = list(results[results['id'] != GRIDID]['id'])\n", - " next_level_results = [recursive_network(x, maxlevel, thislevel+1) for x in gridslist]\n", + " orgslist = list(results[results['id'] != ORGID]['id'])\n", + " next_level_results = [recursive_network(x, maxlevel, thislevel+1) for x in orgslist]\n", " next_level_results = pd.concat(next_level_results)\n", " results = pd.concat([results, next_level_results])\n", " return results\n", @@ -671,11 +664,11 @@ "---- grid.38142.3c :: level = 2\n", "---- grid.116068.8 :: level = 2\n", "---- grid.16753.36 :: level = 2\n", - "---- grid.413735.7 :: level = 2\n", "---- grid.411340.3 :: level = 2\n", "---- grid.412621.2 :: level = 2\n", - "---- grid.33003.33 :: level = 2\n", "---- grid.411818.5 :: level = 2\n", + "---- grid.62560.37 :: level = 2\n", + "---- grid.33003.33 :: level = 2\n", "---- grid.56302.32 :: level = 2\n" ] }, @@ -721,7 +714,7 @@ " grid.412125.1\n", " grid.412125.1\n", " 1\n", - " 1444\n", + " 1435\n", " King Abdulaziz University\n", " KAU\n", " Jeddah\n", @@ -802,7 +795,7 @@ ], "text/plain": [ " id_from id_to level count \\\n", - "0 grid.412125.1 grid.412125.1 1 1444 \n", + "0 grid.412125.1 grid.412125.1 1 1435 \n", "1 grid.412125.1 grid.261112.7 1 106 \n", "2 grid.412125.1 grid.38142.3c 1 98 \n", "3 grid.412125.1 grid.116068.8 1 73 \n", @@ -836,7 +829,7 @@ } ], "source": [ - "collaborators = recursive_network(GRIDID, maxlevel=2)\n", + "collaborators = recursive_network(ORGID, maxlevel=2)\n", "# change column order for readability purposes\n", "collaborators.rename(columns={\"id\": \"id_to\"}, inplace=True)\n", "collaborators = collaborators[['id_from', 'id_to', 'level', 'count', 'name', 'acronym', 'city_name', 'state_name', 'country_name', 'latitude', 'longitude', 'linkout', 'types' ]]\n", @@ -896,7 +889,7 @@ " " ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -945,13 +938,13 @@ "\n", " # calc size based on level\n", " maxsize = int(nodes['level'].max()) + 1\n", - " if row['id_to'] == GRIDID:\n", + " if row['id_to'] == ORGID:\n", " size = maxsize\n", " else:\n", " size = maxsize - row['level']\n", "\n", " # calc color based on level\n", - " if row['id_to'] == GRIDID:\n", + " if row['id_to'] == ORGID:\n", " color = palette[0]\n", " else:\n", " color = palette[row['level'] * 2]\n", @@ -990,10 +983,10 @@ " return g\n", "\n", "#\n", - "# finall, run the viz builder\n", + "# finally, run the viz builder\n", "#\n", "g = build_visualization(collaborators)\n", - "g.show(f\"network_{GRIDID}.html\")" + "g.show(f\"network_{ORGID}.html\")" ] }, { @@ -1006,7 +999,7 @@ "\n", "What if we want to show a collaboration network focusing only on 'government' organizations? \n", "\n", - "That's pretty easy to do, since the GRID database includes information about **organization types**. We can easily see what types are available using the API and a `facet` query:" + "That's pretty easy to do, since the organization data set includes information about **organization types**. We can easily see what types are available using the API and a `facet` query:" ] }, { @@ -1020,8 +1013,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Types: 9\n", - "\u001b[2mTime: 1.00s\u001b[0m\n" + "Returned Types: 8\n", + "\u001b[2mTime: 2.54s\u001b[0m\n" ] }, { @@ -1052,64 +1045,58 @@ " \n", " \n", " 0\n", - " Company\n", - " 30742\n", + " Other\n", + " 165766\n", " \n", " \n", " 1\n", - " Education\n", - " 20761\n", + " Company\n", + " 158994\n", " \n", " \n", " 2\n", - " Nonprofit\n", - " 17573\n", + " Education\n", + " 22805\n", " \n", " \n", " 3\n", - " Healthcare\n", - " 13926\n", + " Nonprofit\n", + " 18361\n", " \n", " \n", " 4\n", - " Facility\n", - " 10168\n", + " Healthcare\n", + " 14865\n", " \n", " \n", " 5\n", " Government\n", - " 6580\n", + " 11545\n", " \n", " \n", " 6\n", - " Other\n", - " 4017\n", + " Facility\n", + " 10692\n", " \n", " \n", " 7\n", " Archive\n", - " 2926\n", - " \n", - " \n", - " 8\n", - " Education,Company\n", - " 1\n", + " 3059\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id count\n", - "0 Company 30742\n", - "1 Education 20761\n", - "2 Nonprofit 17573\n", - "3 Healthcare 13926\n", - "4 Facility 10168\n", - "5 Government 6580\n", - "6 Other 4017\n", - "7 Archive 2926\n", - "8 Education,Company 1" + " id count\n", + "0 Other 165766\n", + "1 Company 158994\n", + "2 Education 22805\n", + "3 Nonprofit 18361\n", + "4 Healthcare 14865\n", + "5 Government 11545\n", + "6 Facility 10692\n", + "7 Archive 3059" ] }, "execution_count": 9, @@ -1133,7 +1120,7 @@ "* **Get more results**. We increase the number of results returned: `..return research_orgs limit 50`. This is to ensure we still have enough results after removing the ones that don't have the chosen 'type'\n", "* **Remove unwanted data**. The new query filter `research_orgs.types in [\"{}\"]` will return also publications with multiple authors/affiliations, even though only one of them has the desired 'type'. So an extra step is required and this is achieved via the `keep_type` function below. This function simply filters out all unwanted organizations data after they're retrieved from the API. \n", "\n", - "That's it! Run the cell below to generate a new visualization showing only \"Government\" collaborators. Or try changing the value of `GRID_TYPE` to see different results. \n" + "That's it! Run the cell below to generate a new visualization showing only \"Government\" collaborators. Or try changing the value of `ORG_TYPE` to see different results. \n" ] }, { @@ -1150,7 +1137,6 @@ "-- grid.412125.1 :: level = 1\n", "---- grid.7327.1 :: level = 2\n", "---- grid.9227.e :: level = 2\n", - "---- grid.20256.33 :: level = 2\n", "---- grid.1089.0 :: level = 2\n", "---- grid.14467.30 :: level = 2\n", "network_grid.412125.1_Government.html\n" @@ -1171,7 +1157,7 @@ " " ], "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -1182,7 +1168,7 @@ "source": [ "#@markdown Try using one of the organization types from the list above\n", "\n", - "GRID_TYPE = \"Government\" #@param {type:\"string\"}\n", + "ORG_TYPE = \"Government\" #@param {type:\"string\"}\n", "\n", "query = \"\"\"search publications {}\n", " where year in [{}:{}] \n", @@ -1193,7 +1179,7 @@ "def keep_only_type(data, a_type, orgid):\n", " clean_list = []\n", " for x in data.research_orgs:\n", - " # include also originating GRID to ensure chart is complete\n", + " # include also originating org to ensure chart is complete\n", " if x['id'] == orgid or a_type in x['types']:\n", " clean_list.append(x)\n", " data.json['research_orgs'] = clean_list\n", @@ -1206,8 +1192,8 @@ " TOPIC_CLAUSE = f\"\"\"for \"{TOPIC}\" \"\"\"\n", " else:\n", " TOPIC_CLAUSE = \"\"\n", - " # include also the GRID_TYPE\n", - " query_full = query.format(TOPIC_CLAUSE, YEAR_START, YEAR_END, orgid, GRID_TYPE)\n", + " # include also the ORG_TYPE\n", + " query_full = query.format(TOPIC_CLAUSE, YEAR_START, YEAR_END, orgid, ORG_TYPE)\n", " if printquery: print(query_full)\n", " data = dsl.query(query_full, verbose=False)\n", " # remove results with unwanted types \n", @@ -1221,7 +1207,7 @@ "#\n", "# RUN THE RECURSIVE QUERY (same code as above)\n", "#\n", - "collaborators = recursive_network(GRIDID, maxlevel=2)\n", + "collaborators = recursive_network(ORGID, maxlevel=2)\n", "collaborators.rename(columns={\"id\": \"id_to\"}, inplace=True)\n", "collaborators = collaborators[['id_from', 'id_to', 'level', 'count', 'name', 'acronym', 'city_name', 'country_name', 'latitude', 'longitude', 'linkout', 'types' ]]\n", "\n", @@ -1229,7 +1215,7 @@ "# BUILD VIZ\n", "#\n", "g = build_visualization(collaborators)\n", - "g.show(f\"network_{GRIDID}_{GRID_TYPE}.html\")\n", + "g.show(f\"network_{ORGID}_{ORG_TYPE}.html\")\n", "\n" ] }, @@ -1272,7 +1258,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.1" + "version": "3.12.8" }, "nteract": { "version": "0.15.0" diff --git a/cookbooks/8-organizations/4-International-collaboration-by-year.ipynb b/cookbooks/8-organizations/4-International-collaboration-by-year.ipynb index cea374f0..40286910 100644 --- a/cookbooks/8-organizations/4-International-collaboration-by-year.ipynb +++ b/cookbooks/8-organizations/4-International-collaboration-by-year.ipynb @@ -24,7 +24,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Jan 25, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": { "Collapsed": "false" }, @@ -59,19 +59,9 @@ "text/html": [ " \n", + " \n", " " ] }, @@ -91,8 +81,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -137,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": { "Collapsed": "false" }, @@ -146,8 +136,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Organizations: 16 (total = 16)\n", - "\u001b[2mTime: 0.57s\u001b[0m\n" + "Returned Organizations: 20 (total = 23)\n", + "\u001b[2mTime: 0.53s\u001b[0m\n" ] }, { @@ -171,225 +161,298 @@ " \n", " \n", " \n", + " id\n", + " name\n", " city_name\n", + " country_code\n", " country_name\n", - " id\n", + " types\n", + " state_name\n", " latitude\n", " linkout\n", " longitude\n", - " name\n", - " state_name\n", - " types\n", " acronym\n", " \n", " \n", " \n", " \n", " 0\n", + " grid.772384.d\n", + " Trelleborg Marine Systems Melbourne Pty Ltd\n", + " Victoria\n", + " AU\n", + " Australia\n", + " [Company]\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 1\n", + " grid.746611.3\n", + " Noyes Bros Melbourne Pty Ltd\n", + " NaN\n", + " AU\n", + " Australia\n", + " [Other]\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 2\n", + " grid.631568.f\n", + " CityLink Melbourne Ltd\n", + " NaN\n", + " AU\n", + " Australia\n", + " [Other]\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 3\n", + " grid.530408.a\n", + " Melbourne Institute of Technology\n", " Melbourne\n", + " AU\n", " Australia\n", + " [Nonprofit]\n", + " Victoria\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 4\n", " grid.511296.8\n", + " Melbourne Genomics Health Alliance\n", + " Melbourne\n", + " AU\n", + " Australia\n", + " [Nonprofit]\n", + " Victoria\n", " -37.797960\n", " [https://www.melbournegenomics.org.au/]\n", " 144.953870\n", - " Melbourne Genomics Health Alliance\n", - " Victoria\n", - " [Nonprofit]\n", " NaN\n", " \n", " \n", - " 1\n", + " 5\n", + " grid.493437.e\n", + " RMIT Europe\n", " Barcelona\n", + " ES\n", " Spain\n", - " grid.493437.e\n", + " [Education]\n", + " NaN\n", " 41.402576\n", " [https://www.rmit.eu]\n", " 2.194333\n", - " RMIT Europe\n", - " NaN\n", - " [Education]\n", " RMIT\n", " \n", " \n", - " 2\n", + " 6\n", + " grid.490309.7\n", + " Melbourne Sexual Health Centre\n", " Carlton\n", + " AU\n", " Australia\n", - " grid.490309.7\n", + " [Healthcare]\n", + " Victoria\n", " -37.803123\n", " [https://www.mshc.org.au/]\n", " 144.963840\n", - " Melbourne Sexual Health Centre\n", - " Victoria\n", - " [Healthcare]\n", " MSHC\n", " \n", " \n", - " 3\n", + " 7\n", + " grid.477970.a\n", + " Melbourne Clinic\n", " Richmond\n", + " AU\n", " Australia\n", - " grid.477970.a\n", + " [Healthcare]\n", + " Victoria\n", " -37.815063\n", " [http://www.themelbourneclinic.com.au/]\n", " 144.999650\n", - " Melbourne Clinic\n", - " Victoria\n", - " [Healthcare]\n", " NaN\n", " \n", " \n", - " 4\n", - " Melbourne\n", + " 8\n", + " grid.474755.0\n", + " Leica Biosystems Melbourne Pty Ltd\n", + " Mt. Waverley\n", + " AU\n", " Australia\n", + " [Company]\n", + " NaN\n", + " NaN\n", + " [http://www.danaher.com/]\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 9\n", " grid.469061.c\n", + " Ridley College\n", + " Melbourne\n", + " AU\n", + " Australia\n", + " [Education]\n", + " Victoria\n", " -37.783780\n", " [https://www.ridley.edu.au/]\n", " 144.957660\n", - " Ridley College\n", - " Victoria\n", - " [Education]\n", " NaN\n", " \n", " \n", - " 5\n", + " 10\n", + " grid.469026.f\n", + " Melbourne School of Theology\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.469026.f\n", + " [Education]\n", + " Victoria\n", " -37.859700\n", " [http://www.mst.edu.au/]\n", " 145.209410\n", - " Melbourne School of Theology\n", - " Victoria\n", - " [Education]\n", " MBI\n", " \n", " \n", - " 6\n", + " 11\n", + " grid.468079.4\n", + " Port of Melbourne Corporation\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.468079.4\n", + " [Government]\n", + " Victoria\n", " -37.824028\n", " [http://www.portofmelbourne.com/]\n", " 144.907070\n", - " Port of Melbourne Corporation\n", - " Victoria\n", - " [Government]\n", " PoMC\n", " \n", " \n", - " 7\n", + " 12\n", + " grid.468069.5\n", + " Melbourne Water\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.468069.5\n", + " [Government]\n", + " Victoria\n", " -37.814007\n", " [http://www.melbournewater.com.au/Pages/home.a...\n", " 144.946700\n", - " Melbourne Water\n", - " Victoria\n", - " [Government]\n", " NaN\n", " \n", " \n", - " 8\n", + " 13\n", + " grid.452643.2\n", + " Melbourne Bioinformatics\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.452643.2\n", + " [Education]\n", + " Victoria\n", " -37.799847\n", - " [https://www.vlsci.org.au/]\n", + " [https://www.melbournebioinformatics.org.au]\n", " 144.964460\n", - " Victorian Life Sciences Computation Initiative\n", - " Victoria\n", - " [Education]\n", - " NaN\n", + " VLSCI\n", " \n", " \n", - " 9\n", + " 14\n", + " grid.449135.e\n", + " Melbourne Free University\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.449135.e\n", + " [Education]\n", + " Victoria\n", " NaN\n", " NaN\n", " NaN\n", - " Melbourne Free University\n", - " Victoria\n", - " [Education]\n", " NaN\n", " \n", " \n", - " 10\n", + " 15\n", + " grid.440113.3\n", + " Royal Dental Hospital of Melbourne\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.440113.3\n", + " [Healthcare]\n", + " Victoria\n", " -37.799260\n", " [https://www.dhsv.org.au]\n", " 144.964630\n", - " Royal Dental Hospital of Melbourne\n", - " Victoria\n", - " [Healthcare]\n", " RDHM\n", " \n", " \n", - " 11\n", + " 16\n", + " grid.438527.f\n", + " Royal Melbourne Institute of Technology Univer...\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.429299.d\n", - " -37.798940\n", - " [http://www.mh.org.au/]\n", - " 144.955930\n", - " Melbourne Health\n", + " [Other]\n", " Victoria\n", - " [Healthcare]\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " \n", " \n", - " 12\n", + " 17\n", + " grid.429299.d\n", + " Melbourne Health\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.416153.4\n", - " -37.798756\n", - " [http://www.rmh.mh.org.au/]\n", - " 144.955930\n", - " Royal Melbourne Hospital\n", - " Victoria\n", " [Healthcare]\n", - " RMH\n", - " \n", - " \n", - " 13\n", - " Clayton\n", - " Australia\n", - " grid.410660.5\n", - " -37.915775\n", - " [http://nanomelbourne.com/]\n", - " 145.143660\n", - " Melbourne Centre for Nanofabrication\n", " Victoria\n", - " [Facility]\n", - " MCN\n", + " -37.798940\n", + " [http://www.mh.org.au/]\n", + " 144.955930\n", + " NaN\n", " \n", " \n", - " 14\n", + " 18\n", + " grid.416153.4\n", + " Royal Melbourne Hospital\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.1017.7\n", - " -37.806747\n", - " [https://www.rmit.edu.au/]\n", - " 144.962570\n", - " RMIT University\n", + " [Healthcare]\n", " Victoria\n", - " [Education]\n", - " RMIT\n", + " -37.798756\n", + " [http://www.rmh.mh.org.au/]\n", + " 144.955930\n", + " RMH\n", " \n", " \n", - " 15\n", + " 19\n", + " grid.413105.2\n", + " St Vincent's Hospital\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.1008.9\n", - " -37.797115\n", - " [http://www.unimelb.edu.au/]\n", - " 144.959980\n", - " University of Melbourne\n", + " [Healthcare]\n", " Victoria\n", - " [Education]\n", + " -37.807000\n", + " [http://www.svhm.org.au/Pages/Home.aspx]\n", + " 144.975000\n", " NaN\n", " \n", " \n", @@ -397,80 +460,96 @@ "" ], "text/plain": [ - " city_name country_name id latitude \\\n", - "0 Melbourne Australia grid.511296.8 -37.797960 \n", - "1 Barcelona Spain grid.493437.e 41.402576 \n", - "2 Carlton Australia grid.490309.7 -37.803123 \n", - "3 Richmond Australia grid.477970.a -37.815063 \n", - "4 Melbourne Australia grid.469061.c -37.783780 \n", - "5 Melbourne Australia grid.469026.f -37.859700 \n", - "6 Melbourne Australia grid.468079.4 -37.824028 \n", - "7 Melbourne Australia grid.468069.5 -37.814007 \n", - "8 Melbourne Australia grid.452643.2 -37.799847 \n", - "9 Melbourne Australia grid.449135.e NaN \n", - "10 Melbourne Australia grid.440113.3 -37.799260 \n", - "11 Melbourne Australia grid.429299.d -37.798940 \n", - "12 Melbourne Australia grid.416153.4 -37.798756 \n", - "13 Clayton Australia grid.410660.5 -37.915775 \n", - "14 Melbourne Australia grid.1017.7 -37.806747 \n", - "15 Melbourne Australia grid.1008.9 -37.797115 \n", + " id name \\\n", + "0 grid.772384.d Trelleborg Marine Systems Melbourne Pty Ltd \n", + "1 grid.746611.3 Noyes Bros Melbourne Pty Ltd \n", + "2 grid.631568.f CityLink Melbourne Ltd \n", + "3 grid.530408.a Melbourne Institute of Technology \n", + "4 grid.511296.8 Melbourne Genomics Health Alliance \n", + "5 grid.493437.e RMIT Europe \n", + "6 grid.490309.7 Melbourne Sexual Health Centre \n", + "7 grid.477970.a Melbourne Clinic \n", + "8 grid.474755.0 Leica Biosystems Melbourne Pty Ltd \n", + "9 grid.469061.c Ridley College \n", + "10 grid.469026.f Melbourne School of Theology \n", + "11 grid.468079.4 Port of Melbourne Corporation \n", + "12 grid.468069.5 Melbourne Water \n", + "13 grid.452643.2 Melbourne Bioinformatics \n", + "14 grid.449135.e Melbourne Free University \n", + "15 grid.440113.3 Royal Dental Hospital of Melbourne \n", + "16 grid.438527.f Royal Melbourne Institute of Technology Univer... \n", + "17 grid.429299.d Melbourne Health \n", + "18 grid.416153.4 Royal Melbourne Hospital \n", + "19 grid.413105.2 St Vincent's Hospital \n", "\n", - " linkout longitude \\\n", - "0 [https://www.melbournegenomics.org.au/] 144.953870 \n", - "1 [https://www.rmit.eu] 2.194333 \n", - "2 [https://www.mshc.org.au/] 144.963840 \n", - "3 [http://www.themelbourneclinic.com.au/] 144.999650 \n", - "4 [https://www.ridley.edu.au/] 144.957660 \n", - "5 [http://www.mst.edu.au/] 145.209410 \n", - "6 [http://www.portofmelbourne.com/] 144.907070 \n", - "7 [http://www.melbournewater.com.au/Pages/home.a... 144.946700 \n", - "8 [https://www.vlsci.org.au/] 144.964460 \n", - "9 NaN NaN \n", - "10 [https://www.dhsv.org.au] 144.964630 \n", - "11 [http://www.mh.org.au/] 144.955930 \n", - "12 [http://www.rmh.mh.org.au/] 144.955930 \n", - "13 [http://nanomelbourne.com/] 145.143660 \n", - "14 [https://www.rmit.edu.au/] 144.962570 \n", - "15 [http://www.unimelb.edu.au/] 144.959980 \n", + " city_name country_code country_name types state_name \\\n", + "0 Victoria AU Australia [Company] NaN \n", + "1 NaN AU Australia [Other] NaN \n", + "2 NaN AU Australia [Other] NaN \n", + "3 Melbourne AU Australia [Nonprofit] Victoria \n", + "4 Melbourne AU Australia [Nonprofit] Victoria \n", + "5 Barcelona ES Spain [Education] NaN \n", + "6 Carlton AU Australia [Healthcare] Victoria \n", + "7 Richmond AU Australia [Healthcare] Victoria \n", + "8 Mt. Waverley AU Australia [Company] NaN \n", + "9 Melbourne AU Australia [Education] Victoria \n", + "10 Melbourne AU Australia [Education] Victoria \n", + "11 Melbourne AU Australia [Government] Victoria \n", + "12 Melbourne AU Australia [Government] Victoria \n", + "13 Melbourne AU Australia [Education] Victoria \n", + "14 Melbourne AU Australia [Education] Victoria \n", + "15 Melbourne AU Australia [Healthcare] Victoria \n", + "16 Melbourne AU Australia [Other] Victoria \n", + "17 Melbourne AU Australia [Healthcare] Victoria \n", + "18 Melbourne AU Australia [Healthcare] Victoria \n", + "19 Melbourne AU Australia [Healthcare] Victoria \n", "\n", - " name state_name types \\\n", - "0 Melbourne Genomics Health Alliance Victoria [Nonprofit] \n", - "1 RMIT Europe NaN [Education] \n", - "2 Melbourne Sexual Health Centre Victoria [Healthcare] \n", - "3 Melbourne Clinic Victoria [Healthcare] \n", - "4 Ridley College Victoria [Education] \n", - "5 Melbourne School of Theology Victoria [Education] \n", - "6 Port of Melbourne Corporation Victoria [Government] \n", - "7 Melbourne Water Victoria [Government] \n", - "8 Victorian Life Sciences Computation Initiative Victoria [Education] \n", - "9 Melbourne Free University Victoria [Education] \n", - "10 Royal Dental Hospital of Melbourne Victoria [Healthcare] \n", - "11 Melbourne Health Victoria [Healthcare] \n", - "12 Royal Melbourne Hospital Victoria [Healthcare] \n", - "13 Melbourne Centre for Nanofabrication Victoria [Facility] \n", - "14 RMIT University Victoria [Education] \n", - "15 University of Melbourne Victoria [Education] \n", + " latitude linkout longitude \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 -37.797960 [https://www.melbournegenomics.org.au/] 144.953870 \n", + "5 41.402576 [https://www.rmit.eu] 2.194333 \n", + "6 -37.803123 [https://www.mshc.org.au/] 144.963840 \n", + "7 -37.815063 [http://www.themelbourneclinic.com.au/] 144.999650 \n", + "8 NaN [http://www.danaher.com/] NaN \n", + "9 -37.783780 [https://www.ridley.edu.au/] 144.957660 \n", + "10 -37.859700 [http://www.mst.edu.au/] 145.209410 \n", + "11 -37.824028 [http://www.portofmelbourne.com/] 144.907070 \n", + "12 -37.814007 [http://www.melbournewater.com.au/Pages/home.a... 144.946700 \n", + "13 -37.799847 [https://www.melbournebioinformatics.org.au] 144.964460 \n", + "14 NaN NaN NaN \n", + "15 -37.799260 [https://www.dhsv.org.au] 144.964630 \n", + "16 NaN NaN NaN \n", + "17 -37.798940 [http://www.mh.org.au/] 144.955930 \n", + "18 -37.798756 [http://www.rmh.mh.org.au/] 144.955930 \n", + "19 -37.807000 [http://www.svhm.org.au/Pages/Home.aspx] 144.975000 \n", "\n", " acronym \n", "0 NaN \n", - "1 RMIT \n", - "2 MSHC \n", + "1 NaN \n", + "2 NaN \n", "3 NaN \n", "4 NaN \n", - "5 MBI \n", - "6 PoMC \n", + "5 RMIT \n", + "6 MSHC \n", "7 NaN \n", "8 NaN \n", "9 NaN \n", - "10 RDHM \n", - "11 NaN \n", - "12 RMH \n", - "13 MCN \n", - "14 RMIT \n", - "15 NaN " + "10 MBI \n", + "11 PoMC \n", + "12 NaN \n", + "13 VLSCI \n", + "14 NaN \n", + "15 RDHM \n", + "16 NaN \n", + "17 NaN \n", + "18 RMH \n", + "19 NaN " ] }, - "execution_count": 17, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -483,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": { "Collapsed": "false" }, @@ -503,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": { "Collapsed": "false" }, @@ -512,8 +591,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Year: 12\n", - "\u001b[2mTime: 0.57s\u001b[0m\n" + "Returned Year: 16\n", + "\u001b[2mTime: 0.55s\u001b[0m\n" ] }, { @@ -524,7 +603,6 @@ }, "data": [ { - "alignmentgroup": "True", "hovertemplate": "year=%{x}
pubs=%{y}", "legendgroup": "", "marker": { @@ -534,45 +612,23 @@ } }, "name": "", - "offsetgroup": "", "orientation": "v", "showlegend": false, "textposition": "auto", "type": "bar", - "x": [ - 2021, - 2020, - 2019, - 2018, - 2017, - 2016, - 2015, - 2014, - 2013, - 2012, - 2011, - 2022 - ], + "x": { + "bdata": "5QfoB+cH5gfkB+MH4gfhB+AH6QffB94H3QfcB9sH6gc=", + "dtype": "i2" + }, "xaxis": "x", - "y": [ - 13015, - 12183, - 11039, - 9954, - 9198, - 8281, - 7720, - 7184, - 6779, - 6030, - 5742, - 816 - ], + "y": { + "bdata": "9kygSotJ9UjhSOlA+T0ZOm42PjZDNBkx3y99K2koDwA=", + "dtype": "i2" + }, "yaxis": "y" } ], "layout": { - "autosize": true, "barmode": "relative", "legend": { "tracegroupgap": 0 @@ -759,57 +815,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 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], "title": { "text": "pubs" - }, - "type": "linear" + } } } }, - "image/png": 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", "text/html": [ - "
\n", + "
" + " }) }; " ] }, "metadata": {}, @@ -6849,7 +6435,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "auallpubs.columns = ['all_count', 'year', ]\n", + "auallpubs.columns = ['year', 'all_count']\n", "\n", "auintpubs = dsl.query(\"\"\"\n", " \n", @@ -6862,7 +6448,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "auintpubs.columns = ['all_int_count', 'year', ]\n", + "auintpubs.columns = ['year', 'all_int_count']\n", "\n", "\n", "audompubs = dsl.query(\"\"\"\n", @@ -6876,7 +6462,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "audompubs.columns = [ 'all_dom_count', 'year',]\n", + "audompubs.columns = ['year', 'all_dom_count']\n", "\n", "auinternalpubs = dsl.query(\"\"\"\n", " \n", @@ -6890,12 +6476,12 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "auinternalpubs.columns = ['all_internal_count', 'year', ]\n", + "auinternalpubs.columns = ['year', 'all_internal_count']\n", "\n", "audf = auallpubs.set_index('year'). \\\n", - " join(auintpubs.set_index('year')). \\\n", - " join(audompubs.set_index('year')). \\\n", - " join(auinternalpubs.set_index('year')). \\\n", + " merge(auintpubs, how='left', on='year'). \\\n", + " merge(audompubs, how='left', on='year'). \\\n", + " merge(auinternalpubs, how='left', on='year'). \\\n", " sort_values(by=['year'])\n", "\n", "px.bar(audf, title=\"Australia: publications collaboration\")" @@ -6912,7 +6498,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "Collapsed": "false" }, @@ -6921,14 +6507,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Year: 12\n", - "\u001b[2mTime: 0.56s\u001b[0m\n", - "Returned Year: 12\n", - "\u001b[2mTime: 0.59s\u001b[0m\n", - "Returned Year: 12\n", - "\u001b[2mTime: 0.61s\u001b[0m\n", - "Returned Year: 12\n", - "\u001b[2mTime: 0.56s\u001b[0m\n" + "Returned Year: 16\n", + "\u001b[2mTime: 0.49s\u001b[0m\n", + "Returned Year: 16\n", + "\u001b[2mTime: 6.01s\u001b[0m\n", + "Returned Year: 16\n", + "\u001b[2mTime: 0.63s\u001b[0m\n", + "Returned Year: 16\n", + "\u001b[2mTime: 0.49s\u001b[0m\n" ] }, { @@ -6939,196 +6525,132 @@ }, "data": [ { - "alignmentgroup": "True", - "hovertemplate": "variable=pubs
year=%{x}
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index=%{x}
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", 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" + " }) }; " ] }, "metadata": {}, @@ -8037,7 +7506,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "allpubs.columns = ['pubs', 'year', ]\n", + "allpubs.columns = ['year', 'pubs']\n", "\n", "\n", "\n", @@ -8053,7 +7522,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "international.columns = ['international_count', 'year', ]\n", + "international.columns = ['year', 'international_count']\n", "\n", "\n", "domestic = dsl.query(f\"\"\"\n", @@ -8068,7 +7537,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "domestic.columns = ['domestic_count', 'year', ]\n", + "domestic.columns = ['year', 'domestic_count']\n", "\n", "internal = dsl.query(f\"\"\"\n", " \n", @@ -8082,13 +7551,13 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "internal.columns = ['internal_count', 'year', ]\n", + "internal.columns = ['year', 'internal_count']\n", "\n", "\n", "jdf = allpubs.set_index('year'). \\\n", - " join(international.set_index('year')). \\\n", - " join(domestic.set_index('year')). \\\n", - " join(internal.set_index('year')) \n", + " merge(international, how='left', on='year'). \\\n", + " merge(domestic, how='left', on='year'). \\\n", + " merge(internal, how='left', on='year')\n", "\n", "px.bar(jdf, title=\"Univ. of Toronto: publications collaboration\")\n" ] @@ -8125,7 +7594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" }, "nteract": { "version": "0.15.0" diff --git a/cookbooks/8-organizations/5-mapping-grid-ids-to-organization-data.ipynb b/cookbooks/8-organizations/5-mapping-organization-ids-to-organization-data.ipynb similarity index 71% rename from cookbooks/8-organizations/5-mapping-grid-ids-to-organization-data.ipynb rename to cookbooks/8-organizations/5-mapping-organization-ids-to-organization-data.ipynb index 4cb5f19b..04fae69d 100644 --- a/cookbooks/8-organizations/5-mapping-grid-ids-to-organization-data.ipynb +++ b/cookbooks/8-organizations/5-mapping-organization-ids-to-organization-data.ipynb @@ -7,19 +7,16 @@ "id": "hu34Y6_eo_8c" }, "source": [ - "# Mapping GRID IDs to Organization Data\n", + "# Mapping Organization IDs to Organization Data\n", "\n", - "In this tutorial, we show how to use the [Dimensions Analytics API](https://www.dimensions.ai/dimensions-apis/) and organization data to extract GRID IDs.\n", - "\n", - "\n", - "[GRID](https://grid.ac/) is a free and openly available global database of research-related organisations, cataloging research-related organizations and providing each with a unique and persistent identifier. Dimensions uses this identifier to link organizations to publications, grants, etc.\n", + "In this tutorial, we show how to use the [Dimensions Analytics API](https://www.dimensions.ai/dimensions-apis/) and organization data to extract organization IDs.\n", "\n", "**Use case scenarios:**\n", "\n", - "* An analyst has a list of organizations of interest, and wants to get details of their publications from Dimensions. To do this, they they need to map them to GRID IDs so they can extract information from the Dimensions database. The organization data can be run through the Dimensions API [extract_affiliations](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations) function in order to extract GRID IDs, which can then be utilized to get publication data statistics.\n", + "* An analyst has a list of organizations of interest, and wants to get details of their publications from Dimensions. To do this, they they need to map them to organization IDs so they can extract information from the Dimensions database. The organization data can be run through the Dimensions API [extract_affiliations](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations) function in order to extract IDs, which can then be utilized to get publication data statistics.\n", "\n", "* A second use case is to standardize messy organization data for \n", - "analysis. For example, an analyst might have a set of affiliation data containing many variants of organization names (\"University of Cambridge\", \"Cambridge University\"). By mapping to GRID IDs, the analyst can standardize the data so it's easier to analyse." + "analysis. For example, an analyst might have a set of affiliation data containing many variants of organization names (\"University of Cambridge\", \"Cambridge University\"). By mapping to IDs, the analyst can standardize the data so it's easier to analyse." ] }, { @@ -33,7 +30,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Jan 25, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -95,8 +92,8 @@ "Logging in..\n", "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -140,20 +137,20 @@ "source": [ "## 1. Importing Organization Data\n", "\n", - "There are several ways to obtain organization data. Below we show examples for 2 different ways to obtain organization data that can be used to run through Dimensions API for GRID ID mapping. *For purposes of this demostration, we will be using method 1*. Please uncomment the other sections if you wish to use those methods instead.\n", + "There are several ways to obtain organization data. Below we show examples for 2 different ways to obtain organization data that can be used to run through the Dimensions API for ID mapping. *For purposes of this demostration, we will be using method 1*. Please uncomment the other sections if you wish to use those methods instead.\n", "\n", "\n", "1. Manually Generate Organization Data\n", "2. Load Organization Data from Local Machine\n", "\n", - "*Note* - To map organizational data to GRID IDs, the data must conform to mapping specifications and contain data (if available) for the following 4 columns (with column headers being lowercase):\n", + "*Note* - To map organizational data to IDs, the data must conform to mapping specifications and contain data (if available) for the following 4 columns (with column headers being lowercase):\n", "* name - name of the organization\n", "* city - city of the organization\n", "* state - state of the organization (use the full name of the state, not acronym)\n", "* country - country of the organization\n", "\n", "\n", - "The user may use structured or unstructured organization data for mapping to GRID IDs like the following:\n", + "The user may use structured or unstructured organization data for mapping to IDs like the following:\n", "\n", "* Structured Data e.g., \n", "`[{\"name\":\"Southwestern University\",\n", @@ -376,7 +373,7 @@ "\n", "The following cells can be utilized to import an excel file of organization data from a local machine.\n", "\n", - "This method is useful for when you need to map hundreds or thousands of organizations to GRID IDs, as the bulk process using the API will be much faster than any individual mapping.\n", + "This method is useful for when you need to map hundreds or thousands of organizations to IDs, as the bulk process using the API will be much faster than any individual mapping.\n", "\n", "\n", "*Please uncomment the cells below if to be utilized*" @@ -423,11 +420,11 @@ "id": "rOeRQ6S7244b" }, "source": [ - "## 2. Utilizing Dimensions API to Extract GRID IDs\n", + "## 2. Utilizing Dimensions API to Extract IDs\n", "\n", - " The following cells will take our organization data and run it through the Dimensions API to pull back GRID IDs mapped to each organization.\n", + " The following cells will take our organization data and run it through the Dimensions API to pull back IDs mapped to each organization.\n", "\n", - "Here, we utilize the \"[extract_affiliations](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations)\" API function which can be used to enrich private datasets including non-disambiguated organizations data with Dimensions GRID IDs.\n", + "Here, we utilize the \"[extract_affiliations](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations)\" API function which can be used to enrich private datasets including non-disambiguated organizations data with Dimensions organization IDs.\n", "\n", "\n" ] @@ -455,7 +452,7 @@ }, "outputs": [], "source": [ - "# Second, we will convert organization data from a dataframe to a dictionary (json) for GRID mapping\n", + "# Second, we will convert organization data from a dataframe to a dictionary (json) for ID mapping\n", "\n", "recs = orgs.to_dict(orient='records')" ] @@ -492,7 +489,7 @@ } ], "source": [ - "# Then we will take the organization data, run it through the API and return GRID IDs\n", + "# Then we will take the organization data, run it through the API and return organization IDs\n", "\n", "# Chunk records to batches, API takes up to 200 records at a time.\n", "def chunk_records(l, n):\n", @@ -502,10 +499,10 @@ "# Use dimcli's from extract_affiliations API wrapper to process data\n", "\n", "chunksize = 200\n", - "grid = pd.DataFrame()\n", + "org_data = pd.DataFrame()\n", "for k,chunk in enumerate(chunk_records(recs, chunksize)):\n", " output = extract_affiliations(chunk, as_json=False)\n", - " grid = grid.append(output,sort = False, ignore_index = True)\n", + " org_data = pd.concat([org_data, output])\n", " # Pause to avoid overloading API with too many calls too quickly\n", " time.sleep(1)\n", " print(f\"{(k+1)*chunksize} records complete!\")" @@ -729,10 +726,10 @@ } ], "source": [ - "# Preview the extracted GRID ID dataframe\n", + "# Preview the extracted organization ID dataframe\n", "# Note: data columns labeled with \"input\" are the original organization data supplied to the API\n", "\n", - "grid.head()" + "org_data.head()" ] }, { @@ -742,7 +739,7 @@ "id": "0xmORlDluF0e" }, "source": [ - "Note: Some records returned in the GRID mapping may require manual review, as some results may give more than one organization of interest (see below). The user can utilize this information to update their original organization data that is inputted to this notebook." + "Note: Some records returned in the mapping may require manual review, as some results may give more than one organization of interest (see below). The user can utilize this information to update their original organization data that is inputted to this notebook." ] }, { @@ -811,218 +808,14 @@ " \n", " \n", " \n", - " \n", - " 10\n", - " null\n", - " United States\n", - " Mayo Clinic\n", - " null\n", - " grid.417468.8\n", - " Mayo Clinic\n", - " Scottsdale\n", - " Arizona\n", - " United States\n", - " True\n", - " 6252001\n", - " United States\n", - " US\n", - " 5551752\n", - " Arizona\n", - " US-AZ\n", - " 5313457\n", - " Scottsdale\n", - " \n", - " \n", - " 11\n", - " null\n", - " United States\n", - " Mayo Clinic\n", - " null\n", - " grid.417468.8\n", - " Mayo Clinic\n", - " Scottsdale\n", - " Arizona\n", - " United States\n", - " True\n", - " 6252001\n", - " United States\n", - " US\n", - " 5551752\n", - " Arizona\n", - " US-AZ\n", - " 4160021\n", - " Jacksonville\n", - " \n", - " \n", - " 12\n", - " null\n", - " United States\n", - " Mayo Clinic\n", - " null\n", - " grid.417468.8\n", - " Mayo Clinic\n", - " Scottsdale\n", - " Arizona\n", - " United States\n", - " True\n", - " 6252001\n", - " United States\n", - " US\n", - " 4155751\n", - " Florida\n", - " US-FL\n", - " 5313457\n", - " Scottsdale\n", - " \n", - " \n", - " 13\n", - " null\n", - " United States\n", - " Mayo Clinic\n", - " null\n", - " grid.417468.8\n", - " Mayo Clinic\n", - " Scottsdale\n", - " Arizona\n", - " United States\n", - " True\n", - " 6252001\n", - " United States\n", - " US\n", - " 4155751\n", - " Florida\n", - " US-FL\n", - " 4160021\n", - " Jacksonville\n", - " \n", - " \n", - " 14\n", - " null\n", - " United States\n", - " Mayo Clinic\n", - " null\n", - " grid.417467.7\n", - " Mayo Clinic\n", - " Jacksonville\n", - " Florida\n", - " United States\n", - " True\n", - " 6252001\n", - " United States\n", - " US\n", - " 5551752\n", - " Arizona\n", - " US-AZ\n", - " 5313457\n", - " Scottsdale\n", - " \n", - " \n", - " 15\n", - " null\n", - " United States\n", - " Mayo Clinic\n", - " null\n", - " grid.417467.7\n", - " Mayo Clinic\n", - " Jacksonville\n", - " Florida\n", - " United States\n", - " True\n", - " 6252001\n", - " United States\n", - " US\n", - " 5551752\n", - " Arizona\n", - " US-AZ\n", - " 4160021\n", - " Jacksonville\n", - " \n", - " \n", - " 16\n", - " null\n", - " United States\n", - " Mayo Clinic\n", - " null\n", - " grid.417467.7\n", - " Mayo Clinic\n", - " Jacksonville\n", - " Florida\n", - " United States\n", - " True\n", - " 6252001\n", - " United States\n", - " US\n", - " 4155751\n", - " Florida\n", - " US-FL\n", - " 5313457\n", - " Scottsdale\n", - " \n", - " \n", - " 17\n", - " null\n", - " United States\n", - " Mayo Clinic\n", - " null\n", - " grid.417467.7\n", - " Mayo Clinic\n", - " Jacksonville\n", - " Florida\n", - " United States\n", - " True\n", - " 6252001\n", - " United States\n", - " US\n", - " 4155751\n", - " Florida\n", - " US-FL\n", - " 4160021\n", - " Jacksonville\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " input.city input.country input.name input.state grid_id \\\n", - "10 null United States Mayo Clinic null grid.417468.8 \n", - "11 null United States Mayo Clinic null grid.417468.8 \n", - "12 null United States Mayo Clinic null grid.417468.8 \n", - "13 null United States Mayo Clinic null grid.417468.8 \n", - "14 null United States Mayo Clinic null grid.417467.7 \n", - "15 null United States Mayo Clinic null grid.417467.7 \n", - "16 null United States Mayo Clinic null grid.417467.7 \n", - "17 null United States Mayo Clinic null grid.417467.7 \n", - "\n", - " grid_name grid_city grid_state grid_country requires_review \\\n", - "10 Mayo Clinic Scottsdale Arizona United States True \n", - "11 Mayo Clinic Scottsdale Arizona United States True \n", - "12 Mayo Clinic Scottsdale Arizona United States True \n", - "13 Mayo Clinic Scottsdale Arizona United States True \n", - "14 Mayo Clinic Jacksonville Florida United States True \n", - "15 Mayo Clinic Jacksonville Florida United States True \n", - "16 Mayo Clinic Jacksonville Florida United States True \n", - "17 Mayo Clinic Jacksonville Florida United States True \n", - "\n", - " geo_country_id geo_country_name geo_country_code geo_state_id \\\n", - "10 6252001 United States US 5551752 \n", - "11 6252001 United States US 5551752 \n", - "12 6252001 United States US 4155751 \n", - "13 6252001 United States US 4155751 \n", - "14 6252001 United States US 5551752 \n", - "15 6252001 United States US 5551752 \n", - "16 6252001 United States US 4155751 \n", - "17 6252001 United States US 4155751 \n", - "\n", - " geo_state_name geo_state_code geo_city_id geo_city_name \n", - "10 Arizona US-AZ 5313457 Scottsdale \n", - "11 Arizona US-AZ 4160021 Jacksonville \n", - "12 Florida US-FL 5313457 Scottsdale \n", - "13 Florida US-FL 4160021 Jacksonville \n", - "14 Arizona US-AZ 5313457 Scottsdale \n", - "15 Arizona US-AZ 4160021 Jacksonville \n", - "16 Florida US-FL 5313457 Scottsdale \n", - "17 Florida US-FL 4160021 Jacksonville " + "Empty DataFrame\n", + "Columns: [input.city, input.country, input.name, input.state, grid_id, grid_name, grid_city, grid_state, grid_country, requires_review, geo_country_id, geo_country_name, geo_country_code, geo_state_id, geo_state_name, geo_state_code, geo_city_id, geo_city_name]\n", + "Index: []" ] }, "execution_count": 10, @@ -1031,9 +824,9 @@ } ], "source": [ - "grid['requires_review'] = grid['requires_review'].astype(str)\n", - "grid_review = grid.loc[grid['requires_review'] == 'True']\n", - "grid_review" + "org_data['requires_review'] = org_data['requires_review'].astype(str)\n", + "org_data_review = org_data.loc[org_data['requires_review'] == 'True']\n", + "org_data_review" ] }, { @@ -1043,9 +836,9 @@ "id": "e2YjFdSk4X6X" }, "source": [ - "## 3. Save the GRID ID Dataset we created\n", + "## 3. Save the ID Dataset we created\n", "\n", - "The following cell will export the GRID ID mapped organization data to a csv file that can be saved to your local machine.\n" + "The following cell will export the ID-mapped organization data to a csv file that can be saved to your local machine.\n" ] }, { @@ -1074,7 +867,7 @@ "outputs": [], "source": [ "# temporarily save pandas dataframe as file in colab environment\n", - "grid.to_csv('file_name.csv')\n", + "org_data.to_csv('file_name.csv')\n", "\n", "if 'google.colab' in sys.modules:\n", " \n", @@ -1093,7 +886,7 @@ "source": [ "## Conclusions\n", "\n", - "In this notebook we have shown how to use the [Dimensions Analytics API](https://www.dimensions.ai/dimensions-apis/) *extract_affiliations* function to assign GRID identifiers to organizations data.\n", + "In this notebook we have shown how to use the [Dimensions Analytics API](https://www.dimensions.ai/dimensions-apis/) *extract_affiliations* function to assign identifiers to organizations data.\n", "\n", "For more background, see the [extract_affiliations function documentation](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations), as well as the other functions available via the Dimensions API. \n", "\n" @@ -1121,7 +914,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/cookbooks/8-organizations/6-organization-groups.ipynb b/cookbooks/8-organizations/6-organization-groups.ipynb index 545ce5ee..6ca8faf2 100644 --- a/cookbooks/8-organizations/6-organization-groups.ipynb +++ b/cookbooks/8-organizations/6-organization-groups.ipynb @@ -11,13 +11,13 @@ "This tutorial shows how use the organization groups in Dimensions (e.g. the [funder groups](https://app.dimensions.ai/browse/facet-filter-groups/publication/funder_shared_group_facet)) in order to construct API queries. \n", "\n", "The Dimensions team maintains various organization groups definitions in the main Dimensions web application. \n", - "These groups are not available directly via the API, but since they are a simple list of GRID identifiers, they can be easily downloaded as a CSV file. \n", + "These groups are not available directly via the API, but since they are a simple list of organization identifiers, they can be easily downloaded as a CSV file. \n", "Once you have a CSV file, it is possible to parse it with Python and use its contents in an API query. \n", "\n", "Outline \n", "\n", "1. Downloading Dimensions' organization groups as a CSV file.\n", - "2. Constructing API queries using a list of GRID IDs\n", + "2. Constructing API queries using a list of organization IDs\n", " " ] }, @@ -32,7 +32,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Feb 21, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": { "Collapsed": "false" }, @@ -75,8 +75,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -118,7 +118,7 @@ "\n", "2. Use the 'Copy to my Groups' command to create a copy of that group in your personal space.\n", "\n", - "3. Go to 'My Groups', where you can select 'Export group definitions' to download a CSV file containing the groups details including GRID IDs. \n", + "3. Go to 'My Groups', where you can select 'Export group definitions' to download a CSV file containing the groups details including organization IDs. \n", "\n", "See below a screenshot of the [Dimensions' groups page](http://api-sample-data.dimensions.ai/data/funder-groups/dimensions-funder-groups-page.jpg). \n", "\n", @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -139,7 +139,7 @@ "" ] }, - "execution_count": 7, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": { "Collapsed": "false" }, @@ -432,7 +432,7 @@ "24 grid.457898.f " ] }, - "execution_count": 9, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -449,7 +449,7 @@ "Collapsed": "false" }, "source": [ - "Let's get the GRID IDs for the NSF and put them into a Python list.\n", + "Let's get the organization IDs for the NSF and put them into a Python list.\n", "\n", "Then we can generate queries programmatically using this list. \n", "\n", @@ -458,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": { "Collapsed": "false" }, @@ -493,14 +493,14 @@ " 'grid.457898.f']" ] }, - "execution_count": 11, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "nsfgrids = data['ID'].to_list()\n", - "nsfgrids" + "nsforgs = data['ID'].to_list()\n", + "nsforgs" ] }, { @@ -511,14 +511,14 @@ "source": [ "### How many grants from the NSF? \n", "\n", - "Let's try a simple API query that uses the contents of `nsfgrids`. \n", + "Let's try a simple API query that uses the contents of `nsforgs`. \n", "\n", "The total number of results should match [what you see in Dimensions](https://app.dimensions.ai/discover/publication?and_facet_funder_shared_group_facet=574603a4-0c27-4844-9f74-7e6810e25cfb).\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": { "Collapsed": "false" }, @@ -532,8 +532,10 @@ " where funders.id in [\"grid.457768.f\", \"grid.457785.c\", \"grid.457799.1\", \"grid.457810.f\", \"grid.457836.b\", \"grid.457875.c\", \"grid.457916.8\", \"grid.457789.0\", \"grid.457813.c\", \"grid.457814.b\", \"grid.457842.8\", \"grid.457821.d\", \"grid.457772.4\", \"grid.457801.f\", \"grid.457891.6\", \"grid.457892.5\", \"grid.457845.f\", \"grid.457922.f\", \"grid.457896.1\", \"grid.431093.c\", \"grid.457758.c\", \"grid.457907.8\", \"grid.473792.c\", \"grid.457846.c\", \"grid.457898.f\"]\n", "return grants[id+title]\n", "\n", - "Returned Grants: 20 (total = 601237)\n", - "\u001b[2mTime: 2.03s\u001b[0m\n" + "Returned Grants: 20 (total = 621348)\n", + "\u001b[2mTime: 0.61s\u001b[0m\n", + "WARNINGS [1]\n", + "Field 'funders' is deprecated in favor of funder_orgs. Please refer to https://docs.dimensions.ai/dsl/releasenotes.html for more details\n" ] }, { @@ -564,133 +566,133 @@ " \n", " \n", " 0\n", - " grant.9752271\n", - " NNA Planning: Developing community frameworks ...\n", + " grant.14880777\n", + " Postdoctoral Fellowship: PRFB: Mapping the Bum...\n", " \n", " \n", " 1\n", - " grant.9890102\n", - " RUI: Exciton-Phonon Interactions in Solids bas...\n", + " grant.14880767\n", + " Postdoctoral Fellowship: PRFB: Using the intro...\n", " \n", " \n", " 2\n", - " grant.9982417\n", - " CAREER: Empowering White-box Driven Analytics ...\n", + " grant.14976921\n", + " Rossbypalooza 2026: A Student-led Summer Schoo...\n", " \n", " \n", " 3\n", - " grant.9982416\n", - " CAREER: Holistic Framework for Constructing Dy...\n", + " grant.14955547\n", + " Postdoctoral Fellowship: PRFB: The Role of Pla...\n", " \n", " \n", " 4\n", - " grant.9982395\n", - " CAREER: Leveraging physical properties of mode...\n", + " grant.14880768\n", + " Postdoctoral Fellowship: PRFB: Testing a role ...\n", " \n", " \n", " 5\n", - " grant.9785674\n", - " BPC-AE Collaborative Research: Researching Equ...\n", + " grant.14973500\n", + " Postdoctoral Fellowship: EAR-PF: Reconstructin...\n", " \n", " \n", " 6\n", - " grant.9785672\n", - " BPC-AE Collaborative Research: Researching Equ...\n", + " grant.14976878\n", + " Conference: Recent Perspectives on Moments of ...\n", " \n", " \n", " 7\n", - " grant.9752397\n", - " Equitable Learning to Advance Technical Education\n", + " grant.14955637\n", + " Conference: Rutgers Gauge Theory, Low-Dimensio...\n", " \n", " \n", " 8\n", - " grant.9995499\n", - " CAREER: New imaging of mid-ocean ridge systems...\n", + " grant.14955550\n", + " Postdoctoral Fellowship: PRFB: Integrating the...\n", " \n", " \n", " 9\n", - " grant.9995464\n", - " CAREER: Reconstructing Parasite Abundance in R...\n", + " grant.14880771\n", + " Postdoctoral Fellowship: PRFB: Eco-evolutionar...\n", " \n", " \n", " 10\n", - " grant.9752334\n", - " Collaborative Research: SWIFT: Intelligent Dyn...\n", + " grant.14976854\n", + " Conference: Meeting in the Middle: Conference ...\n", " \n", " \n", " 11\n", - " grant.9752333\n", - " Collaborative Research: SWIFT: Intelligent Dyn...\n", + " grant.14976778\n", + " MCA: Eavesdropping vectors and disease transmi...\n", " \n", " \n", " 12\n", - " grant.9995542\n", - " CAREER: Learning Mechanisms from Single Cell M...\n", + " grant.14969598\n", + " Conference: Universal Statistics in Number Theory\n", " \n", " \n", " 13\n", - " grant.9995538\n", - " CAREER: A Transformative Approach for Teaching...\n", + " grant.14964639\n", + " Long term compliance observations of the evolv...\n", " \n", " \n", " 14\n", - " grant.9995527\n", - " CAREER: Interlimb Neural Coupling to Enhance G...\n", + " grant.14880779\n", + " Postdoctoral Fellowship: PRFB: Elucidating the...\n", " \n", " \n", " 15\n", - " grant.9995522\n", - " CAREER: Fossil Amber Insight Into Macroevoluti...\n", + " grant.14976745\n", + " What drives spatial variability in water-colum...\n", " \n", " \n", " 16\n", - " grant.9995520\n", - " 2022 Origins of Life GRC and GRS: Environments...\n", + " grant.14976476\n", + " IRES: Exploring New Horizons in the Observable...\n", " \n", " \n", " 17\n", - " grant.9995519\n", - " CAREER: Invariants and Entropy of Square Integ...\n", + " grant.14969702\n", + " MCA Pilot PUI: Can unhatched eggs or trash aff...\n", " \n", " \n", " 18\n", - " grant.9995488\n", - " CAREER: Statistical Learning from a Modern Per...\n", + " grant.14954673\n", + " Conference: Geometry Labs United 2025\n", " \n", " \n", " 19\n", - " grant.9995470\n", - " CAREER: CAS- Climate: Making Decarbonization o...\n", + " grant.14976899\n", + " Collaborative Research: FIRE-MODEL: Advancing ...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id title\n", - "0 grant.9752271 NNA Planning: Developing community frameworks ...\n", - "1 grant.9890102 RUI: Exciton-Phonon Interactions in Solids bas...\n", - "2 grant.9982417 CAREER: Empowering White-box Driven Analytics ...\n", - "3 grant.9982416 CAREER: Holistic Framework for Constructing Dy...\n", - "4 grant.9982395 CAREER: Leveraging physical properties of mode...\n", - "5 grant.9785674 BPC-AE Collaborative Research: Researching Equ...\n", - "6 grant.9785672 BPC-AE Collaborative Research: Researching Equ...\n", - "7 grant.9752397 Equitable Learning to Advance Technical Education\n", - "8 grant.9995499 CAREER: New imaging of mid-ocean ridge systems...\n", - "9 grant.9995464 CAREER: Reconstructing Parasite Abundance in R...\n", - "10 grant.9752334 Collaborative Research: SWIFT: Intelligent Dyn...\n", - "11 grant.9752333 Collaborative Research: SWIFT: Intelligent Dyn...\n", - "12 grant.9995542 CAREER: Learning Mechanisms from Single Cell M...\n", - "13 grant.9995538 CAREER: A Transformative Approach for Teaching...\n", - "14 grant.9995527 CAREER: Interlimb Neural Coupling to Enhance G...\n", - "15 grant.9995522 CAREER: Fossil Amber Insight Into Macroevoluti...\n", - "16 grant.9995520 2022 Origins of Life GRC and GRS: Environments...\n", - "17 grant.9995519 CAREER: Invariants and Entropy of Square Integ...\n", - "18 grant.9995488 CAREER: Statistical Learning from a Modern Per...\n", - "19 grant.9995470 CAREER: CAS- Climate: Making Decarbonization o..." + " id title\n", + "0 grant.14880777 Postdoctoral Fellowship: PRFB: Mapping the Bum...\n", + "1 grant.14880767 Postdoctoral Fellowship: PRFB: Using the intro...\n", + "2 grant.14976921 Rossbypalooza 2026: A Student-led Summer Schoo...\n", + "3 grant.14955547 Postdoctoral Fellowship: PRFB: The Role of Pla...\n", + "4 grant.14880768 Postdoctoral Fellowship: PRFB: Testing a role ...\n", + "5 grant.14973500 Postdoctoral Fellowship: EAR-PF: Reconstructin...\n", + "6 grant.14976878 Conference: Recent Perspectives on Moments of ...\n", + "7 grant.14955637 Conference: Rutgers Gauge Theory, Low-Dimensio...\n", + "8 grant.14955550 Postdoctoral Fellowship: PRFB: Integrating the...\n", + "9 grant.14880771 Postdoctoral Fellowship: PRFB: Eco-evolutionar...\n", + "10 grant.14976854 Conference: Meeting in the Middle: Conference ...\n", + "11 grant.14976778 MCA: Eavesdropping vectors and disease transmi...\n", + "12 grant.14969598 Conference: Universal Statistics in Number Theory\n", + "13 grant.14964639 Long term compliance observations of the evolv...\n", + "14 grant.14880779 Postdoctoral Fellowship: PRFB: Elucidating the...\n", + "15 grant.14976745 What drives spatial variability in water-colum...\n", + "16 grant.14976476 IRES: Exploring New Horizons in the Observable...\n", + "17 grant.14969702 MCA Pilot PUI: Can unhatched eggs or trash aff...\n", + "18 grant.14954673 Conference: Geometry Labs United 2025\n", + "19 grant.14976899 Collaborative Research: FIRE-MODEL: Advancing ..." ] }, - "execution_count": 12, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -700,7 +702,7 @@ "\n", "query = f\"\"\"\n", "search grants \n", - " where funders.id in {json.dumps(nsfgrids)}\n", + " where funders.id in {json.dumps(nsforgs)}\n", "return grants[id+title]\n", "\"\"\"\n", "\n", @@ -727,7 +729,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/cookbooks/8-organizations/7-benchmarking-organizations.ipynb b/cookbooks/8-organizations/7-benchmarking-organizations.ipynb index 4ce0f55c..b38638a8 100644 --- a/cookbooks/8-organizations/7-benchmarking-organizations.ipynb +++ b/cookbooks/8-organizations/7-benchmarking-organizations.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -29,7 +29,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Feb 21, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -70,8 +70,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "Collapsed": "false", "colab": {}, @@ -135,7 +135,7 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 20\n", - "\u001b[2mTime: 21.14s\u001b[0m\n" + "\u001b[2mTime: 12.29s\u001b[0m\n" ] }, { @@ -159,204 +159,204 @@ " \n", " \n", " \n", - " altmetric_median\n", - " count\n", " id\n", " name\n", + " altmetric_median\n", + " count\n", " \n", " \n", " \n", " \n", " 0\n", - " 5.0\n", - " 546592\n", " grid.38142.3c\n", " Harvard University\n", + " 5.292790\n", + " 715128\n", " \n", " \n", " 1\n", - " 3.0\n", - " 484017\n", " grid.26999.3d\n", - " University of Tokyo\n", + " The University of Tokyo\n", + " 3.000000\n", + " 570861\n", " \n", " \n", " 2\n", - " 4.0\n", - " 342764\n", " grid.17063.33\n", " University of Toronto\n", + " 4.019046\n", + " 435895\n", " \n", " \n", " 3\n", - " 3.0\n", - " 320966\n", " grid.214458.e\n", - " University of Michigan\n", + " University of Michigan-Ann Arbor\n", + " 3.968242\n", + " 412146\n", " \n", " \n", " 4\n", - " 3.0\n", - " 310485\n", - " grid.258799.8\n", - " Kyoto University\n", + " grid.168010.e\n", + " Stanford University\n", + " 4.939072\n", + " 393415\n", " \n", " \n", " 5\n", - " 4.0\n", - " 302094\n", - " grid.168010.e\n", - " Stanford University\n", + " grid.4991.5\n", + " University of Oxford\n", + " 5.104038\n", + " 387324\n", " \n", " \n", " 6\n", - " 4.0\n", - " 297558\n", " grid.34477.33\n", " University of Washington\n", + " 4.304326\n", + " 385718\n", " \n", " \n", " 7\n", - " 3.0\n", - " 297094\n", - " grid.19006.3e\n", - " University of California, Los Angeles\n", + " grid.21107.35\n", + " Johns Hopkins University\n", + " 4.374951\n", + " 381545\n", " \n", " \n", " 8\n", - " 5.0\n", - " 289280\n", - " grid.4991.5\n", - " University of Oxford\n", + " grid.19006.3e\n", + " University of California, Los Angeles\n", + " 3.871221\n", + " 373415\n", " \n", " \n", " 9\n", - " 4.0\n", - " 285143\n", - " grid.21107.35\n", - " Johns Hopkins University\n", + " grid.258799.8\n", + " Kyoto University\n", + " 3.000000\n", + " 370973\n", " \n", " \n", " 10\n", - " 4.0\n", - " 282170\n", - " grid.5335.0\n", - " University of Cambridge\n", + " grid.11899.38\n", + " Universidade de São Paulo\n", + " 2.778797\n", + " 367466\n", " \n", " \n", " 11\n", - " 2.0\n", - " 280405\n", - " grid.11899.38\n", - " University of São Paulo\n", + " grid.5335.0\n", + " University of Cambridge\n", + " 4.412618\n", + " 356990\n", " \n", " \n", " 12\n", - " 4.0\n", - " 271170\n", - " grid.25879.31\n", - " University of Pennsylvania\n", + " grid.47840.3f\n", + " University of California, Berkeley\n", + " 4.103148\n", + " 353011\n", " \n", " \n", " 13\n", - " 4.0\n", - " 266337\n", - " grid.83440.3b\n", - " University College London\n", + " grid.25879.31\n", + " University of Pennsylvania\n", + " 4.491342\n", + " 351125\n", " \n", " \n", " 14\n", - " 3.0\n", - " 265592\n", - " grid.136593.b\n", - " Osaka University\n", + " grid.17635.36\n", + " University of Minnesota Twin Cities\n", + " 3.252271\n", + " 324688\n", " \n", " \n", " 15\n", - " 3.0\n", - " 250749\n", - " grid.69566.3a\n", - " Tohoku University\n", + " grid.136593.b\n", + " Osaka University\n", + " 3.000000\n", + " 323974\n", " \n", " \n", " 16\n", - " 3.0\n", - " 244713\n", - " grid.5386.8\n", - " Cornell University\n", + " grid.83440.3b\n", + " University College London\n", + " 4.154059\n", + " 320344\n", " \n", " \n", " 17\n", - " 4.0\n", - " 242749\n", - " grid.47840.3f\n", - " University of California, Berkeley\n", + " grid.14003.36\n", + " University of Wisconsin-Madison\n", + " 3.220404\n", + " 316542\n", " \n", " \n", " 18\n", - " 3.0\n", - " 239283\n", - " grid.17635.36\n", - " University of Minnesota\n", + " grid.410726.6\n", + " University of Chinese Academy of Sciences\n", + " 2.287477\n", + " 313606\n", " \n", " \n", " 19\n", - " 4.0\n", - " 236142\n", - " grid.21729.3f\n", - " Columbia University\n", + " grid.47100.32\n", + " Yale University\n", + " 4.602265\n", + " 305202\n", " \n", " \n", "\n", "" ], "text/plain": [ - " altmetric_median count id \\\n", - "0 5.0 546592 grid.38142.3c \n", - "1 3.0 484017 grid.26999.3d \n", - "2 4.0 342764 grid.17063.33 \n", - "3 3.0 320966 grid.214458.e \n", - "4 3.0 310485 grid.258799.8 \n", - "5 4.0 302094 grid.168010.e \n", - "6 4.0 297558 grid.34477.33 \n", - "7 3.0 297094 grid.19006.3e \n", - "8 5.0 289280 grid.4991.5 \n", - "9 4.0 285143 grid.21107.35 \n", - "10 4.0 282170 grid.5335.0 \n", - "11 2.0 280405 grid.11899.38 \n", - "12 4.0 271170 grid.25879.31 \n", - "13 4.0 266337 grid.83440.3b \n", - "14 3.0 265592 grid.136593.b \n", - "15 3.0 250749 grid.69566.3a \n", - "16 3.0 244713 grid.5386.8 \n", - "17 4.0 242749 grid.47840.3f \n", - "18 3.0 239283 grid.17635.36 \n", - "19 4.0 236142 grid.21729.3f \n", + " id name \\\n", + "0 grid.38142.3c Harvard University \n", + "1 grid.26999.3d The University of Tokyo \n", + "2 grid.17063.33 University of Toronto \n", + "3 grid.214458.e University of Michigan-Ann Arbor \n", + "4 grid.168010.e Stanford University \n", + "5 grid.4991.5 University of Oxford \n", + "6 grid.34477.33 University of Washington \n", + "7 grid.21107.35 Johns Hopkins University \n", + "8 grid.19006.3e University of California, Los Angeles \n", + "9 grid.258799.8 Kyoto University \n", + "10 grid.11899.38 Universidade de São Paulo \n", + "11 grid.5335.0 University of Cambridge \n", + "12 grid.47840.3f University of California, Berkeley \n", + "13 grid.25879.31 University of Pennsylvania \n", + "14 grid.17635.36 University of Minnesota Twin Cities \n", + "15 grid.136593.b Osaka University \n", + "16 grid.83440.3b University College London \n", + "17 grid.14003.36 University of Wisconsin-Madison \n", + "18 grid.410726.6 University of Chinese Academy of Sciences \n", + "19 grid.47100.32 Yale University \n", "\n", - " name \n", - "0 Harvard University \n", - "1 University of Tokyo \n", - "2 University of Toronto \n", - "3 University of Michigan \n", - "4 Kyoto University \n", - "5 Stanford University \n", - "6 University of Washington \n", - "7 University of California, Los Angeles \n", - "8 University of Oxford \n", - "9 Johns Hopkins University \n", - "10 University of Cambridge \n", - "11 University of São Paulo \n", - "12 University of Pennsylvania \n", - "13 University College London \n", - "14 Osaka University \n", - "15 Tohoku University \n", - "16 Cornell University \n", - "17 University of California, Berkeley \n", - "18 University of Minnesota \n", - "19 Columbia University " + " altmetric_median count \n", + "0 5.292790 715128 \n", + "1 3.000000 570861 \n", + "2 4.019046 435895 \n", + "3 3.968242 412146 \n", + "4 4.939072 393415 \n", + "5 5.104038 387324 \n", + "6 4.304326 385718 \n", + "7 4.374951 381545 \n", + "8 3.871221 373415 \n", + "9 3.000000 370973 \n", + "10 2.778797 367466 \n", + "11 4.412618 356990 \n", + "12 4.103148 353011 \n", + "13 4.491342 351125 \n", + "14 3.252271 324688 \n", + "15 3.000000 323974 \n", + "16 4.154059 320344 \n", + "17 3.220404 316542 \n", + "18 2.287477 313606 \n", + "19 4.602265 305202 " ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "Collapsed": "false", "colab": {}, @@ -382,7 +382,7 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 20\n", - "\u001b[2mTime: 6.63s\u001b[0m\n" + "\u001b[2mTime: 4.16s\u001b[0m\n" ] }, { @@ -406,204 +406,204 @@ " \n", " \n", " \n", - " citations_total\n", - " count\n", " id\n", " name\n", + " citations_total\n", + " count\n", " \n", " \n", " \n", " \n", " 0\n", - " 28836616.0\n", - " 546592\n", " grid.38142.3c\n", " Harvard University\n", + " 43542715.0\n", + " 715128\n", " \n", " \n", " 1\n", - " 8545148.0\n", - " 484017\n", " grid.26999.3d\n", - " University of Tokyo\n", + " The University of Tokyo\n", + " 12416944.0\n", + " 570861\n", " \n", " \n", " 2\n", - " 11040840.0\n", - " 342764\n", " grid.17063.33\n", " University of Toronto\n", + " 16896263.0\n", + " 435895\n", " \n", " \n", " 3\n", - " 11710248.0\n", - " 320966\n", " grid.214458.e\n", - " University of Michigan\n", + " University of Michigan-Ann Arbor\n", + " 17899164.0\n", + " 412146\n", " \n", " \n", " 4\n", - " 5928948.0\n", - " 310485\n", - " grid.258799.8\n", - " Kyoto University\n", + " grid.168010.e\n", + " Stanford University\n", + " 22857822.0\n", + " 393415\n", " \n", " \n", " 5\n", - " 14738599.0\n", - " 302094\n", - " grid.168010.e\n", - " Stanford University\n", + " grid.4991.5\n", + " University of Oxford\n", + " 17348878.0\n", + " 387324\n", " \n", " \n", " 6\n", - " 12585381.0\n", - " 297558\n", " grid.34477.33\n", " University of Washington\n", + " 19245227.0\n", + " 385718\n", " \n", " \n", " 7\n", - " 11710928.0\n", - " 297094\n", - " grid.19006.3e\n", - " University of California, Los Angeles\n", + " grid.21107.35\n", + " Johns Hopkins University\n", + " 18542871.0\n", + " 381545\n", " \n", " \n", " 8\n", - " 10879614.0\n", - " 289280\n", - " grid.4991.5\n", - " University of Oxford\n", + " grid.19006.3e\n", + " University of California, Los Angeles\n", + " 17370426.0\n", + " 373415\n", " \n", " \n", " 9\n", - " 12084053.0\n", - " 285143\n", - " grid.21107.35\n", - " Johns Hopkins University\n", + " grid.258799.8\n", + " Kyoto University\n", + " 8426700.0\n", + " 370973\n", " \n", " \n", " 10\n", - " 10814051.0\n", - " 282170\n", - " grid.5335.0\n", - " University of Cambridge\n", + " grid.11899.38\n", + " Universidade de São Paulo\n", + " 6823063.0\n", + " 367466\n", " \n", " \n", " 11\n", - " 4105653.0\n", - " 280405\n", - " grid.11899.38\n", - " University of São Paulo\n", + " grid.5335.0\n", + " University of Cambridge\n", + " 16495121.0\n", + " 356990\n", " \n", " \n", " 12\n", - " 10450691.0\n", - " 271170\n", - " grid.25879.31\n", - " University of Pennsylvania\n", + " grid.47840.3f\n", + " University of California, Berkeley\n", + " 19445292.0\n", + " 353011\n", " \n", " \n", " 13\n", - " 9614297.0\n", - " 266337\n", - " grid.83440.3b\n", - " University College London\n", + " grid.25879.31\n", + " University of Pennsylvania\n", + " 15634591.0\n", + " 351125\n", " \n", " \n", " 14\n", - " 4653874.0\n", - " 265592\n", - " grid.136593.b\n", - " Osaka University\n", + " grid.17635.36\n", + " University of Minnesota Twin Cities\n", + " 13100152.0\n", + " 324688\n", " \n", " \n", " 15\n", - " 3694359.0\n", - " 250749\n", - " grid.69566.3a\n", - " Tohoku University\n", + " grid.136593.b\n", + " Osaka University\n", + " 6486832.0\n", + " 323974\n", " \n", " \n", " 16\n", - " 9370701.0\n", - " 244713\n", - " grid.5386.8\n", - " Cornell University\n", + " grid.83440.3b\n", + " University College London\n", + " 13014090.0\n", + " 320344\n", " \n", " \n", " 17\n", - " 11806056.0\n", - " 242749\n", - " grid.47840.3f\n", - " University of California, Berkeley\n", + " grid.14003.36\n", + " University of Wisconsin-Madison\n", + " 13060297.0\n", + " 316542\n", " \n", " \n", " 18\n", - " 8360048.0\n", - " 239283\n", - " grid.17635.36\n", - " University of Minnesota\n", + " grid.410726.6\n", + " University of Chinese Academy of Sciences\n", + " 8305318.0\n", + " 313606\n", " \n", " \n", " 19\n", - " 9400497.0\n", - " 236142\n", - " grid.21729.3f\n", - " Columbia University\n", + " grid.47100.32\n", + " Yale University\n", + " 14768834.0\n", + " 305202\n", " \n", " \n", "\n", "" ], "text/plain": [ - " citations_total count id \\\n", - "0 28836616.0 546592 grid.38142.3c \n", - "1 8545148.0 484017 grid.26999.3d \n", - "2 11040840.0 342764 grid.17063.33 \n", - "3 11710248.0 320966 grid.214458.e \n", - "4 5928948.0 310485 grid.258799.8 \n", - "5 14738599.0 302094 grid.168010.e \n", - "6 12585381.0 297558 grid.34477.33 \n", - "7 11710928.0 297094 grid.19006.3e \n", - "8 10879614.0 289280 grid.4991.5 \n", - "9 12084053.0 285143 grid.21107.35 \n", - "10 10814051.0 282170 grid.5335.0 \n", - "11 4105653.0 280405 grid.11899.38 \n", - "12 10450691.0 271170 grid.25879.31 \n", - "13 9614297.0 266337 grid.83440.3b \n", - "14 4653874.0 265592 grid.136593.b \n", - "15 3694359.0 250749 grid.69566.3a \n", - "16 9370701.0 244713 grid.5386.8 \n", - "17 11806056.0 242749 grid.47840.3f \n", - "18 8360048.0 239283 grid.17635.36 \n", - "19 9400497.0 236142 grid.21729.3f \n", + " id name citations_total \\\n", + "0 grid.38142.3c Harvard University 43542715.0 \n", + "1 grid.26999.3d The University of Tokyo 12416944.0 \n", + "2 grid.17063.33 University of Toronto 16896263.0 \n", + "3 grid.214458.e University of Michigan-Ann Arbor 17899164.0 \n", + "4 grid.168010.e Stanford University 22857822.0 \n", + "5 grid.4991.5 University of Oxford 17348878.0 \n", + "6 grid.34477.33 University of Washington 19245227.0 \n", + "7 grid.21107.35 Johns Hopkins University 18542871.0 \n", + "8 grid.19006.3e University of California, Los Angeles 17370426.0 \n", + "9 grid.258799.8 Kyoto University 8426700.0 \n", + "10 grid.11899.38 Universidade de São Paulo 6823063.0 \n", + "11 grid.5335.0 University of Cambridge 16495121.0 \n", + "12 grid.47840.3f University of California, Berkeley 19445292.0 \n", + "13 grid.25879.31 University of Pennsylvania 15634591.0 \n", + "14 grid.17635.36 University of Minnesota Twin Cities 13100152.0 \n", + "15 grid.136593.b Osaka University 6486832.0 \n", + "16 grid.83440.3b University College London 13014090.0 \n", + "17 grid.14003.36 University of Wisconsin-Madison 13060297.0 \n", + "18 grid.410726.6 University of Chinese Academy of Sciences 8305318.0 \n", + "19 grid.47100.32 Yale University 14768834.0 \n", "\n", - " name \n", - "0 Harvard University \n", - "1 University of Tokyo \n", - "2 University of Toronto \n", - "3 University of Michigan \n", - "4 Kyoto University \n", - "5 Stanford University \n", - "6 University of Washington \n", - "7 University of California, Los Angeles \n", - "8 University of Oxford \n", - "9 Johns Hopkins University \n", - "10 University of Cambridge \n", - "11 University of São Paulo \n", - "12 University of Pennsylvania \n", - "13 University College London \n", - "14 Osaka University \n", - "15 Tohoku University \n", - "16 Cornell University \n", - "17 University of California, Berkeley \n", - "18 University of Minnesota \n", - "19 Columbia University " + " count \n", + "0 715128 \n", + "1 570861 \n", + "2 435895 \n", + "3 412146 \n", + "4 393415 \n", + "5 387324 \n", + "6 385718 \n", + "7 381545 \n", + "8 373415 \n", + "9 370973 \n", + "10 367466 \n", + "11 356990 \n", + "12 353011 \n", + "13 351125 \n", + "14 324688 \n", + "15 323974 \n", + "16 320344 \n", + "17 316542 \n", + "18 313606 \n", + "19 305202 " ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -616,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "Collapsed": "false", "colab": {}, @@ -629,7 +629,7 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 20\n", - "\u001b[2mTime: 6.54s\u001b[0m\n" + "\u001b[2mTime: 5.11s\u001b[0m\n" ] }, { @@ -653,204 +653,204 @@ " \n", " \n", " \n", - " count\n", " id\n", " name\n", + " count\n", " recent_citations_total\n", " \n", " \n", " \n", " \n", " 0\n", - " 546592\n", " grid.38142.3c\n", " Harvard University\n", - " 5562378.0\n", + " 715128\n", + " 5657002.0\n", " \n", " \n", " 1\n", - " 484017\n", " grid.26999.3d\n", - " University of Tokyo\n", - " 1471000.0\n", + " The University of Tokyo\n", + " 570861\n", + " 1498274.0\n", " \n", " \n", " 2\n", - " 342764\n", " grid.17063.33\n", " University of Toronto\n", - " 2380994.0\n", + " 435895\n", + " 2557162.0\n", " \n", " \n", " 3\n", - " 320966\n", " grid.214458.e\n", - " University of Michigan\n", - " 2370219.0\n", + " University of Michigan-Ann Arbor\n", + " 412146\n", + " 2411193.0\n", " \n", " \n", " 4\n", - " 310485\n", - " grid.258799.8\n", - " Kyoto University\n", - " 1006685.0\n", + " grid.168010.e\n", + " Stanford University\n", + " 393415\n", + " 3172519.0\n", " \n", " \n", " 5\n", - " 302094\n", - " grid.168010.e\n", - " Stanford University\n", - " 2985116.0\n", + " grid.4991.5\n", + " University of Oxford\n", + " 387324\n", + " 2687354.0\n", " \n", " \n", " 6\n", - " 297558\n", " grid.34477.33\n", " University of Washington\n", - " 2411827.0\n", + " 385718\n", + " 2508430.0\n", " \n", " \n", " 7\n", - " 297094\n", - " grid.19006.3e\n", - " University of California, Los Angeles\n", - " 2137101.0\n", + " grid.21107.35\n", + " Johns Hopkins University\n", + " 381545\n", + " 2441471.0\n", " \n", " \n", " 8\n", - " 289280\n", - " grid.4991.5\n", - " University of Oxford\n", - " 2504619.0\n", + " grid.19006.3e\n", + " University of California, Los Angeles\n", + " 373415\n", + " 2151381.0\n", " \n", " \n", " 9\n", - " 285143\n", - " grid.21107.35\n", - " Johns Hopkins University\n", - " 2352686.0\n", + " grid.258799.8\n", + " Kyoto University\n", + " 370973\n", + " 966227.0\n", " \n", " \n", " 10\n", - " 282170\n", - " grid.5335.0\n", - " University of Cambridge\n", - " 2110364.0\n", + " grid.11899.38\n", + " Universidade de São Paulo\n", + " 367466\n", + " 1207947.0\n", " \n", " \n", " 11\n", - " 280405\n", - " grid.11899.38\n", - " University of São Paulo\n", - " 1124894.0\n", + " grid.5335.0\n", + " University of Cambridge\n", + " 356990\n", + " 2258714.0\n", " \n", " \n", " 12\n", - " 271170\n", - " grid.25879.31\n", - " University of Pennsylvania\n", - " 2049126.0\n", + " grid.47840.3f\n", + " University of California, Berkeley\n", + " 353011\n", + " 2404905.0\n", " \n", " \n", " 13\n", - " 266337\n", - " grid.83440.3b\n", - " University College London\n", - " 2197569.0\n", + " grid.25879.31\n", + " University of Pennsylvania\n", + " 351125\n", + " 2063182.0\n", " \n", " \n", " 14\n", - " 265592\n", - " grid.136593.b\n", - " Osaka University\n", - " 727151.0\n", + " grid.17635.36\n", + " University of Minnesota Twin Cities\n", + " 324688\n", + " 1575033.0\n", " \n", " \n", " 15\n", - " 250749\n", - " grid.69566.3a\n", - " Tohoku University\n", - " 644246.0\n", + " grid.136593.b\n", + " Osaka University\n", + " 323974\n", + " 691161.0\n", " \n", " \n", " 16\n", - " 244713\n", - " grid.5386.8\n", - " Cornell University\n", - " 1809884.0\n", + " grid.83440.3b\n", + " University College London\n", + " 320344\n", + " 2241297.0\n", " \n", " \n", " 17\n", - " 242749\n", - " grid.47840.3f\n", - " University of California, Berkeley\n", - " 2057506.0\n", + " grid.14003.36\n", + " University of Wisconsin-Madison\n", + " 316542\n", + " 1508661.0\n", " \n", " \n", " 18\n", - " 239283\n", - " grid.17635.36\n", - " University of Minnesota\n", - " 1519539.0\n", + " grid.410726.6\n", + " University of Chinese Academy of Sciences\n", + " 313606\n", + " 2620498.0\n", " \n", " \n", " 19\n", - " 236142\n", - " grid.21729.3f\n", - " Columbia University\n", - " 1754780.0\n", + " grid.47100.32\n", + " Yale University\n", + " 305202\n", + " 1861426.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " count id name \\\n", - "0 546592 grid.38142.3c Harvard University \n", - "1 484017 grid.26999.3d University of Tokyo \n", - "2 342764 grid.17063.33 University of Toronto \n", - "3 320966 grid.214458.e University of Michigan \n", - "4 310485 grid.258799.8 Kyoto University \n", - "5 302094 grid.168010.e Stanford University \n", - "6 297558 grid.34477.33 University of Washington \n", - "7 297094 grid.19006.3e University of California, Los Angeles \n", - "8 289280 grid.4991.5 University of Oxford \n", - "9 285143 grid.21107.35 Johns Hopkins University \n", - "10 282170 grid.5335.0 University of Cambridge \n", - "11 280405 grid.11899.38 University of São Paulo \n", - "12 271170 grid.25879.31 University of Pennsylvania \n", - "13 266337 grid.83440.3b University College London \n", - "14 265592 grid.136593.b Osaka University \n", - "15 250749 grid.69566.3a Tohoku University \n", - "16 244713 grid.5386.8 Cornell University \n", - "17 242749 grid.47840.3f University of California, Berkeley \n", - "18 239283 grid.17635.36 University of Minnesota \n", - "19 236142 grid.21729.3f Columbia University \n", + " id name count \\\n", + "0 grid.38142.3c Harvard University 715128 \n", + "1 grid.26999.3d The University of Tokyo 570861 \n", + "2 grid.17063.33 University of Toronto 435895 \n", + "3 grid.214458.e University of Michigan-Ann Arbor 412146 \n", + "4 grid.168010.e Stanford University 393415 \n", + "5 grid.4991.5 University of Oxford 387324 \n", + "6 grid.34477.33 University of Washington 385718 \n", + "7 grid.21107.35 Johns Hopkins University 381545 \n", + "8 grid.19006.3e University of California, Los Angeles 373415 \n", + "9 grid.258799.8 Kyoto University 370973 \n", + "10 grid.11899.38 Universidade de São Paulo 367466 \n", + "11 grid.5335.0 University of Cambridge 356990 \n", + "12 grid.47840.3f University of California, Berkeley 353011 \n", + "13 grid.25879.31 University of Pennsylvania 351125 \n", + "14 grid.17635.36 University of Minnesota Twin Cities 324688 \n", + "15 grid.136593.b Osaka University 323974 \n", + "16 grid.83440.3b University College London 320344 \n", + "17 grid.14003.36 University of Wisconsin-Madison 316542 \n", + "18 grid.410726.6 University of Chinese Academy of Sciences 313606 \n", + "19 grid.47100.32 Yale University 305202 \n", "\n", " recent_citations_total \n", - "0 5562378.0 \n", - "1 1471000.0 \n", - "2 2380994.0 \n", - "3 2370219.0 \n", - "4 1006685.0 \n", - "5 2985116.0 \n", - "6 2411827.0 \n", - "7 2137101.0 \n", - "8 2504619.0 \n", - "9 2352686.0 \n", - "10 2110364.0 \n", - "11 1124894.0 \n", - "12 2049126.0 \n", - "13 2197569.0 \n", - "14 727151.0 \n", - "15 644246.0 \n", - "16 1809884.0 \n", - "17 2057506.0 \n", - "18 1519539.0 \n", - "19 1754780.0 " + "0 5657002.0 \n", + "1 1498274.0 \n", + "2 2557162.0 \n", + "3 2411193.0 \n", + "4 3172519.0 \n", + "5 2687354.0 \n", + "6 2508430.0 \n", + "7 2441471.0 \n", + "8 2151381.0 \n", + "9 966227.0 \n", + "10 1207947.0 \n", + "11 2258714.0 \n", + "12 2404905.0 \n", + "13 2063182.0 \n", + "14 1575033.0 \n", + "15 691161.0 \n", + "16 2241297.0 \n", + "17 1508661.0 \n", + "18 2620498.0 \n", + "19 1861426.0 " ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -874,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "Collapsed": "false", "colab": {}, @@ -887,7 +887,7 @@ "output_type": "stream", "text": [ "Returned Year: 20\n", - "\u001b[2mTime: 4.06s\u001b[0m\n" + "\u001b[2mTime: 5.16s\u001b[0m\n" ] }, { @@ -911,161 +911,161 @@ " \n", " \n", " \n", - " count\n", " id\n", + " count\n", " recent_citations_total\n", " \n", " \n", " \n", " \n", " 0\n", - " 6503486\n", - " 2020\n", - " 18375337.0\n", + " 2024\n", + " 7882763\n", + " 13641369.0\n", " \n", " \n", " 1\n", - " 6391947\n", - " 2021\n", - " 4632716.0\n", + " 2023\n", + " 7755821\n", + " 24571792.0\n", " \n", " \n", " 2\n", - " 5792555\n", - " 2019\n", - " 22470145.0\n", + " 2022\n", + " 7279681\n", + " 26740821.0\n", " \n", " \n", " 3\n", - " 5369555\n", - " 2018\n", - " 23030935.0\n", + " 2021\n", + " 7016199\n", + " 27057034.0\n", " \n", " \n", " 4\n", - " 5044596\n", - " 2017\n", - " 21362603.0\n", + " 2020\n", + " 6831663\n", + " 25211581.0\n", " \n", " \n", " 5\n", - " 4598245\n", - " 2016\n", - " 19046830.0\n", + " 2019\n", + " 6004300\n", + " 20340055.0\n", " \n", " \n", " 6\n", - " 4395107\n", - " 2015\n", - " 17010283.0\n", + " 2018\n", + " 5550132\n", + " 17327713.0\n", " \n", " \n", " 7\n", - " 4244049\n", - " 2014\n", - " 15057104.0\n", + " 2017\n", + " 5171177\n", + " 15030351.0\n", " \n", " \n", " 8\n", - " 4046162\n", - " 2013\n", - " 13475978.0\n", + " 2025\n", + " 5064609\n", + " 1745079.0\n", " \n", " \n", " 9\n", - " 3762532\n", - " 2012\n", - " 11970228.0\n", + " 2016\n", + " 4775828\n", + " 13029942.0\n", " \n", " \n", " 10\n", - " 3667073\n", - " 2011\n", - " 10958039.0\n", + " 2015\n", + " 4534568\n", + " 11396769.0\n", " \n", " \n", " 11\n", - " 3430544\n", - " 2010\n", - " 9915351.0\n", + " 2014\n", + " 4382421\n", + " 9976449.0\n", " \n", " \n", " 12\n", - " 3144460\n", - " 2009\n", - " 8991871.0\n", + " 2013\n", + " 4194614\n", + " 8834241.0\n", " \n", " \n", " 13\n", - " 2937393\n", - " 2008\n", - " 7853718.0\n", + " 2012\n", + " 3898366\n", + " 7799039.0\n", " \n", " \n", " 14\n", - " 2915691\n", - " 2007\n", - " 7198101.0\n", + " 2011\n", + " 3761002\n", + " 7104082.0\n", " \n", " \n", " 15\n", - " 2610760\n", - " 2006\n", - " 6579372.0\n", + " 2010\n", + " 3327367\n", + " 6409071.0\n", " \n", " \n", " 16\n", - " 2410569\n", - " 2005\n", - " 5985630.0\n", + " 2009\n", + " 3198543\n", + " 5733537.0\n", " \n", " \n", " 17\n", - " 2246194\n", - " 2004\n", - " 5335870.0\n", + " 2008\n", + " 3001138\n", + " 5037413.0\n", " \n", " \n", " 18\n", - " 2037978\n", - " 2003\n", - " 4730168.0\n", + " 2007\n", + " 2986096\n", + " 4665167.0\n", " \n", " \n", " 19\n", - " 1892417\n", - " 2002\n", - " 4234096.0\n", + " 2006\n", + " 2688556\n", + " 4263341.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " count id recent_citations_total\n", - "0 6503486 2020 18375337.0\n", - "1 6391947 2021 4632716.0\n", - "2 5792555 2019 22470145.0\n", - "3 5369555 2018 23030935.0\n", - "4 5044596 2017 21362603.0\n", - "5 4598245 2016 19046830.0\n", - "6 4395107 2015 17010283.0\n", - "7 4244049 2014 15057104.0\n", - "8 4046162 2013 13475978.0\n", - "9 3762532 2012 11970228.0\n", - "10 3667073 2011 10958039.0\n", - "11 3430544 2010 9915351.0\n", - "12 3144460 2009 8991871.0\n", - "13 2937393 2008 7853718.0\n", - "14 2915691 2007 7198101.0\n", - "15 2610760 2006 6579372.0\n", - "16 2410569 2005 5985630.0\n", - "17 2246194 2004 5335870.0\n", - "18 2037978 2003 4730168.0\n", - "19 1892417 2002 4234096.0" + " id count recent_citations_total\n", + "0 2024 7882763 13641369.0\n", + "1 2023 7755821 24571792.0\n", + "2 2022 7279681 26740821.0\n", + "3 2021 7016199 27057034.0\n", + "4 2020 6831663 25211581.0\n", + "5 2019 6004300 20340055.0\n", + "6 2018 5550132 17327713.0\n", + "7 2017 5171177 15030351.0\n", + "8 2025 5064609 1745079.0\n", + "9 2016 4775828 13029942.0\n", + "10 2015 4534568 11396769.0\n", + "11 2014 4382421 9976449.0\n", + "12 2013 4194614 8834241.0\n", + "13 2012 3898366 7799039.0\n", + "14 2011 3761002 7104082.0\n", + "15 2010 3327367 6409071.0\n", + "16 2009 3198543 5733537.0\n", + "17 2008 3001138 5037413.0\n", + "18 2007 2986096 4665167.0\n", + "19 2006 2688556 4263341.0" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1078,7 +1078,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "Collapsed": "false", "colab": {}, @@ -1086,26 +1086,31 @@ "id": "OZVInY3lZFaJ" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib is building the font cache; this may take a moment.\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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JbonW7qyUtVJSXKSmDHDrlOy2za17JMY6HRUh7rg3pLbW7pa02/d5tTFmo6RekjYccMzSAx6yTFLvE04MAAAAAAha63ZW6oZnVmpvXZMeumKsvjWyh9OROkxkeJgmelI00ZOiH509SOW1Tfp0S6lvc+sSvf31bklSdnp828bWA906KStVMZHhDicH2hzTnkPGmExJSyQNt9ZWHeaYH0kabK293vd1vqS9arsk7VFr7bzDPO5GSTdKUt++fcdt27btGL4NAAAAAECg+NfXu/Sjv3+llLgozfveeA3vleh0JMdYa5VTVLN/VdHy/HI1tXg1pm+SXrv55C65kgqB64Q3pDbGxEv6WNLd1tp/HOaY6ZIeljTVWlvmu62XtXanMSZd0oeSbrfWLjnSa3FZGQAAAAAEH6/X6r4FOXrwoy0a1y9Zj1w1TmkJ0U7HCigNza16emmB/vjuJj169TidPay705EQQg5XDoW188GRkl6T9PwRiqGRkh6XdOG+YkiSrLU7ff8slvS6pInHHh8AAAAAEMhqG1t083Or9OBHW3TZ+N564YZJFEOHEBMZruumepSZGqcHFuYqECeII/QctRwyxhhJT6htw+l7D3NMX0n/kHS1tTbngNtdvk2sZYxxSTpL0jp/BAcAAAAABIYd5XW65G9LtWBjkX55/lD9+ZKRio5gP53DiQgP063TB2j9riot2FjsdBzg6BtSS5oi6WpJa40xa3y3/Y+kvpJkrX1E0i8lpUp6uK1L2j+yPkPS677bIiS9YK19z5/fAAAAAADAOcvyynTLc6vU6rV6as5EnTIwzelIQeHiMb304EdbNHdhjs4Yki7f782AI9ozrexTSUc8S32bT19/iNvzJI067nQAAAAAgID1/PJt+tWb69UvNU6PXzNBHrfL6UhBIyI8TLdNH6CfvPa1PtpUrBlDMpyOhBDWrj2HAAAAAADYp7nVq//3xjr9/PV1mprt1uu3TqEYOg4Xj+2lPimxmsveQ3AY5RAAAAAAoN321jbpe098oWeXbdNNp2TpiWsmqFtMpNOxglJkeJhuPW2Avi6s1OLNJU7HQQijHAIAAAAAtMvmPdW64KFPtWr7Xt172Sj97LwhCg9jr5wT8Z2xvdUrKVb3s3oIDqIcAgAAAAAc1YcbivSdhz9TQ7NXL994kr4ztrfTkbqEqIi2yWVf7ajQxzmsHoIzKIcAAAAAAIdlrdVDi7boxmdXqn96vP5521SN6ZvsdKwu5dJxbauH2HsITqEcAgAAAAAcUn1Tq+54aY3+8v5mfXtkT71y02R1T4xxOlaXExURpltO668vt1fo0y2lTsdBCKIcAgAAAAD8h92V9brs0c/1r6936SfnDNLcmaMVExnudKwu67vje6tHYozmLmD1EDof5RAAAAAA4N+s3r5XF/z1M+WV1Oixq8fr+6cNkDFsPN2RoiPC9f3T+mvltr1aurXM6TgIMZRDAAAAAID9Xl1VqJmPLlNsZLhev3WKzhia4XSkkHHZhD7q3o3VQ+h8lEMAAAAAALV6re5+e4N+9PevNK5fst68dYoGZiQ4HSukREeE65bT+uuLgnJ9nsfqIXQeyiEAAAAACHGV9c269qkVeuyTfF0zuZ+euW6ikl1RTscKSZdP6KOMbtGauyDX6SgIIZRDAAAAABDC8kpqdPHDn+mzLaX6w8Uj9JsLhysynF8VnRITGa6bT+2v5fnlWsbqIXQS/o8HAAAAgBC1JKdEFz70mSrqmvX89ZN0xaS+TkeCpFkT+yotgdVD6DyUQwAAAAAQYqy1euLTfM2e/4V6JcXqzVunaFJWqtOx4LNv9dDneWX6Ir/c6TgIAZRDAAAAABBCGlta9ZNXv9bv/rVBZw7N0Gu3nKw+KXFOx8JBrpzUV+74aM1dmON0FIQAyiEAAAAACBEl1Y264rHl+vuqQt0xI1t/u3KcXNERTsfCIbStHsrSZ1vKtLKA1UPoWJRDAAAAABAC1u2s1AV//VTrd1XqoSvG6r/OHKiwMON0LBzBlZP6yR0fpbkL2XsIHYtyCAAAAAC6uH99vUuXPrJURtKrN5+sb43s4XQktENsVLhuPCVLn+SWatW2vU7HQRdGOQQAAAAAXZTXa/V/H2zWbS98qWE9E/XmbVM1vFei07FwDK46qZ9SXaweQseiHAIAAACALqi2sUU3P7dKD360RZeN760XbpiktIRop2PhGMVFReiGU7K0JKdEX25n9RA6BuUQAAAAAHQxO8rrdMnflmrBxiL98vyh+vMlIxUdEe50LBynq0/qpxRWD6EDUQ4BAAAAQBeyLK9MF/z1U+2qqNdTcybq2qkeGcPG08HMFR2h66d5tHhzidbsqHA6DrogyiEAAAAA6CKeX75NVz2+XMmuKL1x6xSdMjDN6Ujwk+9NzlRSXKQeYPUQOgDlEAAAAAB0AfcvyNHPX1+nqdluvXHrFGWlxTsdCX4UHx2hG6Zl6aNNxfq6sMLpOOhiKIcAAAAAIMi9smKH7l+Qq0vG9tYT10xQt5hIpyOhA3xvcj8lxrJ6CP5HOQQAAAAAQezjnBL97PW1mpbt1p8uGaHwMPYX6qoSYiJ1/VSPFmws1rqdlU7HQRdCOQQAAAAAQWr9rkp9/7lVGpiRoIevHKvIcH7F6+qumZKpbjERTC6DX/HOAQAAAABBaGdFvebMX6HE2Eg9NWeCEriULCR0i4nUtVM9+nBDkdbvYvUQ/INyCAAAAACCTGV9s+bM/0L1za2aP2eiMrrFOB0JnWjOFI8SYiLYewh+QzkEAAAAAEGksaVVNz+7SvmltXr0qnEa1D3B6UjoZImxkZozxaP31xdp4+4qp+OgC6AcAgAAAIAgYa3Vf7/6tT7PK9P/XjpSJw9wOx0JDrluikcJ0awegn9QDgEAAABAkLjng816Y80u/fjsQbp4TG+n48BBiXGRmj0lU++u26NNe1g9hBNDOQQAAAAAQeCF5dv10KKtmjWxj75/Wn+n4yAAXDfVo/joCD24cIvTURDkKIcAAAAAIMAt2lSsX7yxVtMHpel3Fw6XMcbpSAgASXFRuubkfnpn3W7lFFU7HQdBjHIIAAAAAALY2sJK3frCag3t2U1/vWKsIsL5NQ7fuH5qluIiw9l7CCeEdxUAAAAACFA7yus056kVSo6L0pOzJ8gVHeF0JASYZFeUvndypt5eu1u5rB7CcaIcAgAAAIAAVFHXpNnzv1BTS6uevnaC0hNinI6EAHXDtCzFRobrr4vYewjHh3IIAAAAAAJMY0urbnx2lXaU1+ux743XgPQEpyMhgKW4onT15H7651e7tLWkxuk4CEKUQwAAAAAQQLxeqx++8pW+yC/XPZeN0qSsVKcjIQjcOC1L0RHh+utHrB7CsaMcAgAAAIAA8uf3N+lfX+/WT88drAtG9XQ6DoJEany0rp7cT2+u2ak8Vg/hGFEOAQAAAECAeObzAj36cZ6uPqmfbjoly+k4CDI3TMtSVEQYew/hmFEOAQAAAEAA+HBDkX791nqdMSRdv/r2UBljnI6EIJOWEK2rJvXTm2t2qaC01uk4CCKUQwAAAADgsDU7KnT7i6s1oleiHpg1RhHh/KqG43PjqVmKCDOsHsIx4R0HAAAAABy0vaxO1z21QmkJ0Xr8mgmKi4pwOhKCWHpCjK6c1E+vf7lT28pYPYT2oRwCAAAAAIfsrW3S7PlfqNVaPTVnotISop2OhC7gZt/qoYdYPYR2ohwCAAAAAAc0NLfq+mdWqrCiXo9/b7z6p8U7HQldRHq3GM2a2Ff/WL1TO8rrnI6DIEA5BAAAAACdzOu1+sHLa7R6+17df/lojc9McToSuphbTuuvMFYPoZ0ohwAAAACgk939zka9u26Pfn7eEJ03oofTcdAFZXSL0awJffTqqkJWD+GoKIcAAAAAoBM9+Wm+nvg0X7NPztR1Uz1Ox0EXdvNp/RVmjB5evNXpKAhwlEMAAAAA0EneW7dbv3t7g84elqH/d/5QGWOcjoQurEdirC6f0EevrtqhnRX1TsdBAKMcAgAAAIBOsGrbXt350hqN7pOk+y8fo/AwiiF0vFtO6y9Jepi9h3AElEMAAAAA0MHyS2t1/dMr1CMxRo9/b7xio8KdjoQQ0TMpVpeN76NXVu7QLlYP4TAohwAAAACgA5XVNGr2/C9kjNFTcyYqNT7a6UgIMd+fPkCS9Df2HsJhUA4BAAAAQAepb2rVdU+v1J7KBj1+zXhlul1OR0II6pUUq0vH9dHLK3ZodyWrh/CfKIcAAAAAoAO0eq3ufOlLfVVYobkzx2hs32SnIyGEff+0/vJaq0dYPYRDoBwCAAAAAD+z1up3/9qgDzYU6ZfnD9U5w7s7HQkhrk9KnC4d11svrtihoqoGp+MgwFAOAQAAAICfPfFpvp5aWqDrp3o0Z4rH6TiAJOnW6QPk9Vr2HsJ/OGo5ZIzpY4xZZIzZYIxZb4y58xDHGGPMA8aYLcaYr40xYw+47xpjTK7v4xp/fwMAAAAAEEje/nq3fv/2Rp03orv+57whTscB9uuTEqfvjO2lF7/YrmJWD+EA7Vk51CLph9baoZJOknSrMWboQcecKynb93GjpL9JkjEmRdKvJE2SNFHSr4wxXGgLAAAAoEtaUVCuH7yyRuP7Jevey0YrLMw4HQn4N7dNz1aL1+qRj/OcjoIActRyyFq721q72vd5taSNknoddNiFkp6xbZZJSjLG9JB0tqQPrbXl1tq9kj6UdI5fvwMAAAAACABbS2p0/dMr1TspVo99b7xiIsOdjgT8h76pcbp4TC89v3ybiqtZPYQ2x7TnkDEmU9IYScsPuquXpB0HfF3ou+1wtx/quW80xqw0xqwsKSk5llgAAAAA4KiS6kbNnv+FIsKMnpozUcmuKKcjAYd12/QBavFazWP1EHzaXQ4ZY+IlvSbpLmttlb+DWGvnWWvHW2vHp6Wl+fvpAQAAAKBD1DW16LqnV6ikulFPzJ6gvqlxTkcCjijT7dKFo3vqueXbVFLd6HQcBIB2lUPGmEi1FUPPW2v/cYhDdkrqc8DXvX23He52AAAAAAh6La1e3f7Cl1q3s1IPzhqr0X2SnI4EtMvtp2erqcWrxz5h9RDaN63MSHpC0kZr7b2HOewtSd/zTS07SVKltXa3pPclnWWMSfZtRH2W7zYAAAAACGrWWv36n+u1cFOxfnPBMJ05NMPpSEC7edwuXTi6l579fJtKa1g9FOras3JoiqSrJZ1ujFnj+zjPGHOzMeZm3zHvSMqTtEXSY5K+L0nW2nJJv5O0wvfxW99tAAAAABDUHvk4T88t266bTs3S1ZMznY4DHLPbTh+ghpZWVg9BEUc7wFr7qaQjzl+01lpJtx7mviclPXlc6QAAAAAgAL25Zqf+/N4mfXtUT/332YOdjgMcl/5p8fr2yJ569vNtuumU/kphI/WQdUzTygAAAAAg1C3LK9OP//61JnpSdM93Ryos7Ih/lw4EtDtmDFB9M6uHQh3lEAAAAAC0U25RtW58ZqX6psbpsavHKzoi3OlIwAkZkJ6gb43ooWeWFmhvbZPTceAQyiEAAAAAaIfiqgbNnr9C0ZHhmj97ghLjIp2OBPjFHTOyVdfcqsc/ZfVQqKIcAgAAAICjqG1s0ZynVmhvXZOevGaC+qTEOR0J8JuBGQk6b3gPPb10myrqWD0UiiiHAAAAAOAIWlq9uvWF1dq0p1oPXTFWI3onOh0J8LvbZwxQTWOLnvw03+kocADlEAAAAAAchrVWv3hjnRZvLtHvLhyu6YPTnY4EdIjB3bvp3OHdNf+zAlXWNTsdB52McggAAAAADuOhRVv00oodunV6f10xqa/TcYAOdceMbFU3tujJz1g9FGoohwAAAADgEP6xulD3fJCji8f00o/OGuR0HKDDDenRTWcPy9CTn+Wrsp7VQ6GEcggAAAAADvLZllL95NWvNTkrVX++ZKSMMU5HAjrFHTOyVd3Qoqc+K3A6CjoR5RAAAAAAHGDTnird/OwqZaW59MjV4xQVwa9NCB3DeibqzKEZeuLTPFU1sHooVPAuBwAAAAA+eyobNGf+CsVFh2v+nIlKjI10OhLQ6e6cka2qhhY9zeqhkEE5BAAAAACSqhuaNXv+F6qqb9aTsyeoV1Ks05EARwzvlagzhqTr8U/zVc3qoZBAOQQAAAAg5DW3evX951crt7hGD181TsN6JjodCXDUnTMGqrK+Wc98vs3pKOgElEMAAAAAQpq1Vj/7x1p9kluqP148QqcOTHM6EuC4Eb0TdfrgdD32SZ5qGlucjoMORjkEAAAAIKTNXZirV1cV6o4Z2bpsQh+n4wAB484Z2aqoa9Yznxc4HQUdjHIIAAAAQMh6ZeUO3b8gV5eM7a0fnJHtdBwgoIzqk6TTBqXpsSV5qmX1UJdGOQQAAAAgJC3JKdH//GOtpg5w64/fGSFjjNORgIBz54xs7a1r1rPL2HuoK6McAgAAABByNuyq0vefX60B6fH621VjFRXBr0bAoYzpm6xTBratHqprYvVQV8U7IAAAAICQsrWkRtfM/0Lx0RGaP2eCEmIinY4EBLQ7Z2SrrLZJz7F6qMuiHAIAAAAQMraW1GjWvGXyeq2euW6ieiTGOh0JCHjj+iVrWrZb85bkqb6p1ek46ACUQwAAAABCQp6vGGr1Wr1440kamJHgdCQgaNw5I1ulNU16fjmrh7oiyiEAAAAAXV5eSY1m+oqhF26gGAKO1fjMFE0ZkKpHPmb1UFdEOQQAAACgS8svrdWsx74phgZ1pxgCjsedMwaqtKZRL3yx3eko8DPKIQAAAABdVn5prWbO+1zNrRRDwIma6EnR5KxUPfLxVjU0s3qoK6EcAgAAANAl5ZfWata8Zb5iaBLFEOAHd56RrZLqRr3I6qEuhXIIAAAAQJdT4CuGmlq9ev76SRrcvZvTkYAu4aSsVE3ypLB6qIuhHAIAAADQpRSU1mrmvGVqbGnV89dP0pAeFEOAP915RraKqhr18oodTkeBn1AOAQAAAOgyCnybTze2tOqFG06iGAI6wOSsVE3MTNHfFm9VYwurh7oCyiEAAAAAXcK2srZiqKG5Vc9fTzEEdBRjjO48I1t7qhr0CquHugTKIQAAAABBb1tZ26Vk+4qhoT0phoCOdHL/VI3vl6yHWT3UJVAOAQAAAAhq28raNp+upxgCOs2+1UO7Kxv095WFTsfBCaIcAgAAABC0tpfVada8Zaprbtt8mmII6DxTB7g1tm+S/rZ4q5pavE7HwQmgHAIAAAAQlLaX1WnmvM/3F0PDeiY6HQkIKW2rhwZqZ0W9Xl3F6qFgRjkEAAAAIOhsL6vTrMeWqbapVc9dRzEEOOWUbLdG90nSQ4u2sHooiFEOAQAAAAgqO8rbiqGaxhY9f/0kDe9FMQQ4Zd/eQzsr6vWP1aweClaUQwAAAACCxo7yOs2cRzEEBJLTBqZpVO9E/XXRFjW3snooGFEOAQAAAAgKFENAYDLG6MZT+qtwb73W7KhwOg6OA+UQAAAAgIC3rxiqbmimGAIC0Ajf/5P5JbUOJ8HxoBwCAAAAEND+vRg6iWIICEC9kmMVGW6UV0o5FIwohwAAAAAErMK9bZtP7yuGRvSmGAICUXiYUb9Ulwooh4IS5RAAAACAgFS4t23FUFV9s567fhLFEBDgMlNdyqccCkqUQwAAAAACzr5iqNJXDI3sneR0JABHkZXmUn5Zrbxe63QUHCPKIQAAAAABZWdFvWY91lYMPU8xBAQNj9ulphavdlXWOx0Fx4hyCAAAAEDA2FlRr5nzPldFXbOeu45iCAgmHrdLkri0LAhRDgEAAAAICAcWQ89eN0mj+iQ5HQnAMciiHApalEMAAAAAHLerol6z5i1TRW1bMTSaYggIOmkJ0XJFhVMOBSHKIQAAAACO2lVRr5nzlmlvbZOevZ5iCAhWxhhluplYFowohwAAAAA45sBi6JnrJlIMAUHOQzkUlCiHAAAAADhid2XbVLJ9xdCYvslORwJwgrLcLu0or1NTi9fpKDgGlEMAAAAAOt3uyrYVQ2U1TXqaYgjoMjxpLnmttL28zukoOAaUQwAAAAA61e7Kts2ny2raVgyNpRgCugyPO14SE8uCDeUQAAAAgE6zp7JBs+YtUynFENAleVLbxtkXUA4FFcohAAAAAJ1iT2WDZs77XKU1TXr6WoohoCtKjItUiitKeZRDQYVyCAAAAECH21PZoFmPLVNJdaOevnaCxvWjGAK6qraJZTVOx8AxoBwCAAAA0KH2FUPFVQ165rqJGtcvxelIADoQ4+yDD+UQAAAAgA5TVEUxBIQaj9uloqpG1Ta2OB0F7XTUcsgY86QxptgYs+4w9//YGLPG97HOGNNqjEnx3VdgjFnru2+lv8MDAAAACFxFVW2bTxdXNejpaymGgFCR5W7blJrVQ8GjPSuHnpJ0zuHutNb+xVo72lo7WtLPJH1srS0/4JDpvvvHn1BSAAAAAEFjXzFUVNWgp66dqPGZFENAqPCkUQ4Fm6OWQ9baJZLKj3aczyxJL55QIgAAAABBrdhXDO3xFUMTKIaAkNIvhXH2wcZvew4ZY+LUtsLotQNutpI+MMasMsbceJTH32iMWWmMWVlSUuKvWAAAAAA6UXFVg2Y+1lYMPU0xBISk2Khw9UyMYeVQEPHnhtTflvTZQZeUTbXWjpV0rqRbjTGnHO7B1tp51trx1trxaWlpfowFAAAAoDPsL4YqG/TUHIohIJR50lzKoxwKGv4sh2bqoEvKrLU7ff8slvS6pIl+fD0AAAAAAaK4um0q2b5iaKKHYggIZR63S3klNbLWOh0F7eCXcsgYkyjpVElvHnCbyxiTsO9zSWdJOuTEMwAAAADBq7i6bY+h3ZUNmj97AsUQAHnc8apqaNHeumano6AdIo52gDHmRUmnSXIbYwol/UpSpCRZax/xHXaxpA+stQeuGcuQ9LoxZt/rvGCtfc9/0QEAAAA4rbi6QVc8tly7Khr01JwJmpSV6nQkAAHgm3H2NUpxURgHuqOWQ9baWe045im1jbw/8LY8SaOONxgAAACAwFZS3agrHluunXvrKYYA/JvM/eVQncb1oxwKdP7ccwgAAABAiCipbtSsx5Zp5956zacYAnCQ3smxiggzyi+tcToK2oFyCAAAAMAxaVsx1FYMPTl7gk6iGAJwkMjwMPVNiWOcfZCgHAIAAADQbvuKoR176/Tk7Ama3J9iCMChtU0soxwKBpRDAAAAANqltOabYmj+7IkUQwCOyON2qaCsVl4v4+wDHeUQAAAAgKM6sBhixRCA9vCkudTQ7NWeqgano+AoKIcAAAAAHNG+Ymh7eZ2evGaCTu7vdjoSgCDgSW2bWFbAvkMBj3IIAAAAwGGV1jTqyseWf1MMDaAYAtA+nrS2ciiPcijgRTgdAAAAAEBgKqpqaJtKVlGvJyiGAByjjIQYxUaGM7EsCFAOAQAAAPgPhXvrdOXjy1Va3ain5kxkXD2AYxYWZpTpdlEOBQHKIQAAAAD/pqC0Vlc+vlzVDc169vpJGts32elIAIJUltulDburnI6Bo2DPIQAAAAD75RZV67JHP1ddU4teuOEkiiEAJ8Tjdml7eZ2aW71OR8ERUA4BAAAAkCSt31Wpy+ctk9dKL980WcN7JTodCUCQy3S71Oq1Ktxb73QUHAHlEAAAAACt2VGhWfOWKToiTK/cdJIGZiQ4HQlAF+Bxt00syy+tcTgJjoRyCAAAAAhxKwrKddXjy5UYF6lXbpqsrLR4pyMB6CKyfOVQXgmbUgcyNqQGAAAAQthnW0p1/dMr1SMpRi9cf5K6J8Y4HQlAF5LsilJSXCQTywIcK4cAAACAELVoU7HmPLVC/VLj9PKNkymGAHQID+PsAx7lEAAAABCC3l27Wzc+u1IDM+L14g0nKS0h2ulIALooyqHARzkEAAAAhJg3vtyp2178UiN6Jer5609SsivK6UgAujBPqku7KxtU39TqdBQcBuUQAAAAEEJeXrFdP3hljSZkJuvZ6yYpMTbS6UgAujhPWtum1AVlrB4KVJRDAAAAQIh4emmB/vu1tTolO01PzZkoVzTzaQB0vG/G2VMOBSr+NAAAAABCwKMfb9Uf392kM4dm6K9XjFF0RLjTkQCEiMxUyqFARzkEAAAAdGHWWs1dmKv7F+Tq/JE9dN/loxUZzgUEADqPKzpC3bvFKK+EcihQUQ4BAAAAXZS1Vn96b5Me/ThPl47rrT9fMlLhYcbpWABCUNvEshqnY+Aw+CsDAAAAoAvyeq1+/dZ6Pfpxnq46qa/+l2IIgIMy3S4VlNU5HQOHQTkEAAAAdDGtXqv/eX2tnv58m66f6tHvLhyuMIohAA7KcrtUXtukiromp6PgECiHAAAAgC6kpdWrH76yRi+t2KHbTx+gn39riIyhGALgLCaWBTbKIQAAAKCLaGrx6vYXv9Qba3bpx2cP0g/PGkQxBCAgeNIohwIZG1IDAAAAXUBDc6u+//xqfbSpWP/v/KG6bqrH6UgAsF+f5DiFhxnKoQBFOQQAAAAEubqmFt3wzEot3VqmP1w8QldM6ut0JAD4N1ERYeqTHKs8yqGARDkEAAAABLHqhmZd+9QKrdq2V/dcOkqXjOvtdCQAOKRMt0sFlEMBiT2HAAAAgCBVUdekqx5fri+3V+jBWWMphgAENI/bpfzSWllrnY6Cg1AOAQAAAEGorKZRsx5bro27q/XIVeP0rZE9nI4EAEeU5XaprqlVxdWNTkfBQSiHAAAAgCBTXNWgy+ctU35pjZ6YPV5nDM1wOhIAHJXHHS9Jyivh0rJAQzkEAAAABJGdFfW67NHPtbuiXk/Nmahp2WlORwKAdmGcfeBiQ2oAAAAgSGwrq9UVjy1XVUOznr1+ksb2TXY6EgC0W49uMYqOCFN+aY3TUXAQyiEAAAAgCGwprtYVjy1Xc6tXL95wkob3SnQ6EgAck7Awo8xUl/JL65yOgoNQDgEAAAABbsOuKl39xHIZY/TSjZM1qHuC05EA4Lh43C7lFlc7HQMHYc8hAAAAIIB9XVihWY8tU1REmF656SSKIQBBzZPm0vbyOrW0ep2OggNQDgEAAAABamVBua58bLm6xUbolZsmKyst3ulIAHBCPG6XmlutdlbUOx0FB6AcAgAAAALQ0i2luvqJL5SWEK1XbpqsPilxTkcCgBOW5W6bWJbHxLKAQjkEAAAABJhFm4o1+6kV6psSp5dvmqweibFORwIAv/D4yqH8EsqhQMKG1AAAAEAAeW/dHt3+4moN6p6gZ6+dpGRXlNORAMBvUlxRSoiJUEEZ5VAgoRwCAAAAAsSba3bqv175SqN6J2r+nIlKjI10OhIA+JUxRllul/K5rCygcFkZAAAAEABeWbFDd728RhMyk/XsdZMohgB0WR63S3lcVhZQKIcAAAAAhz3zeYF+8trXmpadpvmzJ8oVzQJ/AF2Xxx2vXZX1amhudToKfCiHAAAAAAfNW7JVv3xzvc4cmqHHvjdOsVHhTkcCgA7lSXPJWmlbWZ3TUeBDOQQAAAA4wFqruQty9Yd3Nun8kT308JVjFR1BMQSg69s3zj6/tMbhJNiH9aoAAABAJ7PW6n/f36y/Ld6qS8f11p8vGanwMON0LADoFJn7yyFWDgUKyiEAAACgE1lr9Zt/btBTSwt01Ul99dsLhiuMYghACImPjlBaQjQrhwII5RAAAADQSbxeq5+/sVYvfrFD10/16OffGiJjKIYAhB4P4+wDCnsOAQAAAJ2gpdWrH/39K734xQ7dfvoAiiEAIS2LciigsHIIAAAA6GBNLV7d9fKXemftHv347EG6dfoApyMBgKM8bpdKa5pUWd+sxNhIp+OEPFYOAQAAAB2ooblVtzy3Su+s3aP/d/5QiiEAUFs5JEkFrB4KCJRDAAAAQAepa2rR9U+v1Eebi3X3xcN13VSP05EAICDsL4fKKIcCAZeVAQAAAB2gprFF185foZXbynXPpaN0ybjeTkcCgIDRNzVOxkh5JZRDgYByCAAAAPCzyrpmXTP/C63bWakHZ43Vt0b2cDoSAASU6Ihw9U6OZVPqAEE5BAAAAPhRWU2jrn7iC20prtHfrhqnM4dmOB0JAAKSxx1PORQgjrrnkDHmSWNMsTFm3WHuP80YU2mMWeP7+OUB951jjNlsjNlijPmpP4MDAAAAgaa4qkEz5y1TXmmNHr9mPMUQABzBvnH21lqno4S89mxI/ZSkc45yzCfW2tG+j99KkjEmXNJDks6VNFTSLGPM0BMJCwAAAASqNTsqdNFDn2lXRb2emjNRpwxMczoSAAQ0j9ulmsYWldQ0Oh0l5B21HLLWLpFUfhzPPVHSFmttnrW2SdJLki48jucBAAAAApa1Vs8t26bvPrJUYWFGL980WSdlpTodCwACXub+cfZ1DieBv0bZTzbGfGWMedcYM8x3Wy9JOw44ptB32yEZY240xqw0xqwsKSnxUywAAACg49Q3teqHf/9Kv3hjnaYOcOtft0/V8F6JTscCgKCQ5SuH8ktrHE4Cf2xIvVpSP2ttjTHmPElvSMo+1iex1s6TNE+Sxo8fzwWHAAAACGjbymp183OrtWlPlX5wxkDdfvoAhYUZp2MBQNDomRSrqPAw5bEpteNOuByy1lYd8Pk7xpiHjTFuSTsl9Tng0N6+2wAAAICgtnBjke56eY3Cw4zmz56g0walOx0JAIJOeJhRv9Q45ZdQDjnthMshY0x3SUXWWmuMmai2S9XKJFVIyjbGeNRWCs2UdMWJvh4AAADglFav1X0f5uivi7ZoRK9EPXzlWPVJiXM6FgAELY9vYhmcddRyyBjzoqTTJLmNMYWSfiUpUpKstY9IulTSLcaYFkn1kmbatjl0LcaY2yS9Lylc0pPW2vUd8l0AAAAAHay8tkl3vvSlPskt1cwJffTrC4YpJjLc6VgAENQ8aS4t3lyiVq9VOJfmOuao5ZC1dtZR7v+rpL8e5r53JL1zfNEAAACAwPDVjgp9//nVKqlp1J8vGaHLJ/R1OhIAdAmeVJeaWr3aVVHPSkwH+WtaGQAAANDlWGv1/PJt+u4jn8sY6bWbT6YYAgA/8uyfWMalZU7yx7QyAAAAoMtpaG7VL95Yp1dXFerUgWmaO3O0kuKinI4FAF2KJ+2bcuiUgWkOpwldlEMAAADAQbaX1enm51Zp454q3TkjW3fOyGZMPQB0gLT4aMVHR7ByyGGUQwAAAMABPtpUpLteWiNjjJ6cPUHTGVMPAB3GGCOP26U8yiFHUQ4BAAAAahtTP3dBjh74aIuG9+qmv105js1RAaATeNwufbljr9MxQhrlEAAAAELe3tom3eEbU3/Z+N767YXDGVMPAJ0k0+3Sv77epcaWVkVH8N7rBMohAAAAhLSvCyt0y3NtY+r/9J0RmjmRaWQA0Jmy3C55rbSjvE4D0hOcjhOSGGUPAACAkPXSF9t16d8+lyS9evNkiiEAcMC+cfZ5Jew75BRWDgEAACDkNDS36pdvrtMrKwt1ysA0zb18tJJdjKkHACdkur8ZZw9nUA4BAAAgpOwobxtTv35Xle7wjakPZ0w9ADgmMTZS7vgoyiEHUQ4BAAAgZCzaVKy7Xl4ja63mz56g6YMZUw8AgYBx9s6iHAIAAECX1+q1mrswVw9+lKsh3bvpkavGqW8qY+oBIFBkprq0OKfE6Rghiw2pAQAA0KXtrW3StU+t0AMLc3XJ2N76x/dPphgCgADjSXOppLpRNY0tTkcJSawcAgAAQJe1trBSNz+3SiXVjfrDxSM0a2IfGcP+QgAQaLJ8m1IXlNZqeK9Eh9OEHlYOAQAAoEt6ecV2XfLIUknS32+erCsm9aUYAoAA5XHHSxL7DjmElUMAAADoUhqaW/WrN9fr5ZU7NC3brbkzxyiFMfUAEND6pcbJGCm/hHLICZRDAAAA6DJ2lNfpludXad3OKt1++gDddcZAxtQDQBCIiQxXz8RY5ZfWOB0lJFEOAQAAoEtYvLltTH2r1+qJa8ZrxpAMpyMBAI6Bx+1SPpeVOYI9hwAAABDUvF6r+xfkaM5TK9QjMVb/un0qxRAABKF95ZC11ukoIYeVQwAAAAhaFXVNuuvlNVq8uUSXjO2t3180XLFR4U7HAgAcB4/bpaqGFpXXNik1PtrpOCGFcggAAABBad3OtjH1xVWNuvvi4bpiItPIACCYedLaxtnnl9ZSDnUyLisDAABA0Hll5Q59529L5fVavXLzZF05qR/FEAAEuSx3WznEOPvOx8ohAAAABI2G5lb95p/r9eIXOzR1gFsPzGJMPQB0Fb2SYhUZbtiU2gGUQwAAAAgKhXvrdMtzq7V2Z6Vumz5APziTMfUA0JVEhIepT0qc8ksohzob5RAAAAAC3sc5JbrzpS/V6rV67HvjdeZQppEBQFeU5XapoIxyqLNRDgEAACBgeb1Wf120RfctyNGgjAQ9ctU4Zfr2pAAAdD0et0uf5JbK67UKY3Vop6EcAgAAQECqrGvWD15Zo482Fes7Y3rp7otHMKYeALo4jztejS1e7a5qUK+kWKfjhAzKIQAAAAScdTsrdcvzq7SnskG/u2i4rprEmHoACAUe3+rQ/JJayqFOxCh7AAAABJS/r9yhS/62VC2tVq/cNFlXn8SYegAIFVlpvnKotMbhJKGFlUMAAAAICI0trfr1Wxv04hfbdXL/VD04a4xS46OdjgUA6ETpCdGKiwpXHuPsOxXlEAAAABy3s6Je339ulb4qrNQtp/XXD88cqIhwFrkDQKgxxigz1aUCyqFORTkEAAAARy3xjalvabWad/U4nTWsu9ORAAAO8qS5tH5npdMxQgp/HQMAAABHeL1WDy7M1TXzv1BGtxi9dftUiiEAgLLcLu3YW6+mFq/TUUIGK4cAAADQ6SrqmvTDV77Swk3FunhML9198XDFRfGjKQCgbWJZq9dqx9469U+LdzpOSOBPYAAAAHSqVdvKdfsLX6qkplG/vXAY08gAAP/mwHH2lEOdg3IIAAAAncLrtXpkyVb93wc56p0cq9duOVkjeyc5HQsAEGD2l0NsSt1pKIcAAADQ4UprGvWDl9fok9xSnT+yh/74nRFKiIl0OhYAIAAlxUUpOS5S+WWUQ52FcggAAAAdaumWUt358hpV1Tfrj98ZoZkT+nAZGQDgiDxul/JLKIc6C+UQAAAAOkSr12ruwlw9+FGustwuPXvdRA3u3s3pWACAIOBxx+uzLaVOxwgZlEMAAADwuz2VDbrzpS+1PL9c3x3XW7+5cBjTyAAA7ZaV5tJrqwtV29giVzR/fnQ0/g0DAADArxZtLtYPX/lKDc2tuveyUfrO2N5ORwIABJl9m1IXlNVqWM9Eh9N0fZRDAAAA8IvmVq/ueX+zHl2Sp8HdE/TQlWMZQQwAOC6Zqd9MLKMc6niUQwAAADhhO8rrdMdLX+rL7RW66qS++sW3hiomMtzpWACAIJXpjpMkFTDOvlNQDgEAAOCEvLduj37y6leyVnr4yrE6b0QPpyMBAIJcXFSEeiTGKI9yqFNQDgEAAOC4NDS36o/vbNTTn2/TqN6JenDWWPVNjXM6FgCgi/C4XcqnHOoUlEMAAAA4ZvmltbrthdVav6tK10/16CfnDFZURJjTsQAAXYjH7dLba3c7HSMkUA4BAADgmLy5Zqf+5x9rFRkRpieuGa8ZQzKcjgQA6II8bpcq6pq1t7ZJya4op+N0aZRDAAAAaJf6plb9+q31ennlDk3ITNbcmWPUMynW6VgAgC5q3zj7vNJajaMc6lCUQwAAADiqnKJq3fbCauUW1+i26QN01xnZigjnMjIAQMfZVw4VlNZqXL9kh9N0bZRDAAAAOCxrrf6+slC/fGud4qMj9My1EzUtO83pWACAENAnJU7hYYZNqTsB5RAAAAAOqaaxRT9/fa3eXLNLUwak6r7LRys9IcbpWACAEBEZHqa+KXGUQ52AcggAAAD/Yd3OSt3+4pfaVlarH501ULecNkDhYcbpWACAEONxu5RHOdThKIcAAACwn7VWzy7bpt//a6NSXFF66cbJmuhJcToWACBEedwufb61TF6vVRh/SdFhKIcAAAAgSaqsb9Z/v/q13lu/R6cPTtc93x2lFKbDAAAclOl2qb65VUXVDeqRyITMjkI5BAAAAH25fa9uf/FL7als0M/PG6Lrpnr4G1oAgOOyfBPL8ktrKYc6EPNHAQAAQpjXazVvyVZ995HPJUl/v3mybjgli2IIABAQPAeUQ+g4rBwCAAAIUeW1TfrhK2u0aHOJzh3eXX+6ZKQSYyOdjgUAwH7du8UoJjJM+SWUQx3pqOWQMeZJSedLKrbWDj/E/VdK+m9JRlK1pFustV/57ivw3dYqqcVaO95/0QEAAHC8lueV6Y6XvtTe2mb97sJhuuqkfjKG1UIAgMASFmaUmepi5VAHa8/Koack/VXSM4e5P1/SqdbavcaYcyXNkzTpgPunW2tLTyglAAAA/KLVa/Xwoi26b0GOMlNdenL2BA3rmeh0LAAADisrzaVNu6udjtGlHbUcstYuMcZkHuH+pQd8uUxSbz/kAgAAgJ8VVzXorpfXaOnWMl00uqd+f/EIxUezywAAILBlprr0wfoitbR6FRHO1skdwd8/DVwn6d0DvraSPjDGWEmPWmvnHe6BxpgbJd0oSX379vVzLAAAgND2SW6JfvDyGtU0tuh/Lx2p747rzWVkAICg4HG71OK1Ktxbr0zfBtXwL7+VQ8aY6Worh6YecPNUa+1OY0y6pA+NMZustUsO9XhfcTRPksaPH2/9lQsAACCUtbR6dd+CHD28eKsGpifoxRtOUnZGgtOxAABot6y0byaWUQ51DL+UQ8aYkZIel3SutbZs3+3W2p2+fxYbY16XNFHSIcshAAAA+Neuinrd8eKXWrltr2ZN7KNfnj9MsVHhTscCAOCYeNzxkqS80lpNdzhLV3XC5ZAxpq+kf0i62lqbc8DtLklh1tpq3+dnSfrtib4eAAAAjm7BhiL96NWv1Nzi1dyZo3Xh6F5ORwIA4Lgkx0UqMTZS+aU1Tkfpstozyv5FSadJchtjCiX9SlKkJFlrH5H0S0mpkh72Xbe+b2R9hqTXfbdFSHrBWvteB3wPAAAA8Glq8erP723SE5/ma3ivbvrrrLEswQcABDVjjDxuxtl3pPZMK5t1lPuvl3T9IW7PkzTq+KMBAADgWGwrq9XtL36prwsrNfvkTP3svMGKjuAyMgBA8PO4XVqeV3b0A3FcmF0KAADQBfzr61362WtrZYz06NXjdPaw7k5HAgDAbzxul17/cqcamlsVE8lffPgb5RAAAEAQa2hu1e/+tUHPL9+uMX2T9OCsMeqdHOd0LAAA/Mrju0S6oKxWg7t3czhN10M5BAAAEKS2FNfothdWa9Oeat10apZ+dNYgRYaHOR0LAAC/21cO5ZdQDnUEyiEAAIAg9NqqQv3ijXWKjQrXU3Mm6LRB6U5HAgCgw+wrh/LYlLpDUA4BAAAEkdrGFv3yzfV6bXWhJnlS9MCsMcroFuN0LAAAOpQrOkIZ3aKZWNZBKIcAAACCxMbdVbrthdXKK63VnTOydceMbIWHGadjAQDQKTJTGWffUSiHAAAAApy1Vi98sV2/+ecGJcVG6vnrJ+nk/m6nYwEA0Kmy0lz6YH2R0zG6JMohAACAAGWt1ed5Zbp/Qa6+yC/XKQPTdO9lo+SOj3Y6GgAAnc7jdqmstkmVdc1KjIt0Ok6XQjkEAAAQYKy1+nxrme5f2FYKpSdE63cXDtOVk/opjMvIAAAhyuOOlyTll9VqdFySs2G6GMohAACAALG/FFqQqy8KypXRLVq/uWCYLp/QRzGR4U7HAwDAUfvH2ZfWaHSfJGfDdDGUQwAAAA6z1mrp1jLdvyBHKwr2qnu3GEohAAAO0jclTmFGyi9hU2p/oxwCAABwiLVWn21pK4VWbmsrhX574TBdNp5SCACAg0VFhKl3cpzymFjmd5RDAAAAncxaq0+3lOr+Bbla5SuFfnfhMF02oY+iIyiFAAA4HI/bpYIyyiF/oxwCAADoJNZafZJbqvsX5Gj19gr1SIzR7y4arsvG96YUAgCgHTxul1YWlMtaK2MY0uAvlEMAAAAdzFqrJbmlmusrhXomxuj3Fw3XdymFAAA4JllpLtU2taqkulHp3WKcjtNlUA4BAAB0EGutPs4p0dyFufrSVwrdffFwXTqOUggAgOOxb2JZXmkt5ZAfUQ4BAAD42b5S6P4FuVqzo0K9kmIphQAA8INvxtnX6qSsVIfTdB2UQwAAAH5irdViXyn0la8U+sPFI3TpuN6KighzOh4AAEGvZ2KsoiLClM/EMr+iHAIAADhB1lot3lyi+xfk6KvCSvVKitUfvzNCl4ylFAIAwJ/CwowyU+Moh/yMcggAAOA4WWu1aHOx7l+Qq68LK9U7OVZ/+s4IfYdSCACADuNxu7S1hHLInyiHAAAAjpG1Vh9tKtbchd+UQn++pK0UigynFAIAoCN53PH6aFOxWr1W4WGMs/cHyiEAAIB2stZq4ca2Umjtzkr1SYnV/14yUheP7UUpBABAJ8lyu9TcarVzb736psY5HadLoBwCAAA4in2l0P0Lc7RuZ5X6psTpfy8dqYvHUAoBANDZPGn7xtnXUA75CeUQAADAYVhrtWBjseZSCgEAEDAyU78ZZ3/aIIfDdBGUQwAAAAex1urDDUWauzBX63dVqV9qnP5y6UhdRCkEAIDj3PFRSoiOUAETy/yGcggAAMDHWqsPNhRp7oJcbdjdVgrd891Rumh0T0VQCgEAEBCMMfKkuZRHOeQ3lEMAACDkeb2+UmhhrjburlJmapz+77ujdCGlEAAAAcnjdmnVtr1Ox+gyKIcAAEDIaiuF9mjuwi3auLtKHrdL9142SheMohQCACCQedwuvfXVLjU0tyomMtzpOEGPcggAAIQcr9fq/fV7NHdhrjbtqZbH7dJ9l4/St0dSCgEAEAw8bpeslbaX12lgRoLTcYIe5RAAAAgZB5dCWZRCAAAEJY/bN86+pJZyyA8ohwAAQJfn9Vq9t36P5i7I1eaiamWluXT/5aP17VE9FR5mnI4HAACOUab7m3H2OHGUQwAAoMvyeq3eXbdHDyz8phSaO3O0zh9JKQQAQDDrFhMpd3w04+z9hHIIAAB0OV6v1TvrduuBhbnKKapRf0ohAAC6nCy3i5VDfkI5BAAAuoyDS6EB6fF6YNYYfWtED0ohAAC6GI/bpYWbip2O0SVQDgEAgKBnrdUHG4p034c52rSnWgPS4/XgrDE6j1IIAIAuy5PmUunKRlU1NKtbTKTTcYIa5RAAAAha1lotzinRvR/kaO3OSnncXD4GAECoyExt25S6oLRWI3snORsmyFEOAQCAoLR0S6nu+WCzVm+vUO/kWP3l0pG6eEwvRtIDABAistK+mVhGOXRiKIcAAEBQWVFQrv/7YLOW5ZWre7cY3X3xcH13XB9FRVAKAQAQSvqmxMkYxtn7A+UQAAAICl/tqND/fZijJTklcsdH61ffHqpZE/sqJjLc6WgAAMABMZHh6pUUSznkB5RDAAAgoG3YVaV7P8zRgo1FSo6L1M/OHazvTc5UbBSlEAAAoc7DOHu/oBwCAAABKbeoWvctyNE7a/coISZCPzxzoOZM9Sg+mh9fAABAmyy3S/9YvVPWWhnDMIrjxU9XAAAgoOSX1mrughy9+dUuxUWG6/bTB+j6qVlKjGNELQAA+HeZbpeqG1tUWtOktIRop+MELcohAAAQEHaU1+nBj3L12uqdigw3uvGULN10Sn+luKKcjgYAAAKUx/3NxDLKoeNHOQQAABy1p7JBf12Uq5dX7JCR0fcm99Mtp/VXekKM09EAAECAy3LHS5IKSms10ZPicJrgRTkEAAAcUVLdqL8t3qrnlm+T12t1+YQ+uu30AeqRGOt0NAAAECR6JccqMtwoj02pTwjlEAAA6FR7a5v06JI8Pb20QE2tXn1nTC/dMSNbfVLinI4GAACCTHiYUb9Ul/JLa5yOEtQohwAAQKeorG/WE5/k6cnPClTb1KILRvXUnTOylZUW73Q0AAAQxBhnf+IohwAAQIeqaWzRU5/la96SPFU1tOi8Ed111xkDNTAjweloAACgC8hyu/RxTolavVbhYYyzPx6UQwAAoEPUN7Xq2WUFeuTjPJXXNumMIem664yBGt4r0eloAACgC8l0u9TU4tWuinouUz9OlEMAAMCvGlta9eLy7Xpo8VaVVDdqWrZb/3XmQI3pm+x0NAAA0AXtG2dfUFZLOXScKIcAAIBfNLd69feVhXrwo1ztrmzQRE+KHrpiLGNlAQBAh8rylUP5pbWalp3mcJrgRDkEAABOSEurV2+s2aW5C3O0o7xeY/om6S+XjtKUAakyhuv+AQBAx0pLiJYrKlx5JWxKfbwohwAAwHHxeq3++fUuzV2Qq7zSWg3v1U2/nT1cpw1KoxQCAACdxhgjTxoTy04E5RAAADgm1lq9v75I932Yo81F1RqUkaBHrhqns4dlUAoBAABHeNzx+mpHhdMxghblEAAAaBdrrRZtLta9H+Zo3c4qZaW59MCsMTp/RA+FMTYWAAA4yJMap7e/3qWmFq+iIsKcjhN0KIcAAMARWWv12ZYy/d+Hm/Xl9gr1SYnVPd8dpYtG91REOD98AQAA53nSXPJaaXt5nQakxzsdJ+hQDgEAgMP6Ir9c//fBZi3PL1ePxBj98TsjdOm43oqkFAIAAAHE424rhPJLaymHjkO7yiFjzJOSzpdUbK0dfoj7jaS5ks6TVCdptrV2te++ayT9wnfo7621T/sjOAAA6Dhfbt+rez/M0Se5pUpLiNavvz1UMyf2VUxkuNPRAAAA/oMndd84+xpJGc6GCULtXTn0lKS/SnrmMPefKynb9zFJ0t8kTTLGpEj6laTxkqykVcaYt6y1e08kNAAA6Bjrdlbqvg9ztHBTsVJcUfr5eUN01Un9FBtFKQQAAAJXYlykUl1RTCw7Tu0qh6y1S4wxmUc45EJJz1hrraRlxpgkY0wPSadJ+tBaWy5JxpgPJZ0j6cUTSg0AAPwqp6ha932Yo3fX7VG3mAj9+OxBuubkTMVHcwU6AAAIDh63S3kllEPHw18/8fWStOOArwt9tx3u9v9gjLlR0o2S1LdvXz/FAgAAR5JXUqO5C3P11le75IqK0B0zsnXdVI8SYyOdjgYAAHBMMt0uLckpcTpGUAqYvw601s6TNE+Sxo8fbx2OAwBAl/Z1YYXmLcnTO2t3KzoiXDed0l83nZKlZFeU09EAAACOi8ft0qurClXb2CIXq5+Pib/+be2U1OeAr3v7btuptkvLDrx9sZ9eEwAAHAOv12pxTrHmLcnTsrxyJURH6IZpWbp+WpbSEqKdjgcAAHBCstz7NqWu1fBeiQ6nCS7+KofeknSbMeYltW1IXWmt3W2MeV/SH4wxyb7jzpL0Mz+9JgAAaIfGlla9+eUuPfZJnnKLa9QjMUY/P2+IZk7so4QYLh8DAABdgyeNcuh4tXeU/YtqWwHkNsYUqm0CWaQkWWsfkfSO2sbYb1HbKPs5vvvKjTG/k7TC91S/3bc5NQAA6FiVdc16bvk2PbW0QCXVjRrSo5vuu3yUzh/ZU5HhYU7HAwAA8KvM1G/KIRyb9k4rm3WU+62kWw9z35OSnjz2aAAA4HjsKK/Tk5/l6+UVO1TX1Kpp2W7dd9loTRmQKmOM0/EAAAA6RExkuHolxVIOHQd2aAIAoItYW1ipR5ds1TtrdyvMGF0wqqduOCVLQ3p0czoaAABAp8h0xymPcuiYUQ4BABDEvF6rj3NK9OiSrf+2yfTsKZnqkRjrdDwAAIBO5XG79NaaXbLWsmL6GFAOAQAQhA63yfTlE/uoG5tMAwCAEOVxx6uqoUV765qV4opyOk7QoBwCACCIsMk0AADA4X0zzr5GKa4Uh9MED8ohAACCwKE2mb73slGaOsDNkmkAAAAfj68cyiup1bh+lEPtRTkEAEAAW1tYqXmf5OmdtbtlJF0wqqeun5aloT3ZZBoAAOBgvZNjFRFmmFh2jCiHAAAIMPs2mZ63JE+f55UpPjpC1031aA6bTAMAABxRRHiY+qbEUQ4dI8ohAAACRGNLq95cs0uPLWnbZLp7txj9z3mDNXNiXzaZBgAAaCeP20U5dIwohwAAcFhlXbOe/2KbnvqsQMXVjRrcPUH3XT5K3xrRU1ERbDINAABwLDxulz7bWiqv1yosjL0Z24NyCAAAhxTurdOTnxbopRXb928y/X9sMg0AAHBCPGkuNTR7taeqQT2TuCS/PSiHAADoZOt2VurRJWwyDQAA0BE8+8fZ11IOtRPlEAAAncBaq8U5JZr38b9vMj375Ex+aAEAAPCjLHe8JCmvtFZTBrgdThMcKIcAAOhA+zaZfvyTPOUUsck0AABAR8voFq3YyHDll7ApdXtRDgEA0AEq65v1/PJ/32T63stG6fyRbDINAADQkYwxynS7VFBGOdRelEMAAPjRvk2mX16xXbW+Tabv+e4oTctmk2kAAIDOkuV2acPuKqdjBA3KIQAA/GDdzkrNW5Knt32bTH97VE9dP82jYT0TnY4GAAAQcjxul95bv0fNrV5FhrNq+2gohwAAOE77Npl+bEmelm5t22T62imZmjPFwybTAAAADvK4XWr1Wu0or1NWWrzTcQIe5RAAAMeosaVVb63ZpccO2GT6Z+cO1qxJbDINAAAQCDxp34yzpxw6OsohAADaqbK+WS8s3675n+WzyTQAAEAA86R+Uw7h6CiHAAA4iqqGZv1t8VY9s7RAtU2tmjqATaYBAAACWbIrSklxkZRD7UQ5BADAYbS0evXiF9t134Jcldc26YJRPXXTqVlsMg0AABAEPG4X5VA7UQ4BAHAQa60Wby7R3e9s1JbiGk3ypOgX3xqqEb0phQAAAIKFx+3S51vLnI4RFCiHAAA4wMbdVbr77Y36dEupPG6XHr16nM4amsHlYwAAAEEmy+3SP1bvVF1Ti+KiqD+OhH87AABIKq5q0P99kKNXVu1Qt5hI/fL8obrqpH5sNA0AABCkPO62KWUFpXUa2rObw2kCG+UQACCk1Te16rFP8vTIx1vV3OrVtVM8uv30AUqKi3I6GgAAAE5ApjtOUtvEMsqhI6McAgCEJK/X6vUvd+ov72/WnqoGnTu8u/77nMHKdLucjgYAAAA/yPSNsy8oY1Pqo6EcAgCEnM+3lunudzZo3c4qjeqdqAdmjdFET4rTsQAAAOBHrugIde8Wo7wSyqGjoRwCAISMvJIa/fHdTfpwQ5F6Jsbo/stH64JRPRUWxmbTAAAAXVHbOPsap2MEPMohAECXt7e2SXMX5uq5ZdsUHRGmH589SNdN9SgmMtzpaAAAAOhAnjSX3l272+kYAY9yCADQZTW2tOrZz7fpgYW5qmls0cyJffWDMwYqLSHa6WgAAADoBFlul/bWNWtvbZOSXQwcORzKIQBAl2Ot1Xvr9uiP727S9vI6nTIwTT8/b4gGdU9wOhoAAAA60b5NqfPLaimHjoByCADQpazZUaG7396gFQV7NTAjXk9fO1GnDkxzOhYAAAAc4EnzTSwrrdXYvskOpwlclEMAgC5hZ0W9/ve9TXpzzS6546P0h4tH6LLxvRURHuZ0NAAAADikT3KcwsOM8kuZWHYklEMAgKBW3dCsvy3eqic+zZck3Tq9v245bYDio/kjDgAAINRFRYSpT3Ks8iiHjoifnAEAQaml1auXV+7QfR/mqLSmSReP6aUfnT1IvZJinY4GAACAAOJxu5RfQjl0JJRDAICgs3hzsf7wzkblFNVoYmaKnrhmiEb1SXI6FgAAAAKQxx2vZXnlstbKGON0nIBEOQQACBqb9lTp7rc36pPcUvVLjdMjV43V2cO684c8AAAADsvjjlN9c6uKqhrVPTHG6TgBiXIIABDwiqsbdN+HOXp5xQ7FR0foF98aou9NzlRUBJtNAwAA4Mg87nhJUn5pLeXQYVAOAQACVkNzq574NF8PL9qixhavrjk5U3ecnq1kV5TT0QAAABAk9o2zzy+t1eT+qQ6nCUyUQwCAgOP1Wr351U795b3N2lXZoLOGZuin5w5WVlq809EAAAAQZHp0i1F0RJjyS2ucjhKwKIcAAAHli/xy/f7tDfq6sFIjeiXq3stH66Qs/oYHAAAAxycszLRNLGOc/WFRDgEAAkJBaa3+9O4mvbd+j3okxujey0bpotG9FBbGZtMAAAA4MR63S5uLqp2OEbAohwAAjqqoa9IDC7fo2WUFigwP0w/PHKjrp2UpNirc6WgAAADoIjLdLn24oUgtrV5FhDPU5GCUQwAARzS1ePXssm16YGGuqhuaddn4PvqvswYqPYEJEgAAAPAvj9ulFq/Vzop69Ut1OR0n4FAOAQA6lbVWH2wo0h/f2aiCsjpNy3brf84boiE9ujkdDQAAAF1UlrutEMorraUcOgTKIQBAp1lbWKnfvb1BX+SXa0B6vObPmaDTBqbJGPYVAgAAQMfx+Mqh/JJaTR/kcJgARDkEAOhwuyrqdc/7m/WPL3cq1RWl3180XDMn9OF6bwAAAHSKFFeUusVEMLHsMCiHAAAdpqaxRY9+vFXzluTJSrrltP76/mn9lRAT6XQ0AAAAhBBjjDxp8ZRDh0E5BADwu1av1Ssrd+j/PshRaU2jLhjVUz85Z5B6J8c5HQ0AAAAhypMapxUFe52OEZAohwAAfrOluEbvrdutN9bs0pbiGo3vl6zHvjdOY/omOx0NAAAAIc7jjtcba3apoblVMZHhTscJKJRDAIDjZq3V+l1Ven/9Hr27bo+2FNdIksb2TdJDV4zVeSO6s9k0AAAAAoInrW1T6m1ldRrUPcHhNIGFcggAcEy8Xqsvd1To/fV79N66PdpeXqcwI03ypOp7k/vp7GHdldEtxumYAAAAwL/ZN84+v7SGcugglEMAgKNqafXqi4Jyvb9uj95fX6Q9VQ2KDDeaMsCtW6f315lDuyvFFeV0TAAAAOCwMn3lUB6bUv8HyiEAwCE1tXj12dZSvb9ujz7YUKTy2ibFRIbp1IFp+unwwTp9SLq6MXUMAAAAQSI+OkLpCdHKL6EcOhjlEABgv/qmVn2cU6L31u3Wwk3Fqm5oUXx0hGYMSdc5w7rr1EFpiovijw4AAAAEp0y3i3H2h8BP+AAQ4qobmvXRpmK9t26PFm8uUX1zq5LjInXu8O46Z3h3TRngVnQE0xwAAAAQ/LLcLn24ocjpGAGnXeWQMeYcSXMlhUt63Fr7p4Puv0/SdN+XcZLSrbVJvvtaJa313bfdWnuBH3IDAE7A3tomfbixSO+t26NPc0vV1OpVekK0Lh3XW+cM765JnhRFhIc5HRMAAADwK4/bpbLaJlXWNysxli0S9jlqOWSMCZf0kKQzJRVKWmGMectau2HfMdbaHxxw/O2SxhzwFPXW2tF+SwwAOC7F1Q16f32R3lu3W8vyytXqteqVFKvvTe6nc4Z319i+yQoLY+w8AAAAui6Pb1PqgtJajeqT5GyYANKelUMTJW2x1uZJkjHmJUkXStpwmONnSfqVf+IBAE7EjvK6/SPnV23fK2ulrDSXbj41S+cM66HhvbrJGAohAAAAhIastH3j7CmHDtSecqiXpB0HfF0oadKhDjTG9JPkkfTRATfHGGNWSmqR9Cdr7RuHeeyNkm6UpL59+7YjFgDgULaW1Oi9dW2F0NqdlZKkIT266QdnDNS5w7trQHo8hRAAAABCUp+UOIUZxtkfzN8bUs+U9Kq1tvWA2/pZa3caY7IkfWSMWWut3XrwA6218yTNk6Tx48dbP+cCgC7LWquNu6v13rrdem/9HuUU1UiSRvdJ0s/OHaxzhndXv1SXwykBAAAA50VHhKtXciwTyw7SnnJop6Q+B3zd23fbocyUdOuBN1hrd/r+mWeMWay2/Yj+oxwCALSf12v1VWFF2wqh9Xu0raxOYUaakJmiX397qM4e3l09EmOdjgkAAAAEHI87XvmlNU7HCCjtKYdWSMo2xnjUVgrNlHTFwQcZYwZLSpb0+QG3JUuqs9Y2GmPckqZI+l9/BAeAUNPqtVpRUK731u3R++v3aHdlgyLCjE4e4NbNp/bXmUMz5I6PdjomAAAAENCy3C6t3rZX1lq2W/A5ajlkrW0xxtwm6X21jbJ/0lq73hjzW0krrbVv+Q6dKekla+2Bl4QNkfSoMcYrKUxtew4dbiNrAMBBmlq8+jyvTO+t260P1heprLZJ0RFhOnVgmn589iDNGJLBCE4AAADgGHjcLtU0tqikplHpCTFOxwkI7dpzyFr7jqR3Drrtlwd9/etDPG6ppBEnkA8AQk5Dc6uW5JTovXV7tGBjkaoaWuSKCtfpQzJ0zrDuOm1QmlzR/t4yDgAAAAgN+8bZ55fUUg758NsFAASAmsYWLdpUrPfW7dGizcWqa2pVYmykzhrWXecM666p2W7FRIY7HRMAAAAIevvLodJaTcpKdThNYKAcAgCHlFQ3atGmYn2wYY+W5JaqqcUrd3y0Lh7TS+cM766TslIVGR7mdEwAAACgS+mZFKuo8DAmlh2AcggAOom1VluKa/ThxiIt2FCkL3dUyFqpV1KsrprUT+cM765x/ZIVHsameAAAAEBHCQ8z6pcapzzKof0ohwCgA7W0erWiYK8WbCzSgo1F2lZWJ0ka2TtRPzhjoM4YkqEhPRKYkgAAAAB0Io/bxcqhA1AOAYCfVTc06+OcEi3YUKRFm0tUWd+sqIgwTemfqhtPydKMwRnqnsjGdwAAAIBTPGkuLd5colavZeW+KIcAwC8K99Zp4cZiLdhYpGV5ZWputUpxRenMoRk6Y0iGpmW7mTAGAAAABIgst0tNrV7tqqhXn5Q4p+M4jt9UAOA4eL1W63ZVasGGIn24sVgbd1dJkvqnuXTtVI/OHJKhMX3ZPwgAAAAIRB53vCQpr7SWckiUQwDQbg3Nrfp8a5k+3FikhRuLVFTVqDAjjc9M0c/PG6IZQ9KVlRbvdEwAAAAAR5HpbiuE8ktqdOrANIfTOI9yCACOoKymUR9tartc7JPcUtU1tcoVFa5TB6XpjCEZmj4oXcmuKKdjAgAAADgGafHRio+OYFNqH8ohADiAtVZbS2rbpottKNKq7XtlrdQjMUaXjO2tM4Zm6KSsFEVHhDsdFQAAAMBxMsa0TSzzTRMOdZRDAEJeS6tXq7btGzdfvP9vD4b17KY7Ts/WmUMzNKxnN8bNAwAAAF2Ix+3Slzv2Oh0jIFAOAQhJNY0tWuIbN//R5mJV1DUrKjxMk/un6tqpHs0YnK6eSbFOxwQAAADQQTxul/759S41trSG/JUBlEMAQsauinot3Ng2XWzZ1jI1tXqVFBep0wen68whGZo2ME3xjJsHAAAAQkJWmkvWStvL6pSdkeB0HEfxWxCALstaq/W7qvThhiIt2Fik9bvaxs173C7NnpKpM4ZkaGzfJEWEhzmcFAAAAEBny0x1SWobZ085BABdSGNL27j5BRuLtHBjsXZXNsgYaXy/ZP3s3ME6Y2iG+jNuHgAAAAh5me62coiJZZRDALqA8tomLfKNm1+SU6LaplbFRobrlIFu/deZA3X64HSlxkc7HRMAAABAAEmMjZQ7PkoFlEOUQwCCU15JjW/cfLFWbiuX10oZ3aJ14ZheOnNIhib3T1VMZGhvKgcAAADgyDxul/IohyiHAASHllavVm+v8G0oXaS8krY38CE9uum26QN0xtAMDe+ZqLAwxs0DAAAAaB+P26VFm0ucjuE4yiEAAau8tkkf5xTro00lWpJTosr6ZkWGG52UlaprJmdqxpB09U6OczomAAAAgCDlccfrlZWFqm5oVkJMpNNxHEM5BCBg7JsutmhTsT7aXKw1OypkreSOj9KZQzN0+uB0Tc12q1sIv2kDAAAA8B+Pu+0vmwtK6zSid6LDaZxDOQTAUTWNLfo0t1SLNxdr0eZiFVU1SpJG9U7UnTOyNX1Qukb04nIxAAAAAP7ncbdNMs4rraEcAoDOlF9aq482FWvRpmItzy9Tc6tVQnSEpg10a/qgdJ02KF1pCUwXAwAAANCx+qXGyZi2lUOhjHIIQIdrbGnVF/nl+wuhgrK2N94B6fGaM8Wj6YPSNT4zWZHhYQ4nBQAAABBKYiLD1TMxVvmlNU5HcRTlEIAOUVTV0LZ30KZifbalVLVNrYqKCNPkrFTNmeLR6YPT1SeFzaQBAAAAOCsrzaX8EB9nTzkEwC9avVZrdlTsL4Q27K6SJPVMjNFFY3rp9MHpOrm/W7FR4Q4nBQAAAIBveNwuvf7lTllrZUxo7nVKOQTguFXUNenjnBIt3lyij3NKVF7bpPAwo3F9k/Xf5wzW9MFpGpSRELJvsAAAAAACX2aqS9UNLSqrbZI7PjT3PqUcAtBu1lptLqrev3fQqm175bVSclykThuUrumD03VqdpoS4xg1DwAAACA4eNJcktoG51AOAcAh1De16rMtpfpoc7EWbyrWrsoGSdKwnt106/QBmj44XaN6JymcUfMAAAAAglCW+5tyaEJmisNpnEE5BOA/7Civ00e+vYM+zytTU4tXrqhwTc12644Z2Zo+OF0Z3WKcjgkAAAAAJ6xXUqwiw01Ib0pNOQRAza1erSgo37+Z9NaStjdFj9ulqyb10+mD0zXBk6zoCDaTBgAAANC1RISHqW9KnPJLKIcAhJiS6kYt3lysRZuL9UlOqaobWxQVHqZJWSm6wlcIeXzLKwEAAACgK/O441k5BKDr83qt1u6sbNtMenOxvi6slCRldIvWt0b20PTB6Zo6wC1XNG8LAAAAAEKLxx2nJbkl8nqtwkJwP1V+CwS6sKqGZn2SU6qPNhXr45xildY0yRhpTJ8k/eisgZo+OF1De3Rj1DwAAACAkOZxx6upxatdlfXqnRzndJxORzkEdDG7Kur1/vo9+mB9kVYUlKvFa5UYG6lTB6Zp+uA0nTowXSmuKKdjAgAAAEDA2LelRkFpHeUQgOC0taRG763bo/fX79l/udjAjHjdcEqWTh+crjF9khQRHuZwSgAAAAAITFlp+8bZ12hqttvhNJ2PcggIQtZard9VpffX79F76/Yot7hGkjSqT5L++5zBOntYhrLS4h1OCQAAAADBIT0hWnFR4coL0U2pKYeAIOH1Wq3evlfvrduj99bvUeHeeoUZaZInVVed1E9nDctQj8RYp2MCAAAAQNAxxsjjdoXsxDLKISCANbd69fnWMr23fo8+3FCkkupGRYWHaWq2W3ecnq0ZQ9KVGh/tdEwAAAAACHqZbpfW7ax0OoYjKIeAAFPf1KoluSV6f90eLdhYpKqGFsVFhWv6oHSdPby7pg9KU0JMpNMxAQAAAKBLyXK79O7a3Wpq8SoqIrT2bKUcAgJAVUOzFm0q1nvr9mjx5hLVN7cqMTZSZw3rrrOHdde0bLdiIsOdjgkAAAAAXZbH7ZLXSjv21ql/iO3hSjkEOKS0plEfbijSe+v2aOnWUjW3WqUnROvScb11zvDumuhJUSQTxgAAAACgU+wbZ59fUks5BKDj7Kyo1/u+DaVXFpTLa6W+KXGaM8Wjs4d115g+SQoLM07HBAAAAICQs78cCsFNqSmHgA62pbhG76/fo/fX79HXhW2bmw3unqDbTs/WOcO6a0iPBBlDIQQAAAAATkqKi1KKKyokx9lTDgF+Zq3V+l1V+0fObymukSSN7pOkn547WGcP676/kQYAAAAABI7M1Djll9Y4HaPTUQ4BftDqtVq9fW9bIbRuj3ZW1CvMSJM8qbr6pH46a1iGeiTGOh0TAAAAAHAEHne8Pt1S4nSMTkc5BBynphavluWV6b31e/TB+iKV1jQqKjxM07LduvOMbJ0xJEMpriinYwIAAAAA2ikrzaXXVheqtrFFrujQqUxC5zsF/KC+qVUf55To/fV7tHBjkaoaWhQXFa7pg9N1zrDuOm1QmhJiIp2OCQAAAAA4Dvu2ACkoq9WwnokOp+k8lEPAUVTWN2vRpmK9t26PFucUq6HZq6S4SJ01rLvOGdZdU7PdiokMdzomAAAAAOAEHTixjHIICHGlNY36cEOR3lu3R0u3lqq51SqjW7QuG99H5wzrromeFEWEhzkdEwAAAADgR5mpvnKoJLQmllEOAT6Fe+v0/voivb9+j1YUlMtaqW9KnK6d4tHZw7trdO8khYUxch4AAAAAuqrYqHD1SIxRfoiNs6ccQsiy1mrD7ip9tLFYH2wo0tqdlZKkwd0TdMfp2TpneHcN7p4gYyiEAAAAACBUeNwu5VEOAV1XQ3Orlm4t1cKNxfpoU7F2VzZIkkb3SdJPzx2ss4d133+NKQAAAAAg9HjcLr29drfTMToV5RC6vKKqBl8ZVKRPt5SqodmruKhwTct26wdnDtT0QelKS4h2OiYAAAAAIAB43C5V1DVrb22Tkl1RTsfpFJRD6HK8Xqt1uyq1wFcIrdtZJUnqnRyry8f30YwhGZqUlaLoCCaMAQAAAAD+XVZa29UkeaW1Gkc5BASPuqYWfZrru1xsc7FKqhsVZqSxfZP1k3MG6YwhGcpOj2f/IAAAAADAEXnc8ZLaxtmP65fscJrOQTmEoFW4t06LNhVr4aZiLd1apqYWrxKiI3TKoDTNGJyu0walKyVEWl4AAAAAgH/0To5VeJhRfmmN01E6DeUQgkar12rNjgp9tKlICzcWa9OeaklSZmqcrj6pn2YMTtf4zBRFRYQ5nBQAAAAAEKwiw8PUNyUupMbZt6scMsacI2mupHBJj1tr/3TQ/bMl/UXSTt9Nf7XWPu677xpJv/Dd/ntr7dN+yI0QUd3QrE98l4st3lysstomhYcZje+XrJ+fN0SnD0lX/7R4p2MCAAAAALoQj9ul/NI6p2N0mqOWQ8aYcEkPSTpTUqGkFcaYt6y1Gw469GVr7W0HPTZF0q8kjZdkJa3yPXavX9KjS9peVqcFG4v00aZiLc8vU3OrVWJspE4blKYZQzJ0anaaEuMinY4JAAAAAOiiPG6XPt9aJq/XKiys6+9d256VQxMlbbHW5kmSMeYlSRdKOrgcOpSzJX1orS33PfZDSedIevH44qIramn1avX2Ci3cWKSFm4q1pbjtus4B6fG6dopHM4ZkaGzfJEWEc7kYAAAAAKDjedwu1Te3qqi6QT0SY52O0+HaUw71krTjgK8LJU06xHGXGGNOkZQj6QfW2h2HeWyvQ72IMeZGSTdKUt++fdsRC8Gssq5ZH+eWaOHGIi3eXKLK+mZFhhtN8qTqiol9NWNIuvqlupyOCQAAAAAIQVnutt9H80tqKYeOwT8lvWitbTTG3CTpaUmnH8sTWGvnSZonSePHj7d+yoUAsrWkRh9tLNaCjUVauW2vWr1WKa4onTEkQzOGpGtatlsJMVwuBgAAAABwVqavHMorrdXJA9wOp+l47SmHdkrqc8DXvfXNxtOSJGtt2QFfPi7pfw947GkHPXbxsYZEcGpu9WpFfrkWbirWR5uK9+/0Prh7gm4+NUunD87Q6D5JCg+B6zcBAAAAAMGje7cYxUSGhczEsvaUQyskZRtjPGore2ZKuuLAA4wxPay1u31fXiBpo+/z9yX9wRiT7Pv6LEk/O+HUCFjltU1avLlYCzcVa8nmElU3tigqPEyT+6fq2imZmj44Xb2T45yOCQAAAADAYYWFGWWmulRAOdTGWttijLlNbUVPuKQnrbXrjTG/lbTSWvuWpDuMMRdIapFULmm277Hlxpjfqa1gkqTf7tucGl2DtVa5xTVt08U2Fmv19r3yWiktIVrnjeihGUPSNWWAW65of13BCAAAAABAx8tKc2nT7mqnY3SKdv3Gbq19R9I7B932ywM+/5kOsyLIWvukpCdPICMCTGNLq5bnle+fLla4t16SNLxXN912erZmDE7XiF6JITHuDwAAAADQNXncLn2wvkjNrV5FdvHp2SznwFG1eq027q7SF/nlWpZXps+2lKq2qVUxkWGaOsCt7582QKcPTlf3xBinowIAAAAA4Bced7xavFaFe+vlcXftadqUQ/gPza1erdtZqS/yy7U8v1wrCspV3dAiSeqdHKuLxvTSjCHpOrm/WzGR4Q6nBQAAAADA/zzutv1y80trKIfQ9TW2tOrrwkotzyvT8vxyrdq2V3VNrZLarrE8f2QPTfKkaqInRT2TYh1OCwAAAABAx/O44yVJeSW1On2ww2E6GOVQCKpvatWX2/dqWX65vsgv05fbK9TY4pXUNmb+0nG9NcmTqgmeZKUncKkYAAAAACD0JMdFKjE2MiTG2VMOhYDqhmat2rZXy/PL9UV+ub4urFBzq1WYkYb1TNRVJ/XTJE+KJmSmKNkV5XRcAAAAAAAcZ4zR3RcPV9+UOKejdDjKoS6ooq5JKwr2anlemb4oKNe6nZXyWikizGhE70RdNzVLkzwpGpeZrG4xkU7HBQAAAAAgIJ0/sqfTEToF5VAXUFLdqBUF5fv3DNpcVC1rpaiIMI3uk6Tbpg/QRE+qxvZLUlwU/8kBAAAAAMA3aAqC0J7KBi3PbyuClueVaWtJ2/WPsZHhGtcvWd8a0UMTPSka1SeJaWIAAAAAAOCIKIcCnLVWhXvrtSyvbP9o+e3ldZKkhOgIjc9M1nfH99FET4pG9EpUZHiYw4kBAAAAAEAwoRwKMNZa5ZXWanle2ySx5fnl2l3ZIElKiovUxMwUXXNypiZ5UjSkRzeFhxmHEwMAAAAAgGBGOeQwr9cqp7jaVwa1rQwqrWmUJLnjozUpK0WTPCma5ElVdnq8wiiDAAAAAACAH1EOdbKWVq827q7ev2fQioJyVdQ1S5J6JsZoWrZbEz1thZDH7ZIxlEEAAAAAAKDjUA51sOZWr74urPStCirTyoK9qmlskST1S43TWUMzNNGTqkmeFPVJiXM4LQAAAAAACDWUQx1kwYYizV+ar9XbKlTf3CpJGpAerwtH9/StDEpV98QYh1MCAAAAAIBQRznUQSrrm1Ve26zLJ/TRJE+KJnhS5I6PdjoWAAAAAADAv6Ec6iCXjOutS8b1djoGAAAAAADAEYU5HQAAAAAAAADOoRwCAAAAAAAIYZRDAAAAAAAAIYxyCAAAAAAAIIRRDgEAAAAAAIQwyiEAAAAAAIAQRjkEAAAAAAAQwiiHAAAAAAAAQhjlEAAAAAAAQAijHAIAAAAAAAhhlEMAAAAAAAAhjHIIAAAAAAAghFEOAQAAAAAAhDDKIQAAAAAAgBBGOQQAAAAAABDCKIcAAAAAAABCGOUQAAAAAABACKMcAgAAAAAACGGUQwAAAAAAACGMcggAAAAAACCEUQ4BAAAAAACEMMohAAAAAACAEEY5BAAAAAAAEMKMtdbpDP/BGFMiaZvTOYKMW1Kp0yEQUDgncCicFzgY5wQOhfMCB+OcwKFwXuBgnBOBr5+1Nu3gGwOyHMKxM8astNaOdzoHAgfnBA6F8wIH45zAoXBe4GCcEzgUzgscjHMieHFZGQAAAAAAQAijHAIAAAAAAAhhlENdxzynAyDgcE7gUDgvcDDOCRwK5wUOxjmBQ+G8wME4J4IUew4BAAAAAACEMFYOAQAAAAAAhDDKIQAAAAAAgBBGORQAjDF9jDGLjDEbjDHrjTF3+m5PMcZ8aIzJ9f0z2Xe7McY8YIzZYoz52hgz1nf7aGPM577n+NoYc/lhXm+2MabEGLPG93F95323aC9/nRe++1oP+O/91mFeL9oY87Lv8cuNMZmd8o2i3fz4XjH9gPNhjTGmwRhz0SFej/eKIHAc58Vg358VjcaYHx30XOcYYzb7zpmfHub1eK8IcP46Jw73PId4vdOMMZUHvFf8snO+UxwLP79XFBhj1vr+e688zOsd9ucSBAY/vlcMOujniipjzF2HeD3eK4LAcZwXV/r+H19rjFlqjBl1wHPxc0Uwsdby4fCHpB6Sxvo+T5CUI2mopP+V9FPf7T+V9Gff5+dJeleSkXSSpOW+2wdKyvZ93lPSbklJh3i92ZL+6vT3zUfnnBe++2ra8Xrfl/SI7/OZkl52+t8BHx13ThzwnCmSyiXFHeI+3iuC4OM4zot0SRMk3S3pRwc8T7ikrZKyJEVJ+krS0EO8Hu8VAf7hx3PikM9ziNc7TdK/nP6++eic88J3X4Ek91Fe76h/BvHRdc6JA54zXNIeSf0OcR/vFUHwcRznxcmSkn2fn6tvfjfl54og+2DlUACw1u621q72fV4taaOkXpIulPS077CnJV3k+/xCSc/YNsskJRljelhrc6y1ub7n2SWpWFJa530n8Cd/nRfH8JIHPu+rkmYYY8yJfRfwpw46Jy6V9K61tq6j86NjHOt5Ya0tttaukNR80FNNlLTFWptnrW2S9JLvOQ7Ge0WA89c5cYTnQRDy43tFe53ozyXoYB10TsyQtNVau62jcqNjHcd5sdRau9d3+zJJvX2f83NFkKEcCjC+ZXRjJC2XlGGt3e27a4+kDN/nvSTtOOBhhTrohzVjzES1NbRbD/NSl/iW/71qjOnjp/joIH44L2KMMSuNMcvMIS4fOvjx1toWSZWSUv31PcC//PVeoba/oXnxCC/Fe0UQaed5cTjtOV/+7TjeKwLfCZ4Th3ueQ5lsjPnKGPOuMWbY8SdGZ/DDeWElfWCMWWWMufEwx7T3PQUBwF/vFTr6zxW8VwSR4zgvrlPbikGJnyuCDuVQADHGxEt6TdJd1tqqA++z1lq1/UHcnufpIelZSXOstd5DHPJPSZnW2pGSPtQ3TS0CkJ/Oi37W2vGSrpB0vzGmv/+TorP4+b1ihKT3D3MI7xVBxF/nBboOP75XHPZ5fFar7c+ZUZIelPTGieRGx/LTeTHVWjtWbZeQ3GqMOcX/SdFZ/PheESXpAkl/P8whvFcEkWM9L4wx09VWDv13p4WEX1EOBQhjTKTa/ud73lr7D9/NRfuW3/r+Wey7faekA/8Gv7fvNhljukl6W9LPfUt4/4O1tsxa2+j78nFJ4/z5vcB//HVeWGv3/TNP0mK1/Q3AwfY/3hgTISlR0v9v7+5dJCnCOAD/6lQUzg+UC85E9EAUAzEwkFUEBQUvOBEOP0DvApML/CtMzEzETMHsEnHRwEhPMFBRkTs/TvAjUVAURD0uEqQMuhbadWbXHXvtafp5oNie6Z7qmpmX6uKdqt5fBnw7DGComGgeTbJZa104PVxfMR17jItldouXfxynr1hfA8XEsnr+ptZ6odZ6sW2/meSyUsqhAd4GAxsqLnrjip+TbKZbPrLdv+1TGNFQMdE8lOSTWutPi3bqK6Zjr3FRSrk93Vjx4Vrr1pjAuGJiJIfWQFtT+XKSL2utz/d2vZHkZNs+meT13vMnSueuJL/XWn9s2frNdOu7X93hfP313sfSrSNlzQwYF9eWUi5vdR5KcneS8wtO2a/3eJIz7VcB1sRQMdF73RPZYeq3vmIaVoiLZT5KcnMp5aZ2PXm81bGdvmLNDRUTO9Sz/bjDW/eHaMvaD8TAfu0MGBcHSylXbW0neTDJ5wsO3e0axMgGvH5s2W1coa+YgL3GRSnlhiSvJXmq1vpV73jjiqmpa3BX7LmXJPekm5b3aZKzrRxNt9by7SRfJ3kryXXt+JLkxXT3E/osyZ3t+SfT3SDubK/c0fY9m+RY234uyRfp7hj/TpJbx/4MlH2Ni432+Fz7+3TvHP24uCLdNOBvknyY5MjYn4GyPzHR9t2Y7peaA9vOoa+YWFkhLg6nW/d/Iclvbfvqtu9ouv9K8m26GaiL4kJfseZlqJhYVk97zakkp9r2M72+4oMkG2N/Bsq+xsWR9l2fa997v6/ox8XSa5CyHmXg68fBdImea7adQ18xsbJCXLyU5NfesR/36jKumFAp7QsBAAAAYIYsKwMAAACYMckhAAAAgBmTHAIAAACYMckhAAAAgBmTHAIAAACYMckhAIA9KKW8t+T5V0opx//v9gAA/FeSQwAAe1Br3Ri7DQAAQ7p07AYAAExJKeVirfXKUkpJ8kKSB5J8n+SPcVsGALAaM4cAAFbzSJJbktyW5EQSM4oAgEmSHAIAWM29SU7XWv+stf6Q5MzYDQIAWIXkEAAAAMCMSQ4BAKzm3SSPlVIuKaVcn+S+sRsEALAKN6QGAFjNZpL7k5xP8l2S98dtDgDAakqtdew2AAAAADASy8oAAAAAZkxyCAAAAGDGJIcAAAAAZkxyCAAAAGDGJIcAAAAAZkxyCAAAAGDGJIcAAAAAZuwv51TdJv87ZLsAAAAASUVORK5CYII=\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1115,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "Collapsed": "false", "colab": {}, @@ -1129,7 +1134,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "Collapsed": "false", "colab": {}, @@ -1144,7 +1149,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "Collapsed": "false", "colab": {}, @@ -1155,23 +1160,21 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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176 rows × 3 columns

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193 rows × 3 columns

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176 rows × 4 columns

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193 rows × 4 columns

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"[176 rows x 4 columns]" + "[193 rows x 4 columns]" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1560,7 +1563,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "Collapsed": "false", "colab": {}, @@ -1589,165 +1592,165 @@ " \n", " \n", " \n", - " count\n", " id\n", " name\n", + " count\n", " level\n", " \n", " \n", " \n", " \n", " 0\n", - " 1168442\n", - " 2211\n", - " 11 Medical and Health Sciences\n", + " 80003\n", + " 32 Biomedical and Clinical Sciences\n", + " 1094225\n", " 2\n", " \n", " \n", " 1\n", - " 610238\n", - " 2209\n", - " 09 Engineering\n", + " 80011\n", + " 40 Engineering\n", + " 833052\n", " 2\n", " \n", " \n", " 3\n", - " 335403\n", - " 2206\n", - " 06 Biological Sciences\n", + " 80017\n", + " 46 Information and Computing Sciences\n", + " 425860\n", " 2\n", " \n", " \n", " 4\n", - " 332128\n", - " 2208\n", - " 08 Information and Computing Sciences\n", + " 80002\n", + " 31 Biological Sciences\n", + " 365542\n", " 2\n", " \n", " \n", " 5\n", - " 304680\n", - " 2203\n", - " 03 Chemical Sciences\n", + " 80013\n", + " 42 Health Sciences\n", + " 304968\n", " 2\n", " \n", " \n", - " 7\n", - " 224973\n", - " 2202\n", - " 02 Physical Sciences\n", + " 6\n", + " 80005\n", + " 34 Chemical Sciences\n", + " 301217\n", " 2\n", " \n", " \n", - " 8\n", - " 201573\n", - " 2201\n", - " 01 Mathematical Sciences\n", + " 7\n", + " 80022\n", + " 51 Physical Sciences\n", + " 266544\n", " 2\n", " \n", " \n", - " 12\n", - " 161476\n", - " 2217\n", - " 17 Psychology and Cognitive Sciences\n", + " 8\n", + " 80015\n", + " 44 Human Society\n", + " 227080\n", " 2\n", " \n", " \n", - " 13\n", - " 151455\n", - " 2216\n", - " 16 Studies in Human Society\n", + " 9\n", + " 80006\n", + " 35 Commerce, Management, Tourism and Services\n", + " 191148\n", " 2\n", " \n", " \n", - " 18\n", - " 98630\n", - " 2215\n", - " 15 Commerce, Management, Tourism and Services\n", + " 10\n", + " 80020\n", + " 49 Mathematical Sciences\n", + " 179411\n", " 2\n", " \n", " \n", - 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"execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1804,7 +1807,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "Collapsed": "false", "colab": {}, @@ -1818,7 +1821,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "Collapsed": "false", "colab": {}, @@ -1847,9 +1850,9 @@ " \n", " \n", " \n", - " count\n", " id\n", " name\n", + " count\n", " level\n", " cutoff\n", " \n", @@ -1857,235 +1860,235 @@ " \n", " \n", " 0\n", - " 1168442\n", - " 2211\n", - " 11 Medical and Health Sciences\n", + " 80003\n", + " 32 Biomedical and Clinical Sciences\n", + " 1094225\n", " 2\n", - " 11684\n", + " 10942\n", " \n", " \n", " 1\n", - " 610238\n", - " 2209\n", - " 09 Engineering\n", + " 80011\n", + " 40 Engineering\n", + " 833052\n", " 2\n", - " 6102\n", + " 8330\n", " \n", " \n", " 3\n", - " 335403\n", - " 2206\n", - " 06 Biological Sciences\n", + " 80017\n", + " 46 Information and Computing Sciences\n", + " 425860\n", " 2\n", - " 3354\n", + " 4258\n", " \n", " \n", " 4\n", - " 332128\n", - " 2208\n", - " 08 Information and Computing Sciences\n", + " 80002\n", + " 31 Biological Sciences\n", + " 365542\n", " 2\n", - " 3321\n", + " 3655\n", " \n", " \n", " 5\n", - " 304680\n", - " 2203\n", - " 03 Chemical Sciences\n", + " 80013\n", + " 42 Health Sciences\n", + " 304968\n", " 2\n", - " 3046\n", + " 3049\n", " \n", " \n", - " 7\n", - " 224973\n", - " 2202\n", - " 02 Physical Sciences\n", + " 6\n", + " 80005\n", + " 34 Chemical Sciences\n", + " 301217\n", " 2\n", - " 2249\n", + " 3012\n", " \n", " \n", - " 8\n", - " 201573\n", - " 2201\n", - " 01 Mathematical Sciences\n", + " 7\n", + " 80022\n", + " 51 Physical Sciences\n", + " 266544\n", " 2\n", - " 2015\n", + " 2665\n", " \n", " \n", - " 12\n", - " 161476\n", - " 2217\n", - " 17 Psychology and Cognitive Sciences\n", + " 8\n", + " 80015\n", + " 44 Human Society\n", + " 227080\n", " 2\n", - " 1614\n", + " 2270\n", " \n", " \n", - " 13\n", - " 151455\n", - " 2216\n", - " 16 Studies in Human Society\n", + " 9\n", + " 80006\n", + " 35 Commerce, Management, Tourism and Services\n", + " 191148\n", " 2\n", - " 1514\n", + " 1911\n", " \n", " \n", - " 18\n", - " 98630\n", - " 2215\n", - " 15 Commerce, Management, Tourism and Services\n", + " 10\n", + " 80020\n", + " 49 Mathematical Sciences\n", + " 179411\n", " 2\n", - " 986\n", + " 1794\n", " \n", " \n", - " 20\n", - " 95061\n", - " 2210\n", - " 10 Technology\n", + " 11\n", + " 80001\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 165669\n", " 2\n", - " 950\n", + " 1656\n", " \n", " \n", - " 21\n", - " 94318\n", - " 2220\n", - " 20 Language, Communication and Culture\n", + " 13\n", + " 80008\n", + " 37 Earth Sciences\n", + " 141809\n", " 2\n", - " 943\n", + " 1418\n", " \n", " \n", - " 24\n", - " 88929\n", - " 2213\n", - " 13 Education\n", + " 14\n", + " 80018\n", + " 47 Language, Communication and Culture\n", + " 138186\n", " 2\n", - " 889\n", + " 1381\n", " \n", " \n", - " 25\n", - " 86868\n", - 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"execution_count": 20, + "execution_count": 19, "metadata": { "Collapsed": "false", "colab": {}, @@ -2218,7 +2221,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "Collapsed": "false", "colab": {}, @@ -2255,167 +2258,167 @@ " \n", " \n", " 0\n", - " 28.41\n", - " 11 Medical and Health Sciences\n", - " 2211\n", + " 37.05\n", + " 32 Biomedical and Clinical Sciences\n", + " 80003\n", " \n", " \n", " 0\n", - " 21.35\n", - " 09 Engineering\n", - " 2209\n", + " 28.18\n", + " 40 Engineering\n", + " 80011\n", " \n", " \n", " 0\n", - " 20.52\n", - " 06 Biological Sciences\n", - " 2206\n", + " 45.07\n", + " 46 Information and Computing Sciences\n", + " 80017\n", " \n", " \n", " 0\n", - " 35.44\n", - " 08 Information and Computing Sciences\n", - " 2208\n", + " 28.34\n", + " 31 Biological Sciences\n", + " 80002\n", " \n", " \n", " 0\n", - " 20.51\n", - " 03 Chemical Sciences\n", - " 2203\n", + " 33.82\n", + " 42 Health Sciences\n", + " 80013\n", " \n", " \n", " 0\n", - 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"0 25.02 10 Technology 2210\n", - "0 30.45 20 Language, Communication and Culture 2220\n", - "0 25.34 13 Education 2213\n", - "0 16.52 04 Earth Sciences 2204\n", - "0 33.18 14 Economics 2214\n", - "0 28.80 21 History and Archaeology 2221\n", - "0 20.46 05 Environmental Sciences 2205\n", - "0 15.42 07 Agricultural and Veterinary Sciences 2207\n", - "0 27.68 22 Philosophy and Religious Studies 2222\n", - "0 27.52 18 Law and Legal Studies 2218\n", - "0 16.68 12 Built Environment and Design 2212\n", - "0 27.55 19 Studies in Creative Arts and Writing 2219" + " field_citation_ratio name id\n", + "0 37.05 32 Biomedical and Clinical Sciences 80003\n", + "0 28.18 40 Engineering 80011\n", + "0 45.07 46 Information and Computing Sciences 80017\n", + "0 28.34 31 Biological Sciences 80002\n", + "0 33.82 42 Health Sciences 80013\n", + "0 25.02 34 Chemical Sciences 80005\n", + "0 38.91 51 Physical Sciences 80022\n", + "0 38.81 44 Human Society 80015\n", + "0 44.68 35 Commerce, Management, Tourism and Services 80006\n", + "0 34.54 49 Mathematical Sciences 80020\n", + "0 22.36 30 Agricultural, Veterinary and Food Sciences 80001\n", + "0 23.26 37 Earth Sciences 80008\n", + "0 39.98 47 Language, Communication and Culture 80018\n", + "0 33.78 52 Psychology 80023\n", + "0 39.12 50 Philosophy and Religious Studies 80021\n", + "0 35.81 39 Education 80010\n", + "0 29.06 41 Environmental Sciences 80012\n", + "0 37.09 48 Law and Legal Studies 80019\n", + "0 48.26 38 Economics 80009\n", + "0 31.37 43 History, Heritage and Archaeology 80014\n", + "0 38.82 33 Built Environment and Design 80004\n", + "0 37.37 36 Creative Arts and Writing 80007" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2437,7 +2440,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "Collapsed": "false", "colab": {}, @@ -2451,7 +2454,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "Collapsed": "false", "colab": {}, @@ -2488,167 +2491,167 @@ " \n", " \n", " 0\n", - " 28\n", - " 11 Medical and Health Sciences\n", - " 2211\n", + " 37\n", + " 32 Biomedical and Clinical Sciences\n", + " 80003\n", " \n", " \n", " 0\n", - " 21\n", - " 09 Engineering\n", - " 2209\n", + " 28\n", + " 40 Engineering\n", + " 80011\n", " \n", " \n", " 0\n", - " 20\n", - " 06 Biological Sciences\n", - " 2206\n", + " 45\n", + " 46 Information and Computing Sciences\n", + " 80017\n", " \n", " \n", " 0\n", - " 35\n", - " 08 Information and Computing Sciences\n", - " 2208\n", + " 28\n", + " 31 Biological Sciences\n", + " 80002\n", " \n", " \n", " 0\n", - 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"0 25 10 Technology 2210\n", - "0 30 20 Language, Communication and Culture 2220\n", - "0 25 13 Education 2213\n", - "0 16 04 Earth Sciences 2204\n", - "0 33 14 Economics 2214\n", - "0 28 21 History and Archaeology 2221\n", - "0 20 05 Environmental Sciences 2205\n", - "0 15 07 Agricultural and Veterinary Sciences 2207\n", - "0 27 22 Philosophy and Religious Studies 2222\n", - "0 27 18 Law and Legal Studies 2218\n", - "0 16 12 Built Environment and Design 2212\n", - "0 27 19 Studies in Creative Arts and Writing 2219" + " field_citation_ratio name id\n", + "0 37 32 Biomedical and Clinical Sciences 80003\n", + "0 28 40 Engineering 80011\n", + "0 45 46 Information and Computing Sciences 80017\n", + "0 28 31 Biological Sciences 80002\n", + "0 33 42 Health Sciences 80013\n", + "0 25 34 Chemical Sciences 80005\n", + "0 38 51 Physical Sciences 80022\n", + "0 38 44 Human Society 80015\n", + "0 44 35 Commerce, Management, Tourism and Services 80006\n", + "0 34 49 Mathematical Sciences 80020\n", + "0 22 30 Agricultural, Veterinary and Food Sciences 80001\n", + "0 23 37 Earth Sciences 80008\n", + "0 39 47 Language, Communication and Culture 80018\n", + "0 33 52 Psychology 80023\n", + "0 39 50 Philosophy and Religious Studies 80021\n", + "0 35 39 Education 80010\n", + "0 29 41 Environmental Sciences 80012\n", + "0 37 48 Law and Legal Studies 80019\n", + "0 48 38 Economics 80009\n", + "0 31 43 History, Heritage and Archaeology 80014\n", + "0 38 33 Built Environment and Design 80004\n", + "0 37 36 Creative Arts and Writing 80007" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2670,7 +2673,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "Collapsed": "false", "colab": {}, @@ -2683,49 +2686,49 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.21s\u001b[0m\n", + "\u001b[2mTime: 6.15s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.09s\u001b[0m\n", + "\u001b[2mTime: 1.83s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 3.14s\u001b[0m\n", + "\u001b[2mTime: 5.51s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.30s\u001b[0m\n", + "\u001b[2mTime: 5.82s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.96s\u001b[0m\n", + "\u001b[2mTime: 1.56s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.13s\u001b[0m\n", + "\u001b[2mTime: 6.63s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.06s\u001b[0m\n", + "\u001b[2mTime: 1.82s\u001b[0m\n", "Returned Research_orgs: 1000\n", - 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"execution_count": 26, + "execution_count": 25, "metadata": { "Collapsed": "false", "colab": {}, @@ -2803,7 +2806,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": { "Collapsed": "false", "colab": {}, @@ -2838,109 +2841,114 @@ " \n", " \n", " \n", - " 21\n", - " 11 Medical and Health Sciences\n", - " 22.0\n", + " 15\n", + " 32 Biomedical and Clinical Sciences\n", + " 16.0\n", + " \n", + " \n", + " 173\n", + " 40 Engineering\n", + " 177.0\n", " \n", " \n", - " 103\n", - " 09 Engineering\n", - " 107.0\n", + " 114\n", + " 46 Information and Computing Sciences\n", + " 121.0\n", " \n", " \n", - " 25\n", - " 06 Biological Sciences\n", - " 26.0\n", + " 18\n", + " 31 Biological Sciences\n", + " 19.0\n", " \n", " \n", - " 99\n", - " 08 Information and Computing Sciences\n", - " 105.0\n", + " 12\n", + " 42 Health Sciences\n", + " 12.5\n", " \n", " \n", - " 161\n", - " 03 Chemical Sciences\n", - " 170.5\n", + " 192\n", + " 34 Chemical Sciences\n", + " 212.0\n", " \n", " \n", " 142\n", - " 02 Physical Sciences\n", - " 150.0\n", + " 51 Physical Sciences\n", + " 148.0\n", " \n", " \n", - " 45\n", - " 01 Mathematical Sciences\n", - " 48.5\n", + " 10\n", + " 44 Human Society\n", + " 12.0\n", " \n", " \n", - " 9\n", - " 17 Psychology and Cognitive Sciences\n", - " 11.5\n", + " 81\n", + " 35 Commerce, Management, Tourism and Services\n", + " 90.5\n", " \n", " \n", - " 32\n", - " 16 Studies in Human Society\n", - " 36.0\n", + " 125\n", + " 49 Mathematical Sciences\n", + " 142.0\n", " \n", " \n", - " 66\n", - " 15 Commerce, Management, Tourism and Services\n", - " 88.0\n", + " 21\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 23.5\n", " \n", " \n", - " 35\n", - " 20 Language, Communication and Culture\n", - " 46.0\n", + " 96\n", + " 37 Earth Sciences\n", + " 108.5\n", " \n", " \n", - " 17\n", - " 13 Education\n", - " 22.5\n", + " 10\n", + " 47 Language, Communication and Culture\n", + " 12.0\n", " \n", " \n", - " 196\n", - " 04 Earth Sciences\n", - " 230.0\n", + " 6\n", + " 52 Psychology\n", + " 7.5\n", " \n", " \n", - " 83\n", - " 14 Economics\n", - " 110.0\n", + " 80\n", + " 50 Philosophy and Religious Studies\n", + " 106.5\n", " \n", " \n", - " 263\n", - " 21 History and Archaeology\n", - " 579.5\n", + " 8\n", + " 39 Education\n", + " 10.0\n", " \n", " \n", - " 30\n", - " 05 Environmental Sciences\n", - " 34.5\n", + " 19\n", + " 41 Environmental Sciences\n", + " 21.5\n", " \n", " \n", - " 22\n", - " 07 Agricultural and Veterinary Sciences\n", - " 26.0\n", + " 13\n", + " 48 Law and Legal Studies\n", + " 18.5\n", " \n", " \n", - " 133\n", - " 22 Philosophy and Religious Studies\n", - " 304.0\n", + " 69\n", + " 38 Economics\n", + " 87.5\n", " \n", " \n", - " 23\n", - " 18 Law and Legal Studies\n", - " 37.0\n", + " 51\n", + " 43 History, Heritage and Archaeology\n", + " 65.5\n", " \n", " \n", - " 20\n", - " 12 Built Environment and Design\n", - " 32.5\n", + " 18\n", + " 33 Built Environment and Design\n", + " 21.0\n", " \n", " \n", - " 0\n", - " 19 Studies in Creative Arts and Writing\n", - " 1.0\n", + " 6\n", + " 36 Creative Arts and Writing\n", + " 10.0\n", " \n", " \n", "\n", @@ -2948,30 +2956,31 @@ ], "text/plain": [ " for_name rank\n", - "21 11 Medical and Health Sciences 22.0\n", - "103 09 Engineering 107.0\n", - "25 06 Biological Sciences 26.0\n", - "99 08 Information and Computing Sciences 105.0\n", - "161 03 Chemical Sciences 170.5\n", - "142 02 Physical Sciences 150.0\n", - "45 01 Mathematical Sciences 48.5\n", - "9 17 Psychology and Cognitive Sciences 11.5\n", - "32 16 Studies in Human Society 36.0\n", - "66 15 Commerce, Management, Tourism and Services 88.0\n", - "35 20 Language, Communication and Culture 46.0\n", - "17 13 Education 22.5\n", - "196 04 Earth Sciences 230.0\n", - "83 14 Economics 110.0\n", - "263 21 History and Archaeology 579.5\n", - "30 05 Environmental Sciences 34.5\n", - "22 07 Agricultural and Veterinary Sciences 26.0\n", - "133 22 Philosophy and Religious Studies 304.0\n", - "23 18 Law and Legal Studies 37.0\n", - "20 12 Built Environment and Design 32.5\n", - "0 19 Studies in Creative Arts and Writing 1.0" + "15 32 Biomedical and Clinical Sciences 16.0\n", + "173 40 Engineering 177.0\n", + "114 46 Information and Computing Sciences 121.0\n", + "18 31 Biological Sciences 19.0\n", + "12 42 Health Sciences 12.5\n", + "192 34 Chemical Sciences 212.0\n", + "142 51 Physical Sciences 148.0\n", + "10 44 Human Society 12.0\n", + "81 35 Commerce, Management, Tourism and Services 90.5\n", + "125 49 Mathematical Sciences 142.0\n", + "21 30 Agricultural, Veterinary and Food Sciences 23.5\n", + "96 37 Earth Sciences 108.5\n", + "10 47 Language, Communication and Culture 12.0\n", + "6 52 Psychology 7.5\n", + "80 50 Philosophy and Religious Studies 106.5\n", + "8 39 Education 10.0\n", + "19 41 Environmental Sciences 21.5\n", + "13 48 Law and Legal Studies 18.5\n", + "69 38 Economics 87.5\n", + "51 43 History, Heritage and Archaeology 65.5\n", + "18 33 Built Environment and Design 21.0\n", + "6 36 Creative Arts and Writing 10.0" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -3004,7 +3013,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "Collapsed": "false", "colab": {}, @@ -3017,49 +3026,49 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.47s\u001b[0m\n", + "\u001b[2mTime: 4.39s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.12s\u001b[0m\n", + "\u001b[2mTime: 6.05s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.14s\u001b[0m\n", + "\u001b[2mTime: 1.88s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.09s\u001b[0m\n", + "\u001b[2mTime: 1.80s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.97s\u001b[0m\n", + "\u001b[2mTime: 4.84s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.17s\u001b[0m\n", + "\u001b[2mTime: 1.92s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.29s\u001b[0m\n", + "\u001b[2mTime: 3.19s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.11s\u001b[0m\n", + "\u001b[2mTime: 3.04s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.00s\u001b[0m\n", + "\u001b[2mTime: 3.34s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.02s\u001b[0m\n", + "\u001b[2mTime: 2.38s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 5.36s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 1.38s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.03s\u001b[0m\n", + "\u001b[2mTime: 1.30s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 6.38s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 1.41s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.12s\u001b[0m\n", + "\u001b[2mTime: 4.51s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.14s\u001b[0m\n", + "\u001b[2mTime: 1.98s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.15s\u001b[0m\n", + "\u001b[2mTime: 1.47s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.15s\u001b[0m\n", + "\u001b[2mTime: 6.03s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.10s\u001b[0m\n", + "\u001b[2mTime: 1.30s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.97s\u001b[0m\n", + "\u001b[2mTime: 4.47s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.96s\u001b[0m\n" + "\u001b[2mTime: 1.25s\u001b[0m\n" ] } ], @@ -3086,7 +3095,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "Collapsed": "false", "colab": {}, @@ -3100,7 +3109,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "Collapsed": "false", "colab": {}, @@ -3114,7 +3123,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { "Collapsed": "false", "colab": {}, @@ -3152,38 +3161,38 @@ " \n", " \n", " 0\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " Harvard University\n", - " 845\n", - " 16932\n", + " 767\n", + " 15967\n", " \n", " \n", " 1\n", - " 11 Medical and Health Sciences\n", - " University of Toronto\n", - " 392\n", - " 10281\n", + " 32 Biomedical and Clinical Sciences\n", + " Johns Hopkins University\n", + " 356\n", + " 9182\n", " \n", " \n", " 2\n", - " 11 Medical and Health Sciences\n", - " Johns Hopkins University\n", - " 391\n", - " 10120\n", + " 32 Biomedical and Clinical Sciences\n", + " University of Toronto\n", + " 338\n", + " 8932\n", " \n", " \n", " 3\n", - " 11 Medical and Health Sciences\n", - " University of California, San Francisco\n", - " 365\n", - " 7850\n", + " 32 Biomedical and Clinical Sciences\n", + " Mayo Clinic\n", + " 380\n", + " 8507\n", " \n", " \n", " 4\n", - " 11 Medical and Health Sciences\n", - " Mayo Clinic\n", - " 321\n", - " 7659\n", + " 32 Biomedical and Clinical Sciences\n", + " University of California, San Francisco\n", + " 339\n", + " 7477\n", " \n", " \n", " ...\n", @@ -3193,76 +3202,76 @@ " ...\n", " \n", " \n", - " 12220\n", - " 19 Studies in Creative Arts and Writing\n", - " University of Bamberg\n", - " 1\n", + " 13171\n", + " 36 Creative Arts and Writing\n", + " Adobe Inc\n", " 3\n", + " 7\n", " \n", " \n", - " 12221\n", - " 19 Studies in Creative Arts and Writing\n", - " National University of Quilmes\n", + " 13172\n", + " 36 Creative Arts and Writing\n", + " Polytechnic University of Turin\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", - " 12222\n", - " 19 Studies in Creative Arts and Writing\n", - " Czech University of Life Sciences Prague\n", + " 13173\n", + " 36 Creative Arts and Writing\n", + " University of Electronic Science and Technolog...\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", - " 12223\n", - " 19 Studies in Creative Arts and Writing\n", - " University Hospitals of Cleveland\n", + " 13174\n", + " 36 Creative Arts and Writing\n", + " University of Cyprus\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", - " 12224\n", - " 19 Studies in Creative Arts and Writing\n", - " Grinnell College\n", + " 13175\n", + " 36 Creative Arts and Writing\n", + " Broad Institute\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", "\n", - "

12225 rows × 4 columns

\n", + "

13176 rows × 4 columns

\n", "" ], "text/plain": [ - " for_name \\\n", - "0 11 Medical and Health Sciences \n", - "1 11 Medical and Health Sciences \n", - "2 11 Medical and Health Sciences \n", - "3 11 Medical and Health Sciences \n", - "4 11 Medical and Health Sciences \n", - "... ... \n", - "12220 19 Studies in Creative Arts and Writing \n", - "12221 19 Studies in Creative Arts and Writing \n", - "12222 19 Studies in Creative Arts and Writing \n", - "12223 19 Studies in Creative Arts and Writing \n", - "12224 19 Studies in Creative Arts and Writing \n", + " for_name \\\n", + "0 32 Biomedical and Clinical Sciences \n", + "1 32 Biomedical and Clinical Sciences \n", + "2 32 Biomedical and Clinical Sciences \n", + "3 32 Biomedical and Clinical Sciences \n", + "4 32 Biomedical and Clinical Sciences \n", + "... ... \n", + "13171 36 Creative Arts and Writing \n", + "13172 36 Creative Arts and Writing \n", + "13173 36 Creative Arts and Writing \n", + "13174 36 Creative Arts and Writing \n", + "13175 36 Creative Arts and Writing \n", "\n", - " name count count all \n", - "0 Harvard University 845 16932 \n", - "1 University of Toronto 392 10281 \n", - "2 Johns Hopkins University 391 10120 \n", - "3 University of California, San Francisco 365 7850 \n", - "4 Mayo Clinic 321 7659 \n", - "... ... ... ... \n", - "12220 University of Bamberg 1 3 \n", - "12221 National University of Quilmes 1 2 \n", - "12222 Czech University of Life Sciences Prague 1 2 \n", - "12223 University Hospitals of Cleveland 1 2 \n", - "12224 Grinnell College 1 2 \n", + " name count count all \n", + "0 Harvard University 767 15967 \n", + "1 Johns Hopkins University 356 9182 \n", + "2 University of Toronto 338 8932 \n", + "3 Mayo Clinic 380 8507 \n", + "4 University of California, San Francisco 339 7477 \n", + "... ... ... ... \n", + "13171 Adobe Inc 3 7 \n", + "13172 Polytechnic University of Turin 1 7 \n", + "13173 University of Electronic Science and Technolog... 1 7 \n", + "13174 University of Cyprus 1 7 \n", + "13175 Broad Institute 1 7 \n", "\n", - "[12225 rows x 4 columns]" + "[13176 rows x 4 columns]" ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -3284,7 +3293,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "Collapsed": "false", "colab": {}, @@ -3298,7 +3307,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "Collapsed": "false", "colab": {}, @@ -3323,7 +3332,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "Collapsed": "false", "colab": {}, @@ -3358,109 +3367,114 @@ " \n", " \n", " \n", - " 66\n", - " 11 Medical and Health Sciences\n", - " 41.0\n", + " 73\n", + " 32 Biomedical and Clinical Sciences\n", + " 54.5\n", + " \n", + " \n", + " 842\n", + " 40 Engineering\n", + " 205.0\n", " \n", " \n", - " 840\n", - " 09 Engineering\n", - " 138.0\n", + " 1592\n", + " 46 Information and Computing Sciences\n", + " 191.5\n", " \n", " \n", - " 1498\n", - " 06 Biological Sciences\n", - " 100.0\n", + " 2167\n", + " 31 Biological Sciences\n", + " 71.0\n", " \n", " \n", - " 2294\n", - " 08 Information and Computing Sciences\n", - " 475.5\n", + " 2916\n", + " 42 Health Sciences\n", + " 31.0\n", " \n", " \n", - " 2875\n", - " 03 Chemical Sciences\n", - " 93.5\n", + " 3577\n", + " 34 Chemical Sciences\n", + " 47.0\n", " \n", " \n", - " 3512\n", - " 02 Physical Sciences\n", - " 278.5\n", + " 4211\n", + " 51 Physical Sciences\n", + " 315.0\n", " \n", " \n", - " 4277\n", - " 01 Mathematical Sciences\n", - " 117.0\n", + " 4992\n", + " 44 Human Society\n", + " 115.5\n", " \n", " \n", - " 4921\n", - " 17 Psychology and Cognitive Sciences\n", - " 52.5\n", + " 5642\n", + " 35 Commerce, Management, Tourism and Services\n", + " 241.0\n", " \n", " \n", - " 5584\n", - " 16 Studies in Human Society\n", - " 236.5\n", + " 6253\n", + " 49 Mathematical Sciences\n", + " 87.0\n", " \n", " \n", - " 6165\n", - " 15 Commerce, Management, Tourism and Services\n", - " 150.0\n", + " 7112\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 57.0\n", " \n", " \n", - " 6864\n", - " 10 Technology\n", - " 304.0\n", + " 7557\n", + " 37 Earth Sciences\n", + " 285.5\n", " \n", " \n", - " 7312\n", - " 20 Language, Communication and Culture\n", - " 398.5\n", + " 8174\n", + " 47 Language, Communication and Culture\n", + " 422.0\n", " \n", " \n", - " 7785\n", - " 13 Education\n", - " 377.0\n", + " 8696\n", + " 52 Psychology\n", + " 201.0\n", " \n", " \n", - " 8302\n", - " 04 Earth Sciences\n", - " 346.5\n", + " 9362\n", + " 50 Philosophy and Religious Studies\n", + " 422.0\n", " \n", " \n", - " 8940\n", - " 14 Economics\n", - " 310.5\n", + " 9857\n", + " 39 Education\n", + " 255.0\n", " \n", " \n", - " 9467\n", - " 21 History and Archaeology\n", - " 305.0\n", + " 10448\n", + " 41 Environmental Sciences\n", + " 190.0\n", " \n", " \n", - " 10013\n", - " 05 Environmental Sciences\n", - " 123.0\n", + " 11006\n", + " 48 Law and Legal Studies\n", + " 291.5\n", " \n", " \n", - " 10741\n", - " 07 Agricultural and Veterinary Sciences\n", - " 124.5\n", + " 11405\n", + " 38 Economics\n", + " 301.0\n", " \n", " \n", - " 11176\n", - " 22 Philosophy and Religious Studies\n", - " 311.5\n", + " 11884\n", + " 43 History, Heritage and Archaeology\n", + " 255.0\n", " \n", " \n", - " 11503\n", - " 18 Law and Legal Studies\n", - " 196.0\n", + " 12409\n", + " 33 Built Environment and Design\n", + " 428.0\n", " \n", " \n", - " 11823\n", - " 12 Built Environment and Design\n", - " 181.0\n", + " 12841\n", + " 36 Creative Arts and Writing\n", + " 285.0\n", " \n", " \n", "\n", @@ -3468,30 +3482,31 @@ ], "text/plain": [ " for_name percent rank\n", - "66 11 Medical and Health Sciences 41.0\n", - "840 09 Engineering 138.0\n", - "1498 06 Biological Sciences 100.0\n", - "2294 08 Information and Computing Sciences 475.5\n", - "2875 03 Chemical Sciences 93.5\n", - "3512 02 Physical Sciences 278.5\n", - "4277 01 Mathematical Sciences 117.0\n", - "4921 17 Psychology and Cognitive Sciences 52.5\n", - "5584 16 Studies in Human Society 236.5\n", - "6165 15 Commerce, Management, Tourism and Services 150.0\n", - "6864 10 Technology 304.0\n", - "7312 20 Language, Communication and Culture 398.5\n", - "7785 13 Education 377.0\n", - "8302 04 Earth Sciences 346.5\n", - "8940 14 Economics 310.5\n", - "9467 21 History and Archaeology 305.0\n", - "10013 05 Environmental Sciences 123.0\n", - "10741 07 Agricultural and Veterinary Sciences 124.5\n", - "11176 22 Philosophy and Religious Studies 311.5\n", - "11503 18 Law and Legal Studies 196.0\n", - "11823 12 Built Environment and Design 181.0" + "73 32 Biomedical and Clinical Sciences 54.5\n", + "842 40 Engineering 205.0\n", + "1592 46 Information and Computing Sciences 191.5\n", + "2167 31 Biological Sciences 71.0\n", + "2916 42 Health Sciences 31.0\n", + "3577 34 Chemical Sciences 47.0\n", + "4211 51 Physical Sciences 315.0\n", + "4992 44 Human Society 115.5\n", + "5642 35 Commerce, Management, Tourism and Services 241.0\n", + "6253 49 Mathematical Sciences 87.0\n", + "7112 30 Agricultural, Veterinary and Food Sciences 57.0\n", + "7557 37 Earth Sciences 285.5\n", + "8174 47 Language, Communication and Culture 422.0\n", + "8696 52 Psychology 201.0\n", + "9362 50 Philosophy and Religious Studies 422.0\n", + "9857 39 Education 255.0\n", + "10448 41 Environmental Sciences 190.0\n", + "11006 48 Law and Legal Studies 291.5\n", + "11405 38 Economics 301.0\n", + "11884 43 History, Heritage and Archaeology 255.0\n", + "12409 33 Built Environment and Design 428.0\n", + "12841 36 Creative Arts and Writing 285.0" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -3502,7 +3517,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": { "Collapsed": "false", "colab": {}, @@ -3536,84 +3551,17 @@ " \n", " \n", " \n", - " \n", - " 0\n", - " Harvard University\n", - " 61.5\n", - " \n", - " \n", - " 1\n", - " University of Toronto\n", - " 236.5\n", - " \n", - " \n", - " 2\n", - " Johns Hopkins University\n", - " 220.0\n", - " \n", - " \n", - " 3\n", - " University of California, San Francisco\n", - " 93.5\n", - " \n", - " \n", - " 4\n", - " Mayo Clinic\n", - " 152.0\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 780\n", - " University of Bath\n", - " 425.0\n", - " \n", - " \n", - " 781\n", - " Kuopio University Hospital\n", - " 250.5\n", - " \n", - " \n", - " 782\n", - " Marqués de Valdecilla University Hospital\n", - " 299.0\n", - " \n", - " \n", - " 783\n", - " Policlinico San Matteo Fondazione\n", - " 114.5\n", - " \n", - " \n", - " 784\n", - " Centre Hospitalier Universitaire de Caen\n", - " 351.5\n", - " \n", " \n", "\n", - "

785 rows × 2 columns

\n", "" ], "text/plain": [ - " name percent rank\n", - "0 Harvard University 61.5\n", - "1 University of Toronto 236.5\n", - "2 Johns Hopkins University 220.0\n", - "3 University of California, San Francisco 93.5\n", - "4 Mayo Clinic 152.0\n", - ".. ... ...\n", - "780 University of Bath 425.0\n", - "781 Kuopio University Hospital 250.5\n", - "782 Marqués de Valdecilla University Hospital 299.0\n", - "783 Policlinico San Matteo Fondazione 114.5\n", - "784 Centre Hospitalier Universitaire de Caen 351.5\n", - "\n", - "[785 rows x 2 columns]" + "Empty DataFrame\n", + "Columns: [name, percent rank]\n", + "Index: []" ] }, - "execution_count": 35, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -3646,7 +3594,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": { "Collapsed": "false", "colab": {}, @@ -3665,7 +3613,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": { "Collapsed": "false", "colab": {}, @@ -3679,7 +3627,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { "Collapsed": "false", "colab": {}, @@ -3714,13 +3662,14 @@ " reference count all\n", " id\n", " count all\n", + " name\n", " city_name\n", " count\n", - " country_name\n", + " country_code\n", + " ...\n", " latitude\n", " linkout\n", " longitude\n", - " name\n", " state_name\n", " types\n", " acronym\n", @@ -3732,119 +3681,124 @@ " \n", " \n", " \n", - " 15700\n", + " 17756\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", + " 80003\n", + " 4797\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", + " US\n", + " ...\n", " 42.377052\n", " [http://www.harvard.edu/]\n", " -71.116650\n", - " Harvard University\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", - " \n", - " \n", - " 15701\n", - " grid.1008.9\n", - " University of Melbourne\n", - " 2211\n", - " 5457\n", - " grid.17063.33\n", - " 10281\n", - " Toronto\n", - " 392\n", - " Canada\n", - " 43.661667\n", - " [http://www.utoronto.ca/]\n", - " -79.395000\n", - " University of Toronto\n", - " Ontario\n", - " [Education]\n", - " NaN\n", - " 11 Medical and Health Sciences\n", - " 2.0\n", - " 3.81\n", - " 236.5\n", + " 4.80\n", + " 63.5\n", " \n", " \n", - " 15702\n", + " 17757\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", + " 80003\n", + " 4797\n", " grid.21107.35\n", - " 10120\n", + " 9182\n", + " Johns Hopkins University\n", " Baltimore\n", - " 391\n", - " United States\n", + " 356\n", + " US\n", + " ...\n", " 39.328888\n", " [https://www.jhu.edu/]\n", " -76.620280\n", - " Johns Hopkins University\n", " Maryland\n", " [Education]\n", " JHU\n", - " 11 Medical and Health Sciences\n", - " 3.0\n", - " 3.86\n", - " 220.0\n", + " 32 Biomedical and Clinical Sciences\n", + " 4.0\n", + " 3.88\n", + " 195.5\n", " \n", " \n", - " 15703\n", + " 17758\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", - " grid.266102.1\n", - " 7850\n", - " San Francisco\n", - " 365\n", - " United States\n", - " 37.762800\n", - " [https://www.ucsf.edu/]\n", - " -122.457670\n", - " University of California, San Francisco\n", - " California\n", + " 80003\n", + " 4797\n", + " grid.17063.33\n", + " 8932\n", + " University of Toronto\n", + " Toronto\n", + " 338\n", + " CA\n", + " ...\n", + " 43.661667\n", + " [http://www.utoronto.ca/]\n", + " -79.395000\n", + " Ontario\n", " [Education]\n", - " UCSF\n", - " 11 Medical and Health Sciences\n", - " 6.0\n", - " 4.65\n", - " 93.5\n", + " NaN\n", + " 32 Biomedical and Clinical Sciences\n", + " 8.0\n", + " 3.78\n", + " 220.5\n", " \n", " \n", - " 15704\n", + " 17759\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", + " 80003\n", + " 4797\n", " grid.66875.3a\n", - " 7659\n", + " 8507\n", + " Mayo Clinic\n", " Rochester\n", - " 321\n", - " United States\n", + " 380\n", + " US\n", + " ...\n", " 44.024070\n", " [http://www.mayoclinic.org/patient-visitor-gui...\n", " -92.466310\n", - " Mayo Clinic\n", " Minnesota\n", " [Healthcare]\n", " NaN\n", - " 11 Medical and Health Sciences\n", - " 10.0\n", - " 4.19\n", - " 152.0\n", + " 32 Biomedical and Clinical Sciences\n", + " 3.0\n", + " 4.47\n", + " 96.5\n", + " \n", + " \n", + " 17760\n", + " grid.1008.9\n", + " University of Melbourne\n", + " 80003\n", + " 4797\n", + " grid.266102.1\n", + " 7477\n", + " University of California, San Francisco\n", + " San Francisco\n", + " 339\n", + " US\n", + " ...\n", + " 37.762800\n", + " [https://www.ucsf.edu/]\n", + " -122.457670\n", + " California\n", + " [Education]\n", + " UCSF\n", + " 32 Biomedical and Clinical Sciences\n", + " 6.5\n", + " 4.53\n", + " 89.0\n", " \n", " \n", " ...\n", @@ -3868,210 +3822,242 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", - " 7350128\n", + " 8082796\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.7359.8\n", + " 80007\n", + " 126\n", + " grid.467212.4\n", + " 7\n", + " Adobe Inc\n", + " San Jose\n", " 3\n", - " Bamberg\n", - " 1\n", - " Germany\n", - " 49.893845\n", - " [https://www.uni-bamberg.de/]\n", - " 10.886044\n", - " University of Bamberg\n", + " US\n", + " ...\n", " NaN\n", - " [Education]\n", + " [https://www.adobe.com/]\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 33.33\n", - " 8.0\n", + " California\n", + " [Company]\n", + " NaN\n", + " 36 Creative Arts and Writing\n", + " 59.0\n", + " 42.86\n", + " 1.0\n", " \n", " \n", - " 7350129\n", + " 8082797\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.11560.33\n", - " 2\n", - " Bernal\n", + " 80007\n", + " 126\n", + " grid.4800.c\n", + " 7\n", + " Polytechnic University of Turin\n", + " Turin\n", " 1\n", - " Argentina\n", - " -34.706670\n", - " [http://www.unq.edu.ar/english/sections/158-unq/]\n", - " -58.277500\n", - " National University of Quilmes\n", - " NaN\n", + " IT\n", + " ...\n", + " 45.063095\n", + " [http://www.polito.it/]\n", + " 7.661075\n", + " Piemonte\n", " [Education]\n", - " UNQ\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " NaN\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", - " 7350130\n", + " 8082798\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.15866.3c\n", - " 2\n", - " Prague\n", + " 80007\n", + " 126\n", + " grid.54549.39\n", + " 7\n", + " University of Electronic Science and Technolog...\n", + " Chengdu\n", " 1\n", - " Czechia\n", - " 50.131460\n", - " [http://www.czu.cz/en/]\n", - " 14.373258\n", - " Czech University of Life Sciences Prague\n", + " CN\n", + " ...\n", + " 30.675713\n", + " [http://en.uestc.edu.cn/]\n", + " 104.100270\n", " NaN\n", " [Education]\n", - " CULS\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " UESTC\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", - " 7350131\n", + " 8082799\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.241104.2\n", - " 2\n", - " Cleveland\n", + " 80007\n", + " 126\n", + " grid.6603.3\n", + " 7\n", + " University of Cyprus\n", + " Nicosia\n", " 1\n", - " United States\n", - " 41.506096\n", - " [http://www.uhhospitals.org/]\n", - " -81.604820\n", - " University Hospitals of Cleveland\n", - " Ohio\n", - " [Healthcare]\n", + " CY\n", + " ...\n", + " 35.160270\n", + " [http://www.ucy.ac.cy/en/]\n", + " 33.376976\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " [Education]\n", + " UCY\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", - " 7350132\n", + " 8082800\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.256592.f\n", - " 2\n", - " Grinnell\n", + " 80007\n", + " 126\n", + " grid.66859.34\n", + " 7\n", + " Broad Institute\n", + " Cambridge\n", " 1\n", - " United States\n", - " 41.749737\n", - " [http://www.grinnell.edu/]\n", - " -92.719505\n", - " Grinnell College\n", - " Iowa\n", - " [Education]\n", + " US\n", + " ...\n", + " 42.367890\n", + " [http://www.broadinstitute.org/]\n", + " -71.087030\n", + " Massachusetts\n", + " [Nonprofit]\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", "\n", - "

11685 rows × 20 columns

\n", + "

13176 rows × 21 columns

\n", "" ], "text/plain": [ " reference id reference name for_id reference count all \\\n", - "15700 grid.1008.9 University of Melbourne 2211 5457 \n", - "15701 grid.1008.9 University of Melbourne 2211 5457 \n", - "15702 grid.1008.9 University of Melbourne 2211 5457 \n", - "15703 grid.1008.9 University of Melbourne 2211 5457 \n", - "15704 grid.1008.9 University of Melbourne 2211 5457 \n", + "17756 grid.1008.9 University of Melbourne 80003 4797 \n", + "17757 grid.1008.9 University of Melbourne 80003 4797 \n", + "17758 grid.1008.9 University of Melbourne 80003 4797 \n", + "17759 grid.1008.9 University of Melbourne 80003 4797 \n", + "17760 grid.1008.9 University of Melbourne 80003 4797 \n", "... ... ... ... ... \n", - "7350128 grid.1008.9 University of Melbourne 2219 85 \n", - "7350129 grid.1008.9 University of Melbourne 2219 85 \n", - "7350130 grid.1008.9 University of Melbourne 2219 85 \n", - "7350131 grid.1008.9 University of Melbourne 2219 85 \n", - "7350132 grid.1008.9 University of Melbourne 2219 85 \n", + "8082796 grid.1008.9 University of Melbourne 80007 126 \n", + "8082797 grid.1008.9 University of Melbourne 80007 126 \n", + "8082798 grid.1008.9 University of Melbourne 80007 126 \n", + "8082799 grid.1008.9 University of Melbourne 80007 126 \n", + "8082800 grid.1008.9 University of Melbourne 80007 126 \n", "\n", - " id count all city_name count country_name \\\n", - "15700 grid.38142.3c 16932 Cambridge 845 United States \n", - "15701 grid.17063.33 10281 Toronto 392 Canada \n", - "15702 grid.21107.35 10120 Baltimore 391 United States \n", - "15703 grid.266102.1 7850 San Francisco 365 United States \n", - "15704 grid.66875.3a 7659 Rochester 321 United States \n", - "... ... ... ... ... ... \n", - "7350128 grid.7359.8 3 Bamberg 1 Germany \n", - "7350129 grid.11560.33 2 Bernal 1 Argentina \n", - "7350130 grid.15866.3c 2 Prague 1 Czechia \n", - "7350131 grid.241104.2 2 Cleveland 1 United States \n", - "7350132 grid.256592.f 2 Grinnell 1 United States \n", + " id count all \\\n", + "17756 grid.38142.3c 15967 \n", + "17757 grid.21107.35 9182 \n", + "17758 grid.17063.33 8932 \n", + "17759 grid.66875.3a 8507 \n", + "17760 grid.266102.1 7477 \n", + "... ... ... \n", + "8082796 grid.467212.4 7 \n", + "8082797 grid.4800.c 7 \n", + "8082798 grid.54549.39 7 \n", + "8082799 grid.6603.3 7 \n", + "8082800 grid.66859.34 7 \n", "\n", - " latitude linkout \\\n", - "15700 42.377052 [http://www.harvard.edu/] \n", - "15701 43.661667 [http://www.utoronto.ca/] \n", - "15702 39.328888 [https://www.jhu.edu/] \n", - "15703 37.762800 [https://www.ucsf.edu/] \n", - "15704 44.024070 [http://www.mayoclinic.org/patient-visitor-gui... \n", - "... ... ... \n", - "7350128 49.893845 [https://www.uni-bamberg.de/] \n", - "7350129 -34.706670 [http://www.unq.edu.ar/english/sections/158-unq/] \n", - "7350130 50.131460 [http://www.czu.cz/en/] \n", - "7350131 41.506096 [http://www.uhhospitals.org/] \n", - "7350132 41.749737 [http://www.grinnell.edu/] \n", + " name city_name \\\n", + "17756 Harvard University Cambridge \n", + "17757 Johns Hopkins University Baltimore \n", + "17758 University of Toronto Toronto \n", + "17759 Mayo Clinic Rochester \n", + "17760 University of California, San Francisco San Francisco \n", + "... ... ... \n", + "8082796 Adobe Inc San Jose \n", + "8082797 Polytechnic University of Turin Turin \n", + "8082798 University of Electronic Science and Technolog... Chengdu \n", + "8082799 University of Cyprus Nicosia \n", + "8082800 Broad Institute Cambridge \n", + "\n", + " count country_code ... latitude \\\n", + "17756 767 US ... 42.377052 \n", + "17757 356 US ... 39.328888 \n", + "17758 338 CA ... 43.661667 \n", + "17759 380 US ... 44.024070 \n", + "17760 339 US ... 37.762800 \n", + "... ... ... ... ... \n", + "8082796 3 US ... NaN \n", + "8082797 1 IT ... 45.063095 \n", + "8082798 1 CN ... 30.675713 \n", + "8082799 1 CY ... 35.160270 \n", + "8082800 1 US ... 42.367890 \n", "\n", - " longitude name state_name \\\n", - "15700 -71.116650 Harvard University Massachusetts \n", - "15701 -79.395000 University of Toronto Ontario \n", - "15702 -76.620280 Johns Hopkins University Maryland \n", - "15703 -122.457670 University of California, San Francisco California \n", - "15704 -92.466310 Mayo Clinic Minnesota \n", - "... ... ... ... \n", - "7350128 10.886044 University of Bamberg NaN \n", - "7350129 -58.277500 National University of Quilmes NaN \n", - "7350130 14.373258 Czech University of Life Sciences Prague NaN \n", - "7350131 -81.604820 University Hospitals of Cleveland Ohio \n", - "7350132 -92.719505 Grinnell College Iowa \n", + " linkout longitude \\\n", + "17756 [http://www.harvard.edu/] -71.116650 \n", + "17757 [https://www.jhu.edu/] -76.620280 \n", + "17758 [http://www.utoronto.ca/] -79.395000 \n", + "17759 [http://www.mayoclinic.org/patient-visitor-gui... -92.466310 \n", + "17760 [https://www.ucsf.edu/] -122.457670 \n", + "... ... ... \n", + "8082796 [https://www.adobe.com/] NaN \n", + "8082797 [http://www.polito.it/] 7.661075 \n", + "8082798 [http://en.uestc.edu.cn/] 104.100270 \n", + "8082799 [http://www.ucy.ac.cy/en/] 33.376976 \n", + "8082800 [http://www.broadinstitute.org/] -71.087030 \n", "\n", - " types acronym for_name rank \\\n", - "15700 [Education] NaN 11 Medical and Health Sciences 1.0 \n", - "15701 [Education] NaN 11 Medical and Health Sciences 2.0 \n", - "15702 [Education] JHU 11 Medical and Health Sciences 3.0 \n", - "15703 [Education] UCSF 11 Medical and Health Sciences 6.0 \n", - "15704 [Healthcare] NaN 11 Medical and Health Sciences 10.0 \n", - "... ... ... ... ... \n", - "7350128 [Education] NaN 19 Studies in Creative Arts and Writing 130.0 \n", - "7350129 [Education] UNQ 19 Studies in Creative Arts and Writing 130.0 \n", - "7350130 [Education] CULS 19 Studies in Creative Arts and Writing 130.0 \n", - "7350131 [Healthcare] NaN 19 Studies in Creative Arts and Writing 130.0 \n", - "7350132 [Education] NaN 19 Studies in Creative Arts and Writing 130.0 \n", + " state_name types acronym \\\n", + "17756 Massachusetts [Education] NaN \n", + "17757 Maryland [Education] JHU \n", + "17758 Ontario [Education] NaN \n", + "17759 Minnesota [Healthcare] NaN \n", + "17760 California [Education] UCSF \n", + "... ... ... ... \n", + "8082796 California [Company] NaN \n", + "8082797 Piemonte [Education] NaN \n", + "8082798 NaN [Education] UESTC \n", + "8082799 NaN [Education] UCY \n", + "8082800 Massachusetts [Nonprofit] NaN \n", "\n", - " percentage top 1 percent rank \n", - "15700 4.99 61.5 \n", - "15701 3.81 236.5 \n", - "15702 3.86 220.0 \n", - "15703 4.65 93.5 \n", - "15704 4.19 152.0 \n", - "... ... ... \n", - "7350128 33.33 8.0 \n", - "7350129 50.00 2.5 \n", - "7350130 50.00 2.5 \n", - "7350131 50.00 2.5 \n", - "7350132 50.00 2.5 \n", + " for_name rank percentage top 1 \\\n", + "17756 32 Biomedical and Clinical Sciences 1.0 4.80 \n", + "17757 32 Biomedical and Clinical Sciences 4.0 3.88 \n", + "17758 32 Biomedical and Clinical Sciences 8.0 3.78 \n", + "17759 32 Biomedical and Clinical Sciences 3.0 4.47 \n", + "17760 32 Biomedical and Clinical Sciences 6.5 4.53 \n", + "... ... ... ... \n", + "8082796 36 Creative Arts and Writing 59.0 42.86 \n", + "8082797 36 Creative Arts and Writing 350.5 14.29 \n", + "8082798 36 Creative Arts and Writing 350.5 14.29 \n", + "8082799 36 Creative Arts and Writing 350.5 14.29 \n", + "8082800 36 Creative Arts and Writing 350.5 14.29 \n", "\n", - "[11685 rows x 20 columns]" + " percent rank \n", + "17756 63.5 \n", + "17757 195.5 \n", + "17758 220.5 \n", + "17759 96.5 \n", + "17760 89.0 \n", + "... ... \n", + "8082796 1.0 \n", + "8082797 34.5 \n", + "8082798 34.5 \n", + "8082799 34.5 \n", + "8082800 34.5 \n", + "\n", + "[13176 rows x 21 columns]" ] }, - "execution_count": 38, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -4082,7 +4068,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": { "Collapsed": "false", "colab": {}, @@ -4098,7 +4084,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": { "Collapsed": "false", "colab": {}, @@ -4114,7 +4100,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": { "Collapsed": "false", "colab": {}, @@ -4152,172 +4138,180 @@ " \n", " \n", " \n", - " 15720\n", + " 17779\n", " grid.1008.9\n", - " 2211\n", + " 80003\n", " University of Melbourne\n", - " 11 Medical and Health Sciences\n", - " 15.5\n", + " 32 Biomedical and Clinical Sciences\n", + " 6.0\n", " \n", " \n", - " 738709\n", + " 733648\n", " grid.1008.9\n", - " 2209\n", + " 80011\n", " University of Melbourne\n", - " 09 Engineering\n", - " 40.0\n", + " 40 Engineering\n", + " 85.0\n", " \n", " \n", - " 1131875\n", + " 1168804\n", " grid.1008.9\n", - " 2206\n", + " 80017\n", " University of Melbourne\n", - " 06 Biological Sciences\n", - " 13.0\n", + " 46 Information and Computing Sciences\n", + " 51.0\n", " \n", " \n", - " 1643602\n", + " 1578085\n", " grid.1008.9\n", - " 2208\n", + " 80002\n", " University of Melbourne\n", - " 08 Information and Computing Sciences\n", - " 45.0\n", + " 31 Biological Sciences\n", + " 11.0\n", " \n", " \n", - " 2140491\n", + " 2046557\n", " grid.1008.9\n", - " 2203\n", + " 80013\n", " University of Melbourne\n", - " 03 Chemical Sciences\n", - " 87.5\n", + " 42 Health Sciences\n", + " 5.0\n", " \n", " \n", - " 2627450\n", + " 2644004\n", " grid.1008.9\n", - " 2202\n", + " 80005\n", " University of Melbourne\n", - " 02 Physical Sciences\n", - " 49.0\n", + " 34 Chemical Sciences\n", + " 98.0\n", " \n", " \n", - " 3094798\n", + " 3128285\n", " grid.1008.9\n", - " 2201\n", + " 80022\n", " University of Melbourne\n", - " 01 Mathematical Sciences\n", - " 18.0\n", + " 51 Physical Sciences\n", + " 46.0\n", " \n", " \n", - " 3454060\n", + " 3570941\n", " grid.1008.9\n", - " 2217\n", + " 80015\n", " University of Melbourne\n", - " 17 Psychology and Cognitive Sciences\n", + " 44 Human Society\n", " 4.0\n", " \n", " \n", - " 3909944\n", + " 3958749\n", " grid.1008.9\n", - " 2216\n", + " 80006\n", " University of Melbourne\n", - " 16 Studies in Human Society\n", - " 4.0\n", + " 35 Commerce, Management, Tourism and Services\n", + " 22.0\n", " \n", " \n", - " 4225053\n", + " 4403670\n", " grid.1008.9\n", - " 2215\n", + " 80020\n", " University of Melbourne\n", - " 15 Commerce, Management, Tourism and Services\n", - " 9.0\n", + " 49 Mathematical Sciences\n", + " 37.0\n", " \n", " \n", - " 4915245\n", + " 4832701\n", " grid.1008.9\n", - " 2220\n", + " 80001\n", " University of Melbourne\n", - " 20 Language, Communication and Culture\n", - " 3.0\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 8.0\n", " \n", " \n", - " 5111347\n", + " 5224547\n", " grid.1008.9\n", - " 2213\n", + " 80008\n", " University of Melbourne\n", - " 13 Education\n", - " 8.0\n", + " 37 Earth Sciences\n", + " 44.0\n", " \n", " \n", - " 5429203\n", + " 5606498\n", " grid.1008.9\n", - " 2204\n", + " 80018\n", " University of Melbourne\n", - " 04 Earth Sciences\n", - " 64.0\n", + " 47 Language, Communication and Culture\n", + " 4.0\n", " \n", " \n", - " 5817193\n", + " 5864420\n", " grid.1008.9\n", - " 2214\n", + " 80023\n", " University of Melbourne\n", - " 14 Economics\n", - " 13.0\n", + " 52 Psychology\n", + " 2.0\n", " \n", " \n", - " 6111107\n", + " 6341779\n", " grid.1008.9\n", - " 2221\n", + " 80021\n", " University of Melbourne\n", - " 21 History and Archaeology\n", - " 22.0\n", + " 50 Philosophy and Religious Studies\n", + " 25.0\n", " \n", " \n", - " 6352914\n", + " 6535541\n", " grid.1008.9\n", - " 2205\n", + " 80010\n", " University of Melbourne\n", - " 05 Environmental Sciences\n", - " 6.0\n", + " 39 Education\n", + " 2.0\n", " \n", " \n", - " 6797399\n", + " 6853435\n", " grid.1008.9\n", - " 2207\n", + " 80012\n", " University of Melbourne\n", - " 07 Agricultural and Veterinary Sciences\n", - " 9.0\n", + " 41 Environmental Sciences\n", + " 5.0\n", " \n", " \n", - " 7091867\n", + " 7239785\n", " grid.1008.9\n", - " 2222\n", + " 80019\n", " University of Melbourne\n", - " 22 Philosophy and Religious Studies\n", - " 13.0\n", + " 48 Law and Legal Studies\n", + " 2.5\n", " \n", " \n", - " 7194418\n", + " 7398600\n", " grid.1008.9\n", - " 2218\n", + " 80009\n", " University of Melbourne\n", - " 18 Law and Legal Studies\n", - " 4.0\n", + " 38 Economics\n", + " 24.5\n", " \n", " \n", - " 7281134\n", + " 7643598\n", " grid.1008.9\n", - " 2212\n", + " 80014\n", " University of Melbourne\n", - " 12 Built Environment and Design\n", - " 7.0\n", + " 43 History, Heritage and Archaeology\n", + " 14.0\n", " \n", " \n", - " 7349969\n", + " 7880154\n", " grid.1008.9\n", - " 2219\n", + " 80004\n", " University of Melbourne\n", - " 19 Studies in Creative Arts and Writing\n", - " 1.0\n", + " 33 Built Environment and Design\n", + " 8.0\n", + " \n", + " \n", + " 8082459\n", + " grid.1008.9\n", + " 80007\n", + " University of Melbourne\n", + " 36 Creative Arts and Writing\n", + " 2.0\n", " \n", " \n", "\n", @@ -4325,53 +4319,55 @@ ], "text/plain": [ " id for_id name \\\n", - "15720 grid.1008.9 2211 University of Melbourne \n", - "738709 grid.1008.9 2209 University of Melbourne \n", - "1131875 grid.1008.9 2206 University of Melbourne \n", - "1643602 grid.1008.9 2208 University of Melbourne \n", - "2140491 grid.1008.9 2203 University of Melbourne \n", - "2627450 grid.1008.9 2202 University of Melbourne \n", - "3094798 grid.1008.9 2201 University of Melbourne \n", - "3454060 grid.1008.9 2217 University of Melbourne \n", - "3909944 grid.1008.9 2216 University of Melbourne \n", - "4225053 grid.1008.9 2215 University of Melbourne \n", - "4915245 grid.1008.9 2220 University of Melbourne \n", - "5111347 grid.1008.9 2213 University of Melbourne \n", - "5429203 grid.1008.9 2204 University of Melbourne \n", - "5817193 grid.1008.9 2214 University of Melbourne \n", - "6111107 grid.1008.9 2221 University of Melbourne \n", - "6352914 grid.1008.9 2205 University of Melbourne \n", - "6797399 grid.1008.9 2207 University of Melbourne \n", - "7091867 grid.1008.9 2222 University of Melbourne \n", - "7194418 grid.1008.9 2218 University of Melbourne \n", - "7281134 grid.1008.9 2212 University of Melbourne \n", - "7349969 grid.1008.9 2219 University of Melbourne \n", + "17779 grid.1008.9 80003 University of Melbourne \n", + "733648 grid.1008.9 80011 University of Melbourne \n", + "1168804 grid.1008.9 80017 University of Melbourne \n", + "1578085 grid.1008.9 80002 University of Melbourne \n", + "2046557 grid.1008.9 80013 University of Melbourne \n", + "2644004 grid.1008.9 80005 University of Melbourne \n", + "3128285 grid.1008.9 80022 University of Melbourne \n", + "3570941 grid.1008.9 80015 University of Melbourne \n", + "3958749 grid.1008.9 80006 University of Melbourne \n", + "4403670 grid.1008.9 80020 University of Melbourne \n", + "4832701 grid.1008.9 80001 University of Melbourne \n", + "5224547 grid.1008.9 80008 University of Melbourne \n", + "5606498 grid.1008.9 80018 University of Melbourne \n", + "5864420 grid.1008.9 80023 University of Melbourne \n", + "6341779 grid.1008.9 80021 University of Melbourne \n", + "6535541 grid.1008.9 80010 University of Melbourne \n", + "6853435 grid.1008.9 80012 University of Melbourne \n", + "7239785 grid.1008.9 80019 University of Melbourne \n", + "7398600 grid.1008.9 80009 University of Melbourne \n", + "7643598 grid.1008.9 80014 University of Melbourne \n", + "7880154 grid.1008.9 80004 University of Melbourne \n", + "8082459 grid.1008.9 80007 University of Melbourne \n", "\n", " for_name filtered percent rank \n", - "15720 11 Medical and Health Sciences 15.5 \n", - "738709 09 Engineering 40.0 \n", - "1131875 06 Biological Sciences 13.0 \n", - "1643602 08 Information and Computing Sciences 45.0 \n", - "2140491 03 Chemical Sciences 87.5 \n", - "2627450 02 Physical Sciences 49.0 \n", - "3094798 01 Mathematical Sciences 18.0 \n", - "3454060 17 Psychology and Cognitive Sciences 4.0 \n", - "3909944 16 Studies in Human Society 4.0 \n", - "4225053 15 Commerce, Management, Tourism and Services 9.0 \n", - "4915245 20 Language, Communication and Culture 3.0 \n", - "5111347 13 Education 8.0 \n", - "5429203 04 Earth Sciences 64.0 \n", - "5817193 14 Economics 13.0 \n", - "6111107 21 History and Archaeology 22.0 \n", - "6352914 05 Environmental Sciences 6.0 \n", - "6797399 07 Agricultural and Veterinary Sciences 9.0 \n", - "7091867 22 Philosophy and Religious Studies 13.0 \n", - "7194418 18 Law and Legal Studies 4.0 \n", - "7281134 12 Built Environment and Design 7.0 \n", - "7349969 19 Studies in Creative Arts and Writing 1.0 " + "17779 32 Biomedical and Clinical Sciences 6.0 \n", + "733648 40 Engineering 85.0 \n", + "1168804 46 Information and Computing Sciences 51.0 \n", + "1578085 31 Biological Sciences 11.0 \n", + "2046557 42 Health Sciences 5.0 \n", + "2644004 34 Chemical Sciences 98.0 \n", + "3128285 51 Physical Sciences 46.0 \n", + "3570941 44 Human Society 4.0 \n", + "3958749 35 Commerce, Management, Tourism and Services 22.0 \n", + "4403670 49 Mathematical Sciences 37.0 \n", + "4832701 30 Agricultural, Veterinary and Food Sciences 8.0 \n", + "5224547 37 Earth Sciences 44.0 \n", + "5606498 47 Language, Communication and Culture 4.0 \n", + "5864420 52 Psychology 2.0 \n", + "6341779 50 Philosophy and Religious Studies 25.0 \n", + "6535541 39 Education 2.0 \n", + "6853435 41 Environmental Sciences 5.0 \n", + "7239785 48 Law and Legal Studies 2.5 \n", + "7398600 38 Economics 24.5 \n", + "7643598 43 History, Heritage and Archaeology 14.0 \n", + "7880154 33 Built Environment and Design 8.0 \n", + "8082459 36 Creative Arts and Writing 2.0 " ] }, - "execution_count": 41, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -4412,7 +4408,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": { "Collapsed": "false", "colab": {}, @@ -4432,7 +4428,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": { "Collapsed": "false", "colab": {}, @@ -4446,7 +4442,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": { "Collapsed": "false", "colab": {}, @@ -4460,7 +4456,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": { "Collapsed": "false", "colab": {}, @@ -4494,69 +4490,17 @@ " \n", " \n", " \n", - " \n", - " 515\n", - " University of Michigan\n", - " 24.5\n", - " \n", - " \n", - " 524\n", - " Karolinska Institute\n", - " 24.5\n", - " \n", - " \n", - " 523\n", - " Emory University\n", - " 26.0\n", - " \n", - " \n", - " 528\n", - " University of Pittsburgh\n", - " 27.0\n", - " \n", - " \n", - " 521\n", - " University of Sydney\n", - " 28.0\n", - " \n", - " \n", - " 538\n", - " Monash University\n", - " 29.0\n", - " \n", - " \n", - " 533\n", - " University of British Columbia\n", - " 30.0\n", - " \n", - " \n", - " 520\n", - " University of São Paulo\n", - " 31.0\n", - " \n", - " \n", - " 534\n", - " Shanghai Jiao Tong University\n", - " 32.0\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " name filtered percent rank\n", - "515 University of Michigan 24.5\n", - "524 Karolinska Institute 24.5\n", - "523 Emory University 26.0\n", - "528 University of Pittsburgh 27.0\n", - "521 University of Sydney 28.0\n", - "538 Monash University 29.0\n", - "533 University of British Columbia 30.0\n", - "520 University of São Paulo 31.0\n", - "534 Shanghai Jiao Tong University 32.0" + "Empty DataFrame\n", + "Columns: [name, filtered percent rank]\n", + "Index: []" ] }, - "execution_count": 45, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -4578,7 +4522,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": { "Collapsed": "false", "colab": {}, @@ -4614,11 +4558,11 @@ " reference count all\n", " id\n", " count all\n", + " name\n", " city_name\n", " count\n", - " country_name\n", " ...\n", - " name\n", + " longitude\n", " state_name\n", " types\n", " acronym\n", @@ -4634,122 +4578,122 @@ " \n", " 0\n", " grid.38142.3c\n", - " 2211\n", + " 80003\n", " 1.0\n", " Harvard University\n", - " 16932\n", + " 15967\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", " ...\n", - " Harvard University\n", + " -71.116650\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", + " 4.80\n", + " 63.5\n", " 1.0\n", " 0.0\n", " \n", " \n", " 1\n", - " grid.17063.33\n", - " 2211\n", + " grid.21107.35\n", + " 80003\n", " 2.0\n", - " University of Toronto\n", - " 10281\n", + " Johns Hopkins University\n", + " 9182\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", " ...\n", - " Harvard University\n", + " -71.116650\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", + " 4.80\n", + " 63.5\n", " 1.0\n", " -1.0\n", " \n", " \n", " 2\n", - " grid.17063.33\n", - " 2211\n", + " grid.21107.35\n", + " 80003\n", " 2.0\n", - " University of Toronto\n", - " 10281\n", - " grid.17063.33\n", - " 10281\n", - " Toronto\n", - " 392\n", - " Canada\n", + " Johns Hopkins University\n", + " 9182\n", + " grid.21107.35\n", + " 9182\n", + " Johns Hopkins University\n", + " Baltimore\n", + " 356\n", " ...\n", - " University of Toronto\n", - " Ontario\n", + " -76.620280\n", + " Maryland\n", " [Education]\n", - " NaN\n", - " 11 Medical and Health Sciences\n", - " 2.0\n", - " 3.81\n", - " 236.5\n", + " JHU\n", + " 32 Biomedical and Clinical Sciences\n", + " 4.0\n", + " 3.88\n", + " 195.5\n", " 2.0\n", " 0.0\n", " \n", " \n", " 3\n", - " grid.21107.35\n", - " 2211\n", - " 2.0\n", - " Johns Hopkins University\n", - " 10120\n", + " grid.17063.33\n", + " 80003\n", + " 3.0\n", + " University of Toronto\n", + " 8932\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", " ...\n", - " Harvard University\n", + " -71.116650\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", + " 4.80\n", + " 63.5\n", " 1.0\n", - " -1.0\n", + " -2.0\n", " \n", " \n", " 4\n", + " grid.17063.33\n", + " 80003\n", + " 3.0\n", + " University of Toronto\n", + " 8932\n", " grid.21107.35\n", - " 2211\n", - " 2.0\n", + " 9182\n", " Johns Hopkins University\n", - " 10120\n", - " grid.17063.33\n", - " 10281\n", - " Toronto\n", - " 392\n", - " Canada\n", + " Baltimore\n", + " 356\n", " ...\n", - " University of Toronto\n", - " Ontario\n", + " -76.620280\n", + " Maryland\n", " [Education]\n", - " NaN\n", - " 11 Medical and Health Sciences\n", + " JHU\n", + " 32 Biomedical and Clinical Sciences\n", + " 4.0\n", + " 3.88\n", + " 195.5\n", " 2.0\n", - " 3.81\n", - " 236.5\n", - " 3.0\n", - " 1.0\n", + " -1.0\n", " \n", " \n", " ...\n", @@ -4776,213 +4720,213 @@ " ...\n", " \n", " \n", - " 3721588\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.7359.8\n", + " 4134095\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.467212.4\n", + " 7\n", + " Adobe Inc\n", + " San Jose\n", " 3\n", - " Bamberg\n", - " 1\n", - " Germany\n", " ...\n", - " University of Bamberg\n", " NaN\n", - " [Education]\n", + " California\n", + " [Company]\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 33.33\n", - " 8.0\n", - " 8.0\n", - " 5.5\n", + " 36 Creative Arts and Writing\n", + " 59.0\n", + " 42.86\n", + " 1.0\n", + " 1.0\n", + " -33.5\n", " \n", " \n", - " 3721589\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.11560.33\n", - " 2\n", - " Bernal\n", + " 4134096\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.4800.c\n", + " 7\n", + " Polytechnic University of Turin\n", + " Turin\n", " 1\n", - " Argentina\n", " ...\n", - " National University of Quilmes\n", - " NaN\n", + " 7.661075\n", + " Piemonte\n", " [Education]\n", - " UNQ\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", - " 2.5\n", + " NaN\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", + " 34.5\n", " 0.0\n", " \n", " \n", - " 3721590\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.15866.3c\n", - " 2\n", - " Prague\n", + " 4134097\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.54549.39\n", + " 7\n", + " University of Electronic Science and Technolog...\n", + " Chengdu\n", " 1\n", - " Czechia\n", " ...\n", - " Czech University of Life Sciences Prague\n", + " 104.100270\n", " NaN\n", " [Education]\n", - " CULS\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", - " 2.5\n", + " UESTC\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", + " 34.5\n", " 0.0\n", " \n", " \n", - " 3721591\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.241104.2\n", - " 2\n", - " Cleveland\n", + " 4134098\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.6603.3\n", + " 7\n", + " University of Cyprus\n", + " Nicosia\n", " 1\n", - " United States\n", " ...\n", - " University Hospitals of Cleveland\n", - " Ohio\n", - " [Healthcare]\n", + " 33.376976\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", - " 2.5\n", + " [Education]\n", + " UCY\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", + " 34.5\n", " 0.0\n", " \n", " \n", - " 3721592\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.256592.f\n", - " 2\n", - " Grinnell\n", + " 4134099\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.66859.34\n", + " 7\n", + " Broad Institute\n", + " Cambridge\n", " 1\n", - " United States\n", " ...\n", - " Grinnell College\n", - " Iowa\n", - " [Education]\n", + " -71.087030\n", + " Massachusetts\n", + " [Nonprofit]\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", - " 2.5\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", + " 34.5\n", " 0.0\n", " \n", " \n", "\n", - "

3721593 rows × 23 columns

\n", + "

4134100 rows × 24 columns

\n", "" ], "text/plain": [ " reference id for_id reference filtered percent rank \\\n", - "0 grid.38142.3c 2211 1.0 \n", - "1 grid.17063.33 2211 2.0 \n", - "2 grid.17063.33 2211 2.0 \n", - "3 grid.21107.35 2211 2.0 \n", - "4 grid.21107.35 2211 2.0 \n", + "0 grid.38142.3c 80003 1.0 \n", + "1 grid.21107.35 80003 2.0 \n", + "2 grid.21107.35 80003 2.0 \n", + "3 grid.17063.33 80003 3.0 \n", + "4 grid.17063.33 80003 3.0 \n", "... ... ... ... \n", - "3721588 grid.256592.f 2219 2.5 \n", - "3721589 grid.256592.f 2219 2.5 \n", - "3721590 grid.256592.f 2219 2.5 \n", - "3721591 grid.256592.f 2219 2.5 \n", - "3721592 grid.256592.f 2219 2.5 \n", + "4134095 grid.66859.34 80007 34.5 \n", + "4134096 grid.66859.34 80007 34.5 \n", + "4134097 grid.66859.34 80007 34.5 \n", + "4134098 grid.66859.34 80007 34.5 \n", + "4134099 grid.66859.34 80007 34.5 \n", "\n", " reference name reference count all id \\\n", - "0 Harvard University 16932 grid.38142.3c \n", - "1 University of Toronto 10281 grid.38142.3c \n", - "2 University of Toronto 10281 grid.17063.33 \n", - "3 Johns Hopkins University 10120 grid.38142.3c \n", - "4 Johns Hopkins University 10120 grid.17063.33 \n", + "0 Harvard University 15967 grid.38142.3c \n", + "1 Johns Hopkins University 9182 grid.38142.3c \n", + "2 Johns Hopkins University 9182 grid.21107.35 \n", + "3 University of Toronto 8932 grid.38142.3c \n", + "4 University of Toronto 8932 grid.21107.35 \n", "... ... ... ... \n", - "3721588 Grinnell College 2 grid.7359.8 \n", - "3721589 Grinnell College 2 grid.11560.33 \n", - "3721590 Grinnell College 2 grid.15866.3c \n", - "3721591 Grinnell College 2 grid.241104.2 \n", - "3721592 Grinnell College 2 grid.256592.f \n", + "4134095 Broad Institute 7 grid.467212.4 \n", + "4134096 Broad Institute 7 grid.4800.c \n", + "4134097 Broad Institute 7 grid.54549.39 \n", + "4134098 Broad Institute 7 grid.6603.3 \n", + "4134099 Broad Institute 7 grid.66859.34 \n", "\n", - " count all city_name count country_name ... \\\n", - "0 16932 Cambridge 845 United States ... \n", - "1 16932 Cambridge 845 United States ... \n", - "2 10281 Toronto 392 Canada ... \n", - "3 16932 Cambridge 845 United States ... \n", - "4 10281 Toronto 392 Canada ... \n", - "... ... ... ... ... ... \n", - "3721588 3 Bamberg 1 Germany ... \n", - "3721589 2 Bernal 1 Argentina ... \n", - "3721590 2 Prague 1 Czechia ... \n", - "3721591 2 Cleveland 1 United States ... \n", - "3721592 2 Grinnell 1 United States ... \n", + " count all name \\\n", + "0 15967 Harvard University \n", + "1 15967 Harvard University \n", + "2 9182 Johns Hopkins University \n", + "3 15967 Harvard University \n", + "4 9182 Johns Hopkins University \n", + "... ... ... \n", + "4134095 7 Adobe Inc \n", + "4134096 7 Polytechnic University of Turin \n", + "4134097 7 University of Electronic Science and Technolog... \n", + "4134098 7 University of Cyprus \n", + "4134099 7 Broad Institute \n", "\n", - " name state_name \\\n", - "0 Harvard University Massachusetts \n", - "1 Harvard University Massachusetts \n", - "2 University of Toronto Ontario \n", - "3 Harvard University Massachusetts \n", - "4 University of Toronto Ontario \n", - "... ... ... \n", - "3721588 University of Bamberg NaN \n", - "3721589 National University of Quilmes NaN \n", - "3721590 Czech University of Life Sciences Prague NaN \n", - "3721591 University Hospitals of Cleveland Ohio \n", - "3721592 Grinnell College Iowa \n", + " city_name count ... longitude state_name types \\\n", + "0 Cambridge 767 ... -71.116650 Massachusetts [Education] \n", + "1 Cambridge 767 ... -71.116650 Massachusetts [Education] \n", + "2 Baltimore 356 ... -76.620280 Maryland [Education] \n", + "3 Cambridge 767 ... -71.116650 Massachusetts [Education] \n", + "4 Baltimore 356 ... -76.620280 Maryland [Education] \n", + "... ... ... ... ... ... ... \n", + "4134095 San Jose 3 ... NaN California [Company] \n", + "4134096 Turin 1 ... 7.661075 Piemonte [Education] \n", + "4134097 Chengdu 1 ... 104.100270 NaN [Education] \n", + "4134098 Nicosia 1 ... 33.376976 NaN [Education] \n", + "4134099 Cambridge 1 ... -71.087030 Massachusetts [Nonprofit] \n", "\n", - " types acronym for_name rank \\\n", - "0 [Education] NaN 11 Medical and Health Sciences 1.0 \n", - "1 [Education] NaN 11 Medical and Health Sciences 1.0 \n", - "2 [Education] NaN 11 Medical and Health Sciences 2.0 \n", - "3 [Education] NaN 11 Medical and Health Sciences 1.0 \n", - "4 [Education] NaN 11 Medical and Health Sciences 2.0 \n", - "... ... ... ... ... \n", - "3721588 [Education] NaN 19 Studies in Creative Arts and Writing 130.0 \n", - "3721589 [Education] UNQ 19 Studies in Creative Arts and Writing 130.0 \n", - "3721590 [Education] CULS 19 Studies in Creative Arts and Writing 130.0 \n", - "3721591 [Healthcare] NaN 19 Studies in Creative Arts and Writing 130.0 \n", - "3721592 [Education] NaN 19 Studies in Creative Arts and Writing 130.0 \n", + " acronym for_name rank percentage top 1 \\\n", + "0 NaN 32 Biomedical and Clinical Sciences 1.0 4.80 \n", + "1 NaN 32 Biomedical and Clinical Sciences 1.0 4.80 \n", + "2 JHU 32 Biomedical and Clinical Sciences 4.0 3.88 \n", + "3 NaN 32 Biomedical and Clinical Sciences 1.0 4.80 \n", + "4 JHU 32 Biomedical and Clinical Sciences 4.0 3.88 \n", + "... ... ... ... ... \n", + "4134095 NaN 36 Creative Arts and Writing 59.0 42.86 \n", + "4134096 NaN 36 Creative Arts and Writing 350.5 14.29 \n", + "4134097 UESTC 36 Creative Arts and Writing 350.5 14.29 \n", + "4134098 UCY 36 Creative Arts and Writing 350.5 14.29 \n", + "4134099 NaN 36 Creative Arts and Writing 350.5 14.29 \n", "\n", - " percentage top 1 percent rank filtered percent rank rank_difference \n", - "0 4.99 61.5 1.0 0.0 \n", - "1 4.99 61.5 1.0 -1.0 \n", - "2 3.81 236.5 2.0 0.0 \n", - "3 4.99 61.5 1.0 -1.0 \n", - "4 3.81 236.5 3.0 1.0 \n", - "... ... ... ... ... \n", - "3721588 33.33 8.0 8.0 5.5 \n", - "3721589 50.00 2.5 2.5 0.0 \n", - "3721590 50.00 2.5 2.5 0.0 \n", - "3721591 50.00 2.5 2.5 0.0 \n", - "3721592 50.00 2.5 2.5 0.0 \n", + " percent rank filtered percent rank rank_difference \n", + "0 63.5 1.0 0.0 \n", + "1 63.5 1.0 -1.0 \n", + "2 195.5 2.0 0.0 \n", + "3 63.5 1.0 -2.0 \n", + "4 195.5 2.0 -1.0 \n", + "... ... ... ... \n", + "4134095 1.0 1.0 -33.5 \n", + "4134096 34.5 34.5 0.0 \n", + "4134097 34.5 34.5 0.0 \n", + "4134098 34.5 34.5 0.0 \n", + "4134099 34.5 34.5 0.0 \n", "\n", - "[3721593 rows x 23 columns]" + "[4134100 rows x 24 columns]" ] }, - "execution_count": 46, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -5013,7 +4957,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/cookbooks/8-organizations/file_name.csv b/cookbooks/8-organizations/file_name.csv index 2822ec3e..c69d9955 100644 --- a/cookbooks/8-organizations/file_name.csv +++ b/cookbooks/8-organizations/file_name.csv @@ -9,11 +9,4 @@ 7,Boston,United States,Harvard Medical School,Massachusetts,grid.38142.3c,Harvard University,Cambridge,Massachusetts,United States,False,6252001,United States,US,6254926,Massachusetts,US-MA,4931972,Cambridge 8,Kent,United States,Kent State University,Ohio,grid.258518.3,Kent State University,Kent,Ohio,United States,False,6252001,United States,US,5165418,Ohio,US-OH,5159537,Kent 9,New York,United States,New York University,New York,grid.137628.9,New York University,New York,New York,United States,False,6252001,United States,US,5128638,New York,US-NY,5128581,New York City -10,null,United States,Mayo Clinic,null,grid.417468.8,Mayo Clinic,Scottsdale,Arizona,United States,True,6252001,United States,US,5551752,Arizona,US-AZ,5313457,Scottsdale -11,null,United States,Mayo Clinic,null,grid.417468.8,Mayo Clinic,Scottsdale,Arizona,United States,True,6252001,United States,US,5551752,Arizona,US-AZ,4160021,Jacksonville -12,null,United States,Mayo Clinic,null,grid.417468.8,Mayo Clinic,Scottsdale,Arizona,United States,True,6252001,United States,US,4155751,Florida,US-FL,5313457,Scottsdale -13,null,United States,Mayo Clinic,null,grid.417468.8,Mayo Clinic,Scottsdale,Arizona,United States,True,6252001,United States,US,4155751,Florida,US-FL,4160021,Jacksonville -14,null,United States,Mayo Clinic,null,grid.417467.7,Mayo Clinic,Jacksonville,Florida,United States,True,6252001,United States,US,5551752,Arizona,US-AZ,5313457,Scottsdale -15,null,United States,Mayo Clinic,null,grid.417467.7,Mayo Clinic,Jacksonville,Florida,United States,True,6252001,United States,US,5551752,Arizona,US-AZ,4160021,Jacksonville -16,null,United States,Mayo Clinic,null,grid.417467.7,Mayo Clinic,Jacksonville,Florida,United States,True,6252001,United States,US,4155751,Florida,US-FL,5313457,Scottsdale -17,null,United States,Mayo Clinic,null,grid.417467.7,Mayo Clinic,Jacksonville,Florida,United States,True,6252001,United States,US,4155751,Florida,US-FL,4160021,Jacksonville +10,null,United States,Mayo Clinic,null,grid.417468.8,Mayo Clinic,Scottsdale,Arizona,United States,False,6252001,United States,US,5551752,Arizona,US-AZ,5313457,Scottsdale diff --git a/cookbooks/8-organizations/network_grid.412125.1.html b/cookbooks/8-organizations/network_grid.412125.1.html index f3a07f95..ddeece77 100644 --- a/cookbooks/8-organizations/network_grid.412125.1.html +++ b/cookbooks/8-organizations/network_grid.412125.1.html @@ -271,16 +271,16 @@

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+ + + + + + + + + + + + - - - + - - - - - - - - - + - - - + + + @@ -459,8 +457,8 @@

// parsing and collecting nodes and edges from the python - nodes = new vis.DataSet([{"borderWidthSelected": 5, "color": "rgb(0, 147, 146)", "id": "grid.412125.1", "label": "King Abdulaziz University", "shape": "dot", "title": "\u003ch4\u003eKing Abdulaziz University\u003cbr\u003eJeddah, Saudi Arabia\u003cbr\u003e - grid.412125.1\u003c/h4\u003eLinks:\u003cli\u003eNortheastern University\u003c/li\u003e\u003cli\u003eJamia Millia Islamia\u003c/li\u003e\u003cli\u003eNorthwestern University\u003c/li\u003e\u003cli\u003eAligarh Muslim University\u003c/li\u003e\u003cli\u003eHarvard\u2013MIT Division of Health Sciences and Technology\u003c/li\u003e\u003cli\u003eSuez Canal University\u003c/li\u003e\u003cli\u003eKing Saud University\u003c/li\u003e\u003cli\u003eQuaid-i-Azam University\u003c/li\u003e\u003cli\u003eHarvard University\u003c/li\u003e\u003cli\u003eMassachusetts Institute of Technology", "value": 3}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.261112.7", "label": "Northeastern University", "shape": "dot", "title": "\u003ch4\u003eNortheastern University\u003cbr\u003eBoston, United States\u003cbr\u003e - grid.261112.7\u003c/h4\u003eLinks:\u003cli\u003eUniversity of Porto\u003c/li\u003e\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eMassachusetts General Hospital\u003c/li\u003e\u003cli\u003eBrown University\u003c/li\u003e\u003cli\u003eUniversity of Alberta\u003c/li\u003e\u003cli\u003eHarvard\u2013MIT Division of Health Sciences and Technology\u003c/li\u003e\u003cli\u003eUniversity of Massachusetts Lowell\u003c/li\u003e\u003cli\u003eBrigham and Women\u0027s Hospital\u003c/li\u003e\u003cli\u003eHarvard University\u003c/li\u003e\u003cli\u003eMassachusetts Institute of Technology", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.38142.3c", "label": "Harvard University", "shape": "dot", "title": "\u003ch4\u003eHarvard University\u003cbr\u003eCambridge, United States\u003cbr\u003e - grid.38142.3c\u003c/h4\u003eLinks:\u003cli\u003eBeth Israel Deaconess Medical Center\u003c/li\u003e\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eMassachusetts General Hospital\u003c/li\u003e\u003cli\u003eBoston Children\u0027s Hospital\u003c/li\u003e\u003cli\u003eNortheastern University\u003c/li\u003e\u003cli\u003eStanford University\u003c/li\u003e\u003cli\u003eKoch Institute for Integrative Cancer Research\u003c/li\u003e\u003cli\u003eHarvard\u2013MIT Division of Health Sciences and Technology\u003c/li\u003e\u003cli\u003eBrigham and Women\u0027s Hospital\u003c/li\u003e\u003cli\u003eMassachusetts Institute of Technology\u003c/li\u003e\u003cli\u003eDana-Farber Cancer Institute", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.116068.8", "label": "Massachusetts Institute of Technology", "shape": "dot", "title": "\u003ch4\u003eMassachusetts Institute of Technology\u003cbr\u003eCambridge, United States\u003cbr\u003e - grid.116068.8\u003c/h4\u003eLinks:\u003cli\u003eInstitute of Bioengineering and Nanotechnology\u003c/li\u003e\u003cli\u003eInstitute for Soldier Nanotechnologies\u003c/li\u003e\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eMassachusetts General Hospital\u003c/li\u003e\u003cli\u003eNanyang Technological University\u003c/li\u003e\u003cli\u003eNortheastern University\u003c/li\u003e\u003cli\u003eNational University of Singapore\u003c/li\u003e\u003cli\u003eKoch Institute for Integrative Cancer Research\u003c/li\u003e\u003cli\u003eSingapore-MIT Alliance for Research and Technology\u003c/li\u003e\u003cli\u003eNorthwestern University\u003c/li\u003e\u003cli\u003eBrigham and Women\u0027s Hospital\u003c/li\u003e\u003cli\u003eHarvard University\u003c/li\u003e\u003cli\u003eHarvard\u2013MIT Division of Health Sciences and Technology", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.16753.36", "label": "Northwestern University", "shape": "dot", "title": "\u003ch4\u003eNorthwestern University\u003cbr\u003eEvanston, United States\u003cbr\u003e - grid.16753.36\u003c/h4\u003eLinks:\u003cli\u003eKorea Advanced Institute of Science and Technology\u003c/li\u003e\u003cli\u003ePurdue University West Lafayette\u003c/li\u003e\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eUniversity of Illinois Urbana-Champaign\u003c/li\u003e\u003cli\u003eUniversity of California, Los Angeles\u003c/li\u003e\u003cli\u003eTsinghua University\u003c/li\u003e\u003cli\u003eArgonne National Laboratory\u003c/li\u003e\u003cli\u003eRobert H. Lurie Comprehensive Cancer Center\u003c/li\u003e\u003cli\u003eUniversity of Chicago\u003c/li\u003e\u003cli\u003eMassachusetts Institute of Technology", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.413735.7", "label": "Harvard\u2013MIT Division of Health Sciences and Technology", "shape": "dot", "title": "\u003ch4\u003eHarvard\u2013MIT Division of Health Sciences and Technology\u003cbr\u003eCambridge, United States\u003cbr\u003e - grid.413735.7\u003c/h4\u003eLinks:\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eMassachusetts General Hospital\u003c/li\u003e\u003cli\u003eNortheastern University\u003c/li\u003e\u003cli\u003eKyung Hee University\u003c/li\u003e\u003cli\u003eKoch Institute for Integrative Cancer Research\u003c/li\u003e\u003cli\u003eKonkuk University\u003c/li\u003e\u003cli\u003eBrigham and Women\u0027s Hospital\u003c/li\u003e\u003cli\u003eHarvard University\u003c/li\u003e\u003cli\u003eMassachusetts Institute of Technology\u003c/li\u003e\u003cli\u003eTohoku University", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.411340.3", "label": "Aligarh Muslim University", "shape": "dot", "title": "\u003ch4\u003eAligarh Muslim University\u003cbr\u003eAligarh, India\u003cbr\u003e - grid.411340.3\u003c/h4\u003eLinks:\u003cli\u003eChangwon National University\u003c/li\u003e\u003cli\u003eJawaharlal Nehru Medical College Hospital\u003c/li\u003e\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eInstitute of Microbial Technology\u003c/li\u003e\u003cli\u003eUniversity of Technology Malaysia\u003c/li\u003e\u003cli\u003eJamia Millia Islamia\u003c/li\u003e\u003cli\u003eNational Physical Laboratory of India\u003c/li\u003e\u003cli\u003eKing Fahd University of Petroleum and Minerals\u003c/li\u003e\u003cli\u003eJawaharlal Nehru University\u003c/li\u003e\u003cli\u003eKing Saud University\u003c/li\u003e\u003cli\u003eIntegral University", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.412621.2", "label": "Quaid-i-Azam University", "shape": "dot", "title": "\u003ch4\u003eQuaid-i-Azam University\u003cbr\u003eIslamabad, Pakistan\u003cbr\u003e - grid.412621.2\u003c/h4\u003eLinks:\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eUniversity of Malaya\u003c/li\u003e\u003cli\u003eUniversity of Sargodha\u003c/li\u003e\u003cli\u003eAbdus Salam Centre for Physics\u003c/li\u003e\u003cli\u003eNational University of Sciences and Technology\u003c/li\u003e\u003cli\u003eCOMSATS University Islamabad\u003c/li\u003e\u003cli\u003eKing Fahd University of Petroleum and Minerals\u003c/li\u003e\u003cli\u003eHazara University\u003c/li\u003e\u003cli\u003ePakistan Institute of Nuclear Science and Technology\u003c/li\u003e\u003cli\u003eInternational Islamic University, Islamabad", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.33003.33", "label": "Suez Canal University", "shape": "dot", "title": "\u003ch4\u003eSuez Canal University\u003cbr\u003eIsmailia, Egypt\u003cbr\u003e - grid.33003.33\u003c/h4\u003eLinks:\u003cli\u003eUniversity of Otago\u003c/li\u003e\u003cli\u003eNational Research Centre\u003c/li\u003e\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eF\u0131rat University\u003c/li\u003e\u003cli\u003eAin Shams University\u003c/li\u003e\u003cli\u003eUniversity of Chemical Technology and Metallurgy\u003c/li\u003e\u003cli\u003eUniversity of Tabuk\u003c/li\u003e\u003cli\u003eUniversity of Southampton\u003c/li\u003e\u003cli\u003eTaif University\u003c/li\u003e\u003cli\u003eKing Saud University", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.411818.5", "label": "Jamia Millia Islamia", "shape": "dot", "title": "\u003ch4\u003eJamia Millia Islamia\u003cbr\u003eNew Delhi, India\u003cbr\u003e - grid.411818.5\u003c/h4\u003eLinks:\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eUniversity of Delhi\u003c/li\u003e\u003cli\u003eUniversity of Technology Malaysia\u003c/li\u003e\u003cli\u003eAmity University\u003c/li\u003e\u003cli\u003eNational Physical Laboratory of India\u003c/li\u003e\u003cli\u003eIndian Institute of Technology Delhi\u003c/li\u003e\u003cli\u003eAligarh Muslim University\u003c/li\u003e\u003cli\u003eRowan University\u003c/li\u003e\u003cli\u003eM.J.P. Rohilkhand University\u003c/li\u003e\u003cli\u003eKing Saud University", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.56302.32", "label": "King Saud University", "shape": "dot", "title": "\u003ch4\u003eKing Saud University\u003cbr\u003eRiyadh, Saudi Arabia\u003cbr\u003e - grid.56302.32\u003c/h4\u003eLinks:\u003cli\u003eNational Research Centre\u003c/li\u003e\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eUniversity of Gab\u00e8s\u003c/li\u003e\u003cli\u003eUniversity of Queensland\u003c/li\u003e\u003cli\u003eTanta University\u003c/li\u003e\u003cli\u003eNational University of Singapore\u003c/li\u003e\u003cli\u003eJamia Millia Islamia\u003c/li\u003e\u003cli\u003eChang Gung University\u003c/li\u003e\u003cli\u003eAligarh Muslim University\u003c/li\u003e\u003cli\u003eSuez Canal University\u003c/li\u003e\u003cli\u003eJeonbuk National University\u003c/li\u003e\u003cli\u003eKing Abdulaziz City for Science and Technology", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.17089.37", "label": "University of Alberta", "shape": "dot", "title": "\u003ch4\u003eUniversity of Alberta\u003cbr\u003eEdmonton, Canada\u003cbr\u003e - grid.17089.37\u003c/h4\u003eLinks:\u003cli\u003eNortheastern University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.32224.35", "label": "Massachusetts General Hospital", "shape": "dot", "title": "\u003ch4\u003eMassachusetts General Hospital\u003cbr\u003eBoston, United States\u003cbr\u003e - grid.32224.35\u003c/h4\u003eLinks:\u003cli\u003eNortheastern University\u003c/li\u003e\u003cli\u003eHarvard University\u003c/li\u003e\u003cli\u003eMassachusetts Institute of Technology\u003c/li\u003e\u003cli\u003eHarvard\u2013MIT Division of Health Sciences and Technology", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.40263.33", "label": "Brown University", "shape": "dot", "title": "\u003ch4\u003eBrown University\u003cbr\u003eProvidence, United States\u003cbr\u003e - grid.40263.33\u003c/h4\u003eLinks:\u003cli\u003eNortheastern University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.62560.37", "label": "Brigham and Women\u0027s Hospital", "shape": "dot", "title": "\u003ch4\u003eBrigham and Women\u0027s Hospital\u003cbr\u003eBoston, United States\u003cbr\u003e - grid.62560.37\u003c/h4\u003eLinks:\u003cli\u003eNortheastern University\u003c/li\u003e\u003cli\u003eHarvard University\u003c/li\u003e\u003cli\u003eMassachusetts Institute of Technology\u003c/li\u003e\u003cli\u003eHarvard\u2013MIT Division of Health Sciences and Technology", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.225262.3", "label": "University of Massachusetts Lowell", "shape": "dot", "title": "\u003ch4\u003eUniversity of Massachusetts Lowell\u003cbr\u003eLowell, United States\u003cbr\u003e - grid.225262.3\u003c/h4\u003eLinks:\u003cli\u003eNortheastern University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.5808.5", "label": "University of Porto", "shape": "dot", "title": "\u003ch4\u003eUniversity of Porto\u003cbr\u003ePorto, Portugal\u003cbr\u003e - grid.5808.5\u003c/h4\u003eLinks:\u003cli\u003eNortheastern University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.65499.37", "label": "Dana-Farber Cancer Institute", "shape": "dot", "title": "\u003ch4\u003eDana-Farber Cancer Institute\u003cbr\u003eBoston, United States\u003cbr\u003e - grid.65499.37\u003c/h4\u003eLinks:\u003cli\u003eHarvard University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.516087.d", "label": "Koch Institute for Integrative Cancer Research", "shape": "dot", "title": "\u003ch4\u003eKoch Institute for Integrative Cancer Research\u003cbr\u003eCambridge, United States\u003cbr\u003e - grid.516087.d\u003c/h4\u003eLinks:\u003cli\u003eHarvard University\u003c/li\u003e\u003cli\u003eMassachusetts Institute of Technology\u003c/li\u003e\u003cli\u003eHarvard\u2013MIT Division of Health Sciences and Technology", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.239395.7", "label": "Beth Israel Deaconess Medical Center", "shape": "dot", "title": "\u003ch4\u003eBeth Israel Deaconess Medical Center\u003cbr\u003eBoston, United States\u003cbr\u003e - grid.239395.7\u003c/h4\u003eLinks:\u003cli\u003eHarvard University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.2515.3", "label": "Boston Children\u0027s Hospital", "shape": "dot", "title": "\u003ch4\u003eBoston Children\u0027s Hospital\u003cbr\u003eBoston, United States\u003cbr\u003e - grid.2515.3\u003c/h4\u003eLinks:\u003cli\u003eHarvard University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.168010.e", "label": "Stanford University", "shape": "dot", "title": "\u003ch4\u003eStanford University\u003cbr\u003eStanford, United States\u003cbr\u003e - grid.168010.e\u003c/h4\u003eLinks:\u003cli\u003eHarvard University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.4280.e", "label": "National University of Singapore", "shape": "dot", "title": "\u003ch4\u003eNational University of Singapore\u003cbr\u003eSingapore, Singapore\u003cbr\u003e - grid.4280.e\u003c/h4\u003eLinks:\u003cli\u003eKing Saud University\u003c/li\u003e\u003cli\u003eMassachusetts Institute of Technology", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.429485.6", "label": "Singapore-MIT Alliance for Research and Technology", "shape": "dot", "title": "\u003ch4\u003eSingapore-MIT Alliance for Research and Technology\u003cbr\u003eSingapore, Singapore\u003cbr\u003e - grid.429485.6\u003c/h4\u003eLinks:\u003cli\u003eMassachusetts Institute of Technology", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.512167.6", "label": "Institute for Soldier Nanotechnologies", "shape": "dot", "title": "\u003ch4\u003eInstitute for Soldier Nanotechnologies\u003cbr\u003eCambridge, United States\u003cbr\u003e - grid.512167.6\u003c/h4\u003eLinks:\u003cli\u003eMassachusetts Institute of Technology", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.418830.6", "label": "Institute of Bioengineering and Nanotechnology", "shape": "dot", "title": "\u003ch4\u003eInstitute of Bioengineering and Nanotechnology\u003cbr\u003eSingapore, Singapore\u003cbr\u003e - grid.418830.6\u003c/h4\u003eLinks:\u003cli\u003eMassachusetts Institute of Technology", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.59025.3b", "label": "Nanyang Technological University", "shape": "dot", "title": "\u003ch4\u003eNanyang Technological University\u003cbr\u003eSingapore, Singapore\u003cbr\u003e - grid.59025.3b\u003c/h4\u003eLinks:\u003cli\u003eMassachusetts Institute of Technology", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.187073.a", "label": "Argonne National Laboratory", "shape": "dot", "title": "\u003ch4\u003eArgonne National Laboratory\u003cbr\u003eLemont, United States\u003cbr\u003e - grid.187073.a\u003c/h4\u003eLinks:\u003cli\u003eNorthwestern University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.35403.31", "label": "University of Illinois Urbana-Champaign", "shape": "dot", "title": "\u003ch4\u003eUniversity of Illinois Urbana-Champaign\u003cbr\u003eUrbana, United States\u003cbr\u003e - grid.35403.31\u003c/h4\u003eLinks:\u003cli\u003eNorthwestern University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.516096.d", "label": "Robert H. Lurie Comprehensive Cancer Center", "shape": "dot", "title": "\u003ch4\u003eRobert H. Lurie Comprehensive Cancer Center\u003cbr\u003eChicago, United States\u003cbr\u003e - grid.516096.d\u003c/h4\u003eLinks:\u003cli\u003eNorthwestern University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.12527.33", "label": "Tsinghua University", "shape": "dot", "title": "\u003ch4\u003eTsinghua University\u003cbr\u003eBeijing, China\u003cbr\u003e - grid.12527.33\u003c/h4\u003eLinks:\u003cli\u003eNorthwestern University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.19006.3e", "label": "University of California, Los Angeles", "shape": "dot", "title": "\u003ch4\u003eUniversity of California, Los Angeles\u003cbr\u003eLos Angeles, United States\u003cbr\u003e - grid.19006.3e\u003c/h4\u003eLinks:\u003cli\u003eNorthwestern University", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.169077.e", "label": "Purdue University West Lafayette", "shape": "dot", "title": "\u003ch4\u003ePurdue University West Lafayette\u003cbr\u003eWest Lafayette, United States\u003cbr\u003e - 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"from": "grid.412125.1", "label": 46, "to": "grid.412621.2", "value": 0.07044410413476264}, {"arrows": "none", "from": "grid.412125.1", "label": 40, "to": "grid.411818.5", "value": 0.06125574272588055}, {"arrows": "none", "from": "grid.412125.1", "label": 40, "to": "grid.62560.37", "value": 0.06125574272588055}, {"arrows": "none", "from": "grid.412125.1", "label": 39, "to": "grid.33003.33", "value": 0.05972434915773354}, {"arrows": "none", "from": "grid.412125.1", "label": 39, "to": "grid.56302.32", "value": 0.05972434915773354}, {"arrows": "none", "from": "grid.261112.7", "label": 65, "to": "grid.38142.3c", "value": 0.0995405819295559}, {"arrows": "none", "from": "grid.261112.7", "label": 56, "to": "grid.17089.37", "value": 0.08575803981623277}, {"arrows": "none", "from": "grid.261112.7", "label": 54, "to": "grid.116068.8", "value": 0.08269525267993874}, {"arrows": "none", "from": "grid.261112.7", "label": 29, "to": "grid.32224.35", "value": 0.0444104134762634}, {"arrows": "none", 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"grid.38142.3c", "label": 129, "to": "grid.65499.37", "value": 0.19754977029096477}, {"arrows": "none", "from": "grid.38142.3c", "label": 107, "to": "grid.239395.7", "value": 0.1638591117917305}, {"arrows": "none", "from": "grid.38142.3c", "label": 106, "to": "grid.516087.d", "value": 0.16232771822358347}, {"arrows": "none", "from": "grid.38142.3c", "label": 82, "to": "grid.2515.3", "value": 0.12557427258805512}, {"arrows": "none", "from": "grid.38142.3c", "label": 72, "to": "grid.168010.e", "value": 0.11026033690658499}, {"arrows": "none", "from": "grid.116068.8", "label": 210, "to": "grid.62560.37", "value": 0.3215926493108729}, {"arrows": "none", "from": "grid.116068.8", "label": 191, "to": "grid.516087.d", "value": 0.29249617151607965}, {"arrows": "none", "from": "grid.116068.8", "label": 155, "to": "grid.4280.e", "value": 0.23736600306278713}, {"arrows": "none", "from": "grid.116068.8", "label": 128, "to": "grid.429485.6", "value": 0.19601837672281777}, {"arrows": "none", "from": "grid.116068.8", "label": 112, "to": "grid.512167.6", "value": 0.17151607963246554}, {"arrows": "none", "from": "grid.116068.8", "label": 96, "to": "grid.418830.6", "value": 0.14701378254211334}, {"arrows": "none", "from": "grid.116068.8", "label": 94, "to": "grid.32224.35", "value": 0.1439509954058193}, {"arrows": "none", "from": "grid.116068.8", "label": 89, "to": "grid.59025.3b", "value": 0.1362940275650842}, {"arrows": "none", "from": "grid.116068.8", "label": 75, "to": "grid.168010.e", "value": 0.11485451761102604}, {"arrows": "none", "from": "grid.16753.36", "label": 249, "to": "grid.187073.a", "value": 0.38131699846860645}, {"arrows": "none", "from": "grid.16753.36", "label": 149, "to": "grid.35403.31", "value": 0.22817764165390506}, {"arrows": "none", "from": "grid.16753.36", "label": 86, "to": "grid.516096.d", "value": 0.13169984686064318}, {"arrows": "none", "from": "grid.16753.36", "label": 55, "to": "grid.12527.33", "value": 0.08422664624808576}, {"arrows": "none", "from": "grid.16753.36", "label": 53, "to": "grid.169077.e", "value": 0.08116385911179173}, {"arrows": "none", "from": "grid.16753.36", "label": 52, "to": "grid.19006.3e", "value": 0.07963246554364471}, {"arrows": "none", "from": "grid.16753.36", "label": 47, "to": "grid.170205.1", "value": 0.07197549770290965}, {"arrows": "none", "from": "grid.16753.36", "label": 46, "to": "grid.37172.30", "value": 0.07044410413476264}, {"arrows": "none", "from": "grid.16753.36", "label": 39, "to": "grid.116068.8", "value": 0.05972434915773354}, {"arrows": "none", "from": "grid.411340.3", "label": 61, "to": "grid.56302.32", "value": 0.09341500765696784}, {"arrows": "none", "from": "grid.411340.3", "label": 15, "to": "grid.466808.4", "value": 0.022970903522205207}, {"arrows": "none", "from": "grid.411340.3", "label": 10, "to": "grid.412135.0", "value": 0.015313935681470138}, {"arrows": "none", "from": "grid.411340.3", "label": 10, "to": "grid.417641.1", "value": 0.015313935681470138}, {"arrows": "none", "from": "grid.411340.3", "label": 9, "to": "grid.419701.a", "value": 0.013782542113323124}, {"arrows": "none", "from": "grid.411340.3", "label": 8, "to": "grid.411723.2", "value": 0.01225114854517611}, {"arrows": "none", "from": "grid.411340.3", "label": 7, "to": "grid.410877.d", "value": 0.010719754977029096}, {"arrows": "none", "from": "grid.411340.3", "label": 6, "to": "grid.10706.30", "value": 0.009188361408882083}, {"arrows": "none", "from": "grid.411340.3", "label": 6, "to": "grid.411214.3", "value": 0.009188361408882083}, {"arrows": "none", "from": "grid.412621.2", "label": 39, "to": "grid.418920.6", "value": 0.05972434915773354}, {"arrows": "none", "from": "grid.412621.2", "label": 35, "to": "grid.440530.6", "value": 0.05359877488514548}, {"arrows": "none", "from": "grid.412621.2", "label": 27, "to": "grid.466924.b", "value": 0.04134762633996937}, {"arrows": "none", "from": "grid.412621.2", "label": 18, "to": "grid.412117.0", "value": 0.027565084226646247}, {"arrows": "none", "from": "grid.412621.2", "label": 17, "to": "grid.411727.6", "value": 0.026033690658499236}, {"arrows": "none", "from": "grid.412621.2", "label": 14, "to": "grid.412782.a", "value": 0.021439509954058193}, {"arrows": "none", "from": "grid.412621.2", "label": 13, "to": "grid.420113.5", "value": 0.019908116385911178}, {"arrows": "none", "from": "grid.412621.2", "label": 11, "to": "grid.10347.31", "value": 0.016845329249617153}, {"arrows": "none", "from": "grid.412621.2", "label": 11, "to": "grid.412135.0", "value": 0.016845329249617153}, {"arrows": "none", "from": "grid.411818.5", "label": 35, "to": "grid.419701.a", "value": 0.05359877488514548}, {"arrows": "none", "from": "grid.411818.5", "label": 16, "to": "grid.56302.32", "value": 0.02450229709035222}, {"arrows": "none", "from": "grid.411818.5", "label": 15, "to": "grid.417967.a", "value": 0.022970903522205207}, {"arrows": "none", "from": "grid.411818.5", "label": 9, "to": "grid.8195.5", "value": 0.013782542113323124}, {"arrows": "none", "from": "grid.411818.5", "label": 8, "to": "grid.262671.6", "value": 0.01225114854517611}, {"arrows": "none", "from": "grid.411818.5", "label": 7, "to": "grid.410877.d", "value": 0.010719754977029096}, {"arrows": "none", "from": "grid.411818.5", "label": 7, "to": "grid.411529.a", "value": 0.010719754977029096}, {"arrows": "none", "from": "grid.411818.5", "label": 7, "to": "grid.444644.2", "value": 0.010719754977029096}, {"arrows": "none", "from": "grid.411818.5", "label": 6, "to": "grid.411340.3", "value": 0.009188361408882083}, {"arrows": "none", "from": "grid.62560.37", "label": 53, "to": "grid.32224.35", "value": 0.08116385911179173}, {"arrows": "none", "from": "grid.62560.37", "label": 52, "to": "grid.516087.d", "value": 0.07963246554364471}, {"arrows": "none", "from": "grid.62560.37", "label": 36, "to": "grid.413735.7", "value": 0.055130168453292494}, {"arrows": "none", "from": "grid.62560.37", "label": 34, "to": "grid.189504.1", "value": 0.05206738131699847}, {"arrows": "none", "from": "grid.62560.37", "label": 31, "to": "grid.65499.37", "value": 0.04747320061255743}, {"arrows": "none", "from": "grid.62560.37", "label": 18, "to": "grid.168010.e", "value": 0.027565084226646247}, {"arrows": "none", "from": "grid.33003.33", "label": 18, "to": "grid.411320.5", "value": 0.027565084226646247}, {"arrows": "none", "from": "grid.33003.33", "label": 14, "to": "grid.5491.9", "value": 0.021439509954058193}, {"arrows": "none", "from": "grid.33003.33", "label": 11, "to": "grid.29980.3a", "value": 0.016845329249617153}, {"arrows": "none", "from": "grid.33003.33", "label": 10, "to": "grid.56302.32", "value": 0.015313935681470138}, {"arrows": "none", "from": "grid.33003.33", "label": 8, "to": "grid.412895.3", "value": 0.01225114854517611}, {"arrows": "none", "from": "grid.33003.33", "label": 8, "to": "grid.440760.1", "value": 0.01225114854517611}, {"arrows": "none", "from": "grid.33003.33", "label": 6, "to": "grid.419725.c", "value": 0.009188361408882083}, {"arrows": "none", "from": "grid.33003.33", "label": 5, "to": "grid.7269.a", "value": 0.007656967840735069}, {"arrows": "none", "from": "grid.33003.33", "label": 5, "to": "grid.8905.4", "value": 0.007656967840735069}, {"arrows": "none", "from": "grid.56302.32", "label": 59, "to": "grid.4280.e", "value": 0.0903522205206738}, {"arrows": "none", "from": "grid.56302.32", "label": 54, "to": "grid.411545.0", "value": 0.08269525267993874}, {"arrows": "none", "from": "grid.56302.32", "label": 43, "to": "grid.419725.c", "value": 0.06584992343032159}, {"arrows": "none", "from": "grid.56302.32", "label": 37, "to": "grid.452562.2", "value": 0.05666156202143951}, {"arrows": "none", "from": "grid.56302.32", "label": 35, "to": "grid.1003.2", "value": 0.05359877488514548}, {"arrows": "none", "from": "grid.56302.32", "label": 31, "to": "grid.145695.a", "value": 0.04747320061255743}, {"arrows": "none", "from": "grid.56302.32", "label": 31, "to": "grid.21941.3f", "value": 0.04747320061255743}, {"arrows": "none", "from": "grid.56302.32", "label": 31, "to": "grid.411677.2", "value": 0.04747320061255743}]); nodeColors = {}; allNodes = nodes.get({ returnType: "Object" }); diff --git a/cookbooks/8-organizations/network_grid.412125.1_Government.html b/cookbooks/8-organizations/network_grid.412125.1_Government.html index 1583e2fc..54789a1f 100644 --- a/cookbooks/8-organizations/network_grid.412125.1_Government.html +++ b/cookbooks/8-organizations/network_grid.412125.1_Government.html @@ -267,8 +267,6 @@

- - @@ -277,15 +275,13 @@

- + - + - - - + @@ -339,8 +335,8 @@

// parsing and collecting nodes and edges from the python - nodes = new vis.DataSet([{"borderWidthSelected": 5, "color": "rgb(0, 147, 146)", "id": "grid.412125.1", "label": "King Abdulaziz University", "shape": "dot", "title": "\u003ch4\u003eKing Abdulaziz University\u003cbr\u003eJeddah, Saudi Arabia\u003cbr\u003e - grid.412125.1\u003c/h4\u003eLinks:\u003cli\u003eAustralian Nuclear Science and Technology Organisation\u003c/li\u003e\u003cli\u003eJapan Atomic Energy Agency\u003c/li\u003e\u003cli\u003eCouncil for Scientific and Industrial Research\u003c/li\u003e\u003cli\u003eChinese Academy of Sciences\u003c/li\u003e\u003cli\u003eScience and Technology Facilities Council", "value": 3}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.7327.1", "label": "Council for Scientific and Industrial Research", "shape": "dot", "title": "\u003ch4\u003eCouncil for Scientific and Industrial Research\u003cbr\u003ePretoria, South Africa\u003cbr\u003e - grid.7327.1\u003c/h4\u003eLinks:\u003cli\u003eDepartment of Science and Technology\u003c/li\u003e\u003cli\u003eNational Research Foundation\u003c/li\u003e\u003cli\u003eSouth African Medical Research Council\u003c/li\u003e\u003cli\u003eKing Abdulaziz University", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.9227.e", "label": "Chinese Academy of Sciences", "shape": "dot", "title": "\u003ch4\u003eChinese Academy of Sciences\u003cbr\u003eBeijing, China\u003cbr\u003e - grid.9227.e\u003c/h4\u003eLinks:\u003cli\u003eKing Abdulaziz University", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.20256.33", "label": "Japan Atomic Energy Agency", "shape": "dot", "title": "\u003ch4\u003eJapan Atomic Energy Agency\u003cbr\u003eT\u014dkai-mura, Japan\u003cbr\u003e - grid.20256.33\u003c/h4\u003eLinks:\u003cli\u003eNational Institute of Advanced Industrial Science and Technology\u003c/li\u003e\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eJapan Science and Technology Agency", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.1089.0", "label": "Australian Nuclear Science and Technology Organisation", "shape": "dot", "title": "\u003ch4\u003eAustralian Nuclear Science and Technology Organisation\u003cbr\u003eSydney, Australia\u003cbr\u003e - grid.1089.0\u003c/h4\u003eLinks:\u003cli\u003eCommonwealth Scientific and Industrial Research Organisation\u003c/li\u003e\u003cli\u003eNational Institute of Standards and Technology\u003c/li\u003e\u003cli\u003eKing Abdulaziz University\u003c/li\u003e\u003cli\u003eJapan Science and Technology Agency", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(156, 203, 134)", "id": "grid.14467.30", "label": "Science and Technology Facilities Council", "shape": "dot", "title": "\u003ch4\u003eScience and Technology Facilities Council\u003cbr\u003eSwindon, United Kingdom\u003cbr\u003e - grid.14467.30\u003c/h4\u003eLinks:\u003cli\u003eKing Abdulaziz University", "value": 2}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.424109.a", "label": "Department of Science and Technology", "shape": "dot", "title": "\u003ch4\u003eDepartment of Science and Technology\u003cbr\u003ePretoria, South Africa\u003cbr\u003e - grid.424109.a\u003c/h4\u003eLinks:\u003cli\u003eCouncil for Scientific and Industrial Research", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.425534.1", "label": "National Research Foundation", "shape": "dot", "title": "\u003ch4\u003eNational Research Foundation\u003cbr\u003ePretoria, South Africa\u003cbr\u003e - grid.425534.1\u003c/h4\u003eLinks:\u003cli\u003eCouncil for Scientific and Industrial Research", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.415021.3", "label": "South African Medical Research Council", "shape": "dot", "title": "\u003ch4\u003eSouth African Medical Research Council\u003cbr\u003eCape Town, South Africa\u003cbr\u003e - grid.415021.3\u003c/h4\u003eLinks:\u003cli\u003eCouncil for Scientific and Industrial Research", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.208504.b", "label": "National Institute of Advanced Industrial Science and Technology", "shape": "dot", "title": "\u003ch4\u003eNational Institute of Advanced Industrial Science and Technology\u003cbr\u003eTsukuba, Japan\u003cbr\u003e - grid.208504.b\u003c/h4\u003eLinks:\u003cli\u003eJapan Atomic Energy Agency", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.419082.6", "label": "Japan Science and Technology Agency", "shape": "dot", "title": "\u003ch4\u003eJapan Science and Technology Agency\u003cbr\u003eTokyo, Japan\u003cbr\u003e - grid.419082.6\u003c/h4\u003eLinks:\u003cli\u003eJapan Atomic Energy Agency\u003c/li\u003e\u003cli\u003eAustralian Nuclear Science and Technology Organisation", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.94225.38", "label": "National Institute of Standards and Technology", "shape": "dot", "title": "\u003ch4\u003eNational Institute of Standards and Technology\u003cbr\u003eGaithersburg, United States\u003cbr\u003e - grid.94225.38\u003c/h4\u003eLinks:\u003cli\u003eAustralian Nuclear Science and Technology Organisation", "value": 1}, {"borderWidthSelected": 5, "color": "rgb(238, 180, 121)", "id": "grid.1016.6", "label": "Commonwealth Scientific and Industrial Research Organisation", "shape": "dot", "title": "\u003ch4\u003eCommonwealth Scientific and Industrial Research Organisation\u003cbr\u003eCanberra, Australia\u003cbr\u003e - grid.1016.6\u003c/h4\u003eLinks:\u003cli\u003eAustralian Nuclear Science and Technology Organisation", "value": 1}]); - edges = new vis.DataSet([{"arrows": "none", "from": "grid.412125.1", "label": 4, "to": "grid.7327.1", "value": 0.16}, {"arrows": "none", "from": "grid.412125.1", "label": 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b/docs/.doctrees/nbsphinx/cookbooks/2-publications/Rejected_Article_Tracker.ipynb new file mode 100644 index 00000000..5ad1f5ad --- /dev/null +++ b/docs/.doctrees/nbsphinx/cookbooks/2-publications/Rejected_Article_Tracker.ipynb @@ -0,0 +1,2105 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "_dmqbrsrX1Wm" + }, + "source": [ + "# Rejected Article tracker\n", + "\n", + "This Python notebook shows how publishers can use the [Dimensions Analytics API](https://www.dimensions.ai/dimensions-apis/) to identify whether articles they chose not to publish were ultimately published somewhere else.\n", + "\n", + "In this notebook we will:\n", + "1. Import a .csv file containing rejected articles\n", + "2. Search for publications similar to the rejected articles\n", + "4. Measure the strength of the matches and provide ideas for validation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uPAAo96vdohR", + "outputId": "98c0472c-3b92-4c14-b567-aa0a2d75491c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==\n", + "CHANGELOG\n", + "This notebook was last run on Jan 27, 2025\n", + "==\n" + ] + } + ], + "source": [ + "import datetime\n", + "print(\"==\\nCHANGELOG\\nThis notebook was last run on %s\\n==\" % datetime.date.today().strftime('%b %d, %Y'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qX9CV_XkXVIZ" + }, + "source": [ + "## Prerequisites\n", + "\n", + "This notebook assumes you have installed the [Dimcli](https://pypi.org/project/dimcli/) library and are familiar with the ['Getting Started' tutorial](https://api-lab.dimensions.ai/cookbooks/1-getting-started/1-Using-the-Dimcli-library-to-query-the-API.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ti0txhA9d10c", + "outputId": "a793c666-7d7f-410e-b2e9-1a952f8e5eb5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m241.7/241.7 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.7/162.7 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m56.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.1/51.1 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m54.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for pandasql (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "==\n", + "Logging in..\n", + "API Key: ··········\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.10\u001b[0m\n", + "\u001b[2mMethod: manual login\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install dimcli pandasql levenshtein -U --quiet\n", + "\n", + "import dimcli\n", + "from dimcli.utils import *\n", + "\n", + "import json, sys\n", + "import requests\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pandasql import sqldf\n", + "import pandasql as ps\n", + "import plotly.express as px # plotly>=4.8.1\n", + "if not 'google.colab' in sys.modules:\n", + " # make js dependecies local / needed by html exports\n", + " from plotly.offline import init_notebook_mode\n", + " init_notebook_mode(connected=True)\n", + "#\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "print(\"==\\nLogging in..\")\n", + "# https://digital-science.github.io/dimcli/getting-started.html#authentication\n", + "ENDPOINT = \"https://app.dimensions.ai\"\n", + "if 'google.colab' in sys.modules:\n", + " import getpass\n", + " KEY = getpass.getpass(prompt='API Key: ')\n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "else:\n", + " KEY = \"\"\n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "dsl = dimcli.Dsl()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZAHoWkwhUzjy" + }, + "source": [ + "## 1. Get an example data set\n", + "\n", + "For this tutorial, we are going to use a sample data set of preprints and pretend that the preprints are articles we have rejected. This is a good proof of the concept of finding a similar article that has been published in a peer-reviewed journal: preprints often reappear published in journals and might have subtly different titles or abstracts.\n", + "\n", + "In this simplified example, we'll just use the author names and titles for matching and we'll add a unique (made up) submission ID, as real data is likely to have this. We'll use the preprint publishing date as our rejection date.\n", + "\n", + "Here is the query to get our example data set as a `pandas` data frame, and some code to make it look more like a data set of rejected articles. You don't need to understand this bit necessarily, assuming you will have you're own data you just need to know what the table looks like at the end (which will be shown)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1555 + }, + "id": "OUztuLnzd02w", + "outputId": "2c7307a2-2de4-48a8-f4b4-6b4bd6b8197e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Publications: 10 (total = 730)\n", + "\u001b[2mTime: 0.31s\u001b[0m\n", + "WARNINGS [1]\n", + "Field current_organization_id of the authors field is deprecated and will be removed in the next major release.\n" + ] + }, + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"rejected_publication_data\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"rejected_date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"2020-01-22\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"first_author\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"Joyce\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Scientific Racism 2.0 (SR2.0): An erroneous argument from genetics which inadvertently refines scientific racism\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"abstract\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"

SR2.0 refers to a prominent argument made by some geneticists, often via social and popular media, which inadvertently amounts to a refinement of scientific racism. At face value it is an attack on racism in science. Upon closer inspection its primary, possibly unconscious, purpose appears to be to protect contemporary genetic research from the charge of racism. The argument is often made alongside an emphasis upon long-falsified errors of early science and open expressions of racism in wider society, rather than the intelligence and statistical theory which has informed both genetics and the social construct scientific racism for a century. The core argument is invalid. It also has profound epistemological failings, including misunderstanding the nature of social constructions and how they how they interact with empirical facts. Finally, the proponents do not fully support their own argument; this exposes the argument\\u2019s substantive function as a defensive holding device.

\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"submission_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"879bff9a-169b-425f-a027-11f04e0448ea\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "rejected_publication_data" + }, + "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/e523c247d1e24a05/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"2020-01-22\",\n\"Kong\",\n\"Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis\\u2013Treated Patients Using Stacked Generalization: Model Development and Validation Study (Preprint)\",\n\"\\n BACKGROUND\\n

The increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients.<\\/p>\\n <\\/sec>\\n \\n OBJECTIVE\\n

This study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data.<\\/p>\\n <\\/sec>\\n \\n METHODS\\n

Patient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model.<\\/p>\\n <\\/sec>\\n \\n RESULTS\\n

The final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided <i>t</i> tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy.<\\/p>\\n <\\/sec>\\n \\n CONCLUSIONS\\n

This study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.<\\/p>\\n <\\/sec>\",\n\"6ef9af47-8fa7-4abb-af69-0c8a522b6f9a\"],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"2020-01-22\",\n\"Bowman\",\n\"OSF Prereg Template\",\n\"

Preregistration is the act of submitting a study plan, ideally also with analytical plan, to a registry prior to conducting the work. Preregistration increases the discoverability of research even if it does not get published further. Adding specific analysis plans can clarify the distinction between planned, confirmatory tests and unplanned, exploratory research. This preprint contains a template for the \\u201cOSF Prereg\\u201d form available from the OSF Registry. An earlier version was originally developed for the Preregistration Challenge, an education campaign designed to initiate preregistration as a habit prior to data collection in basic research, funded by the Laura and John Arnold Foundation (now Arnold Ventures) and conducted by the Center for Open Science. More information is available at https://cos.io/prereg, and other templates are available at: https://osf.io/zab38/<\\/p>\",\n\"2239203f-fa3b-4402-a185-1398861aba66\"],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"2020-01-22\",\n\"Di Sia\",\n\"On the Concept of Time in everyday Life and between Physics and Mathematics\",\n\"

In this paper I consider the concept of time in a general way as daily human time andthen within physics with relation to mathematics. I focus the attention on quantum mechanics, with its particular peculiarities, examining peculiar important questions like the temporal asymmetry, the Prigogine\\u2019s position and the time-reversal operator of Wigner. I conclude considering the theme of the temporal asymmetry in relation to decoherence and irreversibility. Interesting imputs related to education science will be done.<\\/p>\",\n\"7dc54f58-2a6c-44eb-b906-98a184d1bac1\"],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"2020-01-22\",\n\"Di Sia\",\n\"Birth and development of quantum physics: a transdisciplinary approach\",\n\"

The last century has been a period of extreme interest for scientific research, marked by the overcoming of the classical frontiers of scientific knowledge.Research oriented towards the infinitely small and infinitely big, in both cases beyondthe borders of the visible. Quantum physics has led to a new Copernican revolution,opening the way to new questions that have led to a new view of reality. At the sametime, new theories have developed, involving every field of science, philosophy and art, rediscovering the link between unity and totality and the importance of humanpotential. In a transdisciplinary approach we consider quantum field theory, new ideason the concepts of vacuum and entanglement, metaphysical aspects of quantum revolution and the introduction of different interpretative approaches on the \\u201cWhole\\u201d.<\\/p>\",\n\"b466b2fd-b0c3-4573-abc5-229532212be1\"],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n\"2020-01-22\",\n\"Bedoya\",\n\"Fabricaci\\u00f3n de capas antirreflejantes y absorbedores solares mediante la t\\u00e9cnica Sol-gel: Un resumen sobre la variaci\\u00f3n de s\\u00edntesis y condiciones experimentales realizadas en la UTP\",\n\"

Se prepararon pel\\u00edculas delgadas de SiO2 en relaci\\u00f3n molar TEOS:H2O:EtOH 1:18:1.8 y CuCoMn en relaci\\u00f3n molar Cu:Co:Mn 1:3:3 por el m\\u00e9todo de recubrimiento por inmersi\\u00f3n (Sol-gel), bajo condiciones fijas de velocidad de dep\\u00f3sito y n\\u00famero de capas. Inicialmente se usaron sustratos de vidrios con el fin de analizar el comportamiento \\u00f3ptico de los recubrimientos utilizando espectroscop\\u00eda UV-Vis y FTIR. Una vez depositados los recubrimientos de SiO2 se sometieron a secado a temperatura ambiente y dentro de un horno tubular a 70 \\u00b0C. Por otro lado, las muestras de CuCoMn se trataron t\\u00e9rmicamente a diferentes temperaturas de recocido (550 \\u00b0C, 600 \\u00b0C y 650 \\u00b0C) durante 12 horas a una rampa de 1 \\u00b0C/min. Los resultados parciales obtenidos muestran que las pel\\u00edculas exhiben una absortancia entre 75% - 95 %, lo cual est\\u00e1 acorde con lo reportado en la literatura para este material. Sin embargo, para aumentar este valor es necesario ampliar el estudio del material, con el fin de definir su estructura, composici\\u00f3n y morfolog\\u00eda. El objetivo es obtener recubrimientos con las propiedades \\u00f3pticas y estructurales adecuadas con el fin de ser usados en la fabricaci\\u00f3n de la superficie absorbedora de calentadores de agua e instalaciones de energ\\u00eda solar.<\\/p>\",\n\"56360b84-72d3-42bc-b963-067e95e1ade3\"],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n\"2020-01-22\",\n\"Coretta\",\n\"Open Science in phonetics and phonology\",\n\"

Open Science is a movement that stresses the importance of a more honest and transparent scientific attitude by promoting a series of research principles and by warning from common, although not necessarily intentional, questionable practices and misconceptions. The term Open Science as a whole refers to the fundamental concepts of 'openness, transparency, rigour, reproducibility, replicability, and accumulation of knowledge' (Cruwell 2018). The goodness of the latter depends in great part on the reproducibility and replicability of the studies that contribute to knowledge accumulation.<\\/p>\",\n\"4c0ca94a-14f2-4f86-b014-ac47f7d5170c\"],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n\"2020-01-22\",\n\"Wekke\",\n\"Merumuskan Masalah Penelitian dengan Metode MAIL\",\n\"

Ringkasan kuliah di pascasarjana STAIN Sorong.<\\/p>\",\n\"494a322a-7a51-460b-8dcb-dcfbb405e9e6\"],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n\"2020-01-22\",\n\"Hern\\u00e1ndez-Caballero\",\n\"Epigen\\u00e9tica en c\\u00e1ncer\",\n\"

Las c\\u00e9lulas contienen informaci\\u00f3n determinada por el genoma propio del organismo al que pertenecen, lo cual le permite el desarrollo y diferenciaci\\u00f3n propios de su especie, en este sentido la informaci\\u00f3n epigen\\u00e9tica constituye una capa adicional de informaci\\u00f3n reguladora que vuelve m\\u00e1s complejos los procesos celulares. La metilaci\\u00f3n del DNA es la marca epigen\\u00e9tica de inactivaci\\u00f3n m\\u00e1s conocida y como el proceso reversible que es, consiste en un fen\\u00f3meno din\\u00e1mico que cambia durante la vida de la c\\u00e9lula. Los cambios epigen\\u00e9ticos inciden directamente en la conformaci\\u00f3n que adquiere la cromatina, con lo que se regula el c\\u00f3mo se expresen los genes y su actividad, a su vez, depende de modificaciones postraduccionales en las prote\\u00ednas histonas. Las histonas al igual que el DNA tambi\\u00e9n pueden presentar modificaciones epigen\\u00e9ticas.El c\\u00e1ncer es una patolog\\u00eda heterog\\u00e9nea que durante mucho tiempo se crey\\u00f3 era el resultado \\u00fanicamente de la adquisici\\u00f3n de mutaciones gen\\u00e9ticas o rearreglos cromos\\u00f3micos, que desembocaban en la p\\u00e9rdida del funcionamiento de genes encargados de evitar el crecimiento celular descontrolado y de la desregulaci\\u00f3n de la actividad de genes encargados de promover la proliferaci\\u00f3n. No obstante, la expresi\\u00f3n adecuada de los genes es fundamental para mantener el fenotipo celular normal, y el control de dicha expresi\\u00f3n va m\\u00e1s all\\u00e1 de la sola presencia de una secuencia gen\\u00e9tica sin cambio. Sin embargo, las alteraciones epigen\\u00e9ticas que preceden y contribuyen al inicio del desarrollo de un c\\u00e1ncer a\\u00fan no se conocen de forma precisa.Actualmente la metilaci\\u00f3n de DNA es la principal marca epigen\\u00e9tica m\\u00e1s ampliamente estudiada. La diversidad en el uso de t\\u00e9cnicas para realizar este cometido va desde m\\u00e9todos sencillos como el uso de enzimas de restricci\\u00f3n sensibles a la metilaci\\u00f3n, para digerir DNA gen\\u00f3mico y analizar peque\\u00f1as regiones de DNA, pasando por el uso de bisulfito de sodio para analizar el estado de metilaci\\u00f3n en las citosinas hasta los m\\u00e9todos actuales de secuenciaci\\u00f3n a gran escala que permiten el an\\u00e1lisis simultaneo de gran cantidad de muestras y de amplias regiones del genoma completo, llegando a analizar hasta 3 millones de variantes gen\\u00e9ticas en un individuo. A la par, se ha desarrollado software especializado en epigen\\u00e9tica, permitiendo conocer la ubicaci\\u00f3n de sitios de metilaci\\u00f3n para luego hacer su b\\u00fasqueda en muestras biol\\u00f3gicas y se han desarrollado programas complejos para el an\\u00e1lisis de datos masivos obtenidos a trav\\u00e9s del uso de plataformas basadas en hibridaci\\u00f3n (microarreglos) y la secuenciaci\\u00f3n masiva con diversas afinidades (DNA-seq, RNA-seq, ChIP-seq, FAIRE-seq, ATAC-seq, MeDIP-seq, MBD-seq) y WGBS. Los cambios epigen\\u00e9ticos aberrantes en el c\\u00e1ncer pueden ser evidentes desde etapas tempranas, lo que ha llevado a pensar que, esta desregulaci\\u00f3n precede de hecho a los eventos tumorales transformadores preliminares cl\\u00e1sicos (mutaciones de supresores y/o protooncogenes e inestabilidad gen\\u00f3mica). Entre las alteraciones epigen\\u00e9ticas m\\u00e1s reconocidas en los tumores est\\u00e1 el silenciamiento asociado a hipermetilaci\\u00f3n de islas CpG en los promotores de los genes supresores como CDKN2A y RASSF1.Aunado a esto, los miRNAs tambi\\u00e9n pueden actuar como supresores u oncogenes en diferentes tipos de c\\u00e1ncer. Es por esto que, las modificaciones epigen\\u00e9ticas son un componente importante en la etiolog\\u00eda del c\\u00e1ncer y debido a su reversibilidad constituyen blancos terap\\u00e9uticos prometedores para diagnostico o tratamiento y potencial como posibles biomarcadores.<\\/p>\",\n\"4745cfed-18ac-465f-80b5-59c437a8ab2d\"],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n\"2020-01-22\",\n\"Joyce\",\n\"Scientific Racism 2.0 (SR2.0): An erroneous argument from genetics which inadvertently refines scientific racism\",\n\"

SR2.0 refers to a prominent argument made by some geneticists, often via social and popular media, which inadvertently amounts to a refinement of scientific racism. At face value it is an attack on racism in science. Upon closer inspection its primary, possibly unconscious, purpose appears to be to protect contemporary genetic research from the charge of racism. The argument is often made alongside an emphasis upon long-falsified errors of early science and open expressions of racism in wider society, rather than the intelligence and statistical theory which has informed both genetics and the social construct scientific racism for a century. The core argument is invalid. It also has profound epistemological failings, including misunderstanding the nature of social constructions and how they how they interact with empirical facts. Finally, the proponents do not fully support their own argument; this exposes the argument\\u2019s substantive function as a defensive holding device.<\\/p>\",\n\"879bff9a-169b-425f-a027-11f04e0448ea\"],\n [{\n 'v': 9,\n 'f': \"9\",\n },\n\"2020-01-22\",\n\"Sinar\",\n\"Functional Features of Forensic Corruption Case in Indonesia\",\n\"

This study examines the multimodal use of language affecting the social interaction in the Indonesian Court for Corruption Crimes as the research data source. The objective is to analyze the metafunction multimodal functional features of law enforcement and witnesses in the proceedings of forensic corruption case in Indonesia. Multimodal theory as a new technology that has been invented by linguists was used in this research to analyse forensic language. The findings showed that the multimodal systems were valuable in analysing the forensic functional features in the court room and the functional features of representational, interactive and compositional meanings were present in the court room involving gestures, postures, gazes, nonverbal communication, eye contacts, etc.<\\/p>\",\n\"e262d61c-32bb-4690-b814-e20ee7add13f\"]],\n columns: [[\"number\", \"index\"], [\"string\", \"rejected_date\"], [\"string\", \"first_author\"], [\"string\", \"title\"], [\"string\", \"abstract\"], [\"string\", \"submission_id\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n

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rejected_datefirst_authortitleabstractsubmission_id
02020-01-22KongPredicting Prolonged Length of Hospital Stay f...<sec>\\n BACKGROUND\\n ...6ef9af47-8fa7-4abb-af69-0c8a522b6f9a
12020-01-22BowmanOSF Prereg Template<p>Preregistration is the act of submitting a ...2239203f-fa3b-4402-a185-1398861aba66
22020-01-22Di SiaOn the Concept of Time in everyday Life and be...<p>In this paper I consider the concept of tim...7dc54f58-2a6c-44eb-b906-98a184d1bac1
32020-01-22Di SiaBirth and development of quantum physics: a tr...<p>The last century has been a period of extre...b466b2fd-b0c3-4573-abc5-229532212be1
42020-01-22BedoyaFabricación de capas antirreflejantes y absorb...<p>Se prepararon películas delgadas de SiO2 en...56360b84-72d3-42bc-b963-067e95e1ade3
52020-01-22CorettaOpen Science in phonetics and phonology<p>Open Science is a movement that stresses th...4c0ca94a-14f2-4f86-b014-ac47f7d5170c
62020-01-22WekkeMerumuskan Masalah Penelitian dengan Metode MAIL<p>Ringkasan kuliah di pascasarjana STAIN Soro...494a322a-7a51-460b-8dcb-dcfbb405e9e6
72020-01-22Hernández-CaballeroEpigenética en cáncer<p>Las células contienen información determina...4745cfed-18ac-465f-80b5-59c437a8ab2d
82020-01-22JoyceScientific Racism 2.0 (SR2.0): An erroneous ar...<p>SR2.0 refers to a prominent argument made b...879bff9a-169b-425f-a027-11f04e0448ea
92020-01-22SinarFunctional Features of Forensic Corruption Cas...<p>This study examines the multimodal use of l...e262d61c-32bb-4690-b814-e20ee7add13f
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Preregistration is the act of submitting a ... \n", + "2

In this paper I consider the concept of tim... \n", + "3

The last century has been a period of extre... \n", + "4

Se prepararon películas delgadas de SiO2 en... \n", + "5

Open Science is a movement that stresses th... \n", + "6

Ringkasan kuliah di pascasarjana STAIN Soro... \n", + "7

Las células contienen información determina... \n", + "8

SR2.0 refers to a prominent argument made b... \n", + "9

This study examines the multimodal use of l... \n", + "\n", + " submission_id \n", + "0 6ef9af47-8fa7-4abb-af69-0c8a522b6f9a \n", + "1 2239203f-fa3b-4402-a185-1398861aba66 \n", + "2 7dc54f58-2a6c-44eb-b906-98a184d1bac1 \n", + "3 b466b2fd-b0c3-4573-abc5-229532212be1 \n", + "4 56360b84-72d3-42bc-b963-067e95e1ade3 \n", + "5 4c0ca94a-14f2-4f86-b014-ac47f7d5170c \n", + "6 494a322a-7a51-460b-8dcb-dcfbb405e9e6 \n", + "7 4745cfed-18ac-465f-80b5-59c437a8ab2d \n", + "8 879bff9a-169b-425f-a027-11f04e0448ea \n", + "9 e262d61c-32bb-4690-b814-e20ee7add13f " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from uuid import uuid4\n", + "\n", + "rejected_publications = []\n", + "\n", + "preprints = dsl.query(\n", + " # This is quite a specific search for preprints published on 2020-01-22\n", + " \"\"\"\n", + " search publications\n", + " where type = \"preprint\" and date = \"2020-01-22\" and abstract is not empty\n", + " return publications[date+title+abstract+authors]\n", + " limit 10\n", + " \"\"\"\n", + ")\n", + "\n", + "for p in preprints.json[\"publications\"]:\n", + " # This will be a row of our data:\n", + " rejected_article_data_row = {\n", + " \"rejected_date\": None, # Initialising the rows with null values\n", + " \"first_author\": None,\n", + " \"title\": None,\n", + " \"abstract\": None\n", + " }\n", + " rejected_article_data_row['rejected_date'] = p['date']\n", + " rejected_article_data_row['title'] = p['title']\n", + " rejected_article_data_row['abstract'] = p['abstract']\n", + " for order, a in enumerate(p[\"authors\"]):\n", + " if order == 0: # i.e. first author\n", + " rejected_article_data_row['first_author'] = a['last_name']\n", + " rejected_publications.append(rejected_article_data_row)\n", + "\n", + "rejected_publication_data = pd.DataFrame(rejected_publications)\n", + "\n", + "rejected_publication_data['submission_id'] = [\n", + " str(uuid4()) for _ in range(len(rejected_publication_data))\n", + "]\n", + "\n", + "rejected_publication_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4gO-Sw9RkPKx" + }, + "source": [ + "## 2. Define the search template\n", + "\n", + "Python concatenates multiple strings one after another in brackets, so we have written it out as shown below so that we can add comments to the query. This format isn't necessary, but hopefully it's helpful!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "id": "UcVe6Ocm6Fiv", + "outputId": "cb54d3a0-9236-4981-c629-d6f56ab69ab2" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'search publications in title_abstract_only for \"{title}\" where date > \"{rejected_date}\" and (authors = \"{first_author}\") return publications[date+doi+title+abstract] limit 1'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "template = (\n", + " 'search publications '\n", + " 'in title_abstract_only ' # Search the whole of the publication\n", + " 'for \"{title}\" ' # Stop words will be automatically excluded\n", + " 'where date > \"{rejected_date}\" '\n", + " 'and ('\n", + " 'authors = \"{first_author}\"'\n", + " # The line below gives an example of how you could also search for\n", + " # the surname of the corresponding author if you have it:\n", + " # ' or authors = \"{corresponding_author}\"'\n", + " ') '\n", + " 'return publications['\n", + " 'date' # Published date\n", + " '+'\n", + " 'doi' # DOI of the published article\n", + " '+'\n", + " 'title' # Title of the published article\n", + " '+'\n", + " 'abstract' # Abstract of the published article\n", + " '] '\n", + " 'limit 1' # Get the most relevant result only\n", + ")\n", + "\n", + "template" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wAIXlRBUkiU8" + }, + "source": [ + "## 3. Iteratively Query the Dimensions API for the retracted articles" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 250, + "referenced_widgets": [ + "a3690f3660a74a0e9af686a2f6ca8304", + "03a4c6bae6ca429f96347855a43adac9", + "62b20a5d1b1649ab8a2d76c3f4ab7981", + "c39293df4cb24387bf8f2638f241c824", + "8ab3cbaeb64a413fb51632f9ef182e9c", + "f2c1b4851d644eb3bdf048721d255a88", + "06fefdea62084b6097b03a497734dbc2", + "bd7d718456174f2794a2a2593cf1144a", + "c96c1481e38f4c82872c15eda63cde58", + "9c5c614ecf7e4fec9e02778e0a938762", + "aeeb243ec979404e8d80b3ef1c0a4070" + ] + }, + "id": "Legvd8_cpPq4", + "outputId": "94828b40-3f54-4327-d93f-d35a9f58958d" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a3690f3660a74a0e9af686a2f6ca8304", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10 [00:00Functional Features of Forensic Corruption Case in Indonesia

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Functional Features of Forensic Corruption ... 2020-02-28 \n", + "\n", + " doi \n", + "0 10.23880/eoij-16000268 \n", + "0 10.31228/osf.io/m3xa6 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import string\n", + "from tqdm.notebook import tqdm\n", + "\n", + "def no_punctuation(s: str) -> str:\n", + " \"\"\"\n", + " Remove punctuation from a python string\n", + " \"\"\"\n", + " return s.translate(str.maketrans('', '', string.punctuation))\n", + "\n", + "# We'll store all our results in this list as we iterate, then join them together at the end...\n", + "results = []\n", + "\n", + "# For each row in the data set as a python dictionary:\n", + "for row in tqdm(rejected_publication_data.to_dict(orient=\"records\")):\n", + " row['title'] = no_punctuation(row['title'])\n", + " query = template.format(**row)\n", + " best = dsl.query(query, verbose=False).as_dataframe()\n", + " best['submission_id'] = row['submission_id']\n", + " results.append(best)\n", + "\n", + "# Join results together\n", + "output = pd.concat(results)\n", + "\n", + "output.head() # .head() shows just a few rows" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zze35aGEHF8Z" + }, + "source": [ + "\n", + "### 4. Join together the input and output data" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1292 + }, + "id": "jtBd5CK-HRSj", + "outputId": "f7fab76f-9fae-40f5-e382-9535c11896a1" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"merged_results\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"rejected_date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"2020-01-22\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"first_author\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"Joyce\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title_x\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Scientific Racism 2.0 (SR2.0): An erroneous argument from genetics which inadvertently refines scientific racism\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"abstract_x\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"

SR2.0 refers to a prominent argument made by some geneticists, often via social and popular media, which inadvertently amounts to a refinement of scientific racism. At face value it is an attack on racism in science. Upon closer inspection its primary, possibly unconscious, purpose appears to be to protect contemporary genetic research from the charge of racism. The argument is often made alongside an emphasis upon long-falsified errors of early science and open expressions of racism in wider society, rather than the intelligence and statistical theory which has informed both genetics and the social construct scientific racism for a century. The core argument is invalid. It also has profound epistemological failings, including misunderstanding the nature of social constructions and how they how they interact with empirical facts. Finally, the proponents do not fully support their own argument; this exposes the argument\\u2019s substantive function as a defensive holding device.

\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"submission_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"879bff9a-169b-425f-a027-11f04e0448ea\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title_y\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Functional Features of Forensic Corruption Case in Indonesia\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"abstract_y\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"

Functional Features of Forensic Corruption Case in Indonesia

\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2020-02-28 00:00:00\",\n \"max\": \"2021-01-01 00:00:00\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"2020-02-28\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"doi\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"10.31228/osf.io/m3xa6\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "merged_results" + }, + "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/e523c247d1e24a05/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"2020-01-22\",\n\"Kong\",\n\"Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis\\u2013Treated Patients Using Stacked Generalization: Model Development and Validation Study (Preprint)\",\n\"\\n BACKGROUND\\n

The increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients.<\\/p>\\n <\\/sec>\\n \\n OBJECTIVE\\n

This study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data.<\\/p>\\n <\\/sec>\\n \\n METHODS\\n

Patient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model.<\\/p>\\n <\\/sec>\\n \\n RESULTS\\n

The final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided <i>t</i> tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy.<\\/p>\\n <\\/sec>\\n \\n CONCLUSIONS\\n

This study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.<\\/p>\\n <\\/sec>\",\n\"6ef9af47-8fa7-4abb-af69-0c8a522b6f9a\",\nNaN,\nNaN,\nNaN,\nNaN],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"2020-01-22\",\n\"Bowman\",\n\"OSF Prereg Template\",\n\"

Preregistration is the act of submitting a study plan, ideally also with analytical plan, to a registry prior to conducting the work. Preregistration increases the discoverability of research even if it does not get published further. Adding specific analysis plans can clarify the distinction between planned, confirmatory tests and unplanned, exploratory research. This preprint contains a template for the \\u201cOSF Prereg\\u201d form available from the OSF Registry. An earlier version was originally developed for the Preregistration Challenge, an education campaign designed to initiate preregistration as a habit prior to data collection in basic research, funded by the Laura and John Arnold Foundation (now Arnold Ventures) and conducted by the Center for Open Science. More information is available at https://cos.io/prereg, and other templates are available at: https://osf.io/zab38/<\\/p>\",\n\"2239203f-fa3b-4402-a185-1398861aba66\",\nNaN,\nNaN,\nNaN,\nNaN],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"2020-01-22\",\n\"Di Sia\",\n\"On the Concept of Time in everyday Life and between Physics and Mathematics\",\n\"

In this paper I consider the concept of time in a general way as daily human time andthen within physics with relation to mathematics. I focus the attention on quantum mechanics, with its particular peculiarities, examining peculiar important questions like the temporal asymmetry, the Prigogine\\u2019s position and the time-reversal operator of Wigner. I conclude considering the theme of the temporal asymmetry in relation to decoherence and irreversibility. Interesting imputs related to education science will be done.<\\/p>\",\n\"7dc54f58-2a6c-44eb-b906-98a184d1bac1\",\n\"On the Concept of Time in Everyday Life and between Physics and Mathematics\",\n\"In this paper I consider the concept of time in a general way as daily human time and then within physics with relation to mathematics. I consider the arrow of time and then focus the attention on quantum mechanics, with its particular peculiarities, examining important concepts like temporal asymmetry, complexity, decoherence, irreversibility, information theory, chaos theory. In conclusion I consider the notion of time connected to a new theory in progress, called \\u201cPrimordial Dynamic Space\\u201d theory.\",\n\"2021-01-01\",\n\"10.23880/eoij-16000268\"],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"2020-01-22\",\n\"Di Sia\",\n\"Birth and development of quantum physics: a transdisciplinary approach\",\n\"

The last century has been a period of extreme interest for scientific research, marked by the overcoming of the classical frontiers of scientific knowledge.Research oriented towards the infinitely small and infinitely big, in both cases beyondthe borders of the visible. Quantum physics has led to a new Copernican revolution,opening the way to new questions that have led to a new view of reality. At the sametime, new theories have developed, involving every field of science, philosophy and art, rediscovering the link between unity and totality and the importance of humanpotential. In a transdisciplinary approach we consider quantum field theory, new ideason the concepts of vacuum and entanglement, metaphysical aspects of quantum revolution and the introduction of different interpretative approaches on the \\u201cWhole\\u201d.<\\/p>\",\n\"b466b2fd-b0c3-4573-abc5-229532212be1\",\nNaN,\nNaN,\nNaN,\nNaN],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n\"2020-01-22\",\n\"Bedoya\",\n\"Fabricaci\\u00f3n de capas antirreflejantes y absorbedores solares mediante la t\\u00e9cnica Sol-gel: Un resumen sobre la variaci\\u00f3n de s\\u00edntesis y condiciones experimentales realizadas en la UTP\",\n\"

Se prepararon pel\\u00edculas delgadas de SiO2 en relaci\\u00f3n molar TEOS:H2O:EtOH 1:18:1.8 y CuCoMn en relaci\\u00f3n molar Cu:Co:Mn 1:3:3 por el m\\u00e9todo de recubrimiento por inmersi\\u00f3n (Sol-gel), bajo condiciones fijas de velocidad de dep\\u00f3sito y n\\u00famero de capas. Inicialmente se usaron sustratos de vidrios con el fin de analizar el comportamiento \\u00f3ptico de los recubrimientos utilizando espectroscop\\u00eda UV-Vis y FTIR. Una vez depositados los recubrimientos de SiO2 se sometieron a secado a temperatura ambiente y dentro de un horno tubular a 70 \\u00b0C. Por otro lado, las muestras de CuCoMn se trataron t\\u00e9rmicamente a diferentes temperaturas de recocido (550 \\u00b0C, 600 \\u00b0C y 650 \\u00b0C) durante 12 horas a una rampa de 1 \\u00b0C/min. Los resultados parciales obtenidos muestran que las pel\\u00edculas exhiben una absortancia entre 75% - 95 %, lo cual est\\u00e1 acorde con lo reportado en la literatura para este material. Sin embargo, para aumentar este valor es necesario ampliar el estudio del material, con el fin de definir su estructura, composici\\u00f3n y morfolog\\u00eda. El objetivo es obtener recubrimientos con las propiedades \\u00f3pticas y estructurales adecuadas con el fin de ser usados en la fabricaci\\u00f3n de la superficie absorbedora de calentadores de agua e instalaciones de energ\\u00eda solar.<\\/p>\",\n\"56360b84-72d3-42bc-b963-067e95e1ade3\",\nNaN,\nNaN,\nNaN,\nNaN]],\n columns: [[\"number\", \"index\"], [\"string\", \"rejected_date\"], [\"string\", \"first_author\"], [\"string\", \"title_x\"], [\"string\", \"abstract_x\"], [\"string\", \"submission_id\"], [\"string\", \"title_y\"], [\"string\", \"abstract_y\"], [\"string\", \"date\"], [\"string\", \"doi\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n

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32020-01-22Di SiaBirth and development of quantum physics: a tr...<p>The last century has been a period of extre...b466b2fd-b0c3-4573-abc5-229532212be1NaNNaNNaNNaN
42020-01-22BedoyaFabricación de capas antirreflejantes y absorb...<p>Se prepararon películas delgadas de SiO2 en...56360b84-72d3-42bc-b963-067e95e1ade3NaNNaNNaNNaN
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Se prepararon películas delgadas de SiO2 en... \n", + "\n", + " submission_id \\\n", + "0 6ef9af47-8fa7-4abb-af69-0c8a522b6f9a \n", + "1 2239203f-fa3b-4402-a185-1398861aba66 \n", + "2 7dc54f58-2a6c-44eb-b906-98a184d1bac1 \n", + "3 b466b2fd-b0c3-4573-abc5-229532212be1 \n", + "4 56360b84-72d3-42bc-b963-067e95e1ade3 \n", + "\n", + " title_y \\\n", + "0 NaN \n", + "1 NaN \n", + "2 On the Concept of Time in Everyday Life and be... \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " abstract_y date \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 In this paper I consider the concept of time i... 2021-01-01 \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " doi \n", + "0 NaN \n", + "1 NaN \n", + "2 10.23880/eoij-16000268 \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_results = pd.merge(\n", + " rejected_publication_data,\n", + " output,\n", + " left_on='submission_id',\n", + " right_on='submission_id',\n", + " how='left')\n", + "\n", + "merged_results.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WHfO5HvqbWoR" + }, + "source": [ + "## 5. Add Matching Score\n", + "\n", + "We have found some publications that might match our rejected articles. Now we need to score them to see whether they are good matches.\n", + "\n", + "In this case we'll measure the edit distance between the titles. The most commonly-used edit distance between strings is [Levensthtein distance](https://en.wikipedia.org/wiki/Jaccard_index), which is [nicely implemented in Python in the `Levenshtein` package](https://rapidfuzz.github.io/Levenshtein/).\n", + "\n", + "The `Levenshtein` package has a function \"ratio\" which uses Levenshtein distance to get a similarity (not distance) score between 0 (disimilar) and 1 (identical). We will use this to compare titles converted to lowercase.\n", + "\n", + "Sorting the results by score descending (from highest to lowest) we can see that there was one good match. If we wanted to make the matching more automatic, we could choose to filter out everything with a score less than e.g. 0.75." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3pxX8k8u52JA", + "outputId": "7570c530-8fba-49cb-e6de-4a7474bbe080" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7111111111111111\n", + "0.9090909090909091\n" + ] + } + ], + "source": [ + "from Levenshtein import ratio\n", + "\n", + "def similarity(string1: str, string2: str) -> float:\n", + " \"\"\"\n", + " Case-insensitive similarity score made by subtracting the normalised\n", + " Levenshtein distance from 1.\n", + " \"\"\"\n", + " if pd.isna(string1) or pd.isna(string2):\n", + " return 0.\n", + " else:\n", + " return ratio(string1.lower(), string2.lower())\n", + "\n", + "print(similarity('The cat sat on the mat', 'The dog sat on the frog'))\n", + "print(similarity('The cat sat on the mat', 'The mat sat on the cat'))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 246 + }, + "id": "wNcx52H3zDnz", + "outputId": "421be014-42a1-4fc2-8362-052bc7accb80" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "repr_error": "0", + "type": "dataframe" + }, + "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/e523c247d1e24a05/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 2,\n 'f': \"2\",\n },\n\"7dc54f58-2a6c-44eb-b906-98a184d1bac1\",\n\"2020-01-22\",\n\"On the Concept of Time in everyday Life and between Physics and Mathematics\",\n\"On the Concept of Time in Everyday Life and between Physics and Mathematics\",\n\"

In this paper I consider the concept of time in a general way as daily human time andthen within physics with relation to mathematics. I focus the attention on quantum mechanics, with its particular peculiarities, examining peculiar important questions like the temporal asymmetry, the Prigogine\\u2019s position and the time-reversal operator of Wigner. I conclude considering the theme of the temporal asymmetry in relation to decoherence and irreversibility. Interesting imputs related to education science will be done.<\\/p>\",\n\"In this paper I consider the concept of time in a general way as daily human time and then within physics with relation to mathematics. I consider the arrow of time and then focus the attention on quantum mechanics, with its particular peculiarities, examining important concepts like temporal asymmetry, complexity, decoherence, irreversibility, information theory, chaos theory. In conclusion I consider the notion of time connected to a new theory in progress, called \\u201cPrimordial Dynamic Space\\u201d theory.\",\n\"10.23880/eoij-16000268\",\n{\n 'v': 0.7055393586005831,\n 'f': \"0.7055393586005831\",\n }]],\n columns: [[\"number\", \"index\"], [\"string\", \"submission_id\"], [\"string\", \"rejected_date\"], [\"string\", \"original_title\"], [\"string\", \"published_title\"], [\"string\", \"abstract_x\"], [\"string\", \"abstract_y\"], [\"string\", \"doi\"], [\"number\", \"score\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n

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In this paper I consider the concept of tim... \n", + "\n", + " abstract_y doi \\\n", + "2 In this paper I consider the concept of time i... 10.23880/eoij-16000268 \n", + "\n", + " score \n", + "2 0.705539 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_results['score'] = merged_results.apply(\n", + " lambda row: similarity(row['abstract_x'], row['abstract_y']),\n", + " axis=1\n", + ")\n", + "\n", + "merged_results = merged_results.sort_values(\"score\", ascending=False)\n", + "\n", + "final_output = merged_results[[\n", + " 'submission_id',\n", + " 'rejected_date',\n", + " 'title_x',\n", + " 'title_y',\n", + " 'abstract_x',\n", + " 'abstract_y',\n", + " 'doi',\n", + " 'score'\n", + "]]\n", + "\n", + "final_output.columns = [\n", + " 'submission_id',\n", + " 'rejected_date',\n", + " 'original_title',\n", + " 'published_title',\n", + " 'abstract_x',\n", + " 'abstract_y',\n", + " 'doi',\n", + " 'score'\n", + "]\n", + "\n", + "final_output" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l639SwiweHiZ" + }, + "source": [ + "## 6. Conclusion\n", + "\n", + "In this tutorial we have shown how to use the Dimensions API to search for articles with titles and abstracts that contain similar terms to the titles of articles that have been rejected in the past.\n", + "\n", + "In terms of next steps, we might choose to do some bibliometric analysis of the articles we rejected. We could also try to improve our search process by extracting keywords from our article abstracts and searching for those too." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "03a4c6bae6ca429f96347855a43adac9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f2c1b4851d644eb3bdf048721d255a88", + "placeholder": "​", + "style": "IPY_MODEL_06fefdea62084b6097b03a497734dbc2", + 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"metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "AuCkj0Qwywjy" + }, + "source": [ + "# The Organizations API: Features Overview\n", + "\n", + "This tutorial provides an overview of the [Organizations data source](https://docs.dimensions.ai/dsl/datasource-organizations.html) available via the [Dimensions Analytics API](https://docs.dimensions.ai/dsl/). \n", + "\n", + "The topics covered in this notebook are:\n", + "\n", + "* How to align your affiliation data with Dimensions using the API [disambiguation service](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations) \n", + "* How to retrieve organizations metadata using the [search fields](https://docs.dimensions.ai/dsl/datasource-organizations.html) available\n", + "* How to use the [schema API](https://docs.dimensions.ai/dsl/data-sources.html#metadata-api) to obtain some statistics about the Organizations data available \n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==\n", + "CHANGELOG\n", + "This notebook was last run on Sep 10, 2025\n", + "==\n" + ] + } + ], + "source": [ + "import datetime\n", + "print(\"==\\nCHANGELOG\\nThis notebook was last run on %s\\n==\" % datetime.date.today().strftime('%b %d, %Y'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "OwTp1dybd2FF" + }, + "source": [ + "## Prerequisites\n", + "\n", + "This notebook assumes you have installed the [Dimcli](https://pypi.org/project/dimcli/) library and are familiar with the ['Getting Started' tutorial](https://api-lab.dimensions.ai/cookbooks/1-getting-started/1-Using-the-Dimcli-library-to-query-the-API.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "Collapsed": "false" + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[2mSearching config file credentials for 'https://app.dimensions.ai' endpoint..\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==\n", + "Logging in..\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", + "\u001b[2mMethod: dsl.ini file\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install dimcli tqdm plotly -U --quiet\n", + "\n", + "import dimcli\n", + "from dimcli.utils import *\n", + "\n", + "import json, sys, time\n", + "import pandas as pd\n", + "from tqdm.notebook import tqdm as pbar\n", + "import plotly.express as px # plotly>=4.8.1\n", + "if not 'google.colab' in sys.modules:\n", + " # make js dependecies local / needed by html exports\n", + " from plotly.offline import init_notebook_mode\n", + " init_notebook_mode(connected=True)\n", + "#\n", + "\n", + "print(\"==\\nLogging in..\")\n", + "# https://digital-science.github.io/dimcli/getting-started.html#authentication\n", + "ENDPOINT = \"https://app.dimensions.ai\"\n", + "if 'google.colab' in sys.modules:\n", + " import getpass\n", + " KEY = getpass.getpass(prompt='API Key: ') \n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "else:\n", + " KEY = \"\"\n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "dsl = dimcli.Dsl()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "JcnVEdOAywj3" + }, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "8zxcg9gPgZAv" + }, + "source": [ + "## 1. Matching affiliation data to Dimensions Organization IDs using `extract_affiliations`\n", + "\n", + "The API function `extract_affiliations` ([docs](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations)) can be used to enrich private datasets including non-disambiguated organizations data with Dimensions IDs, so to then take advantage of the wealth of linked data available in Dimensions.\n", + "\n", + "For example, let's assume our dataset has four columns (*affiliation name*, *city*, *state* and *country*) - any of which can be empty of course. Like this:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "Collapsed": "false", + "colab": {}, + "colab_type": "code", + "id": "cj5zBndjhgMM" + }, + "outputs": [], + "source": [ + "affiliations = [\n", + " ['University of Nebraska–Lincoln', 'Lincoln', 'Nebraska', 'United States'],\n", + " ['Tarbiat Modares University', 'Tehran', '', 'Iran'],\n", + " ['Harvard University', 'Cambridge', 'Massachusetts', 'United States'],\n", + " ['China Academy of Chinese Medical Sciences', 'Beijing', '', 'China'],\n", + " ['Liaoning University', 'Shenyang', '', 'China'],\n", + " ['Liaoning Normal University', 'Dalian', '', 'China'],\n", + " ['P.G. Department of Zoology and Research Centre, Shri Shiv Chhatrapati College of Arts, Commerce and Science, Junnar 410502, Pune, India.', '', '', ''],\n", + " ['Sungkyunkwan University', 'Seoul', '', 'South Korea'],\n", + " ['Centre for Materials for Electronics Technology', 'Pune', '', 'India'],\n", + " ['Institut Necker-Enfants Malades (INEM), INSERM U1151-CNRS UMR8253, Université de Paris, Faculté de Médecine, 156 rue de Vaugirard, 75730 Paris Cedex 15, France', '', '', '']\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "AcAypP1agx3M" + }, + "source": [ + "We want to look up Dimensions Organization identifiers for those affiliations using the **structured** affiliation matching. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 256, + "resources": { + "http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css": { + "data": "/* Copyright (c) Jupyter Development Team.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

*/

 /*
 * The following section is derived from https://github.com/phosphorjs/phosphor/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css 
 * We've scoped the rules so that they are consistent with exactly our code.
 */

 .jupyter-widgets.widget-tab > .p-TabBar {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
  margin: 0;
  padding: 0;
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  list-style-type: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] > .p-TabBar-content {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] > .p-TabBar-content {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
  overflow: hidden;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
  -webkit-box-flex: 0;
      -ms-flex: 0 0 auto;
          flex: 0 0 auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel {
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  overflow: hidden;
  white-space: nowrap;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden {
  display: none !important;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab {
  position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab {
  left: 0;
  -webkit-transition: left 150ms ease;
  transition: left 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab {
  top: 0;
  -webkit-transition: top 150ms ease;
  transition: top 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging {
  -webkit-transition: none;
  transition: none;
}

 /* End tabbar.css */

 :root { /* margin between inline elements */

    /* From Material Design Lite */
}

 .jupyter-widgets {
    margin: 2px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    color: black;
    overflow: visible;
}

 .jupyter-widgets.jupyter-widgets-disconnected::before {
    line-height: 28px;
    height: 28px;
}

 .jp-Output-result > .jupyter-widgets {
    margin-left: 0;
    margin-right: 0;
}

 /* vbox and hbox */

 .widget-inline-hbox {
    /* Horizontal widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
    -webkit-box-align: baseline;
        -ms-flex-align: baseline;
            align-items: baseline;
}

 .widget-inline-vbox {
    /* Vertical Widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
    overflow: auto;
}

 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
}

 .widget-vbox {
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 /* General Button Styling */

 .jupyter-button {
    padding-left: 10px;
    padding-right: 10px;
    padding-top: 0px;
    padding-bottom: 0px;
    display: inline-block;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
    border: 0px solid;
    line-height: 28px;
    -webkit-box-shadow: none;
            box-shadow: none;

    color: rgba(0, 0, 0, .8);
    background-color: #EEEEEE;
    border-color: #E0E0E0;
    border: none;
}

 .jupyter-button i.fa {
    margin-right: 4px;
    pointer-events: none;
}

 .jupyter-button:empty:before {
    content: "\200b"; /* zero-width space */
}

 .jupyter-widgets.jupyter-button:disabled {
    opacity: 0.6;
}

 .jupyter-button i.fa.center {
    margin-right: 0;
}

 .jupyter-button:hover:enabled, .jupyter-button:focus:enabled {
    /* MD Lite 2dp shadow */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

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"metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "YBSdHL4Tywj4", + "toc-hr-collapsed": false + }, + "source": [ + "## 2. Searching the API for organizations \n", + "\n", + "This can be done using full text search and/or fielded search. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "OAwuhlQmd2FK" + }, + "source": [ + "### Full-text search " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1315, + "status": "ok", + "timestamp": 1574702298940, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "qUz8_6M0d2Fa", + "outputId": "c8f58fc6-0e68-4a79-ef20-9dafbd0164f6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Organizations: 10 (total = 352)\n", + "\u001b[2mTime: 5.56s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "

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idnamecountry_codecountry_nametypescity_namestate_name
0grid.798367.4Bank of New YorkUSUnited States[Company]NaNNaN
1grid.798343.2Research Foundation of University of New YorkUSUnited States[Education]NaNNaN
2grid.797561.bNew York Hospital-Cornell Medical CenterUSUnited States[Healthcare]New YorkNew York
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4grid.796173.dBank of New York Mellon Trust Co NAUSUnited States[Company]NaNNaN
5grid.795276.8New York University Medical CenterUSUnited States[Education]New YorkNew York
6grid.794869.dInternational General Electric Company of New ...USUnited States[Other]NaNNaN
7grid.782261.8New York Digital Investment Group LLCUSUnited States[Other]NaNNaN
8grid.778414.9China CITIC Bank International Ltd New York Br...USUnited States[Government]NaNNaN
9grid.777726.4Morgan Guaranty Trust Company of New YorkUSUnited States[Company]NaNNaN
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" + ], + "text/plain": [ + " id name \\\n", + "0 grid.798367.4 Bank of New York \n", + "1 grid.798343.2 Research Foundation of University of New York \n", + "2 grid.797561.b New York Hospital-Cornell Medical Center \n", + "3 grid.796770.8 Research Foundation of City University of New ... \n", + "4 grid.796173.d Bank of New York Mellon Trust Co NA \n", + "5 grid.795276.8 New York University Medical Center \n", + "6 grid.794869.d International General Electric Company of New ... \n", + "7 grid.782261.8 New York Digital Investment Group LLC \n", + "8 grid.778414.9 China CITIC Bank International Ltd New York Br... \n", + "9 grid.777726.4 Morgan Guaranty Trust Company of New York \n", + "\n", + " country_code country_name types city_name state_name \n", + "0 US United States [Company] NaN NaN \n", + "1 US United States [Education] NaN NaN \n", + "2 US United States [Healthcare] New York New York \n", + "3 US United States [Other] NaN NaN \n", + "4 US United States [Company] NaN NaN \n", + "5 US United States [Education] New York New York \n", + "6 US United States [Other] NaN NaN \n", + "7 US United States [Other] NaN NaN \n", + "8 US United States [Government] NaN NaN \n", + "9 US United States [Company] NaN NaN " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york\" \n", + "return organizations limit 10" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 252 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1809, + "status": "ok", + "timestamp": 1574702323641, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "P3UWAR0QkkKg", + "outputId": "9e1be9ab-e3cf-4aca-f621-27f8b93a8a91" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Organizations: 9 (total = 9)\n", + "\u001b[2mTime: 0.62s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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idnamecountry_codecountry_nametypesacronymcity_namelatitudelinkoutlongitudestate_name
0grid.757191.cNew York Community BankUSUnited States[Company]NaNNaNNaNNaNNaNNaN
1grid.507861.dMohawk Valley Community CollegeUSUnited States[Education]MVCCUtica43.076850[https://www.mvcc.edu/]-75.220120New York
2grid.490742.cHealth Foundation for Western & Central New YorkUSUnited States[Nonprofit]NaNBuffalo42.874810[https://hfwcny.org/]-78.849690New York
3grid.480917.3New York Community TrustUSUnited States[Nonprofit]NaNNew York40.758870[http://www.nycommunitytrust.org/]-73.968185New York
4grid.478715.8Central New York Community FoundationUSUnited States[Nonprofit]CNYCFSyracuse43.056038[https://www.cnycf.org/]-76.148210New York
5grid.475804.aCommunity Service Society of New YorkUSUnited States[Other]CSSNew York40.749622[http://www.cssny.org/]-73.974620New York
6grid.475783.aLong Term Care Community CoalitionUSUnited States[Other]LTCCCNew York40.751163[http://www.ltccc.org/]-73.992470New York
7grid.429257.fKorean Community Services of Metropolitan New ...USUnited States[Nonprofit]KCSNew York40.770954[https://www.kcsny.org/]-73.786670New York
8funder.196228Community Health Foundation of Western and Cen...NaNUnited StatesNaNCommunity Health Foundation of Western and CentraNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " id name \\\n", + "0 grid.757191.c New York Community Bank \n", + "1 grid.507861.d Mohawk Valley Community College \n", + "2 grid.490742.c Health Foundation for Western & Central New York \n", + "3 grid.480917.3 New York Community Trust \n", + "4 grid.478715.8 Central New York Community Foundation \n", + "5 grid.475804.a Community Service Society of New York \n", + "6 grid.475783.a Long Term Care Community Coalition \n", + "7 grid.429257.f Korean Community Services of Metropolitan New ... \n", + "8 funder.196228 Community Health Foundation of Western and Cen... \n", + "\n", + " country_code country_name types \\\n", + "0 US United States [Company] \n", + "1 US United States [Education] \n", + "2 US United States [Nonprofit] \n", + "3 US United States [Nonprofit] \n", + "4 US United States [Nonprofit] \n", + "5 US United States [Other] \n", + "6 US United States [Other] \n", + "7 US United States [Nonprofit] \n", + "8 NaN United States NaN \n", + "\n", + " acronym city_name latitude \\\n", + "0 NaN NaN NaN \n", + "1 MVCC Utica 43.076850 \n", + "2 NaN Buffalo 42.874810 \n", + "3 NaN New York 40.758870 \n", + "4 CNYCF Syracuse 43.056038 \n", + "5 CSS New York 40.749622 \n", + "6 LTCCC New York 40.751163 \n", + "7 KCS New York 40.770954 \n", + "8 Community Health Foundation of Western and Centra NaN NaN \n", + "\n", + " linkout longitude state_name \n", + "0 NaN NaN NaN \n", + "1 [https://www.mvcc.edu/] -75.220120 New York \n", + "2 [https://hfwcny.org/] -78.849690 New York \n", + "3 [http://www.nycommunitytrust.org/] -73.968185 New York \n", + "4 [https://www.cnycf.org/] -76.148210 New York \n", + "5 [http://www.cssny.org/] -73.974620 New York \n", + "6 [http://www.ltccc.org/] -73.992470 New York \n", + "7 [https://www.kcsny.org/] -73.786670 New York \n", + "8 NaN NaN NaN " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york AND community\" \n", + "return organizations limit 10" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "Baz2j_cmd2Fd" + }, + "source": [ + "### Fielded search \n", + "\n", + "We can easily look up an organization using its ID, e.g." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 151 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1050, + "status": "ok", + "timestamp": 1574704472898, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "jNBg_c3ed2Fe", + "outputId": "5cbfb9d9-dcdc-4a34-aa1c-d99987b91cb9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Errors: 1\n", + "\u001b[2mTime: 5.84s\u001b[0m\n", + "Query Error\n", + "Semantic errors found:\n", + "\tField / Fieldset 'all' is not present in Source 'organizations'. Available fields: acronym,city_name,cnrs_ids,country_code,country_name,dimensions_url,established,external_ids_fundref,hesa_ids,id,isni_ids,latitude,linkout,longitude,name,nuts_level1_code,nuts_level1_name,nuts_level2_code,nuts_level2_name,nuts_level3_code,nuts_level3_name,organization_child_ids,organization_parent_ids,organization_related_ids,orgref_ids,redirect,ror_ids,score,state_name,status,types,ucas_ids,ukprn_ids,wikidata_ids,wikipedia_url and available fieldsets: basics,nuts\n" + ] + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " where id=\"grid.468887.d\" \n", + "return organizations[all] " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1020, + "status": "ok", + "timestamp": 1574702525174, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "GKh7VSOPk1Ye", + "outputId": "47a339ed-1f50-423c-8c04-e0ca3ad9347e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Organizations: 10 (total = 93)\n", + "\u001b[2mTime: 0.64s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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idnamecountry_codecountry_nametypescity_namestate_namelatitudelinkoutlongitudeacronym
0grid.798343.2Research Foundation of University of New YorkUSUnited States[Education]NaNNaNNaNNaNNaNNaN
1grid.795276.8New York University Medical CenterUSUnited States[Education]New YorkNew YorkNaNNaNNaNNaN
2grid.512545.2State University of New York, KoreaKRSouth Korea[Education]IncheonNaN37.376694[http://www.sunykorea.ac.kr/]126.667170NaN
3grid.511090.cCraig Newmark Graduate School of Journalism at...USUnited States[Education]New YorkNew York40.755230[https://www.journalism.cuny.edu/]-73.988830NaN
4grid.510787.cCenter for Migration Studies of New YorkUSUnited States[Education]New YorkNew York40.761470[https://cmsny.org/]-73.965450CMS
5grid.507867.bNew York State College of CeramicsUSUnited States[Education]AlfredNew York42.253372[https://www.alfred.edu/academics/colleges-sch...-77.787575NaN
6grid.507863.fNew York State School of Industrial and Labor ...USUnited States[Education]IthacaNew York42.439213[https://www.ilr.cornell.edu/]-76.493380ILR
7grid.507861.dMohawk Valley Community CollegeUSUnited States[Education]UticaNew York43.076850[https://www.mvcc.edu/]-75.220120MVCC
8grid.507860.cNew York State College of Agriculture and Life...USUnited States[Education]IthacaNew York42.448290[https://cals.cornell.edu/#]-76.479390CALS
9grid.507859.6New York State College of Veterinary Medicine ...USUnited States[Education]IthacaNew York42.447483[https://www.vet.cornell.edu/]-76.464905NaN
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" + ], + "text/plain": [ + " id name \\\n", + "0 grid.798343.2 Research Foundation of University of New York \n", + "1 grid.795276.8 New York University Medical Center \n", + "2 grid.512545.2 State University of New York, Korea \n", + "3 grid.511090.c Craig Newmark Graduate School of Journalism at... \n", + "4 grid.510787.c Center for Migration Studies of New York \n", + "5 grid.507867.b New York State College of Ceramics \n", + "6 grid.507863.f New York State School of Industrial and Labor ... \n", + "7 grid.507861.d Mohawk Valley Community College \n", + "8 grid.507860.c New York State College of Agriculture and Life... \n", + "9 grid.507859.6 New York State College of Veterinary Medicine ... \n", + "\n", + " country_code country_name types city_name state_name latitude \\\n", + "0 US United States [Education] NaN NaN NaN \n", + "1 US United States [Education] New York New York NaN \n", + "2 KR South Korea [Education] Incheon NaN 37.376694 \n", + "3 US United States [Education] New York New York 40.755230 \n", + "4 US United States [Education] New York New York 40.761470 \n", + "5 US United States [Education] Alfred New York 42.253372 \n", + "6 US United States [Education] Ithaca New York 42.439213 \n", + "7 US United States [Education] Utica New York 43.076850 \n", + "8 US United States [Education] Ithaca New York 42.448290 \n", + "9 US United States [Education] Ithaca New York 42.447483 \n", + "\n", + " linkout longitude acronym \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 [http://www.sunykorea.ac.kr/] 126.667170 NaN \n", + "3 [https://www.journalism.cuny.edu/] -73.988830 NaN \n", + "4 [https://cmsny.org/] -73.965450 CMS \n", + "5 [https://www.alfred.edu/academics/colleges-sch... -77.787575 NaN \n", + "6 [https://www.ilr.cornell.edu/] -76.493380 ILR \n", + "7 [https://www.mvcc.edu/] -75.220120 MVCC \n", + "8 [https://cals.cornell.edu/#] -76.479390 CALS \n", + "9 [https://www.vet.cornell.edu/] -76.464905 NaN " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york\" \n", + " where types in [\"Education\"]\n", + "return organizations limit 10" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 273 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 779, + "status": "ok", + "timestamp": 1574702569063, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "W6_BukMKleWs", + "outputId": "caedaf98-ca87-4504-a505-e4371f623eb2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Organizations: 9 (total = 9)\n", + "\u001b[2mTime: 5.97s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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idnamecity_namecountry_codecountry_namelatitudelinkoutlongitudetypesacronymstate_name
0grid.512545.2State University of New York, KoreaIncheonKRSouth Korea37.376694[http://www.sunykorea.ac.kr/]126.667170[Education]NaNNaN
1grid.479986.dNew York University ParisParisFRFrance48.869614[http://www.nyu.edu/paris.html]2.346863[Education]NaNNaN
2grid.473731.5New York University FlorenceFlorenceITItaly43.795910[http://www.nyu.edu/florence.html]11.265850[Education]NYUNaN
3grid.473728.dNew York Institute of TechnologyVancouverCACanada49.284374[http://nyit.edu/vancouver]-123.116480[Education]NYITBritish Columbia
4grid.449989.1University of New York in PraguePragueCZCzechia50.074043[https://www.unyp.cz/]14.433994[Education]UNYPNaN
5grid.449457.fNew York University ShanghaiShanghaiCNChina31.225506[https://shanghai.nyu.edu/]121.533510[Education]NaNNaN
6grid.444973.9University of New York TiranaTiranaALAlbania41.311060[http://unyt.edu.al/]19.801466[Education]UNYTNaN
7grid.440573.1New York University Abu DhabiAbu DhabiAEUnited Arab Emirates24.485000[https://nyuad.nyu.edu/]54.353000[Education]NaNNaN
8grid.410685.eSUNY KoreaSeoulKRSouth Korea37.377018[http://www.sunykorea.ac.kr/]126.666770[Education]NaNNaN
\n", + "
" + ], + "text/plain": [ + " id name city_name country_code \\\n", + "0 grid.512545.2 State University of New York, Korea Incheon KR \n", + "1 grid.479986.d New York University Paris Paris FR \n", + "2 grid.473731.5 New York University Florence Florence IT \n", + "3 grid.473728.d New York Institute of Technology Vancouver CA \n", + "4 grid.449989.1 University of New York in Prague Prague CZ \n", + "5 grid.449457.f New York University Shanghai Shanghai CN \n", + "6 grid.444973.9 University of New York Tirana Tirana AL \n", + "7 grid.440573.1 New York University Abu Dhabi Abu Dhabi AE \n", + "8 grid.410685.e SUNY Korea Seoul KR \n", + "\n", + " country_name latitude linkout \\\n", + "0 South Korea 37.376694 [http://www.sunykorea.ac.kr/] \n", + "1 France 48.869614 [http://www.nyu.edu/paris.html] \n", + "2 Italy 43.795910 [http://www.nyu.edu/florence.html] \n", + "3 Canada 49.284374 [http://nyit.edu/vancouver] \n", + "4 Czechia 50.074043 [https://www.unyp.cz/] \n", + "5 China 31.225506 [https://shanghai.nyu.edu/] \n", + "6 Albania 41.311060 [http://unyt.edu.al/] \n", + "7 United Arab Emirates 24.485000 [https://nyuad.nyu.edu/] \n", + "8 South Korea 37.377018 [http://www.sunykorea.ac.kr/] \n", + "\n", + " longitude types acronym state_name \n", + "0 126.667170 [Education] NaN NaN \n", + "1 2.346863 [Education] NaN NaN \n", + "2 11.265850 [Education] NYU NaN \n", + "3 -123.116480 [Education] NYIT British Columbia \n", + "4 14.433994 [Education] UNYP NaN \n", + "5 121.533510 [Education] NaN NaN \n", + "6 19.801466 [Education] UNYT NaN \n", + "7 54.353000 [Education] NaN NaN \n", + "8 126.666770 [Education] NaN NaN " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york\" \n", + " where types in [\"Education\"]\n", + " and country_name != \"United States\"\n", + "return organizations limit 10" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "l4V7z5TCd2Fo" + }, + "source": [ + "### Returning facets \n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1147, + "status": "ok", + "timestamp": 1574702640852, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "1fqSIrMkd2Fp", + "outputId": "3add0d42-15b5-4471-c75d-2e5ba6e0d86a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Country_name: 11\n", + "\u001b[2mTime: 0.50s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id count\n", + "0 United States 341\n", + "1 South Korea 2\n", + "2 Albania 1\n", + "3 Canada 1\n", + "4 China 1\n", + "5 Czechia 1\n", + "6 France 1\n", + "7 Italy 1\n", + "8 Panama 1\n", + "9 United Arab Emirates 1\n", + "10 United Kingdom 1" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york\" \n", + "return country_name" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 273 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 867, + "status": "ok", + "timestamp": 1574702673505, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "DMK_imi-l4ln", + "outputId": "b7986146-6e17-4ed9-b64f-74453770f3ff" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Types: 8\n", + "\u001b[2mTime: 5.47s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id count\n", + "0 Education 84\n", + "1 Nonprofit 75\n", + "2 Company 57\n", + "3 Government 46\n", + "4 Other 34\n", + "5 Healthcare 28\n", + "6 Archive 9\n", + "7 Facility 7" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york\" \n", + " where country_name = \"United States\"\n", + "return types" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "l4V7z5TCd2Fo" + }, + "source": [ + "### Returning organizations facets from publications\n", + "\n", + "Organization data is used thoughout Dimensions. \n", + "\n", + "So, for example, one can do a publications search and return organizations as a facet. This allows to take advantage of organization metadata - e.g. latiture and longitude - in order to quickly build a geograpical visualization. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1147, + "status": "ok", + "timestamp": 1574702640852, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "1fqSIrMkd2Fp", + "outputId": "3add0d42-15b5-4471-c75d-2e5ba6e0d86a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Research_orgs: 50\n", + "\u001b[2mTime: 1.16s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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idnamecity_namecountcountry_codecountry_namelatitudelinkoutlongitudestate_nametypesacronym
0grid.38142.3cHarvard UniversityCambridge33545USUnited States42.377052[http://www.harvard.edu/]-71.116650Massachusetts[Education]NaN
1grid.17063.33University of TorontoToronto21731CACanada43.661667[http://www.utoronto.ca/]-79.395000Ontario[Education]NaN
2grid.21107.35Johns Hopkins UniversityBaltimore19419USUnited States39.328888[https://www.jhu.edu/]-76.620280Maryland[Education]JHU
3grid.4991.5University of OxfordOxford19345GBUnited Kingdom51.753437[http://www.ox.ac.uk/]-1.254010Oxfordshire[Education]NaN
4grid.83440.3bUniversity College LondonLondon19047GBUnited Kingdom51.524470[http://www.ucl.ac.uk/]-0.133982NaN[Education]UCL
\n", + "
" + ], + "text/plain": [ + " id name city_name count country_code \\\n", + "0 grid.38142.3c Harvard University Cambridge 33545 US \n", + "1 grid.17063.33 University of Toronto Toronto 21731 CA \n", + "2 grid.21107.35 Johns Hopkins University Baltimore 19419 US \n", + "3 grid.4991.5 University of Oxford Oxford 19345 GB \n", + "4 grid.83440.3b University College London London 19047 GB \n", + "\n", + " country_name latitude linkout longitude \\\n", + "0 United States 42.377052 [http://www.harvard.edu/] -71.116650 \n", + "1 Canada 43.661667 [http://www.utoronto.ca/] -79.395000 \n", + "2 United States 39.328888 [https://www.jhu.edu/] -76.620280 \n", + "3 United Kingdom 51.753437 [http://www.ox.ac.uk/] -1.254010 \n", + "4 United Kingdom 51.524470 [http://www.ucl.ac.uk/] -0.133982 \n", + "\n", + " state_name types acronym \n", + "0 Massachusetts [Education] NaN \n", + "1 Ontario [Education] NaN \n", + "2 Maryland [Education] JHU \n", + "3 Oxfordshire [Education] NaN \n", + "4 NaN [Education] UCL " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "q = \"\"\"\n", + "search publications for \"coronavirus OR covid-19\" \n", + " where year > 2019 \n", + "return research_orgs[basics] limit 50\n", + "\"\"\"\n", + "\n", + "df = dslquery(q).as_dataframe()\n", + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "Collapsed": "false" + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [ + "Cambridge", + "grid.38142.3c", + [ + "Education" + ] + ], + [ + "Baltimore", + "grid.21107.35", + [ + "Education" + ] + ], + [ + "Seattle", + "grid.34477.33", + [ + "Education" + ] + ], + [ + "Stanford", + "grid.168010.e", + [ + "Education" + ] + ], + [ + "Ann Arbor", + "grid.214458.e", + [ + "Education" + ] + ], + [ + "Philadelphia", + "grid.25879.31", + [ + "Education" + ] + ], + [ + "New Haven", + "grid.47100.32", + [ + "Education" + ] + ], + [ + "San Francisco", + "grid.266102.1", + [ + "Education" + ] + ], + [ + "Los Angeles", + "grid.19006.3e", + [ + "Education" + ] + ], + [ + "Boston", + "grid.32224.35", + [ + "Healthcare" + ] + ], + [ + "Chapel Hill", + "grid.10698.36", + [ + "Education" + ] + ], + [ + "Atlanta", + "grid.189967.8", + [ + "Education" + ] + ], + [ + "New York", + "grid.137628.9", + [ + "Education" + ] + ], + [ + "New York", + "grid.21729.3f", + [ + "Education" + ] + ], + [ + "Boston", + "grid.62560.37", + [ + "Healthcare" + ] + ], + [ + "San Diego", + "grid.266100.3", + [ + "Education" + ] + ], + [ + "Durham", + "grid.26009.3d", + [ + "Education" + ] + ], + [ + "Rochester", + "grid.66875.3a", + [ + "Healthcare" + ] + ], + [ + "Minneapolis", + "grid.17635.36", + [ + "Education" + ] + ], + [ + "Pittsburgh", + "grid.21925.3d", + [ + "Education" + ] + ], + [ + "Gainesville", + "grid.15276.37", + [ + "Education" + ] + ], + [ + "Evanston", + "grid.16753.36", + [ + "Education" + ] + ], + [ + "New York", + "grid.59734.3c", + [ + "Education" + ] + ], + [ + "Ithaca", + "grid.5386.8", + [ + "Education" + ] + ], + [ + "St Louis", + "grid.4367.6", + [ + "Education" + ] + ], + [ + "Boston", + "grid.189504.1", + [ + "Education" + ] + ] + ], + "geo": "geo", + "hovertemplate": "%{hovertext}

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country_name=Canada
count=%{marker.size}
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city_name=%{customdata[0]}
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types=%{customdata[2]}", + "hovertext": [ + "University of Toronto", + "University of British Columbia", + "McGill University", + "McMaster University" + ], + "lat": { + "bdata": "1esWgbHURUBxdQDEXaFIQKjg8IKIwEZAwf7r3LShRUA=", + "dtype": "f8" + }, + "legendgroup": "Canada", + "lon": { + "bdata": "4XoUrkfZU8DIDFTGv89ewNsWZTbIZFLAxqcAGM/6U8A=", + "dtype": "f8" + }, + "marker": { + "color": "#EF553B", + "size": { + "bdata": "41TjMF4hzR8=", + "dtype": "i2" + }, + "sizemode": "area", + "sizeref": 83.8625, + "symbol": "circle" + }, + "mode": "markers", + "name": "Canada", + "showlegend": true, + "type": "scattergeo" + }, + { + "customdata": [ + [ + "Oxford", + "grid.4991.5", + [ + "Education" + ] + ], + [ + "London", + "grid.83440.3b", + [ + "Education" + ] + ], + [ + "London", + "grid.13097.3c", + [ + "Education" + ] + ], + [ + "London", + "grid.7445.2", + [ + "Education" + ] + ], + [ + "Cambridge", + "grid.5335.0", + [ + "Education" + ] + ], + [ + "Manchester", + "grid.5379.8", + [ + "Education" + ] + ], + [ + "London", + "grid.8991.9", + [ + "Education" + ] + ], + [ + "Edinburgh", + "grid.4305.2", + [ + "Education" + ] + ] + ], + "geo": "geo", + "hovertemplate": "%{hovertext}

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count=%{marker.size}
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longitude=%{lon}
city_name=%{customdata[0]}
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country_name=Brazil
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", + "hovertext": [ + "Universidade de São Paulo" + ], + "lat": { + "bdata": "6Po+HCSQN8A=", + "dtype": "f8" + }, + "legendgroup": "Brazil", + "lon": { + "bdata": "EtvdA3RdR8A=", + "dtype": "f8" + }, + "marker": { + "color": "#ab63fa", + "size": { + "bdata": "Bzw=", + "dtype": "i2" + }, + "sizemode": "area", + "sizeref": 83.8625, + "symbol": "circle" + }, + "mode": "markers", + "name": "Brazil", + "showlegend": true, + "type": "scattergeo" + }, + { + "customdata": [ + [ + "Melbourne", + "grid.1008.9", + [ + "Education" + ] + ], + [ + "Sydney", + "grid.1013.3", + [ + "Education" + ] + ], + [ + "Melbourne", + "grid.1002.3", + [ + "Education" + ] + ], + [ + "Sydney", + "grid.1005.4", + [ + "Education" + ] + ], + [ + "Brisbane", + "grid.1003.2", + [ + "Education" + ] + ] + ], + "geo": "geo", + "hovertemplate": "%{hovertext}

country_name=Australia
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
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} + }, + "text/html": [ + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter_geo(df, \n", + " lat=\"latitude\", lon=\"longitude\",\n", + " color=\"country_name\",\n", + " size=\"count\", \n", + " projection=\"natural earth\",\n", + " hover_name=\"name\",\n", + " hover_data=['city_name', 'id', 'types']\n", + " )\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "hcg2KOswd2F4" + }, + "source": [ + "## 3. A closer look at the organizations data statistics\n", + "\n", + "The Dimensions Search Language [exposes programmatically metadata](https://docs.dimensions.ai/dsl/data.html#getting-documentation-programmatically), such as supported sources and entities, along with their fields, facets, fieldsets, metrics and search fields. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "Collapsed": "false" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sourcesfieldtypedescriptionis_filteris_entityis_facet
0organizationsacronymstringGRID acronym of the organization. E.g., \"UT\" f...TrueFalseFalse
1organizationscity_namestringGRID name of the organization country. E.g., \"...TrueFalseTrue
2organizationscnrs_idsstringCNRS IDs for this organizationTrueFalseFalse
3organizationscountry_codestringCountry of the organisation, identified using ...TrueFalseTrue
4organizationscountry_namestringGRID name of the organization country. E.g., \"...TrueFalseTrue
5organizationsdimensions_urlstringLink pointing to the Dimensions web applicationFalseFalseFalse
6organizationsestablishedintegerYear when the organization was estabilishedTrueFalseFalse
7organizationsexternal_ids_fundrefstringFundref IDs for this organizationTrueFalseFalse
8organizationshesa_idsstringHESA IDs for this organizationTrueFalseFalse
9organizationsidstringGRID ID of the organization. E.g., \"grid.26999...TrueFalseFalse
10organizationsisni_idsstringISNI IDs for this organizationTrueFalseFalse
11organizationslatitudefloatNoneFalseFalseFalse
12organizationslinkoutstringNoneFalseFalseFalse
13organizationslongitudefloatNoneFalseFalseFalse
14organizationsnamestringGRID name of the organization. E.g., \"Universi...TrueFalseFalse
15organizationsnuts_level1_codestringLevel 1 code for this organization, based on `...TrueFalseTrue
16organizationsnuts_level1_namestringLevel 1 name for this organization, based on `...TrueFalseTrue
17organizationsnuts_level2_codestringLevel 2 code for this organization, based on `...TrueFalseTrue
18organizationsnuts_level2_namestringLevel 2 name for this organization, based on `...TrueFalseTrue
19organizationsnuts_level3_codestringLevel 3 code for this organization, based on `...TrueFalseTrue
20organizationsnuts_level3_namestringLevel 3 name for this organization, based on `...TrueFalseTrue
21organizationsorganization_child_idsstringChild organization IDsTrueFalseFalse
22organizationsorganization_parent_idsstringParent organization IDsTrueFalseFalse
23organizationsorganization_related_idsstringRelated organization IDsTrueFalseFalse
24organizationsorgref_idsstringOrgRef IDs for this organizationTrueFalseFalse
25organizationsredirectstringGRID ID of an organization this one was redire...TrueFalseFalse
26organizationsror_idsstringROR IDs for this organizationTrueFalseFalse
27organizationsscorefloatFor full-text queries, the relevance score is ...TrueFalseFalse
28organizationsstate_namestringGRID name of the organization country. E.g., \"...TrueFalseTrue
29organizationsstatusstringStatus of an organization. May be be one of:\\n...TrueFalseTrue
30organizationstypesstringType of an organization. Available types inclu...TrueFalseTrue
31organizationsucas_idsstringUCAS IDs for this organizationTrueFalseFalse
32organizationsukprn_idsstringUKPRN IDs for this organizationTrueFalseFalse
33organizationswikidata_idsstringWikiData IDs for this organizationTrueFalseFalse
34organizationswikipedia_urlstringWikipedia URLFalseFalseFalse
\n", + "
" + ], + "text/plain": [ + " sources field type \\\n", + "0 organizations acronym string \n", + "1 organizations city_name string \n", + "2 organizations cnrs_ids string \n", + "3 organizations country_code string \n", + "4 organizations country_name string \n", + "5 organizations dimensions_url string \n", + "6 organizations established integer \n", + "7 organizations external_ids_fundref string \n", + "8 organizations hesa_ids string \n", + "9 organizations id string \n", + "10 organizations isni_ids string \n", + "11 organizations latitude float \n", + "12 organizations linkout string \n", + "13 organizations longitude float \n", + "14 organizations name string \n", + "15 organizations nuts_level1_code string \n", + "16 organizations nuts_level1_name string \n", + "17 organizations nuts_level2_code string \n", + "18 organizations nuts_level2_name string \n", + "19 organizations nuts_level3_code string \n", + "20 organizations nuts_level3_name string \n", + "21 organizations organization_child_ids string \n", + "22 organizations organization_parent_ids string \n", + "23 organizations organization_related_ids string \n", + "24 organizations orgref_ids string \n", + "25 organizations redirect string \n", + "26 organizations ror_ids string \n", + "27 organizations score float \n", + "28 organizations state_name string \n", + "29 organizations status string \n", + "30 organizations types string \n", + "31 organizations ucas_ids string \n", + "32 organizations ukprn_ids string \n", + "33 organizations wikidata_ids string \n", + "34 organizations wikipedia_url string \n", + "\n", + " description is_filter is_entity \\\n", + "0 GRID acronym of the organization. E.g., \"UT\" f... True False \n", + "1 GRID name of the organization country. E.g., \"... True False \n", + "2 CNRS IDs for this organization True False \n", + "3 Country of the organisation, identified using ... True False \n", + "4 GRID name of the organization country. E.g., \"... True False \n", + "5 Link pointing to the Dimensions web application False False \n", + "6 Year when the organization was estabilished True False \n", + "7 Fundref IDs for this organization True False \n", + "8 HESA IDs for this organization True False \n", + "9 GRID ID of the organization. E.g., \"grid.26999... True False \n", + "10 ISNI IDs for this organization True False \n", + "11 None False False \n", + "12 None False False \n", + "13 None False False \n", + "14 GRID name of the organization. E.g., \"Universi... True False \n", + "15 Level 1 code for this organization, based on `... True False \n", + "16 Level 1 name for this organization, based on `... True False \n", + "17 Level 2 code for this organization, based on `... True False \n", + "18 Level 2 name for this organization, based on `... True False \n", + "19 Level 3 code for this organization, based on `... True False \n", + "20 Level 3 name for this organization, based on `... True False \n", + "21 Child organization IDs True False \n", + "22 Parent organization IDs True False \n", + "23 Related organization IDs True False \n", + "24 OrgRef IDs for this organization True False \n", + "25 GRID ID of an organization this one was redire... True False \n", + "26 ROR IDs for this organization True False \n", + "27 For full-text queries, the relevance score is ... True False \n", + "28 GRID name of the organization country. E.g., \"... True False \n", + "29 Status of an organization. May be be one of:\\n... True False \n", + "30 Type of an organization. Available types inclu... True False \n", + "31 UCAS IDs for this organization True False \n", + "32 UKPRN IDs for this organization True False \n", + "33 WikiData IDs for this organization True False \n", + "34 Wikipedia URL False False \n", + "\n", + " is_facet \n", + "0 False \n", + "1 True \n", + "2 False \n", + "3 True \n", + "4 True \n", + "5 False \n", + "6 False \n", + "7 False \n", + "8 False \n", + "9 False \n", + "10 False \n", + "11 False \n", + "12 False \n", + "13 False \n", + "14 False \n", + "15 True \n", + "16 True \n", + "17 True \n", + "18 True \n", + "19 True \n", + "20 True \n", + "21 False \n", + "22 False \n", + "23 False \n", + "24 False \n", + "25 False \n", + "26 False \n", + "27 False \n", + "28 True \n", + "29 True \n", + "30 True \n", + "31 False \n", + "32 False \n", + "33 False \n", + "34 False " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%dsldocs organizations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "KZdeyuHvm3ve" + }, + "source": [ + "We can use the fields information above to draw up some quick statistics re. the organizations source. \n", + "\n", + "In order to do this, we use the operator `is not empty` to generate automatically queries like this `search organizations where field_name is not empty return organizations limit 1` and then use the `total_count` field in the JSON we get back for our statistics. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 645, + "resources": { + "http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css": { + "data": "/* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /* We import all of these together in a single css file because the Webpack
loader sees only one file at a time. This allows postcss to see the variable
definitions when they are used. */

 /*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

 /*
This file is copied from the JupyterLab project to define default styling for
when the widget styling is compiled down to eliminate CSS variables. We make one
change - we comment out the font import below.
*/

 /**
 * The material design colors are adapted from google-material-color v1.2.6
 * https://github.com/danlevan/google-material-color
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/dist/palette.var.css
 *
 * The license for the material design color CSS variables is as follows (see
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Dan Le Van
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

 /*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/

 /*
 * Optional monospace font for input/output prompt.
 */

 /* Commented out in ipywidgets since we don't need it. */

 /* @import url('https://fonts.googleapis.com/css?family=Roboto+Mono'); */

 /*
 * Added for compabitility with output area
 */

 :root {

  /* Borders

  The following variables, specify the visual styling of borders in JupyterLab.
   */

  /* UI Fonts

  The UI font CSS variables are used for the typography all of the JupyterLab
  user interface elements that are not directly user generated content.
  */ /* Base font size */ /* Ensures px perfect FontAwesome icons */

  /* Use these font colors against the corresponding main layout colors.
     In a light theme, these go from dark to light.
  */

  /* Use these against the brand/accent/warn/error colors.
     These will typically go from light to darker, in both a dark and light theme
   */

  /* Content Fonts

  Content font variables are used for typography of user generated content.
  */ /* Base font size */


  /* Layout

  The following are the main layout colors use in JupyterLab. In a light
  theme these would go from light to dark.
  */

  /* Brand/accent */

  /* State colors (warn, error, success, info) */

  /* Cell specific styles */
  /* A custom blend of MD grey and blue 600
   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
  /* A custom blend of MD grey and orange 600
   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */

  /* Notebook specific styles */

  /* Console specific styles */

  /* Toolbar specific styles */
}

 /* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /*
 * We assume that the CSS variables in
 * https://github.com/jupyterlab/jupyterlab/blob/master/src/default-theme/variables.css
 * have been defined.
 */

 /* This file has code derived from PhosphorJS CSS files, as noted below. The license for this PhosphorJS code is:

Copyright (c) 2014-2017, PhosphorJS Contributors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

*/

 /*
 * The following section is derived from https://github.com/phosphorjs/phosphor/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css 
 * We've scoped the rules so that they are consistent with exactly our code.
 */

 .jupyter-widgets.widget-tab > .p-TabBar {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
  margin: 0;
  padding: 0;
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  list-style-type: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] > .p-TabBar-content {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] > .p-TabBar-content {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
  overflow: hidden;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
  -webkit-box-flex: 0;
      -ms-flex: 0 0 auto;
          flex: 0 0 auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel {
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  overflow: hidden;
  white-space: nowrap;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden {
  display: none !important;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab {
  position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab {
  left: 0;
  -webkit-transition: left 150ms ease;
  transition: left 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab {
  top: 0;
  -webkit-transition: top 150ms ease;
  transition: top 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging {
  -webkit-transition: none;
  transition: none;
}

 /* End tabbar.css */

 :root { /* margin between inline elements */

    /* From Material Design Lite */
}

 .jupyter-widgets {
    margin: 2px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    color: black;
    overflow: visible;
}

 .jupyter-widgets.jupyter-widgets-disconnected::before {
    line-height: 28px;
    height: 28px;
}

 .jp-Output-result > .jupyter-widgets {
    margin-left: 0;
    margin-right: 0;
}

 /* vbox and hbox */

 .widget-inline-hbox {
    /* Horizontal widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
    -webkit-box-align: baseline;
        -ms-flex-align: baseline;
            align-items: baseline;
}

 .widget-inline-vbox {
    /* Vertical Widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
    overflow: auto;
}

 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
}

 .widget-vbox {
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 /* General Button Styling */

 .jupyter-button {
    padding-left: 10px;
    padding-right: 10px;
    padding-top: 0px;
    padding-bottom: 0px;
    display: inline-block;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
    border: 0px solid;
    line-height: 28px;
    -webkit-box-shadow: none;
            box-shadow: none;

    color: rgba(0, 0, 0, .8);
    background-color: #EEEEEE;
    border-color: #E0E0E0;
    border: none;
}

 .jupyter-button i.fa {
    margin-right: 4px;
    pointer-events: none;
}

 .jupyter-button:empty:before {
    content: "\200b"; /* zero-width space */
}

 .jupyter-widgets.jupyter-button:disabled {
    opacity: 0.6;
}

 .jupyter-button i.fa.center {
    margin-right: 0;
}

 .jupyter-button:hover:enabled, .jupyter-button:focus:enabled {
    /* MD Lite 2dp shadow */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

/*# 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\n", + "" + ], + "text/plain": [ + " filter_by results\n", + "0 All Organizations (no filter) 406172.0\n", + "6 dimensions_url 406172.0\n", + "30 status 406172.0\n", + "15 name 406172.0\n", + "31 types 406087.0\n", + "5 country_name 406058.0\n", + "4 country_code 406012.0\n", + "2 city_name 166300.0\n", + "14 longitude 131622.0\n", + "12 latitude 131622.0\n", + "23 organization_parent_ids 119442.0\n", + "13 linkout 119290.0\n", + "27 ror_ids 94038.0\n", + "7 established 91959.0\n", + "29 state_name 66188.0\n", + "16 nuts_level1_code 51585.0\n", + "18 nuts_level2_code 51585.0\n", + "17 nuts_level1_name 51585.0\n", + "21 nuts_level3_name 51585.0\n", + "19 nuts_level2_name 51585.0\n", + "20 nuts_level3_code 51585.0\n", + "34 wikidata_ids 51499.0\n", + "11 isni_ids 49885.0\n", + "1 acronym 45701.0\n", + "35 wikipedia_url 33440.0\n", + "22 organization_child_ids 21945.0\n", + "25 orgref_ids 14577.0\n", + "8 external_ids_fundref 9406.0\n", + "26 redirect 5669.0\n", + "24 organization_related_ids 4751.0\n", + "3 cnrs_ids 920.0\n", + "33 ukprn_ids 172.0\n", + "9 hesa_ids 171.0\n", + "32 ucas_ids 152.0\n", + "10 id NaN\n", + "28 score NaN" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FIELDS_DATA = dsl_last_results\n", + "\n", + "# one query with `is not empty` for field-filters \n", + "q_template = \"\"\"search organizations where {} is not empty return organizations[id] limit 1\"\"\"\n", + "\n", + "# seed results with total number of orgs\n", + "totorgs = dsl.query(\"\"\"search organizations return organizations[id] limit 1\"\"\", verbose=False).count_total\n", + "stats = [\n", + " {'filter_by': 'All Organizations (no filter)', 'results' : totorgs} \n", + "]\n", + "\n", + "for index, row in pbar(list(FIELDS_DATA.iterrows())):\n", + " # print(\"\\n===\", row['field'])\n", + " q = q_template.format(row['field'], row['field'])\n", + " res = dsl.query(q, verbose=False)\n", + " time.sleep(0.5)\n", + " stats.append({'filter_by': row['field'], 'results' : res.count_total})\n", + "\n", + "\n", + "# save to a dataframe\n", + "df = pd.DataFrame().from_dict(stats)\n", + "df.sort_values(\"results\", inplace=True, ascending=False)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false" + }, + "source": [ + "### Let's visualize the data with plotly" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "Collapsed": "false" + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "filter_by=%{x}
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"shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Fields distribution for GRID data" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "filter_by" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "results" + } + } + } + }, + "text/html": [ + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "px.bar(df, x=\"filter_by\", y=\"results\", \n", + " title=\"Fields distribution for GRID data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "VsEa7X3EsPYH" + }, + "source": [ + "## Where to find out more\n", + "\n", + "Please have a look at the [official documentation](https://docs.dimensions.ai/dsl/data-sources.html) for more information on the organizations data source." + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "Searching GRID organizations using the Dimensions API.ipynb", + "provenance": [ + { + "file_id": "1khRLDKEZ-U_6ARyCJCOocRdH7U-nZKUT", + "timestamp": 1574700652421 + } + ] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/.doctrees/nbsphinx/cookbooks/8-organizations/2-Industry-Collaboration.ipynb b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/2-Industry-Collaboration.ipynb index 310851ec..55967f0e 100644 --- a/docs/.doctrees/nbsphinx/cookbooks/8-organizations/2-Industry-Collaboration.ipynb +++ b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/2-Industry-Collaboration.ipynb @@ -11,7 +11,7 @@ "source": [ "# Identifying the Industry Collaborators of an Academic Institution\n", "\n", - "Dimensions uses [GRID](https://grid.ac/) identifiers for institutions, hence you can take advantage of the GRID metadata with Dimensions queries. \n", + "Dimensions has an enormous amount of data about organizations and you can query this data with the Dimensions Analytics API.\n", "\n", "In this tutorial we identify all organizations that have an `industry` type. \n", "\n", @@ -29,7 +29,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Jan 25, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -64,19 +64,9 @@ "text/html": [ " \n", + " \n", " " ] }, @@ -96,8 +86,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -150,8 +140,8 @@ "id": "L6uIjSVnGRQV" }, "source": [ - "For the purpose of this exercise, we will use [University of Trento, Italy (grid.11696.39)](https://grid.ac/institutes/grid.11696.39) as a starting point. \n", - "You can pick any other GRID organization of course. Just use a [DSL query](https://digital-science.github.io/dimensions-api-lab/cookbooks/8-organizations/1-GRID-preview.html) or the [GRID website](https://grid.ac/institutes) to discover the ID of an organization that interests you. " + "For the purpose of this exercise, we will use University of Trento, Italy (organization ID `grid.11696.39`) as a starting point. \n", + "You can pick any other organization of course. Just use a [DSL query](https://digital-science.github.io/dimensions-api-lab/cookbooks/8-organizations/1-GRID-preview.html) to discover the ID of an organization that interests you. " ] }, { @@ -182,7 +172,7 @@ { "data": { "text/html": [ - "GRID: grid.11696.39 - University of Trento ⧉" + "Organization: grid.11696.39 - University of Trento ⧉" ], "text/plain": [ "" @@ -206,7 +196,7 @@ ], "source": [ "#@markdown The main organization we are interested in:\n", - "GRIDID = \"grid.11696.39\" #@param {type:\"string\"}\n", + "ORGID = \"grid.11696.39\" #@param {type:\"string\"}\n", " \n", "#@markdown The start/end year of publications used to extract industry collaborations:\n", "YEAR_START = 2000 #@param {type: \"slider\", min: 1950, max: 2020}\n", @@ -219,11 +209,11 @@ "# gen link to Dimensions\n", "#\n", "try:\n", - " gridname = dsl.query(f\"\"\"search organizations where id=\"{GRIDID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", + " orgname = dsl.query(f\"\"\"search organizations where id=\"{ORGID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", "except:\n", - " gridname = \"\"\n", - "from IPython.core.display import display, HTML\n", - "display(HTML('GRID: {} - {} ⧉'.format(dimensions_url(GRIDID), GRIDID, gridname)))\n", + " orgname = \"\"\n", + "from IPython.display import display, HTML\n", + "display(HTML('Organization: {} - {} ⧉'.format(dimensions_url(ORGID), ORGID, orgname)))\n", "display(HTML('Time period: {} to {}

'.format(YEAR_START, YEAR_END)))\n" ] }, @@ -273,39 +263,58 @@ "output_type": "stream", "text": [ "Starting iteration with limit=1000 skip=0 ...\u001b[0m\n", - "0-1000 / 30088 (0.63s)\u001b[0m\n", - "1000-2000 / 30088 (0.57s)\u001b[0m\n", - "2000-3000 / 30088 (0.69s)\u001b[0m\n", - "3000-4000 / 30088 (0.52s)\u001b[0m\n", - "4000-5000 / 30088 (0.51s)\u001b[0m\n", - "5000-6000 / 30088 (0.61s)\u001b[0m\n", - "6000-7000 / 30088 (0.52s)\u001b[0m\n", - "7000-8000 / 30088 (0.56s)\u001b[0m\n", - "8000-9000 / 30088 (2.24s)\u001b[0m\n", - "9000-10000 / 30088 (0.56s)\u001b[0m\n", - "10000-11000 / 30088 (0.57s)\u001b[0m\n", - "11000-12000 / 30088 (0.58s)\u001b[0m\n", - "12000-13000 / 30088 (0.62s)\u001b[0m\n", - "13000-14000 / 30088 (1.74s)\u001b[0m\n", - "14000-15000 / 30088 (0.58s)\u001b[0m\n", - "15000-16000 / 30088 (0.49s)\u001b[0m\n", - "16000-17000 / 30088 (0.58s)\u001b[0m\n", - "17000-18000 / 30088 (0.53s)\u001b[0m\n", - "18000-19000 / 30088 (0.57s)\u001b[0m\n", - "19000-20000 / 30088 (0.50s)\u001b[0m\n", - "20000-21000 / 30088 (0.51s)\u001b[0m\n", - "21000-22000 / 30088 (0.51s)\u001b[0m\n", - "22000-23000 / 30088 (0.54s)\u001b[0m\n", - "23000-24000 / 30088 (0.50s)\u001b[0m\n", - "24000-25000 / 30088 (0.53s)\u001b[0m\n", - "25000-26000 / 30088 (0.62s)\u001b[0m\n", - "26000-27000 / 30088 (0.49s)\u001b[0m\n", - "27000-28000 / 30088 (0.48s)\u001b[0m\n", - "28000-29000 / 30088 (0.56s)\u001b[0m\n", - "29000-30000 / 30088 (0.90s)\u001b[0m\n", - "30000-30088 / 30088 (0.61s)\u001b[0m\n", + "0-1000 / 158994 (0.60s)\u001b[0m\n", + "1000-2000 / 158994 (0.59s)\u001b[0m\n", + "2000-3000 / 158994 (4.55s)\u001b[0m\n", + "3000-4000 / 158994 (0.58s)\u001b[0m\n", + "4000-5000 / 158994 (0.62s)\u001b[0m\n", + "5000-6000 / 158994 (2.44s)\u001b[0m\n", + "6000-7000 / 158994 (2.06s)\u001b[0m\n", + "7000-8000 / 158994 (4.03s)\u001b[0m\n", + "8000-9000 / 158994 (1.97s)\u001b[0m\n", + "9000-10000 / 158994 (2.47s)\u001b[0m\n", + "10000-11000 / 158994 (0.63s)\u001b[0m\n", + "11000-12000 / 158994 (0.57s)\u001b[0m\n", + "12000-13000 / 158994 (1.93s)\u001b[0m\n", + "13000-14000 / 158994 (0.65s)\u001b[0m\n", + "14000-15000 / 158994 (2.35s)\u001b[0m\n", + "15000-16000 / 158994 (2.11s)\u001b[0m\n", + "16000-17000 / 158994 (3.86s)\u001b[0m\n", + "17000-18000 / 158994 (6.32s)\u001b[0m\n", + "18000-19000 / 158994 (0.71s)\u001b[0m\n", + "19000-20000 / 158994 (3.59s)\u001b[0m\n", + "20000-21000 / 158994 (0.72s)\u001b[0m\n", + "21000-22000 / 158994 (3.72s)\u001b[0m\n", + "22000-23000 / 158994 (5.98s)\u001b[0m\n", + "23000-24000 / 158994 (0.61s)\u001b[0m\n", + "24000-25000 / 158994 (0.71s)\u001b[0m\n", + "25000-26000 / 158994 (1.70s)\u001b[0m\n", + "26000-27000 / 158994 (1.34s)\u001b[0m\n", + "27000-28000 / 158994 (4.45s)\u001b[0m\n", + "28000-29000 / 158994 (0.70s)\u001b[0m\n", + "29000-30000 / 158994 (0.56s)\u001b[0m\n", + "30000-31000 / 158994 (1.76s)\u001b[0m\n", + "31000-32000 / 158994 (0.64s)\u001b[0m\n", + "32000-33000 / 158994 (0.58s)\u001b[0m\n", + "33000-34000 / 158994 (0.70s)\u001b[0m\n", + "34000-35000 / 158994 (2.38s)\u001b[0m\n", + "35000-36000 / 158994 (2.06s)\u001b[0m\n", + "36000-37000 / 158994 (0.71s)\u001b[0m\n", + "37000-38000 / 158994 (3.82s)\u001b[0m\n", + "38000-39000 / 158994 (0.65s)\u001b[0m\n", + "39000-40000 / 158994 (4.50s)\u001b[0m\n", + "40000-41000 / 158994 (5.95s)\u001b[0m\n", + "41000-42000 / 158994 (0.81s)\u001b[0m\n", + "42000-43000 / 158994 (0.65s)\u001b[0m\n", + "43000-44000 / 158994 (4.53s)\u001b[0m\n", + "44000-45000 / 158994 (0.61s)\u001b[0m\n", + "45000-46000 / 158994 (4.54s)\u001b[0m\n", + "46000-47000 / 158994 (0.60s)\u001b[0m\n", + "47000-48000 / 158994 (0.78s)\u001b[0m\n", + "48000-49000 / 158994 (0.74s)\u001b[0m\n", + "49000-50000 / 158994 (0.68s)\u001b[0m\n", "===\n", - "Records extracted: 30088\u001b[0m\n" + "Records extracted: 50000\u001b[0m\n" ] } ], @@ -350,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "Collapsed": "false", "colab": { @@ -390,18 +399,18 @@ "===\n", "Extracting grid.11696.39 publications with industry collaborators ...\n", "Records per query : 1000\n", - "GRID IDs per query: 200\n" + "Organization IDs per query: 200\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f9ce542da620433da388b9c568a55130", + "model_id": "ec0375ac4b8f455893d5d882ad2543a1", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/151 [00:00\n", " \n", " 0\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/0264-9381/33/23/235015\n", - " pub.1059063534\n", - " 7\n", - " article\n", + " [{'affiliations': [{'city': 'Dublin', 'city_id...\n", + " 10.1109/eucnc.2016.7561056\n", + " pub.1094950798\n", + " 28\n", + " proceeding\n", " 2016\n", " \n", " \n", " 1\n", " [{'affiliations': [{'city': 'Dublin', 'city_id...\n", - " 10.1145/2984356.2984363\n", - " pub.1001653422\n", - " 14\n", + " 10.1109/noms.2016.7503003\n", + " pub.1094654631\n", + " 35\n", " proceeding\n", " 2016\n", " \n", " \n", " 2\n", - " [{'affiliations': [{'city': 'Stuttgart', 'city...\n", - " 10.1016/j.apnum.2016.02.001\n", - " pub.1038596770\n", - " 12\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1002/adem.201400134\n", + " pub.1049335111\n", + " 50\n", " article\n", - " 2016\n", + " 2014\n", " \n", " \n", " 3\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1103/physrevlett.116.231101\n", - " pub.1001053038\n", - " 313\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1111/j.1551-2916.2005.00043.x\n", + " pub.1042663343\n", + " 64\n", " article\n", - " 2016\n", + " 2005\n", " \n", " \n", " 4\n", - " [{'affiliations': [{'city': 'Dublin', 'city_id...\n", - " 10.1109/eucnc.2016.7561056\n", - " pub.1094950798\n", - " 22\n", - " proceeding\n", - " 2016\n", + " [{'affiliations': [{'city': 'Legnaro', 'city_i...\n", + " 10.1016/s0168-583x(03)01322-3\n", + " pub.1041242454\n", + " 14\n", + " article\n", + " 2003\n", " \n", " \n", " 5\n", - " [{'affiliations': [{'city': 'Trento', 'city_id...\n", - " 10.1140/epjds/s13688-016-0064-6\n", - " pub.1033140941\n", - " 15\n", + " [{'affiliations': [{'city': 'MENLO PARK', 'cit...\n", + " 10.1111/jace.12485\n", + " pub.1033867339\n", + " 48\n", " article\n", - " 2016\n", + " 2013\n", " \n", " \n", " 6\n", " [{'affiliations': [{'city': 'Trento', 'city_id...\n", - " 10.1089/big.2014.0054\n", - " pub.1018945654\n", - " 48\n", - " article\n", - " 2015\n", + " 10.1145/2663204.2663254\n", + " pub.1033777395\n", + " 225\n", + " proceeding\n", + " 2014\n", " \n", " \n", " 7\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/1742-6596/610/1/012027\n", - " pub.1031150191\n", - " 1\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1140/epjds/s13688-016-0064-6\n", + " pub.1033140941\n", + " 25\n", " article\n", - " 2015\n", + " 2016\n", " \n", " \n", " 8\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/1742-6596/610/1/012005\n", - " pub.1052522882\n", - " 17\n", - " article\n", - " 2015\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1145/2063518.2063544\n", + " pub.1028019246\n", + " 1\n", + " proceeding\n", + " 2011\n", " \n", " \n", " 9\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/1742-6596/610/1/012026\n", - " pub.1033837350\n", - " 2\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1089/big.2014.0054\n", + " pub.1018945654\n", + " 68\n", " article\n", " 2015\n", " \n", @@ -542,64 +551,64 @@ ], "text/plain": [ " authors \\\n", - "0 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", + "0 [{'affiliations': [{'city': 'Dublin', 'city_id... \n", "1 [{'affiliations': [{'city': 'Dublin', 'city_id... \n", - "2 [{'affiliations': [{'city': 'Stuttgart', 'city... \n", - "3 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", - "4 [{'affiliations': [{'city': 'Dublin', 'city_id... \n", - "5 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "2 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "3 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "4 [{'affiliations': [{'city': 'Legnaro', 'city_i... \n", + "5 [{'affiliations': [{'city': 'MENLO PARK', 'cit... \n", "6 [{'affiliations': [{'city': 'Trento', 'city_id... \n", - "7 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", - "8 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", - "9 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", + "7 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "8 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "9 [{'affiliations': [{'city': 'Trento', 'city_id... \n", "\n", - " doi id times_cited type \\\n", - "0 10.1088/0264-9381/33/23/235015 pub.1059063534 7 article \n", - "1 10.1145/2984356.2984363 pub.1001653422 14 proceeding \n", - "2 10.1016/j.apnum.2016.02.001 pub.1038596770 12 article \n", - "3 10.1103/physrevlett.116.231101 pub.1001053038 313 article \n", - "4 10.1109/eucnc.2016.7561056 pub.1094950798 22 proceeding \n", - "5 10.1140/epjds/s13688-016-0064-6 pub.1033140941 15 article \n", - "6 10.1089/big.2014.0054 pub.1018945654 48 article \n", - "7 10.1088/1742-6596/610/1/012027 pub.1031150191 1 article \n", - "8 10.1088/1742-6596/610/1/012005 pub.1052522882 17 article \n", - "9 10.1088/1742-6596/610/1/012026 pub.1033837350 2 article \n", + " doi id times_cited type \\\n", + "0 10.1109/eucnc.2016.7561056 pub.1094950798 28 proceeding \n", + "1 10.1109/noms.2016.7503003 pub.1094654631 35 proceeding \n", + "2 10.1002/adem.201400134 pub.1049335111 50 article \n", + "3 10.1111/j.1551-2916.2005.00043.x pub.1042663343 64 article \n", + "4 10.1016/s0168-583x(03)01322-3 pub.1041242454 14 article \n", + "5 10.1111/jace.12485 pub.1033867339 48 article \n", + "6 10.1145/2663204.2663254 pub.1033777395 225 proceeding \n", + "7 10.1140/epjds/s13688-016-0064-6 pub.1033140941 25 article \n", + "8 10.1145/2063518.2063544 pub.1028019246 1 proceeding \n", + "9 10.1089/big.2014.0054 pub.1018945654 68 article \n", "\n", " year \n", "0 2016 \n", "1 2016 \n", - "2 2016 \n", - "3 2016 \n", - "4 2016 \n", - "5 2016 \n", - "6 2015 \n", - "7 2015 \n", - "8 2015 \n", + "2 2014 \n", + "3 2005 \n", + "4 2003 \n", + "5 2013 \n", + "6 2014 \n", + "7 2016 \n", + "8 2011 \n", "9 2015 " ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "gridis = list(company_grids.as_dataframe()['id'])\n", + "orgids = list(company_grids.as_dataframe()['id'])\n", "\n", "#\n", - "# loop through all grids\n", + "# loop through all organizations\n", "\n", "ITERATION_RECORDS = 1000 # Publication records per query iteration\n", - "GRID_RECORDS = 200 # grid IDs per query\n", + "ORG_RECORDS = 200 # organization IDs per query\n", "VERBOSE = False # set to True to view full extraction logs\n", - "print(f\"===\\nExtracting {GRIDID} publications with industry collaborators ...\")\n", + "print(f\"===\\nExtracting {ORGID} publications with industry collaborators ...\")\n", "print(\"Records per query : \", ITERATION_RECORDS)\n", - "print(\"GRID IDs per query: \", GRID_RECORDS)\n", + "print(\"Organization IDs per query: \", ORG_RECORDS)\n", "results = []\n", "\n", "\n", - "for chunk in progress(list(chunks_of(gridis, GRID_RECORDS))):\n", - " query = query_template.format(GRIDID, json.dumps(chunk), YEAR_START, YEAR_END)\n", + "for chunk in progress(list(chunks_of(orgids, ORG_RECORDS))):\n", + " query = query_template.format(ORGID, json.dumps(chunk), YEAR_START, YEAR_END)\n", "# print(query)\n", " data = dsl.query_iterative(query, verbose=VERBOSE, limit=ITERATION_RECORDS)\n", " if data.errors:\n", @@ -641,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": { "Collapsed": "false", "colab": { @@ -672,382 +681,48 @@ }, "data": [ { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "type=article
year=%{x}
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" + " }) }; " ] }, "metadata": {}, @@ -2030,7 +1611,7 @@ "px.histogram(pubs, \n", " x=\"year\", \n", " color=\"type\",\n", - " title=f\"Publications per year with industry collaborations for {GRIDID}\")" + " title=f\"Publications per year with industry collaborations for {ORGID}\")" ] }, { @@ -2046,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "Collapsed": "false", "colab": { @@ -2077,7 +1658,6 @@ }, "data": [ { - "alignmentgroup": "True", "hovertemplate": "year=%{x}
times_cited=%{y}", "legendgroup": "", "marker": { @@ -2087,53 +1667,23 @@ } }, "name": "", - "offsetgroup": "", "orientation": "v", "showlegend": false, "textposition": "auto", "type": "bar", - "x": [ - 2000, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, - 2014, - 2015, - 2016 - ], + "x": { + "bdata": "0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH", + "dtype": "i2" + }, "xaxis": "x", - "y": [ - 37, - 106, - 146, - 249, - 449, - 839, - 278, - 592, - 1940, - 550, - 729, - 499, - 634, - 1135, - 3539, - 6701 - ], + "y": { + "bdata": "UwEhAAUAGgEXAhYARACSAJEAyQCuAOwASgRgAZ0f", + "dtype": "i2" + }, "yaxis": "y" } ], "layout": { - "autosize": true, "barmode": "relative", "legend": { "tracegroupgap": 0 @@ -2317,57 +1867,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - 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true, "domain": [ 0, 1 ], - "range": [ - 0, - 7053.684210526316 - ], "title": { "text": "times_cited" - }, - "type": "linear" + } } } }, - "image/png": 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" + " }) }; " ] }, "metadata": {}, @@ -3030,7 +2527,7 @@ "px.bar(pubs_grouped, \n", " x=\"year\", \n", " y=\"times_cited\",\n", - " title=f\"Tot Citations per year for publications with industry collaborations for {GRIDID}\")" + " title=f\"Tot Citations per year for publications with industry collaborations for {ORGID}\")" ] }, { @@ -3120,7 +2617,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": { "Collapsed": "false", "colab": { @@ -3143,6 +2640,16 @@ "outputId": "a997bc97-e4b6-4b32-f63f-1739e921a1df" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/apilab/lib/python3.12/site-packages/dimcli/core/dataframe_factory.py:195: FutureWarning:\n", + "\n", + "Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + "\n" + ] + }, { "data": { "text/html": [ @@ -3181,120 +2688,120 @@ " \n", " \n", " \n", - " 7\n", - " Hamburg\n", - " 2911298.0\n", - " Germany\n", - " DE\n", - " grid.410308.e\n", - " Airbus (Germany)\n", - " Airbus Defence and Space, Claude-Dornier-Stras...\n", - " \n", + " 2\n", + " Paris\n", + " 2988507.0\n", + " France\n", + " FR\n", + " grid.89485.38\n", + " Orange SA\n", + " Orange S.A., France\n", " \n", - " pub.1059063534\n", " \n", - " N\n", - " Brandt\n", + " pub.1094950798\n", + " ur.014561075723.99\n", + " Imen Grida Ben\n", + " Yahia\n", " \n", " \n", - " 8\n", - " Milan\n", - " 3173435.0\n", - " Italy\n", - " IT\n", - " grid.424032.3\n", - " OHB (Italy)\n", - " CGS S.p.A, Compagnia Generale per lo Spazio, V...\n", + " 7\n", + " Madrid\n", + " 3117735.0\n", + " Spain\n", + " ES\n", + " grid.99308.3b\n", + " Telefonica Investigacion y Desarrollo SA\n", + " Telefonica I+D, Spain\n", " \n", " \n", - " pub.1059063534\n", - " ur.014542047336.90\n", - " A\n", - " Bursi\n", + " pub.1094950798\n", + " ur.016452362717.24\n", + " Antonio\n", + " Pastor\n", " \n", " \n", - " 15\n", - " Milan\n", - " 3173435.0\n", - " Italy\n", - " IT\n", - " grid.424032.3\n", - " OHB (Italy)\n", - " CGS S.p.A, Compagnia Generale per lo Spazio, V...\n", - " \n", + " 8\n", + " Madrid\n", + " 3117735.0\n", + " Spain\n", + " ES\n", + " grid.99308.3b\n", + " Telefonica Investigacion y Desarrollo SA\n", + " Telefonica I+D, Spain\n", " \n", - " pub.1059063534\n", " \n", - " D\n", - " Desiderio\n", + " pub.1094950798\n", + " ur.014322160107.95\n", + " Pedro A.\n", + " Aranda\n", " \n", " \n", - " 16\n", - " Milan\n", - " 3173435.0\n", - " Italy\n", - " IT\n", - " grid.424032.3\n", - " OHB (Italy)\n", - " CGS S.p.A, Compagnia Generale per lo Spazio, V...\n", + " 24\n", + " Madrid\n", + " 3117735.0\n", + " Spain\n", + " ES\n", + " grid.99308.3b\n", + " Telefonica Investigacion y Desarrollo SA\n", + " Telefonica I+d\n", " \n", " \n", - " pub.1059063534\n", - " \n", - " E\n", - " Piersanti\n", + " pub.1094654631\n", + " ur.014574231073.91\n", + " Diego R.\n", + " Lopez\n", " \n", " \n", - " 19\n", - " Bristol\n", - " 2654675.0\n", - " United Kingdom\n", - " GB\n", - " grid.7546.0\n", - " Airbus (United Kingdom)\n", - " Airbus Defence and Space, Gunnels Wood Road, S...\n", + " 34\n", + " Paris\n", + " 2988507.0\n", + " France\n", + " FR\n", + " grid.89485.38\n", + " Orange SA\n", + " Orange, France\n", " \n", " \n", - " pub.1059063534\n", - " ur.010504106037.54\n", - " N\n", - " Dunbar\n", + " pub.1094654631\n", + " ur.014561075723.99\n", + " Imen Grida Ben\n", + " Yahia\n", " \n", " \n", "\n", "" ], "text/plain": [ - " aff_city aff_city_id aff_country aff_country_code aff_id \\\n", - "7 Hamburg 2911298.0 Germany DE grid.410308.e \n", - "8 Milan 3173435.0 Italy IT grid.424032.3 \n", - "15 Milan 3173435.0 Italy IT grid.424032.3 \n", - "16 Milan 3173435.0 Italy IT grid.424032.3 \n", - "19 Bristol 2654675.0 United Kingdom GB grid.7546.0 \n", + " aff_city aff_city_id aff_country aff_country_code aff_id \\\n", + "2 Paris 2988507.0 France FR grid.89485.38 \n", + "7 Madrid 3117735.0 Spain ES grid.99308.3b \n", + "8 Madrid 3117735.0 Spain ES grid.99308.3b \n", + "24 Madrid 3117735.0 Spain ES grid.99308.3b \n", + "34 Paris 2988507.0 France FR grid.89485.38 \n", "\n", - " aff_name \\\n", - "7 Airbus (Germany) \n", - "8 OHB (Italy) \n", - "15 OHB (Italy) \n", - "16 OHB (Italy) \n", - "19 Airbus (United Kingdom) \n", + " aff_name aff_raw_affiliation aff_state \\\n", + "2 Orange SA Orange S.A., France \n", + "7 Telefonica Investigacion y Desarrollo SA Telefonica I+D, Spain \n", + "8 Telefonica Investigacion y Desarrollo SA Telefonica I+D, Spain \n", + "24 Telefonica Investigacion y Desarrollo SA Telefonica I+d \n", + "34 Orange SA Orange, France \n", "\n", - " aff_raw_affiliation aff_state \\\n", - "7 Airbus Defence and Space, Claude-Dornier-Stras... \n", - "8 CGS S.p.A, Compagnia Generale per lo Spazio, V... \n", - "15 CGS S.p.A, Compagnia Generale per lo Spazio, V... \n", - "16 CGS S.p.A, Compagnia Generale per lo Spazio, V... \n", - "19 Airbus Defence and Space, Gunnels Wood Road, S... \n", + " aff_state_code pub_id researcher_id first_name \\\n", + "2 pub.1094950798 ur.014561075723.99 Imen Grida Ben \n", + "7 pub.1094950798 ur.016452362717.24 Antonio \n", + "8 pub.1094950798 ur.014322160107.95 Pedro A. \n", + "24 pub.1094654631 ur.014574231073.91 Diego R. \n", + "34 pub.1094654631 ur.014561075723.99 Imen Grida Ben \n", "\n", - " aff_state_code pub_id researcher_id first_name last_name \n", - "7 pub.1059063534 N Brandt \n", - "8 pub.1059063534 ur.014542047336.90 A Bursi \n", - "15 pub.1059063534 D Desiderio \n", - "16 pub.1059063534 E Piersanti \n", - "19 pub.1059063534 ur.010504106037.54 N Dunbar " + " last_name \n", + "2 Yahia \n", + "7 Pastor \n", + "8 Aranda \n", + "24 Lopez \n", + "34 Yahia " ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -3307,7 +2814,7 @@ "# extract affiliations as a dataframe\n", "affiliations = pubsnew.as_dataframe_authors_affiliations()\n", "# focus only on affiliations including a grid from the industry set created above\n", - "affiliations = affiliations[affiliations['aff_id' ].isin(gridis)]\n", + "affiliations = affiliations[affiliations['aff_id' ].isin(orgids)]\n", "# preview the data\n", "affiliations.head(5)" ] @@ -3327,7 +2834,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": { "Collapsed": "false", "colab": { @@ -3358,7 +2865,6 @@ }, "data": [ { - "alignmentgroup": "True", "bingroup": "x", "hovertemplate": "aff_name=%{x}
count=%{y}", "legendgroup": "", @@ -3369,801 +2875,218 @@ } }, "name": "", - "offsetgroup": "", "orientation": "v", "showlegend": false, "type": "histogram", "x": [ - "Airbus (Germany)", - "OHB (Italy)", - "OHB (Italy)", - "OHB (Italy)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "Airbus (Germany)", - "Airbus (Germany)", - "OHB (Italy)", - "OHB (Italy)", - "Airbus (Germany)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "Telefonica Research and Development", - "Robert Bosch (Germany)", - "Robert Bosch (Germany)", - "Robert Bosch (Germany)", - "Airbus (Germany)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "Airbus (Germany)", - "Airbus (Germany)", - "OHB (Italy)", - "Airbus (Germany)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "IBM (Ireland)", - "IBM (Ireland)", - "Orange (France)", - "IBM (Ireland)", - 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"Sweden", + "Sweden", "Italy", - "United Kingdom", - "Germany", - "Germany", - "Germany", "Italy", "Italy", - "Germany", - "United Kingdom", - "United Kingdom", - "United Kingdom", - "United Kingdom", - "Germany", - "Spain", - "Germany", - "Germany", - "Germany", - "Germany", - "United Kingdom", - "Germany", - "Germany", - "Germany", "Italy", - "Germany", - "United Kingdom", - "United Kingdom", + "Italy", + "Italy", + "Italy", + "Italy", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", "United Kingdom", + "Japan", + "Japan", + "Japan", + "Japan", + "Japan", + "Japan", + "Italy", + "Italy", + "United States", + "Italy", + "Italy", + "Sweden", + "Italy", + "Italy", + "Finland", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "Switzerland", + "Switzerland", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", "Germany", - "Ireland", - "Ireland", + "United States", "France", - "Ireland", - "Spain", - "Spain", - "Ireland", "Italy", "Italy", "Italy", - "Spain", "Italy", - "Spain", - "Germany", + "France", "Italy", - "United Kingdom", - "Germany", - "Germany", "Italy", "Italy", + "United States", + "United States", + "Italy", + "Italy", + "Italy", + "Italy", + "United States", + "United States", + "Switzerland", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", 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aff_name=%{x}
count=%{y}", - "legendgroup": "India", + "hovertemplate": "aff_country=Japan
aff_name=%{x}
count=%{y}", + "legendgroup": "Japan", "marker": { "color": "rgb(102,194,165)", "pattern": { "shape": "" } }, - "name": "India", - "offsetgroup": "India", + "name": "Japan", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Venus Remedies (India)", - "Tata Elxsi (India)", - "Samsung (India)", - "IBM (India)" + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=South Korea
aff_name=%{x}
count=%{y}", - "legendgroup": "South Korea", + "hovertemplate": "aff_country=Finland
aff_name=%{x}
count=%{y}", + "legendgroup": "Finland", "marker": { "color": "rgb(50,136,189)", "pattern": { "shape": "" } }, - "name": "South Korea", - "offsetgroup": "South Korea", + "name": "Finland", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Amorepacific (South Korea)" + "Nokia Research Center" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=Slovenia
aff_name=%{x}
count=%{y}", - "legendgroup": "Slovenia", + "hovertemplate": "aff_country=Switzerland
aff_name=%{x}
count=%{y}", + "legendgroup": "Switzerland", "marker": { "color": "rgb(94,79,162)", "pattern": { "shape": "" } }, - "name": "Slovenia", - "offsetgroup": "Slovenia", + "name": "Switzerland", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "EN-FIST Centre of Excellence (Slovenia)" + "Novartis Forschungsstiftung Zweigniederlassung Friedrich Miescher Institute for Biomedical Research", + "Novartis Forschungsstiftung Zweigniederlassung Friedrich Miescher Institute for Biomedical Research", + "Novartis Forschungsstiftung Zweigniederlassung Friedrich Miescher Institute for Biomedical Research" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=Russia
aff_name=%{x}
count=%{y}", - "legendgroup": "Russia", + "hovertemplate": "aff_country=Germany
aff_name=%{x}
count=%{y}", + "legendgroup": "Germany", "marker": { "color": "rgb(158,1,66)", "pattern": { "shape": "" } }, - "name": "Russia", - "offsetgroup": "Russia", + "name": "Germany", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Surface Phenomena Researches Group (Russia)", - "Surface Phenomena Researches Group (Russia)", - "Surface Phenomena Researches Group (Russia)", - "Surface Phenomena Researches Group (Russia)" + "Nokia Solutions and Networks GmbH and Co KG", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "MacDermid Enthone GmbH" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=Greece
aff_name=%{x}
count=%{y}", - "legendgroup": "Greece", + "hovertemplate": "aff_country=South Korea
aff_name=%{x}
count=%{y}", + "legendgroup": "South Korea", "marker": { "color": "rgb(213,62,79)", "pattern": { "shape": "" } }, - "name": "Greece", - "offsetgroup": "Greece", - "orientation": "v", - "showlegend": true, - "type": "histogram", - "x": [ - "Advanced Microwave Systems (Greece)", - "Advanced Microwave Systems (Greece)", - "Advanced Microwave Systems (Greece)", - "Athens Technology Center (Greece)", - "Athens Technology Center (Greece)", - "Athens Technology Center (Greece)" - ], - "xaxis": "x", - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "bingroup": "x", - "hovertemplate": "aff_country=Austria
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count=%{y}", - "legendgroup": "Austria", - "marker": { - "color": "rgb(244,109,67)", - "pattern": { - "shape": "" - } - }, - "name": "Austria", - "offsetgroup": "Austria", + "name": "South Korea", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Vienna Consulting Engineers (Austria)", - "Siemens (Austria)", - "Siemens (Austria)", - "Böhler Edelstahl (Austria)", - "Böhler Edelstahl (Austria)" + "Korea Hydro and Nuclear Power Co Ltd" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", "hovertemplate": "aff_country=Belgium
aff_name=%{x}
count=%{y}", "legendgroup": "Belgium", "marker": { - "color": "rgb(253,174,97)", + "color": "rgb(244,109,67)", "pattern": { "shape": "" } }, "name": "Belgium", - "offsetgroup": "Belgium", - "orientation": "v", - "showlegend": true, - "type": "histogram", - "x": [ - "Aquaplus (Belgium)", - "Aquaplus (Belgium)", - "Aquaplus (Belgium)", - "Aquaplus (Belgium)" - ], - "xaxis": "x", - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "bingroup": "x", - "hovertemplate": "aff_country=Liechtenstein
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count=%{y}", - "legendgroup": "Liechtenstein", - "marker": { - "color": "rgb(254,224,139)", - "pattern": { - "shape": "" - } - }, - "name": "Liechtenstein", - "offsetgroup": "Liechtenstein", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Ivoclar Vivadent (Liechtenstein)", - "Ivoclar Vivadent (Liechtenstein)" + "Vesuvius Group SA" ], "xaxis": "x", "yaxis": "y" } ], "layout": { - "autosize": true, "barmode": "relative", + "height": 900, "legend": { "title": { "text": "aff_country" @@ -8358,57 +5723,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 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"text/html": [ - "
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" + " }) }; " ] }, "metadata": {}, @@ -9070,7 +6383,7 @@ " x=\"aff_name\", \n", " height=900, \n", " color=\"aff_country\",\n", - " title=f\"Top Countries and Industry collaborators for {gridname}-{GRIDID}\",\n", + " title=f\"Top Countries and Industry collaborators for {orgname}-{ORGID}\",\n", " color_discrete_sequence=px.colors.diverging.Spectral)" ] } @@ -9099,7 +6412,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" }, "nteract": { "version": "0.15.0" diff --git a/docs/.doctrees/nbsphinx/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb index 9dc9cbcf..ce8f4f27 100644 --- a/docs/.doctrees/nbsphinx/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb +++ b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb @@ -28,7 +28,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Aug 22, 2023\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -58,33 +58,14 @@ "Collapsed": "false" }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" - ] - }, { "data": { "text/html": [ " \n", + " \n", " " ] }, @@ -104,8 +85,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v1.1)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.7\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -159,9 +140,9 @@ "id": "L6uIjSVnGRQV" }, "source": [ - "For the purpose of this exercise, we will use [grid.412125.1](https://grid.ac/institutes/grid.412125.1) (King Abdulaziz University, Saudi Arabia). \n", + "For the purpose of this exercise, we will use King Abdulaziz University, Saudi Arabia (grid.412125.1). \n", "\n", - "> You can try using a different GRID ID to see how results change, e.g. by [browsing for another GRID organization](https://grid.ac/institutes).\n" + "> You can try using a different organization ID to see how results change." ] }, { @@ -174,7 +155,7 @@ { "data": { "text/html": [ - "GRID: grid.412125.1 - King Abdulaziz University ⧉" + "Organization: grid.412125.1 - King Abdulaziz University ⧉" ], "text/plain": [ "" @@ -209,7 +190,7 @@ } ], "source": [ - "GRIDID = \"grid.412125.1\" #@param {type:\"string\"}\n", + "ORGID = \"grid.412125.1\" #@param {type:\"string\"}\n", " \n", "#@markdown The start/end year of publications used to extract patents\n", "YEAR_START = 2000 #@param {type: \"slider\", min: 1950, max: 2020}\n", @@ -226,11 +207,11 @@ "# gen link to Dimensions\n", "#\n", "try:\n", - " gridname = dsl.query(f\"\"\"search organizations where id=\"{GRIDID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", + " orgname = dsl.query(f\"\"\"search organizations where id=\"{ORGID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", "except:\n", - " gridname = \"\"\n", + " orgname = \"\"\n", "from IPython.display import display, HTML\n", - "display(HTML('GRID: {} - {} ⧉'.format(dimensions_url(GRIDID), GRIDID, gridname)))\n", + "display(HTML('Organization: {} - {} ⧉'.format(dimensions_url(ORGID), ORGID, orgname)))\n", "display(HTML('Time period: {} to {}'.format(YEAR_START, YEAR_END)))\n", "display(HTML('Topic: \"{}\"

'.format(TOPIC)))\n" ] @@ -292,10 +273,10 @@ "Note: \n", "\n", "* **Extra columns**. The resulting dataframe contains two extra columns: a) `id_from`, which is the 'seed' institution we start from; b) `level`, an optional parameter representing the network depth of the query (we'll see later how it is used with recursive querying).\n", - "* **Self-collaboration**. The query returns 11 records - that's because the first one is normally the seed GRID (due to internal collaborations) which we will omit from the results.\n", + "* **Self-collaboration**. The query returns 11 records - that's because the first one is normally the seed organization (due to internal collaborations) which we will omit from the results.\n", "* **Custom changes**. Lastly, it's important to remember that this step can be easily customised by changing the `query_template` sttructure. For example, we could focus on specific research areas (using FOR codes), or set a threshold based on citation counts. The possibilities are endless! \n", "\n", - "For example, let's try it out with our GRID ID:" + "For example, let's try it out with our organization ID:" ] }, { @@ -341,6 +322,7 @@ " acronym\n", " city_name\n", " count\n", + " country_code\n", " country_name\n", " latitude\n", " linkout\n", @@ -358,7 +340,8 @@ " King Abdulaziz University\n", " KAU\n", " Jeddah\n", - " 1444\n", + " 1435\n", + " SA\n", " Saudi Arabia\n", " 21.493889\n", " [http://www.kau.edu.sa/home_english.aspx]\n", @@ -375,6 +358,7 @@ " NU\n", " Boston\n", " 106\n", + " US\n", " United States\n", " 42.339830\n", " [http://www.northeastern.edu/]\n", @@ -391,6 +375,7 @@ " NaN\n", " Cambridge\n", " 98\n", + " US\n", " United States\n", " 42.377052\n", " [http://www.harvard.edu/]\n", @@ -407,6 +392,7 @@ " MIT\n", " Cambridge\n", " 73\n", + " US\n", " United States\n", " 42.359820\n", " [http://web.mit.edu/]\n", @@ -423,6 +409,7 @@ " NU\n", " Evanston\n", " 59\n", + " US\n", " United States\n", " 42.054850\n", " [http://www.northwestern.edu/]\n", @@ -434,27 +421,12 @@ " \n", " \n", " 5\n", - " grid.413735.7\n", - " Harvard–MIT Division of Health Sciences and Te...\n", - " HST\n", - " Cambridge\n", - " 58\n", - " United States\n", - " 42.361780\n", - " [http://hst.mit.edu/]\n", - " -71.086914\n", - " [Education]\n", - " Massachusetts\n", - " grid.412125.1\n", - " 1\n", - " \n", - " \n", - " 6\n", " grid.411340.3\n", " Aligarh Muslim University\n", " AMU\n", " Aligarh\n", - " 47\n", + " 46\n", + " IN\n", " India\n", " 27.917370\n", " [http://www.amu.ac.in/]\n", @@ -465,12 +437,13 @@ " 1\n", " \n", " \n", - " 7\n", + " 6\n", " grid.412621.2\n", " Quaid-i-Azam University\n", " QAU\n", " Islamabad\n", - " 47\n", + " 46\n", + " PK\n", " Pakistan\n", " 33.747223\n", " [http://www.qau.edu.pk/]\n", @@ -481,12 +454,47 @@ " 1\n", " \n", " \n", + " 7\n", + " grid.411818.5\n", + " Jamia Millia Islamia\n", + " JMI\n", + " New Delhi\n", + " 40\n", + " IN\n", + " India\n", + " 28.561607\n", + " [http://jmi.ac.in/]\n", + " 77.280150\n", + " [Education]\n", + " NaN\n", + " grid.412125.1\n", + " 1\n", + " \n", + " \n", " 8\n", + " grid.62560.37\n", + " Brigham and Womens Hospital Inc\n", + " BWH\n", + " Boston\n", + " 40\n", + " US\n", + " United States\n", + " NaN\n", + " [http://www.brighamandwomens.org/]\n", + " NaN\n", + " [Healthcare]\n", + " Massachusetts\n", + " grid.412125.1\n", + " 1\n", + " \n", + " \n", + " 9\n", " grid.33003.33\n", " Suez Canal University\n", " NaN\n", " Ismailia\n", - " 42\n", + " 39\n", + " EG\n", " Egypt\n", " 30.622778\n", " [http://scuegypt.edu.eg/ar/]\n", @@ -497,28 +505,13 @@ " 1\n", " \n", " \n", - " 9\n", - " grid.411818.5\n", - " Jamia Millia Islamia\n", - " JMI\n", - " New Delhi\n", - " 42\n", - " India\n", - " 28.561607\n", - " [http://jmi.ac.in/]\n", - " 77.280150\n", - " [Education]\n", - " NaN\n", - " grid.412125.1\n", - " 1\n", - " \n", - " \n", " 10\n", " grid.56302.32\n", " King Saud University\n", " KSU\n", " Riyadh\n", - " 42\n", + " 39\n", + " SA\n", " Saudi Arabia\n", " 24.723982\n", " [http://ksu.edu.sa/en/]\n", @@ -533,44 +526,44 @@ "" ], "text/plain": [ - " id name acronym \\\n", - "0 grid.412125.1 King Abdulaziz University KAU \n", - "1 grid.261112.7 Northeastern University NU \n", - "2 grid.38142.3c Harvard University NaN \n", - "3 grid.116068.8 Massachusetts Institute of Technology MIT \n", - "4 grid.16753.36 Northwestern University NU \n", - "5 grid.413735.7 Harvard–MIT Division of Health Sciences and Te... HST \n", - "6 grid.411340.3 Aligarh Muslim University AMU \n", - "7 grid.412621.2 Quaid-i-Azam University QAU \n", - "8 grid.33003.33 Suez Canal University NaN \n", - "9 grid.411818.5 Jamia Millia Islamia JMI \n", - "10 grid.56302.32 King Saud University KSU \n", + " id name acronym city_name \\\n", + "0 grid.412125.1 King Abdulaziz University KAU Jeddah \n", + "1 grid.261112.7 Northeastern University NU Boston \n", + "2 grid.38142.3c Harvard University NaN Cambridge \n", + "3 grid.116068.8 Massachusetts Institute of Technology MIT Cambridge \n", + "4 grid.16753.36 Northwestern University NU Evanston \n", + "5 grid.411340.3 Aligarh Muslim University AMU Aligarh \n", + "6 grid.412621.2 Quaid-i-Azam University QAU Islamabad \n", + "7 grid.411818.5 Jamia Millia Islamia JMI New Delhi \n", + "8 grid.62560.37 Brigham and Womens Hospital Inc BWH Boston \n", + "9 grid.33003.33 Suez Canal University NaN Ismailia \n", + "10 grid.56302.32 King Saud University KSU Riyadh \n", "\n", - " city_name count country_name latitude \\\n", - "0 Jeddah 1444 Saudi Arabia 21.493889 \n", - "1 Boston 106 United States 42.339830 \n", - "2 Cambridge 98 United States 42.377052 \n", - "3 Cambridge 73 United States 42.359820 \n", - "4 Evanston 59 United States 42.054850 \n", - "5 Cambridge 58 United States 42.361780 \n", - "6 Aligarh 47 India 27.917370 \n", - "7 Islamabad 47 Pakistan 33.747223 \n", - "8 Ismailia 42 Egypt 30.622778 \n", - "9 New Delhi 42 India 28.561607 \n", - "10 Riyadh 42 Saudi Arabia 24.723982 \n", + " count country_code country_name latitude \\\n", + "0 1435 SA Saudi Arabia 21.493889 \n", + "1 106 US United States 42.339830 \n", + "2 98 US United States 42.377052 \n", + "3 73 US United States 42.359820 \n", + "4 59 US United States 42.054850 \n", + "5 46 IN India 27.917370 \n", + "6 46 PK Pakistan 33.747223 \n", + "7 40 IN India 28.561607 \n", + "8 40 US United States NaN \n", + "9 39 EG Egypt 30.622778 \n", + "10 39 SA Saudi Arabia 24.723982 \n", "\n", - " linkout longitude types \\\n", - "0 [http://www.kau.edu.sa/home_english.aspx] 39.250280 [Education] \n", - "1 [http://www.northeastern.edu/] -71.089180 [Education] \n", - "2 [http://www.harvard.edu/] -71.116650 [Education] \n", - "3 [http://web.mit.edu/] -71.092110 [Education] \n", - "4 [http://www.northwestern.edu/] -87.673940 [Education] \n", - "5 [http://hst.mit.edu/] -71.086914 [Education] \n", - "6 [http://www.amu.ac.in/] 78.077850 [Education] \n", - "7 [http://www.qau.edu.pk/] 73.138885 [Education] \n", - "8 [http://scuegypt.edu.eg/ar/] 32.275000 [Education] \n", - "9 [http://jmi.ac.in/] 77.280150 [Education] \n", - "10 [http://ksu.edu.sa/en/] 46.645840 [Education] \n", + " linkout longitude types \\\n", + "0 [http://www.kau.edu.sa/home_english.aspx] 39.250280 [Education] \n", + "1 [http://www.northeastern.edu/] -71.089180 [Education] \n", + "2 [http://www.harvard.edu/] -71.116650 [Education] \n", + "3 [http://web.mit.edu/] -71.092110 [Education] \n", + "4 [http://www.northwestern.edu/] -87.673940 [Education] \n", + "5 [http://www.amu.ac.in/] 78.077850 [Education] \n", + "6 [http://www.qau.edu.pk/] 73.138885 [Education] \n", + "7 [http://jmi.ac.in/] 77.280150 [Education] \n", + "8 [http://www.brighamandwomens.org/] NaN [Healthcare] \n", + "9 [http://scuegypt.edu.eg/ar/] 32.275000 [Education] \n", + "10 [http://ksu.edu.sa/en/] 46.645840 [Education] \n", "\n", " state_name id_from level \n", "0 NaN grid.412125.1 1 \n", @@ -578,10 +571,10 @@ "2 Massachusetts grid.412125.1 1 \n", "3 Massachusetts grid.412125.1 1 \n", "4 Illinois grid.412125.1 1 \n", - "5 Massachusetts grid.412125.1 1 \n", - "6 Uttar Pradesh grid.412125.1 1 \n", + "5 Uttar Pradesh grid.412125.1 1 \n", + "6 NaN grid.412125.1 1 \n", "7 NaN grid.412125.1 1 \n", - "8 NaN grid.412125.1 1 \n", + "8 Massachusetts grid.412125.1 1 \n", "9 NaN grid.412125.1 1 \n", "10 NaN grid.412125.1 1 " ] @@ -592,7 +585,7 @@ } ], "source": [ - "get_collaborators(GRIDID, printquery=True)" + "get_collaborators(ORGID, printquery=True)" ] }, { @@ -605,9 +598,9 @@ "\n", "What if we want to retrieve the collaborators of the collaborators? In other words, what if we want to generate a larger network?\n", "\n", - "If we think of our collaboration data as a [graph structure](https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)) with nodes and edges, we can see that the `get_collaborators` function defined above is limited. That's because it allows to obtain only the objects *directly* linked to the 'seed' GRID organization. \n", + "If we think of our collaboration data as a [graph structure](https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)) with nodes and edges, we can see that the `get_collaborators` function defined above is limited. That's because it allows to obtain only the objects *directly* linked to the 'seed' organization. \n", "\n", - "We would like to run the same collaborators-extraction step **iteratively** for any GRID ID in our results, so to generate an N-degrees network where N is chosen by us. \n", + "We would like to run the same collaborators-extraction step **iteratively** for any ID in our results, so to generate an N-degrees network where N is chosen by us. \n", "\n", "To this purpose, we can set up a [recursive](https://en.wikipedia.org/wiki/Recursion_(computer_science)) function. This function essentially repeats the `get_collaborators` function as many times as needed. Here's what it looks like:" ] @@ -627,8 +620,8 @@ " print(\"--\" * thislevel, seed, \" :: level =\", thislevel)\n", " if thislevel < maxlevel:\n", " # remove the originating grid-id\n", - " gridslist = list(results[results['id'] != GRIDID]['id'])\n", - " next_level_results = [recursive_network(x, maxlevel, thislevel+1) for x in gridslist]\n", + " orgslist = list(results[results['id'] != ORGID]['id'])\n", + " next_level_results = [recursive_network(x, maxlevel, thislevel+1) for x in orgslist]\n", " next_level_results = pd.concat(next_level_results)\n", " results = pd.concat([results, next_level_results])\n", " return results\n", @@ -671,11 +664,11 @@ "---- grid.38142.3c :: level = 2\n", "---- grid.116068.8 :: level = 2\n", "---- grid.16753.36 :: level = 2\n", - "---- grid.413735.7 :: level = 2\n", "---- grid.411340.3 :: level = 2\n", "---- grid.412621.2 :: level = 2\n", - "---- grid.33003.33 :: level = 2\n", "---- grid.411818.5 :: level = 2\n", + "---- grid.62560.37 :: level = 2\n", + "---- grid.33003.33 :: level = 2\n", "---- grid.56302.32 :: level = 2\n" ] }, @@ -721,7 +714,7 @@ " grid.412125.1\n", " grid.412125.1\n", " 1\n", - " 1444\n", + " 1435\n", " King Abdulaziz University\n", " KAU\n", " Jeddah\n", @@ -802,7 +795,7 @@ ], "text/plain": [ " id_from id_to level count \\\n", - "0 grid.412125.1 grid.412125.1 1 1444 \n", + "0 grid.412125.1 grid.412125.1 1 1435 \n", "1 grid.412125.1 grid.261112.7 1 106 \n", "2 grid.412125.1 grid.38142.3c 1 98 \n", "3 grid.412125.1 grid.116068.8 1 73 \n", @@ -836,7 +829,7 @@ } ], "source": [ - "collaborators = recursive_network(GRIDID, maxlevel=2)\n", + "collaborators = recursive_network(ORGID, maxlevel=2)\n", "# change column order for readability purposes\n", "collaborators.rename(columns={\"id\": \"id_to\"}, inplace=True)\n", "collaborators = collaborators[['id_from', 'id_to', 'level', 'count', 'name', 'acronym', 'city_name', 'state_name', 'country_name', 'latitude', 'longitude', 'linkout', 'types' ]]\n", @@ -896,7 +889,7 @@ " " ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -945,13 +938,13 @@ "\n", " # calc size based on level\n", " maxsize = int(nodes['level'].max()) + 1\n", - " if row['id_to'] == GRIDID:\n", + " if row['id_to'] == ORGID:\n", " size = maxsize\n", " else:\n", " size = maxsize - row['level']\n", "\n", " # calc color based on level\n", - " if row['id_to'] == GRIDID:\n", + " if row['id_to'] == ORGID:\n", " color = palette[0]\n", " else:\n", " color = palette[row['level'] * 2]\n", @@ -990,10 +983,10 @@ " return g\n", "\n", "#\n", - "# finall, run the viz builder\n", + "# finally, run the viz builder\n", "#\n", "g = build_visualization(collaborators)\n", - "g.show(f\"network_{GRIDID}.html\")" + "g.show(f\"network_{ORGID}.html\")" ] }, { @@ -1006,7 +999,7 @@ "\n", "What if we want to show a collaboration network focusing only on 'government' organizations? \n", "\n", - "That's pretty easy to do, since the GRID database includes information about **organization types**. We can easily see what types are available using the API and a `facet` query:" + "That's pretty easy to do, since the organization data set includes information about **organization types**. We can easily see what types are available using the API and a `facet` query:" ] }, { @@ -1020,8 +1013,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Types: 9\n", - "\u001b[2mTime: 1.00s\u001b[0m\n" + "Returned Types: 8\n", + "\u001b[2mTime: 2.54s\u001b[0m\n" ] }, { @@ -1052,64 +1045,58 @@ " \n", " \n", " 0\n", - " Company\n", - " 30742\n", + " Other\n", + " 165766\n", " \n", " \n", " 1\n", - " Education\n", - " 20761\n", + " Company\n", + " 158994\n", " \n", " \n", " 2\n", - " Nonprofit\n", - " 17573\n", + " Education\n", + " 22805\n", " \n", " \n", " 3\n", - " Healthcare\n", - " 13926\n", + " Nonprofit\n", + " 18361\n", " \n", " \n", " 4\n", - " Facility\n", - " 10168\n", + " Healthcare\n", + " 14865\n", " \n", " \n", " 5\n", " Government\n", - " 6580\n", + " 11545\n", " \n", " \n", " 6\n", - " Other\n", - " 4017\n", + " Facility\n", + " 10692\n", " \n", " \n", " 7\n", " Archive\n", - " 2926\n", - " \n", - " \n", - " 8\n", - " Education,Company\n", - " 1\n", + " 3059\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id count\n", - "0 Company 30742\n", - "1 Education 20761\n", - "2 Nonprofit 17573\n", - "3 Healthcare 13926\n", - "4 Facility 10168\n", - "5 Government 6580\n", - "6 Other 4017\n", - "7 Archive 2926\n", - "8 Education,Company 1" + " id count\n", + "0 Other 165766\n", + "1 Company 158994\n", + "2 Education 22805\n", + "3 Nonprofit 18361\n", + "4 Healthcare 14865\n", + "5 Government 11545\n", + "6 Facility 10692\n", + "7 Archive 3059" ] }, "execution_count": 9, @@ -1133,7 +1120,7 @@ "* **Get more results**. We increase the number of results returned: `..return research_orgs limit 50`. This is to ensure we still have enough results after removing the ones that don't have the chosen 'type'\n", "* **Remove unwanted data**. The new query filter `research_orgs.types in [\"{}\"]` will return also publications with multiple authors/affiliations, even though only one of them has the desired 'type'. So an extra step is required and this is achieved via the `keep_type` function below. This function simply filters out all unwanted organizations data after they're retrieved from the API. \n", "\n", - "That's it! Run the cell below to generate a new visualization showing only \"Government\" collaborators. Or try changing the value of `GRID_TYPE` to see different results. \n" + "That's it! Run the cell below to generate a new visualization showing only \"Government\" collaborators. Or try changing the value of `ORG_TYPE` to see different results. \n" ] }, { @@ -1150,7 +1137,6 @@ "-- grid.412125.1 :: level = 1\n", "---- grid.7327.1 :: level = 2\n", "---- grid.9227.e :: level = 2\n", - "---- grid.20256.33 :: level = 2\n", "---- grid.1089.0 :: level = 2\n", "---- grid.14467.30 :: level = 2\n", "network_grid.412125.1_Government.html\n" @@ -1171,7 +1157,7 @@ " " ], "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -1182,7 +1168,7 @@ "source": [ "#@markdown Try using one of the organization types from the list above\n", "\n", - "GRID_TYPE = \"Government\" #@param {type:\"string\"}\n", + "ORG_TYPE = \"Government\" #@param {type:\"string\"}\n", "\n", "query = \"\"\"search publications {}\n", " where year in [{}:{}] \n", @@ -1193,7 +1179,7 @@ "def keep_only_type(data, a_type, orgid):\n", " clean_list = []\n", " for x in data.research_orgs:\n", - " # include also originating GRID to ensure chart is complete\n", + " # include also originating org to ensure chart is complete\n", " if x['id'] == orgid or a_type in x['types']:\n", " clean_list.append(x)\n", " data.json['research_orgs'] = clean_list\n", @@ -1206,8 +1192,8 @@ " TOPIC_CLAUSE = f\"\"\"for \"{TOPIC}\" \"\"\"\n", " else:\n", " TOPIC_CLAUSE = \"\"\n", - " # include also the GRID_TYPE\n", - " query_full = query.format(TOPIC_CLAUSE, YEAR_START, YEAR_END, orgid, GRID_TYPE)\n", + " # include also the ORG_TYPE\n", + " query_full = query.format(TOPIC_CLAUSE, YEAR_START, YEAR_END, orgid, ORG_TYPE)\n", " if printquery: print(query_full)\n", " data = dsl.query(query_full, verbose=False)\n", " # remove results with unwanted types \n", @@ -1221,7 +1207,7 @@ "#\n", "# RUN THE RECURSIVE QUERY (same code as above)\n", "#\n", - "collaborators = recursive_network(GRIDID, maxlevel=2)\n", + "collaborators = recursive_network(ORGID, maxlevel=2)\n", "collaborators.rename(columns={\"id\": \"id_to\"}, inplace=True)\n", "collaborators = collaborators[['id_from', 'id_to', 'level', 'count', 'name', 'acronym', 'city_name', 'country_name', 'latitude', 'longitude', 'linkout', 'types' ]]\n", "\n", @@ -1229,7 +1215,7 @@ "# BUILD VIZ\n", "#\n", "g = build_visualization(collaborators)\n", - "g.show(f\"network_{GRIDID}_{GRID_TYPE}.html\")\n", + "g.show(f\"network_{ORGID}_{ORG_TYPE}.html\")\n", "\n" ] }, @@ -1272,7 +1258,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.1" + "version": "3.12.8" }, "nteract": { "version": "0.15.0" diff --git a/docs/.doctrees/nbsphinx/cookbooks/8-organizations/4-international-collaboration-by-year.ipynb b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/4-international-collaboration-by-year.ipynb index cea374f0..40286910 100644 --- a/docs/.doctrees/nbsphinx/cookbooks/8-organizations/4-international-collaboration-by-year.ipynb +++ b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/4-international-collaboration-by-year.ipynb @@ -24,7 +24,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Jan 25, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": { "Collapsed": "false" }, @@ -59,19 +59,9 @@ "text/html": [ " \n", + " \n", " " ] }, @@ -91,8 +81,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -137,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": { "Collapsed": "false" }, @@ -146,8 +136,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Organizations: 16 (total = 16)\n", - "\u001b[2mTime: 0.57s\u001b[0m\n" + "Returned Organizations: 20 (total = 23)\n", + "\u001b[2mTime: 0.53s\u001b[0m\n" ] }, { @@ -171,225 +161,298 @@ " \n", " \n", " \n", + " id\n", + " name\n", " city_name\n", + " country_code\n", " country_name\n", - " id\n", + " types\n", + " state_name\n", " latitude\n", " linkout\n", " longitude\n", - " name\n", - " state_name\n", - " types\n", " acronym\n", " \n", " \n", " \n", " \n", " 0\n", + " grid.772384.d\n", + " Trelleborg Marine Systems Melbourne Pty Ltd\n", + " Victoria\n", + " AU\n", + " Australia\n", + " [Company]\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 1\n", + " grid.746611.3\n", + " Noyes Bros Melbourne Pty Ltd\n", + " NaN\n", + " AU\n", + " Australia\n", + " [Other]\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 2\n", + " grid.631568.f\n", + " CityLink Melbourne Ltd\n", + " NaN\n", + " AU\n", + " Australia\n", + " [Other]\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 3\n", + " grid.530408.a\n", + " Melbourne Institute of Technology\n", " Melbourne\n", + " AU\n", " Australia\n", + " [Nonprofit]\n", + " Victoria\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 4\n", " grid.511296.8\n", + " Melbourne Genomics Health Alliance\n", + " Melbourne\n", + " AU\n", + " Australia\n", + " [Nonprofit]\n", + " Victoria\n", " -37.797960\n", " [https://www.melbournegenomics.org.au/]\n", " 144.953870\n", - " Melbourne Genomics Health Alliance\n", - " Victoria\n", - " [Nonprofit]\n", " NaN\n", " \n", " \n", - " 1\n", + " 5\n", + " grid.493437.e\n", + " RMIT Europe\n", " Barcelona\n", + " ES\n", " Spain\n", - " grid.493437.e\n", + " [Education]\n", + " NaN\n", " 41.402576\n", " [https://www.rmit.eu]\n", " 2.194333\n", - " RMIT Europe\n", - " NaN\n", - " [Education]\n", " RMIT\n", " \n", " \n", - " 2\n", + " 6\n", + " grid.490309.7\n", + " Melbourne Sexual Health Centre\n", " Carlton\n", + " AU\n", " Australia\n", - " grid.490309.7\n", + " [Healthcare]\n", + " Victoria\n", " -37.803123\n", " [https://www.mshc.org.au/]\n", " 144.963840\n", - " Melbourne Sexual Health Centre\n", - " Victoria\n", - " [Healthcare]\n", " MSHC\n", " \n", " \n", - " 3\n", + " 7\n", + " grid.477970.a\n", + " Melbourne Clinic\n", " Richmond\n", + " AU\n", " Australia\n", - " grid.477970.a\n", + " [Healthcare]\n", + " Victoria\n", " -37.815063\n", " [http://www.themelbourneclinic.com.au/]\n", " 144.999650\n", - " Melbourne Clinic\n", - " Victoria\n", - " [Healthcare]\n", " NaN\n", " \n", " \n", - " 4\n", - " Melbourne\n", + " 8\n", + " grid.474755.0\n", + " Leica Biosystems Melbourne Pty Ltd\n", + " Mt. Waverley\n", + " AU\n", " Australia\n", + " [Company]\n", + " NaN\n", + " NaN\n", + " [http://www.danaher.com/]\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 9\n", " grid.469061.c\n", + " Ridley College\n", + " Melbourne\n", + " AU\n", + " Australia\n", + " [Education]\n", + " Victoria\n", " -37.783780\n", " [https://www.ridley.edu.au/]\n", " 144.957660\n", - " Ridley College\n", - " Victoria\n", - " [Education]\n", " NaN\n", " \n", " \n", - " 5\n", + " 10\n", + " grid.469026.f\n", + " Melbourne School of Theology\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.469026.f\n", + " [Education]\n", + " Victoria\n", " -37.859700\n", " [http://www.mst.edu.au/]\n", " 145.209410\n", - " Melbourne School of Theology\n", - " Victoria\n", - " [Education]\n", " MBI\n", " \n", " \n", - " 6\n", + " 11\n", + " grid.468079.4\n", + " Port of Melbourne Corporation\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.468079.4\n", + " [Government]\n", + " Victoria\n", " -37.824028\n", " [http://www.portofmelbourne.com/]\n", " 144.907070\n", - " Port of Melbourne Corporation\n", - " Victoria\n", - " [Government]\n", " PoMC\n", " \n", " \n", - " 7\n", + " 12\n", + " grid.468069.5\n", + " Melbourne Water\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.468069.5\n", + " [Government]\n", + " Victoria\n", " -37.814007\n", " [http://www.melbournewater.com.au/Pages/home.a...\n", " 144.946700\n", - " Melbourne Water\n", - " Victoria\n", - " [Government]\n", " NaN\n", " \n", " \n", - " 8\n", + " 13\n", + " grid.452643.2\n", + " Melbourne Bioinformatics\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.452643.2\n", + " [Education]\n", + " Victoria\n", " -37.799847\n", - " [https://www.vlsci.org.au/]\n", + " [https://www.melbournebioinformatics.org.au]\n", " 144.964460\n", - " Victorian Life Sciences Computation Initiative\n", - " Victoria\n", - " [Education]\n", - " NaN\n", + " VLSCI\n", " \n", " \n", - " 9\n", + " 14\n", + " grid.449135.e\n", + " Melbourne Free University\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.449135.e\n", + " [Education]\n", + " Victoria\n", " NaN\n", " NaN\n", " NaN\n", - " Melbourne Free University\n", - " Victoria\n", - " [Education]\n", " NaN\n", " \n", " \n", - " 10\n", + " 15\n", + " grid.440113.3\n", + " Royal Dental Hospital of Melbourne\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.440113.3\n", + " [Healthcare]\n", + " Victoria\n", " -37.799260\n", " [https://www.dhsv.org.au]\n", " 144.964630\n", - " Royal Dental Hospital of Melbourne\n", - " Victoria\n", - " [Healthcare]\n", " RDHM\n", " \n", " \n", - " 11\n", + " 16\n", + " grid.438527.f\n", + " Royal Melbourne Institute of Technology Univer...\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.429299.d\n", - " -37.798940\n", - " [http://www.mh.org.au/]\n", - " 144.955930\n", - " Melbourne Health\n", + " [Other]\n", " Victoria\n", - " [Healthcare]\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " \n", " \n", - " 12\n", + " 17\n", + " grid.429299.d\n", + " Melbourne Health\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.416153.4\n", - " -37.798756\n", - " [http://www.rmh.mh.org.au/]\n", - " 144.955930\n", - " Royal Melbourne Hospital\n", - " Victoria\n", " [Healthcare]\n", - " RMH\n", - " \n", - " \n", - " 13\n", - " Clayton\n", - " Australia\n", - " grid.410660.5\n", - " -37.915775\n", - " [http://nanomelbourne.com/]\n", - " 145.143660\n", - " Melbourne Centre for Nanofabrication\n", " Victoria\n", - " [Facility]\n", - " MCN\n", + " -37.798940\n", + " [http://www.mh.org.au/]\n", + " 144.955930\n", + " NaN\n", " \n", " \n", - " 14\n", + " 18\n", + " grid.416153.4\n", + " Royal Melbourne Hospital\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.1017.7\n", - " -37.806747\n", - " [https://www.rmit.edu.au/]\n", - " 144.962570\n", - " RMIT University\n", + " [Healthcare]\n", " Victoria\n", - " [Education]\n", - " RMIT\n", + " -37.798756\n", + " [http://www.rmh.mh.org.au/]\n", + " 144.955930\n", + " RMH\n", " \n", " \n", - " 15\n", + " 19\n", + " grid.413105.2\n", + " St Vincent's Hospital\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.1008.9\n", - " -37.797115\n", - " [http://www.unimelb.edu.au/]\n", - " 144.959980\n", - " University of Melbourne\n", + " [Healthcare]\n", " Victoria\n", - " [Education]\n", + " -37.807000\n", + " [http://www.svhm.org.au/Pages/Home.aspx]\n", + " 144.975000\n", " NaN\n", " \n", " \n", @@ -397,80 +460,96 @@ "" ], "text/plain": [ - " city_name country_name id latitude \\\n", - "0 Melbourne Australia grid.511296.8 -37.797960 \n", - "1 Barcelona Spain grid.493437.e 41.402576 \n", - "2 Carlton Australia grid.490309.7 -37.803123 \n", - "3 Richmond Australia grid.477970.a -37.815063 \n", - "4 Melbourne Australia grid.469061.c -37.783780 \n", - "5 Melbourne Australia grid.469026.f -37.859700 \n", - "6 Melbourne Australia grid.468079.4 -37.824028 \n", - "7 Melbourne Australia grid.468069.5 -37.814007 \n", - "8 Melbourne Australia grid.452643.2 -37.799847 \n", - "9 Melbourne Australia grid.449135.e NaN \n", - "10 Melbourne Australia grid.440113.3 -37.799260 \n", - "11 Melbourne Australia grid.429299.d -37.798940 \n", - "12 Melbourne Australia grid.416153.4 -37.798756 \n", - "13 Clayton Australia grid.410660.5 -37.915775 \n", - "14 Melbourne Australia grid.1017.7 -37.806747 \n", - "15 Melbourne Australia grid.1008.9 -37.797115 \n", + " id name \\\n", + "0 grid.772384.d Trelleborg Marine Systems Melbourne Pty Ltd \n", + "1 grid.746611.3 Noyes Bros Melbourne Pty Ltd \n", + "2 grid.631568.f CityLink Melbourne Ltd \n", + "3 grid.530408.a Melbourne Institute of Technology \n", + "4 grid.511296.8 Melbourne Genomics Health Alliance \n", + "5 grid.493437.e RMIT Europe \n", + "6 grid.490309.7 Melbourne Sexual Health Centre \n", + "7 grid.477970.a Melbourne Clinic \n", + "8 grid.474755.0 Leica Biosystems Melbourne Pty Ltd \n", + "9 grid.469061.c Ridley College \n", + "10 grid.469026.f Melbourne School of Theology \n", + "11 grid.468079.4 Port of Melbourne Corporation \n", + "12 grid.468069.5 Melbourne Water \n", + "13 grid.452643.2 Melbourne Bioinformatics \n", + "14 grid.449135.e Melbourne Free University \n", + "15 grid.440113.3 Royal Dental Hospital of Melbourne \n", + "16 grid.438527.f Royal Melbourne Institute of Technology Univer... \n", + "17 grid.429299.d Melbourne Health \n", + "18 grid.416153.4 Royal Melbourne Hospital \n", + "19 grid.413105.2 St Vincent's Hospital \n", "\n", - " linkout longitude \\\n", - "0 [https://www.melbournegenomics.org.au/] 144.953870 \n", - "1 [https://www.rmit.eu] 2.194333 \n", - "2 [https://www.mshc.org.au/] 144.963840 \n", - "3 [http://www.themelbourneclinic.com.au/] 144.999650 \n", - "4 [https://www.ridley.edu.au/] 144.957660 \n", - "5 [http://www.mst.edu.au/] 145.209410 \n", - "6 [http://www.portofmelbourne.com/] 144.907070 \n", - "7 [http://www.melbournewater.com.au/Pages/home.a... 144.946700 \n", - "8 [https://www.vlsci.org.au/] 144.964460 \n", - "9 NaN NaN \n", - "10 [https://www.dhsv.org.au] 144.964630 \n", - "11 [http://www.mh.org.au/] 144.955930 \n", - "12 [http://www.rmh.mh.org.au/] 144.955930 \n", - "13 [http://nanomelbourne.com/] 145.143660 \n", - "14 [https://www.rmit.edu.au/] 144.962570 \n", - "15 [http://www.unimelb.edu.au/] 144.959980 \n", + " city_name country_code country_name types state_name \\\n", + "0 Victoria AU Australia [Company] NaN \n", + "1 NaN AU Australia [Other] NaN \n", + "2 NaN AU Australia [Other] NaN \n", + "3 Melbourne AU Australia [Nonprofit] Victoria \n", + "4 Melbourne AU Australia [Nonprofit] Victoria \n", + "5 Barcelona ES Spain [Education] NaN \n", + "6 Carlton AU Australia [Healthcare] Victoria \n", + "7 Richmond AU Australia [Healthcare] Victoria \n", + "8 Mt. Waverley AU Australia [Company] NaN \n", + "9 Melbourne AU Australia [Education] Victoria \n", + "10 Melbourne AU Australia [Education] Victoria \n", + "11 Melbourne AU Australia [Government] Victoria \n", + "12 Melbourne AU Australia [Government] Victoria \n", + "13 Melbourne AU Australia [Education] Victoria \n", + "14 Melbourne AU Australia [Education] Victoria \n", + "15 Melbourne AU Australia [Healthcare] Victoria \n", + "16 Melbourne AU Australia [Other] Victoria \n", + "17 Melbourne AU Australia [Healthcare] Victoria \n", + "18 Melbourne AU Australia [Healthcare] Victoria \n", + "19 Melbourne AU Australia [Healthcare] Victoria \n", "\n", - " name state_name types \\\n", - "0 Melbourne Genomics Health Alliance Victoria [Nonprofit] \n", - "1 RMIT Europe NaN [Education] \n", - "2 Melbourne Sexual Health Centre Victoria [Healthcare] \n", - "3 Melbourne Clinic Victoria [Healthcare] \n", - "4 Ridley College Victoria [Education] \n", - "5 Melbourne School of Theology Victoria [Education] \n", - "6 Port of Melbourne Corporation Victoria [Government] \n", - "7 Melbourne Water Victoria [Government] \n", - "8 Victorian Life Sciences Computation Initiative Victoria [Education] \n", - "9 Melbourne Free University Victoria [Education] \n", - "10 Royal Dental Hospital of Melbourne Victoria [Healthcare] \n", - "11 Melbourne Health Victoria [Healthcare] \n", - "12 Royal Melbourne Hospital Victoria [Healthcare] \n", - "13 Melbourne Centre for Nanofabrication Victoria [Facility] \n", - "14 RMIT University Victoria [Education] \n", - "15 University of Melbourne Victoria [Education] \n", + " latitude linkout longitude \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 -37.797960 [https://www.melbournegenomics.org.au/] 144.953870 \n", + "5 41.402576 [https://www.rmit.eu] 2.194333 \n", + "6 -37.803123 [https://www.mshc.org.au/] 144.963840 \n", + "7 -37.815063 [http://www.themelbourneclinic.com.au/] 144.999650 \n", + "8 NaN [http://www.danaher.com/] NaN \n", + "9 -37.783780 [https://www.ridley.edu.au/] 144.957660 \n", + "10 -37.859700 [http://www.mst.edu.au/] 145.209410 \n", + "11 -37.824028 [http://www.portofmelbourne.com/] 144.907070 \n", + "12 -37.814007 [http://www.melbournewater.com.au/Pages/home.a... 144.946700 \n", + "13 -37.799847 [https://www.melbournebioinformatics.org.au] 144.964460 \n", + "14 NaN NaN NaN \n", + "15 -37.799260 [https://www.dhsv.org.au] 144.964630 \n", + "16 NaN NaN NaN \n", + "17 -37.798940 [http://www.mh.org.au/] 144.955930 \n", + "18 -37.798756 [http://www.rmh.mh.org.au/] 144.955930 \n", + "19 -37.807000 [http://www.svhm.org.au/Pages/Home.aspx] 144.975000 \n", "\n", " acronym \n", "0 NaN \n", - "1 RMIT \n", - "2 MSHC \n", + "1 NaN \n", + "2 NaN \n", "3 NaN \n", "4 NaN \n", - "5 MBI \n", - "6 PoMC \n", + "5 RMIT \n", + "6 MSHC \n", "7 NaN \n", "8 NaN \n", "9 NaN \n", - "10 RDHM \n", - "11 NaN \n", - "12 RMH \n", - "13 MCN \n", - "14 RMIT \n", - "15 NaN " + "10 MBI \n", + "11 PoMC \n", + "12 NaN \n", + "13 VLSCI \n", + "14 NaN \n", + "15 RDHM \n", + "16 NaN \n", + "17 NaN \n", + "18 RMH \n", + "19 NaN " ] }, - "execution_count": 17, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -483,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": { "Collapsed": "false" }, @@ -503,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": { "Collapsed": "false" }, @@ -512,8 +591,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Year: 12\n", - "\u001b[2mTime: 0.57s\u001b[0m\n" + "Returned Year: 16\n", + "\u001b[2mTime: 0.55s\u001b[0m\n" ] }, { @@ -524,7 +603,6 @@ }, "data": [ { - "alignmentgroup": "True", "hovertemplate": "year=%{x}
pubs=%{y}", "legendgroup": "", "marker": { @@ -534,45 +612,23 @@ } }, "name": "", - "offsetgroup": "", "orientation": "v", "showlegend": false, "textposition": "auto", "type": "bar", - "x": [ - 2021, - 2020, - 2019, - 2018, - 2017, - 2016, - 2015, - 2014, - 2013, - 2012, - 2011, - 2022 - ], + "x": { + "bdata": "5QfoB+cH5gfkB+MH4gfhB+AH6QffB94H3QfcB9sH6gc=", + "dtype": "i2" + }, "xaxis": "x", - "y": [ - 13015, - 12183, - 11039, - 9954, - 9198, - 8281, - 7720, - 7184, - 6779, - 6030, - 5742, - 816 - ], + "y": { + "bdata": "9kygSotJ9UjhSOlA+T0ZOm42PjZDNBkx3y99K2koDwA=", + "dtype": "i2" + }, "yaxis": "y" } ], "layout": { - "autosize": true, "barmode": "relative", "legend": { "tracegroupgap": 0 @@ -759,57 +815,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 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], "title": { "text": "pubs" - }, - "type": "linear" + } } } }, - "image/png": 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nvnm/U6gEuz6oXdxph1nxztUH1bRPoxdejJh85bB45NFq2owcGbHX7rNiuWXTbZ58KuKCScNjxoz0bcuwxUor9sSO23fH4n2/TqsMu77A93H5sYss8H0o8w50fIjv6emJtT66W5x30hfjI+uvXevLvzz8WGy16+Fx85WnxQrLLV0L8ccf+vn4zlU3xdJLjYnTjt43RowYXmvb3dNT5v637wQIECBAgMA/BWZ190R3d2dc1xcaMSyp3197vTuuuHpW3H1vNYNqgbHLjt2x/joLxfBhacf42JMz4+LLeiod4vfbqzvWWHXhpJopGv/xLzPinAuGVTrEf36Xrlhh2RHJNp2+QX9fMt7pJinH3/EhvsCaHdI/8eF1anb9rcQX//3lV16NG684Od6y4nK9xr6dPqXctCVAgAABAkNb4I//2xVPPpUW4ob2Ec27dx/5UHdDu+xx+v7ZXnqxKyZfVf2V+HHj0m9weZy+oVOtIzbyOH1eNwvxEbVH5zfdaL3YYz6fid/yExvG409Oi789+mRcce6RseSY0TV5IT6vAG1NgAABAgSGkkAR4idf+cbTdlX8W3+97thsUyG+v74tHqdffbX0oCrE1z9ThPgqjiLNOSYhPs+xY0J88fvw3T3dscXOh8UXdt46Nt94/VhoxBuPvlw8eUpcPfvb6RcdGft8pf9vp//3d78j9phwSm2bSad/JUYtMlKIz6s/WxMgQIAAgSElIMTX7w4r8f3bCPFC/OjR6Td/htTAtwB2RojPQ++YEP+lY8+Ln9x2bx+tKZefFCuvtHztMfmJX/tW/OzuX9f+vfgCu7OPOyiWXWbJ2v8vHrc/8+sHxAbve1cU33K/037HxUpvWjbOO/HgeOLZ1/J6wNYECBAgQIDAkBEQ4oX41GIU4oV4IT71rIkQ4tPN5tyiY0L8YJheePHlmPH6zFhm7BKDaV5r43H6QVNpSIAAAQIEhryAEC/EpxapEC/EC/GpZ40Qny7WdwshPlNQiM8EtDkBAgQIEBhCAkK8EJ9ajkK8EC/Ep541Qny6mBCfa9ZneyG+qZxejAABAgQILFABIV6ITy1AIV6IF+JTzxohPl1MiM81E+KbKujFCBAgQIDA0BEQ4oX41GoU4oV4IT71rBHi08WE+FwzIb6pgl6MAAECBAgMHQEhXohPrUYhXogX4lPPGiE+XUyIzzUT4psq6MUIECBAgMDQERDihfjUahTihXghPvWsEeLTxYT4XDMhvqmCXowAAQIECAwdASFeiE+tRiFeiBfiU88aIT5dTIjPNRPimyroxQgQIECAwNAREOKF+NRqFOKFeCE+9awR4tPFhPhcMyG+qYJejAABAgTaLTB1alc89VS737W97/fWt0Y0MrEW4oX41EoV4oX4Rsaa1DqrWvs3LT2qaofU1uPxO/GZ3H5iLhPQ5gQIECDQdoEixF8waXjMmNH2t27LG660Yk+M375biO9He/31umOzTbsb6ocpNw6Pe+7ramjbMmy00w6zYvXVepJ3VYgX4oX45NMmhPh0szm3EOLz/EKIzwS0OQECBAi0XUCIr09uJb6+jRDfv40QL8QL8emXMSE+3UyIzzPrs7UQ30RML0WAAAECbREQ4oX4RgpNiBfiU+vGWJMq1jnthfi8vrYSn+dnJT7Tz+YECBAg0H4BE2shvpGqE+KF+NS6MdakinVOeyE+r6+F+Dw/IT7Tz+YECBAg0H4BE2shvpGqE+KF+NS6MdakinVOeyE+r6+F+Dw/IT7Tz+YECBAg0H4BE2shvpGqE+KF+NS6MdakinVOeyE+r6+F+Dw/IT7Tz+YECBAg0H4BE2shvpGqE+KF+NS6MdakinVOeyE+r6+F+Dw/IT7Tz+YECBAg0H4BE2shvpGqE+KF+NS6MdakinVOeyE+r6+F+Dw/IT7Tz+YECBAg0H4BE2shvpGqE+KF+NS6MdakinVOeyE+r6+F+Dw/IT7Tz+YECBAg0H4BE2shvpGqE+KF+NS6MdakinVOeyE+r6+F+Dw/IT7Tz+YECBAg0H4BE2shvpGqE+KF+NS6MdakinVOeyE+r6+F+Dw/IT7Tz+YECBAg0H4BE2shvpGqE+KF+NS6MdakinVOeyE+r6+F+Dw/IT7Tz+YECBAg0H4BE2shvpGqE+KF+NS6MdakinVOeyE+r6+F+Dw/IT7Tz+YECBAg0H4BE2shvpGqE+KF+NS6MdakinVOeyE+r6+F+Dw/IT7Tz+YECBBolcBLL3bFTTcPa9XLD4nX/fAHu2PcuJ7kfTGxFuKTiyYihHghPrVujDWpYp3TXojP62shPs9PiM/0szkBAgRaJVCE+MlXDYtHHu1q1Vss0NcdOTJir91nCfH99MJKK/bE+O27Y/To9Bscf/zfrph85fAF2retfPP11+uOzTbtbugthHghPrVwhPhUsc5pL8Tn9bUQn+cnxGf62ZwAAQKtEhDi68uaWNe3EeLr2wjxQnzqeG2sSRXrnPZCfF5fC/F5fkJ8pp/NCRAg0CoBIV6ItxI/bw1Yia9/Xuy0w6xYfbX0pzeMNcaaRsaaVl37yvK6QnxeTwnxeX5CfKafzQkQINAqARNrE+tGJtZW4q3Ep45JxhpjTSNjTWqdVa29EJ/Xo0J8np8Qn+lncwIECLRKwMTaxLqRibUQL8SnjknGGmNNI2NNap1Vrb0Qn9ejLQ/xr8+cGdOnvxajFxsVw4YNi1mzuuOB/3koRi0yMtZaY5W8vR8CWz82bfoQ2Au7QIAAAQJzC5hYm1g3MrEW4oX41NHUWGOsaWSsSa2zqrUX4vN6tOUh/lvf+WF8+8ob4+YrT4vFRy8aO3zha/HbP/61ttcT9t4u9hi/Wd4RLOCthfgF3AHengABAnUETKxNrBuZWAvxQnzqoGqsMdY0Mtak1lnV2gvxeT3a8hC/84EnxLtWXyUO2W983HX/7+LzXzo1jp24Wzz73IvxvetujVuvPj3vCBbw1kL8Au4Ab0+AAAEhPrkGfGN0fTIhXohPPaGEeCFeiE89ayKE+HSzObdoeYjfZMeJsc/ntopt/uNDMXtV/u4fnRcvv/JqrL/FvvHj754cb33zcnlHsQC3FuIXIL63JkCgJlD87NOMGdXFWPOd3b4xup/u9Tvx9Wve78TXt/Ht9PVtfDt9/zbGmtaMNdW9ag/uyIT4wTnVa9XyEF+sxL/zHW+NQ/ffMbbe7Yh464rLxdnHHxRPTn0mPrbthLh20tdjtVVXyjuKBbi1EL8A8b01AQK9If6e+7oqq2FibWKdWtxCvBCfWjNFe2ONsSa1bnLGmtT3qlp7IT6vR1se4n8w5Wdx9KnfjsUWXaS2+n7xaRNjg/e9K666/rY49vTvxL0/Pr/2b2X9E+LL2nP2m0B1BIqVeCF+3v70iGv9Gvc4fX0bj9PXtzHW9G9jrDHWeJw+fU4lxKebzblFy0N8T09P/NePfx73//p/Y4N11oqtPrlh7f2POOniWHrsEjFhr23zjmABby3EL+AO8PYECNQepxfihfiUU0GIF+JT6mV2W2ONEJ9aN8aaVLHOaS/E5/V1y0N83u4N/a2F+KHfR/aQQNUFTKxNrFNr3MRaiE+tmaK9scZYk1o3xppUsc5pL8Tn9XVbQvzdD/w+rr7h9vi/vz8es2bNines8ub4z80+HBuuu1be3g+BrYX4IdAJdoFAhwuYWJtYp54CJtZCfGrNCPH1xTxOX9/GWNPImdYZ2wjxef3c8hBfBPg9JpxS28sPrLtWjFx4obj1Fw/W/v/Be34m9txpi7wjWMBbC/ELuAO8PQECVsfq1ICJtYl1I59T9Zn4+nXjhqEbhqmXXCE+Vaxz2gvxeX3d8hC/zR5HxdPPPB83f/8bMXLkwrW9nTlzVhx35uW11fk7f3h2LLXE4nlHsQC3FuIXIL63JkCgJmBibWKdeiqYWNcXE+KF+NTzyQ1DNwwbuWGYWmdVay/E5/Voy0P85p87NDb+0Pvii3N9gd2fH340tt71iLj87MPjve9eLe8oFuDWQvwCxPfWHSXw9NPVP9xllmnsGIV4IT61coR4IT61ZtwwrC8mxAvxQnz6iCLEp5vNuUXLQ/zxZ343Hv7H43HRNyb22dMnnnomPr7dhLjxipPjLSsul3cUC3BrIX4B4nvrjhIoVseunzK8sse81prdsdmm3Q0dnxAvxKcWjhAvxKfWjBAvxI8b15NcNsaaZLKO2UCIz+vqloT4n931q3j0iTeWzZ6c+mxcPHlK7PO5rWLpsWN69/Z3//tw/PTn/y/u/OE5sfBCI/KOYgFuLcQvQHxv3VECHnGt391CvBCfOhiYWAvxqTUjxAvxQvy8NbDSij0xfvvusBKfPqII8elmc27RkhB/8NHnxE9/9v8GtWd33XBujFl8sUG1HYqNhPih2Cv2qYoCQrwQn1rXHnGtLybEC/Gp55MQL8QL8UJ8I+NGvW2E+DzNloT4vF0q19ZCfLn6y96WV0CIF+JTq1eIF+IbWR0z1hhrjDV9BUaOjNhr91khxAvxqefG/NoL8XmaQnyeXwjxmYA2JzBIARNrE+tBlkpvMyFeiBfi562B9dfz/Rv1zoyddpgVq6+W/rlvY42xppGxJvWaVrX2Qnxej7Y8xJ976XXxq9/+qe5envG1A2KxRRfJO4oFuLUQvwDxvXVHCQjxQnxqwZtYm1g3MrE21hhrjDVW4gdbAz4TP1ipedsJ8Y3bFVu2PMR/+8ob47d//Os8e/mT2+6NVVd+U1x5/tExapGReUexALcW4hcgfgXfuvic6u/+0FXBI/vXIa3z3p6GvgDGxNrEOvXEEOKFeCF+3hqwEl//vLAS37+Nx+nr14wQn3pl/ld7Ib5xu7aE+Hq7d96l18Vtv3wwrrzgmBjWVd7QIsTnFaCt+wr4sqn6FSHEC/Gp44UQL8QL8UJ8yrghxAvxKfVStBXiU8WE+MbF+m7Z8pX4ejv6p78+Ep/a7ciYcvlJsfJKyzfreNr+OkJ828kr/YZCvBDfSIH7ibn+1YR4IV6IF+JTxlQhXohPqRchPlWrb3sr8Xl+CyzE3/PAH2L3CScL8Xn9Z+uKCQjxQnwjJS3EC/GpdWOsMdak1kzR3lhjrEmtG2NNqljntBfi8/q65SH+imtujt8/9HDvXvb0RDz/4ktx+y9/FWuvuWpMPu+ovCNYwFtbiV/AHVCxt3exM7FupKRNrE2sU+vGWGOsSa0ZIb6+mKd+6tsYaxo50zpjGyE+r59bHuK/eeHVcf9vHuqzl2NGLxof3mDt+NgH3hvLLrNk3hEs4K2F+AXcARV7exc7E+tGSlqIF+JT68ZYY6xJrRkhXoj3O/Hz1oDPxDcykryxjRDfuF2xZctDfN7uDf2thfih30dl2kMTaxPrRupViBfiU+vGWGOsSa0ZIV6IF+KF+EbGjXrbCPF5mm0L8Q/93yPx6ONToyd64q0rLl/7ebkq/AnxVejFoXMMJtYm1o1UoxAvxKfWjbHGWJNaM0K8EC/EC/GNjBtCfDPV/vVaLQ/xL708Pfaa+I349e//0ucI3v/ed8aJh+0Zy40b25oja9OrCvFtgu6QtzGxNrFupNSFeCE+tW6MNcaa1JoR4oV4IV6Ib2TcEOKbqdbGEP+1078TV15/W0zYa9t437+tHiNGDI97Hvh9XHr1TfHWFZeL755zRGuOrE2vKsS3CbpD3sbE2sS6kVIX4oX41Lox1hhrUmtGiBfihXghvpFxQ4hvplobQ/yHtjkw1l179Tj9mP36HMHka2+O48/8btxy1emx/LLlXY0X4ltTmJ36qibWJtaN1L4QL8Sn1o2xxliTWjNCvBAvxAvxjYwbQnwz1doY4vf88qnx9lXeHIfsN77PETzy+NTYZPzE+OGlx8fbV16xNUfXhlcV4tuA3EFvYWJtYt1IuQvxQnxq3RhrjDWpNSPEC/FCvBDfyLghxDdTrY0h/uaf3x+Hn3RR3HzlaTFm8cV63/mOe34TXzr2vLjjurNj5MILtebo2vCqQnw6cvF7qr/+n670DUshStuHAAAgAElEQVS0xWrv6AkXu+Ze7P74v10x+crhJaqCtF1df73u2GzT7rSN/tlaiBfiUwtHiBfiU2tGiBfizWuaO69p5Bys0ja+nT6vN1v+xXZFUP/JbfcOuJcrr7R8TLn8pAHbDbUGQnx6jxQhfvJVw+KRR6sZ5EeOjNhr91lCfD+lkfN7qkJ8/XNNiBfiU0diIV6IT60ZIV6IF+KF+EbGjXrbCPF5mi0P8bfc+UD849GnBtzL0YuNis9s8ZEB2w21BkJ8eo8I8fXNTKxNrNPPqAghXohPrRtjjbEmtWaEeCFeiBfiGxk3hPhmqv3rtVoe4luz20PnVYX49L4Q4oX40aN7kgvHSnx9MiFeiE89oYR4IT61ZoR4IV6IF+IbGTeE+GaqCfFN05xfiH/hhWo+Lj4n3pgx6WFMiBfihfh5a8Bn4uufFzvtMCtWX81YM7eQj+7Urxkf3alvY6wx1qROgo01rRlrUvuhau09Tp/Xo1bi8/xifiG+WB37+z8y32AIb/6xjbpNrPvpHxe71lzsrMRbiU8dDt0wdMPQDUM3DFPGDTcM+9cyr2nNvCalNqvYVojP61UhPs9vwBB/z33VXY13sXOxSz19rI5ZHUutmaK9scZYk1o3xhpjTWrNGGvqiwnxQnwj59NA2wjxAwnN/9+F+Dw/Id4jrvNUkItday52VuKtxKcO11bircRbibcSnzJuuGHohmFKvRRtc24Ypr5X1doL8Xk9KsTn+QnxQrwQn3AO5VzshHghPqHUak2FeCFeiBfiU8YNIV6IT6kXIT5Vq297IT7PT4jP8xPihXghPuEcEuLrY/myqfo2JtYm1gnDTK2pscZYk1ozRXtjjbEmtW5yxprU96paeyE+r0eF+Dw/IV6IF+ITzqGci52V+PrQfmKufxsr8VbircRbiU+4RAnxdbB8TLB+FeXMa1Jqs4pthfi8XhXiB+F38x33x9prrhrjll5yntYDfTu9L7abF9jE2sTaxNrEehBDb28Tq2NWx1LqxUr8/LU89VPfx1hjrGnnWJP6XlVrL8Tn9agQHxG33PlAHHjkWfNIPvDfF8XIhReK9TbbJ844dv/YcN21hPg5BFzsXOxSh5+cO9ZW4q3Ep9abG4ZuGLph6IZhyrhhXmNek1IvuTcMU9+rau2F+LweFeIjolhpP/zEi+LqC4/to/mWFZeNrq4uIb5OjbnYudilDj9CfH0xq2NWx1LPJ4+41hcz1hhrUs+nor15jXlNat3kjDWp71W19kJ8Xo8K8f8M8cee/p2449p5V+ML3jlX4p959oU49IQL4wPrrhW7bLepz8T7TPw8Z6CJtYl1I8OyEC/Ep9aNscZYk1ozRXtjjbEmtW6MNa0Za1L7oWrthfi8HhXi/xniDzrq7Nh6kw/GIiMXinXWXj022Wi9GD58WE13dohfa41VYpeDT4pVVlo+Tj3qC7V/95n4nuQK9IhrfbKpU7vigknDY8aMZNZSbJBzx9rj9PW72Bfb9W9jrDHWeJx+3hoQ4oX41AmDEC/Ep9bMYNoL8YNRqt9GiI+I3/7xr3HT7ffGEmNGx2NPPB1XXn9b7LjNxnHEQZ/tDfHHH/r5+M5VN8XSS42J047eN0aMGF77t9dndverO2Nmd3zvv7rj7nu78npoCG+9847d8f5/HxHDhqUd4xNTZ8Wky3vikUfTthvCFH12rbjY7bdnd7xjlYWSd/lPf309zrlwWKVD/O47d8Xyy7xx/gz2r7u7J+5+YGZc/r03bqxV8W/D93fH9p8eHguPSDtGY039ajDW1Lcx1vRvY6ypXzPGGmONec28NVAsTjQyr6niPCb1mBZKnO+kvn7V2wvx/fTwNT/+eRx1yiXxm1suqa22Fyvxxd/Lr7waN15xcrxlxeV6t5r6/Gv91khPT8QPf9QVVf52+s+OnxVrrtEVXYlZ/Pnne+K73x9W6RC/9x6zYoXlE2Ei4vEneuL8i6u9Er/T+O5YYkyaTfG8x+9+3xNXfD8t/JdpAN9gve7YcvOe2vdwpPwZa+prGWvq2xhr+rcx1tSvGWONsca8pv8Q38i8JuU6X9W245YYWdVDa8txCfH9MN957//E3l85LR646cIYOXLhWojf8hMbxuNPTou/PfpkXHHukbHkmNG1LT1O73H6uUvIY2f1xy6P09e38YhrfRtfNtW/jbHGWNPITNFYY6xJrRtjTWvGmtR+qFp7j9Pn9agQHxHfu/aWWG3VleJdq68cz7/wUkz8+vm1x+UvOf2Qmu7sz8T/+7vfEXtMOKX23yad/pUYtchIId4X281zBrrYteZi5zPx9V19Jr5/G5+Jr18zvn+jvo2xxliTOrU21hhrGvn+jdQ6q1p7IT6vR4X4iDj9wqtj0uQpvZJrr7lqnHLUPvHmFcb1hvgzv35AbPC+d8VzL7wUO+13XKz0pmXjvBMPjiee7f9x+mJDE2sT69TT08TaxDq1Zow19cVMrE2sG5lYC/FCfOo4bKwx1jQy1qTWWdXaC/F5PSrE/9PvtddmxNRpz8fo0aN6H5UfDK3H6T1OP3edWIm3Ej+YsWPuNh5xra/mcfr+bYw1xhpjTSMCxppUNWNNa8aa1H6oWnshPq9Hhfg8P4/Te5x+ngpysWvNxc7qmNWx1OHa6pjVsUZWx4w1xhpjTV8B85rWzGtS66xq7YX4vB4V4vP8hHghXohPOId8sV19LCvxVscSTqVaUxPr1kyshXghPvVcdMPQDcNGbhim1lnV2gvxeT0qxOf5CfFCvBCfcA4J8UJ8Qrn0NvU4ff9qQrwQ38j55IahG4apdWOsac1Yk9oPVWsvxOf1qBCf5yfEC/FCfMI5JMQL8QnlIsQPgGVi3ZqJtZV4K/Gp45SVeCvxVuJTz5oIIT7dbM4thPg8PyFeiBfiE84hIV6ITygXIV6Ib6RcatsYa4w1jRSPp376V3PDsDU3DBup0SptI8Tn9aYQn+cnxAvxQnzCOWRibWKdUC5CvBDfSLkI8QOoeZy+PpAQL8SnDjo585rU96paeyE+r0eF+Dw/IV6IF+ITzqGci51HXOtDT7lxeNxzX1dCT5SrqYm1iXVqxRpr3DBMrZmivbHGWJNaNzljTep7Va29EJ/Xo0J8np8QL8QL8QnnUM7FTogX4hNKrdbU51Tri02d2hUXTBoeM2akqpajvbFGiG+kUoV4IT61bnLGmtT3qlp7IT6vR4X4PD8hXogX4hPOoZyLnRAvxCeUmhA/AJYQXx/IWGOsMdb0FfCZ+PoVkTOvSa2zqrUX4vN6VIjP8xPihXghPuEcyrnYmVibWCeUmhAvxMf47bujkW+MNtYYa4w1QvxgayBnXjPY96hqOyE+r2eF+Dw/IV6IF+ITzqGci52JtYl1QqkJ8UK8EF+nBnyxXf2Tw+P0/dtYibcSn3r9HUx7IX4wSvXbCPF5fkK8EC/EJ5xDQnx9LBNrE+uEU6nW1MS6NRNrNwzdMEw9F33/Rn0xH91JrabOaS/E5/W1EJ/nJ8QL8UJ8wjkkxAvxCeXS29TqmNWx1Lox1hhrUmumaG+sMdak1k3OWJP6XlVrL8Tn9agQn+cnxAvxQnzCOZRzsbM6ZnUsodRqTa2OWR3zmfh5a8BTP/XPCyFeiE+9zuTMa1Lfq2rthfi8HhXi8/yEeCFeiE84h3IudkK8EJ9QakL8AFgeca0PZKwx1hhr+gr46E79isiZ16TWWdXaC/F5PSrE5/kJ8UK8EJ9wDuVc7EysTawTSk2IF+J9sV2dGrASbyU+dSwV4oX41JoZTHshfjBK9dsI8Xl+QrwQL8QnnENCfH0sE2sT64RTqdbUxLo1E2s3DN0wTD0XfXSnvpinflKrqXPaC/F5fS3E5/kJ8UK8EJ9wDgnxQnxCufQ29TnV/tWEeCG+kfPJDUM3DFPrxljTmrEmtR+q1l6Iz+tRIT7PT4gX4oX4hHNIiBfiE8pFiB8Ay8S6NRNrK/FW4lPHKSvxVuIb+RLN1DqrWnshPq9Hhfg8PyFeiBfiE84hIV6ITygXIV6Ib6RcatsYa4w1jRSPp376V3PDsDU3DBup0SptI8Tn9aYQn+cnxAvxQnzCOWRibWKdUC5CvBDfSLkI8QOoeZy+PpAQL8SnDjo585rU96paeyE+r0eF+Dw/IV6IF+ITzqGci51HXOtDT7lxeNxzX1dCT5SrqYm1iXVqxRpr3DBMrZmivbHGWJNaNzljTep7Va29EJ/Xo0J8np8QL8QL8QnnUM7FTogX4hNKrdbU51Tri/nG6Po2xhpjjbGmr4DH6etXRM68JrXOqtZeiM/rUSE+z0+IF+KF+IRzKOdiZ2JtYp1QakL8AFhCvBCfej4V7T3107+aG4ZuGPpiu/QRRYhPN5tzCyE+z0+IF+KF+IRzSIivj+VzqvVtPOLav43Vsdasjrlh6IZhwmXNDUM3DGP89t0hxKeeNRFCfLqZEJ9n1mfrx6ZNr/tq7li7Y51aalbHrI6l1ozVsfpiVsesjjUysRbihfjUcdhYY6xpZKxJrbOqtRfi83rUSnyen5V4K/FW4hPOISvxVuITyqW3qZV4K/GpdWOsMdak1kzR3lhjrEmtm5yxJvW9qtZeiM/rUSE+z0+IF+KF+IRzKOdiZ3XM6lhCqdWaWh2zOtbI6pixxlhjrOkr4KM79SsiZ16TWmdVay/E5/WoEJ/nJ8QL8UJ8wjmUc7EzsTaxTig1IX4ALB/dqQ9krDHWGGuE+MHWQM68ZrDvUdV2QnxezwrxeX5CvBAvxCecQzkXOxNrE+uEUhPihfiGv2zKWGOsMdYI8YOtgZx5zWDfo6rthPi8nhXi8/yEeCFeiE84h3IudibWJtYJpSbEC/FCfJ0a8EsY9U8On4nv38bj9PVrJmdek3pNq1p7IT6vR4X4PD8hXogX4hPOoZyLnRAvxCeUmhAvxAvxQnzqkOGL7eqICfFCfPLJNIgNhPhBIM2niRCf5yfEC/FCfMI5JMTXx7I6ZnUs4VSqNTWxbs3E2g1DNwxTz0VfollfzPdvpFZT57QX4vP6WojP8xPihXghPuEcEuKF+IRy6W3qEdf+1YR4Ib6R88kNQzcMU+vGWNOasSa1H6rWXojP61EhPs9PiBfihfiEc0iIF+ITykWIHwDLxLo1E2sr8VbiU8cpK/FW4hv5OcvUOqtaeyE+r0eF+Dw/IV6IF+ITziEhXohPKBchXohvpFxq2xhrjDWNFI+nfvpXc8OwNTcMG6nRKm0jxOf1phCf5yfEC/FCfMI5ZGJtYp1QLkK8EN9IuQjxA6h5nL4+kBAvxKcOOjnzmtT3qlp7IT6vR4X4PD8hXogX4hPOoZyLnUdc60NPuXF43HNfV0JPlKupibWJdWrFGmvcMEytmaK9scZYk1o3OWNN6ntVrb0Qn9ejQnyenxAvxAvxCedQzsVOiBfiE0qt1tTnVOuL+cbo+jbGGmONsaavgMfp61dEzrwmtc6q1l6Iz+tRIT7PT4gX4oX4hHMo52JnYm1inVBqQvwAWEK8EJ96PhXtPfXTv5obhm4Y+mK79BFFiE83m3MLIT7PT4gX4oX4hHNIiK+P5XOq9W084tq/jdWx1qyOuWHohmHCZc0NQzcMY/z23SHEp541EUJ8upkQn2fWZ+vHpk2v+2ruWLtjnVpqVsesjqXWjNWx+mJWx6yONTKxFuKF+NRx2FhjrGlkrEmts6q1F+LzetRKfJ6flXgr8VbiE84hK/FW4hPKpbeplXgr8al1Y6wx1qTWTNHeWGOsSa2bnLEm9b2q1l6Iz+tRIT7PT4gX4oX4hHMo52JndczqWEKp1ZpaHbM61sjqmLHGWGOs6Svgozv1KyJnXpNaZ1VrL8Tn9agQn+cnxAvxQnzCOZRzsTOxNrFOKDUhfgAsH92pD2SsMdYYa4T4wdZAzrxmsO9R1XZCfF7PCvF5fkK8EC/EJ5xDORc7E2sT64RSE+KF+Ia/bMpYY6wx1gjxg62BnHnNYN+jqu2E+LyeFeLz/IR4IV6ITziHci52JtYm1gmlJsQL8UJ8nRrwSxj1Tw6fie/fxuP09WsmZ16Tek2rWnshPq9Hhfg8PyFeiBfiE86hnIudEC/EJ5SaEC/EC/FCfOqQ4Yvt6ogJ8UJ88sk0iA2E+EEgzaeJEJ/nJ8QL8UJ8wjkkxNfHsjpmdSzhVKo1NbFuzcTaDUM3DFPPRV+iWV/M92+kVlPntBfi8/paiM/zE+KFeCE+4RwS4oX4hHLpbeoR1/7VhHghvpHzyQ1DNwxT68ZY05qxJrUfqtZeiM/rUSE+z0+IF+KF+IRzSIgX4hPKRYgfAMvEujUTayvxVuJTxykr8VbiG/k5y9Q6q1p7IT6vR4X4PD8hXogX4hPOISFeiE8oFyFeiG+kXGrbGGuMNY0Uj6d++ldzw7A1NwwbqdEqbSPE5/WmEJ/nJ8QL8UJ8wjlkYm1inVAuQrwQ30i5CPEDqHmcvj6QEC/Epw46OfOa1PeqWnshPq9Hhfg8PyFeiBfiE86hnIudR1zrQ0+5cXjcc19XQk+Uq6mJtYl1asUaa9wwTK2Zor2xxliTWjc5Y03qe1WtvRCf16NCfJ6fEC/EC/EJ51DOxU6IF+ITSq3W1OdU64v5xuj6NsYaY42xpq+Ax+nrV0TOvCa1zqrWXojP61EhPs9PiBfihfiEcyjnYmdibWKdUGpC/ABYQrwQn3o+Fe099dO/mhuGbhj6Yrv0EUWITzebcwshPs9PiBfihfiEc0iIr4/lc6r1bTzi2r+N1bHWrI65YeiGYcJlzQ1DNwxj/PbdIcSnnjURQny6mRCfZ9Zn68emTa/7au5Yu2OdWmpWx6yOpdaM1bH6YlbHrI41MrEW4oX41HHYWGOsaWSsSa2zqrUX4vN61Ep8np+VeCvxVuITziEr8VbiE8qlt6mVeCvxqXVjrDHWpNZM0d5YY6xJrZucsSb1varWXojP61EhPs9PiBfihfiEcyjnYmd1zOpYQqnVmlodszrWyOqYscZYY6zpK+CjO/UrImdek1pnVWsvxOf1qBCf5yfEC/FCfMI5lHOxM7E2sU4oNSF+ACwf3akPZKwx1hhrhPjB1kDOvGaw71HVdkJ8Xs8K8Xl+QrwQL8QnnEM5FzsTaxPrhFIT4oX4hr9sylhjrDHWCPGDrYGcec2rrw72XcrbbpFF6u+7EJ/Xr0J8np8QL8QL8QnnUM7FzsTaxDqh1IR4IV6Ir1MDfgmj/snhM/H923icvn7N5M5r7rl3WOqlrTTtx43ric027a67v0J8XlcK8Xl+QrwQL8QnnEO5F7vJVw5PeLdyNTWxNrFOrVgT69ZNrI01/dv61Z3+XXz/Rv1z0Ud36tt0+uKEEJ961e/bXojP8xPihXghPuEcEuLrYwnxQnzCqVRrKsQL8ak1U7Q31hhrUuvGWGOsSa2ZwYw1Qnwjqv/aRojP8xPihXghPuEcEuKF+IRy6W3qEdf+1UysTawbOZ+EeCE+tW6MNcaa1JoR4hsRS9tGiE/zmqf1Y9Om130Fj531T+Oxs/pF57Gz+jad/tjZ/IYqY42xJvVSZqwx1qTWTNHeWGOsSa0bY42xpp6AlfjUs6lveyE+z89KvJX4eSrIHWt3rBsZVqyOWR1LrRtjjbEmtWYGszrmhmFPMqvFCYsTo0en102nL04I8clDTZ8NhPg8PyFeiBfiE84hj9PXxxLihfiEU6nWVIgX4lNrRoifv5iP7vTvY6wx1rRirBHiG1H91zZC/CD9Xnp5erw+c2YstcTifbbwOH36nUd3rN2xdsd63hoQ4oX4QV6OepuZWJtYp9aMEC/EN1IzxhpjTSN1M9C8RohvRFWIH7TaK9Nfi0OOOz9u/cWDtW3WXnPVOOu4A2OZsUvU/r8QL8TPXUwudi52gx5g5mg40MVufq/pc6r967hh6IahG4ZuGKaMx1bi+9cyrzGvSTmPZrcdaF4jxDeiKsQPWm3S5Clx1Y9uj8vPOiIWHTUy9jnk9HjbW1aIr31ldyF+h1mxusfp56klFzsXu0EPMEL8oKhMrE2sB1UoczTy0Z36YgNNrN0wtDhhcWLwI46xpvGxRogffJ3119Lj9AP4bbvX0bHJRuvF53fcvNbyptvviwnHnBu/ve3b0dXVZSVeiBfiE8YgF7vGL3Ym1ibWJtaDH2yMNcaawVfLv1q6YeiGYWrdGGsaH2uE+NRq69teiB/Ab73N9onjDtkjPvmRdWstf//Q36II9nfdcG6MWXwxIV6IF+ITxiAXu8YvdkK8EC/ED36wMdYYawZfLUL8QFaeMKwvZKxpfKwR4gc68+b/70L8fHx6enpirY/uFued9MX4yPpr11r+5eHHYqtdD4+brzwtVlhu6bpbv/Z6d1x+1cx48aW8DhrKW6/z7xEbrrNQDB/WlbSbjz75elx1XfqEPOlNFnDjTTeOeOeqCyfvxR/+PCN+ckvyZqXaYPtPdcWbllsoaZ9ndffEL+59Pe7/ddJmpWq8xOI9sdO2C8XIhYYl7bexpj6Xsaa+jbGmfxtjTf2aMdYYa8xr+q8B85r+XRqd1yRNgjq4sRA/QOcXK/HHH/r5+MSH16m1nHslvoNrx6ETIECAAAECBAgQIECAQJsFhPgBwItH5zfdaL3Yo85n4tvcX96OAAECBAgQIECAAAECBDpYQIgfoPMvnjwlrp797fSLjox9vtL32+k7uHYcOgECBAgQIECAAAECBAi0WUCIHwD85VdejYlf+1b87O43Poy71hqrxNnHHRTLLrNkm7vK280WePW1GfHMcy/G8suOjWFd834ef8brM+PZ51+MZZdesvYLAnP/Fd910N3dE8OHz/vZ4xdefDlefe11/Vuxcuvu6Ylnn3sxFhoxvPaFlP39Pf3M8zF6sVGxyMj+v8tg1qzu6BrW1W/NVYzL4fxToJVjzWzkZ559ofY/xy41hnsFBFo91rwy/bV4/fWZscSY/sexChB25CHkjDXFtWnqtOdiqSUXj5ELz/t9M0VNTn362Vhm7JL9zns6ErwCB5071sxvvjtQTVWArxKHIMQPshuLYi/C4TJjlxjkFpq1QuCAI86MW3/xYO+kd5tNPxgT9t6u9v+LcH7+ZdfHOd++tvffzzn+oFh7zVX77MoNP/1lfPPCH8StV5/e+9+LALfLQSfGw/94ovbfVl35TbHnTlvElp/YsBWH4TXbKHDX/b+Lg446O4obcsXfuu9ZIyZ+Yft41+qr1P7/3x99Mr5w6Dd7+/4/N/twfHXCLjFixPDevSwmWNvtfUzs9dktY4uNN+h377954dVRPLlz94/Oi8VHL9rGI/RWrRBo1VhT7Gsx+Zo0eUpc9oP/jiLEL7boInHvj89vxWF4zTYKtHKsefLpZ+O4b14W9zz4h9oRrfH2t8RhB+wU73zHW9t4hN6qFQI5Y01xzSmuPbP/Ntlo3Th6wq69N3mKBahiIWr29e+YL+0a2265USsOw2u2USBnrBlovjtQTbXxML3VAAJCvBIplUAR0Iuf+3vrisvG3Q/8PvY97Iz4/re+Gu9+59viV7/9c+y0/3Fx+dmH1/7/2ZOuiR/dfHfcfNVptdXTIqztOfEb8chjU2O5cWP7hPinnn4urvvJHbH1Jh+IxRYdFZddfVNcetVP4ufXnlV3ZbZUcB28s/c88Id4atqztV+YmP7ajPja6d+Jnu6e2q9OFH97TfxGbQX+hMP2jCeeeqYW1o/64s69N3BOO//KuOT7N9bannzk3v2G+Ot+cmcccdLFtTZCfDWKrVVjTaFz+gVXxbU/uTO+sPNWselH3x+vv/56bUzyV26BVo41Xznugnju+Rfj3BMPjmFdw+LY0y6Np6Y9F+efPKHcaPa+tvDQ6LzmBz/6Waz0pmVj7XetGn9/9KnY40unxB47bBa7br9pFDefP7zNgbH/btvETp/+RNz2ywdrN7Rv+t6p8eYVxpEvsUDOWDPQfHd+NVViskruuhBfyW7tnIP62LYTYoetP1pbIS0mxn/409/iom9MrAEUA9VHP3Nw/OCiY2urFcXjQU8/81zceueDcdHkKX1C/Nxijzw+NTYZP7F2Q+C9716tc0A74EiLJzEOPf7C+M0tl8TLr0yPDbbcL64458h4z1pvrx398Wd+N554alqcffxBtf//3AsvxWuvzYjx+x4XE/bedp4Qf9+v/xj7HXZGHDtxt/jysd8S4itaQ80aa4rHXjf6z4PjuEP2iG3+40MV1XJYhUAzx5rP7n98vPXNy9V+Laf4K24cnjXpmvlex/RCOQVSxpq5j/Crp1wSjzwxNS45/ZDax0D3PfSb8eBPL46FFxpRa7r55w6NHbfZOHb69MblxLHX/QqkjjVzvshA8905awr/0BIQ4odWf9ibBIG/PfJkbPbZQ2orqsUqa/HI2JJLLB5HHPTZ3ld510a79v777P944233xKnnXTnfyc+1N94RR548Ke647uwYu+TiCXul6VAXOOyEC+PPDz8aV194bPzl4cdiq10Pj9v/64wYt/Qb33Nx+Q/+O67/71/U/n3Ov012nBgH7P7pPiG+qMHiFyzOOHb/WHbcUrH1rkcI8UO9ABrYv2aONbfc+UAceORZscOnPhZ/+r9Hap9h3fKTH4itPumjOw10zZDepJljza2/eCAOOOKs2PhD76vd/Dn1W9+P3Xb4j/jM5h8Z0gZ2Lk2g0bGmeJeZM2fFJ8dPjC02Xr/2McOrb7i99kThlMtP6t2J4tH9Vd6yQu/HENP2TuuhKtDoWFMcz/zmu3PX1FA9/k7dLyG+U3u+5MddfL7rswccH4svNiouPePQGDZsWO2x6OJzgrM/I18c4nqb7RPFZ8A2+/j6vUc8UIj/018fiZ32Oy523naT2mNo/qojMPtu9cWnTYwN3veu3o9g3HXDub1feFdMfL512fXz3OSZO8Q//8LLsd0+x8Su224a47f5eO3GgBBfnVqZfSTNHmsmX3tz7WmP4obQaqu+OR76yyNx9iXXxKlH7dNnnGvOGaIAAA4xSURBVKqeZGcdUTPHmkLu0Seerl3j3vG2N8cv7/ttjBy5cHz7m4fE21desbNgK3y0OWNNwXL0N74dN956T/zospNqX85bfO/GT26/t88N6WKxY7HFRtXmRf6qIZAz1gw03527pqohVp2jEOKr05cdcyTF57yKlawnpj4Tl511eCw5ZnTt2IuLU/HtrIcf2PhKfDFR+twBJ8R671kjTjjs87WbA/6qIVBMfIvvRDh6wi6x3VYfrR3U7JX4n11zZu+XVg52Jf6m2++LCcecW7vZU/wKwrPPvRDX//cvY4etPxaf2eIjvnCqAmXTirGmCPHf/+Gtcf2lJ/QKFasoxfc1FE90+Cu/QLPHmkJk+32OjY02eE98YZet46WXp9cC2x33/CbuuuE83zhe/pKpfX49Z15z3qXXxbmXXhdXnn907VeUij8r8RUojAEOIWesGWi+219NVV+0XEcoxJervzp+b1986ZU44MizYvqrr8UFp3ypN8AXMMVn4v/457/Hhad+ueY092fiZ+PVW4kvVlJ3++LJ8bEP/Ht89Yu7mBhVqNpmB+7i86Sf2vSDvUdW/OrE3J+J//oZl8VTU5/t/Uz87MZzr8QXNwBuufP+3tcqvvH1imtujn0+t1VtRbX4hQN/5RVo1Vgz+3Oqv755Uu8vIBQ3IF959bU494SDywtmz2sCrRhrihXa4qmys48/MD72gffW3ud3Dz0c2+11TFz37ePiHau8mX6JBXLGmuKXLoovX60F9jMOizVX+9evFcwea35188Wx0Ig3PhNfXMd2/swmPhNf4nqZves5Y8385rvzq6kKsFXqEIT4SnVntQ+mCO7FakTxBXXfPGa/2iNhxd/wYcNqvxk/+9vpv3vOEfHuNd4WZ178g5hyyz29305f/ATdzFmz4ie33Vv7ibmbvndK7Vt+i9+Lf+gv/4ht9jgqNt94gzhw90/Xfg+8+Ft01MhYagmfiS9zZf3wpl/E4SdeFIfuv2N87INvTICLv6Jfi/7d88un1h6lLwJ+f99OX9Rbd093bLHzYfGFnbeOzTdev3dCNKeLx+nLXCV9972VY00xYf/4dhNqT3Dsu8vW8T9//GvsuO/X48iDPlf7WIa/8gq0cqwpwtcqK60Qpxy5d4waNTLOuOgHcfsvf1V7oqO4hvkrp0DuWHPUKZfENT/+eW1Ro/is++y/5ceNjRmvvx7rbLp3HLL/jrHTNhv7dvpylki/e50z1gw0351fTRlrhlYRCfFDqz/szXwEit/J/dhn3vhZsDn/xi41Ju649qza78QXP9VS/FZ88Vf89vKFp3y591vHZ4esObctvkzqxMP3imJ1vvhm8bn/Zv+7jimvwNe/eVnt8eW5/2Z/O/jD/3gi9j7ktNpPDxZ/xUr9MV/etTeof+nY82o3fub8K74oaOWVlu/z34T48tbI3HveyrGmeK+5f+O3+Lbo4iaTCVK5a6iVY03xyyvFte3mO+6vXdvW+bfVa4/WFz+n6q+8ArljTXFzZ/a1a06FH3/35NqvGdz2iwdj/yPO7P2nIw/+XIz/lJuF5a2YN/Y8Z6wZaL47UE2V3a5K+y/EV6k3HUtNoPg5sGnPvVhbnS9+H94fgcEIFJOp0YuOqk2Q/REYjEDOWFM84VF8r8fsJ0IG837aVEMgZ6wpHq0vvjF6iTGLVQPDUQxKIGes6e7ujsefeqb2ZXezH6sf1JtqVHqBnLGm9AffAQcgxHdAJztEAgQIECBAgAABAgQIEKiGgBBfjX50FAQIECBAgAABAgQIECDQAQJCfAd0skMkQIAAAQIECBAgQIAAgWoICPHV6EdHQYAAAQIECBAgQIAAAQIdICDEd0AnO0QCBAgQIECAAAECBAgQqIaAEF+NfnQUBAgQIECAAAECBAgQINABAkJ8B3SyQyRAgAABAgQIECBAgACBaggI8dXoR0dBgAABAgQIECBAgAABAh0gIMR3QCc7RAIECBAgQIAAAQIECBCohoAQX41+dBQECBAgQIAAAQIECBAg0AECQnwHdLJDJECAAAECBAgQIECAAIFqCAjx1ehHR0GAAAECBAgQIECAAAECHSAgxHdAJztEAgQIECBAgAABAgQIEKiGgBBfjX50FAQIECBAgAABAgQIECDQAQJCfAd0skMkQIAAAQIECBAgQIAAgWoICPHV6EdHQYAAAQIECBAgQIAAAQIdICDEd0AnO0QCBAgQIECAAAECBAgQqIaAEF+NfnQUBAgQIECAAAECBAgQINABAkJ8B3SyQyRAgACBcgpcfcPtMeWWu+O8E78Yi44a2XsQp194dUx75vk4/tDP1/7bnff+T5x/2fXx4G//FG9+07j41CYfjD132iJGjBgeT059Jg45/sL4y98ei2eefSGWGzc2tt7kA7Hfrp+q/Xvx99VTLomV37JCvGOVFeOGn94VTz39bJz19QNizOKLlRPOXhMgQIAAgQoLCPEV7lyHRoAAAQLlFvjTXx+JT+12ZBw7cbf4zOYfqR3MU08/Fx/9zMFx+IGfjZ0+vXHccc9vYp9DTo+tPrlhfPxD74vf/OH/YtLkKfGlfbaP3Xf4j/j7o0/GGRf9IN7/3jVj6SXHRPGa53z72jh4z8/Ugn7xt+1eR8fvH/pb7X9vtOF7YsTw4fG1ibvHEmOE+HJXkL0nQIAAgSoKCPFV7FXHRIAAAQKVEdj14JPi+Rdfjmsnfb12TBdcfkOcNem/4q4bzq2tlG+zx1ExbuwSceGpX+495gnHnBt/fvjRuP7SE/o4vPzKq/Hs8y/GocdfGKMXGxXnnzyhN8QvNGJEnHPCwTF2ycUrY+dACBAgQIBAFQWE+Cr2qmMiQIAAgcoI3Pzz++Ogr54dV5xzZKy1xirx0W2/GJtutF4ccdBn4/WZM+M9G38+xi41JpYft1TvMf/tkSejCOy/u/3SmDWrOy664kdx1Q231x6tn/33vn9bLS476/DeEP/uNd4WX52wS2XcHAgBAgQIEKiqgBBf1Z51XAQIECBQCYGZM2fVgvsH1lkrNv7Q+2qB/oeXHh9vX3nFWlBfb7N9YtstN4qPf/C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", "text/html": [ - "
\n", + "
" + " }) }; " ] }, "metadata": {}, @@ -6849,7 +6435,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "auallpubs.columns = ['all_count', 'year', ]\n", + "auallpubs.columns = ['year', 'all_count']\n", "\n", "auintpubs = dsl.query(\"\"\"\n", " \n", @@ -6862,7 +6448,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "auintpubs.columns = ['all_int_count', 'year', ]\n", + "auintpubs.columns = ['year', 'all_int_count']\n", "\n", "\n", "audompubs = dsl.query(\"\"\"\n", @@ -6876,7 +6462,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "audompubs.columns = [ 'all_dom_count', 'year',]\n", + "audompubs.columns = ['year', 'all_dom_count']\n", "\n", "auinternalpubs = dsl.query(\"\"\"\n", " \n", @@ -6890,12 +6476,12 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "auinternalpubs.columns = ['all_internal_count', 'year', ]\n", + "auinternalpubs.columns = ['year', 'all_internal_count']\n", "\n", "audf = auallpubs.set_index('year'). \\\n", - " join(auintpubs.set_index('year')). \\\n", - " join(audompubs.set_index('year')). \\\n", - " join(auinternalpubs.set_index('year')). \\\n", + " merge(auintpubs, how='left', on='year'). \\\n", + " merge(audompubs, how='left', on='year'). \\\n", + " merge(auinternalpubs, how='left', on='year'). \\\n", " sort_values(by=['year'])\n", "\n", "px.bar(audf, title=\"Australia: publications collaboration\")" @@ -6912,7 +6498,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "Collapsed": "false" }, @@ -6921,14 +6507,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Year: 12\n", - "\u001b[2mTime: 0.56s\u001b[0m\n", - "Returned Year: 12\n", - "\u001b[2mTime: 0.59s\u001b[0m\n", - "Returned Year: 12\n", - "\u001b[2mTime: 0.61s\u001b[0m\n", - "Returned Year: 12\n", - "\u001b[2mTime: 0.56s\u001b[0m\n" + "Returned Year: 16\n", + "\u001b[2mTime: 0.49s\u001b[0m\n", + "Returned Year: 16\n", + "\u001b[2mTime: 6.01s\u001b[0m\n", + "Returned Year: 16\n", + "\u001b[2mTime: 0.63s\u001b[0m\n", + "Returned Year: 16\n", + "\u001b[2mTime: 0.49s\u001b[0m\n" ] }, { @@ -6939,196 +6525,132 @@ }, "data": [ { - "alignmentgroup": "True", - "hovertemplate": "variable=pubs
year=%{x}
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index=%{x}
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", 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" + " }) }; " ] }, "metadata": {}, @@ -8037,7 +7506,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "allpubs.columns = ['pubs', 'year', ]\n", + "allpubs.columns = ['year', 'pubs']\n", "\n", "\n", "\n", @@ -8053,7 +7522,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "international.columns = ['international_count', 'year', ]\n", + "international.columns = ['year', 'international_count']\n", "\n", "\n", "domestic = dsl.query(f\"\"\"\n", @@ -8068,7 +7537,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "domestic.columns = ['domestic_count', 'year', ]\n", + "domestic.columns = ['year', 'domestic_count']\n", "\n", "internal = dsl.query(f\"\"\"\n", " \n", @@ -8082,13 +7551,13 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "internal.columns = ['internal_count', 'year', ]\n", + "internal.columns = ['year', 'internal_count']\n", "\n", "\n", "jdf = allpubs.set_index('year'). \\\n", - " join(international.set_index('year')). \\\n", - " join(domestic.set_index('year')). \\\n", - " join(internal.set_index('year')) \n", + " merge(international, how='left', on='year'). \\\n", + " merge(domestic, how='left', on='year'). \\\n", + " merge(internal, how='left', on='year')\n", "\n", "px.bar(jdf, title=\"Univ. of Toronto: publications collaboration\")\n" ] @@ -8125,7 +7594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" }, "nteract": { "version": "0.15.0" diff --git a/docs/.doctrees/nbsphinx/cookbooks/8-organizations/5-mapping-organization-ids-to-organization-data.ipynb b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/5-mapping-organization-ids-to-organization-data.ipynb new file mode 100644 index 00000000..04fae69d --- /dev/null +++ b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/5-mapping-organization-ids-to-organization-data.ipynb @@ -0,0 +1,922 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "hu34Y6_eo_8c" + }, + "source": [ + "# Mapping Organization IDs to Organization Data\n", + "\n", + "In this tutorial, we show how to use the [Dimensions Analytics API](https://www.dimensions.ai/dimensions-apis/) and organization data to extract organization IDs.\n", + "\n", + "**Use case scenarios:**\n", + "\n", + "* An analyst has a list of organizations of interest, and wants to get details of their publications from Dimensions. To do this, they they need to map them to organization IDs so they can extract information from the Dimensions database. The organization data can be run through the Dimensions API [extract_affiliations](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations) function in order to extract IDs, which can then be utilized to get publication data statistics.\n", + "\n", + "* A second use case is to standardize messy organization data for \n", + "analysis. For example, an analyst might have a set of affiliation data containing many variants of organization names (\"University of Cambridge\", \"Cambridge University\"). By mapping to IDs, the analyst can standardize the data so it's easier to analyse." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==\n", + "CHANGELOG\n", + "This notebook was last run on Sep 10, 2025\n", + "==\n" + ] + } + ], + "source": [ + "import datetime\n", + "print(\"==\\nCHANGELOG\\nThis notebook was last run on %s\\n==\" % datetime.date.today().strftime('%b %d, %Y'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "lnk_dWT3pINN" + }, + "source": [ + "## Prerequisites \n", + "\n", + "This notebook assumes you have installed the [Dimcli](https://pypi.org/project/dimcli/) library and are familiar with the ['Getting Started' tutorial](https://api-lab.dimensions.ai/cookbooks/1-getting-started/1-Using-the-Dimcli-library-to-query-the-API.html).\n", + "\n", + "To generate an API key from the Dimensions webapp, go to \"My Account\". Under \"General Settings\" there is an \"API key\" section where there is a \"Create API key\" button. More information on this can be found [here](https://dimensions.freshdesk.com/support/solutions/articles/23000018791).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 18999, + "status": "ok", + "timestamp": 1624636934872, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 300 + }, + "id": "p0v3SdNwpDLn", + "outputId": "8c654b2a-5fbe-4c73-feca-89ed1a0987f6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[2mSearching config file credentials for 'https://app.dimensions.ai' endpoint..\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==\n", + "Logging in..\n", + "==\n", + "Logging in..\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", + "\u001b[2mMethod: dsl.ini file\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install dimcli --quiet\n", + "\n", + "import dimcli\n", + "from dimcli.utils import *\n", + "from dimcli.functions import extract_affiliations\n", + "\n", + "import json\n", + "import sys\n", + "import pandas as pd\n", + "import re\n", + "import time\n", + "\n", + "print(\"==\\nLogging in..\")\n", + "# https://digital-science.github.io/dimcli/getting-started.html#authentication\n", + "ENDPOINT = \"https://app.dimensions.ai\"\n", + "\n", + "print(\"==\\nLogging in..\")\n", + "# https://digital-science.github.io/dimcli/getting-started.html#authentication\n", + "ENDPOINT = \"https://app.dimensions.ai\"\n", + "if 'google.colab' in sys.modules:\n", + " import getpass\n", + " KEY = getpass.getpass(prompt='API Key: ') \n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "else:\n", + " KEY = \"\"\n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "dsl = dimcli.Dsl()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "RlPdopEbpeyE" + }, + "source": [ + "## 1. Importing Organization Data\n", + "\n", + "There are several ways to obtain organization data. Below we show examples for 2 different ways to obtain organization data that can be used to run through the Dimensions API for ID mapping. *For purposes of this demostration, we will be using method 1*. Please uncomment the other sections if you wish to use those methods instead.\n", + "\n", + "\n", + "1. Manually Generate Organization Data\n", + "2. Load Organization Data from Local Machine\n", + "\n", + "*Note* - To map organizational data to IDs, the data must conform to mapping specifications and contain data (if available) for the following 4 columns (with column headers being lowercase):\n", + "* name - name of the organization\n", + "* city - city of the organization\n", + "* state - state of the organization (use the full name of the state, not acronym)\n", + "* country - country of the organization\n", + "\n", + "\n", + "The user may use structured or unstructured organization data for mapping to IDs like the following:\n", + "\n", + "* Structured Data e.g., \n", + "`[{\"name\":\"Southwestern University\",\n", + " \"city\":\"Georgetown\",\n", + " \"state\":\"Texas\",\n", + " \"country\":\"USA\"}]`\n", + "* Unstructured Data\n", + "e.g., `[{\"affiliation\": \"university of oxford, uk\"}]`\n", + "\n", + "*For purposes of this notebook, we will be utilizing structured data in a pandas dataframe. Therefore, please ensure your organization dataset resembles the format observed under method 1, below.*\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "X_t-RnDWv3BB" + }, + "source": [ + "### 1.1 Manually Generate Organization Data\n", + "\n", + "The following cell builds an example organization dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 320 + }, + "executionInfo": { + "elapsed": 207, + "status": "ok", + "timestamp": 1624636951400, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 300 + }, + "id": "YtckPfuTpXNi", + "outputId": "70d674be-a82a-4f49-fd52-48f1655e6a73" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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namecitystatecountry
0Augusta UniveristyAugustaGeorgiaUnited States
1Baylor College of MedicineHoustonTexasUnited States
2Brown UniversityProvidenceRhode IslandUnited States
3California Institute of TechnologyPasadenaCaliforniaUnited States
4Duke UniverisityDurhamNorth CarolinaUnited States
5Emory UniversityAtlantaGeorgiaUnited States
6Florida State UniversityTallahasseeFloridaUnited States
7Harvard Medical SchoolBostonMassachusettsUnited States
8Kent State UniversityKentOhioUnited States
9New York UniversityNew YorkNew YorkUnited States
10Mayo ClinicNaNNaNUnited States
\n", + "
" + ], + "text/plain": [ + " name city state \\\n", + "0 Augusta Univeristy Augusta Georgia \n", + "1 Baylor College of Medicine Houston Texas \n", + "2 Brown University Providence Rhode Island \n", + "3 California Institute of Technology Pasadena California \n", + "4 Duke Univerisity Durham North Carolina \n", + "5 Emory University Atlanta Georgia \n", + "6 Florida State University Tallahassee Florida \n", + "7 Harvard Medical School Boston Massachusetts \n", + "8 Kent State University Kent Ohio \n", + "9 New York University New York New York \n", + "10 Mayo Clinic NaN NaN \n", + "\n", + " country \n", + "0 United States \n", + "1 United States \n", + "2 United States \n", + "3 United States \n", + "4 United States \n", + "5 United States \n", + "6 United States \n", + "7 United States \n", + "8 United States \n", + "9 United States \n", + "10 United States " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The following generates a table of organization data with 4 columns\n", + "organization_names = pd.Series(['Augusta Univeristy', 'Baylor College of Medicine', 'Brown University', 'California Institute of Technology', 'Duke Univerisity',\n", + " 'Emory University', 'Florida State University', 'Harvard Medical School', 'Kent State University', 'New York University', 'Mayo Clinic'])\n", + "organization_cities = pd.Series(['Augusta', 'Houston', 'Providence', 'Pasadena', 'Durham',\n", + " 'Atlanta', 'Tallahassee', 'Boston', 'Kent', 'New York'])\n", + "organization_states = pd.Series(['Georgia', 'Texas', 'Rhode Island', 'California', 'North Carolina',\n", + " 'Georgia', 'Florida', 'Massachusetts', 'Ohio', 'New York'])\n", + "organization_countries = pd.Series(['United States', 'United States', 'United States', 'United States', 'United States',\n", + " 'United States', 'United States', 'United States', 'United States', 'United States', 'United States'])\n", + "\n", + "orgs = pd.DataFrame({'name':organization_names, 'city':organization_cities, 'state':organization_states, 'country':organization_countries})\n", + "\n", + "# Preview Dataset\n", + "orgs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "FK2-EG8czdg_" + }, + "source": [ + "### 1.2 Load Organization Data from Local Machine\n", + "\n", + "The following cells can be utilized to import an excel file of organization data from a local machine.\n", + "\n", + "This method is useful for when you need to map hundreds or thousands of organizations to IDs, as the bulk process using the API will be much faster than any individual mapping.\n", + "\n", + "\n", + "*Please uncomment the cells below if to be utilized*" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "Collapsed": "false", + "id": "-OHw5k8Yzcfe" + }, + "outputs": [], + "source": [ + "# # Upload the organization dataset from local machine\n", + "\n", + "# from google.colab import files\n", + "# uploaded = files.upload()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "Collapsed": "false", + "id": "0oEG2QB1xqgH" + }, + "outputs": [], + "source": [ + "# # Load and preview the organization dataset into a pandas dataframe\n", + "\n", + "# import io\n", + "# import pandas as pd\n", + "\n", + "# orgs = pd.read_excel(io.BytesIO(uploaded['dataset_name.xlsx']))\n", + "\n", + "# orgs.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "rOeRQ6S7244b" + }, + "source": [ + "## 2. Utilizing Dimensions API to Extract IDs\n", + "\n", + " The following cells will take our organization data and run it through the Dimensions API to pull back IDs mapped to each organization.\n", + "\n", + "Here, we utilize the \"[extract_affiliations](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations)\" API function which can be used to enrich private datasets including non-disambiguated organizations data with Dimensions organization IDs.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "Collapsed": "false", + "id": "s3QexS3m4OsV" + }, + "outputs": [], + "source": [ + "# First, we replace empty data with 'null' to satisfy mapping specifications\n", + "\n", + "orgs = orgs.fillna('null')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "Collapsed": "false", + "id": "rZqY2QTD26y5" + }, + "outputs": [], + "source": [ + "# Second, we will convert organization data from a dataframe to a dictionary (json) for ID mapping\n", + "\n", + "recs = orgs.to_dict(orient='records')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 3106, + "status": "ok", + "timestamp": 1624636962862, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 300 + }, + "id": "W_AkE-i231b8", + "outputId": "af752023-c2cd-45dc-948d-04b080a91b99" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 records complete!\n" + ] + } + ], + "source": [ + "# Then we will take the organization data, run it through the API and return organization IDs\n", + "\n", + "# Chunk records to batches, API takes up to 200 records at a time.\n", + "def chunk_records(l, n):\n", + " for i in range(0, len(l), n):\n", + " yield l[i : i + n]\n", + "\n", + "# Use dimcli's from extract_affiliations API wrapper to process data\n", + "\n", + "chunksize = 200\n", + "org_data = pd.DataFrame()\n", + "for k,chunk in enumerate(chunk_records(recs, chunksize)):\n", + " output = extract_affiliations(chunk, as_json=False)\n", + " org_data = pd.concat([org_data, output])\n", + " # Pause to avoid overloading API with too many calls too quickly\n", + " time.sleep(1)\n", + " print(f\"{(k+1)*chunksize} records complete!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 321 + }, + "executionInfo": { + "elapsed": 177, + "status": "ok", + "timestamp": 1624636964136, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 300 + }, + "id": "yFRkUViz34hf", + "outputId": "9c2d920b-93e5-4ad1-f84b-0871d9023801" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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input.cityinput.countryinput.nameinput.stategrid_idgrid_namegrid_citygrid_stategrid_countryrequires_reviewgeo_country_idgeo_country_namegeo_country_codegeo_state_idgeo_state_namegeo_state_codegeo_city_idgeo_city_name
0AugustaUnited StatesAugusta UniveristyGeorgiagrid.410427.4Augusta UniversityAugustaGeorgiaUnited StatesFalse6252001United StatesUS4197000GeorgiaUS-GA4180531Augusta
1HoustonUnited StatesBaylor College of MedicineTexasgrid.39382.33Baylor College of MedicineHoustonTexasUnited StatesFalse6252001United StatesUS4736286TexasUS-TX4699066Houston
2ProvidenceUnited StatesBrown UniversityRhode Islandgrid.40263.33Brown UniversityProvidenceRhode IslandUnited StatesFalse6252001United StatesUS5224323Rhode IslandUS-RI5224151Providence
3PasadenaUnited StatesCalifornia Institute of TechnologyCaliforniagrid.20861.3dCalifornia Institute of TechnologyPasadenaCaliforniaUnited StatesFalse6252001United StatesUS5332921CaliforniaUS-CA5381396Pasadena
4DurhamUnited StatesDuke UniverisityNorth Carolinagrid.26009.3dDuke UniversityDurhamNorth CarolinaUnited StatesFalse6252001United StatesUS4482348North CarolinaUS-NC4464368Durham
\n", + "
" + ], + "text/plain": [ + " input.city input.country input.name \\\n", + "0 Augusta United States Augusta Univeristy \n", + "1 Houston United States Baylor College of Medicine \n", + "2 Providence United States Brown University \n", + "3 Pasadena United States California Institute of Technology \n", + "4 Durham United States Duke Univerisity \n", + "\n", + " input.state grid_id grid_name \\\n", + "0 Georgia grid.410427.4 Augusta University \n", + "1 Texas grid.39382.33 Baylor College of Medicine \n", + "2 Rhode Island grid.40263.33 Brown University \n", + "3 California grid.20861.3d California Institute of Technology \n", + "4 North Carolina grid.26009.3d Duke University \n", + "\n", + " grid_city grid_state grid_country requires_review geo_country_id \\\n", + "0 Augusta Georgia United States False 6252001 \n", + "1 Houston Texas United States False 6252001 \n", + "2 Providence Rhode Island United States False 6252001 \n", + "3 Pasadena California United States False 6252001 \n", + "4 Durham North Carolina United States False 6252001 \n", + "\n", + " geo_country_name geo_country_code geo_state_id geo_state_name \\\n", + "0 United States US 4197000 Georgia \n", + "1 United States US 4736286 Texas \n", + "2 United States US 5224323 Rhode Island \n", + "3 United States US 5332921 California \n", + "4 United States US 4482348 North Carolina \n", + "\n", + " geo_state_code geo_city_id geo_city_name \n", + "0 US-GA 4180531 Augusta \n", + "1 US-TX 4699066 Houston \n", + "2 US-RI 5224151 Providence \n", + "3 US-CA 5381396 Pasadena \n", + "4 US-NC 4464368 Durham " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Preview the extracted organization ID dataframe\n", + "# Note: data columns labeled with \"input\" are the original organization data supplied to the API\n", + "\n", + "org_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "0xmORlDluF0e" + }, + "source": [ + "Note: Some records returned in the mapping may require manual review, as some results may give more than one organization of interest (see below). The user can utilize this information to update their original organization data that is inputted to this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 400 + }, + "executionInfo": { + "elapsed": 202, + "status": "ok", + "timestamp": 1624636975652, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 300 + }, + "id": "XhI6un5zxL7L", + "outputId": "26b38de2-f88d-4453-efc0-a174bd3dd739" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
input.cityinput.countryinput.nameinput.stategrid_idgrid_namegrid_citygrid_stategrid_countryrequires_reviewgeo_country_idgeo_country_namegeo_country_codegeo_state_idgeo_state_namegeo_state_codegeo_city_idgeo_city_name
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [input.city, input.country, input.name, input.state, grid_id, grid_name, grid_city, grid_state, grid_country, requires_review, geo_country_id, geo_country_name, geo_country_code, geo_state_id, geo_state_name, geo_state_code, geo_city_id, geo_city_name]\n", + "Index: []" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "org_data['requires_review'] = org_data['requires_review'].astype(str)\n", + "org_data_review = org_data.loc[org_data['requires_review'] == 'True']\n", + "org_data_review" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "e2YjFdSk4X6X" + }, + "source": [ + "## 3. Save the ID Dataset we created\n", + "\n", + "The following cell will export the ID-mapped organization data to a csv file that can be saved to your local machine.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "executionInfo": { + "elapsed": 132, + "status": "ok", + "timestamp": 1623264999048, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 240 + }, + "id": "mvfSL5Ci38ft", + "outputId": "1585e34f-d308-4afd-e2cb-847a0e27a405" + }, + "outputs": [], + "source": [ + "# temporarily save pandas dataframe as file in colab environment\n", + "org_data.to_csv('file_name.csv')\n", + "\n", + "if 'google.colab' in sys.modules:\n", + " \n", + " from google.colab import files\n", + "\n", + " # download file to local machine\n", + " files.download('file_name.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "HWwI1o_q4vxv" + }, + "source": [ + "## Conclusions\n", + "\n", + "In this notebook we have shown how to use the [Dimensions Analytics API](https://www.dimensions.ai/dimensions-apis/) *extract_affiliations* function to assign identifiers to organizations data.\n", + "\n", + "For more background, see the [extract_affiliations function documentation](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations), as well as the other functions available via the Dimensions API. \n", + "\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "[APILAB] Derek Denning - grid_mapping_api.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/.doctrees/nbsphinx/cookbooks/8-organizations/6-organization-groups.ipynb b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/6-organization-groups.ipynb index 545ce5ee..6ca8faf2 100644 --- a/docs/.doctrees/nbsphinx/cookbooks/8-organizations/6-organization-groups.ipynb +++ b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/6-organization-groups.ipynb @@ -11,13 +11,13 @@ "This tutorial shows how use the organization groups in Dimensions (e.g. the [funder groups](https://app.dimensions.ai/browse/facet-filter-groups/publication/funder_shared_group_facet)) in order to construct API queries. \n", "\n", "The Dimensions team maintains various organization groups definitions in the main Dimensions web application. \n", - "These groups are not available directly via the API, but since they are a simple list of GRID identifiers, they can be easily downloaded as a CSV file. \n", + "These groups are not available directly via the API, but since they are a simple list of organization identifiers, they can be easily downloaded as a CSV file. \n", "Once you have a CSV file, it is possible to parse it with Python and use its contents in an API query. \n", "\n", "Outline \n", "\n", "1. Downloading Dimensions' organization groups as a CSV file.\n", - "2. Constructing API queries using a list of GRID IDs\n", + "2. Constructing API queries using a list of organization IDs\n", " " ] }, @@ -32,7 +32,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Feb 21, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": { "Collapsed": "false" }, @@ -75,8 +75,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -118,7 +118,7 @@ "\n", "2. Use the 'Copy to my Groups' command to create a copy of that group in your personal space.\n", "\n", - "3. Go to 'My Groups', where you can select 'Export group definitions' to download a CSV file containing the groups details including GRID IDs. \n", + "3. Go to 'My Groups', where you can select 'Export group definitions' to download a CSV file containing the groups details including organization IDs. \n", "\n", "See below a screenshot of the [Dimensions' groups page](http://api-sample-data.dimensions.ai/data/funder-groups/dimensions-funder-groups-page.jpg). \n", "\n", @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -139,7 +139,7 @@ "" ] }, - "execution_count": 7, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": { "Collapsed": "false" }, @@ -432,7 +432,7 @@ "24 grid.457898.f " ] }, - "execution_count": 9, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -449,7 +449,7 @@ "Collapsed": "false" }, "source": [ - "Let's get the GRID IDs for the NSF and put them into a Python list.\n", + "Let's get the organization IDs for the NSF and put them into a Python list.\n", "\n", "Then we can generate queries programmatically using this list. \n", "\n", @@ -458,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": { "Collapsed": "false" }, @@ -493,14 +493,14 @@ " 'grid.457898.f']" ] }, - "execution_count": 11, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "nsfgrids = data['ID'].to_list()\n", - "nsfgrids" + "nsforgs = data['ID'].to_list()\n", + "nsforgs" ] }, { @@ -511,14 +511,14 @@ "source": [ "### How many grants from the NSF? \n", "\n", - "Let's try a simple API query that uses the contents of `nsfgrids`. \n", + "Let's try a simple API query that uses the contents of `nsforgs`. \n", "\n", "The total number of results should match [what you see in Dimensions](https://app.dimensions.ai/discover/publication?and_facet_funder_shared_group_facet=574603a4-0c27-4844-9f74-7e6810e25cfb).\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": { "Collapsed": "false" }, @@ -532,8 +532,10 @@ " where funders.id in [\"grid.457768.f\", \"grid.457785.c\", \"grid.457799.1\", \"grid.457810.f\", \"grid.457836.b\", \"grid.457875.c\", \"grid.457916.8\", \"grid.457789.0\", \"grid.457813.c\", \"grid.457814.b\", \"grid.457842.8\", \"grid.457821.d\", \"grid.457772.4\", \"grid.457801.f\", \"grid.457891.6\", \"grid.457892.5\", \"grid.457845.f\", \"grid.457922.f\", \"grid.457896.1\", \"grid.431093.c\", \"grid.457758.c\", \"grid.457907.8\", \"grid.473792.c\", \"grid.457846.c\", \"grid.457898.f\"]\n", "return grants[id+title]\n", "\n", - "Returned Grants: 20 (total = 601237)\n", - "\u001b[2mTime: 2.03s\u001b[0m\n" + "Returned Grants: 20 (total = 621348)\n", + "\u001b[2mTime: 0.61s\u001b[0m\n", + "WARNINGS [1]\n", + "Field 'funders' is deprecated in favor of funder_orgs. Please refer to https://docs.dimensions.ai/dsl/releasenotes.html for more details\n" ] }, { @@ -564,133 +566,133 @@ " \n", " \n", " 0\n", - " grant.9752271\n", - " NNA Planning: Developing community frameworks ...\n", + " grant.14880777\n", + " Postdoctoral Fellowship: PRFB: Mapping the Bum...\n", " \n", " \n", " 1\n", - " grant.9890102\n", - " RUI: Exciton-Phonon Interactions in Solids bas...\n", + " grant.14880767\n", + " Postdoctoral Fellowship: PRFB: Using the intro...\n", " \n", " \n", " 2\n", - " grant.9982417\n", - " CAREER: Empowering White-box Driven Analytics ...\n", + " grant.14976921\n", + " Rossbypalooza 2026: A Student-led Summer Schoo...\n", " \n", " \n", " 3\n", - " grant.9982416\n", - " CAREER: Holistic Framework for Constructing Dy...\n", + " grant.14955547\n", + " Postdoctoral Fellowship: PRFB: The Role of Pla...\n", " \n", " \n", " 4\n", - " grant.9982395\n", - " CAREER: Leveraging physical properties of mode...\n", + " grant.14880768\n", + " Postdoctoral Fellowship: PRFB: Testing a role ...\n", " \n", " \n", " 5\n", - " grant.9785674\n", - " BPC-AE Collaborative Research: Researching Equ...\n", + " grant.14973500\n", + " Postdoctoral Fellowship: EAR-PF: Reconstructin...\n", " \n", " \n", " 6\n", - " grant.9785672\n", - " BPC-AE Collaborative Research: Researching Equ...\n", + " grant.14976878\n", + " Conference: Recent Perspectives on Moments of ...\n", " \n", " \n", " 7\n", - " grant.9752397\n", - " Equitable Learning to Advance Technical Education\n", + " grant.14955637\n", + " Conference: Rutgers Gauge Theory, Low-Dimensio...\n", " \n", " \n", " 8\n", - " grant.9995499\n", - " CAREER: New imaging of mid-ocean ridge systems...\n", + " grant.14955550\n", + " Postdoctoral Fellowship: PRFB: Integrating the...\n", " \n", " \n", " 9\n", - " grant.9995464\n", - " CAREER: Reconstructing Parasite Abundance in R...\n", + " grant.14880771\n", + " Postdoctoral Fellowship: PRFB: Eco-evolutionar...\n", " \n", " \n", " 10\n", - " grant.9752334\n", - " Collaborative Research: SWIFT: Intelligent Dyn...\n", + " grant.14976854\n", + " Conference: Meeting in the Middle: Conference ...\n", " \n", " \n", " 11\n", - " grant.9752333\n", - " Collaborative Research: SWIFT: Intelligent Dyn...\n", + " grant.14976778\n", + " MCA: Eavesdropping vectors and disease transmi...\n", " \n", " \n", " 12\n", - " grant.9995542\n", - " CAREER: Learning Mechanisms from Single Cell M...\n", + " grant.14969598\n", + " Conference: Universal Statistics in Number Theory\n", " \n", " \n", " 13\n", - " grant.9995538\n", - " CAREER: A Transformative Approach for Teaching...\n", + " grant.14964639\n", + " Long term compliance observations of the evolv...\n", " \n", " \n", " 14\n", - " grant.9995527\n", - " CAREER: Interlimb Neural Coupling to Enhance G...\n", + " grant.14880779\n", + " Postdoctoral Fellowship: PRFB: Elucidating the...\n", " \n", " \n", " 15\n", - " grant.9995522\n", - " CAREER: Fossil Amber Insight Into Macroevoluti...\n", + " grant.14976745\n", + " What drives spatial variability in water-colum...\n", " \n", " \n", " 16\n", - " grant.9995520\n", - " 2022 Origins of Life GRC and GRS: Environments...\n", + " grant.14976476\n", + " IRES: Exploring New Horizons in the Observable...\n", " \n", " \n", " 17\n", - " grant.9995519\n", - " CAREER: Invariants and Entropy of Square Integ...\n", + " grant.14969702\n", + " MCA Pilot PUI: Can unhatched eggs or trash aff...\n", " \n", " \n", " 18\n", - " grant.9995488\n", - " CAREER: Statistical Learning from a Modern Per...\n", + " grant.14954673\n", + " Conference: Geometry Labs United 2025\n", " \n", " \n", " 19\n", - " grant.9995470\n", - " CAREER: CAS- Climate: Making Decarbonization o...\n", + " grant.14976899\n", + " Collaborative Research: FIRE-MODEL: Advancing ...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id title\n", - "0 grant.9752271 NNA Planning: Developing community frameworks ...\n", - "1 grant.9890102 RUI: Exciton-Phonon Interactions in Solids bas...\n", - "2 grant.9982417 CAREER: Empowering White-box Driven Analytics ...\n", - "3 grant.9982416 CAREER: Holistic Framework for Constructing Dy...\n", - "4 grant.9982395 CAREER: Leveraging physical properties of mode...\n", - "5 grant.9785674 BPC-AE Collaborative Research: Researching Equ...\n", - "6 grant.9785672 BPC-AE Collaborative Research: Researching Equ...\n", - "7 grant.9752397 Equitable Learning to Advance Technical Education\n", - "8 grant.9995499 CAREER: New imaging of mid-ocean ridge systems...\n", - "9 grant.9995464 CAREER: Reconstructing Parasite Abundance in R...\n", - "10 grant.9752334 Collaborative Research: SWIFT: Intelligent Dyn...\n", - "11 grant.9752333 Collaborative Research: SWIFT: Intelligent Dyn...\n", - "12 grant.9995542 CAREER: Learning Mechanisms from Single Cell M...\n", - "13 grant.9995538 CAREER: A Transformative Approach for Teaching...\n", - "14 grant.9995527 CAREER: Interlimb Neural Coupling to Enhance G...\n", - "15 grant.9995522 CAREER: Fossil Amber Insight Into Macroevoluti...\n", - "16 grant.9995520 2022 Origins of Life GRC and GRS: Environments...\n", - "17 grant.9995519 CAREER: Invariants and Entropy of Square Integ...\n", - "18 grant.9995488 CAREER: Statistical Learning from a Modern Per...\n", - "19 grant.9995470 CAREER: CAS- Climate: Making Decarbonization o..." + " id title\n", + "0 grant.14880777 Postdoctoral Fellowship: PRFB: Mapping the Bum...\n", + "1 grant.14880767 Postdoctoral Fellowship: PRFB: Using the intro...\n", + "2 grant.14976921 Rossbypalooza 2026: A Student-led Summer Schoo...\n", + "3 grant.14955547 Postdoctoral Fellowship: PRFB: The Role of Pla...\n", + "4 grant.14880768 Postdoctoral Fellowship: PRFB: Testing a role ...\n", + "5 grant.14973500 Postdoctoral Fellowship: EAR-PF: Reconstructin...\n", + "6 grant.14976878 Conference: Recent Perspectives on Moments of ...\n", + "7 grant.14955637 Conference: Rutgers Gauge Theory, Low-Dimensio...\n", + "8 grant.14955550 Postdoctoral Fellowship: PRFB: Integrating the...\n", + "9 grant.14880771 Postdoctoral Fellowship: PRFB: Eco-evolutionar...\n", + "10 grant.14976854 Conference: Meeting in the Middle: Conference ...\n", + "11 grant.14976778 MCA: Eavesdropping vectors and disease transmi...\n", + "12 grant.14969598 Conference: Universal Statistics in Number Theory\n", + "13 grant.14964639 Long term compliance observations of the evolv...\n", + "14 grant.14880779 Postdoctoral Fellowship: PRFB: Elucidating the...\n", + "15 grant.14976745 What drives spatial variability in water-colum...\n", + "16 grant.14976476 IRES: Exploring New Horizons in the Observable...\n", + "17 grant.14969702 MCA Pilot PUI: Can unhatched eggs or trash aff...\n", + "18 grant.14954673 Conference: Geometry Labs United 2025\n", + "19 grant.14976899 Collaborative Research: FIRE-MODEL: Advancing ..." ] }, - "execution_count": 12, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -700,7 +702,7 @@ "\n", "query = f\"\"\"\n", "search grants \n", - " where funders.id in {json.dumps(nsfgrids)}\n", + " where funders.id in {json.dumps(nsforgs)}\n", "return grants[id+title]\n", "\"\"\"\n", "\n", @@ -727,7 +729,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/docs/.doctrees/nbsphinx/cookbooks/8-organizations/7-benchmarking-organizations.ipynb b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/7-benchmarking-organizations.ipynb index 4ce0f55c..b38638a8 100644 --- a/docs/.doctrees/nbsphinx/cookbooks/8-organizations/7-benchmarking-organizations.ipynb +++ b/docs/.doctrees/nbsphinx/cookbooks/8-organizations/7-benchmarking-organizations.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -29,7 +29,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Feb 21, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -70,8 +70,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "Collapsed": "false", "colab": {}, @@ -135,7 +135,7 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 20\n", - "\u001b[2mTime: 21.14s\u001b[0m\n" + "\u001b[2mTime: 12.29s\u001b[0m\n" ] }, { @@ -159,204 +159,204 @@ " \n", " \n", " \n", - " altmetric_median\n", - " count\n", " id\n", " name\n", + " altmetric_median\n", + " count\n", " \n", " \n", " \n", " \n", " 0\n", - " 5.0\n", - " 546592\n", " grid.38142.3c\n", " Harvard University\n", + " 5.292790\n", + " 715128\n", " \n", " \n", " 1\n", - " 3.0\n", - " 484017\n", " grid.26999.3d\n", - " University of Tokyo\n", + " The University of Tokyo\n", + " 3.000000\n", + " 570861\n", " \n", " \n", " 2\n", - " 4.0\n", - " 342764\n", " grid.17063.33\n", " University of Toronto\n", + " 4.019046\n", + " 435895\n", " \n", " \n", " 3\n", - " 3.0\n", - " 320966\n", " grid.214458.e\n", - " University of Michigan\n", + " University of Michigan-Ann Arbor\n", + " 3.968242\n", + " 412146\n", " \n", " \n", " 4\n", - " 3.0\n", - " 310485\n", - " grid.258799.8\n", - " Kyoto University\n", + " grid.168010.e\n", + " Stanford University\n", + " 4.939072\n", + " 393415\n", " \n", " \n", " 5\n", - " 4.0\n", - " 302094\n", - " grid.168010.e\n", - " Stanford University\n", + " grid.4991.5\n", + " University of Oxford\n", + " 5.104038\n", + " 387324\n", " \n", " \n", " 6\n", - " 4.0\n", - " 297558\n", " grid.34477.33\n", " University of Washington\n", + " 4.304326\n", + " 385718\n", " \n", " \n", " 7\n", - " 3.0\n", - " 297094\n", - " grid.19006.3e\n", - " University of California, Los Angeles\n", + " grid.21107.35\n", + " Johns Hopkins University\n", + " 4.374951\n", + " 381545\n", " \n", " \n", " 8\n", - " 5.0\n", - " 289280\n", - " grid.4991.5\n", - " University of Oxford\n", + " grid.19006.3e\n", + " University of California, Los Angeles\n", + " 3.871221\n", + " 373415\n", " \n", " \n", " 9\n", - " 4.0\n", - " 285143\n", - " grid.21107.35\n", - " Johns Hopkins University\n", + " grid.258799.8\n", + " Kyoto University\n", + " 3.000000\n", + " 370973\n", " \n", " \n", " 10\n", - " 4.0\n", - " 282170\n", - " grid.5335.0\n", - " University of Cambridge\n", + " grid.11899.38\n", + " Universidade de São Paulo\n", + " 2.778797\n", + " 367466\n", " \n", " \n", " 11\n", - " 2.0\n", - " 280405\n", - " grid.11899.38\n", - " University of São Paulo\n", + " grid.5335.0\n", + " University of Cambridge\n", + " 4.412618\n", + " 356990\n", " \n", " \n", " 12\n", - " 4.0\n", - " 271170\n", - " grid.25879.31\n", - " University of Pennsylvania\n", + " grid.47840.3f\n", + " University of California, Berkeley\n", + " 4.103148\n", + " 353011\n", " \n", " \n", " 13\n", - " 4.0\n", - " 266337\n", - " grid.83440.3b\n", - " University College London\n", + " grid.25879.31\n", + " University of Pennsylvania\n", + " 4.491342\n", + " 351125\n", " \n", " \n", " 14\n", - " 3.0\n", - " 265592\n", - " grid.136593.b\n", - " Osaka University\n", + " grid.17635.36\n", + " University of Minnesota Twin Cities\n", + " 3.252271\n", + " 324688\n", " \n", " \n", " 15\n", - " 3.0\n", - " 250749\n", - " grid.69566.3a\n", - " Tohoku University\n", + " grid.136593.b\n", + " Osaka University\n", + " 3.000000\n", + " 323974\n", " \n", " \n", " 16\n", - " 3.0\n", - " 244713\n", - " grid.5386.8\n", - " Cornell University\n", + " grid.83440.3b\n", + " University College London\n", + " 4.154059\n", + " 320344\n", " \n", " \n", " 17\n", - " 4.0\n", - " 242749\n", - " grid.47840.3f\n", - " University of California, Berkeley\n", + " grid.14003.36\n", + " University of Wisconsin-Madison\n", + " 3.220404\n", + " 316542\n", " \n", " \n", " 18\n", - " 3.0\n", - " 239283\n", - " grid.17635.36\n", - " University of Minnesota\n", + " grid.410726.6\n", + " University of Chinese Academy of Sciences\n", + " 2.287477\n", + " 313606\n", " \n", " \n", " 19\n", - " 4.0\n", - " 236142\n", - " grid.21729.3f\n", - " Columbia University\n", + " grid.47100.32\n", + " Yale University\n", + " 4.602265\n", + " 305202\n", " \n", " \n", "\n", "" ], "text/plain": [ - " altmetric_median count id \\\n", - "0 5.0 546592 grid.38142.3c \n", - "1 3.0 484017 grid.26999.3d \n", - "2 4.0 342764 grid.17063.33 \n", - "3 3.0 320966 grid.214458.e \n", - "4 3.0 310485 grid.258799.8 \n", - "5 4.0 302094 grid.168010.e \n", - "6 4.0 297558 grid.34477.33 \n", - "7 3.0 297094 grid.19006.3e \n", - "8 5.0 289280 grid.4991.5 \n", - "9 4.0 285143 grid.21107.35 \n", - "10 4.0 282170 grid.5335.0 \n", - "11 2.0 280405 grid.11899.38 \n", - "12 4.0 271170 grid.25879.31 \n", - "13 4.0 266337 grid.83440.3b \n", - "14 3.0 265592 grid.136593.b \n", - "15 3.0 250749 grid.69566.3a \n", - "16 3.0 244713 grid.5386.8 \n", - "17 4.0 242749 grid.47840.3f \n", - "18 3.0 239283 grid.17635.36 \n", - "19 4.0 236142 grid.21729.3f \n", + " id name \\\n", + "0 grid.38142.3c Harvard University \n", + "1 grid.26999.3d The University of Tokyo \n", + "2 grid.17063.33 University of Toronto \n", + "3 grid.214458.e University of Michigan-Ann Arbor \n", + "4 grid.168010.e Stanford University \n", + "5 grid.4991.5 University of Oxford \n", + "6 grid.34477.33 University of Washington \n", + "7 grid.21107.35 Johns Hopkins University \n", + "8 grid.19006.3e University of California, Los Angeles \n", + "9 grid.258799.8 Kyoto University \n", + "10 grid.11899.38 Universidade de São Paulo \n", + "11 grid.5335.0 University of Cambridge \n", + "12 grid.47840.3f University of California, Berkeley \n", + "13 grid.25879.31 University of Pennsylvania \n", + "14 grid.17635.36 University of Minnesota Twin Cities \n", + "15 grid.136593.b Osaka University \n", + "16 grid.83440.3b University College London \n", + "17 grid.14003.36 University of Wisconsin-Madison \n", + "18 grid.410726.6 University of Chinese Academy of Sciences \n", + "19 grid.47100.32 Yale University \n", "\n", - " name \n", - "0 Harvard University \n", - "1 University of Tokyo \n", - "2 University of Toronto \n", - "3 University of Michigan \n", - "4 Kyoto University \n", - "5 Stanford University \n", - "6 University of Washington \n", - "7 University of California, Los Angeles \n", - "8 University of Oxford \n", - "9 Johns Hopkins University \n", - "10 University of Cambridge \n", - "11 University of São Paulo \n", - "12 University of Pennsylvania \n", - "13 University College London \n", - "14 Osaka University \n", - "15 Tohoku University \n", - "16 Cornell University \n", - "17 University of California, Berkeley \n", - "18 University of Minnesota \n", - "19 Columbia University " + " altmetric_median count \n", + "0 5.292790 715128 \n", + "1 3.000000 570861 \n", + "2 4.019046 435895 \n", + "3 3.968242 412146 \n", + "4 4.939072 393415 \n", + "5 5.104038 387324 \n", + "6 4.304326 385718 \n", + "7 4.374951 381545 \n", + "8 3.871221 373415 \n", + "9 3.000000 370973 \n", + "10 2.778797 367466 \n", + "11 4.412618 356990 \n", + "12 4.103148 353011 \n", + "13 4.491342 351125 \n", + "14 3.252271 324688 \n", + "15 3.000000 323974 \n", + "16 4.154059 320344 \n", + "17 3.220404 316542 \n", + "18 2.287477 313606 \n", + "19 4.602265 305202 " ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "Collapsed": "false", "colab": {}, @@ -382,7 +382,7 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 20\n", - "\u001b[2mTime: 6.63s\u001b[0m\n" + "\u001b[2mTime: 4.16s\u001b[0m\n" ] }, { @@ -406,204 +406,204 @@ " \n", " \n", " \n", - " citations_total\n", - " count\n", " id\n", " name\n", + " citations_total\n", + " count\n", " \n", " \n", " \n", " \n", " 0\n", - " 28836616.0\n", - " 546592\n", " grid.38142.3c\n", " Harvard University\n", + " 43542715.0\n", + " 715128\n", " \n", " \n", " 1\n", - " 8545148.0\n", - " 484017\n", " grid.26999.3d\n", - " University of Tokyo\n", + " The University of Tokyo\n", + " 12416944.0\n", + " 570861\n", " \n", " \n", " 2\n", - " 11040840.0\n", - " 342764\n", " grid.17063.33\n", " University of Toronto\n", + " 16896263.0\n", + " 435895\n", " \n", " \n", " 3\n", - " 11710248.0\n", - " 320966\n", " grid.214458.e\n", - " University of Michigan\n", + " University of Michigan-Ann Arbor\n", + " 17899164.0\n", + " 412146\n", " \n", " \n", " 4\n", - " 5928948.0\n", - " 310485\n", - " grid.258799.8\n", - " Kyoto University\n", + " grid.168010.e\n", + " Stanford University\n", + " 22857822.0\n", + " 393415\n", " \n", " \n", " 5\n", - " 14738599.0\n", - " 302094\n", - " grid.168010.e\n", - " Stanford University\n", + " grid.4991.5\n", + " University of Oxford\n", + " 17348878.0\n", + " 387324\n", " \n", " \n", " 6\n", - " 12585381.0\n", - " 297558\n", " grid.34477.33\n", " University of Washington\n", + " 19245227.0\n", + " 385718\n", " \n", " \n", " 7\n", - " 11710928.0\n", - " 297094\n", - " grid.19006.3e\n", - " University of California, Los Angeles\n", + " grid.21107.35\n", + " Johns Hopkins University\n", + " 18542871.0\n", + " 381545\n", " \n", " \n", " 8\n", - " 10879614.0\n", - " 289280\n", - " grid.4991.5\n", - " University of Oxford\n", + " grid.19006.3e\n", + " University of California, Los Angeles\n", + " 17370426.0\n", + " 373415\n", " \n", " \n", " 9\n", - " 12084053.0\n", - " 285143\n", - " grid.21107.35\n", - " Johns Hopkins University\n", + " grid.258799.8\n", + " Kyoto University\n", + " 8426700.0\n", + " 370973\n", " \n", " \n", " 10\n", - " 10814051.0\n", - " 282170\n", - " grid.5335.0\n", - " University of Cambridge\n", + " grid.11899.38\n", + " Universidade de São Paulo\n", + " 6823063.0\n", + " 367466\n", " \n", " \n", " 11\n", - " 4105653.0\n", - " 280405\n", - " grid.11899.38\n", - " University of São Paulo\n", + " grid.5335.0\n", + " University of Cambridge\n", + " 16495121.0\n", + " 356990\n", " \n", " \n", " 12\n", - " 10450691.0\n", - " 271170\n", - " grid.25879.31\n", - " University of Pennsylvania\n", + " grid.47840.3f\n", + " University of California, Berkeley\n", + " 19445292.0\n", + " 353011\n", " \n", " \n", " 13\n", - " 9614297.0\n", - " 266337\n", - " grid.83440.3b\n", - " University College London\n", + " grid.25879.31\n", + " University of Pennsylvania\n", + " 15634591.0\n", + " 351125\n", " \n", " \n", " 14\n", - " 4653874.0\n", - " 265592\n", - " grid.136593.b\n", - " Osaka University\n", + " grid.17635.36\n", + " University of Minnesota Twin Cities\n", + " 13100152.0\n", + " 324688\n", " \n", " \n", " 15\n", - " 3694359.0\n", - " 250749\n", - " grid.69566.3a\n", - " Tohoku University\n", + " grid.136593.b\n", + " Osaka University\n", + " 6486832.0\n", + " 323974\n", " \n", " \n", " 16\n", - " 9370701.0\n", - " 244713\n", - " grid.5386.8\n", - " Cornell University\n", + " grid.83440.3b\n", + " University College London\n", + " 13014090.0\n", + " 320344\n", " \n", " \n", " 17\n", - " 11806056.0\n", - " 242749\n", - " grid.47840.3f\n", - " University of California, Berkeley\n", + " grid.14003.36\n", + " University of Wisconsin-Madison\n", + " 13060297.0\n", + " 316542\n", " \n", " \n", " 18\n", - " 8360048.0\n", - " 239283\n", - " grid.17635.36\n", - " University of Minnesota\n", + " grid.410726.6\n", + " University of Chinese Academy of Sciences\n", + " 8305318.0\n", + " 313606\n", " \n", " \n", " 19\n", - " 9400497.0\n", - " 236142\n", - " grid.21729.3f\n", - " Columbia University\n", + " grid.47100.32\n", + " Yale University\n", + " 14768834.0\n", + " 305202\n", " \n", " \n", "\n", "" ], "text/plain": [ - " citations_total count id \\\n", - "0 28836616.0 546592 grid.38142.3c \n", - "1 8545148.0 484017 grid.26999.3d \n", - "2 11040840.0 342764 grid.17063.33 \n", - "3 11710248.0 320966 grid.214458.e \n", - "4 5928948.0 310485 grid.258799.8 \n", - "5 14738599.0 302094 grid.168010.e \n", - "6 12585381.0 297558 grid.34477.33 \n", - "7 11710928.0 297094 grid.19006.3e \n", - "8 10879614.0 289280 grid.4991.5 \n", - "9 12084053.0 285143 grid.21107.35 \n", - "10 10814051.0 282170 grid.5335.0 \n", - "11 4105653.0 280405 grid.11899.38 \n", - "12 10450691.0 271170 grid.25879.31 \n", - "13 9614297.0 266337 grid.83440.3b \n", - "14 4653874.0 265592 grid.136593.b \n", - "15 3694359.0 250749 grid.69566.3a \n", - "16 9370701.0 244713 grid.5386.8 \n", - "17 11806056.0 242749 grid.47840.3f \n", - "18 8360048.0 239283 grid.17635.36 \n", - "19 9400497.0 236142 grid.21729.3f \n", + " id name citations_total \\\n", + "0 grid.38142.3c Harvard University 43542715.0 \n", + "1 grid.26999.3d The University of Tokyo 12416944.0 \n", + "2 grid.17063.33 University of Toronto 16896263.0 \n", + "3 grid.214458.e University of Michigan-Ann Arbor 17899164.0 \n", + "4 grid.168010.e Stanford University 22857822.0 \n", + "5 grid.4991.5 University of Oxford 17348878.0 \n", + "6 grid.34477.33 University of Washington 19245227.0 \n", + "7 grid.21107.35 Johns Hopkins University 18542871.0 \n", + "8 grid.19006.3e University of California, Los Angeles 17370426.0 \n", + "9 grid.258799.8 Kyoto University 8426700.0 \n", + "10 grid.11899.38 Universidade de São Paulo 6823063.0 \n", + "11 grid.5335.0 University of Cambridge 16495121.0 \n", + "12 grid.47840.3f University of California, Berkeley 19445292.0 \n", + "13 grid.25879.31 University of Pennsylvania 15634591.0 \n", + "14 grid.17635.36 University of Minnesota Twin Cities 13100152.0 \n", + "15 grid.136593.b Osaka University 6486832.0 \n", + "16 grid.83440.3b University College London 13014090.0 \n", + "17 grid.14003.36 University of Wisconsin-Madison 13060297.0 \n", + "18 grid.410726.6 University of Chinese Academy of Sciences 8305318.0 \n", + "19 grid.47100.32 Yale University 14768834.0 \n", "\n", - " name \n", - "0 Harvard University \n", - "1 University of Tokyo \n", - "2 University of Toronto \n", - "3 University of Michigan \n", - "4 Kyoto University \n", - "5 Stanford University \n", - "6 University of Washington \n", - "7 University of California, Los Angeles \n", - "8 University of Oxford \n", - "9 Johns Hopkins University \n", - "10 University of Cambridge \n", - "11 University of São Paulo \n", - "12 University of Pennsylvania \n", - "13 University College London \n", - "14 Osaka University \n", - "15 Tohoku University \n", - "16 Cornell University \n", - "17 University of California, Berkeley \n", - "18 University of Minnesota \n", - "19 Columbia University " + " count \n", + "0 715128 \n", + "1 570861 \n", + "2 435895 \n", + "3 412146 \n", + "4 393415 \n", + "5 387324 \n", + "6 385718 \n", + "7 381545 \n", + "8 373415 \n", + "9 370973 \n", + "10 367466 \n", + "11 356990 \n", + "12 353011 \n", + "13 351125 \n", + "14 324688 \n", + "15 323974 \n", + "16 320344 \n", + "17 316542 \n", + "18 313606 \n", + "19 305202 " ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -616,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "Collapsed": "false", "colab": {}, @@ -629,7 +629,7 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 20\n", - "\u001b[2mTime: 6.54s\u001b[0m\n" + "\u001b[2mTime: 5.11s\u001b[0m\n" ] }, { @@ -653,204 +653,204 @@ " \n", " \n", " \n", - " count\n", " id\n", " name\n", + " count\n", " recent_citations_total\n", " \n", " \n", " \n", " \n", " 0\n", - " 546592\n", " grid.38142.3c\n", " Harvard University\n", - " 5562378.0\n", + " 715128\n", + " 5657002.0\n", " \n", " \n", " 1\n", - " 484017\n", " grid.26999.3d\n", - " University of Tokyo\n", - " 1471000.0\n", + " The University of Tokyo\n", + " 570861\n", + " 1498274.0\n", " \n", " \n", " 2\n", - " 342764\n", " grid.17063.33\n", " University of Toronto\n", - " 2380994.0\n", + " 435895\n", + " 2557162.0\n", " \n", " \n", " 3\n", - " 320966\n", " grid.214458.e\n", - " University of Michigan\n", - " 2370219.0\n", + " University of Michigan-Ann Arbor\n", + " 412146\n", + " 2411193.0\n", " \n", " \n", " 4\n", - " 310485\n", - " grid.258799.8\n", - " Kyoto University\n", - " 1006685.0\n", + " grid.168010.e\n", + " Stanford University\n", + " 393415\n", + " 3172519.0\n", " \n", " \n", " 5\n", - " 302094\n", - " grid.168010.e\n", - " Stanford University\n", - " 2985116.0\n", + " grid.4991.5\n", + " University of Oxford\n", + " 387324\n", + " 2687354.0\n", " \n", " \n", " 6\n", - " 297558\n", " grid.34477.33\n", " University of Washington\n", - " 2411827.0\n", + " 385718\n", + " 2508430.0\n", " \n", " \n", " 7\n", - " 297094\n", - " grid.19006.3e\n", - " University of California, Los Angeles\n", - " 2137101.0\n", + " grid.21107.35\n", + " Johns Hopkins University\n", + " 381545\n", + " 2441471.0\n", " \n", " \n", " 8\n", - " 289280\n", - " grid.4991.5\n", - " University of Oxford\n", - " 2504619.0\n", + " grid.19006.3e\n", + " University of California, Los Angeles\n", + " 373415\n", + " 2151381.0\n", " \n", " \n", " 9\n", - " 285143\n", - " grid.21107.35\n", - " Johns Hopkins University\n", - " 2352686.0\n", + " grid.258799.8\n", + " Kyoto University\n", + " 370973\n", + " 966227.0\n", " \n", " \n", " 10\n", - " 282170\n", - " grid.5335.0\n", - " University of Cambridge\n", - " 2110364.0\n", + " grid.11899.38\n", + " Universidade de São Paulo\n", + " 367466\n", + " 1207947.0\n", " \n", " \n", " 11\n", - " 280405\n", - " grid.11899.38\n", - " University of São Paulo\n", - " 1124894.0\n", + " grid.5335.0\n", + " University of Cambridge\n", + " 356990\n", + " 2258714.0\n", " \n", " \n", " 12\n", - " 271170\n", - " grid.25879.31\n", - " University of Pennsylvania\n", - " 2049126.0\n", + " grid.47840.3f\n", + " University of California, Berkeley\n", + " 353011\n", + " 2404905.0\n", " \n", " \n", " 13\n", - " 266337\n", - " grid.83440.3b\n", - " University College London\n", - " 2197569.0\n", + " grid.25879.31\n", + " University of Pennsylvania\n", + " 351125\n", + " 2063182.0\n", " \n", " \n", " 14\n", - " 265592\n", - " grid.136593.b\n", - " Osaka University\n", - " 727151.0\n", + " grid.17635.36\n", + " University of Minnesota Twin Cities\n", + " 324688\n", + " 1575033.0\n", " \n", " \n", " 15\n", - " 250749\n", - " grid.69566.3a\n", - " Tohoku University\n", - " 644246.0\n", + " grid.136593.b\n", + " Osaka University\n", + " 323974\n", + " 691161.0\n", " \n", " \n", " 16\n", - " 244713\n", - " grid.5386.8\n", - " Cornell University\n", - " 1809884.0\n", + " grid.83440.3b\n", + " University College London\n", + " 320344\n", + " 2241297.0\n", " \n", " \n", " 17\n", - " 242749\n", - " grid.47840.3f\n", - " University of California, Berkeley\n", - " 2057506.0\n", + " grid.14003.36\n", + " University of Wisconsin-Madison\n", + " 316542\n", + " 1508661.0\n", " \n", " \n", " 18\n", - " 239283\n", - " grid.17635.36\n", - " University of Minnesota\n", - " 1519539.0\n", + " grid.410726.6\n", + " University of Chinese Academy of Sciences\n", + " 313606\n", + " 2620498.0\n", " \n", " \n", " 19\n", - " 236142\n", - " grid.21729.3f\n", - " Columbia University\n", - " 1754780.0\n", + " grid.47100.32\n", + " Yale University\n", + " 305202\n", + " 1861426.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " count id name \\\n", - "0 546592 grid.38142.3c Harvard University \n", - "1 484017 grid.26999.3d University of Tokyo \n", - "2 342764 grid.17063.33 University of Toronto \n", - "3 320966 grid.214458.e University of Michigan \n", - "4 310485 grid.258799.8 Kyoto University \n", - "5 302094 grid.168010.e Stanford University \n", - "6 297558 grid.34477.33 University of Washington \n", - "7 297094 grid.19006.3e University of California, Los Angeles \n", - "8 289280 grid.4991.5 University of Oxford \n", - "9 285143 grid.21107.35 Johns Hopkins University \n", - "10 282170 grid.5335.0 University of Cambridge \n", - "11 280405 grid.11899.38 University of São Paulo \n", - "12 271170 grid.25879.31 University of Pennsylvania \n", - "13 266337 grid.83440.3b University College London \n", - "14 265592 grid.136593.b Osaka University \n", - "15 250749 grid.69566.3a Tohoku University \n", - "16 244713 grid.5386.8 Cornell University \n", - "17 242749 grid.47840.3f University of California, Berkeley \n", - "18 239283 grid.17635.36 University of Minnesota \n", - "19 236142 grid.21729.3f Columbia University \n", + " id name count \\\n", + "0 grid.38142.3c Harvard University 715128 \n", + "1 grid.26999.3d The University of Tokyo 570861 \n", + "2 grid.17063.33 University of Toronto 435895 \n", + "3 grid.214458.e University of Michigan-Ann Arbor 412146 \n", + "4 grid.168010.e Stanford University 393415 \n", + "5 grid.4991.5 University of Oxford 387324 \n", + "6 grid.34477.33 University of Washington 385718 \n", + "7 grid.21107.35 Johns Hopkins University 381545 \n", + "8 grid.19006.3e University of California, Los Angeles 373415 \n", + "9 grid.258799.8 Kyoto University 370973 \n", + "10 grid.11899.38 Universidade de São Paulo 367466 \n", + "11 grid.5335.0 University of Cambridge 356990 \n", + "12 grid.47840.3f University of California, Berkeley 353011 \n", + "13 grid.25879.31 University of Pennsylvania 351125 \n", + "14 grid.17635.36 University of Minnesota Twin Cities 324688 \n", + "15 grid.136593.b Osaka University 323974 \n", + "16 grid.83440.3b University College London 320344 \n", + "17 grid.14003.36 University of Wisconsin-Madison 316542 \n", + "18 grid.410726.6 University of Chinese Academy of Sciences 313606 \n", + "19 grid.47100.32 Yale University 305202 \n", "\n", " recent_citations_total \n", - "0 5562378.0 \n", - "1 1471000.0 \n", - "2 2380994.0 \n", - "3 2370219.0 \n", - "4 1006685.0 \n", - "5 2985116.0 \n", - "6 2411827.0 \n", - "7 2137101.0 \n", - "8 2504619.0 \n", - "9 2352686.0 \n", - "10 2110364.0 \n", - "11 1124894.0 \n", - "12 2049126.0 \n", - "13 2197569.0 \n", - "14 727151.0 \n", - "15 644246.0 \n", - "16 1809884.0 \n", - "17 2057506.0 \n", - "18 1519539.0 \n", - "19 1754780.0 " + "0 5657002.0 \n", + "1 1498274.0 \n", + "2 2557162.0 \n", + "3 2411193.0 \n", + "4 3172519.0 \n", + "5 2687354.0 \n", + "6 2508430.0 \n", + "7 2441471.0 \n", + "8 2151381.0 \n", + "9 966227.0 \n", + "10 1207947.0 \n", + "11 2258714.0 \n", + "12 2404905.0 \n", + "13 2063182.0 \n", + "14 1575033.0 \n", + "15 691161.0 \n", + "16 2241297.0 \n", + "17 1508661.0 \n", + "18 2620498.0 \n", + "19 1861426.0 " ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -874,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "Collapsed": "false", "colab": {}, @@ -887,7 +887,7 @@ "output_type": "stream", "text": [ "Returned Year: 20\n", - "\u001b[2mTime: 4.06s\u001b[0m\n" + "\u001b[2mTime: 5.16s\u001b[0m\n" ] }, { @@ -911,161 +911,161 @@ " \n", " \n", " \n", - " count\n", " id\n", + " count\n", " recent_citations_total\n", " \n", " \n", " \n", " \n", " 0\n", - " 6503486\n", - " 2020\n", - " 18375337.0\n", + " 2024\n", + " 7882763\n", + " 13641369.0\n", " \n", " \n", " 1\n", - " 6391947\n", - " 2021\n", - " 4632716.0\n", + " 2023\n", + " 7755821\n", + " 24571792.0\n", " \n", " \n", " 2\n", - " 5792555\n", - " 2019\n", - " 22470145.0\n", + " 2022\n", + " 7279681\n", + " 26740821.0\n", " \n", " \n", " 3\n", - " 5369555\n", - " 2018\n", - " 23030935.0\n", + " 2021\n", + " 7016199\n", + " 27057034.0\n", " \n", " \n", " 4\n", - " 5044596\n", - " 2017\n", - " 21362603.0\n", + " 2020\n", + " 6831663\n", + " 25211581.0\n", " \n", " \n", " 5\n", - " 4598245\n", - " 2016\n", - " 19046830.0\n", + " 2019\n", + " 6004300\n", + " 20340055.0\n", " \n", " \n", " 6\n", - " 4395107\n", - " 2015\n", - " 17010283.0\n", + " 2018\n", + " 5550132\n", + " 17327713.0\n", " \n", " \n", " 7\n", - " 4244049\n", - " 2014\n", - " 15057104.0\n", + " 2017\n", + " 5171177\n", + " 15030351.0\n", " \n", " \n", " 8\n", - " 4046162\n", - " 2013\n", - " 13475978.0\n", + " 2025\n", + " 5064609\n", + " 1745079.0\n", " \n", " \n", " 9\n", - " 3762532\n", - " 2012\n", - " 11970228.0\n", + " 2016\n", + " 4775828\n", + " 13029942.0\n", " \n", " \n", " 10\n", - " 3667073\n", - " 2011\n", - " 10958039.0\n", + " 2015\n", + " 4534568\n", + " 11396769.0\n", " \n", " \n", " 11\n", - " 3430544\n", - " 2010\n", - " 9915351.0\n", + " 2014\n", + " 4382421\n", + " 9976449.0\n", " \n", " \n", " 12\n", - " 3144460\n", - " 2009\n", - " 8991871.0\n", + " 2013\n", + " 4194614\n", + " 8834241.0\n", " \n", " \n", " 13\n", - " 2937393\n", - " 2008\n", - " 7853718.0\n", + " 2012\n", + " 3898366\n", + " 7799039.0\n", " \n", " \n", " 14\n", - " 2915691\n", - " 2007\n", - " 7198101.0\n", + " 2011\n", + " 3761002\n", + " 7104082.0\n", " \n", " \n", " 15\n", - " 2610760\n", - " 2006\n", - " 6579372.0\n", + " 2010\n", + " 3327367\n", + " 6409071.0\n", " \n", " \n", " 16\n", - " 2410569\n", - " 2005\n", - " 5985630.0\n", + " 2009\n", + " 3198543\n", + " 5733537.0\n", " \n", " \n", " 17\n", - " 2246194\n", - " 2004\n", - " 5335870.0\n", + " 2008\n", + " 3001138\n", + " 5037413.0\n", " \n", " \n", " 18\n", - " 2037978\n", - " 2003\n", - " 4730168.0\n", + " 2007\n", + " 2986096\n", + " 4665167.0\n", " \n", " \n", " 19\n", - " 1892417\n", - " 2002\n", - " 4234096.0\n", + " 2006\n", + " 2688556\n", + " 4263341.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " count id recent_citations_total\n", - "0 6503486 2020 18375337.0\n", - "1 6391947 2021 4632716.0\n", - "2 5792555 2019 22470145.0\n", - "3 5369555 2018 23030935.0\n", - "4 5044596 2017 21362603.0\n", - "5 4598245 2016 19046830.0\n", - "6 4395107 2015 17010283.0\n", - "7 4244049 2014 15057104.0\n", - "8 4046162 2013 13475978.0\n", - "9 3762532 2012 11970228.0\n", - "10 3667073 2011 10958039.0\n", - "11 3430544 2010 9915351.0\n", - "12 3144460 2009 8991871.0\n", - "13 2937393 2008 7853718.0\n", - "14 2915691 2007 7198101.0\n", - "15 2610760 2006 6579372.0\n", - "16 2410569 2005 5985630.0\n", - "17 2246194 2004 5335870.0\n", - "18 2037978 2003 4730168.0\n", - "19 1892417 2002 4234096.0" + " id count recent_citations_total\n", + "0 2024 7882763 13641369.0\n", + "1 2023 7755821 24571792.0\n", + "2 2022 7279681 26740821.0\n", + "3 2021 7016199 27057034.0\n", + "4 2020 6831663 25211581.0\n", + "5 2019 6004300 20340055.0\n", + "6 2018 5550132 17327713.0\n", + "7 2017 5171177 15030351.0\n", + "8 2025 5064609 1745079.0\n", + "9 2016 4775828 13029942.0\n", + "10 2015 4534568 11396769.0\n", + "11 2014 4382421 9976449.0\n", + "12 2013 4194614 8834241.0\n", + "13 2012 3898366 7799039.0\n", + "14 2011 3761002 7104082.0\n", + "15 2010 3327367 6409071.0\n", + "16 2009 3198543 5733537.0\n", + "17 2008 3001138 5037413.0\n", + "18 2007 2986096 4665167.0\n", + "19 2006 2688556 4263341.0" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1078,7 +1078,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "Collapsed": "false", "colab": {}, @@ -1086,26 +1086,31 @@ "id": "OZVInY3lZFaJ" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib is building the font cache; this may take a moment.\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1115,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "Collapsed": "false", "colab": {}, @@ -1129,7 +1134,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "Collapsed": "false", "colab": {}, @@ -1144,7 +1149,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "Collapsed": "false", "colab": {}, @@ -1155,23 +1160,21 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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176 rows × 3 columns

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193 rows × 3 columns

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176 rows × 4 columns

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193 rows × 4 columns

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"[176 rows x 4 columns]" + "[193 rows x 4 columns]" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1560,7 +1563,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "Collapsed": "false", "colab": {}, @@ -1589,165 +1592,165 @@ " \n", " \n", " \n", - " count\n", " id\n", " name\n", + " count\n", " level\n", " \n", " \n", " \n", " \n", " 0\n", - " 1168442\n", - " 2211\n", - " 11 Medical and Health Sciences\n", + " 80003\n", + " 32 Biomedical and Clinical Sciences\n", + " 1094225\n", " 2\n", " \n", " \n", " 1\n", - " 610238\n", - " 2209\n", - " 09 Engineering\n", + " 80011\n", + " 40 Engineering\n", + " 833052\n", " 2\n", " \n", " \n", " 3\n", - " 335403\n", - " 2206\n", - " 06 Biological Sciences\n", + " 80017\n", + " 46 Information and Computing Sciences\n", + " 425860\n", " 2\n", " \n", " \n", " 4\n", - " 332128\n", - " 2208\n", - " 08 Information and Computing Sciences\n", + " 80002\n", + " 31 Biological Sciences\n", + " 365542\n", " 2\n", " \n", " \n", " 5\n", - " 304680\n", - " 2203\n", - " 03 Chemical Sciences\n", + " 80013\n", + " 42 Health Sciences\n", + " 304968\n", " 2\n", " \n", " \n", - " 7\n", - " 224973\n", - " 2202\n", - " 02 Physical Sciences\n", + " 6\n", + " 80005\n", + " 34 Chemical Sciences\n", + " 301217\n", " 2\n", " \n", " \n", - " 8\n", - " 201573\n", - " 2201\n", - " 01 Mathematical Sciences\n", + " 7\n", + " 80022\n", + " 51 Physical Sciences\n", + " 266544\n", " 2\n", " \n", " \n", - " 12\n", - " 161476\n", - " 2217\n", - " 17 Psychology and Cognitive Sciences\n", + " 8\n", + " 80015\n", + " 44 Human Society\n", + " 227080\n", " 2\n", " \n", " \n", - " 13\n", - " 151455\n", - " 2216\n", - " 16 Studies in Human Society\n", + " 9\n", + " 80006\n", + " 35 Commerce, Management, Tourism and Services\n", + " 191148\n", " 2\n", " \n", " \n", - " 18\n", - " 98630\n", - " 2215\n", - " 15 Commerce, Management, Tourism and Services\n", + " 10\n", + " 80020\n", + " 49 Mathematical Sciences\n", + " 179411\n", " 2\n", " \n", " \n", - 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"execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1804,7 +1807,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "Collapsed": "false", "colab": {}, @@ -1818,7 +1821,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "Collapsed": "false", "colab": {}, @@ -1847,9 +1850,9 @@ " \n", " \n", " \n", - " count\n", " id\n", " name\n", + " count\n", " level\n", " cutoff\n", " \n", @@ -1857,235 +1860,235 @@ " \n", " \n", " 0\n", - " 1168442\n", - " 2211\n", - " 11 Medical and Health Sciences\n", + " 80003\n", + " 32 Biomedical and Clinical Sciences\n", + " 1094225\n", " 2\n", - " 11684\n", + " 10942\n", " \n", " \n", " 1\n", - " 610238\n", - " 2209\n", - " 09 Engineering\n", + " 80011\n", + " 40 Engineering\n", + " 833052\n", " 2\n", - " 6102\n", + " 8330\n", " \n", " \n", " 3\n", - " 335403\n", - " 2206\n", - " 06 Biological Sciences\n", + " 80017\n", + " 46 Information and Computing Sciences\n", + " 425860\n", " 2\n", - " 3354\n", + " 4258\n", " \n", " \n", " 4\n", - " 332128\n", - " 2208\n", - " 08 Information and Computing Sciences\n", + " 80002\n", + " 31 Biological Sciences\n", + " 365542\n", " 2\n", - " 3321\n", + " 3655\n", " \n", " \n", " 5\n", - " 304680\n", - " 2203\n", - " 03 Chemical Sciences\n", + " 80013\n", + " 42 Health Sciences\n", + " 304968\n", " 2\n", - " 3046\n", + " 3049\n", " \n", " \n", - " 7\n", - " 224973\n", - " 2202\n", - " 02 Physical Sciences\n", + " 6\n", + " 80005\n", + " 34 Chemical Sciences\n", + " 301217\n", " 2\n", - " 2249\n", + " 3012\n", " \n", " \n", - " 8\n", - " 201573\n", - " 2201\n", - " 01 Mathematical Sciences\n", + " 7\n", + " 80022\n", + " 51 Physical Sciences\n", + " 266544\n", " 2\n", - " 2015\n", + " 2665\n", " \n", " \n", - " 12\n", - " 161476\n", - " 2217\n", - " 17 Psychology and Cognitive Sciences\n", + " 8\n", + " 80015\n", + " 44 Human Society\n", + " 227080\n", " 2\n", - " 1614\n", + " 2270\n", " \n", " \n", - " 13\n", - " 151455\n", - " 2216\n", - " 16 Studies in Human Society\n", + " 9\n", + " 80006\n", + " 35 Commerce, Management, Tourism and Services\n", + " 191148\n", " 2\n", - " 1514\n", + " 1911\n", " \n", " \n", - " 18\n", - " 98630\n", - " 2215\n", - " 15 Commerce, Management, Tourism and Services\n", + " 10\n", + " 80020\n", + " 49 Mathematical Sciences\n", + " 179411\n", " 2\n", - " 986\n", + " 1794\n", " \n", " \n", - " 20\n", - " 95061\n", - " 2210\n", - " 10 Technology\n", + " 11\n", + " 80001\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 165669\n", " 2\n", - " 950\n", + " 1656\n", " \n", " \n", - " 21\n", - " 94318\n", - " 2220\n", - " 20 Language, Communication and Culture\n", + " 13\n", + " 80008\n", + " 37 Earth Sciences\n", + " 141809\n", " 2\n", - " 943\n", + " 1418\n", " \n", " \n", - " 24\n", - " 88929\n", - " 2213\n", - " 13 Education\n", + " 14\n", + " 80018\n", + " 47 Language, Communication and Culture\n", + " 138186\n", " 2\n", - " 889\n", + " 1381\n", " \n", " \n", - " 25\n", - " 86868\n", - 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"execution_count": 20, + "execution_count": 19, "metadata": { "Collapsed": "false", "colab": {}, @@ -2218,7 +2221,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "Collapsed": "false", "colab": {}, @@ -2255,167 +2258,167 @@ " \n", " \n", " 0\n", - " 28.41\n", - " 11 Medical and Health Sciences\n", - " 2211\n", + " 37.05\n", + " 32 Biomedical and Clinical Sciences\n", + " 80003\n", " \n", " \n", " 0\n", - " 21.35\n", - " 09 Engineering\n", - " 2209\n", + " 28.18\n", + " 40 Engineering\n", + " 80011\n", " \n", " \n", " 0\n", - " 20.52\n", - " 06 Biological Sciences\n", - " 2206\n", + " 45.07\n", + " 46 Information and Computing Sciences\n", + " 80017\n", " \n", " \n", " 0\n", - " 35.44\n", - " 08 Information and Computing Sciences\n", - " 2208\n", + " 28.34\n", + " 31 Biological Sciences\n", + " 80002\n", " \n", " \n", " 0\n", - " 20.51\n", - " 03 Chemical Sciences\n", - " 2203\n", + " 33.82\n", + " 42 Health Sciences\n", + " 80013\n", " \n", " \n", " 0\n", - 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"0 25.02 10 Technology 2210\n", - "0 30.45 20 Language, Communication and Culture 2220\n", - "0 25.34 13 Education 2213\n", - "0 16.52 04 Earth Sciences 2204\n", - "0 33.18 14 Economics 2214\n", - "0 28.80 21 History and Archaeology 2221\n", - "0 20.46 05 Environmental Sciences 2205\n", - "0 15.42 07 Agricultural and Veterinary Sciences 2207\n", - "0 27.68 22 Philosophy and Religious Studies 2222\n", - "0 27.52 18 Law and Legal Studies 2218\n", - "0 16.68 12 Built Environment and Design 2212\n", - "0 27.55 19 Studies in Creative Arts and Writing 2219" + " field_citation_ratio name id\n", + "0 37.05 32 Biomedical and Clinical Sciences 80003\n", + "0 28.18 40 Engineering 80011\n", + "0 45.07 46 Information and Computing Sciences 80017\n", + "0 28.34 31 Biological Sciences 80002\n", + "0 33.82 42 Health Sciences 80013\n", + "0 25.02 34 Chemical Sciences 80005\n", + "0 38.91 51 Physical Sciences 80022\n", + "0 38.81 44 Human Society 80015\n", + "0 44.68 35 Commerce, Management, Tourism and Services 80006\n", + "0 34.54 49 Mathematical Sciences 80020\n", + "0 22.36 30 Agricultural, Veterinary and Food Sciences 80001\n", + "0 23.26 37 Earth Sciences 80008\n", + "0 39.98 47 Language, Communication and Culture 80018\n", + "0 33.78 52 Psychology 80023\n", + "0 39.12 50 Philosophy and Religious Studies 80021\n", + "0 35.81 39 Education 80010\n", + "0 29.06 41 Environmental Sciences 80012\n", + "0 37.09 48 Law and Legal Studies 80019\n", + "0 48.26 38 Economics 80009\n", + "0 31.37 43 History, Heritage and Archaeology 80014\n", + "0 38.82 33 Built Environment and Design 80004\n", + "0 37.37 36 Creative Arts and Writing 80007" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2437,7 +2440,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "Collapsed": "false", "colab": {}, @@ -2451,7 +2454,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "Collapsed": "false", "colab": {}, @@ -2488,167 +2491,167 @@ " \n", " \n", " 0\n", - " 28\n", - " 11 Medical and Health Sciences\n", - " 2211\n", + " 37\n", + " 32 Biomedical and Clinical Sciences\n", + " 80003\n", " \n", " \n", " 0\n", - " 21\n", - " 09 Engineering\n", - " 2209\n", + " 28\n", + " 40 Engineering\n", + " 80011\n", " \n", " \n", " 0\n", - " 20\n", - " 06 Biological Sciences\n", - " 2206\n", + " 45\n", + " 46 Information and Computing Sciences\n", + " 80017\n", " \n", " \n", " 0\n", - " 35\n", - " 08 Information and Computing Sciences\n", - " 2208\n", + " 28\n", + " 31 Biological Sciences\n", + " 80002\n", " \n", " \n", " 0\n", - 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"0 25 10 Technology 2210\n", - "0 30 20 Language, Communication and Culture 2220\n", - "0 25 13 Education 2213\n", - "0 16 04 Earth Sciences 2204\n", - "0 33 14 Economics 2214\n", - "0 28 21 History and Archaeology 2221\n", - "0 20 05 Environmental Sciences 2205\n", - "0 15 07 Agricultural and Veterinary Sciences 2207\n", - "0 27 22 Philosophy and Religious Studies 2222\n", - "0 27 18 Law and Legal Studies 2218\n", - "0 16 12 Built Environment and Design 2212\n", - "0 27 19 Studies in Creative Arts and Writing 2219" + " field_citation_ratio name id\n", + "0 37 32 Biomedical and Clinical Sciences 80003\n", + "0 28 40 Engineering 80011\n", + "0 45 46 Information and Computing Sciences 80017\n", + "0 28 31 Biological Sciences 80002\n", + "0 33 42 Health Sciences 80013\n", + "0 25 34 Chemical Sciences 80005\n", + "0 38 51 Physical Sciences 80022\n", + "0 38 44 Human Society 80015\n", + "0 44 35 Commerce, Management, Tourism and Services 80006\n", + "0 34 49 Mathematical Sciences 80020\n", + "0 22 30 Agricultural, Veterinary and Food Sciences 80001\n", + "0 23 37 Earth Sciences 80008\n", + "0 39 47 Language, Communication and Culture 80018\n", + "0 33 52 Psychology 80023\n", + "0 39 50 Philosophy and Religious Studies 80021\n", + "0 35 39 Education 80010\n", + "0 29 41 Environmental Sciences 80012\n", + "0 37 48 Law and Legal Studies 80019\n", + "0 48 38 Economics 80009\n", + "0 31 43 History, Heritage and Archaeology 80014\n", + "0 38 33 Built Environment and Design 80004\n", + "0 37 36 Creative Arts and Writing 80007" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2670,7 +2673,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "Collapsed": "false", "colab": {}, @@ -2683,49 +2686,49 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.21s\u001b[0m\n", + "\u001b[2mTime: 6.15s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.09s\u001b[0m\n", + "\u001b[2mTime: 1.83s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 3.14s\u001b[0m\n", + "\u001b[2mTime: 5.51s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.30s\u001b[0m\n", + "\u001b[2mTime: 5.82s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.96s\u001b[0m\n", + "\u001b[2mTime: 1.56s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.13s\u001b[0m\n", + "\u001b[2mTime: 6.63s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.06s\u001b[0m\n", + "\u001b[2mTime: 1.82s\u001b[0m\n", "Returned Research_orgs: 1000\n", - 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"execution_count": 26, + "execution_count": 25, "metadata": { "Collapsed": "false", "colab": {}, @@ -2803,7 +2806,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": { "Collapsed": "false", "colab": {}, @@ -2838,109 +2841,114 @@ " \n", " \n", " \n", - " 21\n", - " 11 Medical and Health Sciences\n", - " 22.0\n", + " 15\n", + " 32 Biomedical and Clinical Sciences\n", + " 16.0\n", + " \n", + " \n", + " 173\n", + " 40 Engineering\n", + " 177.0\n", " \n", " \n", - " 103\n", - " 09 Engineering\n", - " 107.0\n", + " 114\n", + " 46 Information and Computing Sciences\n", + " 121.0\n", " \n", " \n", - " 25\n", - " 06 Biological Sciences\n", - " 26.0\n", + " 18\n", + " 31 Biological Sciences\n", + " 19.0\n", " \n", " \n", - " 99\n", - " 08 Information and Computing Sciences\n", - " 105.0\n", + " 12\n", + " 42 Health Sciences\n", + " 12.5\n", " \n", " \n", - " 161\n", - " 03 Chemical Sciences\n", - " 170.5\n", + " 192\n", + " 34 Chemical Sciences\n", + " 212.0\n", " \n", " \n", " 142\n", - " 02 Physical Sciences\n", - " 150.0\n", + " 51 Physical Sciences\n", + " 148.0\n", " \n", " \n", - " 45\n", - " 01 Mathematical Sciences\n", - " 48.5\n", + " 10\n", + " 44 Human Society\n", + " 12.0\n", " \n", " \n", - " 9\n", - " 17 Psychology and Cognitive Sciences\n", - " 11.5\n", + " 81\n", + " 35 Commerce, Management, Tourism and Services\n", + " 90.5\n", " \n", " \n", - " 32\n", - " 16 Studies in Human Society\n", - " 36.0\n", + " 125\n", + " 49 Mathematical Sciences\n", + " 142.0\n", " \n", " \n", - " 66\n", - " 15 Commerce, Management, Tourism and Services\n", - " 88.0\n", + " 21\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 23.5\n", " \n", " \n", - " 35\n", - " 20 Language, Communication and Culture\n", - " 46.0\n", + " 96\n", + " 37 Earth Sciences\n", + " 108.5\n", " \n", " \n", - " 17\n", - " 13 Education\n", - " 22.5\n", + " 10\n", + " 47 Language, Communication and Culture\n", + " 12.0\n", " \n", " \n", - " 196\n", - " 04 Earth Sciences\n", - " 230.0\n", + " 6\n", + " 52 Psychology\n", + " 7.5\n", " \n", " \n", - " 83\n", - " 14 Economics\n", - " 110.0\n", + " 80\n", + " 50 Philosophy and Religious Studies\n", + " 106.5\n", " \n", " \n", - " 263\n", - " 21 History and Archaeology\n", - " 579.5\n", + " 8\n", + " 39 Education\n", + " 10.0\n", " \n", " \n", - " 30\n", - " 05 Environmental Sciences\n", - " 34.5\n", + " 19\n", + " 41 Environmental Sciences\n", + " 21.5\n", " \n", " \n", - " 22\n", - " 07 Agricultural and Veterinary Sciences\n", - " 26.0\n", + " 13\n", + " 48 Law and Legal Studies\n", + " 18.5\n", " \n", " \n", - " 133\n", - " 22 Philosophy and Religious Studies\n", - " 304.0\n", + " 69\n", + " 38 Economics\n", + " 87.5\n", " \n", " \n", - " 23\n", - " 18 Law and Legal Studies\n", - " 37.0\n", + " 51\n", + " 43 History, Heritage and Archaeology\n", + " 65.5\n", " \n", " \n", - " 20\n", - " 12 Built Environment and Design\n", - " 32.5\n", + " 18\n", + " 33 Built Environment and Design\n", + " 21.0\n", " \n", " \n", - " 0\n", - " 19 Studies in Creative Arts and Writing\n", - " 1.0\n", + " 6\n", + " 36 Creative Arts and Writing\n", + " 10.0\n", " \n", " \n", "\n", @@ -2948,30 +2956,31 @@ ], "text/plain": [ " for_name rank\n", - "21 11 Medical and Health Sciences 22.0\n", - "103 09 Engineering 107.0\n", - "25 06 Biological Sciences 26.0\n", - "99 08 Information and Computing Sciences 105.0\n", - "161 03 Chemical Sciences 170.5\n", - "142 02 Physical Sciences 150.0\n", - "45 01 Mathematical Sciences 48.5\n", - "9 17 Psychology and Cognitive Sciences 11.5\n", - "32 16 Studies in Human Society 36.0\n", - "66 15 Commerce, Management, Tourism and Services 88.0\n", - "35 20 Language, Communication and Culture 46.0\n", - "17 13 Education 22.5\n", - "196 04 Earth Sciences 230.0\n", - "83 14 Economics 110.0\n", - "263 21 History and Archaeology 579.5\n", - "30 05 Environmental Sciences 34.5\n", - "22 07 Agricultural and Veterinary Sciences 26.0\n", - "133 22 Philosophy and Religious Studies 304.0\n", - "23 18 Law and Legal Studies 37.0\n", - "20 12 Built Environment and Design 32.5\n", - "0 19 Studies in Creative Arts and Writing 1.0" + "15 32 Biomedical and Clinical Sciences 16.0\n", + "173 40 Engineering 177.0\n", + "114 46 Information and Computing Sciences 121.0\n", + "18 31 Biological Sciences 19.0\n", + "12 42 Health Sciences 12.5\n", + "192 34 Chemical Sciences 212.0\n", + "142 51 Physical Sciences 148.0\n", + "10 44 Human Society 12.0\n", + "81 35 Commerce, Management, Tourism and Services 90.5\n", + "125 49 Mathematical Sciences 142.0\n", + "21 30 Agricultural, Veterinary and Food Sciences 23.5\n", + "96 37 Earth Sciences 108.5\n", + "10 47 Language, Communication and Culture 12.0\n", + "6 52 Psychology 7.5\n", + "80 50 Philosophy and Religious Studies 106.5\n", + "8 39 Education 10.0\n", + "19 41 Environmental Sciences 21.5\n", + "13 48 Law and Legal Studies 18.5\n", + "69 38 Economics 87.5\n", + "51 43 History, Heritage and Archaeology 65.5\n", + "18 33 Built Environment and Design 21.0\n", + "6 36 Creative Arts and Writing 10.0" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -3004,7 +3013,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "Collapsed": "false", "colab": {}, @@ -3017,49 +3026,49 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.47s\u001b[0m\n", + "\u001b[2mTime: 4.39s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.12s\u001b[0m\n", + "\u001b[2mTime: 6.05s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.14s\u001b[0m\n", + "\u001b[2mTime: 1.88s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.09s\u001b[0m\n", + "\u001b[2mTime: 1.80s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.97s\u001b[0m\n", + "\u001b[2mTime: 4.84s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.17s\u001b[0m\n", + "\u001b[2mTime: 1.92s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.29s\u001b[0m\n", + "\u001b[2mTime: 3.19s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.11s\u001b[0m\n", + "\u001b[2mTime: 3.04s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.00s\u001b[0m\n", + "\u001b[2mTime: 3.34s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.02s\u001b[0m\n", + "\u001b[2mTime: 2.38s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 5.36s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 1.38s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.03s\u001b[0m\n", + "\u001b[2mTime: 1.30s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 6.38s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 1.41s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.12s\u001b[0m\n", + "\u001b[2mTime: 4.51s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.14s\u001b[0m\n", + "\u001b[2mTime: 1.98s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.15s\u001b[0m\n", + "\u001b[2mTime: 1.47s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.15s\u001b[0m\n", + "\u001b[2mTime: 6.03s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.10s\u001b[0m\n", + "\u001b[2mTime: 1.30s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.97s\u001b[0m\n", + "\u001b[2mTime: 4.47s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.96s\u001b[0m\n" + "\u001b[2mTime: 1.25s\u001b[0m\n" ] } ], @@ -3086,7 +3095,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "Collapsed": "false", "colab": {}, @@ -3100,7 +3109,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "Collapsed": "false", "colab": {}, @@ -3114,7 +3123,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { "Collapsed": "false", "colab": {}, @@ -3152,38 +3161,38 @@ " \n", " \n", " 0\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " Harvard University\n", - " 845\n", - " 16932\n", + " 767\n", + " 15967\n", " \n", " \n", " 1\n", - " 11 Medical and Health Sciences\n", - " University of Toronto\n", - " 392\n", - " 10281\n", + " 32 Biomedical and Clinical Sciences\n", + " Johns Hopkins University\n", + " 356\n", + " 9182\n", " \n", " \n", " 2\n", - " 11 Medical and Health Sciences\n", - " Johns Hopkins University\n", - " 391\n", - " 10120\n", + " 32 Biomedical and Clinical Sciences\n", + " University of Toronto\n", + " 338\n", + " 8932\n", " \n", " \n", " 3\n", - " 11 Medical and Health Sciences\n", - " University of California, San Francisco\n", - " 365\n", - " 7850\n", + " 32 Biomedical and Clinical Sciences\n", + " Mayo Clinic\n", + " 380\n", + " 8507\n", " \n", " \n", " 4\n", - " 11 Medical and Health Sciences\n", - " Mayo Clinic\n", - " 321\n", - " 7659\n", + " 32 Biomedical and Clinical Sciences\n", + " University of California, San Francisco\n", + " 339\n", + " 7477\n", " \n", " \n", " ...\n", @@ -3193,76 +3202,76 @@ " ...\n", " \n", " \n", - " 12220\n", - " 19 Studies in Creative Arts and Writing\n", - " University of Bamberg\n", - " 1\n", + " 13171\n", + " 36 Creative Arts and Writing\n", + " Adobe Inc\n", " 3\n", + " 7\n", " \n", " \n", - " 12221\n", - " 19 Studies in Creative Arts and Writing\n", - " National University of Quilmes\n", + " 13172\n", + " 36 Creative Arts and Writing\n", + " Polytechnic University of Turin\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", - " 12222\n", - " 19 Studies in Creative Arts and Writing\n", - " Czech University of Life Sciences Prague\n", + " 13173\n", + " 36 Creative Arts and Writing\n", + " University of Electronic Science and Technolog...\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", - " 12223\n", - " 19 Studies in Creative Arts and Writing\n", - " University Hospitals of Cleveland\n", + " 13174\n", + " 36 Creative Arts and Writing\n", + " University of Cyprus\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", - " 12224\n", - " 19 Studies in Creative Arts and Writing\n", - " Grinnell College\n", + " 13175\n", + " 36 Creative Arts and Writing\n", + " Broad Institute\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", "\n", - "

12225 rows × 4 columns

\n", + "

13176 rows × 4 columns

\n", "" ], "text/plain": [ - " for_name \\\n", - "0 11 Medical and Health Sciences \n", - "1 11 Medical and Health Sciences \n", - "2 11 Medical and Health Sciences \n", - "3 11 Medical and Health Sciences \n", - "4 11 Medical and Health Sciences \n", - "... ... \n", - "12220 19 Studies in Creative Arts and Writing \n", - "12221 19 Studies in Creative Arts and Writing \n", - "12222 19 Studies in Creative Arts and Writing \n", - "12223 19 Studies in Creative Arts and Writing \n", - "12224 19 Studies in Creative Arts and Writing \n", + " for_name \\\n", + "0 32 Biomedical and Clinical Sciences \n", + "1 32 Biomedical and Clinical Sciences \n", + "2 32 Biomedical and Clinical Sciences \n", + "3 32 Biomedical and Clinical Sciences \n", + "4 32 Biomedical and Clinical Sciences \n", + "... ... \n", + "13171 36 Creative Arts and Writing \n", + "13172 36 Creative Arts and Writing \n", + "13173 36 Creative Arts and Writing \n", + "13174 36 Creative Arts and Writing \n", + "13175 36 Creative Arts and Writing \n", "\n", - " name count count all \n", - "0 Harvard University 845 16932 \n", - "1 University of Toronto 392 10281 \n", - "2 Johns Hopkins University 391 10120 \n", - "3 University of California, San Francisco 365 7850 \n", - "4 Mayo Clinic 321 7659 \n", - "... ... ... ... \n", - "12220 University of Bamberg 1 3 \n", - "12221 National University of Quilmes 1 2 \n", - "12222 Czech University of Life Sciences Prague 1 2 \n", - "12223 University Hospitals of Cleveland 1 2 \n", - "12224 Grinnell College 1 2 \n", + " name count count all \n", + "0 Harvard University 767 15967 \n", + "1 Johns Hopkins University 356 9182 \n", + "2 University of Toronto 338 8932 \n", + "3 Mayo Clinic 380 8507 \n", + "4 University of California, San Francisco 339 7477 \n", + "... ... ... ... \n", + "13171 Adobe Inc 3 7 \n", + "13172 Polytechnic University of Turin 1 7 \n", + "13173 University of Electronic Science and Technolog... 1 7 \n", + "13174 University of Cyprus 1 7 \n", + "13175 Broad Institute 1 7 \n", "\n", - "[12225 rows x 4 columns]" + "[13176 rows x 4 columns]" ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -3284,7 +3293,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "Collapsed": "false", "colab": {}, @@ -3298,7 +3307,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "Collapsed": "false", "colab": {}, @@ -3323,7 +3332,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "Collapsed": "false", "colab": {}, @@ -3358,109 +3367,114 @@ " \n", " \n", " \n", - " 66\n", - " 11 Medical and Health Sciences\n", - " 41.0\n", + " 73\n", + " 32 Biomedical and Clinical Sciences\n", + " 54.5\n", + " \n", + " \n", + " 842\n", + " 40 Engineering\n", + " 205.0\n", " \n", " \n", - " 840\n", - " 09 Engineering\n", - " 138.0\n", + " 1592\n", + " 46 Information and Computing Sciences\n", + " 191.5\n", " \n", " \n", - " 1498\n", - " 06 Biological Sciences\n", - " 100.0\n", + " 2167\n", + " 31 Biological Sciences\n", + " 71.0\n", " \n", " \n", - " 2294\n", - " 08 Information and Computing Sciences\n", - " 475.5\n", + " 2916\n", + " 42 Health Sciences\n", + " 31.0\n", " \n", " \n", - " 2875\n", - " 03 Chemical Sciences\n", - " 93.5\n", + " 3577\n", + " 34 Chemical Sciences\n", + " 47.0\n", " \n", " \n", - " 3512\n", - " 02 Physical Sciences\n", - " 278.5\n", + " 4211\n", + " 51 Physical Sciences\n", + " 315.0\n", " \n", " \n", - " 4277\n", - " 01 Mathematical Sciences\n", - " 117.0\n", + " 4992\n", + " 44 Human Society\n", + " 115.5\n", " \n", " \n", - " 4921\n", - " 17 Psychology and Cognitive Sciences\n", - " 52.5\n", + " 5642\n", + " 35 Commerce, Management, Tourism and Services\n", + " 241.0\n", " \n", " \n", - " 5584\n", - " 16 Studies in Human Society\n", - " 236.5\n", + " 6253\n", + " 49 Mathematical Sciences\n", + " 87.0\n", " \n", " \n", - " 6165\n", - " 15 Commerce, Management, Tourism and Services\n", - " 150.0\n", + " 7112\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 57.0\n", " \n", " \n", - " 6864\n", - " 10 Technology\n", - " 304.0\n", + " 7557\n", + " 37 Earth Sciences\n", + " 285.5\n", " \n", " \n", - " 7312\n", - " 20 Language, Communication and Culture\n", - " 398.5\n", + " 8174\n", + " 47 Language, Communication and Culture\n", + " 422.0\n", " \n", " \n", - " 7785\n", - " 13 Education\n", - " 377.0\n", + " 8696\n", + " 52 Psychology\n", + " 201.0\n", " \n", " \n", - " 8302\n", - " 04 Earth Sciences\n", - " 346.5\n", + " 9362\n", + " 50 Philosophy and Religious Studies\n", + " 422.0\n", " \n", " \n", - " 8940\n", - " 14 Economics\n", - " 310.5\n", + " 9857\n", + " 39 Education\n", + " 255.0\n", " \n", " \n", - " 9467\n", - " 21 History and Archaeology\n", - " 305.0\n", + " 10448\n", + " 41 Environmental Sciences\n", + " 190.0\n", " \n", " \n", - " 10013\n", - " 05 Environmental Sciences\n", - " 123.0\n", + " 11006\n", + " 48 Law and Legal Studies\n", + " 291.5\n", " \n", " \n", - " 10741\n", - " 07 Agricultural and Veterinary Sciences\n", - " 124.5\n", + " 11405\n", + " 38 Economics\n", + " 301.0\n", " \n", " \n", - " 11176\n", - " 22 Philosophy and Religious Studies\n", - " 311.5\n", + " 11884\n", + " 43 History, Heritage and Archaeology\n", + " 255.0\n", " \n", " \n", - " 11503\n", - " 18 Law and Legal Studies\n", - " 196.0\n", + " 12409\n", + " 33 Built Environment and Design\n", + " 428.0\n", " \n", " \n", - " 11823\n", - " 12 Built Environment and Design\n", - " 181.0\n", + " 12841\n", + " 36 Creative Arts and Writing\n", + " 285.0\n", " \n", " \n", "\n", @@ -3468,30 +3482,31 @@ ], "text/plain": [ " for_name percent rank\n", - "66 11 Medical and Health Sciences 41.0\n", - "840 09 Engineering 138.0\n", - "1498 06 Biological Sciences 100.0\n", - "2294 08 Information and Computing Sciences 475.5\n", - "2875 03 Chemical Sciences 93.5\n", - "3512 02 Physical Sciences 278.5\n", - "4277 01 Mathematical Sciences 117.0\n", - "4921 17 Psychology and Cognitive Sciences 52.5\n", - "5584 16 Studies in Human Society 236.5\n", - "6165 15 Commerce, Management, Tourism and Services 150.0\n", - "6864 10 Technology 304.0\n", - "7312 20 Language, Communication and Culture 398.5\n", - "7785 13 Education 377.0\n", - "8302 04 Earth Sciences 346.5\n", - "8940 14 Economics 310.5\n", - "9467 21 History and Archaeology 305.0\n", - "10013 05 Environmental Sciences 123.0\n", - "10741 07 Agricultural and Veterinary Sciences 124.5\n", - "11176 22 Philosophy and Religious Studies 311.5\n", - "11503 18 Law and Legal Studies 196.0\n", - "11823 12 Built Environment and Design 181.0" + "73 32 Biomedical and Clinical Sciences 54.5\n", + "842 40 Engineering 205.0\n", + "1592 46 Information and Computing Sciences 191.5\n", + "2167 31 Biological Sciences 71.0\n", + "2916 42 Health Sciences 31.0\n", + "3577 34 Chemical Sciences 47.0\n", + "4211 51 Physical Sciences 315.0\n", + "4992 44 Human Society 115.5\n", + "5642 35 Commerce, Management, Tourism and Services 241.0\n", + "6253 49 Mathematical Sciences 87.0\n", + "7112 30 Agricultural, Veterinary and Food Sciences 57.0\n", + "7557 37 Earth Sciences 285.5\n", + "8174 47 Language, Communication and Culture 422.0\n", + "8696 52 Psychology 201.0\n", + "9362 50 Philosophy and Religious Studies 422.0\n", + "9857 39 Education 255.0\n", + "10448 41 Environmental Sciences 190.0\n", + "11006 48 Law and Legal Studies 291.5\n", + "11405 38 Economics 301.0\n", + "11884 43 History, Heritage and Archaeology 255.0\n", + "12409 33 Built Environment and Design 428.0\n", + "12841 36 Creative Arts and Writing 285.0" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -3502,7 +3517,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": { "Collapsed": "false", "colab": {}, @@ -3536,84 +3551,17 @@ " \n", " \n", " \n", - " \n", - " 0\n", - " Harvard University\n", - " 61.5\n", - " \n", - " \n", - " 1\n", - " University of Toronto\n", - " 236.5\n", - " \n", - " \n", - " 2\n", - " Johns Hopkins University\n", - " 220.0\n", - " \n", - " \n", - " 3\n", - " University of California, San Francisco\n", - " 93.5\n", - " \n", - " \n", - " 4\n", - " Mayo Clinic\n", - " 152.0\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 780\n", - " University of Bath\n", - " 425.0\n", - " \n", - " \n", - " 781\n", - " Kuopio University Hospital\n", - " 250.5\n", - " \n", - " \n", - " 782\n", - " Marqués de Valdecilla University Hospital\n", - " 299.0\n", - " \n", - " \n", - " 783\n", - " Policlinico San Matteo Fondazione\n", - " 114.5\n", - " \n", - " \n", - " 784\n", - " Centre Hospitalier Universitaire de Caen\n", - " 351.5\n", - " \n", " \n", "\n", - "

785 rows × 2 columns

\n", "" ], "text/plain": [ - " name percent rank\n", - "0 Harvard University 61.5\n", - "1 University of Toronto 236.5\n", - "2 Johns Hopkins University 220.0\n", - "3 University of California, San Francisco 93.5\n", - "4 Mayo Clinic 152.0\n", - ".. ... ...\n", - "780 University of Bath 425.0\n", - "781 Kuopio University Hospital 250.5\n", - "782 Marqués de Valdecilla University Hospital 299.0\n", - "783 Policlinico San Matteo Fondazione 114.5\n", - "784 Centre Hospitalier Universitaire de Caen 351.5\n", - "\n", - "[785 rows x 2 columns]" + "Empty DataFrame\n", + "Columns: [name, percent rank]\n", + "Index: []" ] }, - "execution_count": 35, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -3646,7 +3594,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": { "Collapsed": "false", "colab": {}, @@ -3665,7 +3613,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": { "Collapsed": "false", "colab": {}, @@ -3679,7 +3627,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { "Collapsed": "false", "colab": {}, @@ -3714,13 +3662,14 @@ " reference count all\n", " id\n", " count all\n", + " name\n", " city_name\n", " count\n", - " country_name\n", + " country_code\n", + " ...\n", " latitude\n", " linkout\n", " longitude\n", - " name\n", " state_name\n", " types\n", " acronym\n", @@ -3732,119 +3681,124 @@ " \n", " \n", " \n", - " 15700\n", + " 17756\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", + " 80003\n", + " 4797\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", + " US\n", + " ...\n", " 42.377052\n", " [http://www.harvard.edu/]\n", " -71.116650\n", - " Harvard University\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", - " \n", - " \n", - " 15701\n", - " grid.1008.9\n", - " University of Melbourne\n", - " 2211\n", - " 5457\n", - " grid.17063.33\n", - " 10281\n", - " Toronto\n", - " 392\n", - " Canada\n", - " 43.661667\n", - " [http://www.utoronto.ca/]\n", - " -79.395000\n", - " University of Toronto\n", - " Ontario\n", - " [Education]\n", - " NaN\n", - " 11 Medical and Health Sciences\n", - " 2.0\n", - " 3.81\n", - " 236.5\n", + " 4.80\n", + " 63.5\n", " \n", " \n", - " 15702\n", + " 17757\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", + " 80003\n", + " 4797\n", " grid.21107.35\n", - " 10120\n", + " 9182\n", + " Johns Hopkins University\n", " Baltimore\n", - " 391\n", - " United States\n", + " 356\n", + " US\n", + " ...\n", " 39.328888\n", " [https://www.jhu.edu/]\n", " -76.620280\n", - " Johns Hopkins University\n", " Maryland\n", " [Education]\n", " JHU\n", - " 11 Medical and Health Sciences\n", - " 3.0\n", - " 3.86\n", - " 220.0\n", + " 32 Biomedical and Clinical Sciences\n", + " 4.0\n", + " 3.88\n", + " 195.5\n", " \n", " \n", - " 15703\n", + " 17758\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", - " grid.266102.1\n", - " 7850\n", - " San Francisco\n", - " 365\n", - " United States\n", - " 37.762800\n", - " [https://www.ucsf.edu/]\n", - " -122.457670\n", - " University of California, San Francisco\n", - " California\n", + " 80003\n", + " 4797\n", + " grid.17063.33\n", + " 8932\n", + " University of Toronto\n", + " Toronto\n", + " 338\n", + " CA\n", + " ...\n", + " 43.661667\n", + " [http://www.utoronto.ca/]\n", + " -79.395000\n", + " Ontario\n", " [Education]\n", - " UCSF\n", - " 11 Medical and Health Sciences\n", - " 6.0\n", - " 4.65\n", - " 93.5\n", + " NaN\n", + " 32 Biomedical and Clinical Sciences\n", + " 8.0\n", + " 3.78\n", + " 220.5\n", " \n", " \n", - " 15704\n", + " 17759\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", + " 80003\n", + " 4797\n", " grid.66875.3a\n", - " 7659\n", + " 8507\n", + " Mayo Clinic\n", " Rochester\n", - " 321\n", - " United States\n", + " 380\n", + " US\n", + " ...\n", " 44.024070\n", " [http://www.mayoclinic.org/patient-visitor-gui...\n", " -92.466310\n", - " Mayo Clinic\n", " Minnesota\n", " [Healthcare]\n", " NaN\n", - " 11 Medical and Health Sciences\n", - " 10.0\n", - " 4.19\n", - " 152.0\n", + " 32 Biomedical and Clinical Sciences\n", + " 3.0\n", + " 4.47\n", + " 96.5\n", + " \n", + " \n", + " 17760\n", + " grid.1008.9\n", + " University of Melbourne\n", + " 80003\n", + " 4797\n", + " grid.266102.1\n", + " 7477\n", + " University of California, San Francisco\n", + " San Francisco\n", + " 339\n", + " US\n", + " ...\n", + " 37.762800\n", + " [https://www.ucsf.edu/]\n", + " -122.457670\n", + " California\n", + " [Education]\n", + " UCSF\n", + " 32 Biomedical and Clinical Sciences\n", + " 6.5\n", + " 4.53\n", + " 89.0\n", " \n", " \n", " ...\n", @@ -3868,210 +3822,242 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", - " 7350128\n", + " 8082796\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.7359.8\n", + " 80007\n", + " 126\n", + " grid.467212.4\n", + " 7\n", + " Adobe Inc\n", + " San Jose\n", " 3\n", - " Bamberg\n", - " 1\n", - " Germany\n", - " 49.893845\n", - " [https://www.uni-bamberg.de/]\n", - " 10.886044\n", - " University of Bamberg\n", + " US\n", + " ...\n", " NaN\n", - " [Education]\n", + " [https://www.adobe.com/]\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 33.33\n", - " 8.0\n", + " California\n", + " [Company]\n", + " NaN\n", + " 36 Creative Arts and Writing\n", + " 59.0\n", + " 42.86\n", + " 1.0\n", " \n", " \n", - " 7350129\n", + " 8082797\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.11560.33\n", - " 2\n", - " Bernal\n", + " 80007\n", + " 126\n", + " grid.4800.c\n", + " 7\n", + " Polytechnic University of Turin\n", + " Turin\n", " 1\n", - " Argentina\n", - " -34.706670\n", - " [http://www.unq.edu.ar/english/sections/158-unq/]\n", - " -58.277500\n", - " National University of Quilmes\n", - " NaN\n", + " IT\n", + " ...\n", + " 45.063095\n", + " [http://www.polito.it/]\n", + " 7.661075\n", + " Piemonte\n", " [Education]\n", - " UNQ\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " NaN\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", - " 7350130\n", + " 8082798\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.15866.3c\n", - " 2\n", - " Prague\n", + " 80007\n", + " 126\n", + " grid.54549.39\n", + " 7\n", + " University of Electronic Science and Technolog...\n", + " Chengdu\n", " 1\n", - " Czechia\n", - " 50.131460\n", - " [http://www.czu.cz/en/]\n", - " 14.373258\n", - " Czech University of Life Sciences Prague\n", + " CN\n", + " ...\n", + " 30.675713\n", + " [http://en.uestc.edu.cn/]\n", + " 104.100270\n", " NaN\n", " [Education]\n", - " CULS\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " UESTC\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", - " 7350131\n", + " 8082799\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.241104.2\n", - " 2\n", - " Cleveland\n", + " 80007\n", + " 126\n", + " grid.6603.3\n", + " 7\n", + " University of Cyprus\n", + " Nicosia\n", " 1\n", - " United States\n", - " 41.506096\n", - " [http://www.uhhospitals.org/]\n", - " -81.604820\n", - " University Hospitals of Cleveland\n", - " Ohio\n", - " [Healthcare]\n", + " CY\n", + " ...\n", + " 35.160270\n", + " [http://www.ucy.ac.cy/en/]\n", + " 33.376976\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " [Education]\n", + " UCY\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", - " 7350132\n", + " 8082800\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.256592.f\n", - " 2\n", - " Grinnell\n", + " 80007\n", + " 126\n", + " grid.66859.34\n", + " 7\n", + " Broad Institute\n", + " Cambridge\n", " 1\n", - " United States\n", - " 41.749737\n", - " [http://www.grinnell.edu/]\n", - " -92.719505\n", - " Grinnell College\n", - " Iowa\n", - " [Education]\n", + " US\n", + " ...\n", + " 42.367890\n", + " [http://www.broadinstitute.org/]\n", + " -71.087030\n", + " Massachusetts\n", + " [Nonprofit]\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", "\n", - "

11685 rows × 20 columns

\n", + "

13176 rows × 21 columns

\n", "" ], "text/plain": [ " reference id reference name for_id reference count all \\\n", - "15700 grid.1008.9 University of Melbourne 2211 5457 \n", - "15701 grid.1008.9 University of Melbourne 2211 5457 \n", - "15702 grid.1008.9 University of Melbourne 2211 5457 \n", - "15703 grid.1008.9 University of Melbourne 2211 5457 \n", - "15704 grid.1008.9 University of Melbourne 2211 5457 \n", + "17756 grid.1008.9 University of Melbourne 80003 4797 \n", + "17757 grid.1008.9 University of Melbourne 80003 4797 \n", + "17758 grid.1008.9 University of Melbourne 80003 4797 \n", + "17759 grid.1008.9 University of Melbourne 80003 4797 \n", + "17760 grid.1008.9 University of Melbourne 80003 4797 \n", "... ... ... ... ... \n", - "7350128 grid.1008.9 University of Melbourne 2219 85 \n", - "7350129 grid.1008.9 University of Melbourne 2219 85 \n", - "7350130 grid.1008.9 University of Melbourne 2219 85 \n", - "7350131 grid.1008.9 University of Melbourne 2219 85 \n", - "7350132 grid.1008.9 University of Melbourne 2219 85 \n", + "8082796 grid.1008.9 University of Melbourne 80007 126 \n", + "8082797 grid.1008.9 University of Melbourne 80007 126 \n", + "8082798 grid.1008.9 University of Melbourne 80007 126 \n", + "8082799 grid.1008.9 University of Melbourne 80007 126 \n", + "8082800 grid.1008.9 University of Melbourne 80007 126 \n", "\n", - " id count all city_name count country_name \\\n", - "15700 grid.38142.3c 16932 Cambridge 845 United States \n", - "15701 grid.17063.33 10281 Toronto 392 Canada \n", - "15702 grid.21107.35 10120 Baltimore 391 United States \n", - "15703 grid.266102.1 7850 San Francisco 365 United States \n", - "15704 grid.66875.3a 7659 Rochester 321 United States \n", - "... ... ... ... ... ... \n", - "7350128 grid.7359.8 3 Bamberg 1 Germany \n", - "7350129 grid.11560.33 2 Bernal 1 Argentina \n", - "7350130 grid.15866.3c 2 Prague 1 Czechia \n", - "7350131 grid.241104.2 2 Cleveland 1 United States \n", - "7350132 grid.256592.f 2 Grinnell 1 United States \n", + " id count all \\\n", + "17756 grid.38142.3c 15967 \n", + "17757 grid.21107.35 9182 \n", + "17758 grid.17063.33 8932 \n", + "17759 grid.66875.3a 8507 \n", + "17760 grid.266102.1 7477 \n", + "... ... ... \n", + "8082796 grid.467212.4 7 \n", + "8082797 grid.4800.c 7 \n", + "8082798 grid.54549.39 7 \n", + "8082799 grid.6603.3 7 \n", + "8082800 grid.66859.34 7 \n", "\n", - " latitude linkout \\\n", - "15700 42.377052 [http://www.harvard.edu/] \n", - "15701 43.661667 [http://www.utoronto.ca/] \n", - "15702 39.328888 [https://www.jhu.edu/] \n", - "15703 37.762800 [https://www.ucsf.edu/] \n", - "15704 44.024070 [http://www.mayoclinic.org/patient-visitor-gui... \n", - "... ... ... \n", - "7350128 49.893845 [https://www.uni-bamberg.de/] \n", - "7350129 -34.706670 [http://www.unq.edu.ar/english/sections/158-unq/] \n", - "7350130 50.131460 [http://www.czu.cz/en/] \n", - "7350131 41.506096 [http://www.uhhospitals.org/] \n", - "7350132 41.749737 [http://www.grinnell.edu/] \n", + " name city_name \\\n", + "17756 Harvard University Cambridge \n", + "17757 Johns Hopkins University Baltimore \n", + "17758 University of Toronto Toronto \n", + "17759 Mayo Clinic Rochester \n", + "17760 University of California, San Francisco San Francisco \n", + "... ... ... \n", + "8082796 Adobe Inc San Jose \n", + "8082797 Polytechnic University of Turin Turin \n", + "8082798 University of Electronic Science and Technolog... Chengdu \n", + "8082799 University of Cyprus Nicosia \n", + "8082800 Broad Institute Cambridge \n", + "\n", + " count country_code ... latitude \\\n", + "17756 767 US ... 42.377052 \n", + "17757 356 US ... 39.328888 \n", + "17758 338 CA ... 43.661667 \n", + "17759 380 US ... 44.024070 \n", + "17760 339 US ... 37.762800 \n", + "... ... ... ... ... \n", + "8082796 3 US ... NaN \n", + "8082797 1 IT ... 45.063095 \n", + "8082798 1 CN ... 30.675713 \n", + "8082799 1 CY ... 35.160270 \n", + "8082800 1 US ... 42.367890 \n", "\n", - " longitude name state_name \\\n", - "15700 -71.116650 Harvard University Massachusetts \n", - "15701 -79.395000 University of Toronto Ontario \n", - "15702 -76.620280 Johns Hopkins University Maryland \n", - "15703 -122.457670 University of California, San Francisco California \n", - "15704 -92.466310 Mayo Clinic Minnesota \n", - "... ... ... ... \n", - "7350128 10.886044 University of Bamberg NaN \n", - "7350129 -58.277500 National University of Quilmes NaN \n", - "7350130 14.373258 Czech University of Life Sciences Prague NaN \n", - "7350131 -81.604820 University Hospitals of Cleveland Ohio \n", - "7350132 -92.719505 Grinnell College Iowa \n", + " linkout longitude \\\n", + "17756 [http://www.harvard.edu/] -71.116650 \n", + "17757 [https://www.jhu.edu/] -76.620280 \n", + "17758 [http://www.utoronto.ca/] -79.395000 \n", + "17759 [http://www.mayoclinic.org/patient-visitor-gui... -92.466310 \n", + "17760 [https://www.ucsf.edu/] -122.457670 \n", + "... ... ... \n", + "8082796 [https://www.adobe.com/] NaN \n", + "8082797 [http://www.polito.it/] 7.661075 \n", + "8082798 [http://en.uestc.edu.cn/] 104.100270 \n", + "8082799 [http://www.ucy.ac.cy/en/] 33.376976 \n", + "8082800 [http://www.broadinstitute.org/] -71.087030 \n", "\n", - " types acronym for_name rank \\\n", - "15700 [Education] NaN 11 Medical and Health Sciences 1.0 \n", - "15701 [Education] NaN 11 Medical and Health Sciences 2.0 \n", - "15702 [Education] JHU 11 Medical and Health Sciences 3.0 \n", - "15703 [Education] UCSF 11 Medical and Health Sciences 6.0 \n", - "15704 [Healthcare] NaN 11 Medical and Health Sciences 10.0 \n", - "... ... ... ... ... \n", - "7350128 [Education] NaN 19 Studies in Creative Arts and Writing 130.0 \n", - "7350129 [Education] UNQ 19 Studies in Creative Arts and Writing 130.0 \n", - "7350130 [Education] CULS 19 Studies in Creative Arts and Writing 130.0 \n", - "7350131 [Healthcare] NaN 19 Studies in Creative Arts and Writing 130.0 \n", - "7350132 [Education] NaN 19 Studies in Creative Arts and Writing 130.0 \n", + " state_name types acronym \\\n", + "17756 Massachusetts [Education] NaN \n", + "17757 Maryland [Education] JHU \n", + "17758 Ontario [Education] NaN \n", + "17759 Minnesota [Healthcare] NaN \n", + "17760 California [Education] UCSF \n", + "... ... ... ... \n", + "8082796 California [Company] NaN \n", + "8082797 Piemonte [Education] NaN \n", + "8082798 NaN [Education] UESTC \n", + "8082799 NaN [Education] UCY \n", + "8082800 Massachusetts [Nonprofit] NaN \n", "\n", - " percentage top 1 percent rank \n", - "15700 4.99 61.5 \n", - "15701 3.81 236.5 \n", - "15702 3.86 220.0 \n", - "15703 4.65 93.5 \n", - "15704 4.19 152.0 \n", - "... ... ... \n", - "7350128 33.33 8.0 \n", - "7350129 50.00 2.5 \n", - "7350130 50.00 2.5 \n", - "7350131 50.00 2.5 \n", - "7350132 50.00 2.5 \n", + " for_name rank percentage top 1 \\\n", + "17756 32 Biomedical and Clinical Sciences 1.0 4.80 \n", + "17757 32 Biomedical and Clinical Sciences 4.0 3.88 \n", + "17758 32 Biomedical and Clinical Sciences 8.0 3.78 \n", + "17759 32 Biomedical and Clinical Sciences 3.0 4.47 \n", + "17760 32 Biomedical and Clinical Sciences 6.5 4.53 \n", + "... ... ... ... \n", + "8082796 36 Creative Arts and Writing 59.0 42.86 \n", + "8082797 36 Creative Arts and Writing 350.5 14.29 \n", + "8082798 36 Creative Arts and Writing 350.5 14.29 \n", + "8082799 36 Creative Arts and Writing 350.5 14.29 \n", + "8082800 36 Creative Arts and Writing 350.5 14.29 \n", "\n", - "[11685 rows x 20 columns]" + " percent rank \n", + "17756 63.5 \n", + "17757 195.5 \n", + "17758 220.5 \n", + "17759 96.5 \n", + "17760 89.0 \n", + "... ... \n", + "8082796 1.0 \n", + "8082797 34.5 \n", + "8082798 34.5 \n", + "8082799 34.5 \n", + "8082800 34.5 \n", + "\n", + "[13176 rows x 21 columns]" ] }, - "execution_count": 38, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -4082,7 +4068,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": { "Collapsed": "false", "colab": {}, @@ -4098,7 +4084,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": { "Collapsed": "false", "colab": {}, @@ -4114,7 +4100,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": { "Collapsed": "false", "colab": {}, @@ -4152,172 +4138,180 @@ " \n", " \n", " \n", - " 15720\n", + " 17779\n", " grid.1008.9\n", - " 2211\n", + " 80003\n", " University of Melbourne\n", - " 11 Medical and Health Sciences\n", - " 15.5\n", + " 32 Biomedical and Clinical Sciences\n", + " 6.0\n", " \n", " \n", - " 738709\n", + " 733648\n", " grid.1008.9\n", - " 2209\n", + " 80011\n", " University of Melbourne\n", - " 09 Engineering\n", - " 40.0\n", + " 40 Engineering\n", + " 85.0\n", " \n", " \n", - " 1131875\n", + " 1168804\n", " grid.1008.9\n", - " 2206\n", + " 80017\n", " University of Melbourne\n", - " 06 Biological Sciences\n", - " 13.0\n", + " 46 Information and Computing Sciences\n", + " 51.0\n", " \n", " \n", - " 1643602\n", + " 1578085\n", " grid.1008.9\n", - " 2208\n", + " 80002\n", " University of Melbourne\n", - " 08 Information and Computing Sciences\n", - " 45.0\n", + " 31 Biological Sciences\n", + " 11.0\n", " \n", " \n", - " 2140491\n", + " 2046557\n", " grid.1008.9\n", - " 2203\n", + " 80013\n", " University of Melbourne\n", - " 03 Chemical Sciences\n", - " 87.5\n", + " 42 Health Sciences\n", + " 5.0\n", " \n", " \n", - " 2627450\n", + " 2644004\n", " grid.1008.9\n", - " 2202\n", + " 80005\n", " University of Melbourne\n", - " 02 Physical Sciences\n", - " 49.0\n", + " 34 Chemical Sciences\n", + " 98.0\n", " \n", " \n", - " 3094798\n", + " 3128285\n", " grid.1008.9\n", - " 2201\n", + " 80022\n", " University of Melbourne\n", - " 01 Mathematical Sciences\n", - " 18.0\n", + " 51 Physical Sciences\n", + " 46.0\n", " \n", " \n", - " 3454060\n", + " 3570941\n", " grid.1008.9\n", - " 2217\n", + " 80015\n", " University of Melbourne\n", - " 17 Psychology and Cognitive Sciences\n", + " 44 Human Society\n", " 4.0\n", " \n", " \n", - " 3909944\n", + " 3958749\n", " grid.1008.9\n", - " 2216\n", + " 80006\n", " University of Melbourne\n", - " 16 Studies in Human Society\n", - " 4.0\n", + " 35 Commerce, Management, Tourism and Services\n", + " 22.0\n", " \n", " \n", - " 4225053\n", + " 4403670\n", " grid.1008.9\n", - " 2215\n", + " 80020\n", " University of Melbourne\n", - " 15 Commerce, Management, Tourism and Services\n", - " 9.0\n", + " 49 Mathematical Sciences\n", + " 37.0\n", " \n", " \n", - " 4915245\n", + " 4832701\n", " grid.1008.9\n", - " 2220\n", + " 80001\n", " University of Melbourne\n", - " 20 Language, Communication and Culture\n", - " 3.0\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 8.0\n", " \n", " \n", - " 5111347\n", + " 5224547\n", " grid.1008.9\n", - " 2213\n", + " 80008\n", " University of Melbourne\n", - " 13 Education\n", - " 8.0\n", + " 37 Earth Sciences\n", + " 44.0\n", " \n", " \n", - " 5429203\n", + " 5606498\n", " grid.1008.9\n", - " 2204\n", + " 80018\n", " University of Melbourne\n", - " 04 Earth Sciences\n", - " 64.0\n", + " 47 Language, Communication and Culture\n", + " 4.0\n", " \n", " \n", - " 5817193\n", + " 5864420\n", " grid.1008.9\n", - " 2214\n", + " 80023\n", " University of Melbourne\n", - " 14 Economics\n", - " 13.0\n", + " 52 Psychology\n", + " 2.0\n", " \n", " \n", - " 6111107\n", + " 6341779\n", " grid.1008.9\n", - " 2221\n", + " 80021\n", " University of Melbourne\n", - " 21 History and Archaeology\n", - " 22.0\n", + " 50 Philosophy and Religious Studies\n", + " 25.0\n", " \n", " \n", - " 6352914\n", + " 6535541\n", " grid.1008.9\n", - " 2205\n", + " 80010\n", " University of Melbourne\n", - " 05 Environmental Sciences\n", - " 6.0\n", + " 39 Education\n", + " 2.0\n", " \n", " \n", - " 6797399\n", + " 6853435\n", " grid.1008.9\n", - " 2207\n", + " 80012\n", " University of Melbourne\n", - " 07 Agricultural and Veterinary Sciences\n", - " 9.0\n", + " 41 Environmental Sciences\n", + " 5.0\n", " \n", " \n", - " 7091867\n", + " 7239785\n", " grid.1008.9\n", - " 2222\n", + " 80019\n", " University of Melbourne\n", - " 22 Philosophy and Religious Studies\n", - " 13.0\n", + " 48 Law and Legal Studies\n", + " 2.5\n", " \n", " \n", - " 7194418\n", + " 7398600\n", " grid.1008.9\n", - " 2218\n", + " 80009\n", " University of Melbourne\n", - " 18 Law and Legal Studies\n", - " 4.0\n", + " 38 Economics\n", + " 24.5\n", " \n", " \n", - " 7281134\n", + " 7643598\n", " grid.1008.9\n", - " 2212\n", + " 80014\n", " University of Melbourne\n", - " 12 Built Environment and Design\n", - " 7.0\n", + " 43 History, Heritage and Archaeology\n", + " 14.0\n", " \n", " \n", - " 7349969\n", + " 7880154\n", " grid.1008.9\n", - " 2219\n", + " 80004\n", " University of Melbourne\n", - " 19 Studies in Creative Arts and Writing\n", - " 1.0\n", + " 33 Built Environment and Design\n", + " 8.0\n", + " \n", + " \n", + " 8082459\n", + " grid.1008.9\n", + " 80007\n", + " University of Melbourne\n", + " 36 Creative Arts and Writing\n", + " 2.0\n", " \n", " \n", "\n", @@ -4325,53 +4319,55 @@ ], "text/plain": [ " id for_id name \\\n", - "15720 grid.1008.9 2211 University of Melbourne \n", - "738709 grid.1008.9 2209 University of Melbourne \n", - "1131875 grid.1008.9 2206 University of Melbourne \n", - "1643602 grid.1008.9 2208 University of Melbourne \n", - "2140491 grid.1008.9 2203 University of Melbourne \n", - "2627450 grid.1008.9 2202 University of Melbourne \n", - "3094798 grid.1008.9 2201 University of Melbourne \n", - "3454060 grid.1008.9 2217 University of Melbourne \n", - "3909944 grid.1008.9 2216 University of Melbourne \n", - "4225053 grid.1008.9 2215 University of Melbourne \n", - "4915245 grid.1008.9 2220 University of Melbourne \n", - "5111347 grid.1008.9 2213 University of Melbourne \n", - "5429203 grid.1008.9 2204 University of Melbourne \n", - "5817193 grid.1008.9 2214 University of Melbourne \n", - "6111107 grid.1008.9 2221 University of Melbourne \n", - "6352914 grid.1008.9 2205 University of Melbourne \n", - "6797399 grid.1008.9 2207 University of Melbourne \n", - "7091867 grid.1008.9 2222 University of Melbourne \n", - "7194418 grid.1008.9 2218 University of Melbourne \n", - "7281134 grid.1008.9 2212 University of Melbourne \n", - "7349969 grid.1008.9 2219 University of Melbourne \n", + "17779 grid.1008.9 80003 University of Melbourne \n", + "733648 grid.1008.9 80011 University of Melbourne \n", + "1168804 grid.1008.9 80017 University of Melbourne \n", + "1578085 grid.1008.9 80002 University of Melbourne \n", + "2046557 grid.1008.9 80013 University of Melbourne \n", + "2644004 grid.1008.9 80005 University of Melbourne \n", + "3128285 grid.1008.9 80022 University of Melbourne \n", + "3570941 grid.1008.9 80015 University of Melbourne \n", + "3958749 grid.1008.9 80006 University of Melbourne \n", + "4403670 grid.1008.9 80020 University of Melbourne \n", + "4832701 grid.1008.9 80001 University of Melbourne \n", + "5224547 grid.1008.9 80008 University of Melbourne \n", + "5606498 grid.1008.9 80018 University of Melbourne \n", + "5864420 grid.1008.9 80023 University of Melbourne \n", + "6341779 grid.1008.9 80021 University of Melbourne \n", + "6535541 grid.1008.9 80010 University of Melbourne \n", + "6853435 grid.1008.9 80012 University of Melbourne \n", + "7239785 grid.1008.9 80019 University of Melbourne \n", + "7398600 grid.1008.9 80009 University of Melbourne \n", + "7643598 grid.1008.9 80014 University of Melbourne \n", + "7880154 grid.1008.9 80004 University of Melbourne \n", + "8082459 grid.1008.9 80007 University of Melbourne \n", "\n", " for_name filtered percent rank \n", - "15720 11 Medical and Health Sciences 15.5 \n", - "738709 09 Engineering 40.0 \n", - "1131875 06 Biological Sciences 13.0 \n", - "1643602 08 Information and Computing Sciences 45.0 \n", - "2140491 03 Chemical Sciences 87.5 \n", - "2627450 02 Physical Sciences 49.0 \n", - "3094798 01 Mathematical Sciences 18.0 \n", - "3454060 17 Psychology and Cognitive Sciences 4.0 \n", - "3909944 16 Studies in Human Society 4.0 \n", - "4225053 15 Commerce, Management, Tourism and Services 9.0 \n", - "4915245 20 Language, Communication and Culture 3.0 \n", - "5111347 13 Education 8.0 \n", - "5429203 04 Earth Sciences 64.0 \n", - "5817193 14 Economics 13.0 \n", - "6111107 21 History and Archaeology 22.0 \n", - "6352914 05 Environmental Sciences 6.0 \n", - "6797399 07 Agricultural and Veterinary Sciences 9.0 \n", - "7091867 22 Philosophy and Religious Studies 13.0 \n", - "7194418 18 Law and Legal Studies 4.0 \n", - "7281134 12 Built Environment and Design 7.0 \n", - "7349969 19 Studies in Creative Arts and Writing 1.0 " + "17779 32 Biomedical and Clinical Sciences 6.0 \n", + "733648 40 Engineering 85.0 \n", + "1168804 46 Information and Computing Sciences 51.0 \n", + "1578085 31 Biological Sciences 11.0 \n", + "2046557 42 Health Sciences 5.0 \n", + "2644004 34 Chemical Sciences 98.0 \n", + "3128285 51 Physical Sciences 46.0 \n", + "3570941 44 Human Society 4.0 \n", + "3958749 35 Commerce, Management, Tourism and Services 22.0 \n", + "4403670 49 Mathematical Sciences 37.0 \n", + "4832701 30 Agricultural, Veterinary and Food Sciences 8.0 \n", + "5224547 37 Earth Sciences 44.0 \n", + "5606498 47 Language, Communication and Culture 4.0 \n", + "5864420 52 Psychology 2.0 \n", + "6341779 50 Philosophy and Religious Studies 25.0 \n", + "6535541 39 Education 2.0 \n", + "6853435 41 Environmental Sciences 5.0 \n", + "7239785 48 Law and Legal Studies 2.5 \n", + "7398600 38 Economics 24.5 \n", + "7643598 43 History, Heritage and Archaeology 14.0 \n", + "7880154 33 Built Environment and Design 8.0 \n", + "8082459 36 Creative Arts and Writing 2.0 " ] }, - "execution_count": 41, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -4412,7 +4408,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": { "Collapsed": "false", "colab": {}, @@ -4432,7 +4428,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": { "Collapsed": "false", "colab": {}, @@ -4446,7 +4442,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": { "Collapsed": "false", "colab": {}, @@ -4460,7 +4456,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": { "Collapsed": "false", "colab": {}, @@ -4494,69 +4490,17 @@ " \n", " \n", " \n", - " \n", - " 515\n", - " University of Michigan\n", - " 24.5\n", - " \n", - " \n", - " 524\n", - " Karolinska Institute\n", - " 24.5\n", - " \n", - " \n", - " 523\n", - " Emory University\n", - " 26.0\n", - " \n", - " \n", - " 528\n", - " University of Pittsburgh\n", - " 27.0\n", - " \n", - " \n", - " 521\n", - " University of Sydney\n", - " 28.0\n", - " \n", - " \n", - " 538\n", - " Monash University\n", - " 29.0\n", - " \n", - " \n", - " 533\n", - " University of British Columbia\n", - " 30.0\n", - " \n", - " \n", - " 520\n", - " University of São Paulo\n", - " 31.0\n", - " \n", - " \n", - " 534\n", - " Shanghai Jiao Tong University\n", - " 32.0\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " name filtered percent rank\n", - "515 University of Michigan 24.5\n", - "524 Karolinska Institute 24.5\n", - "523 Emory University 26.0\n", - "528 University of Pittsburgh 27.0\n", - "521 University of Sydney 28.0\n", - "538 Monash University 29.0\n", - "533 University of British Columbia 30.0\n", - "520 University of São Paulo 31.0\n", - "534 Shanghai Jiao Tong University 32.0" + "Empty DataFrame\n", + "Columns: [name, filtered percent rank]\n", + "Index: []" ] }, - "execution_count": 45, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -4578,7 +4522,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": { "Collapsed": "false", "colab": {}, @@ -4614,11 +4558,11 @@ " reference count all\n", " id\n", " count all\n", + " name\n", " city_name\n", " count\n", - " country_name\n", " ...\n", - " name\n", + " longitude\n", " state_name\n", " types\n", " acronym\n", @@ -4634,122 +4578,122 @@ " \n", " 0\n", " grid.38142.3c\n", - " 2211\n", + " 80003\n", " 1.0\n", " Harvard University\n", - " 16932\n", + " 15967\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", " ...\n", - " Harvard University\n", + " -71.116650\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", + " 4.80\n", + " 63.5\n", " 1.0\n", " 0.0\n", " \n", " \n", " 1\n", - " grid.17063.33\n", - " 2211\n", + " grid.21107.35\n", + " 80003\n", " 2.0\n", - " University of Toronto\n", - " 10281\n", + " Johns Hopkins University\n", + " 9182\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", " ...\n", - " Harvard University\n", + " -71.116650\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", + " 4.80\n", + " 63.5\n", " 1.0\n", " -1.0\n", " \n", " \n", " 2\n", - " grid.17063.33\n", - " 2211\n", + " grid.21107.35\n", + " 80003\n", " 2.0\n", - " University of Toronto\n", - " 10281\n", - " grid.17063.33\n", - " 10281\n", - " Toronto\n", - " 392\n", - " Canada\n", + " Johns Hopkins University\n", + " 9182\n", + " grid.21107.35\n", + " 9182\n", + " Johns Hopkins University\n", + " Baltimore\n", + " 356\n", " ...\n", - " University of Toronto\n", - " Ontario\n", + " -76.620280\n", + " Maryland\n", " [Education]\n", - " NaN\n", - " 11 Medical and Health Sciences\n", - " 2.0\n", - " 3.81\n", - " 236.5\n", + " JHU\n", + " 32 Biomedical and Clinical Sciences\n", + " 4.0\n", + " 3.88\n", + " 195.5\n", " 2.0\n", " 0.0\n", " \n", " \n", " 3\n", - " grid.21107.35\n", - " 2211\n", - " 2.0\n", - " Johns Hopkins University\n", - " 10120\n", + " grid.17063.33\n", + " 80003\n", + " 3.0\n", + " University of Toronto\n", + " 8932\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", " ...\n", - " Harvard University\n", + " -71.116650\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", + " 4.80\n", + " 63.5\n", " 1.0\n", - " -1.0\n", + " -2.0\n", " \n", " \n", " 4\n", + " grid.17063.33\n", + " 80003\n", + " 3.0\n", + " University of Toronto\n", + " 8932\n", " grid.21107.35\n", - " 2211\n", - " 2.0\n", + " 9182\n", " Johns Hopkins University\n", - " 10120\n", - " grid.17063.33\n", - " 10281\n", - " Toronto\n", - " 392\n", - " Canada\n", + " Baltimore\n", + " 356\n", " ...\n", - " University of Toronto\n", - " Ontario\n", + " -76.620280\n", + " Maryland\n", " [Education]\n", - " NaN\n", - " 11 Medical and Health Sciences\n", + " JHU\n", + " 32 Biomedical and Clinical Sciences\n", + " 4.0\n", + " 3.88\n", + " 195.5\n", " 2.0\n", - " 3.81\n", - " 236.5\n", - " 3.0\n", - " 1.0\n", + " -1.0\n", " \n", " \n", " ...\n", @@ -4776,213 +4720,213 @@ " ...\n", " \n", " \n", - " 3721588\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.7359.8\n", + " 4134095\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.467212.4\n", + " 7\n", + " Adobe Inc\n", + " San Jose\n", " 3\n", - " Bamberg\n", - " 1\n", - " Germany\n", " ...\n", - " University of Bamberg\n", " NaN\n", - " [Education]\n", + " California\n", + " [Company]\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 33.33\n", - " 8.0\n", - " 8.0\n", - " 5.5\n", + " 36 Creative Arts and Writing\n", + " 59.0\n", + " 42.86\n", + " 1.0\n", + " 1.0\n", + " -33.5\n", " \n", " \n", - " 3721589\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.11560.33\n", - " 2\n", - " Bernal\n", + " 4134096\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.4800.c\n", + " 7\n", + " Polytechnic University of Turin\n", + " Turin\n", " 1\n", - " Argentina\n", " ...\n", - " National University of Quilmes\n", - " NaN\n", + " 7.661075\n", + " Piemonte\n", " [Education]\n", - " UNQ\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", - " 2.5\n", + " NaN\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", + " 34.5\n", " 0.0\n", " \n", " \n", - " 3721590\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.15866.3c\n", - " 2\n", - " Prague\n", + " 4134097\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.54549.39\n", + " 7\n", + " University of Electronic Science and Technolog...\n", + " Chengdu\n", " 1\n", - " Czechia\n", " ...\n", - " Czech University of Life Sciences Prague\n", + " 104.100270\n", " NaN\n", " [Education]\n", - " CULS\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", - " 2.5\n", + " UESTC\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", + " 34.5\n", " 0.0\n", " \n", " \n", - " 3721591\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.241104.2\n", - " 2\n", - " Cleveland\n", + " 4134098\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.6603.3\n", + " 7\n", + " University of Cyprus\n", + " Nicosia\n", " 1\n", - " United States\n", " ...\n", - " University Hospitals of Cleveland\n", - " Ohio\n", - 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Import a .csv file containing rejected articles\n", + "2. Search for publications similar to the rejected articles\n", + "4. Measure the strength of the matches and provide ideas for validation" + ], + "metadata": { + "id": "_dmqbrsrX1Wm" + } + }, + { + "cell_type": "code", + "source": [ + "import datetime\n", + "print(\"==\\nCHANGELOG\\nThis notebook was last run on %s\\n==\" % datetime.date.today().strftime('%b %d, %Y'))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uPAAo96vdohR", + "outputId": "98c0472c-3b92-4c14-b567-aa0a2d75491c" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==\n", + "CHANGELOG\n", + "This notebook was last run on Jan 27, 2025\n", + "==\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Prerequisites\n", + "\n", + "This notebook assumes you have installed the [Dimcli](https://pypi.org/project/dimcli/) library and are familiar with the ['Getting Started' tutorial](https://api-lab.dimensions.ai/cookbooks/1-getting-started/1-Using-the-Dimcli-library-to-query-the-API.html)." + ], + "metadata": { + "id": "qX9CV_XkXVIZ" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install dimcli pandasql levenshtein -U --quiet\n", + "\n", + "import dimcli\n", + "from dimcli.utils import *\n", + "\n", + "import json, sys\n", + "import requests\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pandasql import sqldf\n", + "import pandasql as ps\n", + "import plotly.express as px # plotly>=4.8.1\n", + "if not 'google.colab' in sys.modules:\n", + " # make js dependecies local / needed by html exports\n", + " from plotly.offline import init_notebook_mode\n", + " init_notebook_mode(connected=True)\n", + "#\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "print(\"==\\nLogging in..\")\n", + "# https://digital-science.github.io/dimcli/getting-started.html#authentication\n", + "ENDPOINT = \"https://app.dimensions.ai\"\n", + "if 'google.colab' in sys.modules:\n", + " import getpass\n", + " KEY = getpass.getpass(prompt='API Key: ')\n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "else:\n", + " KEY = \"\"\n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "dsl = dimcli.Dsl()" + ], + "metadata": { + "id": "ti0txhA9d10c", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a793c666-7d7f-410e-b2e9-1a952f8e5eb5" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m241.7/241.7 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.7/162.7 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m56.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.1/51.1 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m54.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for pandasql (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "==\n", + "Logging in..\n", + "API Key: ··········\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.10\u001b[0m\n", + "\u001b[2mMethod: manual login\u001b[0m\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 1. Get an example data set\n", + "\n", + "For this tutorial, we are going to use a sample data set of preprints and pretend that the preprints are articles we have rejected. This is a good proof of the concept of finding a similar article that has been published in a peer-reviewed journal: preprints often reappear published in journals and might have subtly different titles or abstracts.\n", + "\n", + "In this simplified example, we'll just use the author names and titles for matching and we'll add a unique (made up) submission ID, as real data is likely to have this. We'll use the preprint publishing date as our rejection date.\n", + "\n", + "Here is the query to get our example data set as a `pandas` data frame, and some code to make it look more like a data set of rejected articles. You don't need to understand this bit necessarily, assuming you will have you're own data you just need to know what the table looks like at the end (which will be shown)." + ], + "metadata": { + "id": "ZAHoWkwhUzjy" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from uuid import uuid4\n", + "\n", + "rejected_publications = []\n", + "\n", + "preprints = dsl.query(\n", + " # This is quite a specific search for preprints published on 2020-01-22\n", + " \"\"\"\n", + " search publications\n", + " where type = \"preprint\" and date = \"2020-01-22\" and abstract is not empty\n", + " return publications[date+title+abstract+authors]\n", + " limit 10\n", + " \"\"\"\n", + ")\n", + "\n", + "for p in preprints.json[\"publications\"]:\n", + " # This will be a row of our data:\n", + " rejected_article_data_row = {\n", + " \"rejected_date\": None, # Initialising the rows with null values\n", + " \"first_author\": None,\n", + " \"title\": None,\n", + " \"abstract\": None\n", + " }\n", + " rejected_article_data_row['rejected_date'] = p['date']\n", + " rejected_article_data_row['title'] = p['title']\n", + " rejected_article_data_row['abstract'] = p['abstract']\n", + " for order, a in enumerate(p[\"authors\"]):\n", + " if order == 0: # i.e. first author\n", + " rejected_article_data_row['first_author'] = a['last_name']\n", + " rejected_publications.append(rejected_article_data_row)\n", + "\n", + "rejected_publication_data = pd.DataFrame(rejected_publications)\n", + "\n", + "rejected_publication_data['submission_id'] = [\n", + " str(uuid4()) for _ in range(len(rejected_publication_data))\n", + "]\n", + "\n", + "rejected_publication_data" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1555 + }, + "id": "OUztuLnzd02w", + "outputId": "2c7307a2-2de4-48a8-f4b4-6b4bd6b8197e" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Returned Publications: 10 (total = 730)\n", + "\u001b[2mTime: 0.31s\u001b[0m\n", + "WARNINGS [1]\n", + "Field current_organization_id of the authors field is deprecated and will be removed in the next major release.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " rejected_date first_author \\\n", + "0 2020-01-22 Kong \n", + "1 2020-01-22 Bowman \n", + "2 2020-01-22 Di Sia \n", + "3 2020-01-22 Di Sia \n", + "4 2020-01-22 Bedoya \n", + "5 2020-01-22 Coretta \n", + "6 2020-01-22 Wekke \n", + "7 2020-01-22 Hernández-Caballero \n", + "8 2020-01-22 Joyce \n", + "9 2020-01-22 Sinar \n", + "\n", + " title \\\n", + "0 Predicting Prolonged Length of Hospital Stay f... \n", + "1 OSF Prereg Template \n", + "2 On the Concept of Time in everyday Life and be... \n", + "3 Birth and development of quantum physics: a tr... \n", + "4 Fabricación de capas antirreflejantes y absorb... \n", + "5 Open Science in phonetics and phonology \n", + "6 Merumuskan Masalah Penelitian dengan Metode MAIL \n", + "7 Epigenética en cáncer \n", + "8 Scientific Racism 2.0 (SR2.0): An erroneous ar... \n", + "9 Functional Features of Forensic Corruption Cas... \n", + "\n", + " abstract \\\n", + "0 \\n BACKGROUND\\n ... \n", + "1

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In this paper I consider the concept of tim... \n", + "3

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rejected_datefirst_authortitleabstractsubmission_id
02020-01-22KongPredicting Prolonged Length of Hospital Stay f...<sec>\\n BACKGROUND\\n ...6ef9af47-8fa7-4abb-af69-0c8a522b6f9a
12020-01-22BowmanOSF Prereg Template<p>Preregistration is the act of submitting a ...2239203f-fa3b-4402-a185-1398861aba66
22020-01-22Di SiaOn the Concept of Time in everyday Life and be...<p>In this paper I consider the concept of tim...7dc54f58-2a6c-44eb-b906-98a184d1bac1
32020-01-22Di SiaBirth and development of quantum physics: a tr...<p>The last century has been a period of extre...b466b2fd-b0c3-4573-abc5-229532212be1
42020-01-22BedoyaFabricación de capas antirreflejantes y absorb...<p>Se prepararon películas delgadas de SiO2 en...56360b84-72d3-42bc-b963-067e95e1ade3
52020-01-22CorettaOpen Science in phonetics and phonology<p>Open Science is a movement that stresses th...4c0ca94a-14f2-4f86-b014-ac47f7d5170c
62020-01-22WekkeMerumuskan Masalah Penelitian dengan Metode MAIL<p>Ringkasan kuliah di pascasarjana STAIN Soro...494a322a-7a51-460b-8dcb-dcfbb405e9e6
72020-01-22Hernández-CaballeroEpigenética en cáncer<p>Las células contienen información determina...4745cfed-18ac-465f-80b5-59c437a8ab2d
82020-01-22JoyceScientific Racism 2.0 (SR2.0): An erroneous ar...<p>SR2.0 refers to a prominent argument made b...879bff9a-169b-425f-a027-11f04e0448ea
92020-01-22SinarFunctional Features of Forensic Corruption Cas...<p>This study examines the multimodal use of l...e262d61c-32bb-4690-b814-e20ee7add13f
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SR2.0 refers to a prominent argument made by some geneticists, often via social and popular media, which inadvertently amounts to a refinement of scientific racism. At face value it is an attack on racism in science. Upon closer inspection its primary, possibly unconscious, purpose appears to be to protect contemporary genetic research from the charge of racism. The argument is often made alongside an emphasis upon long-falsified errors of early science and open expressions of racism in wider society, rather than the intelligence and statistical theory which has informed both genetics and the social construct scientific racism for a century. The core argument is invalid. It also has profound epistemological failings, including misunderstanding the nature of social constructions and how they how they interact with empirical facts. Finally, the proponents do not fully support their own argument; this exposes the argument\\u2019s substantive function as a defensive holding device.

\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"submission_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"879bff9a-169b-425f-a027-11f04e0448ea\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + }, + "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/e523c247d1e24a05/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"2020-01-22\",\n\"Kong\",\n\"Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis\\u2013Treated Patients Using Stacked Generalization: Model Development and Validation Study (Preprint)\",\n\"\\n BACKGROUND\\n

The increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients.<\\/p>\\n <\\/sec>\\n \\n OBJECTIVE\\n

This study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data.<\\/p>\\n <\\/sec>\\n \\n METHODS\\n

Patient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model.<\\/p>\\n <\\/sec>\\n \\n RESULTS\\n

The final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided <i>t</i> tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy.<\\/p>\\n <\\/sec>\\n \\n CONCLUSIONS\\n

This study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.<\\/p>\\n <\\/sec>\",\n\"6ef9af47-8fa7-4abb-af69-0c8a522b6f9a\"],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"2020-01-22\",\n\"Bowman\",\n\"OSF Prereg Template\",\n\"

Preregistration is the act of submitting a study plan, ideally also with analytical plan, to a registry prior to conducting the work. Preregistration increases the discoverability of research even if it does not get published further. Adding specific analysis plans can clarify the distinction between planned, confirmatory tests and unplanned, exploratory research. This preprint contains a template for the \\u201cOSF Prereg\\u201d form available from the OSF Registry. An earlier version was originally developed for the Preregistration Challenge, an education campaign designed to initiate preregistration as a habit prior to data collection in basic research, funded by the Laura and John Arnold Foundation (now Arnold Ventures) and conducted by the Center for Open Science. More information is available at https://cos.io/prereg, and other templates are available at: https://osf.io/zab38/<\\/p>\",\n\"2239203f-fa3b-4402-a185-1398861aba66\"],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"2020-01-22\",\n\"Di Sia\",\n\"On the Concept of Time in everyday Life and between Physics and Mathematics\",\n\"

In this paper I consider the concept of time in a general way as daily human time andthen within physics with relation to mathematics. I focus the attention on quantum mechanics, with its particular peculiarities, examining peculiar important questions like the temporal asymmetry, the Prigogine\\u2019s position and the time-reversal operator of Wigner. I conclude considering the theme of the temporal asymmetry in relation to decoherence and irreversibility. Interesting imputs related to education science will be done.<\\/p>\",\n\"7dc54f58-2a6c-44eb-b906-98a184d1bac1\"],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"2020-01-22\",\n\"Di Sia\",\n\"Birth and development of quantum physics: a transdisciplinary approach\",\n\"

The last century has been a period of extreme interest for scientific research, marked by the overcoming of the classical frontiers of scientific knowledge.Research oriented towards the infinitely small and infinitely big, in both cases beyondthe borders of the visible. Quantum physics has led to a new Copernican revolution,opening the way to new questions that have led to a new view of reality. At the sametime, new theories have developed, involving every field of science, philosophy and art, rediscovering the link between unity and totality and the importance of humanpotential. In a transdisciplinary approach we consider quantum field theory, new ideason the concepts of vacuum and entanglement, metaphysical aspects of quantum revolution and the introduction of different interpretative approaches on the \\u201cWhole\\u201d.<\\/p>\",\n\"b466b2fd-b0c3-4573-abc5-229532212be1\"],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n\"2020-01-22\",\n\"Bedoya\",\n\"Fabricaci\\u00f3n de capas antirreflejantes y absorbedores solares mediante la t\\u00e9cnica Sol-gel: Un resumen sobre la variaci\\u00f3n de s\\u00edntesis y condiciones experimentales realizadas en la UTP\",\n\"

Se prepararon pel\\u00edculas delgadas de SiO2 en relaci\\u00f3n molar TEOS:H2O:EtOH 1:18:1.8 y CuCoMn en relaci\\u00f3n molar Cu:Co:Mn 1:3:3 por el m\\u00e9todo de recubrimiento por inmersi\\u00f3n (Sol-gel), bajo condiciones fijas de velocidad de dep\\u00f3sito y n\\u00famero de capas. Inicialmente se usaron sustratos de vidrios con el fin de analizar el comportamiento \\u00f3ptico de los recubrimientos utilizando espectroscop\\u00eda UV-Vis y FTIR. Una vez depositados los recubrimientos de SiO2 se sometieron a secado a temperatura ambiente y dentro de un horno tubular a 70 \\u00b0C. Por otro lado, las muestras de CuCoMn se trataron t\\u00e9rmicamente a diferentes temperaturas de recocido (550 \\u00b0C, 600 \\u00b0C y 650 \\u00b0C) durante 12 horas a una rampa de 1 \\u00b0C/min. Los resultados parciales obtenidos muestran que las pel\\u00edculas exhiben una absortancia entre 75% - 95 %, lo cual est\\u00e1 acorde con lo reportado en la literatura para este material. Sin embargo, para aumentar este valor es necesario ampliar el estudio del material, con el fin de definir su estructura, composici\\u00f3n y morfolog\\u00eda. El objetivo es obtener recubrimientos con las propiedades \\u00f3pticas y estructurales adecuadas con el fin de ser usados en la fabricaci\\u00f3n de la superficie absorbedora de calentadores de agua e instalaciones de energ\\u00eda solar.<\\/p>\",\n\"56360b84-72d3-42bc-b963-067e95e1ade3\"],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n\"2020-01-22\",\n\"Coretta\",\n\"Open Science in phonetics and phonology\",\n\"

Open Science is a movement that stresses the importance of a more honest and transparent scientific attitude by promoting a series of research principles and by warning from common, although not necessarily intentional, questionable practices and misconceptions. The term Open Science as a whole refers to the fundamental concepts of 'openness, transparency, rigour, reproducibility, replicability, and accumulation of knowledge' (Cruwell 2018). The goodness of the latter depends in great part on the reproducibility and replicability of the studies that contribute to knowledge accumulation.<\\/p>\",\n\"4c0ca94a-14f2-4f86-b014-ac47f7d5170c\"],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n\"2020-01-22\",\n\"Wekke\",\n\"Merumuskan Masalah Penelitian dengan Metode MAIL\",\n\"

Ringkasan kuliah di pascasarjana STAIN Sorong.<\\/p>\",\n\"494a322a-7a51-460b-8dcb-dcfbb405e9e6\"],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n\"2020-01-22\",\n\"Hern\\u00e1ndez-Caballero\",\n\"Epigen\\u00e9tica en c\\u00e1ncer\",\n\"

Las c\\u00e9lulas contienen informaci\\u00f3n determinada por el genoma propio del organismo al que pertenecen, lo cual le permite el desarrollo y diferenciaci\\u00f3n propios de su especie, en este sentido la informaci\\u00f3n epigen\\u00e9tica constituye una capa adicional de informaci\\u00f3n reguladora que vuelve m\\u00e1s complejos los procesos celulares. La metilaci\\u00f3n del DNA es la marca epigen\\u00e9tica de inactivaci\\u00f3n m\\u00e1s conocida y como el proceso reversible que es, consiste en un fen\\u00f3meno din\\u00e1mico que cambia durante la vida de la c\\u00e9lula. Los cambios epigen\\u00e9ticos inciden directamente en la conformaci\\u00f3n que adquiere la cromatina, con lo que se regula el c\\u00f3mo se expresen los genes y su actividad, a su vez, depende de modificaciones postraduccionales en las prote\\u00ednas histonas. Las histonas al igual que el DNA tambi\\u00e9n pueden presentar modificaciones epigen\\u00e9ticas.El c\\u00e1ncer es una patolog\\u00eda heterog\\u00e9nea que durante mucho tiempo se crey\\u00f3 era el resultado \\u00fanicamente de la adquisici\\u00f3n de mutaciones gen\\u00e9ticas o rearreglos cromos\\u00f3micos, que desembocaban en la p\\u00e9rdida del funcionamiento de genes encargados de evitar el crecimiento celular descontrolado y de la desregulaci\\u00f3n de la actividad de genes encargados de promover la proliferaci\\u00f3n. No obstante, la expresi\\u00f3n adecuada de los genes es fundamental para mantener el fenotipo celular normal, y el control de dicha expresi\\u00f3n va m\\u00e1s all\\u00e1 de la sola presencia de una secuencia gen\\u00e9tica sin cambio. Sin embargo, las alteraciones epigen\\u00e9ticas que preceden y contribuyen al inicio del desarrollo de un c\\u00e1ncer a\\u00fan no se conocen de forma precisa.Actualmente la metilaci\\u00f3n de DNA es la principal marca epigen\\u00e9tica m\\u00e1s ampliamente estudiada. La diversidad en el uso de t\\u00e9cnicas para realizar este cometido va desde m\\u00e9todos sencillos como el uso de enzimas de restricci\\u00f3n sensibles a la metilaci\\u00f3n, para digerir DNA gen\\u00f3mico y analizar peque\\u00f1as regiones de DNA, pasando por el uso de bisulfito de sodio para analizar el estado de metilaci\\u00f3n en las citosinas hasta los m\\u00e9todos actuales de secuenciaci\\u00f3n a gran escala que permiten el an\\u00e1lisis simultaneo de gran cantidad de muestras y de amplias regiones del genoma completo, llegando a analizar hasta 3 millones de variantes gen\\u00e9ticas en un individuo. A la par, se ha desarrollado software especializado en epigen\\u00e9tica, permitiendo conocer la ubicaci\\u00f3n de sitios de metilaci\\u00f3n para luego hacer su b\\u00fasqueda en muestras biol\\u00f3gicas y se han desarrollado programas complejos para el an\\u00e1lisis de datos masivos obtenidos a trav\\u00e9s del uso de plataformas basadas en hibridaci\\u00f3n (microarreglos) y la secuenciaci\\u00f3n masiva con diversas afinidades (DNA-seq, RNA-seq, ChIP-seq, FAIRE-seq, ATAC-seq, MeDIP-seq, MBD-seq) y WGBS. Los cambios epigen\\u00e9ticos aberrantes en el c\\u00e1ncer pueden ser evidentes desde etapas tempranas, lo que ha llevado a pensar que, esta desregulaci\\u00f3n precede de hecho a los eventos tumorales transformadores preliminares cl\\u00e1sicos (mutaciones de supresores y/o protooncogenes e inestabilidad gen\\u00f3mica). Entre las alteraciones epigen\\u00e9ticas m\\u00e1s reconocidas en los tumores est\\u00e1 el silenciamiento asociado a hipermetilaci\\u00f3n de islas CpG en los promotores de los genes supresores como CDKN2A y RASSF1.Aunado a esto, los miRNAs tambi\\u00e9n pueden actuar como supresores u oncogenes en diferentes tipos de c\\u00e1ncer. Es por esto que, las modificaciones epigen\\u00e9ticas son un componente importante en la etiolog\\u00eda del c\\u00e1ncer y debido a su reversibilidad constituyen blancos terap\\u00e9uticos prometedores para diagnostico o tratamiento y potencial como posibles biomarcadores.<\\/p>\",\n\"4745cfed-18ac-465f-80b5-59c437a8ab2d\"],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n\"2020-01-22\",\n\"Joyce\",\n\"Scientific Racism 2.0 (SR2.0): An erroneous argument from genetics which inadvertently refines scientific racism\",\n\"

SR2.0 refers to a prominent argument made by some geneticists, often via social and popular media, which inadvertently amounts to a refinement of scientific racism. At face value it is an attack on racism in science. Upon closer inspection its primary, possibly unconscious, purpose appears to be to protect contemporary genetic research from the charge of racism. The argument is often made alongside an emphasis upon long-falsified errors of early science and open expressions of racism in wider society, rather than the intelligence and statistical theory which has informed both genetics and the social construct scientific racism for a century. The core argument is invalid. It also has profound epistemological failings, including misunderstanding the nature of social constructions and how they how they interact with empirical facts. Finally, the proponents do not fully support their own argument; this exposes the argument\\u2019s substantive function as a defensive holding device.<\\/p>\",\n\"879bff9a-169b-425f-a027-11f04e0448ea\"],\n [{\n 'v': 9,\n 'f': \"9\",\n },\n\"2020-01-22\",\n\"Sinar\",\n\"Functional Features of Forensic Corruption Case in Indonesia\",\n\"

This study examines the multimodal use of language affecting the social interaction in the Indonesian Court for Corruption Crimes as the research data source. The objective is to analyze the metafunction multimodal functional features of law enforcement and witnesses in the proceedings of forensic corruption case in Indonesia. Multimodal theory as a new technology that has been invented by linguists was used in this research to analyse forensic language. The findings showed that the multimodal systems were valuable in analysing the forensic functional features in the court room and the functional features of representational, interactive and compositional meanings were present in the court room involving gestures, postures, gazes, nonverbal communication, eye contacts, etc.<\\/p>\",\n\"e262d61c-32bb-4690-b814-e20ee7add13f\"]],\n columns: [[\"number\", \"index\"], [\"string\", \"rejected_date\"], [\"string\", \"first_author\"], [\"string\", \"title\"], [\"string\", \"abstract\"], [\"string\", \"submission_id\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n

\n \n \n\n\n \n
`;\n parentElement.appendChild(quickchartButtonContainerElement);\n }\n\n appendQuickchartButton(table);\n " + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 2. Define the search template\n", + "\n", + "Python concatenates multiple strings one after another in brackets, so we have written it out as shown below so that we can add comments to the query. This format isn't necessary, but hopefully it's helpful!" + ], + "metadata": { + "id": "4gO-Sw9RkPKx" + } + }, + { + "cell_type": "code", + "source": [ + "template = (\n", + " 'search publications '\n", + " 'in title_abstract_only ' # Search the whole of the publication\n", + " 'for \"{title}\" ' # Stop words will be automatically excluded\n", + " 'where date > \"{rejected_date}\" '\n", + " 'and ('\n", + " 'authors = \"{first_author}\"'\n", + " # The line below gives an example of how you could also search for\n", + " # the surname of the corresponding author if you have it:\n", + " # ' or authors = \"{corresponding_author}\"'\n", + " ') '\n", + " 'return publications['\n", + " 'date' # Published date\n", + " '+'\n", + " 'doi' # DOI of the published article\n", + " '+'\n", + " 'title' # Title of the published article\n", + " '+'\n", + " 'abstract' # Abstract of the published article\n", + " '] '\n", + " 'limit 1' # Get the most relevant result only\n", + ")\n", + "\n", + "template" + ], + "metadata": { + "id": "UcVe6Ocm6Fiv", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "outputId": "cb54d3a0-9236-4981-c629-d6f56ab69ab2" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'search publications in title_abstract_only for \"{title}\" where date > \"{rejected_date}\" and (authors = \"{first_author}\") return publications[date+doi+title+abstract] limit 1'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 3. Iteratively Query the Dimensions API for the retracted articles" + ], + "metadata": { + "id": "wAIXlRBUkiU8" + } + }, + { + "cell_type": "code", + "source": [ + "import string\n", + "from tqdm.notebook import tqdm\n", + "\n", + "def no_punctuation(s: str) -> str:\n", + " \"\"\"\n", + " Remove punctuation from a python string\n", + " \"\"\"\n", + " return s.translate(str.maketrans('', '', string.punctuation))\n", + "\n", + "# We'll store all our results in this list as we iterate, then join them together at the end...\n", + "results = []\n", + "\n", + "# For each row in the data set as a python dictionary:\n", + "for row in tqdm(rejected_publication_data.to_dict(orient=\"records\")):\n", + " row['title'] = no_punctuation(row['title'])\n", + " query = template.format(**row)\n", + " best = dsl.query(query, verbose=False).as_dataframe()\n", + " best['submission_id'] = row['submission_id']\n", + " results.append(best)\n", + "\n", + "# Join results together\n", + "output = pd.concat(results)\n", + "\n", + "output.head() # .head() shows just a few rows" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 250, + "referenced_widgets": [ + "a3690f3660a74a0e9af686a2f6ca8304", + "03a4c6bae6ca429f96347855a43adac9", + "62b20a5d1b1649ab8a2d76c3f4ab7981", + "c39293df4cb24387bf8f2638f241c824", + "8ab3cbaeb64a413fb51632f9ef182e9c", + "f2c1b4851d644eb3bdf048721d255a88", + "06fefdea62084b6097b03a497734dbc2", + "bd7d718456174f2794a2a2593cf1144a", + "c96c1481e38f4c82872c15eda63cde58", + "9c5c614ecf7e4fec9e02778e0a938762", + "aeeb243ec979404e8d80b3ef1c0a4070" + ] + }, + "id": "Legvd8_cpPq4", + "outputId": "94828b40-3f54-4327-d93f-d35a9f58958d" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/10 [00:00Functional Features of Forensic Corruption ... 2020-02-28 \n", + "\n", + " doi \n", + "0 10.23880/eoij-16000268 \n", + "0 10.31228/osf.io/m3xa6 " + ], + "text/html": [ + "\n", + "
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Functional Features of Forensic Corruption Case in Indonesia

\",\n \"In this paper I consider the concept of time in a general way as daily human time and then within physics with relation to mathematics. I consider the arrow of time and then focus the attention on quantum mechanics, with its particular peculiarities, examining important concepts like temporal asymmetry, complexity, decoherence, irreversibility, information theory, chaos theory. In conclusion I consider the notion of time connected to a new theory in progress, called \\u201cPrimordial Dynamic Space\\u201d theory.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2020-02-28\",\n \"max\": \"2021-01-01\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"2020-02-28\",\n \"2021-01-01\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"doi\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"10.31228/osf.io/m3xa6\",\n \"10.23880/eoij-16000268\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + }, + "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/e523c247d1e24a05/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"7dc54f58-2a6c-44eb-b906-98a184d1bac1\",\n\"On the Concept of Time in Everyday Life and between Physics and Mathematics\",\n\"In this paper I consider the concept of time in a general way as daily human time and then within physics with relation to mathematics. I consider the arrow of time and then focus the attention on quantum mechanics, with its particular peculiarities, examining important concepts like temporal asymmetry, complexity, decoherence, irreversibility, information theory, chaos theory. In conclusion I consider the notion of time connected to a new theory in progress, called \\u201cPrimordial Dynamic Space\\u201d theory.\",\n\"2021-01-01\",\n\"10.23880/eoij-16000268\"],\n [{\n 'v': 0,\n 'f': \"0\",\n },\n\"e262d61c-32bb-4690-b814-e20ee7add13f\",\n\"Functional Features of Forensic Corruption Case in Indonesia\",\n\"

Functional Features of Forensic Corruption Case in Indonesia<\\/p>\",\n\"2020-02-28\",\n\"10.31228/osf.io/m3xa6\"]],\n columns: [[\"number\", \"index\"], [\"string\", \"submission_id\"], [\"string\", \"title\"], [\"string\", \"abstract\"], [\"string\", \"date\"], [\"string\", \"doi\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n

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02020-01-22KongPredicting Prolonged Length of Hospital Stay f...<sec>\\n BACKGROUND\\n ...6ef9af47-8fa7-4abb-af69-0c8a522b6f9aNaNNaNNaNNaN
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42020-01-22BedoyaFabricación de capas antirreflejantes y absorb...<p>Se prepararon películas delgadas de SiO2 en...56360b84-72d3-42bc-b963-067e95e1ade3NaNNaNNaNNaN
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "merged_results", + "summary": "{\n \"name\": \"merged_results\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"rejected_date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"2020-01-22\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"first_author\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"Joyce\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title_x\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Scientific Racism 2.0 (SR2.0): An erroneous argument from genetics which inadvertently refines scientific racism\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"abstract_x\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"

SR2.0 refers to a prominent argument made by some geneticists, often via social and popular media, which inadvertently amounts to a refinement of scientific racism. At face value it is an attack on racism in science. Upon closer inspection its primary, possibly unconscious, purpose appears to be to protect contemporary genetic research from the charge of racism. The argument is often made alongside an emphasis upon long-falsified errors of early science and open expressions of racism in wider society, rather than the intelligence and statistical theory which has informed both genetics and the social construct scientific racism for a century. The core argument is invalid. It also has profound epistemological failings, including misunderstanding the nature of social constructions and how they how they interact with empirical facts. Finally, the proponents do not fully support their own argument; this exposes the argument\\u2019s substantive function as a defensive holding device.

\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"submission_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"879bff9a-169b-425f-a027-11f04e0448ea\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title_y\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Functional Features of Forensic Corruption Case in Indonesia\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"abstract_y\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"

Functional Features of Forensic Corruption Case in Indonesia

\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2020-02-28 00:00:00\",\n \"max\": \"2021-01-01 00:00:00\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"2020-02-28\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"doi\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"10.31228/osf.io/m3xa6\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + }, + "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/e523c247d1e24a05/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"2020-01-22\",\n\"Kong\",\n\"Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis\\u2013Treated Patients Using Stacked Generalization: Model Development and Validation Study (Preprint)\",\n\"\\n BACKGROUND\\n

The increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients.<\\/p>\\n <\\/sec>\\n \\n OBJECTIVE\\n

This study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data.<\\/p>\\n <\\/sec>\\n \\n METHODS\\n

Patient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model.<\\/p>\\n <\\/sec>\\n \\n RESULTS\\n

The final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided <i>t</i> tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy.<\\/p>\\n <\\/sec>\\n \\n CONCLUSIONS\\n

This study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.<\\/p>\\n <\\/sec>\",\n\"6ef9af47-8fa7-4abb-af69-0c8a522b6f9a\",\nNaN,\nNaN,\nNaN,\nNaN],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"2020-01-22\",\n\"Bowman\",\n\"OSF Prereg Template\",\n\"

Preregistration is the act of submitting a study plan, ideally also with analytical plan, to a registry prior to conducting the work. Preregistration increases the discoverability of research even if it does not get published further. Adding specific analysis plans can clarify the distinction between planned, confirmatory tests and unplanned, exploratory research. This preprint contains a template for the \\u201cOSF Prereg\\u201d form available from the OSF Registry. An earlier version was originally developed for the Preregistration Challenge, an education campaign designed to initiate preregistration as a habit prior to data collection in basic research, funded by the Laura and John Arnold Foundation (now Arnold Ventures) and conducted by the Center for Open Science. More information is available at https://cos.io/prereg, and other templates are available at: https://osf.io/zab38/<\\/p>\",\n\"2239203f-fa3b-4402-a185-1398861aba66\",\nNaN,\nNaN,\nNaN,\nNaN],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"2020-01-22\",\n\"Di Sia\",\n\"On the Concept of Time in everyday Life and between Physics and Mathematics\",\n\"

In this paper I consider the concept of time in a general way as daily human time andthen within physics with relation to mathematics. I focus the attention on quantum mechanics, with its particular peculiarities, examining peculiar important questions like the temporal asymmetry, the Prigogine\\u2019s position and the time-reversal operator of Wigner. I conclude considering the theme of the temporal asymmetry in relation to decoherence and irreversibility. Interesting imputs related to education science will be done.<\\/p>\",\n\"7dc54f58-2a6c-44eb-b906-98a184d1bac1\",\n\"On the Concept of Time in Everyday Life and between Physics and Mathematics\",\n\"In this paper I consider the concept of time in a general way as daily human time and then within physics with relation to mathematics. I consider the arrow of time and then focus the attention on quantum mechanics, with its particular peculiarities, examining important concepts like temporal asymmetry, complexity, decoherence, irreversibility, information theory, chaos theory. In conclusion I consider the notion of time connected to a new theory in progress, called \\u201cPrimordial Dynamic Space\\u201d theory.\",\n\"2021-01-01\",\n\"10.23880/eoij-16000268\"],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"2020-01-22\",\n\"Di Sia\",\n\"Birth and development of quantum physics: a transdisciplinary approach\",\n\"

The last century has been a period of extreme interest for scientific research, marked by the overcoming of the classical frontiers of scientific knowledge.Research oriented towards the infinitely small and infinitely big, in both cases beyondthe borders of the visible. Quantum physics has led to a new Copernican revolution,opening the way to new questions that have led to a new view of reality. At the sametime, new theories have developed, involving every field of science, philosophy and art, rediscovering the link between unity and totality and the importance of humanpotential. In a transdisciplinary approach we consider quantum field theory, new ideason the concepts of vacuum and entanglement, metaphysical aspects of quantum revolution and the introduction of different interpretative approaches on the \\u201cWhole\\u201d.<\\/p>\",\n\"b466b2fd-b0c3-4573-abc5-229532212be1\",\nNaN,\nNaN,\nNaN,\nNaN],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n\"2020-01-22\",\n\"Bedoya\",\n\"Fabricaci\\u00f3n de capas antirreflejantes y absorbedores solares mediante la t\\u00e9cnica Sol-gel: Un resumen sobre la variaci\\u00f3n de s\\u00edntesis y condiciones experimentales realizadas en la UTP\",\n\"

Se prepararon pel\\u00edculas delgadas de SiO2 en relaci\\u00f3n molar TEOS:H2O:EtOH 1:18:1.8 y CuCoMn en relaci\\u00f3n molar Cu:Co:Mn 1:3:3 por el m\\u00e9todo de recubrimiento por inmersi\\u00f3n (Sol-gel), bajo condiciones fijas de velocidad de dep\\u00f3sito y n\\u00famero de capas. Inicialmente se usaron sustratos de vidrios con el fin de analizar el comportamiento \\u00f3ptico de los recubrimientos utilizando espectroscop\\u00eda UV-Vis y FTIR. Una vez depositados los recubrimientos de SiO2 se sometieron a secado a temperatura ambiente y dentro de un horno tubular a 70 \\u00b0C. Por otro lado, las muestras de CuCoMn se trataron t\\u00e9rmicamente a diferentes temperaturas de recocido (550 \\u00b0C, 600 \\u00b0C y 650 \\u00b0C) durante 12 horas a una rampa de 1 \\u00b0C/min. Los resultados parciales obtenidos muestran que las pel\\u00edculas exhiben una absortancia entre 75% - 95 %, lo cual est\\u00e1 acorde con lo reportado en la literatura para este material. Sin embargo, para aumentar este valor es necesario ampliar el estudio del material, con el fin de definir su estructura, composici\\u00f3n y morfolog\\u00eda. El objetivo es obtener recubrimientos con las propiedades \\u00f3pticas y estructurales adecuadas con el fin de ser usados en la fabricaci\\u00f3n de la superficie absorbedora de calentadores de agua e instalaciones de energ\\u00eda solar.<\\/p>\",\n\"56360b84-72d3-42bc-b963-067e95e1ade3\",\nNaN,\nNaN,\nNaN,\nNaN]],\n columns: [[\"number\", \"index\"], [\"string\", \"rejected_date\"], [\"string\", \"first_author\"], [\"string\", \"title_x\"], [\"string\", \"abstract_x\"], [\"string\", \"submission_id\"], [\"string\", \"title_y\"], [\"string\", \"abstract_y\"], [\"string\", \"date\"], [\"string\", \"doi\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n

\n \n \n\n\n \n
`;\n parentElement.appendChild(quickchartButtonContainerElement);\n }\n\n appendQuickchartButton(table);\n " + }, + "metadata": {}, + "execution_count": 18 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 5. Add Matching Score\n", + "\n", + "We have found some publications that might match our rejected articles. Now we need to score them to see whether they are good matches.\n", + "\n", + "In this case we'll measure the edit distance between the titles. The most commonly-used edit distance between strings is [Levensthtein distance](https://en.wikipedia.org/wiki/Jaccard_index), which is [nicely implemented in Python in the `Levenshtein` package](https://rapidfuzz.github.io/Levenshtein/).\n", + "\n", + "The `Levenshtein` package has a function \"ratio\" which uses Levenshtein distance to get a similarity (not distance) score between 0 (disimilar) and 1 (identical). We will use this to compare titles converted to lowercase.\n", + "\n", + "Sorting the results by score descending (from highest to lowest) we can see that there was one good match. If we wanted to make the matching more automatic, we could choose to filter out everything with a score less than e.g. 0.75." + ], + "metadata": { + "id": "WHfO5HvqbWoR" + } + }, + { + "cell_type": "code", + "source": [ + "from Levenshtein import ratio\n", + "\n", + "def similarity(string1: str, string2: str) -> float:\n", + " \"\"\"\n", + " Case-insensitive similarity score made by subtracting the normalised\n", + " Levenshtein distance from 1.\n", + " \"\"\"\n", + " if pd.isna(string1) or pd.isna(string2):\n", + " return 0.\n", + " else:\n", + " return ratio(string1.lower(), string2.lower())\n", + "\n", + "print(similarity('The cat sat on the mat', 'The dog sat on the frog'))\n", + "print(similarity('The cat sat on the mat', 'The mat sat on the cat'))" + ], + "metadata": { + "id": "3pxX8k8u52JA", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7570c530-8fba-49cb-e6de-4a7474bbe080" + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.7111111111111111\n", + "0.9090909090909091\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "merged_results['score'] = merged_results.apply(\n", + " lambda row: similarity(row['abstract_x'], row['abstract_y']),\n", + " axis=1\n", + ")\n", + "\n", + "merged_results = merged_results.sort_values(\"score\", ascending=False)\n", + "\n", + "final_output = merged_results[[\n", + " 'submission_id',\n", + " 'rejected_date',\n", + " 'title_x',\n", + " 'title_y',\n", + " 'abstract_x',\n", + " 'abstract_y',\n", + " 'doi',\n", + " 'score'\n", + "]]\n", + "\n", + "final_output.columns = [\n", + " 'submission_id',\n", + " 'rejected_date',\n", + " 'original_title',\n", + " 'published_title',\n", + " 'abstract_x',\n", + " 'abstract_y',\n", + " 'doi',\n", + " 'score'\n", + "]\n", + "\n", + "final_output" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 246 + }, + "id": "wNcx52H3zDnz", + "outputId": "421be014-42a1-4fc2-8362-052bc7accb80" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " submission_id rejected_date \\\n", + "2 7dc54f58-2a6c-44eb-b906-98a184d1bac1 2020-01-22 \n", + "\n", + " original_title \\\n", + "2 On the Concept of Time in everyday Life and be... \n", + "\n", + " published_title \\\n", + "2 On the Concept of Time in Everyday Life and be... \n", + "\n", + " abstract_x \\\n", + "2

In this paper I consider the concept of tim... \n", + "\n", + " abstract_y doi \\\n", + "2 In this paper I consider the concept of time i... 10.23880/eoij-16000268 \n", + "\n", + " score \n", + "2 0.705539 " + ], + "text/html": [ + "\n", + "

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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "repr_error": "0" + }, + "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/e523c247d1e24a05/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 2,\n 'f': \"2\",\n },\n\"7dc54f58-2a6c-44eb-b906-98a184d1bac1\",\n\"2020-01-22\",\n\"On the Concept of Time in everyday Life and between Physics and Mathematics\",\n\"On the Concept of Time in Everyday Life and between Physics and Mathematics\",\n\"

In this paper I consider the concept of time in a general way as daily human time andthen within physics with relation to mathematics. I focus the attention on quantum mechanics, with its particular peculiarities, examining peculiar important questions like the temporal asymmetry, the Prigogine\\u2019s position and the time-reversal operator of Wigner. I conclude considering the theme of the temporal asymmetry in relation to decoherence and irreversibility. Interesting imputs related to education science will be done.<\\/p>\",\n\"In this paper I consider the concept of time in a general way as daily human time and then within physics with relation to mathematics. I consider the arrow of time and then focus the attention on quantum mechanics, with its particular peculiarities, examining important concepts like temporal asymmetry, complexity, decoherence, irreversibility, information theory, chaos theory. In conclusion I consider the notion of time connected to a new theory in progress, called \\u201cPrimordial Dynamic Space\\u201d theory.\",\n\"10.23880/eoij-16000268\",\n{\n 'v': 0.7055393586005831,\n 'f': \"0.7055393586005831\",\n }]],\n columns: [[\"number\", \"index\"], [\"string\", \"submission_id\"], [\"string\", \"rejected_date\"], [\"string\", \"original_title\"], [\"string\", \"published_title\"], [\"string\", \"abstract_x\"], [\"string\", \"abstract_y\"], [\"string\", \"doi\"], [\"number\", \"score\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n let quickchartButtonContainerElement = document.createElement('div');\n quickchartButtonContainerElement.innerHTML = `\n

\n \n \n\n\n \n
`;\n parentElement.appendChild(quickchartButtonContainerElement);\n }\n\n appendQuickchartButton(table);\n " + }, + "metadata": {}, + "execution_count": 26 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 6. Conclusion\n", + "\n", + "In this tutorial we have shown how to use the Dimensions API to search for articles with titles and abstracts that contain similar terms to the titles of articles that have been rejected in the past.\n", + "\n", + "In terms of next steps, we might choose to do some bibliometric analysis of the articles we rejected. We could also try to improve our search process by extracting keywords from our article abstracts and searching for those too." + ], + "metadata": { + "id": "l639SwiweHiZ" + } + } + ] +} \ No newline at end of file diff --git a/docs/_sources/cookbooks/8-organizations/1-Organization-data-preview.ipynb.txt b/docs/_sources/cookbooks/8-organizations/1-Organization-data-preview.ipynb.txt new file mode 100644 index 00000000..ee0a14f7 --- /dev/null +++ b/docs/_sources/cookbooks/8-organizations/1-Organization-data-preview.ipynb.txt @@ -0,0 +1,5308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "AuCkj0Qwywjy" + }, + "source": [ + "# The Organizations API: Features Overview\n", + "\n", + "This tutorial provides an overview of the [Organizations data source](https://docs.dimensions.ai/dsl/datasource-organizations.html) available via the [Dimensions Analytics API](https://docs.dimensions.ai/dsl/). \n", + "\n", + "The topics covered in this notebook are:\n", + "\n", + "* How to align your affiliation data with Dimensions using the API [disambiguation service](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations) \n", + "* How to retrieve organizations metadata using the [search fields](https://docs.dimensions.ai/dsl/datasource-organizations.html) available\n", + "* How to use the [schema API](https://docs.dimensions.ai/dsl/data-sources.html#metadata-api) to obtain some statistics about the Organizations data available \n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==\n", + "CHANGELOG\n", + "This notebook was last run on Sep 10, 2025\n", + "==\n" + ] + } + ], + "source": [ + "import datetime\n", + "print(\"==\\nCHANGELOG\\nThis notebook was last run on %s\\n==\" % datetime.date.today().strftime('%b %d, %Y'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "OwTp1dybd2FF" + }, + "source": [ + "## Prerequisites\n", + "\n", + "This notebook assumes you have installed the [Dimcli](https://pypi.org/project/dimcli/) library and are familiar with the ['Getting Started' tutorial](https://api-lab.dimensions.ai/cookbooks/1-getting-started/1-Using-the-Dimcli-library-to-query-the-API.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "Collapsed": "false" + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[2mSearching config file credentials for 'https://app.dimensions.ai' endpoint..\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==\n", + "Logging in..\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", + "\u001b[2mMethod: dsl.ini file\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install dimcli tqdm plotly -U --quiet\n", + "\n", + "import dimcli\n", + "from dimcli.utils import *\n", + "\n", + "import json, sys, time\n", + "import pandas as pd\n", + "from tqdm.notebook import tqdm as pbar\n", + "import plotly.express as px # plotly>=4.8.1\n", + "if not 'google.colab' in sys.modules:\n", + " # make js dependecies local / needed by html exports\n", + " from plotly.offline import init_notebook_mode\n", + " init_notebook_mode(connected=True)\n", + "#\n", + "\n", + "print(\"==\\nLogging in..\")\n", + "# https://digital-science.github.io/dimcli/getting-started.html#authentication\n", + "ENDPOINT = \"https://app.dimensions.ai\"\n", + "if 'google.colab' in sys.modules:\n", + " import getpass\n", + " KEY = getpass.getpass(prompt='API Key: ') \n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "else:\n", + " KEY = \"\"\n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "dsl = dimcli.Dsl()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "JcnVEdOAywj3" + }, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "8zxcg9gPgZAv" + }, + "source": [ + "## 1. Matching affiliation data to Dimensions Organization IDs using `extract_affiliations`\n", + "\n", + "The API function `extract_affiliations` ([docs](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations)) can be used to enrich private datasets including non-disambiguated organizations data with Dimensions IDs, so to then take advantage of the wealth of linked data available in Dimensions.\n", + "\n", + "For example, let's assume our dataset has four columns (*affiliation name*, *city*, *state* and *country*) - any of which can be empty of course. Like this:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "Collapsed": "false", + "colab": {}, + "colab_type": "code", + "id": "cj5zBndjhgMM" + }, + "outputs": [], + "source": [ + "affiliations = [\n", + " ['University of Nebraska–Lincoln', 'Lincoln', 'Nebraska', 'United States'],\n", + " ['Tarbiat Modares University', 'Tehran', '', 'Iran'],\n", + " ['Harvard University', 'Cambridge', 'Massachusetts', 'United States'],\n", + " ['China Academy of Chinese Medical Sciences', 'Beijing', '', 'China'],\n", + " ['Liaoning University', 'Shenyang', '', 'China'],\n", + " ['Liaoning Normal University', 'Dalian', '', 'China'],\n", + " ['P.G. Department of Zoology and Research Centre, Shri Shiv Chhatrapati College of Arts, Commerce and Science, Junnar 410502, Pune, India.', '', '', ''],\n", + " ['Sungkyunkwan University', 'Seoul', '', 'South Korea'],\n", + " ['Centre for Materials for Electronics Technology', 'Pune', '', 'India'],\n", + " ['Institut Necker-Enfants Malades (INEM), INSERM U1151-CNRS UMR8253, Université de Paris, Faculté de Médecine, 156 rue de Vaugirard, 75730 Paris Cedex 15, France', '', '', '']\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "AcAypP1agx3M" + }, + "source": [ + "We want to look up Dimensions Organization identifiers for those affiliations using the **structured** affiliation matching. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 256, + "resources": { + "http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css": { + "data": "/* Copyright (c) Jupyter Development Team.
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    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

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"metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "YBSdHL4Tywj4", + "toc-hr-collapsed": false + }, + "source": [ + "## 2. Searching the API for organizations \n", + "\n", + "This can be done using full text search and/or fielded search. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "OAwuhlQmd2FK" + }, + "source": [ + "### Full-text search " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1315, + "status": "ok", + "timestamp": 1574702298940, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "qUz8_6M0d2Fa", + "outputId": "c8f58fc6-0e68-4a79-ef20-9dafbd0164f6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Organizations: 10 (total = 352)\n", + "\u001b[2mTime: 5.56s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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0grid.798367.4Bank of New YorkUSUnited States[Company]NaNNaN
1grid.798343.2Research Foundation of University of New YorkUSUnited States[Education]NaNNaN
2grid.797561.bNew York Hospital-Cornell Medical CenterUSUnited States[Healthcare]New YorkNew York
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6grid.794869.dInternational General Electric Company of New ...USUnited States[Other]NaNNaN
7grid.782261.8New York Digital Investment Group LLCUSUnited States[Other]NaNNaN
8grid.778414.9China CITIC Bank International Ltd New York Br...USUnited States[Government]NaNNaN
9grid.777726.4Morgan Guaranty Trust Company of New YorkUSUnited States[Company]NaNNaN
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" + ], + "text/plain": [ + " id name \\\n", + "0 grid.798367.4 Bank of New York \n", + "1 grid.798343.2 Research Foundation of University of New York \n", + "2 grid.797561.b New York Hospital-Cornell Medical Center \n", + "3 grid.796770.8 Research Foundation of City University of New ... \n", + "4 grid.796173.d Bank of New York Mellon Trust Co NA \n", + "5 grid.795276.8 New York University Medical Center \n", + "6 grid.794869.d International General Electric Company of New ... \n", + "7 grid.782261.8 New York Digital Investment Group LLC \n", + "8 grid.778414.9 China CITIC Bank International Ltd New York Br... \n", + "9 grid.777726.4 Morgan Guaranty Trust Company of New York \n", + "\n", + " country_code country_name types city_name state_name \n", + "0 US United States [Company] NaN NaN \n", + "1 US United States [Education] NaN NaN \n", + "2 US United States [Healthcare] New York New York \n", + "3 US United States [Other] NaN NaN \n", + "4 US United States [Company] NaN NaN \n", + "5 US United States [Education] New York New York \n", + "6 US United States [Other] NaN NaN \n", + "7 US United States [Other] NaN NaN \n", + "8 US United States [Government] NaN NaN \n", + "9 US United States [Company] NaN NaN " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york\" \n", + "return organizations limit 10" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 252 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1809, + "status": "ok", + "timestamp": 1574702323641, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "P3UWAR0QkkKg", + "outputId": "9e1be9ab-e3cf-4aca-f621-27f8b93a8a91" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Organizations: 9 (total = 9)\n", + "\u001b[2mTime: 0.62s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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idnamecountry_codecountry_nametypesacronymcity_namelatitudelinkoutlongitudestate_name
0grid.757191.cNew York Community BankUSUnited States[Company]NaNNaNNaNNaNNaNNaN
1grid.507861.dMohawk Valley Community CollegeUSUnited States[Education]MVCCUtica43.076850[https://www.mvcc.edu/]-75.220120New York
2grid.490742.cHealth Foundation for Western & Central New YorkUSUnited States[Nonprofit]NaNBuffalo42.874810[https://hfwcny.org/]-78.849690New York
3grid.480917.3New York Community TrustUSUnited States[Nonprofit]NaNNew York40.758870[http://www.nycommunitytrust.org/]-73.968185New York
4grid.478715.8Central New York Community FoundationUSUnited States[Nonprofit]CNYCFSyracuse43.056038[https://www.cnycf.org/]-76.148210New York
5grid.475804.aCommunity Service Society of New YorkUSUnited States[Other]CSSNew York40.749622[http://www.cssny.org/]-73.974620New York
6grid.475783.aLong Term Care Community CoalitionUSUnited States[Other]LTCCCNew York40.751163[http://www.ltccc.org/]-73.992470New York
7grid.429257.fKorean Community Services of Metropolitan New ...USUnited States[Nonprofit]KCSNew York40.770954[https://www.kcsny.org/]-73.786670New York
8funder.196228Community Health Foundation of Western and Cen...NaNUnited StatesNaNCommunity Health Foundation of Western and CentraNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " id name \\\n", + "0 grid.757191.c New York Community Bank \n", + "1 grid.507861.d Mohawk Valley Community College \n", + "2 grid.490742.c Health Foundation for Western & Central New York \n", + "3 grid.480917.3 New York Community Trust \n", + "4 grid.478715.8 Central New York Community Foundation \n", + "5 grid.475804.a Community Service Society of New York \n", + "6 grid.475783.a Long Term Care Community Coalition \n", + "7 grid.429257.f Korean Community Services of Metropolitan New ... \n", + "8 funder.196228 Community Health Foundation of Western and Cen... \n", + "\n", + " country_code country_name types \\\n", + "0 US United States [Company] \n", + "1 US United States [Education] \n", + "2 US United States [Nonprofit] \n", + "3 US United States [Nonprofit] \n", + "4 US United States [Nonprofit] \n", + "5 US United States [Other] \n", + "6 US United States [Other] \n", + "7 US United States [Nonprofit] \n", + "8 NaN United States NaN \n", + "\n", + " acronym city_name latitude \\\n", + "0 NaN NaN NaN \n", + "1 MVCC Utica 43.076850 \n", + "2 NaN Buffalo 42.874810 \n", + "3 NaN New York 40.758870 \n", + "4 CNYCF Syracuse 43.056038 \n", + "5 CSS New York 40.749622 \n", + "6 LTCCC New York 40.751163 \n", + "7 KCS New York 40.770954 \n", + "8 Community Health Foundation of Western and Centra NaN NaN \n", + "\n", + " linkout longitude state_name \n", + "0 NaN NaN NaN \n", + "1 [https://www.mvcc.edu/] -75.220120 New York \n", + "2 [https://hfwcny.org/] -78.849690 New York \n", + "3 [http://www.nycommunitytrust.org/] -73.968185 New York \n", + "4 [https://www.cnycf.org/] -76.148210 New York \n", + "5 [http://www.cssny.org/] -73.974620 New York \n", + "6 [http://www.ltccc.org/] -73.992470 New York \n", + "7 [https://www.kcsny.org/] -73.786670 New York \n", + "8 NaN NaN NaN " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york AND community\" \n", + "return organizations limit 10" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "Baz2j_cmd2Fd" + }, + "source": [ + "### Fielded search \n", + "\n", + "We can easily look up an organization using its ID, e.g." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 151 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1050, + "status": "ok", + "timestamp": 1574704472898, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "jNBg_c3ed2Fe", + "outputId": "5cbfb9d9-dcdc-4a34-aa1c-d99987b91cb9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Errors: 1\n", + "\u001b[2mTime: 5.84s\u001b[0m\n", + "Query Error\n", + "Semantic errors found:\n", + "\tField / Fieldset 'all' is not present in Source 'organizations'. Available fields: acronym,city_name,cnrs_ids,country_code,country_name,dimensions_url,established,external_ids_fundref,hesa_ids,id,isni_ids,latitude,linkout,longitude,name,nuts_level1_code,nuts_level1_name,nuts_level2_code,nuts_level2_name,nuts_level3_code,nuts_level3_name,organization_child_ids,organization_parent_ids,organization_related_ids,orgref_ids,redirect,ror_ids,score,state_name,status,types,ucas_ids,ukprn_ids,wikidata_ids,wikipedia_url and available fieldsets: basics,nuts\n" + ] + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " where id=\"grid.468887.d\" \n", + "return organizations[all] " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1020, + "status": "ok", + "timestamp": 1574702525174, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "GKh7VSOPk1Ye", + "outputId": "47a339ed-1f50-423c-8c04-e0ca3ad9347e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Organizations: 10 (total = 93)\n", + "\u001b[2mTime: 0.64s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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idnamecountry_codecountry_nametypescity_namestate_namelatitudelinkoutlongitudeacronym
0grid.798343.2Research Foundation of University of New YorkUSUnited States[Education]NaNNaNNaNNaNNaNNaN
1grid.795276.8New York University Medical CenterUSUnited States[Education]New YorkNew YorkNaNNaNNaNNaN
2grid.512545.2State University of New York, KoreaKRSouth Korea[Education]IncheonNaN37.376694[http://www.sunykorea.ac.kr/]126.667170NaN
3grid.511090.cCraig Newmark Graduate School of Journalism at...USUnited States[Education]New YorkNew York40.755230[https://www.journalism.cuny.edu/]-73.988830NaN
4grid.510787.cCenter for Migration Studies of New YorkUSUnited States[Education]New YorkNew York40.761470[https://cmsny.org/]-73.965450CMS
5grid.507867.bNew York State College of CeramicsUSUnited States[Education]AlfredNew York42.253372[https://www.alfred.edu/academics/colleges-sch...-77.787575NaN
6grid.507863.fNew York State School of Industrial and Labor ...USUnited States[Education]IthacaNew York42.439213[https://www.ilr.cornell.edu/]-76.493380ILR
7grid.507861.dMohawk Valley Community CollegeUSUnited States[Education]UticaNew York43.076850[https://www.mvcc.edu/]-75.220120MVCC
8grid.507860.cNew York State College of Agriculture and Life...USUnited States[Education]IthacaNew York42.448290[https://cals.cornell.edu/#]-76.479390CALS
9grid.507859.6New York State College of Veterinary Medicine ...USUnited States[Education]IthacaNew York42.447483[https://www.vet.cornell.edu/]-76.464905NaN
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" + ], + "text/plain": [ + " id name \\\n", + "0 grid.798343.2 Research Foundation of University of New York \n", + "1 grid.795276.8 New York University Medical Center \n", + "2 grid.512545.2 State University of New York, Korea \n", + "3 grid.511090.c Craig Newmark Graduate School of Journalism at... \n", + "4 grid.510787.c Center for Migration Studies of New York \n", + "5 grid.507867.b New York State College of Ceramics \n", + "6 grid.507863.f New York State School of Industrial and Labor ... \n", + "7 grid.507861.d Mohawk Valley Community College \n", + "8 grid.507860.c New York State College of Agriculture and Life... \n", + "9 grid.507859.6 New York State College of Veterinary Medicine ... \n", + "\n", + " country_code country_name types city_name state_name latitude \\\n", + "0 US United States [Education] NaN NaN NaN \n", + "1 US United States [Education] New York New York NaN \n", + "2 KR South Korea [Education] Incheon NaN 37.376694 \n", + "3 US United States [Education] New York New York 40.755230 \n", + "4 US United States [Education] New York New York 40.761470 \n", + "5 US United States [Education] Alfred New York 42.253372 \n", + "6 US United States [Education] Ithaca New York 42.439213 \n", + "7 US United States [Education] Utica New York 43.076850 \n", + "8 US United States [Education] Ithaca New York 42.448290 \n", + "9 US United States [Education] Ithaca New York 42.447483 \n", + "\n", + " linkout longitude acronym \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 [http://www.sunykorea.ac.kr/] 126.667170 NaN \n", + "3 [https://www.journalism.cuny.edu/] -73.988830 NaN \n", + "4 [https://cmsny.org/] -73.965450 CMS \n", + "5 [https://www.alfred.edu/academics/colleges-sch... -77.787575 NaN \n", + "6 [https://www.ilr.cornell.edu/] -76.493380 ILR \n", + "7 [https://www.mvcc.edu/] -75.220120 MVCC \n", + "8 [https://cals.cornell.edu/#] -76.479390 CALS \n", + "9 [https://www.vet.cornell.edu/] -76.464905 NaN " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york\" \n", + " where types in [\"Education\"]\n", + "return organizations limit 10" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 273 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 779, + "status": "ok", + "timestamp": 1574702569063, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "W6_BukMKleWs", + "outputId": "caedaf98-ca87-4504-a505-e4371f623eb2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Organizations: 9 (total = 9)\n", + "\u001b[2mTime: 5.97s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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idnamecity_namecountry_codecountry_namelatitudelinkoutlongitudetypesacronymstate_name
0grid.512545.2State University of New York, KoreaIncheonKRSouth Korea37.376694[http://www.sunykorea.ac.kr/]126.667170[Education]NaNNaN
1grid.479986.dNew York University ParisParisFRFrance48.869614[http://www.nyu.edu/paris.html]2.346863[Education]NaNNaN
2grid.473731.5New York University FlorenceFlorenceITItaly43.795910[http://www.nyu.edu/florence.html]11.265850[Education]NYUNaN
3grid.473728.dNew York Institute of TechnologyVancouverCACanada49.284374[http://nyit.edu/vancouver]-123.116480[Education]NYITBritish Columbia
4grid.449989.1University of New York in PraguePragueCZCzechia50.074043[https://www.unyp.cz/]14.433994[Education]UNYPNaN
5grid.449457.fNew York University ShanghaiShanghaiCNChina31.225506[https://shanghai.nyu.edu/]121.533510[Education]NaNNaN
6grid.444973.9University of New York TiranaTiranaALAlbania41.311060[http://unyt.edu.al/]19.801466[Education]UNYTNaN
7grid.440573.1New York University Abu DhabiAbu DhabiAEUnited Arab Emirates24.485000[https://nyuad.nyu.edu/]54.353000[Education]NaNNaN
8grid.410685.eSUNY KoreaSeoulKRSouth Korea37.377018[http://www.sunykorea.ac.kr/]126.666770[Education]NaNNaN
\n", + "
" + ], + "text/plain": [ + " id name city_name country_code \\\n", + "0 grid.512545.2 State University of New York, Korea Incheon KR \n", + "1 grid.479986.d New York University Paris Paris FR \n", + "2 grid.473731.5 New York University Florence Florence IT \n", + "3 grid.473728.d New York Institute of Technology Vancouver CA \n", + "4 grid.449989.1 University of New York in Prague Prague CZ \n", + "5 grid.449457.f New York University Shanghai Shanghai CN \n", + "6 grid.444973.9 University of New York Tirana Tirana AL \n", + "7 grid.440573.1 New York University Abu Dhabi Abu Dhabi AE \n", + "8 grid.410685.e SUNY Korea Seoul KR \n", + "\n", + " country_name latitude linkout \\\n", + "0 South Korea 37.376694 [http://www.sunykorea.ac.kr/] \n", + "1 France 48.869614 [http://www.nyu.edu/paris.html] \n", + "2 Italy 43.795910 [http://www.nyu.edu/florence.html] \n", + "3 Canada 49.284374 [http://nyit.edu/vancouver] \n", + "4 Czechia 50.074043 [https://www.unyp.cz/] \n", + "5 China 31.225506 [https://shanghai.nyu.edu/] \n", + "6 Albania 41.311060 [http://unyt.edu.al/] \n", + "7 United Arab Emirates 24.485000 [https://nyuad.nyu.edu/] \n", + "8 South Korea 37.377018 [http://www.sunykorea.ac.kr/] \n", + "\n", + " longitude types acronym state_name \n", + "0 126.667170 [Education] NaN NaN \n", + "1 2.346863 [Education] NaN NaN \n", + "2 11.265850 [Education] NYU NaN \n", + "3 -123.116480 [Education] NYIT British Columbia \n", + "4 14.433994 [Education] UNYP NaN \n", + "5 121.533510 [Education] NaN NaN \n", + "6 19.801466 [Education] UNYT NaN \n", + "7 54.353000 [Education] NaN NaN \n", + "8 126.666770 [Education] NaN NaN " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york\" \n", + " where types in [\"Education\"]\n", + " and country_name != \"United States\"\n", + "return organizations limit 10" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "l4V7z5TCd2Fo" + }, + "source": [ + "### Returning facets \n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1147, + "status": "ok", + "timestamp": 1574702640852, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "1fqSIrMkd2Fp", + "outputId": "3add0d42-15b5-4471-c75d-2e5ba6e0d86a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Country_name: 11\n", + "\u001b[2mTime: 0.50s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id count\n", + "0 United States 341\n", + "1 South Korea 2\n", + "2 Albania 1\n", + "3 Canada 1\n", + "4 China 1\n", + "5 Czechia 1\n", + "6 France 1\n", + "7 Italy 1\n", + "8 Panama 1\n", + "9 United Arab Emirates 1\n", + "10 United Kingdom 1" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york\" \n", + "return country_name" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 273 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 867, + "status": "ok", + "timestamp": 1574702673505, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "DMK_imi-l4ln", + "outputId": "b7986146-6e17-4ed9-b64f-74453770f3ff" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Types: 8\n", + "\u001b[2mTime: 5.47s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id count\n", + "0 Education 84\n", + "1 Nonprofit 75\n", + "2 Company 57\n", + "3 Government 46\n", + "4 Other 34\n", + "5 Healthcare 28\n", + "6 Archive 9\n", + "7 Facility 7" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%dsldf \n", + "search organizations \n", + " for \"new york\" \n", + " where country_name = \"United States\"\n", + "return types" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "l4V7z5TCd2Fo" + }, + "source": [ + "### Returning organizations facets from publications\n", + "\n", + "Organization data is used thoughout Dimensions. \n", + "\n", + "So, for example, one can do a publications search and return organizations as a facet. This allows to take advantage of organization metadata - e.g. latiture and longitude - in order to quickly build a geograpical visualization. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 1147, + "status": "ok", + "timestamp": 1574702640852, + "user": { + "displayName": "Michele Pasin", + "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mBu8LVjIGgontF2Wax51BoL5KFx8esezX3bUmaa0g=s64", + "userId": "10309320684375994511" + }, + "user_tz": 0 + }, + "id": "1fqSIrMkd2Fp", + "outputId": "3add0d42-15b5-4471-c75d-2e5ba6e0d86a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned Research_orgs: 50\n", + "\u001b[2mTime: 1.16s\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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idnamecity_namecountcountry_codecountry_namelatitudelinkoutlongitudestate_nametypesacronym
0grid.38142.3cHarvard UniversityCambridge33545USUnited States42.377052[http://www.harvard.edu/]-71.116650Massachusetts[Education]NaN
1grid.17063.33University of TorontoToronto21731CACanada43.661667[http://www.utoronto.ca/]-79.395000Ontario[Education]NaN
2grid.21107.35Johns Hopkins UniversityBaltimore19419USUnited States39.328888[https://www.jhu.edu/]-76.620280Maryland[Education]JHU
3grid.4991.5University of OxfordOxford19345GBUnited Kingdom51.753437[http://www.ox.ac.uk/]-1.254010Oxfordshire[Education]NaN
4grid.83440.3bUniversity College LondonLondon19047GBUnited Kingdom51.524470[http://www.ucl.ac.uk/]-0.133982NaN[Education]UCL
\n", + "
" + ], + "text/plain": [ + " id name city_name count country_code \\\n", + "0 grid.38142.3c Harvard University Cambridge 33545 US \n", + "1 grid.17063.33 University of Toronto Toronto 21731 CA \n", + "2 grid.21107.35 Johns Hopkins University Baltimore 19419 US \n", + "3 grid.4991.5 University of Oxford Oxford 19345 GB \n", + "4 grid.83440.3b University College London London 19047 GB \n", + "\n", + " country_name latitude linkout longitude \\\n", + "0 United States 42.377052 [http://www.harvard.edu/] -71.116650 \n", + "1 Canada 43.661667 [http://www.utoronto.ca/] -79.395000 \n", + "2 United States 39.328888 [https://www.jhu.edu/] -76.620280 \n", + "3 United Kingdom 51.753437 [http://www.ox.ac.uk/] -1.254010 \n", + "4 United Kingdom 51.524470 [http://www.ucl.ac.uk/] -0.133982 \n", + "\n", + " state_name types acronym \n", + "0 Massachusetts [Education] NaN \n", + "1 Ontario [Education] NaN \n", + "2 Maryland [Education] JHU \n", + "3 Oxfordshire [Education] NaN \n", + "4 NaN [Education] UCL " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "q = \"\"\"\n", + "search publications for \"coronavirus OR covid-19\" \n", + " where year > 2019 \n", + "return research_orgs[basics] limit 50\n", + "\"\"\"\n", + "\n", + "df = dslquery(q).as_dataframe()\n", + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "Collapsed": "false" + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [ + "Cambridge", + "grid.38142.3c", + [ + "Education" + ] + ], + [ + "Baltimore", + "grid.21107.35", + [ + "Education" + ] + ], + [ + "Seattle", + "grid.34477.33", + [ + "Education" + ] + ], + [ + "Stanford", + "grid.168010.e", + [ + "Education" + ] + ], + [ + "Ann Arbor", + "grid.214458.e", + [ + "Education" + ] + ], + [ + "Philadelphia", + "grid.25879.31", + [ + "Education" + ] + ], + [ + "New Haven", + "grid.47100.32", + [ + "Education" + ] + ], + [ + "San Francisco", + "grid.266102.1", + [ + "Education" + ] + ], + [ + "Los Angeles", + "grid.19006.3e", + [ + "Education" + ] + ], + [ + "Boston", + "grid.32224.35", + [ + "Healthcare" + ] + ], + [ + "Chapel Hill", + "grid.10698.36", + [ + "Education" + ] + ], + [ + "Atlanta", + "grid.189967.8", + [ + "Education" + ] + ], + [ + "New York", + "grid.137628.9", + [ + "Education" + ] + ], + [ + "New York", + "grid.21729.3f", + [ + "Education" + ] + ], + [ + "Boston", + "grid.62560.37", + [ + "Healthcare" + ] + ], + [ + "San Diego", + "grid.266100.3", + [ + "Education" + ] + ], + [ + "Durham", + "grid.26009.3d", + [ + "Education" + ] + ], + [ + "Rochester", + "grid.66875.3a", + [ + "Healthcare" + ] + ], + [ + "Minneapolis", + "grid.17635.36", + [ + "Education" + ] + ], + [ + "Pittsburgh", + "grid.21925.3d", + [ + "Education" + ] + ], + [ + "Gainesville", + "grid.15276.37", + [ + "Education" + ] + ], + [ + "Evanston", + "grid.16753.36", + [ + "Education" + ] + ], + [ + "New York", + "grid.59734.3c", + [ + "Education" + ] + ], + [ + "Ithaca", + "grid.5386.8", + [ + "Education" + ] + ], + [ + "St Louis", + "grid.4367.6", + [ + "Education" + ] + ], + [ + "Boston", + "grid.189504.1", + [ + "Education" + ] + ] + ], + "geo": "geo", + "hovertemplate": "%{hovertext}

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country_name=Canada
count=%{marker.size}
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city_name=%{customdata[0]}
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types=%{customdata[2]}", + "hovertext": [ + "University of Toronto", + "University of British Columbia", + "McGill University", + "McMaster University" + ], + "lat": { + "bdata": "1esWgbHURUBxdQDEXaFIQKjg8IKIwEZAwf7r3LShRUA=", + "dtype": "f8" + }, + "legendgroup": "Canada", + "lon": { + "bdata": "4XoUrkfZU8DIDFTGv89ewNsWZTbIZFLAxqcAGM/6U8A=", + "dtype": "f8" + }, + "marker": { + "color": "#EF553B", + "size": { + "bdata": "41TjMF4hzR8=", + "dtype": "i2" + }, + "sizemode": "area", + "sizeref": 83.8625, + "symbol": "circle" + }, + "mode": "markers", + "name": "Canada", + "showlegend": true, + "type": "scattergeo" + }, + { + "customdata": [ + [ + "Oxford", + "grid.4991.5", + [ + "Education" + ] + ], + [ + "London", + "grid.83440.3b", + [ + "Education" + ] + ], + [ + "London", + "grid.13097.3c", + [ + "Education" + ] + ], + [ + "London", + "grid.7445.2", + [ + "Education" + ] + ], + [ + "Cambridge", + "grid.5335.0", + [ + "Education" + ] + ], + [ + "Manchester", + "grid.5379.8", + [ + "Education" + ] + ], + [ + "London", + "grid.8991.9", + [ + "Education" + ] + ], + [ + "Edinburgh", + "grid.4305.2", + [ + "Education" + ] + ] + ], + "geo": "geo", + "hovertemplate": "%{hovertext}

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count=%{marker.size}
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longitude=%{lon}
city_name=%{customdata[0]}
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country_name=Brazil
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
city_name=%{customdata[0]}
id=%{customdata[1]}
types=%{customdata[2]}", + "hovertext": [ + "Universidade de São Paulo" + ], + "lat": { + "bdata": "6Po+HCSQN8A=", + "dtype": "f8" + }, + "legendgroup": "Brazil", + "lon": { + "bdata": "EtvdA3RdR8A=", + "dtype": "f8" + }, + "marker": { + "color": "#ab63fa", + "size": { + "bdata": "Bzw=", + "dtype": "i2" + }, + "sizemode": "area", + "sizeref": 83.8625, + "symbol": "circle" + }, + "mode": "markers", + "name": "Brazil", + "showlegend": true, + "type": "scattergeo" + }, + { + "customdata": [ + [ + "Melbourne", + "grid.1008.9", + [ + "Education" + ] + ], + [ + "Sydney", + "grid.1013.3", + [ + "Education" + ] + ], + [ + "Melbourne", + "grid.1002.3", + [ + "Education" + ] + ], + [ + "Sydney", + "grid.1005.4", + [ + "Education" + ] + ], + [ + "Brisbane", + "grid.1003.2", + [ + "Education" + ] + ] + ], + "geo": "geo", + "hovertemplate": "%{hovertext}

country_name=Australia
count=%{marker.size}
latitude=%{lat}
longitude=%{lon}
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} + }, + "text/html": [ + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter_geo(df, \n", + " lat=\"latitude\", lon=\"longitude\",\n", + " color=\"country_name\",\n", + " size=\"count\", \n", + " projection=\"natural earth\",\n", + " hover_name=\"name\",\n", + " hover_data=['city_name', 'id', 'types']\n", + " )\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "hcg2KOswd2F4" + }, + "source": [ + "## 3. A closer look at the organizations data statistics\n", + "\n", + "The Dimensions Search Language [exposes programmatically metadata](https://docs.dimensions.ai/dsl/data.html#getting-documentation-programmatically), such as supported sources and entities, along with their fields, facets, fieldsets, metrics and search fields. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "Collapsed": "false" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sourcesfieldtypedescriptionis_filteris_entityis_facet
0organizationsacronymstringGRID acronym of the organization. E.g., \"UT\" f...TrueFalseFalse
1organizationscity_namestringGRID name of the organization country. E.g., \"...TrueFalseTrue
2organizationscnrs_idsstringCNRS IDs for this organizationTrueFalseFalse
3organizationscountry_codestringCountry of the organisation, identified using ...TrueFalseTrue
4organizationscountry_namestringGRID name of the organization country. E.g., \"...TrueFalseTrue
5organizationsdimensions_urlstringLink pointing to the Dimensions web applicationFalseFalseFalse
6organizationsestablishedintegerYear when the organization was estabilishedTrueFalseFalse
7organizationsexternal_ids_fundrefstringFundref IDs for this organizationTrueFalseFalse
8organizationshesa_idsstringHESA IDs for this organizationTrueFalseFalse
9organizationsidstringGRID ID of the organization. E.g., \"grid.26999...TrueFalseFalse
10organizationsisni_idsstringISNI IDs for this organizationTrueFalseFalse
11organizationslatitudefloatNoneFalseFalseFalse
12organizationslinkoutstringNoneFalseFalseFalse
13organizationslongitudefloatNoneFalseFalseFalse
14organizationsnamestringGRID name of the organization. E.g., \"Universi...TrueFalseFalse
15organizationsnuts_level1_codestringLevel 1 code for this organization, based on `...TrueFalseTrue
16organizationsnuts_level1_namestringLevel 1 name for this organization, based on `...TrueFalseTrue
17organizationsnuts_level2_codestringLevel 2 code for this organization, based on `...TrueFalseTrue
18organizationsnuts_level2_namestringLevel 2 name for this organization, based on `...TrueFalseTrue
19organizationsnuts_level3_codestringLevel 3 code for this organization, based on `...TrueFalseTrue
20organizationsnuts_level3_namestringLevel 3 name for this organization, based on `...TrueFalseTrue
21organizationsorganization_child_idsstringChild organization IDsTrueFalseFalse
22organizationsorganization_parent_idsstringParent organization IDsTrueFalseFalse
23organizationsorganization_related_idsstringRelated organization IDsTrueFalseFalse
24organizationsorgref_idsstringOrgRef IDs for this organizationTrueFalseFalse
25organizationsredirectstringGRID ID of an organization this one was redire...TrueFalseFalse
26organizationsror_idsstringROR IDs for this organizationTrueFalseFalse
27organizationsscorefloatFor full-text queries, the relevance score is ...TrueFalseFalse
28organizationsstate_namestringGRID name of the organization country. E.g., \"...TrueFalseTrue
29organizationsstatusstringStatus of an organization. May be be one of:\\n...TrueFalseTrue
30organizationstypesstringType of an organization. Available types inclu...TrueFalseTrue
31organizationsucas_idsstringUCAS IDs for this organizationTrueFalseFalse
32organizationsukprn_idsstringUKPRN IDs for this organizationTrueFalseFalse
33organizationswikidata_idsstringWikiData IDs for this organizationTrueFalseFalse
34organizationswikipedia_urlstringWikipedia URLFalseFalseFalse
\n", + "
" + ], + "text/plain": [ + " sources field type \\\n", + "0 organizations acronym string \n", + "1 organizations city_name string \n", + "2 organizations cnrs_ids string \n", + "3 organizations country_code string \n", + "4 organizations country_name string \n", + "5 organizations dimensions_url string \n", + "6 organizations established integer \n", + "7 organizations external_ids_fundref string \n", + "8 organizations hesa_ids string \n", + "9 organizations id string \n", + "10 organizations isni_ids string \n", + "11 organizations latitude float \n", + "12 organizations linkout string \n", + "13 organizations longitude float \n", + "14 organizations name string \n", + "15 organizations nuts_level1_code string \n", + "16 organizations nuts_level1_name string \n", + "17 organizations nuts_level2_code string \n", + "18 organizations nuts_level2_name string \n", + "19 organizations nuts_level3_code string \n", + "20 organizations nuts_level3_name string \n", + "21 organizations organization_child_ids string \n", + "22 organizations organization_parent_ids string \n", + "23 organizations organization_related_ids string \n", + "24 organizations orgref_ids string \n", + "25 organizations redirect string \n", + "26 organizations ror_ids string \n", + "27 organizations score float \n", + "28 organizations state_name string \n", + "29 organizations status string \n", + "30 organizations types string \n", + "31 organizations ucas_ids string \n", + "32 organizations ukprn_ids string \n", + "33 organizations wikidata_ids string \n", + "34 organizations wikipedia_url string \n", + "\n", + " description is_filter is_entity \\\n", + "0 GRID acronym of the organization. E.g., \"UT\" f... True False \n", + "1 GRID name of the organization country. E.g., \"... True False \n", + "2 CNRS IDs for this organization True False \n", + "3 Country of the organisation, identified using ... True False \n", + "4 GRID name of the organization country. E.g., \"... True False \n", + "5 Link pointing to the Dimensions web application False False \n", + "6 Year when the organization was estabilished True False \n", + "7 Fundref IDs for this organization True False \n", + "8 HESA IDs for this organization True False \n", + "9 GRID ID of the organization. E.g., \"grid.26999... True False \n", + "10 ISNI IDs for this organization True False \n", + "11 None False False \n", + "12 None False False \n", + "13 None False False \n", + "14 GRID name of the organization. E.g., \"Universi... True False \n", + "15 Level 1 code for this organization, based on `... True False \n", + "16 Level 1 name for this organization, based on `... True False \n", + "17 Level 2 code for this organization, based on `... True False \n", + "18 Level 2 name for this organization, based on `... True False \n", + "19 Level 3 code for this organization, based on `... True False \n", + "20 Level 3 name for this organization, based on `... True False \n", + "21 Child organization IDs True False \n", + "22 Parent organization IDs True False \n", + "23 Related organization IDs True False \n", + "24 OrgRef IDs for this organization True False \n", + "25 GRID ID of an organization this one was redire... True False \n", + "26 ROR IDs for this organization True False \n", + "27 For full-text queries, the relevance score is ... True False \n", + "28 GRID name of the organization country. E.g., \"... True False \n", + "29 Status of an organization. May be be one of:\\n... True False \n", + "30 Type of an organization. Available types inclu... True False \n", + "31 UCAS IDs for this organization True False \n", + "32 UKPRN IDs for this organization True False \n", + "33 WikiData IDs for this organization True False \n", + "34 Wikipedia URL False False \n", + "\n", + " is_facet \n", + "0 False \n", + "1 True \n", + "2 False \n", + "3 True \n", + "4 True \n", + "5 False \n", + "6 False \n", + "7 False \n", + "8 False \n", + "9 False \n", + "10 False \n", + "11 False \n", + "12 False \n", + "13 False \n", + "14 False \n", + "15 True \n", + "16 True \n", + "17 True \n", + "18 True \n", + "19 True \n", + "20 True \n", + "21 False \n", + "22 False \n", + "23 False \n", + "24 False \n", + "25 False \n", + "26 False \n", + "27 False \n", + "28 True \n", + "29 True \n", + "30 True \n", + "31 False \n", + "32 False \n", + "33 False \n", + "34 False " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%dsldocs organizations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "KZdeyuHvm3ve" + }, + "source": [ + "We can use the fields information above to draw up some quick statistics re. the organizations source. \n", + "\n", + "In order to do this, we use the operator `is not empty` to generate automatically queries like this `search organizations where field_name is not empty return organizations limit 1` and then use the `total_count` field in the JSON we get back for our statistics. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 645, + "resources": { + "http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css": { + "data": "/* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /* We import all of these together in a single css file because the Webpack
loader sees only one file at a time. This allows postcss to see the variable
definitions when they are used. */

 /*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

 /*
This file is copied from the JupyterLab project to define default styling for
when the widget styling is compiled down to eliminate CSS variables. We make one
change - we comment out the font import below.
*/

 /**
 * The material design colors are adapted from google-material-color v1.2.6
 * https://github.com/danlevan/google-material-color
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/dist/palette.var.css
 *
 * The license for the material design color CSS variables is as follows (see
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Dan Le Van
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

 /*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/

 /*
 * Optional monospace font for input/output prompt.
 */

 /* Commented out in ipywidgets since we don't need it. */

 /* @import url('https://fonts.googleapis.com/css?family=Roboto+Mono'); */

 /*
 * Added for compabitility with output area
 */

 :root {

  /* Borders

  The following variables, specify the visual styling of borders in JupyterLab.
   */

  /* UI Fonts

  The UI font CSS variables are used for the typography all of the JupyterLab
  user interface elements that are not directly user generated content.
  */ /* Base font size */ /* Ensures px perfect FontAwesome icons */

  /* Use these font colors against the corresponding main layout colors.
     In a light theme, these go from dark to light.
  */

  /* Use these against the brand/accent/warn/error colors.
     These will typically go from light to darker, in both a dark and light theme
   */

  /* Content Fonts

  Content font variables are used for typography of user generated content.
  */ /* Base font size */


  /* Layout

  The following are the main layout colors use in JupyterLab. In a light
  theme these would go from light to dark.
  */

  /* Brand/accent */

  /* State colors (warn, error, success, info) */

  /* Cell specific styles */
  /* A custom blend of MD grey and blue 600
   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
  /* A custom blend of MD grey and orange 600
   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */

  /* Notebook specific styles */

  /* Console specific styles */

  /* Toolbar specific styles */
}

 /* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /*
 * We assume that the CSS variables in
 * https://github.com/jupyterlab/jupyterlab/blob/master/src/default-theme/variables.css
 * have been defined.
 */

 /* This file has code derived from PhosphorJS CSS files, as noted below. The license for this PhosphorJS code is:

Copyright (c) 2014-2017, PhosphorJS Contributors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

*/

 /*
 * The following section is derived from https://github.com/phosphorjs/phosphor/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css 
 * We've scoped the rules so that they are consistent with exactly our code.
 */

 .jupyter-widgets.widget-tab > .p-TabBar {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
  margin: 0;
  padding: 0;
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  list-style-type: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] > .p-TabBar-content {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] > .p-TabBar-content {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
  overflow: hidden;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
  -webkit-box-flex: 0;
      -ms-flex: 0 0 auto;
          flex: 0 0 auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel {
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  overflow: hidden;
  white-space: nowrap;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden {
  display: none !important;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab {
  position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab {
  left: 0;
  -webkit-transition: left 150ms ease;
  transition: left 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab {
  top: 0;
  -webkit-transition: top 150ms ease;
  transition: top 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging {
  -webkit-transition: none;
  transition: none;
}

 /* End tabbar.css */

 :root { /* margin between inline elements */

    /* From Material Design Lite */
}

 .jupyter-widgets {
    margin: 2px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    color: black;
    overflow: visible;
}

 .jupyter-widgets.jupyter-widgets-disconnected::before {
    line-height: 28px;
    height: 28px;
}

 .jp-Output-result > .jupyter-widgets {
    margin-left: 0;
    margin-right: 0;
}

 /* vbox and hbox */

 .widget-inline-hbox {
    /* Horizontal widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
    -webkit-box-align: baseline;
        -ms-flex-align: baseline;
            align-items: baseline;
}

 .widget-inline-vbox {
    /* Vertical Widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
    overflow: auto;
}

 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
}

 .widget-vbox {
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 /* General Button Styling */

 .jupyter-button {
    padding-left: 10px;
    padding-right: 10px;
    padding-top: 0px;
    padding-bottom: 0px;
    display: inline-block;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
    border: 0px solid;
    line-height: 28px;
    -webkit-box-shadow: none;
            box-shadow: none;

    color: rgba(0, 0, 0, .8);
    background-color: #EEEEEE;
    border-color: #E0E0E0;
    border: none;
}

 .jupyter-button i.fa {
    margin-right: 4px;
    pointer-events: none;
}

 .jupyter-button:empty:before {
    content: "\200b"; /* zero-width space */
}

 .jupyter-widgets.jupyter-button:disabled {
    opacity: 0.6;
}

 .jupyter-button i.fa.center {
    margin-right: 0;
}

 .jupyter-button:hover:enabled, .jupyter-button:focus:enabled {
    /* MD Lite 2dp shadow */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0idXRmLTgiPz4KPCEtLSBHZW5lcmF0b3I6IEFkb2JlIElsbHVzdHJhdG9yIDE5LjIuMSwgU1ZHIEV4cG9ydCBQbHVnLUluIC4gU1ZHIFZlcnNpb246IDYuMDAgQnVpbGQgMCkgIC0tPgo8c3ZnIHZlcnNpb249IjEuMSIgaWQ9IkxheWVyXzEiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyIgeG1sbnM6eGxpbms9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkveGxpbmsiIHg9IjBweCIgeT0iMHB4IgoJIHZpZXdCb3g9IjAgMCAxOCAxOCIgc3R5bGU9ImVuYWJsZS1iYWNrZ3JvdW5kOm5ldyAwIDAgMTggMTg7IiB4bWw6c3BhY2U9InByZXNlcnZlIj4KPHN0eWxlIHR5cGU9InRleHQvY3NzIj4KCS5zdDB7ZmlsbDpub25lO30KPC9zdHlsZT4KPHBhdGggZD0iTTUuMiw1LjlMOSw5LjdsMy44LTMuOGwxLjIsMS4ybC00LjksNWwtNC45LTVMNS4yLDUuOXoiLz4KPHBhdGggY2xhc3M9InN0MCIgZD0iTTAtMC42aDE4djE4SDBWLTAuNnoiLz4KPC9zdmc+Cg");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

/*# 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ogV2lkZ2V0IFRleHQgYW5kIFRleHRBcmVhIFN0eWluZyAqL1xuXG4ud2lkZ2V0LXRleHRhcmVhLCAud2lkZ2V0LXRleHQge1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aCk7XG59XG5cbi53aWRnZXQtdGV4dCBpbnB1dFt0eXBlPVwidGV4dFwiXSwgLndpZGdldC10ZXh0IGlucHV0W3R5cGU9XCJudW1iZXJcIl17XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LXRleHQgaW5wdXRbdHlwZT1cInRleHRcIl06ZGlzYWJsZWQsIC53aWRnZXQtdGV4dCBpbnB1dFt0eXBlPVwibnVtYmVyXCJdOmRpc2FibGVkLCAud2lkZ2V0LXRleHRhcmVhIHRleHRhcmVhOmRpc2FibGVkIHtcbiAgICBvcGFjaXR5OiB2YXIoLS1qcC13aWRnZXRzLWRpc2FibGVkLW9wYWNpdHkpO1xufVxuXG4ud2lkZ2V0LXRleHQgaW5wdXRbdHlwZT1cInRleHRcIl0sIC53aWRnZXQtdGV4dCBpbnB1dFt0eXBlPVwibnVtYmVyXCJdLCAud2lkZ2V0LXRleHRhcmVhIHRleHRhcmVhIHtcbiAgICBib3gtc2l6aW5nOiBib3JkZXItYm94O1xuICAgIGJvcmRlcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLWNvbG9yKTtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJhY2tncm91bmQtY29sb3IpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWNvbG9yKTtcbiAgICBmb250LXNpemU6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKTtcbiAgICBwYWRkaW5nOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpIGNhbGMoIHZhcigtLWpwLXdpZGdldHMtaW5wdXQtcGFkZGluZykgKiAgMiApO1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgZmxleC1zaHJpbms6IDE7XG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xufVxuXG4ud2lkZ2V0LXRleHRhcmVhIHRleHRhcmVhIHtcbiAgICBoZWlnaHQ6IGluaGVyaXQ7XG4gICAgd2lkdGg6IGluaGVyaXQ7XG59XG5cbi53aWRnZXQtdGV4dCBpbnB1dDpmb2N1cywgLndpZGdldC10ZXh0YXJlYSB0ZXh0YXJlYTpmb2N1cyB7XG4gICAgYm9yZGVyLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWZvY3VzLWJvcmRlci1jb2xvcik7XG59XG5cbi8qIFdpZGdldCBTbGlkZXIgKi9cblxuLndpZGdldC1zbGlkZXIgLnVpLXNsaWRlciB7XG4gICAgLyogU2xpZGVyIFRyYWNrICovXG4gICAgYm9yZGVyOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWxheW91dC1jb2xvcjMpO1xuICAgIGJhY2tncm91bmQ6IHZhcigtLWpwLWxheW91dC1jb2xvcjMpO1xuICAgIGJveC1zaXppbmc6IGJvcmRlci1ib3g7XG4gICAgcG9zaXRpb246IHJlbGF0aXZlO1xuICAgIGJvcmRlci1yYWRpdXM6IDBweDtcbn1cblxuLndpZGdldC1zbGlkZXIgLnVpLXNsaWRlciAudWktc2xpZGVyLWhhbmRsZSB7XG4gICAgLyogU2xpZGVyIEhhbmRsZSAqL1xuICAgIG91dGxpbmU6IG5vbmUgIWltcG9ydGFudDsgLyogZm9jdXNlZCBzbGlkZXIgaGFuZGxlcyBhcmUgY29sb3JlZCAtIHNlZSBiZWxvdyAqL1xuICAgIHBvc2l0aW9uOiBhYnNvbHV0ZTtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtYmFja2dyb3VuZC1jb2xvcik7XG4gICAgYm9yZGVyOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1ib3JkZXItY29sb3IpO1xuICAgIGJveC1zaXppbmc6IGJvcmRlci1ib3g7XG4gICAgei1pbmRleDogMTtcbiAgICBiYWNrZ3JvdW5kLWltYWdlOiBub25lOyAvKiBPdmVycmlkZSBqcXVlcnktdWkgKi9cbn1cblxuLyogT3ZlcnJpZGUganF1ZXJ5LXVpICovXG4ud2lkZ2V0LXNsaWRlciAudWktc2xpZGVyIC51aS1zbGlkZXItaGFuZGxlOmhvdmVyLCAud2lkZ2V0LXNsaWRlciAudWktc2xpZGVyIC51aS1zbGlkZXItaGFuZGxlOmZvY3VzIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1hY3RpdmUtaGFuZGxlLWNvbG9yKTtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkgc29saWQgdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYWN0aXZlLWhhbmRsZS1jb2xvcik7XG59XG5cbi53aWRnZXQtc2xpZGVyIC51aS1zbGlkZXIgLnVpLXNsaWRlci1oYW5kbGU6YWN0aXZlIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1hY3RpdmUtaGFuZGxlLWNvbG9yKTtcbiAgICBib3JkZXItY29sb3I6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWFjdGl2ZS1oYW5kbGUtY29sb3IpO1xuICAgIHotaW5kZXg6IDI7XG4gICAgdHJhbnNmb3JtOiBzY2FsZSgxLjIpO1xufVxuXG4ud2lkZ2V0LXNsaWRlciAgLnVpLXNsaWRlciAudWktc2xpZGVyLXJhbmdlIHtcbiAgICAvKiBJbnRlcnZhbCBiZXR3ZWVuIHRoZSB0d28gc3BlY2lmaWVkIHZhbHVlIG9mIGEgZG91YmxlIHNsaWRlciAqL1xuICAgIHBvc2l0aW9uOiBhYnNvbHV0ZTtcbiAgICBiYWNrZ3JvdW5kOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1hY3RpdmUtaGFuZGxlLWNvbG9yKTtcbiAgICB6LWluZGV4OiAwO1xufVxuXG4vKiBTaGFwZXMgb2YgU2xpZGVyIEhhbmRsZXMgKi9cblxuLndpZGdldC1oc2xpZGVyIC51aS1zbGlkZXIgLnVpLXNsaWRlci1oYW5kbGUge1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSk7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSk7XG4gICAgbWFyZ2luLXRvcDogY2FsYygodmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSAtIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKSkgLyAyIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG4gICAgbWFyZ2luLWxlZnQ6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItaGFuZGxlLXNpemUpIC8gLTIgKyB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpKTtcbiAgICBib3JkZXItcmFkaXVzOiA1MCU7XG4gICAgdG9wOiAwO1xufVxuXG4ud2lkZ2V0LXZzbGlkZXIgLnVpLXNsaWRlciAudWktc2xpZGVyLWhhbmRsZSB7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKTtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKTtcbiAgICBtYXJnaW4tYm90dG9tOiBjYWxjKHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKSAvIC0yICsgdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG4gICAgbWFyZ2luLWxlZnQ6IGNhbGMoKHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLXRyYWNrLXRoaWNrbmVzcykgLSB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSkpIC8gMiAtIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkpO1xuICAgIGJvcmRlci1yYWRpdXM6IDUwJTtcbiAgICBsZWZ0OiAwO1xufVxuXG4ud2lkZ2V0LWhzbGlkZXIgLnVpLXNsaWRlciAudWktc2xpZGVyLXJhbmdlIHtcbiAgICBoZWlnaHQ6IGNhbGMoIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLXRyYWNrLXRoaWNrbmVzcykgKiAyICk7XG4gICAgbWFyZ2luLXRvcDogY2FsYygodmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSAtIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLXRyYWNrLXRoaWNrbmVzcykgKiAyICkgLyAyIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG59XG5cbi53aWRnZXQtdnNsaWRlciAudWktc2xpZGVyIC51aS1zbGlkZXItcmFuZ2Uge1xuICAgIHdpZHRoOiBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci10cmFjay10aGlja25lc3MpICogMiApO1xuICAgIG1hcmdpbi1sZWZ0OiBjYWxjKCh2YXIoLS1qcC13aWRnZXRzLXNsaWRlci10cmFjay10aGlja25lc3MpIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSAqIDIgKSAvIDIgLSB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpKTtcbn1cblxuLyogSG9yaXpvbnRhbCBTbGlkZXIgKi9cblxuLndpZGdldC1oc2xpZGVyIHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcblxuICAgIC8qIE92ZXJyaWRlIHRoZSBhbGlnbi1pdGVtcyBiYXNlbGluZS4gVGhpcyB3YXksIHRoZSBkZXNjcmlwdGlvbiBhbmQgcmVhZG91dFxuICAgIHN0aWxsIHNlZW0gdG8gYWxpZ24gdGhlaXIgYmFzZWxpbmUgcHJvcGVybHksIGFuZCB3ZSBkb24ndCBoYXZlIHRvIGhhdmVcbiAgICBhbGlnbi1zZWxmOiBzdHJldGNoIGluIHRoZSAuc2xpZGVyLWNvbnRhaW5lci4gKi9cbiAgICBhbGlnbi1pdGVtczogY2VudGVyO1xufVxuXG4ud2lkZ2V0cy1zbGlkZXIgLnNsaWRlci1jb250YWluZXIge1xuICAgIG92ZXJmbG93OiB2aXNpYmxlO1xufVxuXG4ud2lkZ2V0LWhzbGlkZXIgLnNsaWRlci1jb250YWluZXIge1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBtYXJnaW4tbGVmdDogY2FsYyh2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSkgLyAyIC0gMiAqIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkpO1xuICAgIG1hcmdpbi1yaWdodDogY2FsYyh2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1oYW5kbGUtc2l6ZSkgLyAyIC0gMiAqIHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWJvcmRlci13aWR0aCkpO1xuICAgIGZsZXg6IDEgMSB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aC1zaG9ydCk7XG59XG5cbi53aWRnZXQtaHNsaWRlciAudWktc2xpZGVyIHtcbiAgICAvKiBJbm5lciwgaW52aXNpYmxlIHNsaWRlIGRpdiAqL1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKTtcbiAgICBtYXJnaW4tdG9wOiBjYWxjKCh2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpIC0gdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKSkgLyAyKTtcbiAgICB3aWR0aDogMTAwJTtcbn1cblxuLyogVmVydGljYWwgU2xpZGVyICovXG5cbi53aWRnZXQtdmJveCAud2lkZ2V0LWxhYmVsIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG59XG5cbi53aWRnZXQtdnNsaWRlciB7XG4gICAgLyogVmVydGljYWwgU2xpZGVyICovXG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLXZlcnRpY2FsLWhlaWdodCk7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXRpbnkpO1xufVxuXG4ud2lkZ2V0LXZzbGlkZXIgLnNsaWRlci1jb250YWluZXIge1xuICAgIGZsZXg6IDEgMSB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aC1zaG9ydCk7XG4gICAgbWFyZ2luLWxlZnQ6IGF1dG87XG4gICAgbWFyZ2luLXJpZ2h0OiBhdXRvO1xuICAgIG1hcmdpbi1ib3R0b206IGNhbGModmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItaGFuZGxlLXNpemUpIC8gMiAtIDIgKiB2YXIoLS1qcC13aWRnZXRzLXNsaWRlci1ib3JkZXItd2lkdGgpKTtcbiAgICBtYXJnaW4tdG9wOiBjYWxjKHZhcigtLWpwLXdpZGdldHMtc2xpZGVyLWhhbmRsZS1zaXplKSAvIDIgLSAyICogdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItYm9yZGVyLXdpZHRoKSk7XG4gICAgZGlzcGxheTogZmxleDtcbiAgICBmbGV4LWRpcmVjdGlvbjogY29sdW1uO1xufVxuXG4ud2lkZ2V0LXZzbGlkZXIgLnVpLXNsaWRlci12ZXJ0aWNhbCB7XG4gICAgLyogSW5uZXIsIGludmlzaWJsZSBzbGlkZSBkaXYgKi9cbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1zbGlkZXItdHJhY2stdGhpY2tuZXNzKTtcbiAgICBmbGV4LWdyb3c6IDE7XG4gICAgbWFyZ2luLWxlZnQ6IGF1dG87XG4gICAgbWFyZ2luLXJpZ2h0OiBhdXRvO1xufVxuXG4vKiBXaWRnZXQgUHJvZ3Jlc3MgU3R5bGluZyAqL1xuXG4ucHJvZ3Jlc3MtYmFyIHtcbiAgICAtd2Via2l0LXRyYW5zaXRpb246IG5vbmU7XG4gICAgLW1vei10cmFuc2l0aW9uOiBub25lO1xuICAgIC1tcy10cmFuc2l0aW9uOiBub25lO1xuICAgIC1vLXRyYW5zaXRpb246IG5vbmU7XG4gICAgdHJhbnNpdGlvbjogbm9uZTtcbn1cblxuLnByb2dyZXNzLWJhciB7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ucHJvZ3Jlc3MtYmFyIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC1icmFuZC1jb2xvcjEpO1xufVxuXG4ucHJvZ3Jlc3MtYmFyLXN1Y2Nlc3Mge1xuICAgIGJhY2tncm91bmQtY29sb3I6IHZhcigtLWpwLXN1Y2Nlc3MtY29sb3IxKTtcbn1cblxuLnByb2dyZXNzLWJhci1pbmZvIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC1pbmZvLWNvbG9yMSk7XG59XG5cbi5wcm9ncmVzcy1iYXItd2FybmluZyB7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtd2Fybi1jb2xvcjEpO1xufVxuXG4ucHJvZ3Jlc3MtYmFyLWRhbmdlciB7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtZXJyb3ItY29sb3IxKTtcbn1cblxuLnByb2dyZXNzIHtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC1sYXlvdXQtY29sb3IyKTtcbiAgICBib3JkZXI6IG5vbmU7XG4gICAgYm94LXNoYWRvdzogbm9uZTtcbn1cblxuLyogSG9yaXNvbnRhbCBQcm9ncmVzcyAqL1xuXG4ud2lkZ2V0LWhwcm9ncmVzcyB7XG4gICAgLyogUHJvZ3Jlc3MgQmFyICovXG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aCk7XG4gICAgYWxpZ24taXRlbXM6IGNlbnRlcjtcblxufVxuXG4ud2lkZ2V0LWhwcm9ncmVzcyAucHJvZ3Jlc3Mge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBtYXJnaW4tdG9wOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpO1xuICAgIG1hcmdpbi1ib3R0b206IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtcGFkZGluZyk7XG4gICAgYWxpZ24tc2VsZjogc3RyZXRjaDtcbiAgICAvKiBPdmVycmlkZSBib290c3RyYXAgc3R5bGUgKi9cbiAgICBoZWlnaHQ6IGluaXRpYWw7XG59XG5cbi8qIFZlcnRpY2FsIFByb2dyZXNzICovXG5cbi53aWRnZXQtdnByb2dyZXNzIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtdmVydGljYWwtaGVpZ2h0KTtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgtdGlueSk7XG59XG5cbi53aWRnZXQtdnByb2dyZXNzIC5wcm9ncmVzcyB7XG4gICAgZmxleC1ncm93OiAxO1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLXByb2dyZXNzLXRoaWNrbmVzcyk7XG4gICAgbWFyZ2luLWxlZnQ6IGF1dG87XG4gICAgbWFyZ2luLXJpZ2h0OiBhdXRvO1xuICAgIG1hcmdpbi1ib3R0b206IDA7XG59XG5cbi8qIFNlbGVjdCBXaWRnZXQgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LWRyb3Bkb3duIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoKTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbn1cblxuLndpZGdldC1kcm9wZG93biA+IHNlbGVjdCB7XG4gICAgcGFkZGluZy1yaWdodDogMjBweDtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgYm9yZGVyLXJhZGl1czogMDtcbiAgICBoZWlnaHQ6IGluaGVyaXQ7XG4gICAgZmxleDogMSAxIHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXNob3J0KTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgYm94LXNpemluZzogYm9yZGVyLWJveDtcbiAgICBvdXRsaW5lOiBub25lICFpbXBvcnRhbnQ7XG4gICAgYm94LXNoYWRvdzogbm9uZTtcbiAgICBiYWNrZ3JvdW5kLWNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJhY2tncm91bmQtY29sb3IpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWNvbG9yKTtcbiAgICBmb250LXNpemU6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKTtcbiAgICB2ZXJ0aWNhbC1hbGlnbjogdG9wO1xuICAgIHBhZGRpbmctbGVmdDogY2FsYyggdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1wYWRkaW5nKSAqIDIpO1xuXHRhcHBlYXJhbmNlOiBub25lO1xuXHQtd2Via2l0LWFwcGVhcmFuY2U6IG5vbmU7XG5cdC1tb3otYXBwZWFyYW5jZTogbm9uZTtcbiAgICBiYWNrZ3JvdW5kLXJlcGVhdDogbm8tcmVwZWF0O1xuXHRiYWNrZ3JvdW5kLXNpemU6IDIwcHg7XG5cdGJhY2tncm91bmQtcG9zaXRpb246IHJpZ2h0IGNlbnRlcjtcbiAgICBiYWNrZ3JvdW5kLWltYWdlOiB2YXIoLS1qcC13aWRnZXRzLWRyb3Bkb3duLWFycm93KTtcbn1cbi53aWRnZXQtZHJvcGRvd24gPiBzZWxlY3Q6Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lkZ2V0LWRyb3Bkb3duID4gc2VsZWN0OmRpc2FibGVkIHtcbiAgICBvcGFjaXR5OiB2YXIoLS1qcC13aWRnZXRzLWRpc2FibGVkLW9wYWNpdHkpO1xufVxuXG4vKiBUbyBkaXNhYmxlIHRoZSBkb3R0ZWQgYm9yZGVyIGluIEZpcmVmb3ggYXJvdW5kIHNlbGVjdCBjb250cm9scy5cbiAgIFNlZSBodHRwOi8vc3RhY2tvdmVyZmxvdy5jb20vYS8xODg1MzAwMiAqL1xuLndpZGdldC1kcm9wZG93biA+IHNlbGVjdDotbW96LWZvY3VzcmluZyB7XG4gICAgY29sb3I6IHRyYW5zcGFyZW50O1xuICAgIHRleHQtc2hhZG93OiAwIDAgMCAjMDAwO1xufVxuXG4vKiBTZWxlY3QgYW5kIFNlbGVjdE11bHRpcGxlICovXG5cbi53aWRnZXQtc2VsZWN0IHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuXG4gICAgLyogQmVjYXVzZSBGaXJlZm94IGRlZmluZXMgdGhlIGJhc2VsaW5lIG9mIGEgc2VsZWN0IGFzIHRoZSBib3R0b20gb2YgdGhlXG4gICAgY29udHJvbCwgd2UgYWxpZ24gdGhlIGVudGlyZSBjb250cm9sIHRvIHRoZSB0b3AgYW5kIGFkZCBwYWRkaW5nIHRvIHRoZVxuICAgIHNlbGVjdCB0byBnZXQgYW4gYXBwcm94aW1hdGUgZmlyc3QgbGluZSBiYXNlbGluZSBhbGlnbm1lbnQuICovXG4gICAgYWxpZ24taXRlbXM6IGZsZXgtc3RhcnQ7XG59XG5cbi53aWRnZXQtc2VsZWN0ID4gc2VsZWN0IHtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1iYWNrZ3JvdW5kLWNvbG9yKTtcbiAgICBjb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1jb2xvcik7XG4gICAgZm9udC1zaXplOiB2YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSk7XG4gICAgZmxleDogMSAxIHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXNob3J0KTtcbiAgICBvdXRsaW5lOiBub25lICFpbXBvcnRhbnQ7XG4gICAgb3ZlcmZsb3c6IGF1dG87XG4gICAgaGVpZ2h0OiBpbmhlcml0O1xuXG4gICAgLyogQmVjYXVzZSBGaXJlZm94IGRlZmluZXMgdGhlIGJhc2VsaW5lIG9mIGEgc2VsZWN0IGFzIHRoZSBib3R0b20gb2YgdGhlXG4gICAgY29udHJvbCwgd2UgYWxpZ24gdGhlIGVudGlyZSBjb250cm9sIHRvIHRoZSB0b3AgYW5kIGFkZCBwYWRkaW5nIHRvIHRoZVxuICAgIHNlbGVjdCB0byBnZXQgYW4gYXBwcm94aW1hdGUgZmlyc3QgbGluZSBiYXNlbGluZSBhbGlnbm1lbnQuICovXG4gICAgcGFkZGluZy10b3A6IDVweDtcbn1cblxuLndpZGdldC1zZWxlY3QgPiBzZWxlY3Q6Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lnZXQtc2VsZWN0ID4gc2VsZWN0ID4gb3B0aW9uIHtcbiAgICBwYWRkaW5nLWxlZnQ6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtcGFkZGluZyk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgLyogbGluZS1oZWlnaHQgZG9lc24ndCB3b3JrIG9uIHNvbWUgYnJvd3NlcnMgZm9yIHNlbGVjdCBvcHRpb25zICovXG4gICAgcGFkZGluZy10b3A6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KS12YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSkvMik7XG4gICAgcGFkZGluZy1ib3R0b206IGNhbGModmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KS12YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSkvMik7XG59XG5cblxuXG4vKiBUb2dnbGUgQnV0dG9ucyBTdHlsaW5nICovXG5cbi53aWRnZXQtdG9nZ2xlLWJ1dHRvbnMge1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LXRvZ2dsZS1idXR0b25zIC53aWRnZXQtdG9nZ2xlLWJ1dHRvbiB7XG4gICAgbWFyZ2luLWxlZnQ6IHZhcigtLWpwLXdpZGdldHMtbWFyZ2luKTtcbiAgICBtYXJnaW4tcmlnaHQ6IHZhcigtLWpwLXdpZGdldHMtbWFyZ2luKTtcbn1cblxuLndpZGdldC10b2dnbGUtYnV0dG9ucyAuanVweXRlci1idXR0b246ZGlzYWJsZWQge1xuICAgIG9wYWNpdHk6IHZhcigtLWpwLXdpZGdldHMtZGlzYWJsZWQtb3BhY2l0eSk7XG59XG5cbi8qIFJhZGlvIEJ1dHRvbnMgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LXJhZGlvIHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LXJhZGlvLWJveCB7XG4gICAgZGlzcGxheTogZmxleDtcbiAgICBmbGV4LWRpcmVjdGlvbjogY29sdW1uO1xuICAgIGFsaWduLWl0ZW1zOiBzdHJldGNoO1xuICAgIGJveC1zaXppbmc6IGJvcmRlci1ib3g7XG4gICAgZmxleC1ncm93OiAxO1xuICAgIG1hcmdpbi1ib3R0b206IHZhcigtLWpwLXdpZGdldHMtcmFkaW8taXRlbS1oZWlnaHQtYWRqdXN0bWVudCk7XG59XG5cbi53aWRnZXQtcmFkaW8tYm94IGxhYmVsIHtcbiAgICBoZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtcmFkaW8taXRlbS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLXJhZGlvLWl0ZW0taGVpZ2h0KTtcbiAgICBmb250LXNpemU6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKTtcbn1cblxuLndpZGdldC1yYWRpby1ib3ggaW5wdXQge1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1yYWRpby1pdGVtLWhlaWdodCk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtcmFkaW8taXRlbS1oZWlnaHQpO1xuICAgIG1hcmdpbjogMCBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpICogMiApIDAgMXB4O1xuICAgIGZsb2F0OiBsZWZ0O1xufVxuXG4vKiBDb2xvciBQaWNrZXIgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LWNvbG9ycGlja2VyIHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgpO1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbn1cblxuLndpZGdldC1jb2xvcnBpY2tlciA+IC53aWRnZXQtY29sb3JwaWNrZXItaW5wdXQge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBmbGV4LXNocmluazogMTtcbiAgICBtaW4td2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLXdpZHRoLXRpbnkpO1xufVxuXG4ud2lkZ2V0LWNvbG9ycGlja2VyIGlucHV0W3R5cGU9XCJjb2xvclwiXSB7XG4gICAgd2lkdGg6IHZhcigtLWpwLXdpZGdldHMtaW5saW5lLWhlaWdodCk7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIHBhZGRpbmc6IDAgMnB4OyAvKiBtYWtlIHRoZSBjb2xvciBzcXVhcmUgYWN0dWFsbHkgc3F1YXJlIG9uIENocm9tZSBvbiBPUyBYICovXG4gICAgYmFja2dyb3VuZDogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1iYWNrZ3JvdW5kLWNvbG9yKTtcbiAgICBjb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1jb2xvcik7XG4gICAgYm9yZGVyOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci13aWR0aCkgc29saWQgdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1ib3JkZXItY29sb3IpO1xuICAgIGJvcmRlci1sZWZ0OiBub25lO1xuICAgIGZsZXgtZ3JvdzogMDtcbiAgICBmbGV4LXNocmluazogMDtcbiAgICBib3gtc2l6aW5nOiBib3JkZXItYm94O1xuICAgIGFsaWduLXNlbGY6IHN0cmV0Y2g7XG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xufVxuXG4ud2lkZ2V0LWNvbG9ycGlja2VyLmNvbmNpc2UgaW5wdXRbdHlwZT1cImNvbG9yXCJdIHtcbiAgICBib3JkZXItbGVmdDogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLWNvbG9yKTtcbn1cblxuLndpZGdldC1jb2xvcnBpY2tlciBpbnB1dFt0eXBlPVwiY29sb3JcIl06Zm9jdXMsIC53aWRnZXQtY29sb3JwaWNrZXIgaW5wdXRbdHlwZT1cInRleHRcIl06Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lkZ2V0LWNvbG9ycGlja2VyIGlucHV0W3R5cGU9XCJ0ZXh0XCJdIHtcbiAgICBmbGV4LWdyb3c6IDE7XG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBsaW5lLWhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBiYWNrZ3JvdW5kOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJhY2tncm91bmQtY29sb3IpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWNvbG9yKTtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgZm9udC1zaXplOiB2YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSk7XG4gICAgcGFkZGluZzogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1wYWRkaW5nKSBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpICogIDIgKTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgZmxleC1zaHJpbms6IDE7XG4gICAgYm94LXNpemluZzogYm9yZGVyLWJveDtcbn1cblxuLndpZGdldC1jb2xvcnBpY2tlciBpbnB1dFt0eXBlPVwidGV4dFwiXTpkaXNhYmxlZCB7XG4gICAgb3BhY2l0eTogdmFyKC0tanAtd2lkZ2V0cy1kaXNhYmxlZC1vcGFjaXR5KTtcbn1cblxuLyogRGF0ZSBQaWNrZXIgU3R5bGluZyAqL1xuXG4ud2lkZ2V0LWRhdGVwaWNrZXIge1xuICAgIHdpZHRoOiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS13aWR0aCk7XG4gICAgaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWlubGluZS1oZWlnaHQpO1xufVxuXG4ud2lkZ2V0LWRhdGVwaWNrZXIgaW5wdXRbdHlwZT1cImRhdGVcIl0ge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBmbGV4LXNocmluazogMTtcbiAgICBtaW4td2lkdGg6IDA7IC8qIFRoaXMgbWFrZXMgaXQgcG9zc2libGUgZm9yIHRoZSBmbGV4Ym94IHRvIHNocmluayB0aGlzIGlucHV0ICovXG4gICAgb3V0bGluZTogbm9uZSAhaW1wb3J0YW50O1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtaGVpZ2h0KTtcbiAgICBib3JkZXI6IHZhcigtLWpwLXdpZGdldHMtaW5wdXQtYm9yZGVyLXdpZHRoKSBzb2xpZCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LWJvcmRlci1jb2xvcik7XG4gICAgYmFja2dyb3VuZC1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1iYWNrZ3JvdW5kLWNvbG9yKTtcbiAgICBjb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1jb2xvcik7XG4gICAgZm9udC1zaXplOiB2YXIoLS1qcC13aWRnZXRzLWZvbnQtc2l6ZSk7XG4gICAgcGFkZGluZzogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1wYWRkaW5nKSBjYWxjKCB2YXIoLS1qcC13aWRnZXRzLWlucHV0LXBhZGRpbmcpICogIDIgKTtcbiAgICBib3gtc2l6aW5nOiBib3JkZXItYm94O1xufVxuXG4ud2lkZ2V0LWRhdGVwaWNrZXIgaW5wdXRbdHlwZT1cImRhdGVcIl06Zm9jdXMge1xuICAgIGJvcmRlci1jb2xvcjogdmFyKC0tanAtd2lkZ2V0cy1pbnB1dC1mb2N1cy1ib3JkZXItY29sb3IpO1xufVxuXG4ud2lkZ2V0LWRhdGVwaWNrZXIgaW5wdXRbdHlwZT1cImRhdGVcIl06aW52YWxpZCB7XG4gICAgYm9yZGVyLWNvbG9yOiB2YXIoLS1qcC13YXJuLWNvbG9yMSk7XG59XG5cbi53aWRnZXQtZGF0ZXBpY2tlciBpbnB1dFt0eXBlPVwiZGF0ZVwiXTpkaXNhYmxlZCB7XG4gICAgb3BhY2l0eTogdmFyKC0tanAtd2lkZ2V0cy1kaXNhYmxlZC1vcGFjaXR5KTtcbn1cblxuLyogUGxheSBXaWRnZXQgKi9cblxuLndpZGdldC1wbGF5IHtcbiAgICB3aWR0aDogdmFyKC0tanAtd2lkZ2V0cy1pbmxpbmUtd2lkdGgtc2hvcnQpO1xuICAgIGRpc3BsYXk6IGZsZXg7XG4gICAgYWxpZ24taXRlbXM6IHN0cmV0Y2g7XG59XG5cbi53aWRnZXQtcGxheSAuanVweXRlci1idXR0b24ge1xuICAgIGZsZXgtZ3JvdzogMTtcbiAgICBoZWlnaHQ6IGF1dG87XG59XG5cbi53aWRnZXQtcGxheSAuanVweXRlci1idXR0b246ZGlzYWJsZWQge1xuICAgIG9wYWNpdHk6IHZhcigtLWpwLXdpZGdldHMtZGlzYWJsZWQtb3BhY2l0eSk7XG59XG5cbi8qIFRhYiBXaWRnZXQgKi9cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiIHtcbiAgICBkaXNwbGF5OiBmbGV4O1xuICAgIGZsZXgtZGlyZWN0aW9uOiBjb2x1bW47XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciB7XG4gICAgLyogTmVjZXNzYXJ5IHNvIHRoYXQgYSB0YWIgY2FuIGJlIHNoaWZ0ZWQgZG93biB0byBvdmVybGF5IHRoZSBib3JkZXIgb2YgdGhlIGJveCBiZWxvdy4gKi9cbiAgICBvdmVyZmxvdy14OiB2aXNpYmxlO1xuICAgIG92ZXJmbG93LXk6IHZpc2libGU7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciA+IC5wLVRhYkJhci1jb250ZW50IHtcbiAgICAvKiBNYWtlIHN1cmUgdGhhdCB0aGUgdGFiIGdyb3dzIGZyb20gYm90dG9tIHVwICovXG4gICAgYWxpZ24taXRlbXM6IGZsZXgtZW5kO1xuICAgIG1pbi13aWR0aDogMDtcbiAgICBtaW4taGVpZ2h0OiAwO1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAud2lkZ2V0LXRhYi1jb250ZW50cyB7XG4gICAgd2lkdGg6IDEwMCU7XG4gICAgYm94LXNpemluZzogYm9yZGVyLWJveDtcbiAgICBtYXJnaW46IDA7XG4gICAgYmFja2dyb3VuZDogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IxKTtcbiAgICBib3JkZXI6IHZhcigtLWpwLWJvcmRlci13aWR0aCkgc29saWQgdmFyKC0tanAtYm9yZGVyLWNvbG9yMSk7XG4gICAgcGFkZGluZzogdmFyKC0tanAtd2lkZ2V0cy1jb250YWluZXItcGFkZGluZyk7XG4gICAgZmxleC1ncm93OiAxO1xuICAgIG92ZXJmbG93OiBhdXRvO1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIge1xuICAgIGZvbnQ6IHZhcigtLWpwLXdpZGdldHMtZm9udC1zaXplKSBIZWx2ZXRpY2EsIEFyaWFsLCBzYW5zLXNlcmlmO1xuICAgIG1pbi1oZWlnaHQ6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi1oZWlnaHQpICsgdmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiIHtcbiAgICBmbGV4OiAwIDEgdmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi13aWR0aCk7XG4gICAgbWluLXdpZHRoOiAzNXB4O1xuICAgIG1pbi1oZWlnaHQ6IGNhbGModmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi1oZWlnaHQpICsgdmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG4gICAgbGluZS1oZWlnaHQ6IHZhcigtLWpwLXdpZGdldHMtaG9yaXpvbnRhbC10YWItaGVpZ2h0KTtcbiAgICBtYXJnaW4tbGVmdDogY2FsYygtMSAqIHZhcigtLWpwLWJvcmRlci13aWR0aCkpO1xuICAgIHBhZGRpbmc6IDBweCAxMHB4O1xuICAgIGJhY2tncm91bmQ6IHZhcigtLWpwLWxheW91dC1jb2xvcjIpO1xuICAgIGNvbG9yOiB2YXIoLS1qcC11aS1mb250LWNvbG9yMik7XG4gICAgYm9yZGVyOiB2YXIoLS1qcC1ib3JkZXItd2lkdGgpIHNvbGlkIHZhcigtLWpwLWJvcmRlci1jb2xvcjEpO1xuICAgIGJvcmRlci1ib3R0b206IG5vbmU7XG4gICAgcG9zaXRpb246IHJlbGF0aXZlO1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYi5wLW1vZC1jdXJyZW50IHtcbiAgICBjb2xvcjogdmFyKC0tanAtdWktZm9udC1jb2xvcjApO1xuICAgIC8qIFdlIHdhbnQgdGhlIGJhY2tncm91bmQgdG8gbWF0Y2ggdGhlIHRhYiBjb250ZW50IGJhY2tncm91bmQgKi9cbiAgICBiYWNrZ3JvdW5kOiB2YXIoLS1qcC1sYXlvdXQtY29sb3IxKTtcbiAgICBtaW4taGVpZ2h0OiBjYWxjKHZhcigtLWpwLXdpZGdldHMtaG9yaXpvbnRhbC10YWItaGVpZ2h0KSArIDIgKiB2YXIoLS1qcC1ib3JkZXItd2lkdGgpKTtcbiAgICB0cmFuc2Zvcm06IHRyYW5zbGF0ZVkodmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG4gICAgb3ZlcmZsb3c6IHZpc2libGU7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiLnAtbW9kLWN1cnJlbnQ6YmVmb3JlIHtcbiAgICBwb3NpdGlvbjogYWJzb2x1dGU7XG4gICAgdG9wOiBjYWxjKC0xICogdmFyKC0tanAtYm9yZGVyLXdpZHRoKSk7XG4gICAgbGVmdDogY2FsYygtMSAqIHZhcigtLWpwLWJvcmRlci13aWR0aCkpO1xuICAgIGNvbnRlbnQ6ICcnO1xuICAgIGhlaWdodDogdmFyKC0tanAtd2lkZ2V0cy1ob3Jpem9udGFsLXRhYi10b3AtYm9yZGVyKTtcbiAgICB3aWR0aDogY2FsYygxMDAlICsgMiAqIHZhcigtLWpwLWJvcmRlci13aWR0aCkpO1xuICAgIGJhY2tncm91bmQ6IHZhcigtLWpwLWJyYW5kLWNvbG9yMSk7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1UYWJCYXItdGFiOmZpcnN0LWNoaWxkIHtcbiAgICBtYXJnaW4tbGVmdDogMDtcbn1cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyIC5wLVRhYkJhci10YWI6aG92ZXI6bm90KC5wLW1vZC1jdXJyZW50KSB7XG4gICAgYmFja2dyb3VuZDogdmFyKC0tanAtbGF5b3V0LWNvbG9yMSk7XG4gICAgY29sb3I6IHZhcigtLWpwLXVpLWZvbnQtY29sb3IxKTtcbn1cblxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyIC5wLW1vZC1jbG9zYWJsZSA+IC5wLVRhYkJhci10YWJDbG9zZUljb24ge1xuICAgIG1hcmdpbi1sZWZ0OiA0cHg7XG59XG5cbi5qdXB5dGVyLXdpZGdldHMud2lkZ2V0LXRhYiA+IC5wLVRhYkJhciAucC1tb2QtY2xvc2FibGUgPiAucC1UYWJCYXItdGFiQ2xvc2VJY29uOmJlZm9yZSB7XG4gICAgZm9udC1mYW1pbHk6IEZvbnRBd2Vzb21lO1xuICAgIGNvbnRlbnQ6ICdcXGYwMGQnOyAvKiBjbG9zZSAqL1xufVxuXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYkljb24sXG4uanVweXRlci13aWRnZXRzLndpZGdldC10YWIgPiAucC1UYWJCYXIgLnAtVGFiQmFyLXRhYkxhYmVsLFxuLmp1cHl0ZXItd2lkZ2V0cy53aWRnZXQtdGFiID4gLnAtVGFiQmFyIC5wLVRhYkJhci10YWJDbG9zZUljb24ge1xuICAgIGxpbmUtaGVpZ2h0OiB2YXIoLS1qcC13aWRnZXRzLWhvcml6b250YWwtdGFiLWhlaWdodCk7XG59XG5cbi8qIEFjY29yZG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\n", + "" + ], + "text/plain": [ + " filter_by results\n", + "0 All Organizations (no filter) 406172.0\n", + "6 dimensions_url 406172.0\n", + "30 status 406172.0\n", + "15 name 406172.0\n", + "31 types 406087.0\n", + "5 country_name 406058.0\n", + "4 country_code 406012.0\n", + "2 city_name 166300.0\n", + "14 longitude 131622.0\n", + "12 latitude 131622.0\n", + "23 organization_parent_ids 119442.0\n", + "13 linkout 119290.0\n", + "27 ror_ids 94038.0\n", + "7 established 91959.0\n", + "29 state_name 66188.0\n", + "16 nuts_level1_code 51585.0\n", + "18 nuts_level2_code 51585.0\n", + "17 nuts_level1_name 51585.0\n", + "21 nuts_level3_name 51585.0\n", + "19 nuts_level2_name 51585.0\n", + "20 nuts_level3_code 51585.0\n", + "34 wikidata_ids 51499.0\n", + "11 isni_ids 49885.0\n", + "1 acronym 45701.0\n", + "35 wikipedia_url 33440.0\n", + "22 organization_child_ids 21945.0\n", + "25 orgref_ids 14577.0\n", + "8 external_ids_fundref 9406.0\n", + "26 redirect 5669.0\n", + "24 organization_related_ids 4751.0\n", + "3 cnrs_ids 920.0\n", + "33 ukprn_ids 172.0\n", + "9 hesa_ids 171.0\n", + "32 ucas_ids 152.0\n", + "10 id NaN\n", + "28 score NaN" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FIELDS_DATA = dsl_last_results\n", + "\n", + "# one query with `is not empty` for field-filters \n", + "q_template = \"\"\"search organizations where {} is not empty return organizations[id] limit 1\"\"\"\n", + "\n", + "# seed results with total number of orgs\n", + "totorgs = dsl.query(\"\"\"search organizations return organizations[id] limit 1\"\"\", verbose=False).count_total\n", + "stats = [\n", + " {'filter_by': 'All Organizations (no filter)', 'results' : totorgs} \n", + "]\n", + "\n", + "for index, row in pbar(list(FIELDS_DATA.iterrows())):\n", + " # print(\"\\n===\", row['field'])\n", + " q = q_template.format(row['field'], row['field'])\n", + " res = dsl.query(q, verbose=False)\n", + " time.sleep(0.5)\n", + " stats.append({'filter_by': row['field'], 'results' : res.count_total})\n", + "\n", + "\n", + "# save to a dataframe\n", + "df = pd.DataFrame().from_dict(stats)\n", + "df.sort_values(\"results\", inplace=True, ascending=False)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false" + }, + "source": [ + "### Let's visualize the data with plotly" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "Collapsed": "false" + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "filter_by=%{x}
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+ "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Fields distribution for GRID data" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "filter_by" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "results" + } + } + } + }, + "text/html": [ + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "px.bar(df, x=\"filter_by\", y=\"results\", \n", + " title=\"Fields distribution for GRID data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "colab_type": "text", + "id": "VsEa7X3EsPYH" + }, + "source": [ + "## Where to find out more\n", + "\n", + "Please have a look at the [official documentation](https://docs.dimensions.ai/dsl/data-sources.html) for more information on the organizations data source." + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "Searching GRID organizations using the Dimensions API.ipynb", + "provenance": [ + { + "file_id": "1khRLDKEZ-U_6ARyCJCOocRdH7U-nZKUT", + "timestamp": 1574700652421 + } + ] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/_sources/cookbooks/8-organizations/2-Industry-Collaboration.ipynb.txt b/docs/_sources/cookbooks/8-organizations/2-Industry-Collaboration.ipynb.txt index 310851ec..55967f0e 100644 --- a/docs/_sources/cookbooks/8-organizations/2-Industry-Collaboration.ipynb.txt +++ b/docs/_sources/cookbooks/8-organizations/2-Industry-Collaboration.ipynb.txt @@ -11,7 +11,7 @@ "source": [ "# Identifying the Industry Collaborators of an Academic Institution\n", "\n", - "Dimensions uses [GRID](https://grid.ac/) identifiers for institutions, hence you can take advantage of the GRID metadata with Dimensions queries. \n", + "Dimensions has an enormous amount of data about organizations and you can query this data with the Dimensions Analytics API.\n", "\n", "In this tutorial we identify all organizations that have an `industry` type. \n", "\n", @@ -29,7 +29,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Jan 25, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -64,19 +64,9 @@ "text/html": [ " \n", + " \n", " " ] }, @@ -96,8 +86,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -150,8 +140,8 @@ "id": "L6uIjSVnGRQV" }, "source": [ - "For the purpose of this exercise, we will use [University of Trento, Italy (grid.11696.39)](https://grid.ac/institutes/grid.11696.39) as a starting point. \n", - "You can pick any other GRID organization of course. Just use a [DSL query](https://digital-science.github.io/dimensions-api-lab/cookbooks/8-organizations/1-GRID-preview.html) or the [GRID website](https://grid.ac/institutes) to discover the ID of an organization that interests you. " + "For the purpose of this exercise, we will use University of Trento, Italy (organization ID `grid.11696.39`) as a starting point. \n", + "You can pick any other organization of course. Just use a [DSL query](https://digital-science.github.io/dimensions-api-lab/cookbooks/8-organizations/1-GRID-preview.html) to discover the ID of an organization that interests you. " ] }, { @@ -182,7 +172,7 @@ { "data": { "text/html": [ - "GRID: grid.11696.39 - University of Trento ⧉" + "Organization: grid.11696.39 - University of Trento ⧉" ], "text/plain": [ "" @@ -206,7 +196,7 @@ ], "source": [ "#@markdown The main organization we are interested in:\n", - "GRIDID = \"grid.11696.39\" #@param {type:\"string\"}\n", + "ORGID = \"grid.11696.39\" #@param {type:\"string\"}\n", " \n", "#@markdown The start/end year of publications used to extract industry collaborations:\n", "YEAR_START = 2000 #@param {type: \"slider\", min: 1950, max: 2020}\n", @@ -219,11 +209,11 @@ "# gen link to Dimensions\n", "#\n", "try:\n", - " gridname = dsl.query(f\"\"\"search organizations where id=\"{GRIDID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", + " orgname = dsl.query(f\"\"\"search organizations where id=\"{ORGID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", "except:\n", - " gridname = \"\"\n", - "from IPython.core.display import display, HTML\n", - "display(HTML('GRID: {} - {} ⧉'.format(dimensions_url(GRIDID), GRIDID, gridname)))\n", + " orgname = \"\"\n", + "from IPython.display import display, HTML\n", + "display(HTML('Organization: {} - {} ⧉'.format(dimensions_url(ORGID), ORGID, orgname)))\n", "display(HTML('Time period: {} to {}

'.format(YEAR_START, YEAR_END)))\n" ] }, @@ -273,39 +263,58 @@ "output_type": "stream", "text": [ "Starting iteration with limit=1000 skip=0 ...\u001b[0m\n", - "0-1000 / 30088 (0.63s)\u001b[0m\n", - "1000-2000 / 30088 (0.57s)\u001b[0m\n", - "2000-3000 / 30088 (0.69s)\u001b[0m\n", - "3000-4000 / 30088 (0.52s)\u001b[0m\n", - "4000-5000 / 30088 (0.51s)\u001b[0m\n", - "5000-6000 / 30088 (0.61s)\u001b[0m\n", - "6000-7000 / 30088 (0.52s)\u001b[0m\n", - "7000-8000 / 30088 (0.56s)\u001b[0m\n", - "8000-9000 / 30088 (2.24s)\u001b[0m\n", - "9000-10000 / 30088 (0.56s)\u001b[0m\n", - "10000-11000 / 30088 (0.57s)\u001b[0m\n", - "11000-12000 / 30088 (0.58s)\u001b[0m\n", - "12000-13000 / 30088 (0.62s)\u001b[0m\n", - "13000-14000 / 30088 (1.74s)\u001b[0m\n", - "14000-15000 / 30088 (0.58s)\u001b[0m\n", - "15000-16000 / 30088 (0.49s)\u001b[0m\n", - "16000-17000 / 30088 (0.58s)\u001b[0m\n", - "17000-18000 / 30088 (0.53s)\u001b[0m\n", - "18000-19000 / 30088 (0.57s)\u001b[0m\n", - "19000-20000 / 30088 (0.50s)\u001b[0m\n", - "20000-21000 / 30088 (0.51s)\u001b[0m\n", - "21000-22000 / 30088 (0.51s)\u001b[0m\n", - "22000-23000 / 30088 (0.54s)\u001b[0m\n", - "23000-24000 / 30088 (0.50s)\u001b[0m\n", - "24000-25000 / 30088 (0.53s)\u001b[0m\n", - "25000-26000 / 30088 (0.62s)\u001b[0m\n", - "26000-27000 / 30088 (0.49s)\u001b[0m\n", - "27000-28000 / 30088 (0.48s)\u001b[0m\n", - "28000-29000 / 30088 (0.56s)\u001b[0m\n", - "29000-30000 / 30088 (0.90s)\u001b[0m\n", - "30000-30088 / 30088 (0.61s)\u001b[0m\n", + "0-1000 / 158994 (0.60s)\u001b[0m\n", + "1000-2000 / 158994 (0.59s)\u001b[0m\n", + "2000-3000 / 158994 (4.55s)\u001b[0m\n", + "3000-4000 / 158994 (0.58s)\u001b[0m\n", + "4000-5000 / 158994 (0.62s)\u001b[0m\n", + "5000-6000 / 158994 (2.44s)\u001b[0m\n", + "6000-7000 / 158994 (2.06s)\u001b[0m\n", + "7000-8000 / 158994 (4.03s)\u001b[0m\n", + "8000-9000 / 158994 (1.97s)\u001b[0m\n", + "9000-10000 / 158994 (2.47s)\u001b[0m\n", + "10000-11000 / 158994 (0.63s)\u001b[0m\n", + "11000-12000 / 158994 (0.57s)\u001b[0m\n", + "12000-13000 / 158994 (1.93s)\u001b[0m\n", + "13000-14000 / 158994 (0.65s)\u001b[0m\n", + "14000-15000 / 158994 (2.35s)\u001b[0m\n", + "15000-16000 / 158994 (2.11s)\u001b[0m\n", + "16000-17000 / 158994 (3.86s)\u001b[0m\n", + "17000-18000 / 158994 (6.32s)\u001b[0m\n", + "18000-19000 / 158994 (0.71s)\u001b[0m\n", + "19000-20000 / 158994 (3.59s)\u001b[0m\n", + "20000-21000 / 158994 (0.72s)\u001b[0m\n", + "21000-22000 / 158994 (3.72s)\u001b[0m\n", + "22000-23000 / 158994 (5.98s)\u001b[0m\n", + "23000-24000 / 158994 (0.61s)\u001b[0m\n", + "24000-25000 / 158994 (0.71s)\u001b[0m\n", + "25000-26000 / 158994 (1.70s)\u001b[0m\n", + "26000-27000 / 158994 (1.34s)\u001b[0m\n", + "27000-28000 / 158994 (4.45s)\u001b[0m\n", + "28000-29000 / 158994 (0.70s)\u001b[0m\n", + "29000-30000 / 158994 (0.56s)\u001b[0m\n", + "30000-31000 / 158994 (1.76s)\u001b[0m\n", + "31000-32000 / 158994 (0.64s)\u001b[0m\n", + "32000-33000 / 158994 (0.58s)\u001b[0m\n", + "33000-34000 / 158994 (0.70s)\u001b[0m\n", + "34000-35000 / 158994 (2.38s)\u001b[0m\n", + "35000-36000 / 158994 (2.06s)\u001b[0m\n", + "36000-37000 / 158994 (0.71s)\u001b[0m\n", + "37000-38000 / 158994 (3.82s)\u001b[0m\n", + "38000-39000 / 158994 (0.65s)\u001b[0m\n", + "39000-40000 / 158994 (4.50s)\u001b[0m\n", + "40000-41000 / 158994 (5.95s)\u001b[0m\n", + "41000-42000 / 158994 (0.81s)\u001b[0m\n", + "42000-43000 / 158994 (0.65s)\u001b[0m\n", + "43000-44000 / 158994 (4.53s)\u001b[0m\n", + "44000-45000 / 158994 (0.61s)\u001b[0m\n", + "45000-46000 / 158994 (4.54s)\u001b[0m\n", + "46000-47000 / 158994 (0.60s)\u001b[0m\n", + "47000-48000 / 158994 (0.78s)\u001b[0m\n", + "48000-49000 / 158994 (0.74s)\u001b[0m\n", + "49000-50000 / 158994 (0.68s)\u001b[0m\n", "===\n", - "Records extracted: 30088\u001b[0m\n" + "Records extracted: 50000\u001b[0m\n" ] } ], @@ -350,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "Collapsed": "false", "colab": { @@ -390,18 +399,18 @@ "===\n", "Extracting grid.11696.39 publications with industry collaborators ...\n", "Records per query : 1000\n", - "GRID IDs per query: 200\n" + "Organization IDs per query: 200\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f9ce542da620433da388b9c568a55130", + "model_id": "ec0375ac4b8f455893d5d882ad2543a1", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/151 [00:00\n", " \n", " 0\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/0264-9381/33/23/235015\n", - " pub.1059063534\n", - " 7\n", - " article\n", + " [{'affiliations': [{'city': 'Dublin', 'city_id...\n", + " 10.1109/eucnc.2016.7561056\n", + " pub.1094950798\n", + " 28\n", + " proceeding\n", " 2016\n", " \n", " \n", " 1\n", " [{'affiliations': [{'city': 'Dublin', 'city_id...\n", - " 10.1145/2984356.2984363\n", - " pub.1001653422\n", - " 14\n", + " 10.1109/noms.2016.7503003\n", + " pub.1094654631\n", + " 35\n", " proceeding\n", " 2016\n", " \n", " \n", " 2\n", - " [{'affiliations': [{'city': 'Stuttgart', 'city...\n", - " 10.1016/j.apnum.2016.02.001\n", - " pub.1038596770\n", - " 12\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1002/adem.201400134\n", + " pub.1049335111\n", + " 50\n", " article\n", - " 2016\n", + " 2014\n", " \n", " \n", " 3\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1103/physrevlett.116.231101\n", - " pub.1001053038\n", - " 313\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1111/j.1551-2916.2005.00043.x\n", + " pub.1042663343\n", + " 64\n", " article\n", - " 2016\n", + " 2005\n", " \n", " \n", " 4\n", - " [{'affiliations': [{'city': 'Dublin', 'city_id...\n", - " 10.1109/eucnc.2016.7561056\n", - " pub.1094950798\n", - " 22\n", - " proceeding\n", - " 2016\n", + " [{'affiliations': [{'city': 'Legnaro', 'city_i...\n", + " 10.1016/s0168-583x(03)01322-3\n", + " pub.1041242454\n", + " 14\n", + " article\n", + " 2003\n", " \n", " \n", " 5\n", - " [{'affiliations': [{'city': 'Trento', 'city_id...\n", - " 10.1140/epjds/s13688-016-0064-6\n", - " pub.1033140941\n", - " 15\n", + " [{'affiliations': [{'city': 'MENLO PARK', 'cit...\n", + " 10.1111/jace.12485\n", + " pub.1033867339\n", + " 48\n", " article\n", - " 2016\n", + " 2013\n", " \n", " \n", " 6\n", " [{'affiliations': [{'city': 'Trento', 'city_id...\n", - " 10.1089/big.2014.0054\n", - " pub.1018945654\n", - " 48\n", - " article\n", - " 2015\n", + " 10.1145/2663204.2663254\n", + " pub.1033777395\n", + " 225\n", + " proceeding\n", + " 2014\n", " \n", " \n", " 7\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/1742-6596/610/1/012027\n", - " pub.1031150191\n", - " 1\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1140/epjds/s13688-016-0064-6\n", + " pub.1033140941\n", + " 25\n", " article\n", - " 2015\n", + " 2016\n", " \n", " \n", " 8\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/1742-6596/610/1/012005\n", - " pub.1052522882\n", - " 17\n", - " article\n", - " 2015\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1145/2063518.2063544\n", + " pub.1028019246\n", + " 1\n", + " proceeding\n", + " 2011\n", " \n", " \n", " 9\n", - " [{'affiliations': [{'city': 'Madrid', 'city_id...\n", - " 10.1088/1742-6596/610/1/012026\n", - " pub.1033837350\n", - " 2\n", + " [{'affiliations': [{'city': 'Trento', 'city_id...\n", + " 10.1089/big.2014.0054\n", + " pub.1018945654\n", + " 68\n", " article\n", " 2015\n", " \n", @@ -542,64 +551,64 @@ ], "text/plain": [ " authors \\\n", - "0 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", + "0 [{'affiliations': [{'city': 'Dublin', 'city_id... \n", "1 [{'affiliations': [{'city': 'Dublin', 'city_id... \n", - "2 [{'affiliations': [{'city': 'Stuttgart', 'city... \n", - "3 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", - "4 [{'affiliations': [{'city': 'Dublin', 'city_id... \n", - "5 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "2 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "3 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "4 [{'affiliations': [{'city': 'Legnaro', 'city_i... \n", + "5 [{'affiliations': [{'city': 'MENLO PARK', 'cit... \n", "6 [{'affiliations': [{'city': 'Trento', 'city_id... \n", - "7 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", - "8 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", - "9 [{'affiliations': [{'city': 'Madrid', 'city_id... \n", + "7 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "8 [{'affiliations': [{'city': 'Trento', 'city_id... \n", + "9 [{'affiliations': [{'city': 'Trento', 'city_id... \n", "\n", - " doi id times_cited type \\\n", - "0 10.1088/0264-9381/33/23/235015 pub.1059063534 7 article \n", - "1 10.1145/2984356.2984363 pub.1001653422 14 proceeding \n", - "2 10.1016/j.apnum.2016.02.001 pub.1038596770 12 article \n", - "3 10.1103/physrevlett.116.231101 pub.1001053038 313 article \n", - "4 10.1109/eucnc.2016.7561056 pub.1094950798 22 proceeding \n", - "5 10.1140/epjds/s13688-016-0064-6 pub.1033140941 15 article \n", - "6 10.1089/big.2014.0054 pub.1018945654 48 article \n", - "7 10.1088/1742-6596/610/1/012027 pub.1031150191 1 article \n", - "8 10.1088/1742-6596/610/1/012005 pub.1052522882 17 article \n", - "9 10.1088/1742-6596/610/1/012026 pub.1033837350 2 article \n", + " doi id times_cited type \\\n", + "0 10.1109/eucnc.2016.7561056 pub.1094950798 28 proceeding \n", + "1 10.1109/noms.2016.7503003 pub.1094654631 35 proceeding \n", + "2 10.1002/adem.201400134 pub.1049335111 50 article \n", + "3 10.1111/j.1551-2916.2005.00043.x pub.1042663343 64 article \n", + "4 10.1016/s0168-583x(03)01322-3 pub.1041242454 14 article \n", + "5 10.1111/jace.12485 pub.1033867339 48 article \n", + "6 10.1145/2663204.2663254 pub.1033777395 225 proceeding \n", + "7 10.1140/epjds/s13688-016-0064-6 pub.1033140941 25 article \n", + "8 10.1145/2063518.2063544 pub.1028019246 1 proceeding \n", + "9 10.1089/big.2014.0054 pub.1018945654 68 article \n", "\n", " year \n", "0 2016 \n", "1 2016 \n", - "2 2016 \n", - "3 2016 \n", - "4 2016 \n", - "5 2016 \n", - "6 2015 \n", - "7 2015 \n", - "8 2015 \n", + "2 2014 \n", + "3 2005 \n", + "4 2003 \n", + "5 2013 \n", + "6 2014 \n", + "7 2016 \n", + "8 2011 \n", "9 2015 " ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "gridis = list(company_grids.as_dataframe()['id'])\n", + "orgids = list(company_grids.as_dataframe()['id'])\n", "\n", "#\n", - "# loop through all grids\n", + "# loop through all organizations\n", "\n", "ITERATION_RECORDS = 1000 # Publication records per query iteration\n", - "GRID_RECORDS = 200 # grid IDs per query\n", + "ORG_RECORDS = 200 # organization IDs per query\n", "VERBOSE = False # set to True to view full extraction logs\n", - "print(f\"===\\nExtracting {GRIDID} publications with industry collaborators ...\")\n", + "print(f\"===\\nExtracting {ORGID} publications with industry collaborators ...\")\n", "print(\"Records per query : \", ITERATION_RECORDS)\n", - "print(\"GRID IDs per query: \", GRID_RECORDS)\n", + "print(\"Organization IDs per query: \", ORG_RECORDS)\n", "results = []\n", "\n", "\n", - "for chunk in progress(list(chunks_of(gridis, GRID_RECORDS))):\n", - " query = query_template.format(GRIDID, json.dumps(chunk), YEAR_START, YEAR_END)\n", + "for chunk in progress(list(chunks_of(orgids, ORG_RECORDS))):\n", + " query = query_template.format(ORGID, json.dumps(chunk), YEAR_START, YEAR_END)\n", "# print(query)\n", " data = dsl.query_iterative(query, verbose=VERBOSE, limit=ITERATION_RECORDS)\n", " if data.errors:\n", @@ -641,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": { "Collapsed": "false", "colab": { @@ -672,382 +681,48 @@ }, "data": [ { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "type=article
year=%{x}
count=%{y}", - "legendgroup": "article", + "hovertemplate": "type=proceeding
year=%{x}
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" + " }) }; " ] }, "metadata": {}, @@ -2030,7 +1611,7 @@ "px.histogram(pubs, \n", " x=\"year\", \n", " color=\"type\",\n", - " title=f\"Publications per year with industry collaborations for {GRIDID}\")" + " title=f\"Publications per year with industry collaborations for {ORGID}\")" ] }, { @@ -2046,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "Collapsed": "false", "colab": { @@ -2077,7 +1658,6 @@ }, "data": [ { - "alignmentgroup": "True", "hovertemplate": "year=%{x}
times_cited=%{y}", "legendgroup": "", "marker": { @@ -2087,53 +1667,23 @@ } }, "name": "", - "offsetgroup": "", "orientation": "v", "showlegend": false, "textposition": "auto", "type": "bar", - "x": [ - 2000, - 2002, - 2003, - 2004, - 2005, - 2006, - 2007, - 2008, - 2009, - 2010, - 2011, - 2012, - 2013, - 2014, - 2015, - 2016 - ], + "x": { + "bdata": "0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH", + "dtype": "i2" + }, "xaxis": "x", - "y": [ - 37, - 106, - 146, - 249, - 449, - 839, - 278, - 592, - 1940, - 550, - 729, - 499, - 634, - 1135, - 3539, - 6701 - ], + "y": { + "bdata": "UwEhAAUAGgEXAhYARACSAJEAyQCuAOwASgRgAZ0f", + "dtype": "i2" + }, "yaxis": "y" } ], "layout": { - "autosize": true, "barmode": "relative", "legend": { "tracegroupgap": 0 @@ -2317,57 +1867,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - 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true, "domain": [ 0, 1 ], - "range": [ - 0, - 7053.684210526316 - ], "title": { "text": "times_cited" - }, - "type": "linear" + } } } }, - "image/png": 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" + " }) }; " ] }, "metadata": {}, @@ -3030,7 +2527,7 @@ "px.bar(pubs_grouped, \n", " x=\"year\", \n", " y=\"times_cited\",\n", - " title=f\"Tot Citations per year for publications with industry collaborations for {GRIDID}\")" + " title=f\"Tot Citations per year for publications with industry collaborations for {ORGID}\")" ] }, { @@ -3120,7 +2617,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": { "Collapsed": "false", "colab": { @@ -3143,6 +2640,16 @@ "outputId": "a997bc97-e4b6-4b32-f63f-1739e921a1df" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/apilab/lib/python3.12/site-packages/dimcli/core/dataframe_factory.py:195: FutureWarning:\n", + "\n", + "Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + "\n" + ] + }, { "data": { "text/html": [ @@ -3181,120 +2688,120 @@ " \n", " \n", " \n", - " 7\n", - " Hamburg\n", - " 2911298.0\n", - " Germany\n", - " DE\n", - " grid.410308.e\n", - " Airbus (Germany)\n", - " Airbus Defence and Space, Claude-Dornier-Stras...\n", - " \n", + " 2\n", + " Paris\n", + " 2988507.0\n", + " France\n", + " FR\n", + " grid.89485.38\n", + " Orange SA\n", + " Orange S.A., France\n", " \n", - " pub.1059063534\n", " \n", - " N\n", - " Brandt\n", + " pub.1094950798\n", + " ur.014561075723.99\n", + " Imen Grida Ben\n", + " Yahia\n", " \n", " \n", - " 8\n", - " Milan\n", - " 3173435.0\n", - " Italy\n", - " IT\n", - " grid.424032.3\n", - " OHB (Italy)\n", - " CGS S.p.A, Compagnia Generale per lo Spazio, V...\n", + " 7\n", + " Madrid\n", + " 3117735.0\n", + " Spain\n", + " ES\n", + " grid.99308.3b\n", + " Telefonica Investigacion y Desarrollo SA\n", + " Telefonica I+D, Spain\n", " \n", " \n", - " pub.1059063534\n", - " ur.014542047336.90\n", - " A\n", - " Bursi\n", + " pub.1094950798\n", + " ur.016452362717.24\n", + " Antonio\n", + " Pastor\n", " \n", " \n", - " 15\n", - " Milan\n", - " 3173435.0\n", - " Italy\n", - " IT\n", - " grid.424032.3\n", - " OHB (Italy)\n", - " CGS S.p.A, Compagnia Generale per lo Spazio, V...\n", - " \n", + " 8\n", + " Madrid\n", + " 3117735.0\n", + " Spain\n", + " ES\n", + " grid.99308.3b\n", + " Telefonica Investigacion y Desarrollo SA\n", + " Telefonica I+D, Spain\n", " \n", - " pub.1059063534\n", " \n", - " D\n", - " Desiderio\n", + " pub.1094950798\n", + " ur.014322160107.95\n", + " Pedro A.\n", + " Aranda\n", " \n", " \n", - " 16\n", - " Milan\n", - " 3173435.0\n", - " Italy\n", - " IT\n", - " grid.424032.3\n", - " OHB (Italy)\n", - " CGS S.p.A, Compagnia Generale per lo Spazio, V...\n", + " 24\n", + " Madrid\n", + " 3117735.0\n", + " Spain\n", + " ES\n", + " grid.99308.3b\n", + " Telefonica Investigacion y Desarrollo SA\n", + " Telefonica I+d\n", " \n", " \n", - " pub.1059063534\n", - " \n", - " E\n", - " Piersanti\n", + " pub.1094654631\n", + " ur.014574231073.91\n", + " Diego R.\n", + " Lopez\n", " \n", " \n", - " 19\n", - " Bristol\n", - " 2654675.0\n", - " United Kingdom\n", - " GB\n", - " grid.7546.0\n", - " Airbus (United Kingdom)\n", - " Airbus Defence and Space, Gunnels Wood Road, S...\n", + " 34\n", + " Paris\n", + " 2988507.0\n", + " France\n", + " FR\n", + " grid.89485.38\n", + " Orange SA\n", + " Orange, France\n", " \n", " \n", - " pub.1059063534\n", - " ur.010504106037.54\n", - " N\n", - " Dunbar\n", + " pub.1094654631\n", + " ur.014561075723.99\n", + " Imen Grida Ben\n", + " Yahia\n", " \n", " \n", "\n", "" ], "text/plain": [ - " aff_city aff_city_id aff_country aff_country_code aff_id \\\n", - "7 Hamburg 2911298.0 Germany DE grid.410308.e \n", - "8 Milan 3173435.0 Italy IT grid.424032.3 \n", - "15 Milan 3173435.0 Italy IT grid.424032.3 \n", - "16 Milan 3173435.0 Italy IT grid.424032.3 \n", - "19 Bristol 2654675.0 United Kingdom GB grid.7546.0 \n", + " aff_city aff_city_id aff_country aff_country_code aff_id \\\n", + "2 Paris 2988507.0 France FR grid.89485.38 \n", + "7 Madrid 3117735.0 Spain ES grid.99308.3b \n", + "8 Madrid 3117735.0 Spain ES grid.99308.3b \n", + "24 Madrid 3117735.0 Spain ES grid.99308.3b \n", + "34 Paris 2988507.0 France FR grid.89485.38 \n", "\n", - " aff_name \\\n", - "7 Airbus (Germany) \n", - "8 OHB (Italy) \n", - "15 OHB (Italy) \n", - "16 OHB (Italy) \n", - "19 Airbus (United Kingdom) \n", + " aff_name aff_raw_affiliation aff_state \\\n", + "2 Orange SA Orange S.A., France \n", + "7 Telefonica Investigacion y Desarrollo SA Telefonica I+D, Spain \n", + "8 Telefonica Investigacion y Desarrollo SA Telefonica I+D, Spain \n", + "24 Telefonica Investigacion y Desarrollo SA Telefonica I+d \n", + "34 Orange SA Orange, France \n", "\n", - " aff_raw_affiliation aff_state \\\n", - "7 Airbus Defence and Space, Claude-Dornier-Stras... \n", - "8 CGS S.p.A, Compagnia Generale per lo Spazio, V... \n", - "15 CGS S.p.A, Compagnia Generale per lo Spazio, V... \n", - "16 CGS S.p.A, Compagnia Generale per lo Spazio, V... \n", - "19 Airbus Defence and Space, Gunnels Wood Road, S... \n", + " aff_state_code pub_id researcher_id first_name \\\n", + "2 pub.1094950798 ur.014561075723.99 Imen Grida Ben \n", + "7 pub.1094950798 ur.016452362717.24 Antonio \n", + "8 pub.1094950798 ur.014322160107.95 Pedro A. \n", + "24 pub.1094654631 ur.014574231073.91 Diego R. \n", + "34 pub.1094654631 ur.014561075723.99 Imen Grida Ben \n", "\n", - " aff_state_code pub_id researcher_id first_name last_name \n", - "7 pub.1059063534 N Brandt \n", - "8 pub.1059063534 ur.014542047336.90 A Bursi \n", - "15 pub.1059063534 D Desiderio \n", - "16 pub.1059063534 E Piersanti \n", - "19 pub.1059063534 ur.010504106037.54 N Dunbar " + " last_name \n", + "2 Yahia \n", + "7 Pastor \n", + "8 Aranda \n", + "24 Lopez \n", + "34 Yahia " ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -3307,7 +2814,7 @@ "# extract affiliations as a dataframe\n", "affiliations = pubsnew.as_dataframe_authors_affiliations()\n", "# focus only on affiliations including a grid from the industry set created above\n", - "affiliations = affiliations[affiliations['aff_id' ].isin(gridis)]\n", + "affiliations = affiliations[affiliations['aff_id' ].isin(orgids)]\n", "# preview the data\n", "affiliations.head(5)" ] @@ -3327,7 +2834,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": { "Collapsed": "false", "colab": { @@ -3358,7 +2865,6 @@ }, "data": [ { - "alignmentgroup": "True", "bingroup": "x", "hovertemplate": "aff_name=%{x}
count=%{y}", "legendgroup": "", @@ -3369,801 +2875,218 @@ } }, "name": "", - "offsetgroup": "", "orientation": "v", "showlegend": false, "type": "histogram", "x": [ - "Airbus (Germany)", - "OHB (Italy)", - "OHB (Italy)", - "OHB (Italy)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "Airbus (Germany)", - "Airbus (Germany)", - "OHB (Italy)", - "OHB (Italy)", - "Airbus (Germany)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "Telefonica Research and Development", - "Robert Bosch (Germany)", - "Robert Bosch (Germany)", - "Robert Bosch (Germany)", - "Airbus (Germany)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "Airbus (Germany)", - "Airbus (Germany)", - "OHB (Italy)", - "Airbus (Germany)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (United Kingdom)", - "Airbus (Germany)", - "IBM (Ireland)", - "IBM (Ireland)", - "Orange (France)", - "IBM (Ireland)", - 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bsdLQ3ar3WcLAxLbDfnRBdeZ49x03qubrlq3M9b3u1vurML3e177SEVwn1ceehvgy5/czn/xER1gvr7f7IadVgenaC4+OTy04XzWOxvsuBxO+tdHXqrPJZZXuTb97RHXQoRwAKYG2HIxZf9uDqoBywckHdCykWK4AKWddu/Z3Ypb/fuGVmHvwwG5n7i+6+rY4/scj4rC9vl2dIS7bhC6nn5xxNMJsCb1l7vpyX1qsuspg5plmjJ0POHmS+8uEenrRVbdWV25ceMoBVZhubD39LHY94LXjlt+ItVddtlrV/IPRY+Jjc49/wcUSYNfd5sBq3y7+cw9uX0yyHPxZe8v9xhvix/e+y34xdojv6eexp/9afFQhvvG7qfzOKgekui6qWQ42nXzELjHvPHNWw9rjsB/Hr+75Xfz0pP3HuUtAI+D/6a4LonevXvGdfX5YHfwY+yBKeZ79dt48vr3p2j19q9Xj9j7izLjlrt9W/116uedOm8THh8xVff3Ek8/G0B1+UF1pdOGpB1av39huvuuh2OeIs7p9dibrhT2YAAECBAgQIDCdCNQqxJdVjz+/+vbV2d3rLjymWwsb4bkxL7UR4ssfmeWMZtetzDktweKOK0+aYIhoPH5SIb6cMS9nzhtb44/YcguzsqJ62Rqr3N97zWndLu0eew7u5IT4Ruib2IrYPQnxj/7qvHFuvdVYxKpcrrz/97eo3sMFV9wcPzrriugausf3GWr0qBwkueni48e7EvXk9LGnIb6MpQT3p555IZ578ZXqDGa5nPz2e38fZx67R8fUgAnNif/p5TfHiWdfEY2zhCWol8B+6pG7xhorfanjrTb2q65nHxshfnyWjcJylvXp516sDhj8/al/VwcCykGecjCgbBMK8ZMzjkaIv+ysQ7utFt71+XuygvrYfR1fiJ+cHjZC/NjztCf2O7iszH7cj0eMs781DiaN70z8+N73+Pafnn4ee/pvxEcV4htnscvBiC02XD0W++8tFa+95b7qaqOyqN3lZ/6gOjBz0x0Pxr5HnV39Pjlwly2r34nlCoOyz5fxlK2xSGIj1JdbAn591S9X03KeevbFOPuia6sDBZN7Znz3H5wef3vyX9WVAGWsB+32rY5FA0ePGRMbbndIPP3si9Xl+5uut0rMOGOfKKvbl4NX5fdj1ztN9NTY4wgQIECAAAEC05NArUJ8OeO+5ub7jPcy3Nt//fvY/dDT45Ddt67m2U4sxJewVkJbT+ZmTm6IbwTxsgJzWYm5ETq6hr7GDpgJ8Y3FxspzlbOVX/rcZ6qrDcq838aZ/kmF+Andaq38Ib7C+rtUw/z1NadVl2J/fav9qmB87y9Pr87sTmhr9KisZH7CId8d78Mmp489DfElrB9x0s86bp3V9YXLAmGNKzEmFOLveeAPsfOBp3Rc7t+4SqLrlQ6N5/zG1gdUIaURkia2WF45k37AMed0u4y88TzlDGg5EzqxED8542iE+K5XUDReqyf7y4R6Or4QPzk9bIT4sQ94TewXcbmdWbntXjlYVwJqY5tYiB/f+x57/5mcz2NP/6H4qEJ8ueS9XPo+9gru5YBJucVfmdt+6Y8PiaWW+FQ1tLJWwdkXXddtmCXUl2krXQ90loMWZRv7toqNBffK+gI/+dG+PX27HY8rV64MO+DkKph3Hde//v1SNS++MV2oUVACfzlocPrRu8VqK3xxsl9PAQECBAgQIEBgehGoVYhvrFi/0TorxZH7bd+th417NTfO9k0sxDfmGPfk1muTG+IbqzA3QnwJeyX0jW+V/UyIL2++BJQy773r/bvLZbflD+qyQNuHDfHluRvzkY87eKfqzF25JLlMB9h1++7zXMf+IDUu/x5fjxqPnZw+9iTENx5TQkKZrvD5zy4c835scNxx3+9j+CkXV6t89zTEl3tZb7vZ2h2X+zcur+/6PsvlwiW4PH7XBdUBkwmF+HKbxBX+ezCkBPaVlvt8ddnxW2+/G2W18J6E+MYl6z0Zx8RCfE/2l8kJ8ZPTww8T4g869rxqLYErzj6sWtfgowrxk/N57Ok/Eh9ViG84lc9Y+ax13c6/7KY46Zwr44h9t4uNv9F5i7Yyv/3P//d0vPve+9U0kDI94etb7R+rrfCFOP3o3aunaKxM/4fbz6umjzS2cuu3z622fXU2feyA39P33rh6oHzuut6qrhx4+O0f/lqtm1AWFyy3wLvg8purAzNX/+SI+OynJ7ymRk9f2+MIECBAgAABAs0qUKsQX+Z8f2mtnap5uWV+btftsl/eUQW2kw7/frVw28RCfGNF5fuv+3HHqu0T2gGyIb4xjnIpa5m/2nUbO8Q3VpYe34JfYy9s1/V5xoxpqeY7lxBX5tX+5If7VCv1N0L8+K44mNSt1ko4WGuLfas58CUY3Hr3w/GrK07smJM7Ia9Gj8a3MF6jZnL62JMQX+Zrl7PFZVG+srp+Yyurlh983Hk9CvGN/ee04btVK/w3znL+7NQDq/tidw0+y627c8w154BqPYKyTciyse98Z6t1q3UHGls5U1mCVk9C/OSMY1IhvvH6E9pfJtTT8Z2Jn5wefpgQX+4zXqbINK6saYwteyZ+cj6PPf1H4aMK8Y0DI+P7/DeuTCgH1cqaEhPaGtNCuj6ucSDopkuOrxbHbGyNq4bKOhh3XnVST99ut8c1pt6U22+W23BOaHv7nXerRTsHDug7wWk2H2oAiggQIECAAAECTShQqxBf+tM4q3T9z46JTy7QfpltOeuz8U6HVWdHy4rMZT72hEL8/z35r/jmDod2W1F5Yn3Phvjy3Kttsle3RdPK98o9l3c5+JTqLHpjYbtyGftSa+xYXR5//UXHdiwK9ae//jM2/96R46wCXW6h1vWWaGXxvLJoV5mjutU314jLrrkjhp/aeWCj6/ucVIgvjy2LoZW5uGVbY8UvxalH7dqjj0jjTHXXS2xLYbnM97kXX63ma/e0jz0J8WU17HJGsOsiX2WfOP6MEdX84EmdiS/uZZX1chVBCTMl1JT3Xd5/CUwlEDW2RmjpeqXBhCwbl0d/f9sNY+dtN+x4jsYCX11DfJlnXKYElHtoNxZxKwWTM46JhfjiM6n9ZULNndDCdj3t4YcJ8Y1LvctBpHKpdzlbXG6BeNZF18aFV9wy3oXtenI5/eR8Hstjy/736wcei0U/vcAE76/+UYX4sh+utcV+1e+Kmy89vrozQeP32/Lr7Vxdit71997Y/SoH8sqCl2X/ve2yH3b8bmhMpSi31Tx87207ysrt3spt37pOfSnTJC65+raYbdZZuq0uX24d99nPLFAtmNnYyhoUuxx4SrWPHn/Id2PdNZYf7y5UFukr0wSuu+03cfQBO3bMn+/RLxMPIkCAAAECBAhMhwK1C/GN+xCXuZ8lHJU/7q+5+d4qDG++4Wrxgz22qdrc9RZz5dL2snJ5OdNdFgor29grbY9v3ygh7I9//Wf84qZfV/c9Lrd+GrrOitUtnSYUTMa+nL48b+Msb/njepN1V463R75X3Zu9hNqydb0dXbmfc5n7Wi6HXWqJT8cf//JkFe7K1nVefTkzX85qDV17xep+0uWP77L4XPmD+eZLT4g5B/brWIm/vO52m61d3aJrsUUWrG5J15MQ3wiQ5bXPO3Hfqq4n28OP/S223b39LHUJr/PPN3d1gKVcSlsuuS33bu9pH3sS4svzljOV5eBNCd3lEvdiVl6zbGOH+HKbuLLo1jJLLVKtkF76W77X9WxiuQ/4Vt8fXl3hUM6MrrT8kvHvF16trnYoW9fL2ydk2VjksOyj5fXKFQ2lt2XfKVvXEF+mRZzyk6urq0zKlSQvvfJGbL7BatVt6Xo6jomF+J7sLxPq7YRCfE97+GFCfBlLY1X18t/lwFa5FL6xjW9hu56G+Mn5PDYuYx97jYdykOjK6+6qhvOb3z1e3QWh/J5ZdOH5o3+/2TvunFF+Xs54lwXnynbJL35VvY+9dtqkCsplsbqykntjK/ttOaBTPrPf22b9Krhfdu0d1fzysW89WQ5m9O7TO6KtLcoq/CUkl33tjGP3iGWW7LyLQNeDnOXgU7lrwd0PPFbdLrJsXQ8YNA4EjH2JfeMgU9mPF1n449VdPcptLsvnpsy/L7eYa6yVUcZ66TW3Vz17+dU3qpXsq2kMaywfRx+wQ7dL+nvy+8RjCBAgQIAAAQLTm8B0G+Ibt5ga31zysojdQcf9pNstmMpK37vusFHHSutdbzFXmt4IzOWP4yP33a7bZdcT2inK/ZbH3u7++SnVpdSNANP1NmHlsY0Q3/UP7vJH9EnnXlmdQWxsZQG6spW5/I2rB8rX5Wzwzged3LEoVPljutzCqQS0EtbLHOGylfmx5Uxa19tQlT+aD9lj625huwT78rjGIlONRbN6EuLL1QLLfP277bfju/SEbreLmtQHqfgcfdol3YJXmdt84C5bdSzM1ZM+Ns7Idr2sunGp9TXnH1WFoOJ76A9/GuXy+cZWzuCWebgl4J9xzB7Vgn9la6wkX95T14W3SoDfbYeNul3Z8J+3RsYRJ11YTSVobKXuxEN37jZPe2KWjbOdjfrGgZyy5kCZe1/m4Jet7K+n/uTn1TzwRk8bi7r1dBynnvfzOPeS67vdUq/xuj3dX8bX18Yt8caeWlAe25MeTuizMql9qBxg+ellN0XZB8oUgHIQa61Vlq1W8u+6sv/E3vf49p/J+Tw2Lk8fO8SX+6R/cc3vjPctjH33jHKAcfu9jh/vY7veAaLxgHJw4EdnX9Hts13myJfbNXZdVLIRrEtd+T3x5S98troKZ8h/b0PX9QXLQb4jTrww7n/48Y5vl8/O8P22j8UX6VxzYEIh/uob74kzL7y2ukqg61Zc9h62aXXLwMbWuNqp8XX5vVRu51hW3LcRIECAAAECBAhETLchflLNLZdylntwv/f+B9U8z7FXTO96OX25Ldorr/+nCqElgE+trVwOXO5zXRag6td39gkOo4SMMm+6nFH+xLxzj/c2baW4nC0uc4TLAYpyVn7OQf3HG7TL48pZ5Nlnn6Xb5bCTcmjMKc/cEuqtt0dWt74qVwb0nWO2cV5yUn2c1Bi7/rwcQHnl9Tdj8MB+1ZnMiW1lUa8y778E5hIyZp1l5gk+vOxLz/775ZhzYP9ul7r3dGxlPvCzz78Ss806c8z/8XkmejCkXFJdbkc3eNCA6vFdt+w4erq/9PR9NR73UfZwUq9932//FN/d78TqYFU2FPb08zipMU2Jn5ffAeV3xZiWlup3wPjuCFEWTizhfEC/Oaq1Khp3pZjYeEpN2b8+7L5cDiiVIN+rd6+Y72NzjbOPltcun61nnnspyroJk/psTQk7z0mAAAECBAgQmNYFahviJ9WYiS1sN6laP28/QLDBdgdXVwY8cP0ZEz3owIvAlBAoazrMMces8akF56sOAD35zPNxzGmXVFdQNNYumBKv6zkJECBAgAABAgQITEkBIX4CukJ8brdrXAI99jzc3LOqJtBzga4LK3atmtgiaj1/do8kQIAAAQIECBAgMHUEhPgJuJfLUcv84nLJZ7m9m23yBMqCfv946rlY7kuLT/K2cpP3zB5NoGcCz7/0WpQ7M7z6+n+ipbU1PjFkrvj8YgtXUzNsBAgQIECAAAECBJpVQIhv1s4ZNwECBAgQIECAAAECBAjUTkCIr13LvWECBAgQIECAAAECBAgQaFYBIb5ZO2fcBAgQIECAAAECBAgQIFA7ASG+di33hgkQIECAAAECBAgQIECgWQWE+GbtnHETIECAAAECBAgQIECAQO0EhPjatdwbJkCAAAECBAgQIECAAIFmFRDim7Vzxk2AAAECBAgQIECAAAECtRMQ4mvXcm+YAAECBAgQIECAAAECBJpVQIhv1s4ZNwECBAgQIECAAAECBAjUTkCIr13LvWECBAgQIECAAAECBAgQaFYBIb5ZO2fcBAgQIECAAAECBAgQIFA7ASG+di33hgkQIECAAAECBAgQIECgWQWE+GbtnHETIEDOyyzCAAAgAElEQVSAAAECBAgQIECAQO0EhPjatdwbJkCAAAECBAgQIECAAIFmFRDim7Vzxk2AAAECBAgQIECAAAECtRMQ4mvXcm+YAAECBAgQIECAAAECBJpVQIhv1s4ZNwECBAgQIECAAAECBAjUTkCIr13LvWECBAgQIECAAAECBAgQaFYBIb5ZO2fcBAgQIECAAAECBAgQIFA7ASG+di33hgkQIECAAAECBAgQIECgWQWE+GbtnHETIECAAAECBAgQIECAQO0EhPjatdwbJkCAAAECBAgQIECAAIFmFRDim7Vzxk2AAAECBAgQIECAAAECtRMQ4mvXcm+YAAECBAgQIECAAAECBJpVQIhv1s4ZNwECBAgQIECAAAECBAjUTkCIr13LvWECBAgQIECAAAECBAgQaFYBIb5ZO2fcBAgQIECAAAECBAgQIFA7ASG+di33hgkQIECAAAECBAgQIECgWQWE+GbtnHETIECAAAECBAgQIECAQO0EhPjatdwbJkCAAAECBAgQIECAAIFmFRDim7Vzxk2AAAECBAgQIECAAAECtRMQ4mvXcm+YAAECBAgQIECAAAECBJpVQIhv1s4ZNwECBAgQIECAAAECBAjUTkCIr13LvWECBAgQIECAAAECBAgQaFYBIb5ZO2fcBAgQIECAAAECBAgQIFA7ASG+di33hgkQIECAAAECBAgQIECgWQWE+GbtnHETIECAAAECBAgQIECAQO0EhPjatdwbJkCAAAECBAgQIECAAIFmFRDim7Vzxk2AAAECBAgQIECAAAECtRMQ4mvXcm+YAAECBAgQIECAAAECBJpVoFYhvrWtLaKtLXr37j1Ov8rPXnn1jRg8aED06TPuz98Z+V6MHjMmBvbv26y9Nm4CBAgQIECAAAECBAgQaHKB2oT4tra2OPzEC6t2HbHPdt3ads+Dj8W+R54VI999v/r+4XtvG5ust0r13+++Nyr2H3523Hn/o9XXSy62cJw2fLcYPKh/k7fe8AkQIECAAAECBAgQIECg2QRqEeJvvfvhGH7qxfH6G2/Fxuuu3C3Evz/qg1hp6G6xy3ZDY6tvfi3u+s2jsfsPTo9bL/thfHzIXHH+iBvjyhvujotPOzhmm3XmGLb/SfHJ+YfEkftt32y9Nl4CBAgQIECAAAECBAgQaHKBWoT4994fFW+9PTJOPvfqmHnmGbuF+HIWfucDTo5Hf3VezDTjDFU7v7H1AbHl0DViq2+uEZvsdFistcqyseOW36h+Vg4I7HX4GfH4XRdEr169mrz9hk+AAAECBAgQIECAAAECzSRQixDfaMhRJ18UY1pauoX4q66/Oy688pa48eLjOvq268GnxkLzD4m9vrtpLLvOsBi+/w6x5srLVD//yxPPVMH+gevPiH59Z2+mXhsrAQIECBAgQIAAAQIECDS5QO1DfLlc/pa7fxtXnXtERyvL/PjZZ581Dtvr27HEqtvFmcftGSsvt2T18yeffj7W3/aguP2KE2PIPHM2efsNnwABAgQIECBAgAABAgSaSaD2Ib4nZ+KPPmDH+NpKS1d9HftM/POvvddM/TZWAgQIECBAgAABAgQITFWBeeecdaq+frO/eO1DfGNO/B9uPy9mnKF9TvxaW+4b22y8Vsec+LVXWTZ2mMCceCG+2T8Cxk+AAAECBAgQIECAwP9SQIjPadcixLe2tkZLa2sMP+XiGDOmJQ7fZ9vo06dP9O7VK8qid0uv/d3Yf5ctY6uha4yzOv15I26Mqxqr0882cwzbr/vq9EJ8bgdUTYAAAQIECBAgQIBAvQSE+Fy/axHir7zurjjipJ91kzpqv+3jm+usVH3vrvsfjV0OPrXj54fssXVsseHq1dfl3vFljnw5Y1+2JRZdKE4fvnvMPXhA9bUQn9sBVRMgQIAAAQIECBAgUC8BIT7X71qE+J4QlbP1L7z8ehXOG5fVd60rt6j7YPSYGDyof7enE+J7ousxBAgQIECAAAECBAgQaBcQ4nN7ghCf83MmPumnnAABAgQIECBAgACBegkI8bl+C/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5v/jp5aO6PcPaX2tNPqNyAgQIECBAgAABAgQITL8CQnyut0J8zi/Ou3RUPPjb3tWzDOjfFnvt3pJ8RuUECBAgQIAAAQIECBCYfgWE+FxvhficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hfgufi++/HrMPdfA6N2r1ziq74x8L0aPGRMD+/ft9rPzLh0VD/62d/W9Af3bYq/dW3IdUU2AAAECBAgQIECAAIHpWECIzzVXiI+Ii66+LS79xa9i9OiWKqgP/fqKsddOm1Sy7743KvYffnbcef+j1ddLLrZwnDZ8txg8qH/1tRCf2wFVEyBAgAABAgQIECBQLwEhPtfv2of4Pz/xdGy60+Fx4SkHxDJLLRpP/euFWHebA2PEmT+oAvv5I26MK2+4Oy4+7eCYbdaZY9j+J8Un5x8SR+63vRCf2/dUEyBAgAABAgQIECBQQwEhPtf02of43z7619huz+Pj5kuPj/nnm6fSXHHobrHfzpvHel/7Smyy02Gx1irLxo5bfqP62a13Pxx7HX5GPH7XBdGrVy9n4nP7n2oCBAgQIECAAAECBGomIMTnGl77EP/B6DGx494nxN/+8a/Ydftvxsh3349b73k4Ljr1wOg7x2yx7DrDYvj+O8SaKy9TSf/liWeqYP/A9WdEv76zC/G5/U81AQIECBAgQIAAAQI1ExDicw2vfYgvfOeNuDGuu+3+mHWWmePxvz1VnXXfbYeNonfvXrHEqtvFmcftGSsvt2Ql/eTTz8f62x4Ut19xYgyZZ8645Koxcfd9bdXPBg2MOPKg9kXubAQIECBAgAABAgQIECAwrsAsM/XBkhCofYi/96E/VvPcH7zhzOrM+28efjz2OOzHsfewzWKz9VetzsQffcCO8bWVlq6Yxz4Tf8Hlo+P+B9o7MHBAxIH7tAd6GwECBAgQIECAAAECBAiMKzCo70xYEgK1D/Gn/OTquPP+R+K6C4/pYPz+QafE7LPNGicc8t3q0vm1V1k2djAnPrGbKSVAgAABAgQIECBAgEC7gMvpc3tC7UP8TXc+FPseeVacffxe8dVlPxfPvfBKrL3lfrHv9zaPbTdbu7rU/qrG6vSzzRzD9rM6fW6XU02AAAECBAgQIECAQJ0FhPhc92sf4lvb2uLci6+Pa265N9548+2YY/bZYoO1Vojvb7thzDBDn2qhuxLy73nwsUp6iUUXitOH7x5zDx5Qfe0+8bkdUDUBAgQIECBAgAABAvUSEOJz/a59iO/K9/xLr8XH5h4UvXv1Gkf1rbdHRlnJfvCg/t1+JsTndkDVBAgQIECAAAECBAjUS0CIz/VbiM/5OROf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hfix/F5/463qO4MG9uv2k3dGvhejx4yJgf37dvv+eZeOigd/27v63oD+bbHX7i25jqgmQIAAAQIECBAgQIDAdCwgxOeaK8RHRGtbW5w/4sa46OrbooT42WebJX5709mV7LvvjYr9h58dd97/aPX1kostHKcN3y0GD+pffS3E53ZA1QQIECBAgAABAgQI1EtAiM/1W4iPiJPOuTKuueW++N4268faq345Ro8eHfPMNaiSLeH+yhvujotPOzhmm3XmGLb/SfHJ+YfEkfttL8Tn9j3VBAgQIECAAAECBAjUUECIzzW99iH+ldfejFU22iOG779DDP36iuNobrLTYbHWKsvGjlt+o/rZrXc/HHsdfkY8ftcF0atXL2fic/ufagIECBAgQIAAAQIEaiYgxOcaXvsQf8d9j8Ruh5wWm2+4Wvz9n8/FzDPNGOutuUKsv+ZXKtll1xlWBfw1V16m+vovTzwTJdg/cP0Z0a/v7EJ8bv9TTYAAAQIECBAgQIBAzQSE+FzDax/iR1xzexx96iWx6/bfjM8s/PF44snn4vSf/iJ++INh8fXVvhxLrLpdnHncnrHycktW0k8+/Xysv+1BcfsVJ8aQeeaMEVe3xJ33tlY/GzQw4thDZ8h1RDUBAgQIECBAgAABAgSmY4E+vXtNx+9uyr81If6a2+Pya++M6y48pkP7wGPOjfdGfRCnHLFLdSb+6AN2jK+ttHT187HPxJ8/4oN44KH2nXBA/4h997Q6/ZTfbb0CAQIECBAgQIAAAQLNKjDPwFmadejTxLhrH+LvefCx2PmAk+Ox28+PGWboUzVl3yPPinffHxVnHLNHden82qssGzuYEz9N7LAGQYAAAQIECBAgQIBAcwu4nD7Xv9qH+LffeTdW33Sv2GaTtWLnb28Qf/rbU7HlzkfFIbtvHVsMXT3OG3FjXNVYnX62mWPYflanz+1yqgkQIECAAAECBAgQqLOAEJ/rfu1DfOF74Pd/jt1/cHqMfPf9SnPLoWvEAbtsGX369K6+V87MlzP2ZVti0YXi9OG7x9yDB1Rfu098bgdUTYAAAQIECBAgQIBAvQSE+Fy/hfj/+rW0tMaLr7weA/v3re4HP/b21tsj44PRY2LwoP7dfiTE53ZA1QQIECBAgAABAgQI1EtAiM/1W4jP+TkTn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv4X4nJ8Qn/RTToAAAQIECBAgQIBAvQSE+Fy/hficnxCf9FNOgAABAgQIECBAgEC9BIT4XL+F+JyfEJ/0U06AAAECBAgQIECAQL0EhPhcv6doiD/8xAvj0wt9PLb65hrdRvnEk8/GsANOjp+fd0QM7N839w6mcvV5l46KB3/buxrFgP5tsdfuLVN5RF6eAAECBAgQIECAAAEC066AEJ/rzRQN8bsefGosvshCMWyb9buN8pXX3oxVNtojrv7JEfHZTy+QewdTuVqIn8oN8PIECBAgQIAAAQIECDSVgBCfa9cUCfF//fszMXpMS5xwxmXxyfmHxMbrrdIxyjFjxsTNd/42Rlxze/z+1nNjlplnyr2DqVwtxE/lBnh5AgQIECBAgAABAgSaSkCIz7VrioT4FYfuFq+/8dYERzZoYL/YcYt14tubrp0b/TRQLcRPA00wBAIECBAgQIAAAQIEmkZAiM+1aoqE+Ceffj7GtIyJo0+9JD614Hyx2QardoxyxhlmiAXnHxK9e/XKjXwaqRbip5FGGAYBAgQIECBAgAABAk0hIMTn2jRFQnxjSO+9Pyp69+4dM880Y26U03C1ED8NN8fQCBAgQIAAAQIECBCY5gSE+FxLpmiIbwzt6WdfjOdeeGWckS7/pcWjT5/2ld2bdRPim7Vzxk2AAAECBAgQIECAwNQQEOJz6lM0xP/5/56KvY44M557ftwAX4b9wPVnRL++s+fewVSuFuKncgO8PAECBAgQIECAAAECTSUgxOfaNUVDfLnF3BNPPRdH7bt9DJlnzpihT59uo51n7kFNPzdeiM/tgKoJECBAgAABAgQIEKiXgBCf6/cUDfGrbbJXbLreKuPcJz435GmrWoiftvphNAQIECBAgAABAgQITNsCQnyuP1M0xB9w9LkxesyYOPGwnXOjnIarhfhpuDmGRoAAAQIECBAgQIDANCcgxOdaMkVD/D0PPhY7H3BynHHMHjHPXAPHGekiC3+iWr2+mTchvpm7Z+wECBAgQIAAAQIECPyvBYT4nPgUDfFlTvyd9z86wRFa2C7XPNUECBAgQIAAAQIECBBoNgEhPtexKRrin3nupXjr7ZETHOFin1nQLeZy/VNNgAABAgQIECBAgACBphIQ4nPtmqIhPje05qh2OX1z9MkoCRAgQIAAAQIECBCYNgSE+FwfpmiIv+2eh+Nf/355giPceuM1Y+aZZsy9g6lcLcRP5QZ4eQIECBAgQIAAAQIEmkpAiM+1a4qG+AOPOTfuuO+RcUY48t33q+89eMOZ0XeO2XLvYCpXC/FTuQFengABAgQIECBAgACBphIQ4nPtmqIhfkJD22/4OdHS0jJd3HpOiM/tgKoJECBAgAABAgQIEKiXgBCf6/dUCfGP/eXJ2HLno+Kuq0+JuQcPyL2DqVwtxE/lBnh5AgQIECBAgAABAgSaSkCIz7VrqoT4fzz979hg24PjqnOPiMU+s0DuHUzlaiF+KjfAyxMgQIAAAQIECBAg0FQCQnyuXVM0xD/4yF/ixZdf7zbCt955N665+d74z1sj4/YrfhS9e/fOvYOpXC3ET+UGeHkCBAgQIECAAAECBJpKQIjPtWuKhvhdDz417rz/0XFGuNYqy8TmG6wWy37hs7nRTwPVQvw00ARDIECAAAECBAgQIECgaQSE+FyrpmiIHzOmpVrArus2wwwzRJ8+zX32vev7EeJzO6BqAgQIECBAgAABAgTqJSDE5/o9RUN816G9/sZb8f4Ho2OewQOF+FzPVBMgQIAAAQIECBAgQKBpBYT4XOumeIj/5S33xYnnXBklxDe2zdZfNfb4zsbRr+/sudFPA9XOxE8DTTAEAgQIECBAgAABAgSaRkCIz7Vqiob4G25/IPYffiOPJuEAACAASURBVE4ss9Si8dVlPxcDB/SNhx75a9x4+wOx8nJLxhnH7hG9evXKvYOpXC3ET+UGeHkCBAgQIECAAAECBJpKQIjPtWuKhvhv7XJ0NbpLfnxwt1FefcM9cdiPLohfXXFizDvPnLl3MJWrhfip3AAvT4AAAQIECBAgQIBAUwkI8bl2TdEQv+LQ3WK7zb4e22/+9W6jLLedW33TveLCUw+IZZZcNPcOpnK1ED+VG+DlCRAgQIAAAQIECBBoKgEhPteuKRrih+1/Ujz/0qvxywuOjt5dLps/95Lr49Tzfh53XX1KzD14QO4dTOVqIX4qN8DLEyBAgAABAgQIECDQVAJCfK5dUzTE//6PT8Q2ux0Tgwb2i68us0QMHtQ/7nv48XjiyWdjo3VWiiP32z43+mmgWoifBppgCAQIECBAgAABAgQINI2AEJ9r1RQN8WVoj/zpiTjrZ9fGY395Mka++34svOC8scm6q8TmG64WM84wQ27000C1ED8NNMEQCBAgQIAAAQIECBBoGgEhPteqKRriW1pa49333o/ZZp2lujd8W1tbtRr9OyPfixlm6BOzzDxTbvTTQLUQPw00wRAIECBAgAABAgQIEGgaASE+16opGuJ/duUtccKZl8etl/0wPj5kro6R7nzAyfHK62/GVecekRv9NFAtxE8DTTAEAgQIECBAgAABAgSaRkCIz7Vqiob47fc6Phb8+Mfi0L2+3W2U5dL6LXc+Ku686qSYZ65BuXcwlauF+KncAC9PgAABAgQIECBAgEBTCQjxuXZN0RD/ja0PqOa/b7vZ2t1G+fKrb8aqG+8RV557eCz+mQVz72AqVwvxU7kBXp4AAQIECBAgQIAAgaYSEOJz7ZqiIf77B50Sz7/0Wlxz/lHdRtm4zP7ea06rVq5v5k2Ib+buGTsBAgQIECBAgAABAv9rASE+Jz5FQ/zdv/lDlCC/4pc/H6ut8IUYPGf/uP/hx+P6234TS39+kTjzuD1zo58GqoX4aaAJhkCAAAECBAgQIECAQNMICPG5Vk3REF+GduV1d8WPzr6iur1cYyuB/rC9t63uG9/smxDf7B00fgIECBAgQIAAAQIE/pcCQnxOe4qH+DK8UR+MjmeffznefW9UfGLeuWJg/765UU9D1UL8NNQMQyFAgAABAgQIECBAYJoXEOJzLfqfhPjcEKftaiF+2u6P0REgQIAAAQIECBAgMG0JCPG5fgjxOb8Q4pOAygkQIECAAAECBAgQqJWAEJ9rtxCf8xsnxG+4fmu8/U7nk849V8SQj7UlX0U5AQIECBAgQIAAAQIEpg8BIT7XRyE+5zfeEH/hxX06nvV7O7UI8Ulj5QQIECBAgAABAgQITD8CQnyul0J8zk+IT/opJ0CAAAECBAgQIECgXgJCfK7fQnzOT4hP+iknQIAAAQIECBAgQKBeAkJ8rt9CfM5PiE/6KSdAgAABAgQIECBAoF4CQnyu30J8zk+IT/opJ0CAAAECBAgQIECgXgJCfK7fQnzOT4hP+iknQIAAAQIECBAgQKBeAkJ8rt9CfM5PiE/6KSdAgAABAgQIECBAoF4CQnyu30J8zk+IT/opJ0CAAAECBAgQIECgXgJCfK7fQnzOT4hP+iknQIAAAQIECBAgQKBeAkJ8rt9CfA/93hn5XoweMyYG9u/breK8S0fFg7/tXX1vQP+22HD91rjw4j4dj/neTi0x5GNtPXwVDyNAgAABAgQIECBAgMD0LSDE5/orxHfx+/eLr8bQ7Q+JzTdcPfbaaZPqJ+++Nyr2H3523Hn/o9XXSy62cJw2fLcYPKh/9bUQn9sBVRMgQIAAAQIECBAgUC8BIT7XbyH+v37lTPuW3z8qnnz6+dhhy290hPjzR9wYV95wd1x82sEx26wzx7D9T4pPzj8kjtxveyE+t++pJkCAAAECBAgQIECghgJCfK7pQnxEtLS0xvcPOiU+NvegePudd2O+IXN1hPhNdjos1lpl2dhxy29U0rfe/XDsdfgZ8fhdF0SvXr2cic/tf6oJECBAgAABAgQIEKiZgBCfa7gQHxHHnn5p/P2p5+KcE/aOA44+t1uIX3adYTF8/x1izZWXqaT/8sQzUYL9A9efEf36zh7nj/ggHnioV/WzAf0jvrlBa/z0ovY58mXb5butMWSIOfG53VQ1AQIECBAgQIAAAQLTi8A8A2eZXt7KVHkftQ/xl/3yjrjwylviyrMPj/79Zo+9jzizI8S3tbXFEqtuF2cet2esvNySVYPK5fbrb3tQ3H7FiTFknjljxNUtcee9rdXPBg2M+PYWfeLkM1s6mnnIPn3iE/O1h3wbAQIECBAgQIAAAQIE6i7Qp7d8lNkHah/i19py31hgvnniUwt9vHK8477fR785ZqvOvH9nq3WjnIk/+oAd42srLV39fOwz8Ra2y+x+agkQIECAAAECBAgQqJuAy+lzHa99iL/iurviP2+906F47a33V7eRW2/Nr8Rm669aXTq/9irLVovdlc2c+NwOp5oAAQIECBAgQIAAgXoLCPG5/tc+xI/N1/Vy+vKz80bcGFc1VqefbeYYtp/V6XO7nGoCBAgQIECAAAECBOosIMTnui/Ej+U3dogf+e77se+RZ8U9Dz5WPXKJRReK04fvHnMPHlB97XL63A6omgABAgQIECBAgACBegkI8bl+C/E99Hvr7ZHxwegxMXhQ/24VQnwPAT2MAAECBAgQIECAAAECESHE53YDIT7n50x80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+QnzSTzkBAgQIECBAgAABAvUSEOJz/Rbic35CfNJPOQECBAgQIECAAAEC9RIQ4nP9FuJzfkJ80k85AQIECBAgQIAAAQL1EhDic/0W4nN+PQrxF/ysT8erLL9ca6y6cmvyVZUTIECAAAECBAgQIECgOQWE+FzfhPicX49C/E8v7BOjPmh/oRLghfgkunICBAgQIECAAAECBJpWQIjPtU6Iz/kJ8Uk/5QQIECBAgAABAgQI1EtAiM/1W4jP+QnxST/lBAgQIECAAAECBAjUS0CIz/VbiM/5CfFJP+UECBAgQIAAAQIECNRLQIjP9VuIz/kJ8Uk/5QQIECBAgAABAgQI1EtAiM/1W4jP+QnxST/lBAgQIECAAAECBAjUS0CIz/VbiM/5CfFJP+UECBAgQIAAAQIECNRLQIjP9VuIz/kJ8Uk/5QQIECBAgAABAgQI1EtAiM/1W4jP+QnxST/lBAgQIECAAAECBAjUS0CIz/VbiM/5CfFJP+UECBAgQIAAAQIECNRLQIjP9VuIz/kJ8Uk/5QQIECBAgAABAgQI1EtAiM/1W4jP+QnxST/lBAgQIECAAAECBAjUS0CIz/VbiM/5CfFJP+UECBAgQIAAAQIECNRLQIjP9VuIz/kJ8Uk/5QQIECBAgAABAgQI1EtAiM/1W4jP+QnxST/lBAgQIECAAAECBAjUS0CIz/VbiP+v31tvj4z3R42OuQcPGK/oOyPfi9FjxsTA/n27/fy8S0fFg7/tXX1vQP+22HD91rjw4j4dj/neTi3x0wv7xKgP2r+16sqt1f9sBAgQIECAAAECBAgQqKOAEJ/reu1D/Kuv/ye+vfux8fSzL1aSCy84b3xnq3Vjva99pfr63fdGxf7Dz44773+0+nrJxRaO04bvFoMH9a++FuJzO6BqAgQIECBAgAABAgTqJSDE5/pd+xD/8qtvxi9vuTc2WGuFmH22WeOiq26NC6+8JX59zWkxy8wzxfkjbowrb7g7Lj7t4Jht1plj2P4nxSfnHxJH7rf9hw7xvXp1b9pcc7XFK690fnOpJduqs/o2AgQIECBAgAABAgQITG8CQnyuo7UP8WPzPffCK7HWFvvGxacfFF/83Gdik50Oi7VWWTZ23PIb1UNvvfvh2OvwM+Lxuy6IXr16fagz8a+/3ise+1N7aB/ysbZYacXWuOKqzkvw99q9RYjP7deqCRAgQIAAAQIECBCYRgWE+FxjhPix/K65+d445Pjz495fnh6DBvSNZdcZFsP33yHWXHmZ6pF/eeKZKtg/cP0Z0a/v7EJ8bv9TTYAAAQIECBAgQIBAzQSE+FzDhfgufn9/6rnY6vvDY5tN1opdthsabW1tscSq28WZx+0ZKy+3ZPXIJ59+Ptbf9qC4/YoTY8g8c8YFl4+O+x9of5KBAyI2GdoW517QeWn8Ht9vi7PO6xWjRrU/5murRbz2WsQjj7V/Pe+QiNVXaYuLL+usOXCftuq5bAQIECBAgAABAgQIEJjeBAb1nWl6e0v/0/cjxP+X+98vvhpb73pMLLvUonHMgTtG797tK86XM/FHH7BjfG2lpauvxz4Tf8lVY+Lu+9rnrw8aGLHVpr3i9HM657MfsEfvOPms1o4Qv86aveKVVyMefqT9MR+fL2Kt1XrF+Rd31hx5UO/quWwECBAgQIAAAQIECBCY3gRmmalzKvH09t7+F+9HiI+Ifzz979huz+NjtRW+EIfu+e3o06c9wJetXDq/9irLxg7mxP8v9kevQYAAAQIECBAgQIDAdC7gcvpcg2sf4p948tkYusMP4htrLB+7bf/N6NW7/bL2shJ9uSf8eSNujKsaq9PPNnMM2y+/Or2F7XI7rWoCBAgQIECAAAECBJpXQIjP9a72If7mux6KfY44axzF9df8Shx70E4x8t33Y98jz4p7HmyfxL7EogvF6cN3j7kHt09a/zD3iZ9UiN9j15aYoU/3W8z165drtGoCBAgQIECAAAECBAhMCwJCfK4LtQ/xPeV76+2R8cHoMTF4UP9uJVMqxJ9yeuc8kXXWbo3llm3t6VA9jgABAgQIECBAgAABAtOsgBCfa40Qn/ObYmfihfhkY5QTIECAAAECBAgQIDBNCgjxubYI8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5v/9ZiP/SF1onOtLRoyNmnHHib2ZSP09SKCdAgAABAgQIECBAgMAkBYT4SRJN9AFCfM7vfxbi//GPXvHEP3pVo/3kQm2xxOJtcd0NvTtGv8euY+KU02fo+HqjoS3x0MO947nn2msW/2xbbLZJS/LdKidAgAABAgQIECBAgEBOQIjP+QnxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOb+mCvHzztsWL7zYfsu5sm26kVvOJduvnAABAgQIECBAgACByRQQ4icTbKyHC/E5v6YL8b+6o/3e8n16Rxx2yJjku1dOgAABAgQIECBAgACByRMQ4ifPa+xHC/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOb+mDvE7D2uJs87t0yGwxaYt8elPtSVFlBMgQIAAAQIECBAgQGDCAkJ8bu8Q4nN+TR/iTz+zM8RvvaUQn9wdlBMgQIAAAQIECBAgMAkBIT63iwjxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOT8hPumnnAABAgQIECBAgACBegkI8bl+C/E5PyE+6aecAAECBAgQIECAAIF6CQjxuX4L8Tk/IT7pp5wAAQIECBAgQIAAgXoJCPG5fgvxOb/pLsTfdGvv+OCDXpXKoou0xnrrtCaFlBMgQIAAAQIECBAgQKBTQIjP7Q1CfM5vugzxr73WHuKXWVqIT+4eygkQIECAAAECBAgQGEtAiM/tEkJ8zk+IT/opJ0CAAAECBAgQIECgXgJCfK7fQnzOb7oP8V9csm2yheabb/JrJvtFFBAgQIAAAQIECBAg0JQCQnyubUJ8zm+6D/EzzxRx3296V0p9+0ZstnFLnHdBnw617+7QEhdf1ifefbf9Wyt9tTXWWM08+uRupZwAAQIECBAgQIDAdCsgxOdaK8Tn/IR4IT65ByknQIAAAQIECBAgUC8BIT7XbyE+5yfEC/HJPUg5AQIECBAgQIAAgXoJCPG5fgvxOT8hfjwhfmzSj8/XFs/9u33F+7J9cam2GDTIvPnkrqecAAECBAgQIECAQFMKCPG5tgnxOT8hfjwhfuTIiN8/2j6Pfu6522KNVVtjxBWd8+j32KVFiE/ud8oJECBAgAABAgQINKuAEJ/rnBCf8xPihfjkHqScAAECBAgQIECAQL0EhPhcv4X4nJ8QL8Qn9yDlBAgQIECAAAECBOolIMTn+i3E5/yE+A8Z4kdc2X65fdnKveif/leveP2N9q9nnaV93vz9D3bOo99qs5a4tMsl+Sss1xaP/KFXvPd+e82ggRELzt8WjzzWWbPLsJZkd5UTIECAAAECBAgQIPBRCwjxOVEhPucnxH/IEH/KjzvnyK/9tdYqxP/t/9oD+ALzt1Uh/prrOoP+nruOiZNPn6GjW0PXb61C/DP/aq9ZdJG2KsTf8qvOmiMPHZPsrnICBAgQIECAAAECBD5qASE+JyrE5/yEeCE+uQcpJ0CAAAECBAgQIFAvASE+128hPucnxE/DIb6sgj+tbG6pN610wjgIECBAgAABAgSmtoAQn+uAEJ/zE+Kn8RDf9bL9LTdridvv6h0vv9x+Cf4XlmqLvnO0xa/va78Ef9ZZI7bZsiXOOb/zUv8dtm2JK3/eJ95+u31H+cryrTF6dMTDv2uvKeH8G2u3xsUjOmvKXPyzz+sTY/57Nf/qq7bGyiu2Jvc05QQIECBAgAABAgSmDwEhPtdHIT7nJ8QL8UJ88jOknAABAgQIECBAoF4CQnyu30J8zk+IF+KF+ORnSDkBAgQIECBAgEC9BIT4XL+F+JyfEC/E9yjEz9i5sH61x83Rty3eebvzdniLLtoaf/tb58r6M84Y1WX7Xbc+M0S0dFlwf+yafv3a4q23Op9zfLt2mQ5gI0CAAAECBAgQIDA1BYT4nL4Qn/MT4oX4HoX4l17uFY//uT1gzzdfWyz/5da4+hed8+j32HVMnNLlFnrrrdMaf/lbr3jyn+01n1q4LRb5TFvceHNn0B+7ZtONWuK+3/SO519or/n8Em0xeHBb3Hl3e83MM0UcfIDb7iU/8soJECBAgAABAgSSAkJ8DlCIz/kJ8UK8EJ/8DCknQIAAAQIECBCol4AQn+u3EJ/zE+KFeCE++RlSToAAAQIECBAgUC8BIT7XbyE+5yfEC/FNFeJ33K4lbuhySf6qK7fGXfd0XqI/5GNt0at3xPPPd86tX22V1o5L8svHZZ21W+OmWzprPrlQW7z+eq948z+dH6blvtwaDz7U+ZihG7TGNdd2fr34Ym3xj3/0ilEftNf06ROxxOJt8dgfO1936AYtcc21nVMOlv5Sa/zu953PMcfsEfPO2xZP/L2zZv11W+O6Gzofs+IKrXHv/Z1fzzW4LWaZJeLZ5zprvrZ6a/zqjs7HrLVGa9x6e+fX83+iLcpjbAQIECBAgAABAh+NgBCfcxTic35CvBDfdCH+jHM6g/G2W7fEtdf3iTfebP8gLLdsaxXiH3iwPcQO6N8WJYBfcFFnzfd2aokLftYn3h/VXlMOBJQQ/9if2oNxORCw0oqtccVVXef8t8Qpp3d+XQ4ElBD/xD/aa8qBgBLiuwbwsef8bzS0JR56uHc8998Avvhn26oQ3wjgfXpH7DysJU4/s/N1vrVFS9x8W+947bX211n6i61ViC9rB5StHAjYfNOWOO+Czpqdtm+JSy7vE+++2/7+yoEAIT75i1I5AQIECBAgQKCLgBCf2x2E+JyfEC/EC/FCfPK3iHICBAgQIECAQL0EhPhcv4X4nJ8QL8QL8TUI8V/9ykd/Of2oUREzzzx5v4BaWyN6d17pP3nFU+HRs846FV7USxIgQIAAAQLTvIAQn2uREJ/zE+KFeCG+BiG+XFr/+0fb03OZV7/mGq1x6eWdl+Dv/v2WOO3MPtHW1v4Lpfy8XPZfbhNYtjKvfukvtsUvuqwLsOeuY+LkLrcV3GC91mpNgKefaa8ptxT85IJt1XSAxrb7Li1x6o87X7dMBShrGrz0UnvNF5Zqi3592+Kee9trSoj+9rda4uyfdNbssG1LXPWLPvHWW+3P+pXlW6NlTFRTFco2aFBblFsc/uySzppdhrXEOef3idGj22tWX7U1JnXbxH33HBN9+yZ/wSonQIAAAQIEpksBIT7XViE+5yfEC/FCvBAfQnxbLP/l1rj6F53BX4hP/uOinAABAgQITMcCQnyuuUJ8zk+IF+KFeCFeiJ9PiE/+U6KcAAECBAjUSkCIz7VbiM/5CfFCvBAvxAvx4wnx5e4CzzwzeRP4F1igtVvNjDO2xejRnbcDHN+v63mHtMXzL3Q+pl+/tnjrrYnXDBjQFm++2fmYeeZp65iSMKF/EmaepS1Gvd9Zs8ACbfHMf6c+9PSfkQUWbI1nnu406dUrOqZgTOg5ylSMfz3b+bqzz94WI0dO/P2VKR+vvNr5mLG/Ht9rjf28Y7/u+GrGHv+CC7TG05PZ8/nnb4t//atzrGM7j+91v7DUR79GRU976HEECBAg8NEICPE5RyE+5yfEC/FCvBAvxE8gxJ/SZc5/mWdf1gh48p/tge1TC7dV8/5vvLkz1I59W8FNN2qpbgfYCOmf/3/2rgJKimOLvhWS4CFIgCBBAgR3t+Du7u5ui7u7u7u7u7s7BA2WoCHEYXf/uW+p2Z5hprt6uljIp+ucf37Y6a6uflVd9eS++1IHU4wYwbR7b8g94cIRoSSgtmwiOADWbwwtm5gtSxD5+oWWTYwShahiuUCnsonNGgfS3PmhZRPz5Qmil78RnT0X8hwY+SiluHS557KJxYsE0a07PnTtesj7fZswmNKlDaZ1GzTv1+YNjR3v7zh1KpQNopOnfRxGesoUwRQvXjBt3xlyD4zkNi0Cadwk57KJ23b4Oox0lE0E98GBQ6FlE2tUDaTps53LJi5e5ke//xHyaJRN/OsvopOnQ3keihYOooVLPj6e/SgYGAAAIABJREFUh/TpgihqFHLwPHzxOVH3gDcWT277dlsCtgRsCdgS+NASsI14azNgG/GS8vv9j7/o9Zs3FC2qM1PTzEX/0NHjoTW1y5UJorkLnGtqz57rR//8G/Kgj7mmdu0agbR5W2hN7SyZg+jzz0JraoOkqmol55raTW0j3jbibSPeNuJtI55sI57ofZA12ka8pJJiX2ZLwJaALYH/mARsI97ahNlGvIH8/vzrHwoYOJV2HzrDV6ZLmYTGD2xDMb6Kyv+2jfhAWrDEj8DejZY3dxD98Ucok3esWMFU6IcgQhRItHatAmmshmG7WOEguvOTD129FhLBSpggmDKmD6Y160MjWK7KYfkyQXT6rA/dfQvDTJE8mL5NEExbd2iiXi7PQXRq5x5fevw4lMk7cqRg2n8wlMm7To0QFm7RwOS9fJUfvXoV8hcweYOh+8TJUCbvksWCaMFiZybvqTP96M3bYJEMk7drBPJ9RC3hkGlU3zlqWa92IK3bEBq1zJ41iHx8Q6OWX0YNpvJlg5yils2bBNKceaFRy4/ZMVWreiCzuz97FjLniFp+8UWoYypSRCIwvM+c4xy1XLg0dE0jammz05tnpw+LNW1H4kPWtB2Jt6YI2XfbErAlYEvAlkDYS8A24q3J3DbiDeQ3a/EmWr5xLy0Y34MihP+cmgWMpsQJ4lD/Lg1sI56I7Eh8sB2JtyPxdiTejsTbkXgKu0h8vTqBtHd/qMMWlRGOHAv9d7RowRQUSPRSw42AtApRRhGHd8H8QbTrbVoG/g1uhadPfejft2UU8be0qYPo/MXQfosVCaSt20MdfkkSBzvSQ4Qq8V2SYPrxZmiOv+s9qVMF0cVLoX36+xN9HSuYHjwMvQeObzicRXMde9QoweTnT/T8eeg9uXIE0aEjoffkzxvkJCM847dXPpxGIRpQDiJlBH8rViSItmpKWiZKGEy3XXgfkAIjUkb4nqKBtHVbqEyQEiJKa+J3Xx/iFBEtr0PRQkG07W3KCK7JlCHIUcIT/44YIZgiRCAnXoe8uYJo/9uUEVyTO2cQp9qIFiN6MP39N9HvGr4IOLhEygiuK1wwiHbsCr0nfrxgevjQhwI1FAupUgbRpcue5zzZd8F0/UdnTorEiYLp1m3NnBcOcgoopEuL8qGhfcKhjlKej34OvQfOcJQLFc3dmg4OIvr1Zeg9cLoLJOh/bU1nyRREJ06Fvm/kyMGcHqW3pvPlDqJ9b4MueN9YMYPp1e/OazpjegR4NPPnMhcI9iBopG3fJw+mK2+DSPh78SJw/oeuadffcY1rPwhGaYNIruPAmo4Ykejxk9Bnu76PuzUNFO+rV6H3uMrtg63pNEF07oK5Ne26XhEkalTzc2tW7Cd+t23EGyyAyk36UNH8WalRjZJ85ba9J6hD30l0cc8c8vHxsSPxNpzeNuJtI9424m0j3jbiw9iInzojVMmuXyeQVq31o99+CznQc2QPYiNeGO3RviQqUyqQ5i0Mvadl0xDeACCr0ArkD2Ij/vzFEIUZRj2UaiCxRHNFl5QsHsQG7Y23RjuMehixGzZ75nmoVCGQHQ4PHoQ8J3WqYDbid7012mHIwDk+cWroc8HzgD6FgQOjHkb84bdGO3geKlcIpFlzQ+8BzwPeVxjt4HmAEX/mbMhzPyaeh5rVApkHQpAxwqiHES94HvDftao58zwAVQaOCsHzgLmCES+M9ujRg/mb1PI8tG4RSJOn+jmMdhhAMOIvXQmRCZwNkO2qNZ7nvEwpOGF8HEZ7sqTBlDRpMG3e6hkFWLVyIO0/4Osw2tOlCWYjXhjt4HmoXzeQpkx3XtNr1vk6jHasaRjxwmjHmi5bOtApfRNrGqgykb75saxpOKqaNXJe00jf3LQ1dE0jfRNrX6xppG+CE0W7pvFdzF8cuqaB/IQRL9Y0jPpCBT5O5CfWNIx4gfzEmq5d3Rn5iTW9bGUo8hNrGnMpkJ9Y0yWKOiM/P9iarhTI7yIcUa5rGo6qBvWc1zSQn2vXh65pGPW2EW/NC2Eb8Qbyy1qiGQ0MaEhF8mXhKy9fv0sw7I9smERRIke0jXjbiLeNeNuIt41424i3jXjbiCfbiCfyhqzRNuJtI9424kPSHW0j3ppR+6ndbRvxOjMeHBxMqX+oT5OHtqd82dPxlTfvPKQy9brTzmWjKM7X0WnJqkDatT8EixX9Kx+qV92PRk0KZc7t3dmfho9/Q3//E/KgMsX96PGTYDp6MuSeBPF8qGRhX5oyJ9AxksE9/an7wNA+qlf0o4tXgujC5WC+5vtkPpQlgy/NXxZ6z5De4ahb/1AcYKPafrT7ANiSQ+7JlN6Xvo3vQ6s2hNzj50fUN8Cfeg0OfU67Zv78Pr88Cbknfy5fzh/euitkrF9GJWpW35+Gjg29p0cHfxo37Y3DG16yiC/99orowJGQe76J40PlS/rRxJmh9wzu5U/dB4T+u0o5P7p+M5jOXgi557vEPpQ7uy/NWez5/erX8KODR4Pox1shY02fxpeSJfGh5Ws1cnR5TqtG/rRmUyA9eBRyT65svvxOm7aHPDdiBCLIYNDo0LEFtPGnafPe0K8vQ6anyA++9O+/RHsPhdwTK4YP1ajkR2Onht7Tr6s/DRz5hl6//RPe//7DYDpxJuSeRAl9qFA+X5oxXzvWcNR9QOj81arsR6fPB9HlayFjTf29D6VN5UuLV2ru6R2OumvmvGk9P56ru/dC7smWyZfifO1DazeH3ANvf9d2/tR3WOhYO7b0p3lLA+nps5B7Cub1JV9foh17Q9d0g5p+NGKC85rGv//6O0QmH/OabtvUn5auDl3TeXP6UoTwoWs6SmSiFg2d13T39v40fnromi5eyJfXt1jTkGmlMn40YUaoTAb18KdeQ95Q0FtYJn7Ht4c5REuayIfw7NmLQudvaO9w1FUzf3Wr+fG+cO1GyFykS+1DKb7zpWVrPK/pFg39aP2WIF5fYk0jQrNxW+ia7tDCnwaMdF7T0+cH0otfQ+7BmgZ/A/YLsaZrVfGj0ZOd1zS+C6x9tI9lTX/2GRHmS7um8b7YG8WaLpDHl/c7saajfelD2B+1a7pXJ38aOTF0TZcq6kvPXxAdPh4ik3hxfahMcV+aPMvzPl21vB9d/TGIzl0MkWvypD6UPbMvf1+iuc45vq39h4Poxu2QezKm9aXE3/rQyvUh9+BbHNDNn3oMCp0LrGnsc49+CbkH6wp715adIWPFmsZeN3hM6D2QEfZg7M1oWNN//En8bDSsaezD2MtF+1jWdM6svvRVtNA1Hf4Lok6tnNd059b+NHNB6JounN+XAgND13SM6D5Up6rzmsb5BxmJNV2uhB/L9NipEJkkjO9DxQr60rS5mnOoVzjqptmnsfefvxREF6+EzEXK5D48hwtXeL6ncR0/2rkviG7fDbkHZznWF84mNKxpnKt9hobOBdb0wuWB9PhpyD1Y04hubt8TMlas6SZ1/GjY+NB7sKbxDWOe0bCmX/xKdOjYx7emWzf25zUv1nSeHL4EvhKxpvHfbZo4r2mcZZNnha5pzNWff4Wu6a9j+lC1Cs5rekD3kL0CawOtYmk/unMvmE6dDZEJvj3IFmtJtCEuc451hLP8yvWQuUiT0odSf+/LupNorjpO8/p+tGlHEP10P+Qe7AuxYvrQ+i0h92BNYw33HxE6f/g3zotnz0PuwZrG+SL0TaxpnBlafRNrGvqZ0Dc/ljUdzp+oZyfnNQ1dC/qMWNPQN7H2xZqGbta0rr/TmsZ3AV1LrGnom9DNxJqOG9uHKpT6OPVNrGnszULfxJrGXq7VN7Gmp84J1TexpoEuEfom1jTsAa2++aHWdLP6frRZZ01D3+zSxnlNQ9+cuyR0TUPfxPvYzXsJ2Ea8gewQiR/UtREVzpuZr3SNxHsvevtOWwK2BGwJ2BKwJWBLwJaALQFbArYEbAnYErAlYE4CthFvIC9A54vlz0oNPeTEmxO3fbUtAVsCtgRsCdgSsCVgS8CWgC0BWwK2BGwJ2BLwXgK2EW8gu5mLN9EKwU4f4XNq1sWZnd570dt32hKwJWBLwJaALQFbArYEbAnYErAlYEvAloAtAXMSsI14A3n98eff1Ln/FNp39BxfmTpFIpowsC3FivGlOUnbV9sSsCVgS8CWgC0BWwK2BGwJ2BKwJWBLwJaALQGLErCNeEkB/vbqD/r39RuK8VVUyTvsy2wJ2BKwJWBLwJaALQFbArYEbAnYErAlYEvAloBaCdhGvFp52r3ZErAlYEvAloAtAVsCtgRsCdgSsCVgS8CWgC2B9yYB24h/b6L9sB3//c+/9Psff9nIgQ87DfbTbQnYEngrgf+nPen/6V3sBWpLwJaALQGVEvh/2x//395H5VzbfX1YCdhGvAn5//7nX1S79WCqVDIfFcmXmWJG9y4v/trNn+jegydUIHcG8kUhYCLCJnHt5j36LlE8ihD+cxOjcn/p5t3HaPHqnbRwYg/LfdkdfFgJ3H/4hG7ceUBPn7+kb+LEoFTJvqUokSOG2aBUrHsVfbzvF75+8x5t3HWU7j98TKP7tpR+3J17P/P8vPj1FX0TOwYlT5qAokeLYnh/YGAQHTxxgfJlT+f2WqTwoN+MaZIZ9vVfuEB2T1K5P76vb0f2XVTMy6e2TlTITKYPFXJVua9ZXfcqxqKiDxnZy15jVSZ6zzGz3z9++iv5+flK7euy7/ahrguLOZbZH8NiHKpkLPM+2mfdvf8L3bz7gJ6/eMU62/ffJaQvo0QyHM7HticZDtjEBd7qSSYe8UleahvxJqb9zZtAmrFoI63ddpCgHObJlpbKFstF+XOkp/BfyBveKFsX9+sYNG5Aa376gWPnqVnAaP7vr6JFoaVTerMxYKXJbDrYRLOVaK77mGwZv6fZowM8XvOx9IEBfixjUTEOvM8///xLo6Ytp0Wrd7L8I0b4gkC0iP/v26k+lSiQzXCJqBiLinWvog8V7+IqMChnW/cepzVbDhCUum/jx6b61Yqzo86ovfr9T+o7ai5t3XPcaX7wj3aNK1HjmqV0uwBSJlvJ5rR2zkC31/146z4tXrNL1xGnQiYq+jCSFX6X2ZNwnYr9UcW3o/dOsu+iQrb/b+tEiUxUnF0Kvj8V+5pYZ1bXvYqxqOhDxfyqkomK/T4oOJjyVWhLzeuUoRrlC72zLbToOobSfJ+Ymtct+5/Qk1TMsdF+L7M/qhiHyrWmYr+HfjZ4/EJau/XgOzpB3471qHLp/O9dJ1AhV1X6NPqxqicZrbVP/XfbiPdiBQQHB1PAoOl09cZd+vnxczasKpbIS6UK56DM6ZI7ouvuun752x+Us0xLWj69L0dU0VeFRr0pacK41LROGd4AsmdMSU1qlTYcGbx2ntrm3Udp2bo9+gbAWyVmRO/mFNFN9P/MxRt08tw1qT5G9mlOEd46MvqNnk9VSuen779LQGcv3aQTZ6/q9sHex+PnHa+CcUeNEpGK/ZCV/yYzDlwXFBRE5y/fciuSs5du0IgpSylD6u90x6KiD9f3wYAGT1hEhfJkoqzpU/D4EEnHWPTa1PnrafbSzWyw497Pwvmzo2LRqp00ftYqWjmjH3t49Zq7sfQcPpuqly1AqZJ/S8fPXqVzl25KITasrHsxRit9qJgbjOPPv/6hvUfO0totB+jQiYvsOCtfPA8V/yGroTy1su4+ZCadunCN+nWq75jXy9fv0sHjF2jC7NU0olczKlEwu2fF7u33pzd/RutVGHhDujehqJEjcFfatSYzv6q+Pzzb6p6kan9U9e1Y2V+FPLR7G+amTJFclDr5t9J7m5hjK+tExT6g6vvz1M9f//xDKzfuY6cY0CmTh7Y3/HasnDsq5KpiX0MfqtY9+rKyx6p4H1XrRJVMrO73D395RoWrdqTD6yexXoIG5y/2288//4zWbTtEc5dvpTWzBhiuVyu6liq5qphjFXu9inGo2EvEOKyeXehnyIRF7Kzu36k+5cqaxqGzrdywj/XPWaO7sH7vqX1Me5KKMwPvaVVP0lVw7R/JNuK9XAR9Rs6hBN98TfWqFKNjpy/Txp1Had22gxydnzqsg8deAY8tW68Hnd05k8L5+xNgNyVqBTgMsl0HT9P0hRto2dQ+uiNT8bH/9fc/lLlYUzq0fqJbqM/2fSc4Ejh3bFfDTefY5ikUKUJ4vq58w17UtWUNQhR/z6EzNGvJZikjUTxkwNj5FPOrL6lZnTL8p50HTtHcZVtN9SH6uv3TIzaotu09QeWK5WZvebw4MU3Nuoo+arQYQLUrF6HiPxhHz8UBmbZgAxrTryUVyZflnfEOm7iY4fVQCsy2ojU6U5/2dSlnltTS0VHxDG/XvXaMKvrwdn6h0GUp3pRvr1QqH5UsmN3Q8eZOvqIfT44UrNfdh07T/PHdDb+d/WvGu73m8vU7NG3BBikn2oE149kZgaZdazIREdeHe/v9qdiTVOyPUD6sfjsq3sXdpGJuGlQvwU45NOyxC1fteO/rxN1YrOwD3n5/7saBSM2Kjfto6vx1FDvWV7xHF8mbheHLnpqYHyvnjujDyvenal9Tse5VjUXlXm9lnaiQiYr9/tL1O1SlSV+6tHeuQ8TQcRCZx9mM3+u3G0rHN0/1uF5V6FruOreqm3h7FqveH70dh6tMvNlL0IeK90H1qgyFG9HMUZ0pR6ZU70zXxDlr6MqPd2nS4HaG+9rHsCepODNU6Elm9dtP7XrbiPdyxl03nU27jjKEJkv6FLpG708PfqHiNQPo2KYpFClieNq5/xS17T2BzuyYyV47RK079JtMUMr1GhTV0xeve7zk6MnLdOzMFUPDN1X+erRx/hBKlCDOO30tWbOLTp6/RqP6tPD4HMBWMxZtQvtWj3OQ6JWs3ZXaNKxIRfNnoQ07DtP6bYdoxsjOUpIGrLnnsFms5Ip0g3nLt3LEWG/zc+0cCIkp89dxZKdArgzUplFF5hsw01T0IZ5n1oi//+gJFa3e2bEufnn6gsJ//pkjF/7EuavUd+Rc2rRgqJlX4mutKO/erns9BVP229H24e3cADWTtUQz+jrmV1S1zA9UvEBWdsaZbYi4A/6qVey0fVy8epsadBimq9ghinDp2h2GYrprOAAf/PxEd90K5UOVEW/l+1OxJ6nYH1V8Oyrexd2cdhs8nWLFiEbtm1TmnyfPXcu8B3ocDCrWiQqFTMX35zqOVZv3U+/hsyl1ikTUom5ZypM9Hfn6+Bh+jirOHdVytbI3qlj3qvdYK+8jxuLtPo37VchExX4PZzng9Ktn9afkSRI40vZKFspBQ7o1JpxfY2espN0rQlIiPTWrutb7+P68nWPV+6O349DKxNu9BH2oeB84VErV6UYXds92i8Y9e/EG6/Z66+Rj2pNUnBlwWlRqbE1PMjwMPvELbCPeiwXgDq4Go7NM0VyUO2sa+vyzcB57ff3mDeUq04o6NK3CkPP2fSbRy1d/OAz/BSu30xaQ0k3u5cXIQm+RjcBVbdaPiubPSg2qFXd6Ht6xfvth7JRoWa+c7lhgFA3u1pgNb5Bx5SjdkpJ8G5caVi9Ji9fsZAWtV7s6un1g88JBiKh9p+ZVacHKHZQtQwr6Jk5MmjJvHXVtVYNqVypiKJMXL1/R7CWbafbSLTx2KMvpUiYxvE97gYo+XB9o1oi/fus+1Wo10GEEdug7ibKkS0HVyxfkrs9fuUUtu481dPao2IhFH1bWvao+VMzN819fcQQUBiuMbawPfLtF8mZ2RLP1Fsy+o+doxsKNdObijx5RLDv2n6TJ89bpQizxDBjq7pq/vx879YyaKiNe1fenN16ZPUnF/vg+vx3xfjLv4k4Wh09cpMadR3JuLeZ4/opthmkXKtaJqn1AxfenHQvQKgPHLqTYMaNxClneHHJGPPpQce6gH6DhTp2/xrmb8b+JxWg6oOTMNKt7o4p1r2qPRT9W30fFOlElE6v7PeTRZeA02nv4DBXMnZHPXuh5py9cp5+fvKDnL36jVvXL6+bEow8VupYKuapcJ56+ETP7o9W1JsZgZS8x+tZl3kcY8Se2THNLTg3uqz4j5+oa8UBsID2jWtkCTkN69uI3DsrJ8CC9z/k1GwB68uxXyl+xnWU9yWh+PuXfbSPexOyDMGLm4k20ZusBJraD8g+IduF8mSla1MjSPa3YsJcJsUQT8Jt//n3NUfryxXJT64YVpPtzd6HMpoP7Nu48QgEDpxFyCwWEEXnX42asYgN8y6JhhpHKqQvWs+EsDri03yem1CkS07iZK9kInziorS5RH7yg7XpPoN2HztC04R35gATJ2LwV2+jC1VuUP2cGatuwoi68Eu8iIGv4b6AAfsiV4R3RYJ7Qv6emog8oH8iB0rYZizdRjkwpKXXyRPznGNGjUuG8mT2OQ8CQ9q4ay1UQXI345ev3MPLDG2cPDoTIkSI4kB97Dp+lLi2qeRyLinWvog8Vc+P6kmBM3bzrKK3ctJ9+efKcc3Grlv2B8uVI71YewkkFIwxe5q9jRqOBAQ0dxJZQSK7e+Ina9ZnIqRMguPPU9CB86L9H21qGewAiTcVqdqH1cwc59iAQLVUqnY8K5MpISM9ZvXm/LopF1fcnBou9EQgeyCph/NiUO0sa/nZl9ySr++P7/HbEO8q+i6e9eenaXYRvAtG8mhXeJcvS3qdinYj+Hvz8lHx8fCju19HJ7D7wPr4/jAvrb9ve44RzBP8NQkhwSRg5sayeO3i2dq2B1BL7Qby4MWnJ5N701ZfGZ7qKfU3MjdV1r2IsKvpQuU6sysTqfi/uB1R6yZqdbMAn/fYbqle1GL1+/YbT9EA0W6xANkMEiVVdS5VcVcwx5IKKSkh1RMNZo62qJLM/qhqHdo693UtEH54cejLvA70vfaFGrO+60z2Rpvb02UsHwtTd2QCZ5i3fhnXpPh3r0Reff0aI4LfuNZ7tAgT/jNr7kKt4ptkzA/eVqded0YTe6klG7/up/24b8SZWAJSpMvV6UOVS+VjJSBjPPAxXPO7c5Zt0+dodypw+uQMui4/v4S9PKXq0qHwwWGmAlEIh0TNYRf9L1+2mAWPm8z8BM4Yxg+dPH96J0qdOajgMsLfCqDx+5gqlSp6IKpTIY8qpAbkWrxVAs0Z2pmRJ4hs+z9MFUN7rth2se3+yxPFpUNdGHq9R1Ue15v10xwFSQ5CR6bV67YbSTw8eM5kOnBo929bmSDwUivINerIDyYgBHf3DGzp+1momc0PUACiJRjVKUZkiOaVkrWLdq+hDxdx4emGs4TMXfqRNO48QiIw88VqA42HA2AW0a/koRtDUbDmQ5YvoHQ7c/cfOs4yBPpk3rhv/Te+Zt+8+cvr539evqW7bIXzQu8urk5owkxep+v7wWJEeBEMIxjxaymQJac7YrvTry9+l9ySr+6PVbwcKM/pw14BcQolR2f3V5HS8cznWpop1AiVy6drd3D/QVS3qleNqK3C0ykR4VH1/ULQRNXNtQUHBjJABsV3OzKkMU7D0zh2Zus6C2bp3h7pMTAtkBL7d5t3GcFnHgJbVDadOxb6mfYiVda9iLCr6ULVOhFysyMTqfm+4AExeYEXXUiVXFXOM10Y6GYxeNJxVOLN6DJ1Jcb6OzrqJ0f6oYhyq9hK8g55D78+//jZ8H/QxauoyOnX+OqexivLR+DtSQ2q2GsSGrKeSsmIpIXWyc/8p9OqPv6hovizM6VSnclFGlRo5NtGHCrmKsVjVHdEPdNdarQdxl97oSSY/sU/uctuINznliLQhkqGywSjbdfAUs9zLlLbCsxG1BwOzUatVsbBUbVOwrZ6/fJPuPXpMKZIm4IgxorVh0eC8AOQNirNVWGNYjDesnnHp2m26cPW243EZU3/HTg4YmYBmIdcfUXq9BkW3evP+zNxfqlAOihs7Bp0+f53mr9zukTTPXX8q1r3oAxFaRAQBXRVkiGElUzzH6GDSe1dwEQQMnO6AxOFdEHXGwX3zzkOuCZspTTKurKA9xM28H9JHcJCjJI1R09sHgIhxFxFw7RPfH5QxOHfcNShK+DbB4aHXcF2O0i2odYMKnPqSp3wbWjGtL/UaPovSpkzCf5dt6AuOSMgX60Smxq62b6vfDqIqa7eElAkS7d6jJzRr8SZ2zqAKiUyDMQmFTK8lShiXOrzNkZfpU1xjZp1gPRWo1J7TtgKDgqhhh+F0att0Ll8JAkU93hMzY5K5FsYzxqLXYETrEcQaPUcmcibyNc/vmu2E8oJDGukwsjwuYr8Amzqc8Kr2NW/0AjEW3CsaHI8YV80Khcnfz9dwX1Kx1xvNj7e/eyMTo2cZvS/Ozms37+l2gxLDQHLItA+pa4nxGb2z0XvcuvuQStftTgfWTuDUC0SP8d9wwh05eUk32qztG/3gnBSyg16AvV42iKVqL1Hh0DOSmZnfsZeUqtuNnYpIde3YrKqZ23lOrNopqnRHDByOhVWb9hFS3aB/YY+0qieZEsj/8cW2EW9icqHINu400u0dyAeHB3Ld1oNuS+PAEwf2aK3Rgoj3qk37uTwJDPgBXRpQhRJ5pUYE5bDXsFlO1+KAA7xJlCPDj8gvR3Rdr0FhBoEW5wTGjeUgT5MZiFHEyjXX3lOfVmGN2n6t5jjqwcRkZCKusToO9IO5uffwMW+CMMBlIJ7aMQpyoO1LRzqlNMBjjANTj1BL9KPKEEHpNTiekEsuGpxMXVpUN0yVMCN3vWtVHkyqxuTaD4wzGFYTBrU1fITePoAII8j7jJqAa3si6btw5RYNm7TEkCQT67RYjS50Yc8chpbCiN+8YCgdOH6By/lNH9HJaCj8O3gKOvaf7Ijk42+IRHRuUc0Qsqp9gJV9zdNAEX0C+sVdxQh398DJMnH2asdPy9bv4ahVgm9i8d9u/fSIy4shcmO2mVkngulbkC7hPXq2q8MlUldu3CttsJod44e6XsaIR35x7rKt2VmACBEaDLYW3cZyugG+H5mGMxfpZIgYi4b12qlZVcN9TbWHhz88AAAgAElEQVRegEgveHUQBXXXYAzoncmqznM829vzT6VMrOhsQn4qmMtFX1b3JOz37pq/nx+9CQxkjhUjHUHFHEN3KFKtE53eNp3L7NVqNYjaNqrIwRgYazJ7vXD69mhbm8oWzcVoUKxfGPBIZ/HkVJb5Js1eo8Khh70eyCajVqNcQV3uHZDmtu87kaJEikAViuelgeMWsGwb1Shp6IDDs1XML/pRoTsaycL+3boEbCPehAwRnYHR7a4hTypC+C/o4tVbVMWN4gwCI5SYqFauIGVKm4yNfeRTIbKKnEjkz8p6H/WG3LrHOMqZObWDAM3o9eAZa99notOhb0ZhVhGxUukFVeEM0IOJgcBGpumNAzKD8mxEuIdcqIDB05yMGTh5+nSoy9BPmSY85gfXTXBKcUDpsvNXbkox/gslBgahpzUa9+sYumtOsEmXLpyT6lYpyikjqLHeZ8QcJgXCmjNqYp3oXYeyhrNHB3i8ROXBhCjxwRMXCUYulH/REEUEJ4NRczXA4WCAYXHs9BUa1781FcobUobMm2ZmHxDz62lu4WA0qlePMYpymSKyKYx45C7DeYnSYUYNckRqDdJN6lQqyo6r42evUP/R86hb65pUvngeoy74d6v7mqeHgEk9UqQIuhwSegMEuRVY2AXngkz5TBXrRMh1QOcGlDXD98yxUapwDq6OcO/hExreM6Tsol5T8f2J/lU4SlXUdQaZKhRvnMPxYsdg8ig0lI6UqVohODKwN4KMCqlPqKQCg0QgUvRkqlIvEDnTIJlNljge+fqGIAY37TpG9x8+pqa1S3PpVlGK0t24VJzn6NfKOaxSJlZ0Nq18ENl0bZd/vMvOG6R/NKtdxpDHyOqeZMSNkTxpfJqzdIthxRp3c4y/DRq3kMb2b6XL1aOVAQx3pOSVKJSdKwrlypyajpy6xHpGr/b6RMboB+lWIEo7t3MWR2cLVe3I+gh4XAAbl+kD/Vy7+RPde/CECuTO4DBysb8APYFcbG2uvqdvUYVDD8/sPmSG0TbKaTqeAmuCzwXpPeDEgYMEcPRmXcdwmqqM/qk3v0h5EOVNjQaqQndUgRQ0Guen/rttxIfRCoDCse/oWa4JDCUdDYc8DlarsBftK6AsHKDWk4fqwxVxDw6mCo1680bXsVkVLgWBnJ3hk5ZQ3cpF3TojZMVlJmKlwguKcalwBqiAiRmNAx5mKCrr53rO3weqomCVDvRDjvQ8D6iffPbSDeo3eh7Vr1pcN5qinSOsuxK1A5iMB6ka3M/FGzRmxgqOFLlzOLnOsTsGdNl1IK4TxjPKq2gPMDi2kMYh47kX4wCXQNTIIakegycs4kMpa/oUrDyfu3RTN6qp4mAS74Q1/tvvf1KmNMkpXLhQpwqqIiB1wai5O+CiRo5IObOkZiXcSjOzDwi5wnHgrt2+94j2HTlnGC0WxD5zxgSwoQgjHtEERAW3Lh7OKB+jJsrD7Vk5lmLFCE0VmTBrNd25/7MU7FvlvuYaOUNN6IgRwhNSFbxpbXtNoEzpklOdt5U2ME8HT1zQdaapWCcw8FAmCw3lRMGkjAYHzYwRnXjNGTUV35/22/GUTyujqKqMjsLwwJkskGioVGEUzRTvAQOiQsPedGTDJCcE25jpK5j40mhfU6kXCCeaK6JmydpddOvuIymiTE9rwMx5bnT+GXENqJSJ0Zr25nekVM1avJn1LCDJ6lYpxsgNvaZiT4KT98btB24fAwg6sjwfP/uVHaDetGETF1O4z8JJpfZ4+v7gIJo/rpvbksWuY8JeX73FAK6wA+TMyCnLOE0NwS3ZaD76xNpEEEGUJMa8NAsIKfeH8Syd0luXWFmMy6pDzxuZu94DRwCcQuAU0LZff/ud9yiZ4ICnccBJg5Q4RPVlmgrd0R1S8NHj54zGNIM8lhnvp3qNbcSbmHlVXiV4ZJet283QIRh0tSsWYaI8M5F4V7KjYApmWCaUh2wZvjf0CuO1kZv1Q6V2dHLrNGbXFpEzlLjDRigD5/UkPjMRKxVeUIxDhTNABUzMaBxQ7HCo6+VMY42AvO7YpilOucjzlm+lI6cum8oXRa426rBqYewgtUJkVKYmswojHgdC/krtmLlVi0BYuWkf59DJ5OSqKKem4mDCWhPlZI5tnvJB8vrFd6diH4DDCHuRMCxdv2nsE8fOXCagKIwayu9FDP8F54yjkkfihHEYGaRH8KftUzh7XPOUYYggSjq2XyujISjb16xGztwNFPnWI6cuowFdGjKaBlVK6lUpRg2rlzB8LysXgPdg/fZDTl2EC+dP33+XkB18Mk3F94fnqHCUqqjrLPPORtcINmlUCdHKEZFoOAdk0pXEM6zqBXCiLV69kxEBiOCJhvMIRoAVokwz57nR+SfLNYDxW5UJ+gBaAgbajgOnOMcYRJvN6pRlBnCzbfjkpYQzGJHSNo0qUoyvokp1oUrXwrmDoI+3OeR6g0VgASlvRo4n9IFz56e3pHaiT+xncWJFN0wh0Y4BkfhaFQqz4Q5duF+n+oxYffTLM10CYtEHdN6cZVrS8ul92XkhnCVJE8alpnXK0ODxCyl7xpRcvlKmWXHooX/sS6hCcPTUJXbwA80DlI4sX4LMGL29BtWm9h89H6a6o7uxApVZvFZX5mQyQqN6+66f0n22EW9itlV7lXC4rN12iOYu28qM8IiCy8JFPXlC4alDjjEirkYNeZKoXw5DEQeDMOKRMgBlWjYnUGxe2rx6sxErFV5QVc4AqzAxo3Fky5jSI3eCmDOQ1xWu2pE2zBtMiROGEo4BVvz7n39LwV9d5x9RN0RJYkSLauqgtWLEw3uM+rpoR89cocDAQIbdiQY292L5s0o5nVQZEVadGhg7lOJcZVqRa5qC0Tfn+juUDCj/QESAjG3Zuj30bYI4jIRBWTajpmIfMHpGWP4unD3dWtVgx6bYW8o37MmKM6JeRk3FvqYicuZunHi/QeMWEHLj0UBOObxXM0eJQk/3HDt92e1rA9XiTe6oN8RHqr4/FY5SozUgkxOvIncUCinQbIDsZk4bSnaIfQ2oGqGkNqhewpCEVLyTFb1AlRHhSiwJw1wWgWJ0/pnRK1TIBOU+4TiC0xrpWyjfCYPGmwogMKLhnATxYeXS+dkABReSUVOxJ6nKIXfl6YBB/turP7nMI9YpUgO8bUgte/7ylSEDu+gfRjMcI4DPj+zdnOLFiUmd+k9hPVimspLg+ji7cyaF8/d3pHQhHQYOSkDzpy/cQMum9vH2lUzd12v4bC7pCgZ6VGg5evoyE96K8Rh1poLDwRVOj73+xcvfaeHqHdSiTlnpVFvtWL3VHT29b/chM7k8rywqwEhun/LvthFvcfZVeJWwOR88fp5+/e0PJviQbcif0bbPPwsnpfiLe/BhZi3RzGEowogvnCcTexLhLZf1kqmKWFn1guK9rDoDVMDEjMbx+nUg4fDRg0Zh423edQxHIsB0HifWV3TqwnXase8kzRzV2VRUxXWdiPmH51yqZMlbNultS0aYKh2I5xw+cZHhYXotS4YUUlFeVUaEdv1749TA/cgxbtplFH0bP8473ywgfEbwSvQh8mm3LBpGsWNFZ1I4EJ7dvPuQ86arlysotRVY3QfwkNHTlnO6BtjLgRDB2JZv2MtlI/PnTM9jMUr70VNAtC8ytEcTVtY8NUSt0AScXpA7wjEpE9FXsa+pipx5ekecG1CegYAyap4YmPGeDWuUlIK/4hmIQo6ZsdJRahJ54Cg1J5sjqfL7s+ooNZKZjBGvIncU8wiYqlFr1aCCU3qI0fX43Ru9wKoRgeeqIJa0eg57ko9ZmYg9dueyUWxsi0AFqjIgzQd7kTcNUHBErtEPoM+oG4+0RE9NxZ6kKoccwShRUliM9/WbQHa27101TiqHHPeByHHjjiOcky7a/UdPCd9VovixGREiHLFmZIxKGn///a9UCWeB3BKIRVHi9MyOmazbnDh7lTr0m8yQfb2mAmWLvP78FdsRUDla3bljv8l8bumVNg6dB+95t0Qf7pyT0b+MQnlzpKMyRXJJzy/6s6o7ijHBcQv9QehFcKKh+pUMus/M2vkUr7WNeAWz3t2kV0kVJEoFOdCkuWvZcMDH1KLrGEoYPzYVzpuJ6+TKNJURK6tlpcR4rTgDVMHEMBYr48D9UN6B0kBqAyK/SRLEpdqVi5gy4I3IcECe8l9pUISK1exC6+cOcjgTsGYrlc5HBXJlZK87vOAgx9Frnhh+tffAIaZntKqQ64+371ODDsNZwUCqAwyb45uncpQHZaFEjp/s/ODAvXv/Z87/ixI5ouxtBKg1IImdm1fj6BIa2OiRl4hoCKCOMoRHeiRS2sGgJrmn8akqnWl1X1MROdO+M5T+6Qs20IVrt+nBoyeULHF8ZkSH8o+1ZrZhr6zUpA+1a1xJOurVoMMw+uXJC6pQPA/FixuLDp+8SCs37iPBYWA0BlXfnypHqd54IW+jOtWe7jebO2okt7D6XYURoYpYUsX5p0JucILXajWQ91U0YcTjrNh/9JxUugMcNaOmLXc7HMC+dx86I0X8aXVPUpVD7kmuMDYRvQYLulETiKn8OdJzSWKRlnfs7BV6+uwllSyYndKmSmKYnw8eCuhJiFaLBufAsxcv+UzPnikly9ZTw5mTq0wr6tC0ClUpnZ/a95lEL1/9wc5oNFRqQHooDGsjncC12pPZ3G2RRiIcCOJ5SAXbffC0VxVAvEFMGc2d7O8qdBw8a8DY+bR07W5+LJzGSOUEmeg3cWISdAG7WZOAbcRbkx/fbcarpAoSheeqYFG3+vqqIlYqvP+e3gWH36PHzyhLuhRWX9f0/d4aVqYf5OEG15xpXHb/5ydcu3rF9H5S3m6VrNRIG7l47Q4FB4Wy/aK2OpSHsGgyhFgYB0gHjWDKkK27Bk5oo6g17oNyCKVy3+px7Kw5dOIik8et2XKAD309TgooPVv2HGO2WrDXIs8a94uGKEj3NrWkkDkidQPOBKAIhIwQpYJzDzmg127ep8HdGr33KVJZOtPKYFVEzsTzsbeBlR55kZAn8mhRjg/KHRQZQD1lEDGu74N5OX/llhSfxNPnL5nYbvWs/pQ8SQJHVz2GziQ/X1/q36WBFXHxvRgLkBtGRoBKR6nlQbvpwJvcUW03gBSD6wOkjrL1nVVA+1UYESqIJVXNiQpkD9An2GPF3ob/hrNy5uKN7ACDoWjUsCfhO9FrIIuUIWQ0epbR71ZzyPX6B8kmOE1QctGowUFWsnZXskqiWL5hL/rzr78pWSJUUwhJHwOR6u9//E1pUiSi0kVyGiKFtJUQcL9AKsIhXLxmAJUvllsqXc/1nc2ibLFOMhVtwuugWjlUqohEN27dp04DplDhPJmlx2AVMYX3gE6weO0u5ghoUK0E63k4c8BXo1edQisDFbojUBUFKrVnp0pgUBA17DCcTm2bzggWlNGV4UIyWouf+u+2EW9iBaiIEqmCRKkgB8KrWyV9URGxUun9F9MJZwk87djgcTBBsWzfpLLhbFtRplQaVhgoIivjZ612wF9hVDaqUYrLulhtTTqPpB9yZZCCbAujbmSf5hThLfy33+j57Pn+/rsEdPbSTYatGdW67j5kJq3bdpAPEeSviSZbOkVcDyTLjEWb6OK1W1yqD1HNHJlTMWu/UTkZPYZfrUxhcMkYVvieT56/xt8RUCy5s6SRMpzFs+BMAVcADm7A7QDNxNykT5WUPdaeGiIMWN9Ie0EEH6kRqDYQK0Y0Hk/fkfKl+3DgQ5kS9d0FKmDvqrGcx4t/t+k1wRCSqGI/8fS+ZkrmqYBGYhxWI2fiXcA7glSA4T2bOZWGBClTlWZ9qUqp/AyLN9NARth35Fzy95MzwHFeVGve3xGRFM8CcgUQVJlqJkbjk4Gwu/bxIZ2cKnNHsUdyxG/tLlagkRPbuFYp3SiiVhYqoP0qjAgVxJIYB75Xo9avcwPdtCMVyB6MoV67oVQgd0Ym7oQRHy1qJK4eUr9acadzyGi8H8PvVnPI8Q7QjXYfOu30OiBhQ7k8oHRk9iI4ObsMnMpGmDbFCboW0AlwIhs14TA6vX2GExrJm2oKiN5fvnaHMqdP7khrAMLs4S9PuZytGdJo7bihs5jJ3UbJUFQg0TYgCaYN7yg9BquIKYGSSJM8Eac2wJBGUAIkqtC7rKIvzeiOgrPgwu7Z7KRB4LFnuzqst63cuNcrdILRuvrUfreNeBMzLg7JXFlSM2kNb4hBQQw3BUGR2Mw6Na/qsQ6kKkiUKnIgq6QvKiJWKr3/OEDWbj3IyhQMIzCDgiQljWRJKCvKlErDCsZm9eb9OfcaCgfqZZ8+f53mr9zOrJ5F8hnXIddb2ogsIFIU0KqG4RcgjHgtEzuMvq4taxDqsgP6PWvJZl0jXnhk184ZqJs7aDQYEOXhkAPDcMHcmRxRzdVbDrABv2pmf6/Y4r1Ba4j8OxDYwJhHw7jmjO0qPQbM877DZ9m4w76CCD7K5IHVXQ8SP2XeOnbygCQKvBZQpgDPFm3y3LWc4y7DNCxI+gSJ4pxlW2j20i0Oo337vhM0buYqw/rDeLbV/cTT/JspmaeagNRoTer9Drho9lItaNfy0W7JRmH4grFewD/d9eUuJx77LhTT2aMDKHWKRIZDRFQpY9EmtGhiT0qfOqnj+pbdx/L3iKik1WZkxKt2clodr4rcUZwXQ8YvYqJCkAxWK/sD553KEMvKjN8stN+qEaGCWBIOpiVrdvLrYU08efaS6lYu8s7rli2Wm1AaLSwb9gYZXo2wHFNYPwt7R+m63Z0eGz1aZMqbLR3VqVyUokaRS8Wyms4pUBKIzGrnBPshHPUyuolq2anI3Qax46Vrd5idPn6cmLw/yyDz8C4qEFMCkXN623RAAilPudZMrHf6wo+0fe8Jyw5bM7qjCNAN6NyAy8526DuJShXOwfK59/CJVwTNquf8v96fbcSbmEEBHT+3c5ZTRAW5RGlSJOb8RpmmChJllRxIFemL1YiVCu8/5C5g31BuASWGoWsUmZWZL1wjo0ypNKyETLYvHelU43TU1GWEg8ZM6SIhm1Pnr3M+7rfxYlOiBHHpiy/CSRHVCQMAsG9RUgdQujYNKzI534Ydh2n9tkO6XlUB2cbBoi1/JCt/cR2+HZRK6t2+jlOJPsx97daD2ZDtIIG4QH/eojXEvTlKt6DWDSpQ7bcRnhXT+lKv4bMobcok/HczDYqvaHCKIIJQs0JhjrS6K0UIRxWUnUWTejLBHgiEtLVlAecFqaDsOmncaQTzLsAAmb5oIzPBC8Ou2+Dp9Nc//xqWd1Oxn6gomedO7mahkaqi+SB9wroUOblw9CVJGNdRlx1VPFDTWI94CevUlZ0epEApk31rCvUBbo0vvvjMkVYEJXzTrqNcLULG6MQanb10s8dlffXGT5wP6wmRo9LJaebbep/XCic2zhxEmIrky6zUSPQG2m/FiICsrBJLCnkjEtp96EzatPMIQ4zBDm+m4RtE7qy7ljp5IjY2UXZSj03d3bcj+vO2soOZd1B9Lc5dQMzxzjgDgNRLljgeDevRVNr4VjWmjyGdE3sY0Gx6LVHCuFI6gcrc7ee/vmIou2hwUsigAVQgpkR1iEPrJ7KTDJFzlDF99utvtGXXMdNGPHQrb3VHOEqRxoWGlBM4Z9Cwb84Y0clxDqpak59iP7YRb2LWhYfr6MbJzKwoGkqOnDx3TSqXCPeogESpIAdSQfpiQnweL1Xh/UfnUBrGz1rFNa+jfRmZYaqliuSkr2NEszxMGWVKpWEl0iVcS5hNW7CBzl+5aUjepn1hGP6IrOIQgVwQNUbEeNaoLtIEaIj2Du7WmPPThLEGeH/D6iWZEwLe5l7t6ujKGYYijE3ZMoqunT178RvlLd+GxOHk+jsggpCPUTkZq2gNPBf5ZWCTFxB0QZh04PgFWrvlgFQEHP1grcK4QY6hu4ac2gbVir/zE+agZJ1ulPCbr9mhCEb7Um9LsuFilLiCE0u2hAuQEiD2QfQejpA+Hery2sBhizJ48J4b1ZlWsZ+8z5J53U1AI/Vy84F+AMJHpoEDokDlDiTq3iMSAW6O6uVDKg+gLvOQCYukUA6CpR8yAjLnqy8jywzBcQ2Uyi17jtPBYxfo5avfKcm331D1sgWkSmShEyhkqJjhqQEZEi1qZI9GvEonpxiDVcJATySX/n5+9CYwkNmZjeQMhX3D9kOM/nrx6ysqWzQ3lSuWi1IlN0ZIiPdQBe1H5DtyxPD8DSNdY/+xc1yrWrbSDMZjNcUOfUCuCG5c+fEn6tOxLvUcNosqlcwnvR+JProPmeF2ueXMkppTfYCGGqDD56CqsoOpD83NxZhfOFUxbm0qmZl+RRDpyIZJfFuO0i0Z+g6EHhBxZh3HcJKcvXiDK+UATZYzcyrpsalI57SSuijkJs4LlA384vN3CUJv/fSIvwOjVD9VudtA5MDRKZB5YpwYnwyMXQViCudE7daDuHRt4XyZ2REGzqHjZ68yYtjMOrGqO0InX7/9kNMyDxfOn8eD8dnNugRsI96EDHEwwYjAIV2zQiEuk3Tz7gNq3nUsQ2H1DhOjx5gpq4G+VJADqSB9URWxUuX9h2ywEYJ9dvGaXZzPixJZjWuUcoKRepoPK8qUSsMKG3GJ2gG80UH5QaQMB+6YGSs49xl1xWUaDNZCVTtSv071qULJvMwii0g+IOlF8maWJl6aumA958wVzJ2RCazSfp+YUqdITONmrmRyromD2johBlzHpkeOh1xFGegcopoVGvami3vmuIWnIS8OjgIR9XQnH1Vojbv3f6EStQIcxpkw4iGnSBHCS0WdoMRkLtaUnSOIpvj6ghKPaNOuY3T/4WNqWrs0xfzqS49ENPC4b9h+mGFpWPOuTbZ0nwoFE89WsZ+gHxUl89zNvRkCUk/flpncfLFPZy/ZnFBiDNBKKFQgtxNGfP/R8+j3P/82hBXi2w8YPM1JOaxQIi87W+DEkWkT56whGNLIV4XzCLma2B9R4hCGntVmBKdX6eTEWK0SBhqxLydPGp/mLN0i5WARcw1iP/CwoKymLA8L7lUB7RfpfvPGdaPM6ZJTnTaDCegIOOJAVqZNt9Gba6spMVDckYeO/Wn26C6cZgBkWc1Wg6hxzVKcm/4hmzeVHayOV4vYgBO7ZKEcTNwmC7PG82E41203lFE7QNXASXJkw2Tavv8Erdt6yDCIhJrlsxZvYsQcUkDhUEQD/wucyHDKzx/fXSrFQUU6p7vUxXuPnvAYxRo2krs4z+HYcJd+hvQSkMYaGfEqcrfFHGNPBqu+v4b3B4RysmkKVhFTooqIVnZwHGFP6Nm2lnTgRpXuaDSH9u/WJGAb8SblJ+pOQmEVLUv6FDS0exMpSCLusVpWw+SQdS+3SvqiImKlgjDQ00uijNfqTfsZwi2T92lVmVJlWOF9kEPaZ+QcVrRFA9kZYImipIvRWhAec1cYO2rc7j1ylnNqZRqcRoBvQ0lFhAlkdIi6yTYoTkAQuGvRo0WRMiJEvti2xSM4cuDaQNIFUhyw7ntqqtAaUEDSF2rkKM8FIx4cA1CGti4eTvHjxjIUjXAEWGX4NXyQwQUqFEzxCKv7CfpRUQIQ8wAHByKRl6/f5UgkolZwQllpZnLzxXNGT19BS9fucjy2S4vqVKlUPl4rVZr2fYfPwHV8gLEXrNKBfsiRnp137NC7dIP6jZ7HZI7ukBqufQgDb8GE7lw+VDidxkxfwZcCXWC1GRnxKp2cGKtVwkA9kkvAUH18iB4/+9WwTJY7uQGV8ODRUynHsVW5i/sFFBc1s3+8dZ/JKlEfHfNy7eY9Q0cR+lGREgPnSMOOw9mo1J4R2O9A/tesThnpV8YcoRb5kZMXOaKaKGEcTjMS9aalO3K50ExlB3ffkjd59dhnQfw2ac4a/vZxhsFBX/yHbG7PM9fnIr8Y/Bpzx3ajBSu3cc41yqkCyYX9wIjpG7w1F67e4rQowPKzZfieerStzSk5QAyh3CkCHmDvl2lW0zk9PQMwfTh7ZHh/sD7S/FCfti0ZwUE117Zq837af+ScYblWFbnb0NfK1OtOgsRNRoburoGutHHnETp66hLPMRysQH7B2RKWTYXuqCrIF5bv/V97lm3EezFjiHrduPuQ/vjjLzZoZHJdtI9RUVZDRfkVVYeTOxHCm58pTTLOFzZqKstKWSVbMRrrh/gdhz88zjGiRTWVA4ux4kDAwdKjTS2n/CPA61FTHBF6oybyzaFwIOcTMEZvmytxjNl+cMCDf8LV6MA3WaZ+DypXNLdUFNwKWkOMGQoZPOzwcCOlBmR0OTOnls6JhSNg8eqdHBnV8gQgbQf56UbwdTG/x89ecSvG2DG/4ijtvQePDXPPrCqY7gbgDYmUihKAmNtC1Tpx5Bv5oyj1c/TkJYJCN3lIO8qXI73hsntfufmGD3ZzAdIUyjfoSTDOIkUM77gChsiRU5cNI3C4AQpm9Rah7PTCiD944oJUFM/TuLXl1DKlTc7lEccNaO3xNVU5OVUQBmKQyNFENFQoyNifYMCbOdPXbz9Mk+auodevAxk6DmZ6pPYARSNLqCq+ZSvKO2RbtHpnOrppCjtcgTwBKzWMeHCWyJQOU5ESgzMH3747GZqte41UHpSjAtIx/jexaM+hs2xwenLkynxfZis7IHVqxcZ9dOX6HQLaC3sl0AXpUyWhtN8n4XJiZox6OOYjhP+Cy37hewGiJFPaZFSrYmFDwxVR5RFTlvJrIlqN+0rV6Ua1KhR2oHs8yWD6wg3MCo6zE9//6D4tCAEo0eCg3HHgpJRjX0U6p6dx9h4+myJFikBdWsg5E8CTA4g4CIBdG/hc8D0bIf1U5G4L9KQgcZNZi+6u6TV8NiEggX0Ejh4gKLB/g5xOthSvCke4Ct1RRZDPWzl+KvfZRryXM+0tcYWqshpWyq+ApAK5mHoNcGmUIPO2mak56ukZZqGr6EcF2YoKT6i3dTrxbBwoUJaLTbUAACAASURBVNbhHb5w9TYdOn6BYNiBoKVyqXxSUV4hU0CT81cMIRbRklfhMNf+DXB4T5BaRK8BA1677SDDeQHLLFssF+XPkZ7Cvy05J7NOVBDHQA6IKqHMnpbwDekoR05eotxZ0zjI92TGhGvMojVEv1YNAMy1VQPcU84nxgjvPcreAXUBZV6mWVEwVZBIqSgBKNIuXGHiXQZO47QDPSNTyOh95ubLzIP2GkEKKaoHiN9kofi4XkAjBRM0lPiFE3rQ8MlL+DuuXDq/9LCsllOTfpDOhaoIA0FOiWhk2aK5aMCY+ZxmAONzyeTeDC82agLFApLPwMBAmgvHyobJNGH2aq4egbKRsk2F8g50Apyspy/+SDXKFWSHJr5pGJndWtc0HIqKlBi9AAM4VeJ8HZ3WbT1oSLAlvkHXVACkgiVPHN/QMMPLWqnsAB1r3vJtBLQKCFyLF8jGHCTgQwIqDMb36i376dmLV9SvUz3pdAXMB87ahtVL8HzAGQxyNpRKNYJ943qsq88/CycNixaTDv6AEVOXEghYR05ZxmlwSNkSDVVJgBCQceyrSOfEc90FXcAZFTFCeE7Zk2lT56+n5Rv2MioOjhHR4LyCXAX6SK8vFbnbeJey9XtwOhjKu2lb3hzppMruYW7zV2xHiyf3cuKxQOoEvmGZ/USFIxxjV6E7epK5N7q9zFr4FK+xjXiTs26VuOJjKKux78hZhqihYaMDoYko9SJK5vVsW9vQs4v7obgfPH7eIUUQcgICNHPxRiZw6tVen+xMT/xmoasqyFYwHqvKlJU6nYjEQAFEpAH/jwMKHlkcuIAGw4heM3sg51DLNBxOW/YcM7wUiryWrNHdDYiiBAyaTldv3GWPPhRYsJiD9AzRaHcs6qIfVcQxhi8ieYFVkjDcb9UAUG2Ae5ozzJve3Gjvs6JgfiwkUoDd5izTknNHv4oWxfF6yMlGrrJMRFIoMVrZQHEG9DSsG+YPhHKIkhb7ISvFifUVnbpwnXbsO0kzR4VUazBqULoLVenIxGIwWGHE4ywCIRbgtXrlDEXfqsqpqXD2qCAMxF6KKB6qzUB5BncI4MngU/ksnL/U2SXIbgWEFv2NH9Cabt59ZIrgUoXyjnkCtwwcCeH8/RiSDIcEDFE4XmWcEujDakqMXoABHC+IQl+8esuQ1wV5ynBKgEhYu38BUbNr/ylDJ4DQT7yt7IByriAsHBTQiJIlie/2E8O3uXXvceozYg4TqoKB26hpHaVASCC6j7lpUK2EU4URd/14GxwQsmgWMIoOn7zE+yK+f+2awJkOxy/IVMOqqQi6YL31GDqLqyBgnSNYge8STpaurWpIoUE9vS/2TXybMuTIQHgAHeWuIYKOAINRE/vJmR0zeQ8SDWmCuw+elqqrrsIRjueq1B1d39usbm8kt0/5d9uINzH7qogr8EgoECfPX+McNETLcmdJY1pBtMoiK5Rd1zqd8PrBC1q3inHJPHckGng/HBCuzOp6G+XtuyGlJ9CCKZjz3+ABR95W64Zy5bpUkK2oUKas1OkEu/qde49oSPcmrGi75ryiXAgiLTIeWRNLW/pSYeShZAmUo407j9K6bQf58NQzjlQQx0gP0uBCFSRhKgwAo/eBgihjgLsSwYl+AaXXKgJGz8PvVhRMd/17QyIFpRaROuyPuF80UVrJ6D1wT42WAyhxgjhUME8mx+XgxgAzLvpBA/kQSAg9NRXM5UZjlf0dDhJAaUF6hFSLJAniUu3KRaQMePEMKOkwhmLF+JLA7YJSd1onh9FYVJVTU+HsgaJqlTAQqLjqLQawswfObEQnd68YzaRfqzbtk6owIchul0/ry5B8RJgqlc5PD39+SkdPXZZCfUDuKpR3o/nz5ndvUmK8eY67e0R+/o5lo5xy4FHuFSg1KwECmTFiH/ouUTypMrVAcyH/X5Rgddc/9mnw0IiceHx7THJXMBslT5LAcEhWggOic5wnl6/fYXJb5Cu7toTxYktHwA0HbHCBqqALHoP1AOcbqkSBeyFV8m95b0RwwZuGdB0Euhau3sEpcjLM8t48x/UewV2CkoxI00Aq2I1b96nTgClUOE9maV3Y3Viw/9979NhRYlTFeI36+JjS0ozG+l/93TbiTcycKuIKwJra9p7A+S6iFAVKfs0Z21VXqXQdqlUWWVGLfM/KsazYiWa2ZJ7ruLChQjmqW7kolxQzaiqhq1bJVlQoU1bqdEJRh0IAIx1RHdf8qnXbDrHRLEtIZyR7s7+7GvGoM40IJ3Lr5o7t6rE7FcQxZsfq7noVJGHoV4UBoB2f2Trx4l4jhm1Z5cOqgqk3N2ZIpISiCgUMCCEt8zqgrDI5xjA2S9ftbrhcXOGX2huM5GqWudxwMF5cYCZKhO49OXu0j0YdeT3STBXl1Ny9qjfOHquEgRgH9ljkE8NwR1QSUGKw+CP9QMZRCkMI0USkFWXPmJLAhv11zGgcBezQtIoUhBbjeJ/Ku5mlpZfio+0nfaqkuqlUViLG2uegmgIg0kKPgJyQ9gAeEZm9QEUZMzPy83Qtvr0sxZvyz0gFQ4WKbBlTmgrcWAkOePrmHvz8hImWQcQqg8ZRIQvRh4qgi9XxgM8C3zrSiVBKEut22YY9tHTtbnZw1q5YmB0tsjxAKghVsYe07TXB6dVQSWTa8I66XB3Yi4AW0zY4bY6fuUpL1u1i5BbIXTs0qWxVbNL3q9TtpR/6iV1oG/EmJlwFcYWA4YKIA6RviLYiR6nX8FmUNmUS6RqOKlhkYVjlq9iO8mZLy/lZyNOCAQnnQOa0yaWUGE/iAxTt1Llr0pEIFWWlVJCtqFCmrNTpBF9B866jafzANgyfgkHSsWkVh5hnLN7EUEkoiGHd3MHpkeNYpmguhoq5HiDa8akgjlHxvipIwsQ4rBoA6MfbOvFiDK6ebvz939evqW7bIfztyUCtVSiYnubGLIkUlCgQf1pl+LW6VlQxl3O60YkLnBLjrmEfB0oFjPGy7dmL35h5H4pZ3mzppKJEqvIktevO23Jqnt7TjLNHVlZG1yH/dvjkpYxYGdm7OTNcd+o/hRV3Gfgrzot+o+Y6PQblnHCWi5KeRmMQv3urvMv2L3OdXoqP9n6BPHDXp4qIsejXat17d2XM8DdE87E/4vySbXDcTl+wgS5cu00PHj3hHHYg0OpVLaZ79oXsyW9o297jVCBXRlOkidqxWQkOuL4jzsH2fSZyDrxodSoXpc4tqklXvpGVm951VoMuVscAVF6/MfPYeEcgDZVM4DxGCdB82dObcrKoIFQV74O5vnTtDqemgqA1tUQ5Quyf4FcAehPVWPDtoHIB5hglDauV/YHLipopa2hVvrhfhW6vYhz/r33YRryJmVVBXAGm02I1utCFPXN4sxQswQeOXzCVQ6eCRRavDshY+z6TmPFVNHzow3s1kyrjAsMOSqW2QWEcPmkJs8nKkOmIe3H4W/EMqyJbsapMWa3TCRK5sTNWelyZNcoXklLcTSxt3UuRGwV0xpqtBxg5ggOiXLHcVDhfZukycyqIY1S8jwqSMDEOqwaA1TrxevJABAs8BH071jMUmwoFEw+xQiIlBimcaJsWDLVUUud9VamATP/++18nAiU9AQvjee2cgW4vQymwxWt2GRJaYW87efYq1yCHcgYUV/WyBZmPQg/CKx4KBTNj0SbMaC3KfoGIrWGnEewkRMUHNOzZZlMwVJRTM+vsUZFXb/hhfIALvFHeP8AwdR+pKmKsqu69u8HCiAd5LDgiZBqQFVWb9eM9CVF0fHPQ5ZCrDL4a5MO7+25UEtVaCQ5o3xE6W4VGvTlVoGOzKlSpcR8aGNCQdTagJ1HGMiyaiqCLinFCHgieoJoDEDngCYGOBZ4gLRLM6FkqCFWNnqH3OxAFCJ6ByFY0RN7rVCoidUZYebbMvTDmURHpm9gxwhz1ITO+/+o1thFvYuZUEFeI2tDnd81mL58w4qcuWM9QerDJyjQVLLLiOXivG7cfMKwKuUTaMkZGY/G0EcPQG9ytsbQi/rF4hsX7fmhlCkr7s+cv2YPv2rBOokaJaDQ1yn7HHJep14OZ8QFr1DLAKnuIREeIoICUB4bPtRs/0aPHzylpom+YqRieahl4pQqSML2hmjHy3medeBjxyH2cMCikMkFYNHeGFcgSUyb7VjqigTmu22YIK0+uNd1BLiWDLMC7qiBMwn4IRw3SqESD0/PZi5ccUUNOPRyeek0mAo4+9FiphfMYDjQ4zyqWzGs6ooJIU63Wg+j45qlOwwUHByKMA7o0kFoiKngCVDh7PPWBlwhLyKhVZwKcm+72eO1keMNtITWZOhdhbzp1/loI1PqbWBxxBsJApqmKGKuoe+9pvDDY9h89L01yCYI9EKYN79nMybADd0+VZn2pSqn8vO5cm0qiWqvBATE2kB/+UKkdndw6jVMihA66Zfcx5tyQOTOwD4D5Xa+hmo4edFtV0EVmTcpeg7zxFRv3cgQbZV+RalOhhBycXhWhquxYPV2HvRH5/CIKDyO+Uqn80sSWVp+P+3FmglAZuf04e0ZOXUaHTlx0dI10mO5taknrBSrG9P/ah23Eh/HMQklNX6gRl8PImuF73kCjRIrAkJeti4ebKh9mlUVWxavDKBLlykR/YMQ1k1+lyjP8PvPfwOj/8OdnUoz9kINKsjEr84SUCZRk02s4yEWdZE/Xma3vK/pRVc4QsLfeI2fzWoMhAxbgqJEjMhvz2cs3ON8LzN0BLWs48Tu4ex8VJGHo16qRp6JOvGsdVihGUKKPnb5C4/q3pkJ55eGiVtaZ9l4rzP94H5CDuWsoeYkIiVFTRZgEWP+ff/1NyRLFc7Bj3773iH7/429KkyIRE+QZwXGFEb9/zXi3w4ajBYa0nhGPKDoMcEA9K5XKR+WL5aF0qZKYgkWK/FPX0kWoo/zi5e9SxowqngAVzh53wsReULFxb46Kxv06utEyUfK7VZI+QGAB59drZpBXSM+4dfcRp8UBIYF9HYzwIM/VS3fSPh9oj75vUwRwP3QTID9Qdg95w0ZNVcRYRd17Vzg9zjGsdxCWtahTVuo8xz6fvVQL2rV8tFOZViEHIGOWr9/jlhPmYySqFaz/xzZN4T1EGPGrNu0n8CShjrxRE3sB2Oyh76Gt3XaIUiX7lr5L9A39ePsB4Rq9fQ1n1cNHTylmjC+l16bRuFT9jj13276TtGDlNkqf6jsp5KMqQlVV7wD5gnx44aodtPfwWUYYwKg2k7qFscDRCKfek+e/colFOLOMIPlwIOw/eo7Z9JEyAUdkp2ZVKVaMaExY23fkHB4LUjjsZk0CthFvUn6Dxy/kQxGQo0vXbtOydXvo2wRxGIYkW34IeSsRw3/BzJmAKSdOGIcZMFEH0tv2IVlkvR2zuE+FZxh9uct/u/foCc1avIlhpDJMpUaRs0xpk1GdSkV1jSMjZVeGbEyv1q5W3kN7NOE8Tk/N6H1wn1Ek0Mr8qihnuHnXUVYqUfoGJe3cQdyg8E2cvYY27DjMNdFx0LzvpsLIwxiRynLx2h0KDgp2DPmbODEIZWmMGshsUIZQ2+DcyJkltXQZQqNnmPldBfO/mee5u1YFYRKi00Wrd6bT22c4KZiAz8JIkvmGMTY40ZDb6AklAmcfUogAbTVqgPSu3LSPIfUwrqBAI9cxuqaMnl4fqDqCSBsQDjDqrly/y0Rs/TrXp0ol8xk9nlTxBBg+yMIFHfpOohRJE1CTWqH1ry1059WtZkj6UGng2fPfuMY89pMZIzrR1zFD9i6R7gBy0/w50+uOBcb7uJmrmGMDayN5kvjsSH/85AUrzWhQouEE0is3CadEthLN2ZATey1Qf827jWHlP6BldUOZqIoY40FW6967c+xH/zIKoW53mSK5pJjnAZOu3XqwA8UCAwWVHbDHol26foeaBYzmKgeuTTVRrYrggKgysWHeYEqcMC4b8YXzZCKgBmB0AUVp1IReoS3jiblCxYziP2TjlJ/Fq3fqGvGiD+SiTxjY9p0ze8f+k7zXTR/RyWg4Xhmahp2+vQBknjLOKxWEqrJjMnsdnDOQpb+/v3QKCSLps5dsZucxHDWRIkZgXQX/Dc6sdo0reSy9CCQgnIjYR7KWaEaj+rRgNI9ok+eupbOXbkjNrdl3/dSut414EzMuyOS2LBpGsWNF59z2BN/Eopt3H1KLumWpermCJnqzfqkKh4LVUehFv7V94yP2ZGyq8AzrvQegtaiZWyRfFsPXhdJ94ertd65DrhTIbDKlS04TZ6+mQ+sneoQXqiAb06u1qx1ciQLZDFEPiD64tss/3uUNGnWzm9UuY6l0iaFQibhkIGp3e1POELKHdx8Kh1GDEQ/mZLDtvs+mysjrPmQmVxsAE64WrgoIHzzV/6WmivlfhaJqlTBJpCu5rldE3G7/9IgCWtWQnhoV76N9GCKDUJIXrd5B2TKklHYoIKICJwAMefSBihJICciSLoX0u+DdEYURyB04TL6MEsk0WZfV8qieBgzm8mhfRv7g345Zkj5RKUZw5Yj3QyQX505/nXSH0xeusxEJJblprdLvKNaY90MnL9KoqcsYPj19eCeP6Vgin12k+4lxYN3DqIKRF5ZNRd17q+OF4VKgcgcSMoGjCN8MyM/QDh6/QEMmLCLweLg2lUS1KoIDYnyT5q5l3RX5/S26jmGnXuG8maSjtCqNeOxDQCShRK02SizjCLBiaFpdF/+1+2VT/bCng6zzxp2H1LhmSSqcN7Mjp54dubfu08ZdRzk4hhSSlvXKvYOkQMUi7BmLJvWkpl1GcRomEJSi4Rw6fOIije7b8r8mxo9uvLYRb2JKAFFr0GE4e1zPXPyRYSLIMcThtufQGSkmdlWQ74/FoQDFPUPhRmwEwsON9iYwkAaOXcD5/V/HiMZ/K5o/i0djU4VnWG8aew+fTZEiRaAuLaqZmG3nS7UROHiuceAAOmammSEbM9OvmWtPnLtKsxZvZkW+VsXCVLdKsTCBnr6vcoZm3l3ltSqMPByqBSq1J5CeyURiPY1fhUGEaNO9B0+oQO4Mjkgd0D1IxZCtlayC+V+FoqqKMEkFOZ6K9xHzDuUKBrQWKo51aKbWu5VvQFRW6dG2NpUtmotLfSHyi8gMoNYo0SbbrJZHFc+xkrutKtXI9Z3NkvThfuHkxLmijVgh3QHEsXrRyEWrd7LjAg5dvQZjfuC4BVS7YhGPcyXy2bXjgJxadBvL604Gai27BsLqOqvl7mC4ZC/ZnFo1qMBM4SCehfErjPj+o+fR73/+TcN7hpSQc22qiGpVBAdUyVylEb9v9TgClH/8rFXUp0NdB7GenhGvwtBUJQvXfsBdAvQLzmU4R3JnSSON0lXB+4PxWEn1gwMS+yoqFcDp56nhPdv3nUhDuzd9Zz/Bu5es043h90BOItBZSlNuev+x8xxUkyWWfF9z9f/Qr23Em5hF5MnAgMOmA5gUiBqQ87NmywEuByZDCKJX8mRs/1bs9ZJpKhwKMs8xugb5clWa9n2HMGnYpCXk6+tDnZvLGc5WPcN64wSJzes3gZzyINtc83r9fH0J0OWY0aNyDmOjGiWlYazimWbJxhBB37LnOB08doFevvqdknz7DVUvW4DieJnviXFjgwZMsk2jimHKWKqynKGVUj+y8x8W1wmm/NPbpjOJjrdNhUEEtErcr2M4HJFw8iC6hwYjcemU3swqq9dUMP/rKarw2suU/VJFmKSCHE+V4j1g7HyuXYyGyEeLeuXYmAAztpHxJuYM8Mbxs1bT3iNnCcY/jO5GNUpx3WqZBqUNZRXP7ZzFUMlCVTvSpMHtaNfB08zM3at9HZluWLnNUbol7Vw2ivcykZMLYxTnCVKEZJrV3G0VqUYqSPrEuyJlCKlD+XNmoMQJ4nC63u5DZ7jCBOpYe2o4q2RT+XCm4H96kHpURsHaApFkvNgx6NiZK/zolTP6cRna/1JTVe5u9PQVtHTtLserd2lRnVMThP7jChd2ldH7JKo1Gxxwl4Ilxpv2+8QE7hGjptKIF5B85G237D6W04S6tq7JqT6eIPkqDE2jd/Tm9537T1Hb3hOYQwL7JRrSBeaM7crE1XpNJe+PlVQ/BBdE8M1IBtj/fMjHLRILDkGUQr338AnBdnJtWTKkYGeY3axJwDbiTcoPjJxHz1xhJWhQ10YMEWnSeSRDeKFYeduGTVxM4T4Lp8vmqe1bhUNBBcMooPBl6/WgbYtH8MYl2pjpKxgiFdbwO7BHg5kTkUXR7j96yvnyieLHJrBiAtpjtJkGDJ7m2IRxbYUSedlLLFNyRBXZ2MQ5awgHNMaMiBdy14EAQTqHN8oUoLDgYAByBEohmFdlHQKjpy3nHKa5Y7uyAghFfPmGvYRa0cjXRCqJEdmJ1XKGmAdvS/14+12+7/sadxrB6xF1qb1pKgwiEQVcPr0vo0sE0WTShHGpaZ0yhLSd7BlTGuYZv0/mf7Ms6t7IUnuPKnI8T+Mwo3gLxAa+vcCgIGrYYTinpcDoxR4LA8KowYio3rw/5+iXKpSD4saOQafPX+eSRGP6tZRKNYLzrHqLAYxEQ5Rs5JRltHvFaC7LtGrTPun8RhXlUVXkbkNm+H5Eq9tuKDWpVYpyZQ7JdQZ3DThv9Mi5VJL0oS+k1gDVB/I1TnfImJIJqcK6oSoDyDGZnT5uLCpTNJdUXnBYj9PoearK3Rk9R/Z3K6SfenuJmUokrvoJ+gV6BEYzkBYwoo2acJQCki8cQihxBh4GIHOArsTa0eOmcecIAKt5i+5jKOZXXzJpKMbk7vtTZWgavaeZ3wVSqXWDClS7UhF2Tq6Y1pd6DZ9FaVMmIfzdU1PJ+6Mq1c91rNATUJkFZ4cV/i4zMrWvNZaAbcQby8jpCmxe+w6fZWMuV5bUbLicu3STyenMMLK7Pha1HZFbJUPiIe616lAQm2jNCoUcOS3L1u9hwhbAxm799IhhfkZKTKUmfZioD3m8MC5RAgywPZALhSVcRnjdUd8T5Ea+Pj4sqmNnr9DTZy+pZMHslDZVEl0YvIq8XhVkY6JG7oIJ3TlPTESr4BxBswJrxCaP9QYjAE6oelWL6cK5AcNEPjtQFSIiBKQFFHdERqHEy6JIrJQzxHt7W+rH9XvzhnHV5FZheLkwRNxdiLIwMnnXKgwi4Yg7u3Mm5+WL0neIvIFcD5HW6Qs3MOu3UVPF/O/6HDiRUAZz8tD2RkPg3+EwWrf1IMMasS+IBkZ5cEAYNRXkeHrPMIPKEfNzYfdsVpiBEOjZrg5Xali5ca+Uo1Sks2xfOtIJUYE8abyrbG4iIvFw/uGbRyS/X6f6BGcjFHg4tWWaivKo7yN3G9VeQFgrkA1wVEC+s0cHGL4W1hj2VTgFUJINPAFh3fT2EzEWOARk3iesx/6+nqeq3J0KVJxV0k9VwQFPskZVEBAsizQBoznxVG5Sex8qInhy7rsz4nEvvqFO/acw0vV9ku66ez8resG9h4+ZJ0vwWgid7cDxC7R2ywFd3V4l74+KVD+tbBD0GTJhMZPaafeR3u3rGlY1wvUqvh2jtfgp/24b8WE8+zDwQIwmGpwCv736k7btPU4NqpeQUjC191pxKIhNFHn9okwIYDjdWtXg8neICMxaslnXiMdYoEwOn7yEFTs0sJvmzZ6O6lctZgkmbHZqAG0rWbsrXdo71+lWM4zSKvJ6zY7b3fXwSFdv0d+RpiAOhIMnLtC6rYekSkKhXyA2Rk1b7nZIULwB1zQ6KAVMWsDexLoB7BVwKEDbrt28T4O76SvxVg99K6V+hAA+JiIcKP7nr9x0OzdgHJdBW6gwiISBh5JDkSKGJwEJPLNjJsOkT5y9Sh3AbO6hTJqK9S76AAlczVYDnbp8+vwlI59gaIJbw6gJZx5qyufMnMoJPYMcPU9M8a79WiXHQ38qFG9Ez4vXCiCwlGNfBrFWqcI5mPUeUEVPubja9xHIgoPrJlC0qKFlwoBwwBoELF6mIUKLtBysi5G9mzNZKRRuIElkUh3EM6yWR30fudvIbQZb/Mg+LdgBjHx0sDkP0CGVw/sAHdSx/2Qn5BZKJyGnVDiSZWSLs0s4mUAMhdSHZInj0bAeTT0S0Wn7xbo/ePy806N6Dp/NKVipkn9Lx89e5YCDnlNeFWePdhAfsnKOqnJ3VlFxH0twQG8dLlmzi7lyZBylMqkoeBYqxXjiyhAVeCYPafdOAAzzBtQg9j5wK+k1oCyRanT09GWCroKqDtoGdNmQ7vopOir0AuH8FgSIQmebumA9Q+mN3kNmjwjra+DIRLAQ6IziBbMx7xWcvggCwVG+eeEww7RMq99OWL/zf+15thEfxjOGAw2EQNqGfO29h8/Q3lXjpEqeqBoyFOYsxZuSVrErU687l/LKlz0dG+UwgAHjlGlwSPz77+sPBrUBhKvLwKkML9XCfQCLhMEKWLpRU5HXy4fX9sM0ae4aev06kPp0rMvy3H3oNMPEZIwIjBc5p4IdGwfCwgk92FkCpIFejqSrAtVj6Ezd10bNdT0mdBADwbkjPMyC1HHvqrEUM/qXDPFv02uCrpGn4tC3UurnYybCcZ2cC1du0fOXr3jNyDSrBhGUoFxlWlGHplWoSun81L7PJHr56g/Hd4+SSltQMmhyL93hqMi1RCQEpY60DX/Dut8wf4hUrp5jvb6NXMvI0PUaVeR4KlA5UHbzVWjLQ8S3irQYNOx3KEkmSl3pvSeU4hK1A7g8KsrJAeaKyOCYGSu49Bgi0B+qCSNPlj1ZjFN17rZwAoN5HznmcKRizeuV3BIOFhgKKD0KqOnxs1cIDoFurWtKp8mIMqtHNkzi1wNnAJifkfKA6LkeFFdv3oCc6NO+Lq8RGbZvFWVaEZFcsXEfXbl+hxV9rFOUzUufKgml/T4JVStXQFpHsFr3XkW5OxWouI8lOIC14srTEUzBjLgEyi9bhu+lKtXolZvUrkd8S3D4Ge1NKLMpUjfMIlq7DJzG+nO1cgUpdsxoUS2LUgAAIABJREFU70T+Y3wV1SPXlEq9AN9O+kKNaM6YAHa2QmeLEikC8yZsXTzcVMUc7PlwvD559pJTqLQNJWT1yiXrlSdGigJSKIFSM3LWIIKet0JbAlLXFb2G+W/WZRRH4ru3qeVxelV8Ox/qXPqvPNc24j+SmUINX0BXQZgWli1V/noORUXk9GAj79KyOk2YvYaHYhTpwX1QvI+eukS//f4nRxDhuRNliMLyfTxFe/39/Jg1H44LvZqfKvJ6Bdt+m4YV2Ss8d/lWOrJhMk2YvZoJoQrmyUhb95zQlSs2yUJVOnI6ApigcSAgGgmFbmy/VpZSN8zOB6JTucq0IlFXds6yLTR76RaH0b593wmuUeyuxI54lopD30qpH9VEODiwl6/fqytKKA9GkWMVHA7aQXgb9dKShKG/maM6EyLZMEKL1wyg8sVyGyp37nItHz1+zk4eRDPBK+FtQ/QX36ZMnWqhOGA9ersHqSLH8/Z9tffBibF++yGnrsKF8+fzAka5bINR2mfkHJ4P0cDjggiRbMTYKjmeeK4V9mTt+6rO3QYqZdPOo3xWwFlq5HAV+ad7Vo6lWDG+dAxtwqzVdOf+z1J8BbgJCjty8oF2QTQU+gDOjO37T5hCXrmuBbNGvKe1JFOmFXvivOXb2BjEvle8QDZmp44cKQIBTQPEwuot++nZi1fUr1M9JxZ+1+eqqnsv+23oXacCFacqOKDifTw5KDFnIOzTy2MXzxdpcSBt0zY4ta7c+Em6cg+cG+37TGRDVzQzKBaBRFszeyCjVsw21XoBAkZILYWRDSQB0myRomAmhxyIgs4DprK+hyZQsuLd4FTU45rSK0+M8yJC+C/o4tVbho5b7PX5K7YjgdBzlS3eFfXe9dLsVHw7Zuf0U7veNuI/khkHlAkfBUq7hGVDlBY1w5GDC5IyQIIQXQWhCNr88d0pU9pkukPqNXw2rd68nyOHILfDJoSPV+TUyryPVY87nmFUzil50vg0Z+kWXWOT+/nzL64+AGUKBmySBHGpduUibNTINJGvKXJYoUiNH9Cabt59xLlR7ZtUpkvX73BETK8hTQE5sFAOAWlOkjCuV+WkVDhZQMAGWZQpkoumL9rIDPftGlfi4QN2+tc//7Jz4X02K6V+VBPhiLUGqKCfn987r43IBkoyGfFJ5K/Ujo0Fbzkc8GBVOWdwKFy+docyp0/u4EiAAfnwl6cUPVpU07XAMTakcxSv1ZXJ0/QimkbrBt8j9iej6AH6gRJTt80QhtEXzJ3RqWswbst+x0Zjkv0dMkC9aOyL2F+hSAFJI6Ms6z3DbORa9AUnI/a4GNGiSrOa414V5HhiDFbYk2XlLnMdnLpG7YsvPvPo5BCpKK511YFgA6u77J4I4yd7qRY0d2w3WrByGzvDkeIAFAwIRWXIC929hyojXqZMK0gSN2w/RIMCGr1Tq16MDXvV1r3Hqc+IOaz8A1ni2lTWvTeaW5nfVaDiVAQHZMYqe43rukfuumyFAzxD8C9onbNIcQkYOI0SxotNPdp6js5q10KFRr35rOnYrApVatyHBgY0pOGTlnAlIRl0EPSo+u2GvlMdSVYOqvUC2ed6ug5nV4HKHZh8FFVIkN5mtWHtGZEOu3sG9Ni6bYc4ZIs9IF3qpKz7oV2+fpfqtQv93V0fKr4dq+///36/bcSH8QzDoAKsWttwYM9espkqFM/DELqwbDDMAP0DqR4Y9gGt/C5xPIZsRo0SybCMmvDWucIOEUmA99GI7Eilx10v2guiIfDcPX72q7SX2Nt5QCQwb/k2tHxaX44EgjCmUun89PDnp3T01GU+sG7f+1kaLu3tOMR9KpwsOOx6DZvFCiXqGIOpH7A3GARgL0eOrpFxZJVsDO9jtdQP+oACA8gZEBnaw81MqSZhxHvyUuMbn71ki64Rr4LDAe+jIufMKoJFb412HzKTvo4ZTYrk0h2HAzhDNuw4zKXHZErS4F3wzblrKJ9Uo3whw09KD5KovRljQl64pwbjrE6bIXT91j3OGcc+tPfIOSYJWjd3kHQkXUXk2qozTxU5nir2ZKv7iYoUH8G/AB4ZUfUEfyvfsCcru3WrFDNca+ICOKpGTFnK/5w3rhs7z0vV6cZEgrJkY64PQ415RMIFt8Wew2epSwu5sq/avoB4iBghPKEEmaeG+YBBFiG859rS4l6UyAU3A9BKrk1l3Xtp4etcqAoVZzU4oOJdVPYBnRH8HFj3JQtlpw59J7PjelTvFk6Vijw9U6SQnNw6jeuRixxypG4hgCJTslmg81AlAykb3jacGXAeIK0QJVW9aSr4JIROgDKeMpWQPI0TUfwxM1Y6yonCeQ2nAOD0sk3wjpzYMo2/acx1lnQpHHsR0m2R779m1gDP599HhCiVfe//2nW2EW9ixuAlO3zyErOvX7vxEwEqmjTRN5Q8cXxKnSKRIfQOj4LRU7pud6enRo8WmfJmS0eAEUWNEtHEiEIv9RZG69XDNDeJqLMgwRI/IRKx++BpXdjPx+Zxx9hV5PWiD8APcTChRBBQDTBiACdE3jHgUUali6zOi7jfqpNFbxxQbtC/TE1RVWRjKuTSousYevTkOa2a2Z8jbECNDJ24iL9tHHKDuzU2jDrjfdIWbMAlttwpD+BE2Lr7mG7kWAWHg4qcM1UIFk9zs3jNTjYkZAxwGPFDJi526grOouwZvuc8QyuKjZm1owdJ1PYDJnO9HE6sLfCMuCKaQJqXMN7Xhk5O8SwVkWurzjxV5Hgq2JNV7CeuucGQ9b+vX3P0qV/n+pQsUXwWP9jm9fJ6YYygCTi9KCMGpIUZGC36wH6KqKjZvGCxTjwhC/DdGOUmow9VaUJmvjXXa804U2Xq3lsZi7hXFSpOxVg+pj7g3EcEHd80Kt306VhPap3hHVB1A9Vm4AiHM10Y8as27Sc4DGUq8GD+UeIZ44D+DF3ENeqMs8cIBSZI6QR60lXGSIc4dvqyLseFCj4JwZ0g+Ie8nesGHYbRL09ecGAwXtxYdPjkRVq5cZ8jX1+2X5BtpvwuIcWMEY12HTxF9SoXcxjxkDvQoUZVdOxvR1ba3l1nG/GScgMJUO+Rs5mJHZsVYGAgmMABfvbyDdqx7yQV+yErBbSs4ZQbJ9m96ctUkMfA2JyxaKPhs2uUK+jROymMCHgwQViD6P2NW/ep04ApVDhPZt082vfhcbfK8Ksirxd99BvlzJCP0l2oFVqhZF7p/FPDiZG4wIqTxVP3iApu3HGEFq7ewfleMtA5FWRjYjz3Hz7h0mFAcSSMH5tyZ0kjDQWEtzxzsaactgJEARqitj8/eU6VS+WnqQs2ULPapaWgfFlLNKNxA1q7RSAgovbq1Z/U34DZWrwTcgxFQ1UIQNhrVihM/n6+jjq87uZDRc7Zx4JgkVjO/7lLXOGE4gVWbd5Pm3YekSr3pSJyrcKZ9zGR46ncT1wXFcr/wSjo27Ge1HrDPgSSvR0HTrExgzzhZnXKvpPKIdWZ5iK8I0q+IgoGslmjZuSMk9mn9VAsf/39L/MpGFUzcezTj57Q9AUb6MK12/Tg0RNKljg+77koaQpHxX+lYd3DgHPX4MD1xL7uer1V3UT0Z6UMmkqZY71BzwFhIpzfpy/+SGP6ttQlXdM+X3AHCa4dGPGF82RifiXkfBsZ3qIvoEmRm410VOgGrg2IFjhR9ZpIDxAVeFyvPXnuGn/jeulxnvqX4ZMQ9wp9GmRxQN/IcpVonw3+CRChrp7Vn5InSeD4Camzfr6+0joJbsRcIBgnGqqQ4BsGYgAcT01rlfaYOqNyrdl9eZaAbcRLrI7Nu45S31Fz+SAFRM5dNAjQk4mz1zDsE2U19PIdselFjhiePwbkzu4/do7J4GQ2LZXkMdgwug+ZYSgBkEnpQZUQaW7ba4JTPzjopw3vqBvRVO1xf18Mv6ryeg0F/R4usOJkQTQZRgjydwE9Z6Vywx4u5wLIWe2KhdkzDQ4Fo6aCbAzPEOXPwL0gDmwozXPGduUyLkZNQIJFWUWxZgSRG0o7bd97Qir3Gmkot356xA4BbcRNRBgGdGloSGy3dN1uznvVEvto3wF7ToNqxT2+1vvIOfM2h85I9jK/6yFhtPfXqljYMNVH5nmertFzcKZOnogRU8h5Nqo5D1nWbj2Y10HJgtkdj5uxeNP/2DsPaCuKpI+3oq4BAwYUEBETiqCgIChKzllyBsk5Z8kgSaIEAUmiooCSMxIl56ywgCgIriiKYddPl/U7v9J+O+9yZ6bnTt8nLq/O2bP4bk+n6enuqvrXv4QvQGeFwPjpFpNqw3Nty5hngxzPBnuyrf0k2vtHiT989KQRnJfn2/YeK8R0EAQS6024BugTNwOfs024TuA90bwznO9rNu6RPQGFGSJT1shTWb15aagzGrLg9JfnJEXUnEl9BfkRi+Ddn7fsI4HOwrbdrlFlVTivNywX1FnVpn0lnAwEDrB5HA8g9NKluUvi4f2QAfHIe8897dvvfpAsAqYoCfpRsFK7RFOHAooQ/ti+cWXfabVxN7GRBs23o4YF2B9BGGGMGTOgjawtDJPETYNkg4zXRMZNn6/uS5da1ggIOYzyRfI+bbTeTeoPUobveP/hE5JFgdSSTvn6/Hfql1/+HZMSb8In4WwLHaLrK5PkjgXbPvuDU0D+ejkH2IuqNftvemL9LLxV3J9MeGWCzFty2T93BpKVeIP5J/bj4Yzp1AMZ0vqW5gMktjx92tRRy+rLBzFvMFjWaT1QfXLsc4HZO72Dbg3ZIo/xHUjAAhyO5C0mvj99mrskvCAWMg0uvcTARqbVwCPpBzGMF8MvU9E9QFwv5YkTh8AKcpZDRz4V+Pz996URwpYgBDL6NYQJl4jVyAL6pO/IN0V5R1GGyIS821iI8+XOFmgcNsjGdPYE0i3VrlRU4HdzJvZRPYdOEZSDSRomFOxy9V5Wez+crEBHbN5xUDXqNExtW/q6GAEYc+POw4yIcvDW1W83RDxlxAUC5fv7p1+od+aukvjnoT2aesK/NSqASw/MuldffZW87iWrt6nTZ75STWqXkZSEXjF6tuI1bcTQBdwyPPfHPDmzCNIJYS9gDy6YJ3vCxbtjs6qhYiD9+upl4CRdl5B/btjlm0PcRty1X19Nfg9jzItWf6zkeNRlgz3Zxn4SibriW+Ic27b7YzW6XytfRZWx4IUnHdyHs4ZL6iYNCQZlhmEOzgQv0XmY2V/hFJm3fKMYdyqWyqcql85ndOfwe//AXuGCqF6+kF/RRL/j4cRgO/W9paKkNalVVhV8PrsnMkhXAEwaR0bkHojTokrTPqpK6fy+/D/wSaBURRO4WUA7maACeK8LV2xSwyfOTmD8pk6eBZ1AloegglJesVEvMUYQB+4nYe4mNtOg+fXT9HcMGwNHv6N6ta+TyBiy/+MTasvOQ3J2JZWEDSHR/WRP2bB1v/AW/ee3xGndKAP3yUtV3Q3qlGFP0aTQBfM8JXcDEz6JyLnCOA/C99zX311yF+bsgfjOTdg/nirWWL0ztofKluWhhGItuo8SzgpNSGzyfuKFMjJpO7mM2QwkK/Fm82StlLaSEQdEbD2xjlwAOMyPHD/lm87NFnmMc0CQgxw8clL99p/fEv6cLs2dMR1usU4UB9XgMe+oNZv2RK3C6LCOE8MvHQoS16svdsveGaLuSX2HKl6js1ibj392RjWvW87oMmUjXMI5kbEaWTCqwKzN+FGk8AzhaYJRPUh8sg2yMeaEudTxYvrC/NH2A8L6P+nVjr7LjwM/Z4kmCTHKpC5bv2VvQsYCPPFvzlnhSdYSOa947jbtOCiXdtA0eZ99UjWqUdrXyKHj8A6tSxx6gbfqxGdnjcIU6IuNmDNbMXS+L8CngPZYRRL7QJSZ9dEHBIr7VxK+H+Ip/QQDkNv3ZMNzTfuxGvN0323BiiPnIhbkh439JBrqA8MRRhrTdFXEsNZqOSDB6Kf3pNUbd6sNW/epEX1a+L16iYOfu/QjCU/CmNageklVq2JRa2F5wGjxoPvFrkZ2FA8i3lX2+26taxpDewm3gml/9ewRURGJ3HVmL1yrpo/q6js3kQUg3AXGy1lEWCPoBy9iSZ4H7dR/5AwhGSyQJ5u647ZbFPcNnRGIcxokZFCB6IusIo1r+SusYbIP2E6DZoPbibniXeCo0Wk8mVOIOyNTornNqw0iOBshJEHfu1d5oPOc6whGOdA4fH8Y+DTqymZ7bnVBDEhWDYjoEPbLJau3qjw5sgTKihIGZZQU40xuQ6lkJT6GVUCMYqyxXihTxap3UluXvC4HGcoR8HsONqzFSZ1irvugyWrBio3i7cMzqaVCyReSbNPBUl61SR/1t+uuEwsu0Dtid5zChmRy0Npg+KU/xHxv2XlQwh0yZkgj8ckmFnf6DPNu/fZDJecvkEhIrIBvr9qwUxHvzMbuJjbDJSLbiDXuWteDsjhn8TqBev7tb9cJazLrxAROH8NndskjWunV6Zz0hRmYJ150LnQm8srot+W7w7OL0QgSK9L9aegzZJWm8bDO9lg3QWLYeNcz536oqpUrKPOpBegzXjA/tn/K21CsbMbQOecjFvSIhn1vXTxeyPC0kHOXuMRY9sdY+qEvPpFeFZP15Vbm/Hc/yBrTApzX5MJrw3Ot24zVmMfzNmDFuh+XC/IjzPvkWR3qoGNp2ZM6NaumJs9cLB4vvHGmwntmvb01Z6XCWF+q8LOqatkC6qmsDxuj2kBNzZy/WtZZ/WolxYMOuuiWW25UD9znjyR09pXzglDC12cskNzSjWuVVkXz5vQ1Th45/rmEkHDmIZwXEGBhHEFIC9a0ywg5H02Fs4d+QM7Fvt26YcWENJhedWjlmbAk5jJSMA5y7xjYraFpVxLKYRhIddvNxvekWO8mNtOg2eJ20qi4l9vUFug8c4GxhP3s3fG9jHgCohHB8TfOZ+5IJkzqXuSUJuEszpce7dvBcUDedxPGek38+dH8MfL9kaGIf69cv0PQCV73Pt0PG8TK1BV2LNQRFmUU7YOK9SwO/HFeQQ8kK/EBX7aNWC+gZig+EIFAGiexdMOmCSypW6uaRj2ywSLL4UCs1/xpA4wORKOOxVAIT1WRqh3U5oXjYmbndzYbluEXKDxwSCC9MBOv3bRXUkKtmPmqUeoU4Exc5tbPHS355vHSQohCXCEkIV6pU+IRLhE27jrylTK+Fet3Sj7jbI//Dkk0kbAhBqz5bIUbJjCsMsd4mPCAL5851DWEJbJvEAPhbT/wyQlRlDXPBV56DlxQH37xo1xitu/9OOqw77nrdvGqnvriq4SLq9v8hM3xbkOxshVDZwM9otMzliv2vKpZobB42Y5/9oVq1nWUfI/kJfYTG/2gDVteFcjJgCRHki7h4TT9dvzGbPI7F14/CWKEoq6gsGLdfqzIDwzon//h6XIbSxCyMXg/xk2fp3799aLq3aGupP0kPSRhLFk90qk5267XdrAq+PxTqs4fIT6pbk0pcNeXqpVIZBj3m3vn7yi6QNmJYzWNu9aM/VkzZZSQBc53HATw+WCgj3WtoQiDLJj09iL1zbc/qHaNK3lmmtBpv7SxNTI1FanJBo15JwH95DUvGJ1Ivzv1vWUqZ7ZHVbvGlY24g3SdeIeLVuuodq98IyqZ3tbdh0UBXfLWYM/XYwuyHXk3kW8S42+E0yLIWjEta5Pbib2sWI1OCsQUYypctYMaN7CtrBO4Dnq2q2ParUvKocSTH71Nw4ox1xGUnNLGt5Ow1lZM+t3J0fIVGQPG2w+WrDdCCkYjVsaYhnEPtv5ohqjISbIxFuq0gTKydRbHvBCugAeTlfiAL9lGrBcXn+mzl6trr0mhGtUsLdbLN2evEKI7U6ZTDpWaLQck6j3WNy4wkC7hQcZz7MWmqZXn3X9sOgGnwlpxYq3rtR1kFIdsrVGXijQ8K5KfgEsnhCKmkETIhLbu+Vg8Na90bSjQP2IT4UtoXq+86zBsh0vYiLv2mnMOKEjv/MRGiAFtwECLZRw+CbyzD2RIIwz5piRFbv3k4sk3Qz5VE4mmPOvnOGgh6JkxZ4Vcor3ERo73yPqDKlZhY+hso0cg+2rfd3yi+FUu8IO7N/aEAtrshw2vCu9FMzD3bl9X5X46cyLCJNaxaUpRLsqvTZmbkPeXc6JhjdKqbNHnTJarimdsfhBYMZ0Ng/wg1GTkpDkyZk0u5kQz8Dfg6KTy9BP9blo3qKguXrwoZ/KWReMFrs18s28HFdueJs6Pz7/4KlFsq1ufNIqF81yRsqt8K/X+G33V7gN/Nybr5F6B8cpNMJj6hbahmOYu1Uy1rF9B+HHIgANxmc51DyHoj//82Td0UJ9d9AViSOL6I4Vc8/CPuInOYuCWdxt0T7s+4zxRATYh2+zNJ0+dTeguyhnKH1w5pALzQ/uxx2HcwfjA/Y1165THH7lfDeoenYfBJrcTxrTqzfvLvIEiHfb6LEm3ShumCqvbOwMlR2x6LKgrXWdQckob3w5to7izJ5csnFv1GDJF4OvExLNOwxg2yKDDPUd/Q177kq2xhEEZ2TyLg+7BV1r5ZCU+wBuPZ6xXgG64FsUy2rtdXfH+sbEC1fVLidGo46tCygXL+J8lKE/PlW0hfeWC8GeKZhYHzuu0jhMbuDoAsycXmfWb94pHFg8isWP7Dh0XpdOPoM/m+G3EXduAbIcJMXDOhy2PCGtu94GjchnCM8TllDyzZGKwISb5i23keHfra1DFKkwMXTzQIxgWjn12Rv3007/U45kyGsHObfbDhleFd6PzxLvlHzZZa+wl1Zv1U3hE8fDCrL17/1HFeEf2baGK5svpWw3PYixFUIS37DqsXu3Z9BLvJASuztAO34qVEk9mEFixDeSH/nY2zHstIUsB81SrxQDhTTCZE33Z1e+G8/O1/q3U8c/OGnNsMD+sVXhD2EvYbyE0JaOHV4aaaPMaFqmE1/r5cq3UpoVjJTYZo3G9KsXVN999r5at3mbESs3le97SjzxfO9BiP6jziElz1HvzVyfU07l5dVWpdD7ZZ6s06aOG926ekOLTrTH2+rptvI2gpK3zMrZohvvZE3vLPhIpGILZ/73i870g23AeeBkRnO11GzhJgfxwExwEoDm8pPOAiWrd5j2qWvlCwlweSR5MKGKRvDlMPtvQZfheCKlDcceo2LfjSwqjNKRsJgawSDg9Z+a3F34UbojmdcoZKaw2yCmZCBvfjpuxh+9lxuhukpY6VoG/gTPahFnexlh0P2NFGdk8i2OdsyvluWQlPsCbDhPrdeDjE5I7HUKw3E9llpQytpW5oEq8VwoXDhMTr7MtkpQhY2fKpZQ4QC4/kbBOiEFMoEQBXmfUotpjvGrW8ERWcSBeXITDWFOD9m3ExNkKFl4uGBgU6NvsRevU9j0fq/zPZROCPL8MADbirt28zuL1MkyxEybEQM+bDY8Ih37dNoMUYTGkqcuV7TH19JOZBCEB+aDffDrfYTQm2iDv2EaOd7f2gihWXkYaZ/3ZsjwsbLuRYhs9Qv3ANXfuPyJrHlTD8zmz+sbi2u6HDa+Kzq3ev1N99Uz2x4Isj4SyOi3iyveGqXT33Jnw9+ETZokXz4Q8TT+EJ7Bhx6FiXAD5Napfy9AolqCDCov8oD2UQfJu71oxKVH/p81apjhrTeZEh27MnthHyLnwdlUqk1+d+fJrYag2iWHlTKjTepA6euKUKHMoz+u27JPwqwXTXxGF3kRsIJVYa7Vb/d5mkXw5xAMO8/r2vZ9IHLlJ9g6Tvv7VysDKjQOGjEDO/R20BWuoY9OqkkUmqEx8a5HCI20S4vPNt99LjHRk2FcQElPtGZ03dYAx8aLXmEA6YFA7982FSxjQIXgE7eYleJghhgU+P6xXMwl96tjvdXEImRg2ohHbQToIMWzZonminjWR/bFBTkmdNr4djD2RoT44cdKkvsP37NLjijQY/aZ+E4Qg6KNc2R9TrRpU8F2mNsYSrZEgKCPbZ7HvoK/gAslKfICXHybWCysjMFFirYFQcYEKAxeK1u2gSjwf+/6Pj0edgTtS3eJLJGeLJIUOMD/MC5enL8+dl03VKVh6IQxKCgGKRUw0CAWEzQulCAIy0zjJsP0kbht0AmPGq4MMGfeuQNU4ILF+cwE3sbozl4s/3KK27jokKQAhCMQgolllY+krdVZq3FvIm4gjNZFYQwzcDjj+HjQPMod+z6FT1ZIPt4ghBI9d3txPCNwtqISNmbaV4531GU2uSZFC/fviRYVHyyvkwctI44Qqa2Un6DwFLU/qtja9xoiRRceRk4Jr2qiuQmCYFGLLq8K7KffSy/IOiFV2CpdV9hQ/0dD+jQvGJFqnKBHs38SimgjGgHpth4gCMLBrQzEqIzx/w/WXGmdM6oy1TBjkB21qQwCG5loVi4jRF4UazxHfsokCzl7AN8zYMaxz/tx9Vyox8AHHN3k3Gmkx47XuCbne6R8GIM4QE48k5W0glTCqFq/ZOdErIRYeZaxHm1pGTgPmBOU/mmTJlFHCP7bt+Vg1rV3W89WH5frQlduoBwMJRKHA1Z3OAZRIFHkyyPjlrI82WIzsxz79wsg7qkMXNU+Ari+IEg9PwkttB1sJOwQ10qn/hISQpUiCTbKsvDGsU6yf91/uORvfjo1Bu507hJOAZjFB99gaiy2SPRvzklyH+wwkK/EBVoetWC8upvs/OaFKFswVoHX/ouOnz1fF8j8j0CZTOD21opB98eU5sVaT394EIWCTJMV/ZMFKoAADazx3/juVId3dsvEF8bDSWiyewGC99C6tD33Neqw3d/IOE19I2pkjx08bseqitEKQhLKNYsQBzuWTeMlYcuTqntMH8sICjTSReIUYxJIHGeVq886DatX6nULGw6WFHLCaQdlvPDZipm3kePdDJ2R6KL2a9t4yX+KmyPHGYqRhTjHUeEnGDGlV+8aVXYto1mO8hrX/IAqbM7GP6jl0inoi84PG3sSwKBYbXhUGCSkR30kZRfCdAAAgAElEQVQ04dsz8Vhpbz4eVrIosJ9hQB35xhxjL6JGwrAHDOzeSIjOeF/Nu41UKGcmMeR+30RS/06IE2nQUEAyP3K/Onz0pMTJg1yCQ8FPGH/f4YnTOzIvrLMKpfIaZZqINMTpNukbhsKpI7r4dUN+t4FUMmrIpxBz0n3QG1FLsTdCyIuRzc/7bIvrw0Y9YY3Y0fiH4HXAMw7iAwXLT9hPDh85KSnpnKkk8dCjLPnFwlO/diIRew73UawCOq9g5fYSmtOiXnkhkQsqtow9Ye9aNvsRdA7iVT4yZPBv111r7Mm32adoJHtnvzovGZf4/iuUzOvbnA0Cbt9GrvACyUp8wAVgI9YrYJMxFcfyjJX54Yz3ej4PA2W73mMFnqiF2OBOzat5XmJskqTYiLmm78vWbhMmW2I/udilvOlGOfj4d44nMonX2IS47HLwBGpCHp0TXaeqW/fBKLlI8d+te47xTdPDGshfsa2aOb5nIlZfUutABmfqKYpcRCgnfYZNV9ekuFr1M2AMj2kRGz4Uax5kmFNXf7Rb0uax/nu0qW0Uh0e3bMVMR8vxfhtEgYaMxVwO8QZFE6C9V12l1FfffKcgPAoqQY00rAnI/KIJxgY8fH6kWLyT4jU6K73udRrBj7YfMI5TtoliCTpn8SqP0Y0MJnz3WiDIJLOJCaM88w8kE4ZyJ9cH3sh1W/aqEgXsGpTjNQ+R9WJMI/sHCKPsjz+k3EI+4tUfnZYSRa7UH8gt2npj5hJRzHVu6FtvSel7EQ+LVKJdW5whkfMVhEXdFteHrXrCGrHZT0CyOYW/DR3/rlo0Y5C6+85UvsvLhrLJWsNgTdYB7mi0G+mcIC0nBmkv0aEobmR/voP5A51ow9gT9q4V1uhkO9uFydz5lQmLrKN+tzqcbWMcCOrcYk8rUaurcLH4rTO9H4Ul4Pabryv992Ql/i+6AmxAXTgUKjTsJYp+h6ZVVKVGvdWALg3U0HHvCltqLHFisUxn2JhrFCo8KsdOnlGNapYSiDkEL4goOSdOq8Wrt6opM5dIDDfWZzawaGLLExjLPDifwQiTp2xLtejNgeqBDGkVsZ6k2dG5dUmFNnryB74eVk3etGfV5ESQQWB8pLszgcy5pTHDOIKnKcujlxIGhR1/kOeJ87322hRGkHjeL3mLl6zeIsYeOBi4gEM+GJTh3kbMNOMMy1gcZK5My9oy0lAPISAokJCfQRzolTtbEzFq2KlW4ie8tVCg9CitfmITxeLXlsnvYQnLnG3gaeZ7vDPVrb5KoUnfwpQJEiMZpp14PWvDeGyT9T8sUskPlRMkxVyYPckW14eNeuJlxGZNEg/OHcqEDDWssqm/Ae4FIC7J0hKZspIycC0R2uElOG5erN8jwVAar+/Lr97L4a5lM9uF33hNfvf7hk2QdTb3pGh97j5osoQdxZoCMGjYr8m8XcllkpX4GN7+nw21pss2oC4c1AUqtVU7l0+UuEB9YV62ZpswYXrlM9fThuW/YYeh4umNJsT6TXtvuRHs2/l8EDgvHkMu/6AHvGI7eW/t+oxVg7s3cU3lZ8MTaIvsj8wBHNqQvEx6Z7HkMwdNgMB0+6//+0WN6tvScwVrbwYeoWrlCyo8Qhg1iIct8kIOY6KUbbsPJ2oHiz8Q1hQpro7hC4rtkWhEOLomwkjqVyvhW7FOK0VB5jN3jsdV1kczCilPEKu0rZjpsIzFNuYkHkYa4pMJ6RkxcY7krG5dv4IqX+J537zZlM1WuKGaNrKLEMGxJ92S8kZBSkSSQrm9bFsoFt/FZFDAFmFZ5Penmw6SE92gu55F/tdy/rqteybBlLATJQ6jkZ/gNXXCqP3Kx/K7F4s6HAHPPv24UbVh9yRbXB826rFhxHabtOmzlgvJrAljuNHEJ1EhfSfo3rqWIM9MkDzx6JqNu5azXxiMtazdtEed+cfXqmaFIoIWdKKPoo3FRrYLG3NkA1nnVYezj3AixcIFQQpA7n+EdcYiyUp8LLPm/kyyEh9wPsPCfwI2F6h4UKiLTqe2bcnrosBoJf6DJRsUJEi92tf1bV8rM8ULPBO1LOkufvnl376p7qI9bArnBV5mAmmjDS5uV6mrXFNWhfUE2iT7Y1w9h0wRhnqIEMk1DV8BiijevdJFnjW6mEHY1KbnmERTDKx54tAORqm79IMYVoCfoZikT5damJiTUiJT0tA2fyNzgCnJH8+AYoHBGqJJoLhAlEEVtGtU2RhObyNm2gZjsdeccHH3SwXFfETzSMZqpEGp2bB1nxo+cbYiVKBpnXKqxouFAqEc8DKRQx1CLlJAkZaRHLmmSAlbKBYba9sGYVlYpFLYcVxJOX8xbFds1EvNmtDbKE457NwmxfMQtXKW9OlQz7c5G3uSDa4POmqjHhtGbO5V7GdO+f6Hf6pFqzYrzVHjN7E2UB+0YStcgr53fWWSIv0ZqeoildxMD6SPe5hc2LuWnvP3FqwRdJ0zJNT5Pjo0repr4LeR7YI2uRud+OysEFWCAkFRhsuEDCtu6E+/tcPv3FfOX/jBmETYpE6/MrbWbGQ7yUq838wH+z1ZiQ8wXzbgP3ioLvzwkxH0N0DXEop2DwB10V5JDdlGiS/ywtMS/wXM2iTmRSvxMARHEzYyLkZ++eojn40Vzssm6kbMx/vDc+lF5BLGE5hUZH+xwFgxphw6clJiR9OnuUsg8EE8zzA2d+g3PhGEz4Q7IZY1HPQZ0hNee921noRpXnWiJO058HusMcaSoMLF6rPTX0r6LxNSSF2/DcZit75i2GCdB4G8xUJw6Wwf40iD9kPFKEKIAt5MUhU55bprr1Wp77wt6BQHLm8DxRK40SgPxIuwLAhSKew4bOf89Uptqvua66nHjEnhwo4v8vn2fcYJAVnjWmVsV/2n1IcSD+mfCbLO1p4UjesDhTGo2KgnrBGbb3jQ2JmJus4+nzv7Y4IWMkFY2DDE+UGtg4RLMBiQDnsPH1Pnvv7ukhRz8O5AfBdPCXPX0v3iPpejeBM1sFsjybpx9dVXyU9LVm9Tp898pZrULqPuuv02MVZ4SdhsF9w7CW/EoIDinunB9HIX+Orct5IqFSGVYaXS+XxRAaRnW7xqiyKltZbTZ78Wh0XG9PdI1gydOSme78fGmo3Wv1gJuOM51r9y3clKfIC3ZwP+ozdi8raWK/a8eiFXVvW3v10XoBeJixIPjjKm2U2DQl3GTZ8v+bGBxjTvOlIshkXyPq2eyvqIUZ80I7XbBQEL56yFaz3jxmzBefXcHlqXmHFYDwRrJmna/AwKsXoCbZL9OScfBXzXvqOS2mfDtn0qb64nhaDKVMJa7zE8lajVRcjR6lQqptLec6favvdj1W/Em6pbq5qSF9ZPbIUYRGsHMrWN2w+oSa929OvGfw/FGPKQ8zBxmhAoEp4AbHvYhFnizdfCAQtM0STMwAZjsduA2Qc2bN1vnMYyVoJLZ/smsXh+xHY2wgPoU1gUi831aoOwLNp7NkUqGX8ULgVt5/zFALFx+/6E1mYtWCupyzSaa8/BY2rnviO++3TYcbk9TzpR+Bs0KV282rFdb2SIHfsLZ8e23R+r0f1aqcJ5n/Zt0m1P+uTYKclekzObd+5w3UDYMyeyo2Gg0tQV1ojtO3ExFAhqiLMVLhFDV+P2SKx3Ld0h7c2PvPMFSd2n64o128XuA0dV0y4jxAHQpFaZS8iTIUHctPOgGj5hloR7ThraUfa7aMKayF+prcr/bDYxJOpQh217P1Zff3NByDOfePzBmIhqbbxE+letWV/VvG55VSBP9tBVmhJwh27of7iCZCU+wMu1Af8BbsoFhbhzlFtgvCjQQKOffPyhQPFJ/UfNUO/NXyMjgKwNtmIYoNOluct6+roA0yRFg7DZ2oLzaiUiMuep7jvIAz8lIug441UeJWLs1HmSBg0CNmJfn306s0CMcz2V2RjqacN6D4S+WPVOau37oxJ5UcdMmatOnv7SN8WcrRADPL1jp85NmHLWGLDGFeu2q/rVS/rmLtYPhgmJAbYHXBykCsR2eGGwsKe+M5VY3PsMmyaXf1AKSSGRcHr2l28v/KjenrtKNa9Tzig8wCbBJR4JL0mRIoVnCEe08IBTZ88JKeWbo7vJ+o+32Fqvup9hCcuijTcoUsmmUcL2/HOO4S1rWuf33ON4Tok39jO2hoWuxgsuant+TOuLRnYLEobUcHgpTQVlfcuuQ2Kw1IIh55tvLwgpZe6nM8s56iY2zhxdtw2otA2DAgbbmfNXC5Fd/WolVYZ771Y4dQj78fPyes27DUNckHAJ+mKDFDlyTLGgA3Ud3KtRhC98/5N4sVGGTYzgPM++NnPuh+KddjrD4EJAQTTlgdB9iSXbxTtzPxSjn1/KaJT5AaPfUrUrFnXlZNKwfhtGCdPvPWg5kFm8Lz8+Juq1ZZQP2scrqXyyEh/gbduA/zibI2UQuUavvfYahRcXRa1y6XyqVOHc6r50d3v2DE9TwUrtJCfuxf/8R2Csu1ZMUmwoQOdMc3cHGL5n0TBstrb6oC8PeB2iyaenzqr1W/b5Xg5t9Ado/aGjJ1WHJlUEPsVFfv+h42rXgaMq91OPqcczeTO6s/kVrd5J1kfNCoUlZ3b6tKkDdy2a9f70l+ckn/ecSX3lMuIn8COUqNlFacZwXR5rN+gAr83cZogBFwW8ZE759d8X1brNe9S6D0arG2/4m99QJP772TLNJd94LHnIuTARIgJfxDMlm8p35oTgAxWDwyAIKsC30x4Foh2Sd9x2i8r77JNCiGgyJzYILp1dZI654PI9gtq4nZR5IaVy496qUc3Sqmg+95zMNtIF2VyvkUOO1ZsYFqlkyygRj5y/85Z9pHoMmSLcDXA4ICg22/d+osYNbBt11diCrsYLLhrLUr+cjCwvNuip/vmvn9UjGYEn/05cytn5408/CwlomaLPeXJt2DhztAIQFiptw6CgvaNZM2UUpZG718LpA1Wf4dOFqDMIKs65NoIa4tzWVZBwCeqIRopMXzCeca5VLVvAdwnbIrlcsGKT0qnq7k17l4TrZX4kg5o+qpsxXw+GlWVrt6uN2w6oCz/8qB68P52qXq6gSnP3Hb7jiCwQiWw1qYD1YWp0oK/8z41oD0dT5wET5F7h5IABsUD4A8aKpBSnUf6335SgcYaMmyl9e7VXM9+uXA5Ged9O/sULJCvxAV9gWPiPszmUeJT1BtVLCiHGqo92KeJ7H3kgva+iCSlduXovqwNrpsqGwCW3R9s6Qib1/uJ1RqnDAg7dtXhYNlsUkUYdh0Wtn8sdm/GC5Rt9WWA5iLDc16lUNGpdKCvb9hyOmVXTdL40IQ/oivaNK8tjWC95t6AE2Kgj87ZHqxsYOx6Qlet3Ck8BafOw9gI7jUWhd7ZBvlngUNXLF/Idlr7EdGtZIyEWi7+92KCHMLzXrVLctY54hRg4GyTn/WMPZ1ANa5TyHUvYkJj5yzeq2QvXqnfG9VBNOg+X+Shf/PmEdt9fsl5t3nFQjejTwrcvl0sBGwSXeiwojF0GTkzEnVChZF4hZTSJHXWbk15Dp6qUKW9UnZtXc502G+mC4rFew3oTwyCVbBol8Gh65fzlxWR+OIN6pWtD36XN3jbqjffVlHeXqo7Nqqq33l+lcmV/VFBkKCVdW9YQI1uk2ISuRutkULioDcOGLSML4+HsIZ52y86D4tnMmCGNMHTrcDu/F6NRV7tXvpGIiCsWeHJkW0HOHJ61AZW2AUHXDPe7V0xSCgLg8q3U+2/0VbsP/F2tXLfD917CWMIa4qjDRriE1/tv9fJoIRCFsd5NbJJcggh4qmgj4W3hDgHxG0o06wR0qkbm+K3ZsdPmyZ6BgsteC0oEXpZl7wzxdYY567aJbEVRByWIg80pMOWb8OaEyYBlK2OUlwEMBxDGlljFxCgfa91X2nPJSnzAN24DmqWb1Eo8nrwVa7er2YvXiee1xouFfa27Ok65f6f6Qq4CGQ9KI+Rlp86cU0N7NAk4stiK22Cz5WCAET+awOx54w3Xq4OfnIg5b31SM3vq3LQrZr6qsC5jXChUpb2qVaGI5LGHdAzol5dC4pwLDoT9H59QA0bNEGg9RoouLWvE9sL+eOrlwZMldZdpPRhAEE1Kpr2t96S+3Zg1PFSHPR5+d95qyZs7YUh73ybChsRgbCtVp5vKkO5uUUqPf3ZGlS6UO6HdDdv2i7c4CKGcb6ejFHCSNLIXHPjkU7Vp+wHFhTNjhrSC6DE19NgguKSLep0XeDabfKusDVAJfUe8qV6qWsKXIThymE7CQFAtN914g3risQd8p+tySRdER20RL/kO2qVAPIwSzqZqNO+valcuqkoUyGXcRdZu215j1JpNeyRDxvPPZBV+iTfnrFAHPjmh8j+XXbVpUDGqd8smdNWtw0Hgovqi++D9aRWhIghjYd/n3EKRRoF2Cw2waWShbbKWMEd5cmaRDCJrN+1V/zh3XumzyO8lcf+A4BZUn9MTiOHy08/PGp8X0doJeubYhko7+xQEgk48/fPlWqlNC8dKRhaUzHpViqtvvvteLVu9zUiJD2OI0/22FS7htgY4R0kt7JUyzybJJeupdJ1uCSmOdb8IWdu6+7ArEsfZf73XvzWmu/A46QxLIyfNkWImGZYoZwvZihFi8Jh3ZG+LJibhnGHC/WjTVsYoDGCQAzqFkICGHYepkX1bGBFfu601E6O8316V/PvvM5CsxAdYCTagWbo5POZdB06SNFcIVq0KJfKqIvlyiMfVT7gc5qvQRoplvC+NHLAIF/I3Xu0ocXBJIbbYbKP1NUhcvX4+3sye6zbvVfNXbPSEkEfmqdaWfG0Z5oDCgMPFyk1Q3GGEB52BZ4ULDWyxkA5mgzvhD5hjrO8Ypfzaa1MYZUnwiqFztk+GgjtcWGBRNI8cP+XZXUhfiIlzEy5CazbtTvQzbPtT312qKpR4QdjQ/cRGSAyXukUrN4uxDFbbSMmZ/VEjtIdWviOfX7l+h0DnBnRp6AqHB5kxZupcWUP8/4QZCyX9DJ5MyA+x5M+bOsA4FjYswSVjgBzvxfo9FCkrnRkggEdv2XXY08hikzDQRrog1jwX/baNKkVdUryftZv3iMHVS2x4E/3W9J/5eyxKPOcoRJlThnW6hATKbyw2oau0FRYuqu8E25dOSID/AkcHtYRxnfOCNIluSrxNI4vuC8ZMZ4hP/fZDFCnDTA22zEsssGLnu4vm7MC4fcP11wl/iIl4cRY4n8+W5WGjsCHnM0Eg6PSjdqtXJFUY9zN4h0B+EfIBQTGhWX8liUQn/KZ+E2MTim+u7I+pVg3cx2OT5FKHcW1b+rpKeeMNCVM44a2F6rNTX6pB3Rv7TivnRvXm/RTfH6KV+I07DqgFyzcZGfZ5zgaylXmt2qSP+tt11wkrPvf4FBH3tOuvv84THRA23I+xxCtjlH4Zk95epE6e+oca2M0fcaWfidxP4NwwNcr7LoIrvECyEh9gAdiI9eLS36TLCFHeIUSpUjq/KlEolxwQQQSL2MKVmxI9Qmw9h0ssdXHZPHf+O/Ew4kEzTT9mk2E7bFx9UjB7Ll2zTYhUvEiXtFV3zfsjJX89cZ+Dxryjti55XYgLsXb3HjZdrZkzwvWVOw1GKAr1qhaXFGZ/hnjF0BHucN2110i3gMXC6xBNbDCXY6AqU7d7ourvSHWzsPVDJOfG+BrZH5shMWHeh8mcEAceTZGc+BYH6Vm56HBxifR04y0iTZAJtDnMGJzPaoOeTlmpfyOLwY///NkTHWSTMDBsuiDnRcjNk/PF2XMCp/UjX4unNzGW94bxF/Kmc99cuATqCQlaUOLAWJR4zq7z3/2gfvnlVyGDRJEmK8rzObMax5Y6xx4rdNUGXFTXsWXRuASYLEp8p6ZVxZCOV23G+yvUjNcS71uxvDu/Z3RYzNbF4xMZeWHdXr1hl5HHmDbCwoptOTu8OAuc5LWzJ/ZxNf7agKBz7hSv2TnxXeuaa+Rb6dGmlhE8GqMgyn80yZIpo5xd8Ms0rf07uWM0sVGHc2+LbIO0oJ2bV5f7X1JJvbaDVdF8ORKMobyvig17qZb1XzRC92BMLVy1QwJyhLPw7TEvq6Hj3xWG98pl8hsNxQayVZ9/mxeOM76LRHYubLgf9dnIGOU1aWR4+urrb415t8LuJ0Yv8AoulKzEW3j5QWK9uEQNeu0dIYd5+olHQntUw3SfVFl4MYFocyimvOlGgd7x7xxPZBLl4ZEH0/s2sXDlZjVu+jz1668XVe8OdcUjiMcUxuGsBvBXGggbV08dScHsaaLEY9goUbOzypXtMVWtfCH18pDJ6uGM9yYoMeQTJTwAhnM3oY59h47JwU6M9a79R8XoA9tq+WJ5kgxp4fXyTWLo9PP6Yrdq1nCV8sbr5c+1Wr2i2jSspHI+mUng8KSa8lOKfBdjgAJ4H87842uBnTo9ASZVxIPhV7dL/CkXctAFVZr0VU4FQZeBvfvvn54WJb1YjU5Kh9Xo3yEMWrBio1GubVtjQZlq1nWkeOThbUiT+nYhcly1fqeaPLyTJ1OwbcLAWNMFRa5XN2TIv37+xRMmreu5nBjQQQB16j9BQraQyCweT2Z+MDCXSixKPG2HhYxSR1joqg24KHVkLfCSchquUCQqlconITUQXYLEMsnRzpjCGFkwhjxbpoVij3XGwBO+hZLSs10d363NBqzYKw4drhDCJ2KVoGnZ4g1BNx0HipUmcIt8BmMPBle+if6d67tWaaMOXXkkUoJ4dFNyNuqwFXetv2NnmmTTOaWc5h/iWytXLI8YtNnfcj31mKAlTeLP9XcXFtnKPbpe20EJqIAg49Blw4b7xdKm2zO8Y2LXnQKaBqPWtJFdBGnkJzb2E782rvTfk5V4CyvAJNYL74OTnThas8TYaq+mX7fCXLy5/PQdPl0dO3lGYrSL5P0vhJ9N8diJ02rx6q2S1gmIMunr2OSjiY6nbd2gorp48aKaDnR20XiB+PLBm3gCbcTV0zcbzJ5colBE3IQxpbr1Zl9lUxMw0Scuy9pbwH9XaNhT0tSYsMA6D929B/8uED4OfBjr/2wxiaHTfdRKvBNqjccKXgAME6s37lbT3lvmO682Uv3AwovxCmigFjz5pIozvcjYYPh1e38ooCc/P6s6NK0qlxLgsY8/cn+i4hh1mnUdoV4b0Fqt2bhb1j6ZELS8MXOJuvaaFKq9429u7UUby9mvzgs5EJdKiOlMBe8ZBgbQJqT4efC+tBIz7ZfqJx6EgbGkC4pcr5GpfvTvhLoMHjvTd726EVpRD3urJr40nd9Yy4EIKFi5vYTksJ87wx1M62QsuUp6MxI/l+NxX0OADcioDeiq27iDwkXxJgIRZ1637/lYvTZ1rvCNEOqDQsEZ6CS/dGvXhpEFYxgZRyDcRHRGD0i/TAzqNmDFbuMDPQRxnpeiarIWbaRlM2lHl7mcDHFB+h3Psrbirm14aQlNJbwQvh4Qrg9mSBs47Z8NZCtOgefKtpAzwSsFo9d7sRHuF0Y3cPaNOSG0zyk3p7xRZc/6sLHTI577STzX91+p7mQl3sLbMokv5uAZOv49z9ZMCO10BWGUCPqCxa9T82qKOGQ3Ia62XZ+xanD3Jq55LXW8t2bJxyv4Wv9W6vhnZ9X8ZR8ZpdqyGVfPvKCkIeS0NUmx5Rw/hhYUPDdhvBgdTDzGWDIhBiHlialyaGE5Wq8iTAyd8yL0RKH66sNZwxNSv7BWGtUorSqVzqdmLVyr1m7a4xnDZiPVj/ZWYUDhYguMEcMIqet02rkwExgEnaDb4Vvctf+IpG8BFUA8K6mL8Gyv/mi3oC6irWPgmTB8u0mQ/SSyDiDpJWp1DU1gYzqX8SAMDBvXG63vsfB0RNbDeVGxUS81a0JvI9ZwG3wSGqW078MpMWcJ4Ptbt2Wv5ys1geTbgIzagK66DSQoXBTDYoOOryYgHEhxykWX/QyOELKA+IkNI4tuIwyztQ1YsdtYR0ycrY59+oUxrD9aPbGkZStVu6sgH4GqYyx8bcpc4QoZ8nITI9jz5ZSKUM9JrOkqed5GNiAbcde2vLScVRi1MYJxlhJOCow+KcMC9HshAxHkf6UKPyvtEz7pFDIt+Tlvwob7hdEN/PapoL/Hcz8J2pf/1fLJSnyANxstJ7N+vFj+ZzzZl/FKfXP+e/FW44GEfE7HD/M3LgFAYvM/ly1Ajy4taqJEsHkSq20iHGBXqatcc3ayYeR9sXWCp5n2K5XJr858+bXauutwQu5fr7ZsxtUD/2EjR/D+kXsYpASbZ8uXXjQZsmcZEzh9ZAUo/Tv3HVHbdh+WfhDn7Cc2DlpnGxAfvvHOEnXwyAlJQ0gaw2dzPC6x1H6GDrcYx6AxdFymNAGdJv8jRCBvrifUqg07pS/N6pZznRobqX6OHP9cVWjQ6xKIOqQ+1B82v3sQdAIDnbNoneQbRoBuo2zBbP3u+F5G+dX5dr45fyEqyocQAVOegGiT3n3QZHX3XamMmfbDegBsEQYyFhseHuoJy9PhtpjJJvLoQ/epxrXK+G0FCURFXgX9WI816eCBtdMuuVj6dsByARuQURvQVRtwUT01KFV8uxnSpZbMI0HFhpGFNsOGKdggzI2WivDr8xfEyAGcnnPDRGykZdPkaYQkyZ2gTAtBwOzef1Qg17GS0gWF9ZuM16RM2HSVtGEjG5CNuGsbXlqUxDqtB6mjJ05JmAbZA9Zt2SdhoQum/05GaCJhzy7dBoZ3nEjcfb88d16xTpxCFotOzdzTpDrLhgn3izbmtr3HqqezPhI1dafbHIUNlbWxn5i8vyu5TLISH+Dts/nNX7Yx0RP8jZizUf1aCizdTz7/4h+qRM0uKvIyBdQMsqR+HjFRfnXze1AlAg+YW9yQM42VW9tsfijOePRzP5VZNjAu/sBNgfLi7TQR+oFXETZ2DnvY+pvWKacKPf+UyeNSBvgsxGcfzR8jXkyMC/wbSNCWnYeMDAp+jZko8exp+MIAACAASURBVBwsm3YcFGjXR9sPSMohlNXnc2ZR5Yo/L/PkJzYOWt3Gtt0fKxiKmdNCzz8trKl4w+Yu+0gU+A8m9/OFR4WNoaMv+oLJXPCOMarkfjqzenf+GulT6wYVPNPV2Uj1o41OM8f3THTAo0zDmGqa390GOkHDkyFPq1gyr3hImZdm3UZKupwuLar7LZO4/j5z3ocK+Bw5e03EJizfpD23MrY8PDZ4Otz6CPIj1W03GxkWtRFt8YxBCdkfGnQYqqqWK6iK5s2hVq7fKR5GL3SQTsXUvXUtyQMd6SEynW/26Y07DirSdrLPaWG9mipnNiCjNqCrNuCinJHb934cdfruuet2+aZPffGVL4eJDSOLjTAFG7Bi6iB7hlP4G2Rji2YMMnYg2EjLxp2gbtvB6qN5r0mIT4e+4yXcb+WGHYHYy6O94KSG9cc7XSX75s8//yLhGEkhNry0sNOXrdddiCPhmNJSq+UrMg6TcE6euZy81zbC/aK9vyCpeHneRqisjf0kKdbiX7mNZCXewtsDQnPtddcaxTfqy0dkGhgujHhsTT2BVpSIn/6lcpVqptziPrmoAS30uxwSX+8UoMBPZH5QVSiV1/iyiJWQAxdPbO9XpwlbKUoEnnS/eFrdNvDZotU6qt0rJok3hI0cwhOYkD9Yst54br0MCuRrx6Pu5U13eq6JXSfe+t40d1lYab8TuajffgtEiAhsnTns1a5OoudQImu3Gijwbb/Y3GgpgxhQEB4HyuMJ33PwmGRRIEYziDJhI9UP0LsKDXuJ8QLyRi3kdwcKDLkXUr96SeEecBMb6ASNLNi/emqicAtyMoNM8CI+tLKY/qjERgpBt/7EAsvHALds7Xa1cdsBdeGHHyUcpXq5gglhGH5jt+HhscXTYUPJ02sNRQQDGOIklDMxLPLMolWbVddXJkkd99yV6pI9hDRkfkZkDLbEej+dNZOkqNSSM9ujEm9vKmEho7RjA7pq2l/XfeCf/1IFK7WL+jOwWVj3Z8xZoRZOH+jZlA0ji40whbDz4fU84YR820GNk3xDX3x57vdwo7SpjcnK6AuKYu7SzdX0Ud3UW++vkLU7bmBbRTaMvYeOGTNsR44rFlh/2Lm1ma6SucRojRKsBY6Yb769IGGIGNe94rpteK5teGkj2en1WOCVWfLhFiNiV6/3YoJs1c/b4E6wEe5HPzZu358wLK6MrPvJMxernE8+akRwycM2QmXDrvnk5/1nIFmJ958j3xIc0hu3HzBWEoHPLl29VeV/Lrt64L406tCRT9WaTXtUnw71jFNi2FAidB2RLMV6wFji/GCavpNjUEBvXDpeWuf6fGfuhwJRHPyyf75Q3QyKe9miz6mShXOrHkOmqDw5sshhBRmdCTsv9dgwKABfx6LKxfnbCz9Kn0jH9sRjDwaKjw8L59XKyKaFYwVqFilkEQAFQnyu60X1D2NPtN+Dxl2Hide0kurn/34R5IyftKxfQYhyvCQsOkEjC5wGPS6dzbuNklhpt/Rmfn0P+ruNFIJebXYPCMsfO22e5GgHxQN8lD0Ikr1l7wzxzLGr+2DDw2OLp8Mtnpa+mip5tpR42uTSu/fwMXXu6+8uSTGH0cpLEWdPK12nm4rM6xx0vVGeumCk1uz/GGDZn9zOomht2ICuhoWL+o2dPvI/iLf8JKyRxUaYglcaM2f/a5QvFJg8DMJLiP/GD45u9Ig2PyAU2vUeK/cALRjF4fMxNQDT7quv/85H9ObobuKxZR3XqlBEUCl+YgPWH9kGe+71AcMubKarJKTzn//6WT2S8d6EtfnpqbPqx59+VlkfzSgcAtxX3MSG59qGl5ZvC0cEKKBSf5A50meIXTEg6xDKW29JGejepccdBNlqgzvBRrhftHsS4wHlt3HBGLkLm4iNUFkbxh6Tvl7JZZKV+ABvnwU5durchCfwin7/wz/VinXbxXPnlePT2QyWMtI/QXyDgocXA4g1MVpBJKwSoS+HEPFEEzb19Vv2eXribRz6HNS1Wg5ISM2hlXgYyzds3WcMcXYzbOB5mjG6m8p4Xxrf6bVpUNCNsTFjUCCOs06loqpLyxq+/aCADTivPhQOrp0ml+ZIwfreqOOrnmlRoqUMOv3lOdWp3+tqzqS+xvC7sPGaRpOWxIX4lvGAsfbS3nOnURy7s4uEkEBQRyrHe++5U1IKIu+/0ddIYY3ncIN4Ibz6EQSWrz2Sb43pLiEFei+AswAxMWzY8PDY5OnwmhsTJc+mEh9mvcDrkqdsy0AXwWjtadj3y21qS1ooQgsw1qDAwwVB3GhSiA24qO6nGzll0HGEMbLYCFPwSmPmHAvedM3pEzlGlKfhE2cn+jP3JIwUGORNw3P4NkBNkZ61Q9MqqlKj3mpAlwZq6Lh3Vd3KxVSVsgWMp5esMmTYMU05FnlfA33nFMKMMj9yv7FiyBkxZ/F69fHRk5IVhbXH/GV7/EEx7FcrX9BIqQ+LUmIMZAgoVr2T2r3yjURZh0hteuKzs+rlNrWM5zWyoK0zw7QDbne+yOdBwnjtKzaQrW57XaXGvSVdM6mX/cRWuF9kOxi2qzfvL9+Nzlzh1xcbobI2jD1+/bzSf09W4gOsAJ2uxfnIr/++qNZt3qPWfTDalyAsQFPGRbkQsSmjfMJsHc3b6lYZkDAuTyiW0QQv8LY9hz0PXRuHPhZCLusaLsq/If8A/sPmB7zLRNiIP/+D1E6XB+6dJvUdxoetLYMCGyBsqSvX7RBoNEyleLlKFMwlkEA/sQXnhVCI/KcrZr4qhGmRMnfpBsXhjTIeVBp3GibMy9XL+3szbMRr0j8bsH4bFyH6svfgMdVl4EQFukAL6dh6t68biAEcpAi8BRouWrZYnsDGgKDvzqR8EC9EtPpiIXQE3lm9eb9LDHobdxwwjmG14eFhPDa9tGEQKOyxhCy93LZ2AncFSAWyFhD+gVKwecdBT1JIGx4RLoJNOg9X96dPI8q3UzCUOnOTe60v5oIQH5jyUa4KV+0gEGeMtqRYNUFM2YCu2oKLhiWnNPkWTcvYCFMwbcutHEr8oLEzE/2M8pw7+2OSW5oz2UQ0Kd3O5ROFc0cb9Jat2Sbx7WNeaWNSjZSB6OzgkZPqt//8lvBMujR3SlhXrAJB7KmzXwlE2U0wrLw5e4XCCIm3mPM/Q7q7hWuEsxnuoLnLNqhvvv1B9e1YT0LbvCQsSom69X1r14pJiQwHhHGBkDF1MkTrZ9gzI+i74CwHNeUnEDl7rTsbyFa3PgThTrAV7hetL7Dm79p3xJgbSp87zrpiCZWN1pekNvb4rY+/8u/JSryFtwdZCodBwxqlXGsjBQaQey954rEHjNLR6Do4ADr0G59IiQgKNYvWH2Lhz1/4wchyaGH6pApy7RZ8/ikxKHBYp7o1pSi9L1UrIem2kkpsGBRQNPNXbCPWduDmNV4sZIQCcI7RFpyXOoljzfroA5d4MTkwyr70sipf7HlPBcBt7mH9Jx+yyaFvI17Ty+oeBNZv4yKEAaxQlfaqwLPZxCOEkYYYy74j3hSW/frVSiTVkhUDHh6Uv396WpQiIMqw8hKPi/fJT2x5IWwQOkbGOLIXvD3mZSHFyv9sNuNwI78x+/1u00t7OSBQbHhEbH1/GJ3xCmG0JZ5/2Ouz1Jo5I9SKdTuMuUtsQFdtwEVtkVOGMbLA+XL7beYQWdZ+UCi33/cSj9/htoD7YduS1wVFppX4D5ZsUBAEm6By6Ff3QZMF+YihyXmXqFDyBU9iSd5J5P6J0rh9zyfq3QWr1ar1O4Xp3otPBsVp0cpN6pUuDQVtFU2oc/m67cIFRFibG2LQBkrJ2X4oroHfflOffnY2obrf1G8KvieMFbmyP6ZaNagQjyUR1zrDIlujdS4odwJ3srDhfqwnDOhOYe8GwYKjr1urmnGdR5PKk9rYY9Knv2qZZCXewpszYX1cv2WvWrzqd8ZWLi7P5Xg8wWt+8T//kQtMjza1jWK0qEPHfT7+yP2qTqViAuWFJbffiDflI32xxAtGI8OLQ7+AXWs5ffZrSUOSMf09EpdqCr/Rz7Nxrd64S5TYSqXyGfXDWSiWeDGe90rL5qwfSJ8X0VxYgwKewGmzliUw1OMBh0SN2F5CJkw88awJLgs5n8yUyIKM9Z7LRbp77jSeVzxOR46fkrh8Z2wmbLSw9pOaBXZ4P4mEjjNOyK1MYqxsxGtGg/X/8uuvqm6bQcYEiLYuQppNmgtmyptuSJg6rO5bdh32zHevC4dJWUkdKO+jJ38gaBoU90wPphe46FfnvlU79x+RZjo2raoqlc7nGZNrywthg9CRd1y4SgchpMTby8Udwxrfzai+LY3gsF4hPlkyZZS0e4QteIU/2fLS2kCghF0nXt91UI+IEGtGEQJ1ooXruLWNJ56YZM49YK59O76kMK5hxDFllI6sO2jaLxtwUVvklGGMLOw533z3g2pV/0VPgzcGPhwOvdvXi3vIAhlZZs5fLXwA9auVlJArDLk33XC9cSy9NqQtenOgeiBDWtkLirzwtDDfQ/qpSUi91rfOVDF/2gCB5QcR5hVkA0ZZ2uLeBiGepAMs/KyqVq6AnOle6577Fe36pXGlXxhhOUvdzmIbKCU9/rBcA7bOjCDvIx5lbRrA4sGdEMuY3d4Na3hgt0YJPCR+dYcxLOq6bTkI/Pp6Jf+erMQHePtcEiACcwqsj1PfXZqQ/9qvOs1OHwll4nDFE1+3SnG/KuR3Hde09v1RiQi4xkyZq06e/tKIdZXx5K/UVjxc5CzWRDHb9n6svv7mghCFPPH4gwpDQaRgCceyTS5qLUDWsJJPn71cFPj+nesr4MUmwsG4aOVmtWHbPokdZ8PByh0kxZxXWjZnH0oWzGWkDPAMF2igfLEKlzM8tDv2fKKABDMu0jz5id6IYbeHxEd7EGYtXKuOffpFoLg13jMXn627DglL6X3p7hZSLU0q5dcXfg8LHbcRr+nWT6DFXNYghvQTWxchjZTQF0zdLka0H//5sxrao4lfV8RQFpmy8tTZc2rKzCVCwJTjyf+y50dWtvvAUdW0ywiBXzapVeYSLw8Glk07D6rhE2bJ+p00tKNnznhbXggbhI7AVDE2QSxImsYHM6Q1vvgzT14hPkDQIXDDO87+5CY2vLTUbQOB4pXalOwdXgRUfoswqEfERjgLfSKEBLZy4PPDejUTo2rHfq+L8RmjYqwSBLpqAy4ab3JKEyMLZ2f3QW+oCz/8pJrWKSvhZ06CQEgDYeqePHOJoNs6t6geV0+8vldkzZRR9jj2ZmKSIfTlHAsScz1u+nx1X7rUEtLXvOtIQRcVyfu08GWYiN6ndcYak2d0GQxKeNIhLtbCnQS0oInRO0hbJmVtoZRscQ3YOjNMxh6vMjYNYDZSItoYJ++XM9Qp7AdB+SDCGBZ12/8rxh4b7yVedSQr8QFmFsWUPOROuSPVzSpvricljRgeHj/ReeIjlW8O2J37jhh58GhD1xOZnor4ZrxMeK38RCzKtbtekmLOhOCEgw3PSbXyhYTtdcHyjeJVATKG4lmiQC5jpmEgRIWrdVTp09wlrKgwiW7deUiRJmT8oLYq37PZ/Ibi+TvWQLwQxEWZCJ6/kW+8r9Zt2SteQMbUol75UJdl3S4IChOmYr354aW6JeVNalS/lnJxiEWJ7zl0qiL2HWIVUAHE6qPMQp5mEhNoCzoer3hNlPjDR08axUjavAg16zpS4dEoXuAZlSb17WrXgaMCsZw8/PeUfrEK4Q+kMCyaL6drFWRuIM84BikvQZkfMPotVbtiUV/vW5i47Wh9iJXQ0ZaiGOv885wNLy312ECguI0D2CUoEFALfmLDI2ILTu/X11h/DwpdjbWdyOfiSU5pamThXMGAPvndJRJeB2kadxPWn84yAyonW5aHbA3btR6NTkBxVsDgy7eSs2b3gb8LR0wQdnobnYW4FTShKToxsk08rKAVtRceJb5S6fy++6mzHva0hh2Gqpnje0YdEl74ae8tVwO7NXQdsg2UEpXb5BqgPhxTZ/7xtUC1nU4d03fHGcU6PXf+O+ELIDQtCKrHtJ1o5S43A1iYsTifRQEnOxKCUc8ECWLatolhMXLtO/+bEJUUKfyzdZj250ovl6zEJ/EK4LDNV7GtypvrCdWgeknxirKBk9YMyLUplFBbu7u1rJEAd+dvLzbooSqWzGvk0edw7zxggnjtnTFyKFsoO0Dp3YS21m/dq97+YJWQciGt6ldQTWqXCbwBawb1yBRSnQdMlDQheJtiEYjCOHzfnrtKPZcji7EHoH77Ieof574VdMW9aVOrzTsPqvcXr1fTRnYRYh4T8co1b/K8vjBvXzpB9Xp1qtq+9xM19pU26pNjnwfyxGO8yF+xrVwenPBDkB+8c5P1ZgM6bjJmvzKRlmEuNXjCWH9kWCic1z0ljq7b1kWI+rjckb4IkiWYux+8L62qXbloKAWeensNnapSprxRdW5ezfP7Mz0ITRjQbcVthyV0tKEo2iA9s+Gl5eXFE4EC6/+GrfuNDL82PCI2wln0gmZfem3K3ARDKcbKhjVKS8iPidiCrjImzogtOw+KMpIxQxpVs0IRY5I+3dew5JQ2jCy6L7zr45+dEeI0vNggrmLllYkltE2jE3RaUwhQ61Uprr757nu1bPU2YyXeBpxXcxZEW1NBMsXwPO8IlnruPOs275UQH1KYmaAC9PeHwTeaMGe//PJvz0xAPBcWpUQdtrgGUBJBoRIyoAVnFsYik7Np2dpt8jzISzzFKW+6UQgI+Td3YUiN3XgETPYI0zK2DGBe4ZygpdLcfYc4u5LCiIUjAMMIgkOBOzQcRvRBp90znZ/IcqaGRedz9AX0IHss+xEIQpM1Emsfr6TnkpX4gG/bRqwXm1673uNkw9JCbNXQnk0DXR6wqCI6n7WOWcaSaUpcY8Nih5I3a8Eaic3lMobXD8u3ac5fHWKg2en1nMxfvlEtX7vd95IKizRGh8pl8gvBD+9o1qK16r35awSGW7tiEbHCA6P1E83mPndKP5XpwfsSirMBprj6atXPA4LrrDtsrnl96B9aN13iCqfNWi7QaNYJ3nNTSKL2iuxZNVmgq1pAW6zZuFtiC/3EBnTcBl9BtEvdrTffJCzdjzxgHu9o4yLkN2dhfkchuOnGGyS8xk/45gnTcEtfgzGJS5vXRdNG3Db9tEHoaCOdoQ3SM795D/J7WARKJJye/YDUpBgnm9cpZ8yjEi/4a5BwFuaNd1y9WT/hdQHeDZ/L7v1HBbo8sm8LTwSKnndb0NWBr72tQLXkyZlFPIlrN+2Vc9ktm0e0924j570NI0uQNelW1kYqNN5N7VavCLlmkXw5JIUmZxaG6IJ5soux30SiwXnPfnVe7Tn4d+NQPfqy/+P/KpnOdu9IdUvMaTxBQpKV4JprrjFCwuj3W6tikahDx6jFfe7tsS97Tg1ODUiSQdOhGDHH3Hu485mKDa4BnYqXsDycPaBQeb+ki+T91nbJeEQfCe8g28axk2dUo5qlVJG8ORLCE9gbjp04rRav3iphZYQvgIL0ImgdMXG2hCxOH9VVUI70bfaidWr7no9V/ueySfYcU89+GAOYVzgn7+nGG65XBz85ESg1ouk7dZY78dkZQQx/NH+M3B3zkvlp/hi1cv0O4UAydYrZMiwuWLFJwn0QkKAghTI/kkFNH9XNWEeIZR6ulGeSlfgAb9pmrBfQP2Kb8RY/nun+RORYpl0K6+2lHZsWO/ozf8Um8U5yESKnqwmEjXmt0aK/euC+NKrQC//1ps5dskFde+01ArFHcj+dOSpci3jtviPfFOWdzQHLLsSB1V8spPLlzhbI4scGWK3Zf1Nc6XcBHB1vpYkV1UaueacSr/uActem5xhVrtjzxkq8JnLD+kouWkIVOCQ79n9dFXkhhxGLLAdBWOh4PPgK9LwECZew4aXV7RKKwtqEIA2DE55FjAlDXm5iFFpj+p17ldPrBOKmaPL3E6fVzHmrPS+HNuK2adsGoaPbWIOkM4xWR1DSM8pDFOon2R5/yJczI6ySF43Y7o7bblF5n31SlS2axxgqyV4QTa5JkUL9++JFMcKYspw76wkSzsJzOhRs5XvDEhF0YqTkgj+iTwu/abfyu/52Jgxpnyi1F0isTA+kN8q6YTPnfbyMLCaTZTMVGkpi8ZqdEzULEgCOjx5tagWOzXVWhBJbolZXMfaYENuZjD0pyvDtder3umvIF7BuQuW6tKju2h2MXnVaD1JHT5wS3gjSCa/bsk/uWgum/240MZWwXAMaPbll0bhE7xN2ehwHk17t6NoV4tAxQMD148U3hLLXrs9YNbh7E9fQBc6c58q2kJTEGDOQIePelSwXzBEhnoQiYij4s0TIQH/7zSiM0kYf2UOLVuuoNA9ErZaviKEJEj/mxevdONu3YVjE8fJU0UbSPnxfGGPoH+c5PBdweCRLuBlIVuIDzJ+tWC+3y5SzKyx2P+thWG+vLYtd5BSKd3A7EOOfLsknHG26o3ENRCsHnB2W22iCoomFGogpGzdQN9KOQdpnmpeWerkkPFWssXpnbI9E8YMtuo8SllkgXn5iI9e8m4eV1CFsjKY5mekrsDeUf6fg0Z84tIOxJTRe0HG/ufT6PZZwCVteWh1XyCUGebZMC/Ea4FFk7Zl6m8KMn2e94Oe6bt61l4cnHnHbsRI6us1HkHSGbnUEIT1zWyeRdc+e2MeTINKmkhdmrfiFKWR6KL2a9t4yteStwa7N2AhnoXJ97mxcMCZRZouJby0Sryk54/3EBnRVw4q3Lh6f6IINF8tqQ4OtjZz3fmNNit9tpkKLd3+7D5qs7r4rlZEHPAwCbO2mPerhB+71zGSjx0pZoN9BssbwLKR/P//8i+u9xjmX8NiUrdddzXitu/AQaUFJ415kEhoX+W5ijWfXxJ+E6TmNB6ATcDZ4GeIYsyk/EfvwVeoq13uKRglqFKfe58hAhJLInn/k+GlPrgHb65W7wclT/02/x/0LxbVu5WISnul1dzM5d0DVYXj0EtYEoUklC+dWPYZMUXlyZJH3QvaDnu3qGA85rGERA3bpOt3UzuUTExls4JcATWKy1xt39gotmKzEB3jxNmK9TC7ddAk2V6DpbmLD2xvGYmczNUeAV2BUFLj0nMXrhIjmb3+7TlIZkRPWBE5PA8Q4X3/9dSrnk49KexxYS1ZvlY3QBLZmI9c87dpAWugJY+0eOnJS2OkhEMzyaEZfI5HfZJtcQFgnh458Kp4uxjNn8XrFhQdIJKiJ+tVLCgmil9gMl4jWTlAvLXWgiNRtO1hyXbNe4BjYsmi8Wrlhh1qwfJPvIes3t6a/6/1kw7zXoj4C4R/KkZcSH8+4bd0pU0JHt3FzMTJNZxitjj+L9CyMkse6RAmBvI75O/DJp2rT9gPi6cqYIa2qXDqfUbpK5kNgqp9+EXV68ehddZVSX33zXdRMJPohW+EsjKtk7S5y+ScFKXsqaKqRb8yReNoqZQv4Ln8b0FV9hq6aNTzRxRrCQObb5LJrI+c9g7VhlPCdNI8CNlOh0Uw8ySkx1N+c8kZR0vwkDAIMdBXhFmQ9KV4wV0L2HmebjHPiWwuF+T8SWRLZN4zOKFIo41qY92++vSDkYyANMbi6SSQpqy6H0YnsA1NHdPGbjoTfw8az4+yo0LCXoICIX9eyYdt+RYibRklwvpveu4w77ygI+vLFBj3VgbXT5P1wr0CBXffBKGmX/27dc4yc00kh3QZOUtxX3KRLyxqS3cBN2BtJz4zgwB83fZ78u0W9F2WPRtivvbiZ3HQMQktnjO6mMt6XJuapCJo+Wjs7ti19PRGKdsJbC9Vnp75Ug7o3jrkvyQ/+PgPJSnyAlWAj1svrMuXsCuQPzhjmyG7a8PZSZ6wWO5upOQK8gkBFOWhWrN+p3np/hcr2+MNGEHQvuLWz8WxZHvaEsYbNNU9bYZEWur82LlSxXkCwzL+/ZL2aNaG3KLp7Dh5TdSsXVfekvkNIgoAQjnmltVxi3MRmuIRbG0G8tNTBJT936eYS18X6wjiCVRnDEfF5kEUmhdAPjDNZXeLnefdffHnON0dy2LhtPVYuMFw8fv31ourdoa7E6pOW867bb3Pto+15skV6FtmvoBcYng+j5JEWcszUuRKfzf9PmLFQ5jNdmrskFScGgnlTBwTig4gck4khzvb7oT4Umd7DpsklW0vzeuVVs7rloipLpn0IOh5CAfBiwuGCYLAlrpc4X7dvKrIvNnLe2zBKmM5RvMv5oT5M+VwwGhFP7yc1yhcKlIJS9m9DiDMG2t7DpksXXizxvChAGBCIYd9/6Lhk0IFPAYNP+rSpPbuKsvnPf/2sHsl4bwLy49NTZ9WPP/2ssj6aUUKzvNJFgjas3WqgKpY/p6T/1fLGzCWCINSEZYTMeZGGhYln123SHsYuP2lZv0KiFMiR5Vn3cBfhlcWrfvHixURFSG/spehBKJunbEulU71Om7VMTX1vWYLSThz46MkfeCKM/MZg+jsoSeLPl88cmmgtmGR7itYGd1EyCy1YsVHBpYABQKeB9uoTa/vzP0jtdDnQqGlS32EUWmo7fTR34aL5cggyVu+xFRv2Ui3rv+jrwDGd+yu5XLISH+DtxzPWK0A3pKgNb28Yi91fLTWHKXLAC27tJOrzg9E632csDL82kBb0wdaFKtYLCJfkL8+dV307vqSeKdlU9WpfV8istHCR/+7Cj75kK7bCJaJ9Z7F6aeF+ePX196RK8roDcQQ6BvIDPgY/QQE/cvyUZzFiBjHomQjj0ALagbQ/MG1fk+LqJInH04RJrRtUlMvY9NnLBZ2AAsqlNxa4p8m4I8uEIT2zfYGhb7EqeSAogGVyiX3hxdbqpaolVP1qJRKGS1wh3ibTeY3VEGcTERD5rlgz7Ll3prrV6ILpfD7W8UT2IWxqxXjlvKefQY0SsXwvzmdQqmYvXOdZDalOUSTdxFYWA85NTYjlvnT2PgAAIABJREFU1SHiyEmp5yVhIM70Y82mPerIsc8FBcN5RmhdpgfTi6HHJJ0oxrxi1Tup3SvfSETSFkTBs4XiDBPPHnZ9RT5PBqJ1m/dIquJ77kp1CTqQteYXz04KQZR5+EEmvbNYsjPpsEc84//6v1+MUi6HHZuG9kdL+3zis7NGTiTdB9YcTg+yILC/w7eTK/ujqn/nBoHCQzHif3b6SwnzMM0TbzN9tB4PZwjOBPZsjF2mfQn7Tq6E55OV+CR+yzZhc2G9vWEtdrZSc4R9BRysD2RI68lgqtvgkk4qkyAETrHArcOyyNpCWnhdqIhbg/zFT8JcQLikbN11WJR0SBSBhcEYq2XRqs1qyYdbA8HPw4RL2PbSopzCX6EPJVMPD+M3uZT5xbNTD1khdA7jaO+yQ9OqiZS/yDK29iTNGXJgzVQxGqC8vta/lTr+2Vk1f9lHxoQ6fuvR7XcMPX48In51x+MCE6uSh5GI9KNc4pjL/p3qJ4JRwvqLl8YURhurIc42IsAWuWSs43GuAVupFXWdYdagLaOE3xr3+l3vSYTypUiRImrRzA9nMDYcOSsImsUgzDicz4aFONvoh3a67FoxKVHmoNkL1yrihvGy+glrC0XRT4g39+IBChPP7lTK3Ig/77nrdmn/1BdfSeYYN9FzYgNN1HPIFEHAEbbXu31dOY8xEBIOUbrIs0aGFr959fuds//wkZNi4PnH1+cTFNZf/30xEI8RDqeW3UepU2fPqSnDOgnXAsa8+u2GCPnzsF7NJEw0UkA3kbYPRAZhBsMmzFKbdhxMKAa6qHvrWr7GUpvpo2mcu2y73mMVjj8tpCKE2NAEWeA371f678lKfMAVEBaaHC/YXCze3oBD9yweJjVH2H4AhV69cbew4d+XLjrxHW0Ajes+eLKaPrKrJ99AtP4EgVvbYJG1gbTwmle8fCjn/Q1S5oW5gGjSNKCyHOoYUYiT00IGgkwP3WdEUhQ5HlvhEsAjMz9yv+/hFm0+w3h4qA/EhRZi7BvXKi3cCwgQ91kL1nrGs6OA5yjeRA3s1khg1Vdf/Xvg3JLV29TpM1+pJrXLCJSdeDg3sbUn6cuhRqm0enm0qlQmvzrz5dcJhpyw37rX8xiMSFfmlm5PP8v3CYty+eIvXLIP2L7AhBkvRJ3Nuo5Qrw1oLekguZR2aFIloUpgtNdek0K1d/zNrb0whjjbiAAb5JJhxqPnyFZqRfbHkW+8n5Dznks3abG8oNHR3pMNo0SY9cazWomPTPcatl6eD5rFgGfCpsC1DXEOOw/wEGFoDEJMG7bNaOdm2Hh2LwI20s5lSH+PwiAKt5ObHDp6Ur3UdrDavnSC7SEah0vYbjiswgqarkzdbuIYmDS0YyIOJvaZBh1fFSPB0B5NLuk6hvwNW/dJ2mDCZDGkwDGS+s5Uauf+I6rPsGmi4KNAm0rY9NEYnlhr9LlD0yqqUqPeck8fOu5dIfoz4T8x7euVWi5ZiQ/w5m1Bk51NBrXc22RMDcPcGmDajIpiHKE/eMid3jQuWl7xXVTOc8SLQi4DlKpEoVwqw733qJtvukGR9/3gkU/VvGUfqYOffKr6dHxJlSjwTCCPXVC4tS0W2bBIC6+JJ7cqRFcmKfOMXqBHoR17PxFoNZbYf/18aZorSAd1PF+sbZmGS8Raf7TnbHt4eN8caiUL/k70t3TNNvX+4nWenlZtJDm07ve4TS1BYJpucxIUyksMK2gLQgByP5VZsiLAIs13h6KJJ8BPMChs3nlIkRoP+Cp5oR/KmE5SfkHG6BWnTFxl6x6vSXxzwxqlLmGVZq8lBnvY67MEwg2fgRciJ+wFxm+sJr8TEzzqjfddixJnaBJjHMYQZxsREG0wQdFOYcaj27eVWpGUdP84962qUOIFYZ/evPOgen/xekU2FS8CKuc82DBKmKwnvzI2lHhbWQzoa9gUuDYhzn5z5/d7/1EzJP4bwciDYZvvG44Lvef71WHjd1vx7F59Ya/lfyCyXM+Xc+dVwcrt1Zo5I3zDIfzGHdaY7le/ye82FFa+v5YvjxYEWzTIOUb/CW8tUp2bV7ukSxjJQAYSskjoIrw8IBO0jJ8+X9AKpinmnA3Emj5aE9tpdnrCwpa+NVgtW7NNnGpjXmljMrXJZTxmIFmJD7A8bMV6hbHc22RMDcPcGmDajIo27zpSnT13Xn0wuZ9AbFCEB499Ry70eDTwNDpj0qNVyqX7vfmrFRZeFAeEZ7j8k5YDVlC/WBwbcGubLLJ6nLEiLTCO1GyZOIc4hg3WIHB6r9hG3Xa0PNX6t2L5n/GEahu9fJ9CNsIlbBq/6G48PDz9RrwpsX3DejeXbwAjwTXXXOOJluAbnjn3Q1GQnRA75oy6TOI1GY8NKC9rtO/wxMYE8kM/kflBVaFUXl/oHASGvYZNVYRLlC/+O4kUTMdcBPYePqZWrd+pihd4RnVpUcOVLIlLDFZ+DCAwJJMiCqIn6ty574jkVG7fuLKqU6WYom8mEusFxqRukzKcO9+cv6CcnAf6uZQ33qBuveUmk2qkTCyxiTYRAV4dDYJ20vXEMh79rI3Uiuyl+Sq0UXOn9FOZHrwvYXikRExx9dWqnwHSiYdsGCWMF4FHQRtKvK0sBjZS4GqI86MP3ZcIYh5LqtYw84tBtGCldmr6qK7q4n/+oxq0H6qA1r8z90NFBpGkIkINM4bIZ8OiJFB64fVgbvAOEwYQGQ4FSk4z3bv13bYxPdY5sqGwsl7hkzE9m5x9RTcgPOOdcT1Uk87DxZjNOaoFguHNOw56pv/zG3vQ9NE6jee2Ja/Lu9VK/AdLNggqE4NDsoSbgWQlPtz8ydNBY73CWu5tMqZGGz4bCZdh01yeYadQQ4LJfakth0BxIZCpXDq/WB6b1i4TCHrDZnPhh58Cxb7rSy6s6U4JCre2wSJrK2703/++qIhpdQp/Gzr+XbVoxiCjd4yiOH/ZxkR1SLzWzCVC6Jbjyf+mmPFaCxz60eSaFCnUvy9elLRE0TyjNsIlbBq/GEM8PDwgFUrV7ipEdqBPMGSRh9fvEkP828z5q8XzUb9aSWHbxst40w3XG7M2xxPKa7KfLF29VfUZPl0Rvw+aJlpMJ2kSx06dp+BRAKbple4RpZ14cgx7KFrM6cMZ0wmsj+85Fgl6gYmljXg+EwbqaQsR4Da+oGgn6gkzHp63kVoRRbNas36XQILnLt2giLcPinQKY5SwsXZsKPFu/TDZB5zPhkmBq+vxYrjPkimjGMC27flYNa1d1sb0udaBMlOu3stK84WAMOjRto4YF0FbAYH+q0lYlATjxdCMh5jQMQgmIwWy2BmvdXedmngY02N9DzYU1jD8NBibS9XppjKku1vOz+OfnVGlHVkMSP9XNF9O39DFoOhgr/nSZLc6ewBKfJEXnpY7KWve724T67u4kp5LVuItvO0gsV62LPc2GFMjh443bvGqLertuavUczmyGME0LUyfWORK1OwiFyE859qiOXl4J/EkooCtXLcj8IXIRt9iqcOEsIx6UUQgEIombogAyjaoUUq8iWFk6Pj3ROmD2TdW4RBvVLO0HAx+4heKkumh9Grae8uipoKxFS5h0/gVLw8P3wJEfxg18j+bzTfNFZf+/JXaqqyZMopSgleDdYVCjDXfBGodLyhvkP1kxbodomRDUOknKPHZHn/IN6WTXz3Rfrd5gYml/WjPhLnY6fpsQD1tIQJsoJ1sjIe5CZtaEWjyU8Uaq3fG9lDZsjyU8PpadB8lBiPNkm2yFsIaJUza8CvDO8YwkTF9Gt8wNr+69O9B9oHIOmNNgavr8WK4h3SN7A4YW0y4YUzHG60cPBwlanVJIKds32ecEK6RHvTUmXNRY5zDtBfvZ22gJGz0MR7G9Fj7ZUNhDctPg6F70crNsqbYmyIlZ/ZHVZkiz3kO0Qa3jLOBcdPnq/vSpZZ2QdzCl1Ak79OCjk2W8DOQrMQHmEMbsV62LfcBup9QlFzOQL4rl8kvnk88ebMWrZV4LQiwalcsol4s8YIccEkh2kq998PJongA+WnUaZjatvR1BVwUmG3jzsPiQoASOb5oUEBd5onHHlAF8mT3nRJbLLKRDWHcqNiol+RcD0uMQ5zr9j0fhzKM9Bo6VaVMeWPU+KzIvnM5JAY/mtx2S0p11VVKffXNd4q8sG5iI1zClvHLhofHBtpCM8LvXjFJMYkvlG+l3n+jr9p94O/Ghq+wUN7LbT/x/UA9Cti+wOimQL8A3z53/jvxlIAiMGXSD3uxow82oJ56LGHJucKkANR9sDmeMOuFZzEOXn/9dSrnk49KVewxS1ZvFYJKL7SIs11bRomwY+H5fYePqwXLNwoZFu9KC3nMvTzWtveBMClwbcyDzTowxBF2gRAmBCM9guL3xqsdPVncnf2Ay4a4ZmD5xJvjfZ29aJ2c5WR9qV6+kPG+EmZ8NlAStB+WKDpexvRY5+Z/QWG1zS0T61wmP2c2A8lKvNk8SSkbsV42LfcBup6oKEpx35FvivKe+ZEM6vDRz9RzOR6X3Nb5cmezZoE37R8bec4STQQ2BXwKL/H6LXsTvLJ44t+cs0LNm9LftMqYy0UaaqgIqCckXcTvwLxqKvGARmLBJ76vca0yRt1gvQ2fODtR2e9/+KdAkge/3NjXKut8kPEA0+ZyBQs4+WZvuvEGhXEjqSXWcAlb/bTh4bGBtsDy/ny5VmrTwrEKYwgxhvWqFFfffPe9WrZ6m7GRhkslSqXOSc8ljfr8eCiYz3jsJ1x6MXie++aCxJA6hTh50xCOoO/b9gWGlD9T310qeyxzSXpL4vL5d44nMomnFjbzeIsNqCd9tEnOFcYYYGs8sSqs+n2xD4VNtUVdl4tRQiN7QMBxJ3CGtWB88iKWtL0PhE2By7zaMJTa+DYx4i1cuSlRVddee4167OEM6qH70xk1QR3PlW2hOjWrJg4YZMi4d9UHS9ZLqljQTKP6tfTNq27UmEGhsCgJP3SeCYqMbmI8Gjd9nvr114uqd4e6kp1kzabdkpXFa70aDDHJi9hwItnYk8Jyy9jmIEryF/EXajBZibfwsoLGetmw3IftNpZ/CItmzvtQNv9cTz2mYDoGwuuVYzRsu27PvzL6belLwTzZ1ZpNe1TfTi+pSqXyCeS7dquBwlDdp0O9eDXvWy8x+oQYYOgwkXhBI/uPnKFS3XazMZs7SvygsTMTdRlyv9zZHxPmZNN3zQWty8CJieLWKpTMKzlZTesIe2E2mfe/epmgaAsO7NqtXpGLYJF8OYTtmIvh9r2fyLfUqn4F3ynRqbZeblNblSuWR7HGyD2Povnu+F5G6Rht7ico0p36TxCyLyTSkEAcXTxjSMNeYOgzCiokf8dOnlGNapaSi/Wdt98q4xFUyonTavHqrcIrQXgMbNWkFYomNi52NqCeNsm5whoDbIwnjMKq35ONVFvUZcso4fux+xTAsA8/ho7dDlqfzX2AtsN6am2kMww6B/Eqr6HjOv2fVoK1MR7umCPHT6uB3Rr6dgED/KkvzqmCz2dPYJDHMH3k+CkJA7nxhr951mEDJWGDKFrvA60bVBRCOLLgbFk0Xo2ZOld4nV7p6j8XvpMVoAC8NsDZN2zbJ4Zbzir290LPP2VUiw0nkq09iQ7Hyi1jm4PIaPKu0ELJSnyIFx9rrJcNIqoQ3b7kUT7UOYvXKfJMwnBdq0IRRdqvpILT0yGszHjbD3xyQuLgNbkVh/jK9TtU9iwPC2HXnyXvzlstsEkToiJb0Eg3IjjnHHDxN4Xlxjp3IBEKVWmvCjybTcgFgYgC6es74k31UtUSRuz0Ni7MsfY/Xs9xCYFDYsvOg+rC9z+pjBnSqJoVioQOdQiCtuASU7xm50RDJCQFT3WPNrV8szHwIIRCxWp0Uvs+nCIXn8JVO6hxA9uq1Rt3q+uuvUb1bFcn0BSG2U+AjpN2qHThZ0WxTXnTDYHatlk41gsMfeBCDXy+U/NqknLPTZj7dn3GqsHdm7gaS2xc7Gg/LNTTFjmXLWNA2PGEVVhN1ppJqi3qsWGUMOmPXxnW2tPFGgsKTqNy/J5x+z3MPkCdtjy1kf0Lms4w1vE7n/MKv3KWq1G+kCsZacJ6XTtNMn2QLhNv+LoPRsldjf9u3XOMQsn3E/hs0t59pxrdv5UU5W7TtMsI+Tchle+93kulu+dO12psoCTcKg9CFK3DybTRiXOM9GzHPzur5i/7KKZ0an5z5/Y7DpPC1Tqq9GnuUoSekBVl685D6oOlG9T4QW1VvmezxVq1CupE8mrIdE+KubN/PGiTgyhsX/6Xn09W4g3ers1YLxtEVAZdjqkIm9CK9TvVW++vUNkef9iIFCumhgwfCpqn2rBaz2KR1uHf1G+ioI2cNEflyv6YatXA37NpAxoZlhwPsrLPT//Dc6x333W7kZcVVMGL9Xso0oQ4lSoUlS27DiuyCvhJUlyY/fpg+/eBr70tKYLy5Myi0qdLrdZu2itQ6RUzX1X3pr0r5uaCoi1ibuiPB1kr1Zv3l8sfqdnIo07uXhA6QDVjyStL1bHsJ5qhH4OCKcIj7Pjj8Tx7l2l2DzwnV6mrjEIXnH2N9WLHfnbmH1/LmoVzxFRskXPZMgaY9tutnE2FFWMMMeTEKEPc9HzOrIHD0sIaJcLOB89jRKvbepB8e5HeQ8I+TFNWOvsSyz7A8zY8tW5zEks6wzDz6xV+5awXolnO5WgCk3uesi2VZvqeNmuZmvresgSlHWfH6MkfRCWHddbH9w8sf/akPsJBo50OD2VIq5rUKas413I/lTlqyF5SEH8GIYpmXvO+2FrNnthHjE7siZXK5Fdnvvxabd11OMFIEebdmT4LuqFCg15q2TtD1H3p/utw6jxgopyF2mBiWp+zXBAnEs+FRbDE0sdoz9jiILLVn//FepKVeIO3ajPWywYRlUGXQxc5/90PgdOzhWnURp7qMO3rZ92UZ/Kpd25e3YioyAY00osIzjlODi68pZECQReGBwQvD+KEJfO3BtVLqvZNqvhOm4bx6cuDfoC85j/+82cjZl2bF2bfDidBAb1OnGkRaZb0kZkeSK+6tKxh3As8trv2H5Fc7ShWpFkMkifWBmIDDwYIHBR3Mib07fiSGjttnhBg2oAkmu4n2mB04A9vk/EkXoEFg17s4PUgPp+wFi3kZ+7YtKqRwmmLnMuWMSAsdNWWwgq7eZteY8Rwp9NkwTUzbVTXQEYS/U5iNbLY+ATYS1CEogmkroTchRHTfcCrjSCe2mj1xJLOMMyYbT7bqOOrkpatbNE8atI7iwWxqDMgkC/9X//3ixrVt6Vnk5FEwpw/JWt1EUJUQrFAYE16e5EQ6Ea7V8CHQ9y5l/CNc/8oX/wFY4QRdx44Xrbt/liN7tdKFc77tO/UgXAAVQDaCcMDe9zdd6VSBz/5VO421coV9K3DVgFtHNHhDrpeoOXL1243cnbYcCLFC8Fia56S67E7A8lKvOF82or1skVEZdjtqMUuR9KJeOapDjpXkVZMIOvk7TYVm9DIMORP9FcrzxvmvabuSHWLDIGDolaLAape1eJG6eFY+826jpS8zMULPKPSpL5d7TpwVK1av1PpNIB+c2PrwuzXTlL9rg01WxePT4gppG2gc6sD5Iees2idpINDMMigmKAMEItO5gg/CYvY0PVv2XVICCUxCA3r1Uzdm+Yu1bHf65KlAtIkL8EwSWo4t5hu57Ok0IPczW1ser12b11L+CeAjf7VhbVP5g9i/TGIEbvpFLxhg7o3dh2mjYsdHuJny7QQYk4utuTHhjcB1Ae8CbUrFfWdZhvkXDRiwxhgA7pqQ2HVfBJ6DsmDPGdiH9Vz6BT1ROYHjTgp9MSHNbL4vsA4F0iqe0UQT62NdIbxmjbJCrRwrcSfd2ha1agZED49h0yRcDaMvXDSwHHDnQMPOmnr/NASOqWvRtZpI9SeVZNl/9+x9xPVvu/4qLB8m8SfNoii+YbhHnEKBnC+vQql8ibp+cFeUKNFf/XAfWlUoRf+a4CYu2SDgsQQiD2S++nMrsY9G06keCJYjBZpcqEknYFkJT6G6Q4T62WDiCqGLid65HIjnYhXnuow8xTWO2oDGhmW/Inxa3jyrhWT1PV/uy5hSoDiHfj4hBrRp4XRNHEZIi0dcU54Ah68L62qXbmo74VBV27jwmzU0SQqpJWiVbOGJ4qBh6ARL4RJHDlzmqtkM8l6oDkgIHNr1m2k5FAFWuknYREbfvWb/A40Fe/NgC4NEsEII59l7XQfPFlNH9nVM4yDzAldX5kksZn33JUqkZGEOkE69Otc36Rrl0UZ4JTrNu9R1coXkvFEclhAeAfxnZvYuNhpqOeWReMS8STgLcMIE2vIRCwTbMMYEE/oKt+U+u23S9ZdtLGSraN4jc5KI0dQ4pe+NVh9tP1AoJhcG0aWWN5F5DOstV6vTlX9OzdIhNzaue+IKHfN6pZzbcb2vcJGSl8b6QzDzCtzRio4su4gGPTWbNwj/EPEsEMo3PKlF0PlzA6yXnUf8pRtKZ7qKmXyq3a9x6kLP/wkaesQ+rZszTY1c3zPqEO3QfwZZk5Nnv0zQjExpJSp2923e9NGdvHkdwrrRHLrQFgEi+/Akgv8KTOQrMSHmPZYYr1sEFGF6HLCo5cT6UTYPNU25sNZR1jvqI3+2CJ/0ikNgXfXqlhELNMomfXaDlapbr05VJyWjXH+levgUIRssWSh3DIMLp14NvF0mqS20aE1+1dPTYT0mL1wrVq1YWcoBnYMjafOfpWQu9pvnsOk6cGzOmHGQjV55hIxRpQolEtluPcedfNNN6ivz19QB498quYt+0ggjn06vqRKFHjGl4wRGP/ew8fUua+/uyTFHCROEN/9FUTvbfOmDlCPPHBvzF0Oe7HTsaNczJ0prdjrQGGYGPNssOTHPAERD9qAruoq4TA5eer3vN0IHnEQUHUrF1P3pk3tSVSpocj6G9ZK/IS3Foq3zUvpdQ7pcjGyoGS+1HaIwJrHvtJG0EEwfsOT0aNNbd/sLDbvFTY8tc455hv67PSXQtiG9zopBI6RTv1el1S+eMjnLd8o8dEVS+VTlUvnEwRTUAmzXnVbzjsOf9OIOua8RM0u6sXiz/vy/4Qh/qRNL7K/LJkyClpo256PVdPaZT2n6HIJxQz6Hr3KMzeE8YL4A5lHuscgIXbR6g6CYLE5luS64jsDyUq8pfm1EetlqSvG1VxupBNhoePGA/coaMM7auOya5P8CYh3r6FTxbOS+ZH71eGjJwV+h+U9Z7ZHfafNRq5dG3X4djSJC3BBQ1EF1ohSQVoZCG1IK2MiOrTGGVePgaV5t1GiOOChdxPWWCR8nbCH7Xs+Ue8uWC2hDqS2ad+4sm9XbKXpIdzivfmr1aGjJ0VhR1hzWR7NKJ6mOpWKJtnl2XfQSVSAuXip7WC1femE0C3yDX3x5bnfuRPSpg40lygOFRr2Eugu+em1bNi2X916800Ja7Z+9ZKuWUmiseSf/eq8eBT7d66vSDlpIjb2RxvQVfpKHDEGLDfB+Mm6dROU3myFGyq8a6TsRIm/JeWNgoBaPnOovCcTsWFkMWnHpAxzO376fIUhgvOB84JwjyBpskgTe+TY54Ly+PLceUlblunB9GLc9IN7m/TRr8zxk2fUsrXbxMsNbH3YhFlq046DCY9haCVkJ0iYnF+bbr/juZ679CP19txVkjYTLppaFYuq1HfeFrjKsOvV2SDcGIePnFQ5smWS94OAkoH08o5UtwYm2gw6GC+yv+dyZpF9CKg/e4uXXE6hmEHnQJcnTIF0o6ROhZeGDDWIDrGDp2bGa93Vbbek9G3CBoLFt5HkApfNDCQr8QavwmbMp7M5CFa0EE/G5kmKqmtSXG0E5TPo+l+miA3ouI3B2vCO2kgJZYv8Sc/Jic/OyCXm+x//qbI//pDKluVh31yw+lkbuXZt1GHj/dqqQ8duvzm6m6Rzq9N6oPrk2OdiHIkku/Nqc9Qb70tud5if773nTvE8IJAMORluI+sAwr5+6z5J8YfRAIMCMEgJnyj8rKpWroCkZTRJPxiPND0oAkA0TeL6nWOzoeDZesc26iFbASnzYPt3Y502aQcDSbveY+X9aoGUjhR2JrwBKPGEevhJy/oVAikX1FuiVlc1sm8LY+OVjf3RBnT1m2+/F2brSGUbUtATn501zs7Cd3jTDdfLPgAa5YEMadRzObIkCl/ym3cbRha/NoL8DjKgYcdhonSi8L7cptafcidhXnbtPyp8EqAeQJFULpPfl2CWvXDD1n2iFJGGDbZ9CBxT35lKsgj0GTZNFHy+oaQSDD6gPN6as1LIJdmn4ah4KqvZPm1rvbqNl/C68xd+8CWti/d8BQkPuBxDMWOZnynvLpX0ypASlqrdVTIhvdymthiZOEPqtx+q8j+XTXVqVs23etsIFt8Gkwv8qTOQrMQbTL/tmM/3FqxJuHBHax6Sk/rVShj07H+jiC3ouI3ZCOMd9Ws/SEooG+RPzv7YRjnYyLVLHdWa9VXN65ZXMB//lQSjSLVm/STt3t9Jwdegp/pw1nBRpo8cP2XE2K/HC5wZRl7tYS1bLI+v8gvcfMb7K9WMOSsSpg3PO15DYqyDyOWUpseGghdk7PEuCzqicadhij0OhYG0c5GGlZtT3uipAOsUUHjLOjStoio16i38A0PHvSuQ7yplC8R7GJ71dx80WRih2zSsGKofQfbHUA398bDOuhEZzhJUibfRl3gZWYL2jbUG2ZqEBZUvqKqWKaDa9RknEPTB3Ru75jAP2o5JeQzZdVoPUkdPnBJyTbyQ67bsE6VmwfRXEoWFRNYHdBgPOGimZ0o2VcN7NxfElBaQBpDDJSUXhLOPIHQgu5y7dIMxYsrmesWIsHjVFoWxRsvps19LzH7G9PeI4UaHiZm8qzBlwoQHXG6hmLHOA9kACE9gvYLmGdG7eSKU5P+zdxXgUVxb+DykLe4QEiC4Q3GXAEESSHDBDliXAAAgAElEQVQJkOAWnARCsODugWABgrsTXEOQIMG1uLZYm0LltY/2ff+hs92Eldnd2dmd3Tnf977XNjNz7z0zO3PPOf/5fyiRHD51kVbMDjF3CPU8B/WAGsSLuLFS9nziWhUa9aLJoT24RzJZss/sy9FH4+j5y9fUy9+HsmXOKOvHUoQLrHqIlNBxKSZqbnXU2NimSEJJQf4kzMdaKAcptHYRiMZfv2dUFseYb+X+O5I9Df2G0rnoRYQe9vU7jtDuqMkcxO8+eFqUnIz2nM2VlQLCAZsxoQqPIL5VEw+DxHFJfWVPMj367qPcAZ6UzxOIIBE0oGIrSJBpXx+kV4BK6jNscuu0GkQXDyxhKSWh7xrkU+hBDp80UMrpmnytddsP8zlAkVliprwfLRlHOBcVP8CJixbMw5VawVDxxG8CLS3GDN9z8IvosoYelRSXjBdIFKeO7Ek+9T+zaeMdM3JKJFexUZGXywCJ9+08gn8bAjEcxkZlHVwkhqQvQbKH9/K6haOo17BZHJA2a1RDM/Wt0SfpzIUborggrLleBKFPX7ymMiULGh1GiucVgyB57tFqEHlULcPPvoDkibtym96+S6DG9apQ6RIFWEPe2iZVe4AlrUbWXqOY66NtYMbijaxsAf4Jt5zZOBYQDETEQGFB+lWfwQd4H6VNk4p5j67feUSnz1/nlpZ87q7MwSC2vUfMnNVj7MMDahBvwn2QoudTIMK5eSKxLIYtsv8mLN2qh0oNHZdisuZUR7U3h4+e/EuU9Df9zT3TYIIGTKp/txZSTFHUNayFcjBHaxcMzIKBABqV52kL1zPsdMaYPqLWY08HtQ+cwH178Te+o/bN6jGJVdjMlbye0P4dRE1VKlkpbPDi4m/R2m2H6cSZKyaxHtuTTI8+p8kd4Im6eTIdJMgZAvWBKr4QxG+LjiHIRRniTrDmFPH7RQIJvb6Aj4sN8KSQzJNiXVIQa6FyuXN/bKLp4L+hdWHehP7kqSU1JcWcrX0NvNdB/ib0SGt/09DjDh1xuQxoI8+2QZRUWQUcL9FHzhqsSuJb0zgglNzdcnCC5sGTl9TkHwJSzB9cEA1qV7QYPWLMF1K3YlpCQCrMVVCssfUeVKr2AEtbjYzdQ1P+DvJEBNJoI9NGXCHANsS/gL/3DplFZy7e5AIekjvogxcMVXq0XhiSItx75CyFr9hOB9fP4P8H2WztKt9yQgB8PUggW0qwaoov1GPl8YAaxJvpZ3N7PvGBX7/9CMOVvtaS/MLLHhUbOUhfzFyy1U6TGjputYmKvLAUklBS9QZLgXKQQmvXkJ75lqXjmL1XaYYKKZibU6ZITj06NGEioFWbDzJsU/sDrG9d1pKVQmAH9uEUKVJYfZMq9T2zlwBPynUlZZYXro3gArrMxkwgHtyzajKzWSOIr1+zPGHThp5fsUSKxsbR93cEDgio0IuMzSnrW+85znBgbDj9W9an5l419RLiJb2uFO9Hc9eifZ5UxFq65oIgHhUxS1sMpFinqdcApH7/8fMUG3edEj58pAJ53civaV3KKQKZYOpYho7HPPz7T6aGHhW5OizYsvXRzPCOnnZYhvRpdQZIQEvtOXSGnr18w8cntYpli2rQBlLOW/taUrZiSkVAiusMm7iYWwy0ZWeBFMLvHHtTOUyK9gB7azUKHD6HXr15T9sixzPCAWiSqQvWcXCOhB4QuNgn6DKsBSSSaH3E/i+pQfGldLH8em/NkjV7WGUDJJT4RoAvR7stF21dKDoYQrDIcd/VMaT1gBrES+tPo1dD8H/+ymfyqqTmki0zZ42fvXhNYOd0FpMSOm6pz5BQ6BE80+hlADfMlTOb3uOkkIQaPW15ouujSoLKLSpvyMqKMSlQDlJo7SI4Q7uItuG+gzzJFFIsMWtWyjH2IitlT8oB9hLgSfUMGUpetW/uKbp6vTBqJ+Vxy85BBzaK7rldqH6t8hbpS4tdI6SOxs1ZxcE7km237j1hySO/5vWodpUyZjF8W/p+FDt3Wx2H9pqYc9dMbqux1Xy1x12wcgehpxzBHPh7QJAJBYL966YZJNuUeu6GfjvaY6GNSUzSVOr5ibmelK2YUhKQIoGFvQSsbvVyokluxaxZ7DFStAfYU6uR0CqrTWyLNjCoM7Ru4kGL1+yh3v4+VuMwidp0gL579JyD9Ibth9KEoV1ZMUOwXQdP066DsWpfvdgHVCHHqUG8zDdKH0s3poHADJszkFXhw+TsBij477//wf1vchmQEoCpGjPvupW/kHgyRWYQH1GYdibc2Jj4u6m9wfaOcgChy+NnP9Dk0O5ilu9Qx1giKwU1i0L5cxlMJAnOwrFgvwc5lS6zN+UARwrwkiIL4P8//vyTOg2cwnBrU5BXlvZ9Imn07MUbqlujrIZpHM8giBgBn4b8nD5DlQgs4QhOIYFUuVwxQhICfbXa/eSm/ECxHrBLA5GS2y27KPkkU64v5ljcH7QEnL14g1ue8rnn5N5+Mf3wuH5SOD389GPCR24xCAxoalRXXcwc5TxGUN1YEz6CE0RC6wZawWBytm7Al6jWGjOQRZr7DBq7tlR/l6IVU0oC0tY9w5jtH4Z3EN5FI6dGMtpCQDhItXZD18FvHxxEh09dYgg5koS9A5qKljO0p1YjIOC8OoSwnCiq7UKCIXLWUPYxeBoOnbhAEVMHG3Qt2h2AIAEEHglTIK1AWmtM4hHv5z7DZ9P8iQPoWGw8K+UE9WqjGQsIFqAGh2j9NznusTqGdT2gBvHW9a/JV8eHC/9LliyZyecq+QT0V6IPHfAjwcCg+u7HBM4UVylfnCsC9myAzr376QP179qcUqbQD5MFa27QuAgKG9LZ5AqCqb3B9oRy0HXvpi3cQK/f/sjQPmczS2SlsCGYPH8tjQ3qTI3qVtYpM4ZgeMma3Sx7dWjjTL1BvC6/S6E+4Gz305T1otKJJCXunxiTou8TG3fXHFl5ww4DKV7vkNn8z4DFb1w0RtQzgv7MLXtPMJkiWsI6tqhPLbzFw+kx3o07jyhofEQioj9TJPMQ0CDoMGT58uQ0GpDgN7Ru+xGqXrEkJxKOn77C7OfoK83lqh9pJYyri9guS8b0VKvqt+TboLpJFU5LkixiniExx+D76xc4ngMRmBDEx164TrsOmE7YKWZMQ8c8evqKe4uhlw0D1Bgs9fogyZaOJ8f55rZiSkVACmUVn04j6NTOcN5rQmYR/3zo5AU6e/Gm5v0ghy8GhS0gzIf5ZGas5MQgEoViE5y2bjXS9pHQunjlSCTv/0Cc2GPoTIrbt4jSpk5FQDT1HDZT89vS5V/sCTzbBVPunNnIp0E1bhU5d/EmgQciYsogql21jMHbArlaJEX0mSnoLznuvzqG5R5Qg3jLfWjWFZAFvXTt7mdZKbfs3EdrKPAzaxAFnQSJrl9/+50K5wNj/+cExqNnr+jjL79TqaL5+IUmF0kQguz5y7fTibNXNAQj3ds3Id8Gn9l69RkyqCOmLGN97N4Bvpx80N5sYAMCQh4EVU08q9Kwvn56K/GO1huMgBJBhLbBz/gIr5wTkgj2paDH1qKpWiorhSAsbOZngszmXjUIQQvkyuDXazcf8IcfwcnowQFmsdJKoT5gqoMMtbPg949K0a4DsUarGaaOK/fxCOLR/yiGWV6Kvk9Umav59qXNS8cy67RwzYLurtQrwJcTQlXKFaeeHf9lRDbmEzy/B09epDVbD1KZEoVEtwYILT6YR0CrhuTqkpVbzMbPXsWEkOivN2YYe+bizZrDsPFHoAeCPcFy5cxKndo00nspAa6tDX/FwV2HTKMi+XNTSL/2xqYh6d+lSrJYMqmkZHII4teGj6TpERsYdQFeBLkMwW5Vn0DWy27asDrL3gHej2/qhogxJifA5Zq3tcaRioAU+5AG7YIp/uDSz0m4fpOYuwFIwm3RJ2WT3RM4YSDNive6kDBCUg17KbQsijFbthppzw97nIpevTRqCtMjNtLJs1coes1n9Qok3ldtOUg7lk/QuyyhxS5p68qwiUuY10FIwBryC/aO794nENovkxqSCRnSpxHjVvUYhXhADeJtcKNAOjV21ufNNzYeeGEh648PE4iDnM0AqYRcV/yhZfT1Vyk1y7cFYz9egH59xrNEBwJtbDDjr91jTW70boPR1uAL9K+/GI4fuSGaq0w5smWmLJnSMXQNASvQBMG92xqVlJGqN1gqgjxLn0kgApDp1zYEnGVLFeIstWrmeQAbu2OnLxOYo9Ezif47QKOLFMhNpYrlNwmurT0Dc9QHzFtB4rMMtbMUzOtGqVN9QzfuPLRaX6EUa9C+RlLde7xfQLgVF3+b5o3vT561yhsdUoq+z6RVIkElZeuyccw4fjQ2ntDasmlx4kSb0cn9c4AprUTC+/741rmUPWtGzRDhy7fT4+ffm4XKwbmL1+xmyTAQoYkxAYp7bm9EIuQbkl9HYy6JThRZCsnHXK2RZBHjg6THYC2ebYI4qEPgjMAKMGe0T8wd1++LFjJzxhB7Dr6f6O29emQ5JybBVL9w8iB+VkEIieSkasSIHlPbDhG4oyjh7VmFRk1bTtUrlGQkZKYM6WTzK9BFHftN/AL1gfsbc+6qzeX/zHm2QGiJhGLd6mX5uzxuaBdq1bg2J01B0lgwn5tB9JXwHji1Y34imWkkAA4cP69Ijg1z/KieI94DahAv3leSHIn+08refbi3rKV3Le7lwkeyT+gc7kEL6esnyThKugjWj81CUikZ6LwCTidnRUToa0oKP561eBND+bBJFGsIxCFt8/Z9ApNSIWFjCtpCit7gpEEE5m4OQZ7YNTvTcagkPHzyislksMnE/UWgCV4L7WSUMZ8Avnvj7mP6+6+/NYe65cwqm5yTFOoDxtZo6d/N2ahaOqYU5+tKomVIl4aJSwvnzyVqCCn6PoX3GmTqwJoOXeKBY8Lp8uFIDoguXLlDQ8ZFEDaPSU1K/gVcW5jLtaMrEpHiIWkbd/k2B4um2IqN+wnv56YNazBxk7BxNnYNoRJ4eNOsRD3w2IgjiSs2SLQUko95WjvJYswX2n9HuwTQcEiw4Lko4O6aKKAw5VqWHItkj1/gBH4m9x2LY/3sY1tmMyeDnBVjS9Yg9blStB3qKxCgpWb1vFBGdMlhwr5PCFixBxzapx1Frt9Lg3q0YiSjGGvsP5yRmr39fbnaDRQl3q3TRvaSveqMYgWq7dfvPOQEurDHx14ORQwUcQxxPAF90r7vBMqfJyfV05Kn3B4dQylTpuB1wtBeqhY/xDwdjn+MGsTLfI8FhtGkGxgErIdjLrJkkLMaMu8Xr91loiMEQjUqljKL+dgS/wn9YrG7wjkrLRjkO67dfsCVADkNARYIS168ekN5c7lQ5XLFJfGJqQR55gasUgcAcvpe31jwxbzIbQztROCOqnf6dGno9Zsf+fmFAW3Rqklto9wWI6ZEcuCBDZR2ggc9xnIRDEmhPiDlfZFioyrlfGx9LSn6PoFwqO7bj0mN2vh40OCwhdz2EzV3OC8P/e37j8XR+ojRXyxXav4FPG8erQZRaL/25P2PdBj+W/Nuo3jTawgCrz05BNrg1Fi77TAzMjdrVIPOX75NXQZPYz1lbXklffcQbQ3YVAvzQNITsG0wswPJYsykguRbkmQxNkdT/m5PShWYNyrx4FxA4A4G+nHBXQjs+YD9i5HKEoomhnwAlMGK2SGmuMlmx0rRdgi0xdN/SO2EhaCYlDN7Fkn2FqY4p/OgqVS3RjkKaNWACzmZMqRlBGSXdl6iCh4CSunsnoU8bFWfvkwCB/Qk7mv/ri1MmY5VjjUlAY13PfgKjBlaEOUkfDY2H/XvtvOAGsTL7HtAKWs07c+wGPTBw7AZCQydy9UAOdlfZV66weGEyhDaChDMw8BUunLucFkzjtjEePuHcEUVMCiX7JmZkGTOsi0cmLURKe0mhW9RXUKVCT2AmTKmY7/AJ8tnDbMY1iiWIM/SgFXqAEAKv1pyjfjr95gMDL/dXh19mPVd25CJP33xBlcGU33zNS2dHqy3GoCPe91Wg2nnyokMgbe1AWkCEimxzNzWmq8UG1Vrzc3U66ISD7IhXVaySD5+NlB9RhXJkEnR96ndxoWxBNZkzBGsys0b1aD+3XRveqXmX8DmGybA6fHeffbyNb9vxSh24PihExZxcLdsRnAiSVYQovYInkFd23kzN4khs1RqSypIviVJFlOfSUPH25tSBSDe6C0GWmTmmD6sxhE8fhHzJtSoVMro0oUkC7SzM6RLzcdPDl/H/DqVyhSl81fu0NWbD2jtgpFGr2XrA6zddnj99kN6n/CBalf51iZLxW9RzG9fe3IounQaNJXRGnhHgTD47J4IOhRzwSZEjGoC2iaPjlMPqgbxNrj9YI/Exg4BQC6XrLyJg6E/MY+bfHJqNli6ziEFAhtkTf3/ychuWTKWRk9fTqWLF5A9mwqG3rCZK1kbV7DAzs2YQTXZf/4ji9sEgiFUHlo0rsXjIsgC6VKDWhW40iTGLCHIkypglToAELNuax0D0h0kVCAxaMgQzE+ct4b8WzbQS8AE+aT6bYM0BEPWmrOY606Yu5o27jzGh/bt3IzwvOMd5ZYzm9G1irm+2GOsvVEVOw+pjsPGFGSXugyQ+mxZMjK0fcKwrlINafA6CHBv3X1MFcoU0SSO8Ky+/OEtZcmUwSDrt5T8C5ZKSyE48+oYQkunB+lsOwFBFGC1xpBTlkptSQXJx02zJMlizYcH3+d2fcZRYKdmVKd6WWsOJfm1hSBeu8e4feAE8m/dgLzqVGaY/vrtRxQRxEvZdoj3AKQV8TsR7PmrtyyZmC+3CyNRBHSK5DdF64IgawPS8Fz8LeYNQvEE5IlI5okxFMCqNAmkqLmhTLD588df+TcPZNGVm/fN4tcQM66+Y6RKQKvE15bcBec6Vw3ibXS/kWEGuRGz07tmJ9+G1Z2S1A7uRwWmUfthdP34Sg5WBZbSU+ev0879p2RjS036KADahMpE1kwZZIeZCbB+gUFWmNvqLQeZNV8s/M8SgjwpA1YpAwAb/WR5WGxokycXJ/8oRi4SFUNslsQwcltr3QIiANDqT3/9Rd2GTGd+Ctx/MKjLKf8n5UbVWv6S4rpIrpFIKVFrklPaqvpmqbQUEg/goQCrtT7Du9tQ36hUUluWQvK1529JkkWK51LfNUDsiqSuqXwF5sxJyhYsRwriBV/iG/Ti+zeavSNauUwxoZ0FagNFC+bRFCbirtymt+8SqHG9KlS6RAFWsbCmIQAPGDCF7j18xqgKSAeeOHuVJR53RU3igF6MRW06QDMWbeRDV80LpfKlC1OTgFBuw/BrXk/MJSQ5RqoEtBTE17sPnaGFUTvozz8/UVhQJ0ZXHDsdT9kyZxTVJiSJQ9SLyOIBNYiXxc3qIIY8IDAlCzwBQhAPtmFswlABt7bhwwZ5K5A+4eNy/c4jOn3+OjN+53N3pdZNapsl02XuvDEf384jaOSAjomgooDXP3n+PfcGijVzCfKkDljFzldpxyFY//nDrxwAa1uK5MkMtj0Y6tdEj6BchI4Cqdb1Yyu4hx/VyVGDAggEV1v3nrAJT4elG1V7e4YAH3/87JVmWkdOXWJkTafWDSmXa3aDLQy6yClfvX7PSCFU8Ft41xK1XHupvkkhLSVFm4KUUlvW4nOxRZIF90cw5JpQaJi2cD1DnWeM6SPqWbPkIClbsBwtiAej++CwBaxoJFhA64Y0NLCdaJQgzgUZ3M0TnxWSBJNbDQiIR+xxVs8fwYG3YGDOR7+3GM4D4Rwk9EAma2pCw5LnNOm5UiSgpSC+FjhUBnRrSZ8+faKozQe4xSB8xXZOfJriVyn9o17LOh5Qg3jr+FXvVREogsxDlzX0qCSKjEfmKVt9OEC4ynh21+iFI4hPnzY1f6gOrJ8uS/C898hZfskdXD+D/3/x6t2cvQScOCbuKvej71gxUTSjtKVOQ+Dt0XIgX0YbWobASvu/LZg00OQWDLDTH429xJJ36PsXY4DdPXvxhurWKKsha0NwcffBM4blpk71tZjLONQxCAKmhq9jKRldBiZaQ72WCFRBlqjLsmRKb/J9Nde5gm73hKFdqVLZYjRk7EJqUr8q3bz7mJ69fEPTR/Uy99JmnSfFRtWsga10UujkpYTKiD5DsgZJG1MMMFSvjsNZ9vLb4gWMnmov1TdMVAppKanaFKSQ2pKKz8Uekiz6kFu4b1uWjmNOFjlMqhYsfOMadRhGu6MmaYhqA4fPoVY+tZn9HHJm2/fFGG27kGPNxsZAsrhF9zH8vQ3q3YZa9QijiSHdaPrCDZwMFMvXA58Mm7iYEVbaPegnz11lwkBA6eUwoWUwqSoRJB6jj5w1iDZEcSW/u6soFRiQRqZNk1o2pKsl3DJSEF8L1xCS8iCHnD+hPz148sqmyFY5nilnHEMN4mW+6whYd+6PTTQq/htkbeZN6M+EK85o+ICkSfUNVfi2CEWuj6b87jmpWoWSJhOdmOs7sM+jUgYCHCQRurT1SpRQ6Tl0JvevypXFBFx0//E4o8sBJA5660kNHy4wnmvDSQFTg4Y9MrP4kJtSxUN11jVHVn5GYdhkgeANhnE2LhpDbi5Zjc7XUQ4AHLptr7H09VdfUS9/H8qaOQMlT5YYZv/NN1/JFohb4lckFmu3+JwwgrwQZB1heEaSkoZZMo6Yc6XaqIoZS45j3v34M9VqPuCLZKQUVa8RUyIpR7ZMrOltzOyl+oZ5SiUtZWzNxv4uhdSWVHwu9pJkwXvt+cvXiVyHb1H34JmiE0bG/C72747SgiV2vcaOE5jYLx5YwqSpAmIRqhL4HodP+vwOF2N4z+4/fp5i465TwoePVCCvG/k1rWuwPUXMdU05RtBOb+hRkSH8gi1bH01IUgrqLBnSp/2ihW3V5gOcgEESwxCPFPwyYmokRc0ZrpebxpQ5GzvWUm4ZKYiv8bvBN2fzkrGsngMlolY+HvTy+7d07tItzR7O2FrUvyvDA2oQbyf3CUE8oNxiNmR2MmVJp4HAAczYeOnAkM1EjxSY2eUw9FVB7xtBOjKXQlVSGHvXwdMsBSa2F12OORsaA73zkOJp16weQ9V2HYhlJmeQKXZo4cmkPmJ9m/DzL1TNty9tXjqW++SEQKuguyv1CvAl6CRXKVecenb0sfWyZRtfIKU7s3uh2Vq0+NjiA2vMxg3talXGeGzSdx86nWga0KQtVshddF+isTWI/buUG1WxY1rzOOE50aWJ/vDJKxo5sKPZw6/fcYQTeD71P2sHGzJ7qb4Jc7RUWkoKKTQppLak4nOxpySLrudo6VokuX+gyaHdjT1qdvV3a/JJyL1QQQkhLnoR75WEIB6JeSTtTVE2wt4AXA6oukMqFagxtOfsXzdNtsSzIdSHtm93R03+IgBH4hloSRR8IEvpVa8yuedyoXRpUtHb9wl04+4j2rH/FN2484jGBnchrzqV2GfWNKm4ZSwlvsYzj6ILEj3Yl6F1C8le+AISo3IhLazpa/Xa/3pADeLt5GnAhizm3DWWnnM2E6oZIwf6U9OG1VmnFx8WBJkbIsbIkkEFQ2qf4bNp/sQBdCw2nquQQb3aaG4FssMpUyTnl6A1TSpSH/j05LkrrKEMAkUY2P9RNTb1Yyb0TF85EsnarQKHAdQUEOghI45N3qbFYdZ0jV1d+9a9J9R50BQ6v2+x2fNCW8OGHUf4fLAkv3mXQJ1afwmrbtqoBie0nMGk3Kjag78QKIINHgRS0GIWDBV6bLbEyPlJ0f+NcS2VU7OWP82RlrIXKTSp+FzsLcmS9F5PW7iBXr/9UVaSSymeN118EnjvIrBB0NtWRslYS9cj9DrvWTWZoeQI4uvXLE9oBVw2c6iothrhPVC+YU9aEz6CypUqrEkGzFm6hadoSjLAkjWhGIAkpzHLkTVTonen9vFozdm48yjdvPeYg1QY9o0li+bjtaFNSa4+eSm5ZSwhvsYzP25WYr4D7Nug9CQoHRnzufp35XhADeJlvldJ4fR4kf2Y8JHWbj9MgQFNZWXTlHnpeodDvzmq31ePLGfiDc+2QdyjhuAQ+rCjBwfIMlVIaiELqs/aN/e0qHImZhFSkvoI4+FDt2nXMU6MFMjryrJnYEQXW4lHlh860qgAAC0i9IBePhzJ9+fClTs0ZFwEa7U6iwnoBPS8o4phiaESDsgf+gABIZSDyFF7voaCRO3j2jerx60T1jSpNqrWnKOp17ZUTk2q/m9L5dRMXbfcxyNx2apnGA3q0UqU1jWqeT2CZxqd5tSRPVmfXJdJyediD0kWcLHgOdE2fJPxu1w5J4Q5MxzBgIBCu56c7OVS+G1h1E7K45ad0Tfo7XfP7UL1a5XngFWsgVDOL3C8JgEtVPRjL1y3iba62HkbOw6//4QPv8jW+550PvbGLWPMX+rfHcMDahAv833URWyXJWN6qlX1W/JtUN0pCcIgzeEXOIGDQFQkZy7aRMe2zGb497bok7JKzKFy9u59AiFbn9TQX54hvWlyLuY8XlKR+iQdG8HEzoOnCa0D6I9HP5kYaTNsVKv79mMUQhsfDxoctpA/lpAkg0GTFX156yNGm7NcxZ4zbcF6gvRSY8+qTD4IeURtg/yVsUoPNu5B4yLo9ndPWQpm1LTlTDYoZ1uNoSBRez0hff0oRzZx+r2W3FQpNqqWjC/1uZbKqUkxH6nk1KSYizWvgV7Za7cfiqoY470GKLIx865b2WA1Tyo+F3tIsiCheOjkhUQuQctG2VKFDMr1GfOhvf19w46j3EceMXWwvU3N6vNJSiiHIH5t+EiaHrGBwLEDnXbVTPeAFNwyUhBfO1ILiel3wfnOUIN457vndrliVOKh64nAHdViSKihbwsfHLnI5Aw5Bqzwz169porfFpXFf9Yk9UHGOvb8Nfrp51+4fUGMaWuX4vjIWUOpavkSDAlGlb55oxrUv1sLMZdymGOAogEsE2Qx3795zxry2obneGifdixpD24AACAASURBVHrXiw0zeoNBZrNi9jAOkIF66NBvEvXo0MRkxnKHcew/CwHa4eUPbym3W3bFBhBSyKnBHUllIoV7DYg+0DDGTEo5NWNj2ervSLyOnRlFkHYcP6yrraZh1rjOkmQxyzkWnISk/KMn/0o7/k1/E94rgI5XLltMcd8sSMP5NKhGvf19Cai9+cu3s2LOtJG9RBcY4BPPNkGcKMb3H0E8yCYrlytGc8f1kw1+bsFttctTpeCWMUR8PXd8P6pfq4LRtTtSC4nRxaoHkBrE2+AhABFYwbxuLAly8+4j2rTrOOXNk5NlQpInT8xwbYPp2WRI9ABNj9jIG9KZY/owfDF4/CKuFNeoVMrqc0IwCp1RbUOQdv7yHdqw6ygdPnmRurVvTEN6trb6XOx1AEggob+3QpkiLHMDw4cLgVaWTBlEw/PtdX1yzwvEPt2CpjMPRqYM6TTDo88WyIbeAb5yT0kz3r0Hz2jT7uOMDArq3VbWeSAxsmLDPsLzJhi0kIN7t1Xc+1EKOTVDBFCmtPhIIacm64NgYDBdPfGAfKNFCOSj6IkVY1IERWLGMXaMvSRZDFXxtNfQsWV9ggymvZu+3w4Y0YcF+iWSb7X3tQikn2f3LOSpVvXpy3uS+Gv3OAAH541YQ1EiWbJklD1rRm6HK+DuavVWKbFzc7Tjrt9+SO8TPohq8dG3dqD+Un6V0qL9p1JbSBzteZB6PWoQL7VHjVxPqMyABdQlexZq1H4Y9zg9ePKSAjs1Jb9m9WSekTocPAAYJmCRkJaD7jJg/YCJM2uwZ1Vq17QO9z6bSgqnZO8iQNfVVqC9JrGVQCX7QdfcpWDHxjWQNU/KTQCW299++69GqcFavsPmDRs5qBfAUAU4FnuZn3swFWNjiB59U/otLZ2r8H5EGwJYdNG+cv7KHSa7xCbV30Q9dUvnY+n5UsipJa0mYk5//PkndRo4heWCgIgxZlLIqRkbQ+zfESiCGRu967oM6KvjZy4TEhT6TNfvD7Dv4oXzik70SBkUiV27oePsIcmC9xFIz6pXLEkZ0n1uHfv011+MkKtbvaxG8jW4T1tZWmuk8GtSFAuS9UoslgCt0WnQVG47RCsA2rDO7omgQzEXTO5lx/4GTO41K5dmZEJM3FVmpcfeRzXzPYDE897DZ+nug6eaizx/9Za/rflyu/A3DXxEphrUhmLPX7eotdSZW0hM9beSjleDeJnvFmTMug6Zzi9ibJTx4QbD9eGYiwRmckGHW+Zp2WQ4qZjYpZg8No7ob8bLUjBkucFuCg1wZzQkNoCOMGSmVAIdyYdSsWN/+PgrAYUCsiHBsBF492MC1a1ejqqUL24xcZ4+v2MjN3T8Iipe2J0DwR0HYlmft2Xj2tS6SW1mQJbbsPlp0W0ModqkzSoM+Ovt755YtImRey3CeJbKqembNwJhJHzGBnU2ujQp5NSMDiLyACGhoI8F+8WrNxR//TsCaaQ1TcqgyNJ52kuSRUhsgGRWW00BAWOpovmpc9tGli7VZucjmH/y/Htyc8mqSMg4iNOqNAmkqLmhtGbrQfr5469MAIyk65Wb90XxQMD5QqJm1bxQqvBtEQoYMJnu3H/K5IVAhSGwl8tmL9nMcwe/DhLKSOJu3nOCzl++TR7VynBRSymFEyQWPVoNYl4BqJEIHDlxV27T23cJ1LheFSpdogDL9OozJDgXrNiu+TPe2z9/+JUOnjhPXf28uY3CmDlaC4mx9Tr739UgXuYnAJtk9CCd3D6PCcZOX7jBmxVoWkLaLHzSQJlnZLvhrMHEbulqEJwhkypU4RHEt2riIYvMnaVzl/r8n37+SO/e/0yfPn2i5t1G07IZwZrqC/5bt+AZNGFoV/7YqkbcE28KOzZ8Br/++tvvVDhfLt7EwB49e0Uff/mdShXNx/2PnjXLW829YJ7evu8Uq2OgatzNz5s6tmzAMEtbGDaYtZoPYJJEtBwJBk4GJDtmj+1ri2lJNqY5cmr6BkcQf+veY5O/GdroGiRS0Q7ToUV97iUXnkHJFqzjQkLAmje3i85hfvv9D5be0xXEv//pA7egIdDAhn/L3pOc/EZCHMkobHS96lQWNX2pgiJRgxk5yF6SLEiUteoRRuf2RhCQDYJBj/vi1buKkcBFUnT/8ThGEqE1aObiTbzXEgwV0REDOiquIo8944xFnxPrCMKBomoSEMp8QmKZ9pG8atdnPKvNfPfwOX+DjmyaxejDuw+e0fRRvaR4pI1eA0i/ar59mTdGINODlCHIjNFCCfSH2D5wo4PJcACjNv2H080TieXdNuw8Sg+fvBKlbITvA1Bn2vbn/z7RiTOX6cS2eaKIrx2phUSG26b4IdQg3ga3ENWvc5dv86YZpG3NGtWgnkNnUpkSBSmwczMbzMh2Q1qLid3SFWFTFRd/i3XWT5y5YhNosaVrkOp8QWLu+vGViRjYl6zZQ6iaKY1ESiq/6LqOKezYUGVo6DeU4g8tS8THYMpHX6q1AO6HXvQ1Ww5xLzpaSABpL1dK3hYSJDlbdB/Dm5UKpYtolhcTd43hvQLcE8Fatiy2STSY6nOs6dK1e3Qu/haB7wDJCWxaoWggxpISFeHdBDLEuPjbNG98f/KsJS7JA4lJITmpa1xwH3Rt5yVmShYdI2wyk252hYtC73nqgvU6g3gkc7ZGn6RNi8MYTnz5xn3q1LoBt6bhfQ0eh/BJAxjFIsYsDYrwu9m8+4TBoYDkQg+2OSZFP60p4wpJtKYNa1CHFp7MTfPgyQvqM3wuQ+wnKIQwEM95zLmrrJ8OtCNQBeDUyJ41E128dpfGzlzJAT64NpRmSLyiJcBc/XO8O/DdORe9iDbvPk7rdxyh3VGTOYjfffC0bIkaaMTXbxvEqFRIlwrvBUg6QkIP39K7D57T5NDuirhFQDIMm7iYERHffP2VZs5o0wTSE4kjcw3vumKF3Kl7+8aiLuEoLSSiFuvkB6lBvA0eAGzCTp65wh8WfBgBF7p68wHld89p9ovZBsuQbEhrMrFLMUkEsdg8pkiRQlbpLynmjk3mmYs3OeN+9/5TevX6PRXM50ZF8udm8qdSxfIbHUbQRE8KtQudvJTe/fizIiHORhdtxgGmsmML/dKXDi5N9NHHxurR01cU0q+9GbOw/JSb9x7Txp3HaPu+GNnJHBHwTpq31ugi+nVtYTO0gNHJaR2Aam/AgCl07+Ezri5lTJ+WTpy9yhKPu6ImJUIb6LuuLrIxJDSqVSzJzNRiDNJFFRr1osmhPficZMk+yyFGH42j5y9fUy9/H8qWOaPdk1sBfQAlCKiXVPLuTYDkN/GsqnFB2MyV9FPCR5Pa0iwJioTAA0oUyZMn13krihdyF6WwYq1+WjHPh/Yx4MoYMi6CiwyCVSxTlKaO6Ck68WTqmFIfj+cE9xXPB54TBFbaMPGIqJ0M4146I1jqoSW/HtARaG1KSryrayDsVdKmSW1UK7194AROgsbf+I7aN6tHfTo1Jfx2EHyG9u8g+Rp0XRAICaAAhOKA0F56Yttcnhv+fcDocA7ylWTPX77hRBGQQu65XahGxVIWIz7Qz45kAPZgppgjKLyYsl5nPFYN4p3xrqtrdgoPXLlxn8bMXEFgogXaI1+enFzNRN/jlVv3mXG/UZ1KFNK3vdGAaOysKNp39Bx5VCtL+fPkZEjrsdOXuR/XGXVlpWLHtucHEZv4py9eU5mSBe15mnY9N8B6fTuPoNXzR2gIBDFhVAfdc+UQFdxJsUAgALw7hlgE9ZRiHriGJaSQQKlA0hHcMdBV79u5eaJ2nj2Hz1D0kXMmb3bNXZsQxAvVRHOvI0U/rblj6zoPybT7T17SL7/8RiWK5FOc8gha9ZAMXbdwFPUaNovJxPANFAxojjMXbiiiPQcV6aOx8TQxpBuTz+kzoBpHTI2kqDnDjbb/YQ8QtfkApUyRnOVMQa66avNBTnQgISWHoV2vum8/2rNqMicpVm7aTys27tcE7YdOXqB5kdsoes1UOaYjyRhHYi7RwDHhlMs1GyGYh6HNZ+Xc4aJkUvEeOHY6PtFcwH0AtZYWXjU5qS7GHEnhRcx6nfkYNYh35ruvrt1hPYCAG4E3ILItvWslIikSFg1Y3YIVOwgbX8DpDMF78XHZdTCW+09/TPhIqMxUKVec2wyc0aRgx4bfUH3bdSCWM/faOvOCFrBcvlXZiq3jacAoPdsGUVK0xbZ9MRR95CzLoekzKfu/gchZv/0IQzq/1oJ6osqHzbQYhnupPGQJKaSQjEDb2bMXrwmVR7RWCLY9OoaKFMwjG2JKqiBein5aqe4PrgM/X7p2l0C8mdstOwd3KVOkkHIIq14LVdDGAaHk7paDv31Q/2mixQqO9pwGtSvK9pxYsligaBav3k3gJcC33KteZXLP5cLs8m/fJ9CNu4+YUwltKGODu5BXnUo6yeAgmWtvJHE9gmfw+8e3QXVaum4vr09QrQDS77f//sHa9UowfL+r+gRqVFTAfbVlyVgaPX05lS5eQJQEICD5Pp1GJFpulkzpqFblb7n1A2otxszRFF6MrdfZ/64G8c7+BKjrd0gPgBSmUD43UQzjCOLBx5DbNbtD+sJeFyVU3xBAVatQIlGiBZtPMa0OUqzNntiKpViPPV0DG2f//pO5JxrsxIItWx/NSgDoy4VlSJ/2C8il1P3fmMv+4+cpNu46JXz4SAXyupFf07qUM0cWm7vMFFJIwL1RRUTgCxK8pNbCu6bGr9ZemFRBvDX7aU31AZ47JIBhIB+En1FZ3BAxxihM29SxrHk8ktR7Dp2hZy/f8G8tqVUsW5R7r5Vi9x4+p407jxLanRCww1BBR1scZEBBwmuoTx4oFleXrEb1ytECBDWQZo1qWr0qD3WN0dOWc2sDEkVhQzrxGvB7mDx/LTWpX1XWBKMlz8Kzl69ZMlpoD0AQv2/NVDp1/jrt3H9KttYNR1R4seS+OPq5ahDv6HdYXZ/qAQk8sPvQGVoYtYP+/PMThQV14o0AYF/oo5Ur2JRgGZJdAtWRHsEzdV4PbPIIjFBhj5g6WO+Ymp7AYytkYQXXNxF7YSuW7ObY0YX0MQUnnSKQMElhrFL3fy9YuYP12VGNB8ld2ZKFuO90/7ppBmG6crnTFFJIueZkbBypgniMYw9JFqAkKnv34V5yAcGFtpo+oXM4UAzp62fMJerfZfAAkl4JH34xKakCYs0Bo+ZzawEI0kBaqG14/vA+mLloE+E5gJRd5ozpZFhN4iHAGUV//23Tb6I5ixZQQteOruCErBDEL16zm6H04B3QZ0BdifU1ku4wbfI87es6usKLOffGkc9Rg3gb3V1kGkFuBM1SbXijjaajDuvgHkDQiWDtzbsE+vTXX4lWiz556MXqMzyrIAca0K0ly82hCnZ2TwSFr9jO5EFQWHA2Azx5W3SMzmWDfTx1qm/oxp2H1Ma3jl7XCBVw9Pzpk9uSw6/2wlYsx1rlHgMbY7AwG7McWTN90fIiZf+38KytCR/BwZiwwUTFDaZPs93YvKX6u6mkkBjXUtb/pHM3R/4PAQdaEsB9ALJAsOJDWcEcs4ckiyAxJwQiwjrQX3445iKzvSvJQAq5bN1enVMuWSQfw5PjLt8Wpb+tpHXrmyu+19MXbmAmeih9FMqfi1FA4M2BhCD2pEN6tqaANg1la59Af/7jZ680U0Y/94vv31Kn1g0pl2t2lptUgmFPUMazO62cE0KVyhbjd2z6tKkZyXJg/XSDSEckMN/99IH6d21u0O+4f2CqDxvSWS9KwhEVXpRw/201RzWIt4Hn8TEcNGYBjww4FHp+EMiv3nKQ0qZJxT1BSpFPsoH7ZBkSGqbIrL55/xP31aFf3N76ycQ6Ahn4oRMWa9iG8cxpGz7mhjZnwsbu+j8V44bth9L8Cf3pwZNXssLExK7XlscBHvj7738waZkxw0e/04ApHLzVq5FYEqtwgdyywgjtga1Y8BfggM9evKG6NcpqqjEIsKBhXChfLrODJGP3w97+LmX/N4JMv8DxdH7fYl6mEMTHXrhOuw7IJyuFsaUghZSC9R/wV+jM3773mLkpkKzMkS0zlSlRgEoXK0DtmtXVW+3SflZAsId7BUNrDEj3Rk6NZDSO0C5h7NmylyQLEno1mvZnYkCBzR2+Dgydy8GUrZM9xvyY9O/w64gpy3SeBnUH7LNARqYU6TxT16/veATt3z16ToDoo68eSWS03+H9mi5taqmGMXod9L0D5afPoNCCNgGlGBjk06T6hosi4DCA4lS1CiWNvkcQ6OM5Bbqid4AvJwO192lIaoBDBdeEGsewvn56r+loCi9Kufe2mqcaxNvA8439h1PpYvmpfXNPluiasmAdM1n6NqhGCALSpUltkkSODZbgsEPuPx7HTKC37j3hlyjkWpCdxj9DtxoJFgRYSjEEinVbD+EXf9/OzThJZKoJ8KzNS8byx77/yHnUyseDXn7/VsMUbeo1HeF4kD6dvXSTq3CCIRh492MCf4SrlC/OkGV9Br/Cl7qsTvWy/H6Qy+yBrVhYK4Ii1xxZNe9AsC73DpnNf4ae8MZFYxjB5AwmVf93UoI9BPFrw0fS9IgN5FG1jKwKE1KQQlrC+o93Ipi4gUIAV4FX3cqcqEXwwkRhdx7R9v0x9O7HDzQuuHMiabKkzxzQTSCiOrUznOHwtZoP4H8Gs/bZizdFf8ftKckyd9lWrl7jO5fLJStXqmFbl42zi7YLZ/jdO8MasffF7yVplRoIpIdPXtHIgR0V5wZIw6LYIyDrEHxDVjRp4UTXwpAsA7ovckM0xwNIKILUDglCJBixlwju3VZVi1HcU2HdCatBvHX9+8XV8SNvEhBK5/ZG8KYBTJId+0/iDCi0TLGxr98uWHHamDK7UfLh8LIdNyuK7j9+ST06NKb6tSpQ1swZeBxAJu8/fE57j56j5eujWeYDAbEY3VbJJ2riBQXW46tHlutkqBdzOUASEVil+uZrZqQH3C1Htky82R3Sqw332DqjQeP2199+p8L5oLudjF3w6Nkr+vjL71SqaD4Cwzz645ViCK5efP/mMyO1a3aDJEnWWhN0bav59qXNS8dSicJ5OTBq0X0MFXR3pV4Bvkx2hGewZ0cfa03BIa+Ld5hnmyBm427asDpX4tHrDHUJIMEMEWLZo0MsYf1fvfUQ7Tl0miaFdNebkMVzd+DEeQqbsZI2LQ5jeU5dhu9Gg3bBFH9wKaPpIB0IH6PHdVv0SdFkVvaUZME6kZyMi7+teRf4NqwuumfX3p6XX3/7r84pAQH1VUrlMO7bm18tnQ/ajOq3DaKkrRtKDeIFdvqRA/35HTthzmrmHkEAD1JIU6T7wLUBRQUkFfO4ZeekgJLUISx9NtTzxXtADeLF+0qSI4VNKoKq3//4g7oNmc7B4PNXb2lAtxb0/Zv3FH/tnuiPvySTUi9C6ElCxnNoYDsOVvUZMqSDxy6gqSN6mfRStpWLAZdr3nWUhjHVnHkgsYQEh7bhgwLZlBaNa1Gy//zHnMsq+pznr95QQ7+hFH9oWaJkjqkbEKn7es11Kp6TwWELuH9PMEja4Pcg5/29//gFNe08kq4cieRNiwApRxWwWCF31kteunYPB1aqmeYBQGiRbMqeNSOhwl/A3ZWRDXIbkoKQzNJlQKgBhWLMLGH9B1pGbFsGIMeZMqTTJHR1zQuBO1B03p5VaNS05VS9QkkOgnHe6MEBxpbCf3e0JIuoRctwkCFiSSCdlFjtlcFtsgyBZ/7W3cfcN54iZQpN7zsq9HhHKKUXXnAW9oZoNcTeHr3rkBZdOHkQf7OQLBL7LpDF+eogDuMBNYi3wa1EvxxgzTfvPubqKPrPTp69ynqS6NEaP7QrlS9d2AYzc94h0cYAcikxhp7O/9B/REGkxFzPmscIvZYjBnQkv+b1ZA3IrLkuW18bVUxUM5Pqf4MACmgb9PIZMyn6eo2NIebvQrUbgU1Q7zbUqkcYTQzpxgRIIBcyRM4n5vqmHAPdb68OIRQXvYjfkehXHTgmnC4fjuSNEILPIeMiVKSSKU7951iQWUFbGr3OSCbHxF1leDQ4MeQ0vJMgK6VtILYDwgc9120NkEEK51jC+i/lWvXNA8mR1fNC9Vbwdc3BXpIsSHJAWePitbuE6qJgQBb19veV0n1WvxYCxUdP/iVNw4B//PkndRo4hVsdwGGgmu08MGHuatq48xhPAOjGwM7NuJXDLWc28q5b2XYTM2NkJPb9AifwtwnvWrD8H9symyD3awoqx4yh1VOc2ANqEG+Dmy9U4zF0hxaeTICDjRUqI38TqYGWDe6JriER3Pz84dcv2NxTJE+mKPgpdOCHT1rKVTeXbJm+kG4pkj83jR/WVa/XsZE7f+VzX2RSc8mWmRNRz168JhAFOZtZAkG3pK9XSj+jH75Oq0F08cASRqEIpGf7j8UR+tHDJw2UcjiD10K/cnXfftym0cbHgwaHLWSyn6i5w/m8NVsPEea1PmK0bHOydKDZSzazDjLWgEo4Wqg27zlB5y/fJo9qZcivWT2rk2YKybxV80KZdClgwGS6c/8p91pqk5hZulZLzgc/BEigkGw0Zpaw/ie9NnS3L4GZ++2P9Ndf+AL/a6gG+hsg1kKQ+PQfUjvhLLwPc2bPwjJTppg9JFnwPvNoNYiD22oVSiRqwQJvgKPIiUJqEfd7bFBnU26ReqyEHoD/67YazO9FKOYAlYqk+LrtR+jWvcfcXqo0QyW+Y4v6HLgDPj8uuAtBdQLtMs6o4qO0+6fE+apBvMx3DR/JPsNn88YUWceTZ67Q4+ffU9d23lSjUimZZ6MOp8sD6HOcGr6Ojp2+rNNBIBhZu2CkopyHj8iVW/fpzdufvkhKAP0B4jt9potNWjgWVTP33C6srACta2cySyHolvT1SulnQNjBTo/qN0h5hCAeJDuojMvNSL1lzwkaq9W+ETlrKAcVgFiiSt+8UQ3q362FlC6w2rWgcoEe/6F92mnI46Yt3MCVGbzvsdmbO74fc3BY00DA1q7PeL7H36HFpttoOrJpFleMwPo/fVQvaw4v6tobdhzlpFHE1MGijsdB+J6CZR4VcVeXrCb3baONanrERipe2J3JFJMG3u65XLjH3dpmL0mWew+e8bMhKJFYe922uj6CeASKciYobbVWex1XaJ0SnjXw7owaFMByd1v3nlCcnCH8jDYavE+AGps5pg/lypmNgscvouZeNW2yvwfEH35Gb71bzqzMM6M0/hN7fX7tZV5qEC/zncBmo8vgqbR81jD1xySz78UMh8pK215j6euvvqJe/j7cC5n8H9Iy4fxvvvlKZenVciaqYvifQO4mxs9KP0YKCLolfb1S+g/V2ErevWnPqsmU392Vg/j6NcvT3iNneSMlN9waawOkF/2SFcoU4f5lGALilz+8pSyZMiiilQVzFsibALEEEkaAX08d2ZN86ldjLo67D57T5NDuUt7SL64F6TBwOJyLXkRo+Vi/4wgn3RDE7z4or8RcUojz3/Q3w/vBFl+5bDHRCZorN+5TyOQlzOQsWAvvWhQ2pJMoEk/huZ89ti+z1Jtjv/3+X+o8aKrRU1FVxIZen9lLkkVIJkSvmaph2Da6ODs+IGnrBp49/BZA2jdvfH/yrKUc4lE7drNZU0M7mVfHEJowtCvrqg8Zu5Ca1K/KbabPXr6xi8SiWQuzg5PAtTNryWZGNcBArof3Hf5/bHAXxbUq2IFL7XYKahAv861B5eDDL7+y7IRq9ucBYdN9ZvdCypA+jf1N0MQZSUEihSFVht/EjpcCgm4vfb1Y2cKoncyCi8AycPgcRlfUr1WeypWyD26O67cf0vuED1S7yrcm/gJse7imsnl8JbdJXb7xHTOYn9g2l/lP8O8DRocb7PFHi8GZize5gn73/lN69fo9FcznRmiDKVk0n2iIM9AWGDP+xnfUvlk96tOpKYXNXMl6w6H9O8jmKH3PPQLpYYF+5JI9s9G5oIe+XpshVKdqGeZswDloWRg3exV1aetFXdt5Gb3G3QdPqUW3MXTj+Eqz2xkwj7L1u3PiIUvGzySB//v0iSbOXcP+FXhWsDZDFTB7SbLgWes0YAonQerVKJfIh5CcU1oPua7vX4Z0abj1q3D+z8lB1WzjASTAarf43KoF9QdwycAQbC6bEayY9rzjpy9Tofy5DCbpBA/jWPyOrC2RCuLQFRv3ccAOhRwgA4CoXLftCM1fvo3lIkEUq5ryPaAG8cq/h+oKJPQA9OE7D5pC5/ctlvCqtruUtUmknJXhVwoIupR9vbZ7wqQfGVX4vYfPEoIswaDegQAjX24XljT0rldF+oGtcMWffv7IPf4CymHlpv20YuN+TdAOPfF5kdsIlU9dhmrzmJkrGGLarFEN3uwiCEESCe0xh09epEZ1KlFI3/bMOm/IcE7U5gOUMkVy6tGhCVdloJfOfCx5Xa2wev2XTJoUhEKLKT3kguqGQIAojARkw9lLt7jP35gJJIpJySmNnaf9d6g5tOk19ovvBVomkiX7D7dRiDV7SLLgewFuAl0G1QC871VTPSCFB4Cs2n3odKJLpUyZgoPLgnndpBhClmvsPBDL0qfgV2hUt7JOTiu875as2U2R66Pp0MaZVg3iUSgsXa8rzRnXlxrU/hJhNG3BeobXzxjTRxb/qINY1wNqEG9d/6pXV5gHBNJB9Lyj991RzRQSKZXh98unwJoQdLDcvnr9jip+W1SWx88QJLihRyVRVU2pJioQa3lULUNFC+bRbIjirtymt+8SqHG9KlS6RAHu7VOK9QieQQjmfRtUp6Xr9lJL71o0qEcrnn7o5KX023//YK32pLbv6DnmBgjq3ZbPQXU0qaGCu2DFDgJ5JeDxSavYSBSB58AeDfKBl67d/axF7padkwlitZAFxJSQHBHWN372Kvr46++ioLh41rz9Q+jb4gWpT4AvoxSS+gqJBSAV9JnQ13tw/QzK5fovXB6tAei5RjuKWLOnJIvYOSvhh9t5xwAAIABJREFUOBBJzl22lQ6fukRQFQH/Qe+Apl8gDZSwFmeYIwjvfv/9D3LPlUMxywWXR9jMzzK8zb0+J1vTpU3NUnPXbj6gbftiqHrFkiwzl9s1u1XXJcjfCoou8Geqr7/SIIEuXL1DY2dG6U0cW3Vy6sUl94AaxEvuUvWCSvcAMpWrtx6ixp5VeVOcVCcbagJiZJDs2Q/mkEglXY+zM/xKCUFHQBFz7iqB1O3kuavUvX1jGtyztSyPECrcO/fHJhoL/23SvLUswwQ4nlyGymZj/+F088TnDZFgG3YepYdPXilS1xmbKEiqAe6NQBU924BWIxGECg76QHXBlEF6VyifG/MUGDME8WVKFPxigwi/gfDNWBsC+lMReDZrVFOWqrw2eWHe3C6E+44geEPEGFHkdEhO9Bk+h1CRBxIhZ/bMdOn6PUYmCESIxnyGvwN5NXzyEoJShC4zRmKK322rnmGUJtU31MK7JnOloO1h4rw11LOjj1FSPHtLsmA9cfG3dPoiR7bMsjwbYu6bKccMCltA4Bzg9pEZKxlNAE4IVWLOFC9a51gk8EAGp/37AxLr3Y8JVLd6OapSvrhiiilAsYAMGS1Pt797Qt+/ec98LkUK5OaWJ7laUfBO7NhvogYdBK4BFAQE1Y9rtx9S3xFzVZlW6zzSsl9VDeJld7k6oL17ABsraBafu3SLX8TaWrmYO6CnpsAkbbleqUikdK1BZfi1/M6CoR5wvPU7j3KVCMkhMNnag5QTgnhotcvBzi14EoHtsImLWV5IuwKKxAZ8BSi9I5hc1aZz8bdowKj53H6AxFBScjW869CXD01j9ExGzQ0VFURbcg8wTmXvPqx6ICAM8Oz3CZ3DHAwhff1EXZ7nu+kAM9oD6VAgjyv5t25g8mYZ70hUzN6+g3JHYom51Km+NgrtRavD9IgNrDQAAxFkrSrfUpe2jehrA1V8HGtvSRZdSiT4TcK6tW9MQ2RKLIp6AEQchCp8VZ++rMTAUr7NB9C+NVOZ8AuJIxBMqmY7D0AJ4dfffqfC+XJpiHEfPXtFH3/5nUoVzUc+DarJmkS2nSekGxnQ/YpevTS8K0mDeBCb8p5DQTKt0nnH8a6kBvEy31Nk64aOX2Rw1Hzuror7WMrsRnU4kR6QgkRKZfjV7WxsAvccOkMxcVe5oofNOza6SQmh9N0qIZhBb/KIAR25fw1Bg70YqlUx566J6i+Wcs543pBEg6EaY08+MWedllabsCnrHjRd76bru0fPaeXGA3oZ7hGgTl+4gZno8YyChClD+rTcZ38R+uhv3vP3JqBNQ9FwdnP8IJyDKlWrHmF07eiKRH3w2FwejrloEgTdknlIfS6SAX/88adB+H3SMe0xyZJ0joD5t+wxhjYtDiPXHFmkdptVr5e0KikE8Udj4xn5BGUC1WzjAQH2HX9oGYETQzAlo65s48kvR4VixtMXr5mcGeSqowb6cyUeRJzNu45ifhXwoqimfA+oQbzM9xBsqQtWbNeMumn3ca4cgBka9vDpK5bbUZoOucxutOpwjsbEbimJlMrw++XjBgkXz3bBlDtnNq4WICg6d/Em975FTBlEtauWMfqMgtgHTLEbdx2jTBnTUZsmHtSkQTUNo7XRC0h0QFI4PaqzPyZ8pLXbD1NgQFMNDE+i4YxeBnrB6JeG4d0I2OvIqZFcSevXpbnR8+3tAEurTUIiDrBxXYa++D/++J/RbwaCdgT8CGxAbAQYO+D6gHyif1Muw3xrNO3PySG0F8AA5w8MnctBIir0xkwK1Q2puCCA1IIc47lLN+nnj78ypB6IGvhXjNlbkkXXnFHNA0cFWgSUZEB4IHAXJB7xz0DRRa7fy7wUSBKqZhsPCPcmKbEkknlgqg/p1942E3OAUW/efUTX7zzSrKRcyULMig8uESCX6lYvyxwgqinfA2oQb+N72Lb3OArs1FSz6UcFChBBNYi3zY0xJPulVCZ2VDZ1WYrkyVkOCUF+5ozpbONwhY4qyFPtXzeNN+2CDZu4hBDgI/AUazgelaH1O44ytNmjWhnq0b4JlSlZUOwlLDpOVzADuaxaVb9lMjY5K+HoXfXpNIJO7QwnJBNqYQO+M5zA4n724k2T/GqRUyQ6WYpqk/BO6tiyvs5ZIQhEtVRJ3wwQjS1bt5c3lrlcslLc5du8Nkgfaf+e9N0GKVQ3pOKCGD19BW3fF8O8A+jrR3UdPb6myjjZS5JFl88nzFnNiUYlJtFQlaxboxwFtGrAAX2mDGmpiWdV6tLOSxbkiUSvEoe9DJJgL75/85ng0jW7QSlGh3WCFRam+tUKTrXDS6pBvI1vysDR4VT+2yL8gYGBcCz2wnVaOHmQjWfmnMM7GhO7saREkYK5aeXG/TqZSgF7BamWNtRN31MBuaa0aVI7TTJAUDEQKjyCX9BrduD4ebMh6KiUbo+O4V5agcHcmX6JL75/Sw3aBVP8waXsA2iqoyf//U8faFv0SVo6I1hR7pCi2iS0YIVP+qypnNTQ1gFEl9hecntxIAit4uJvazbvvg2rW/z+AIlZ+VKFyf+f76k5azWFCwIJFI+Wg7jVAa0KggWNi2BY/aTh3c2Zgs3PAdIB8oZg30diolqFEg4R8OK3ZEhtwOaOd7IJABU0OGwB8xMIFtC6IQ0NbKdTqs3J3GP2clW/mu06xZ2oBvE2vmWADs1cvIkmDOvGEkKQFOrcphF18/O28czU4bU9oFQmdiQl7j96ofNmZkyflqA+9frdTzolu6C5jArxxJBuBqtjgGeNmBpJUXOGK5K92JwnHVnu9n0nUP48OameFns7AnBo3QJiDwO7btrUqcwZwmnPQeDu26AaeXtWoVHTllP1CiWZwThThnQs0aNEQ3IC8mVS9hTjt01//60hhFKiX7TnLIW0IpLgIEEUoxOvz1+mcEEI/f2CnJNwTfT1HouNV0x/P9ADy9dH83xB0Af4PExQDgCZ6+r5IwjfDKUZED37j5+n2LjrlPDhIxXI60Z+Tetye45qtvMA7kuL7mO4nSeodxvmycBeA/wdnVo3pDa+dWw3OQWPrPpVwTfPjKmrQbwZTpPyFAQDk+at4UoKDL0q00f3plTf2A/BlZTrVeq1HI2JXQw7NmDWi1fvpsj10cwi7VWvMrnncqF0aVJxT+2Nu49ox/5TdOPOIxob3IW86lSyW01qqZ87MDYD9m3MVs4J0at3C//2CJ6p8xKQdcMmc9eBWIqYOtjYMA7zd33IkcyZ0tPqeaGsv6s0mzB3NW3ceYyn3bdzMwrs3Iyh5G45s5F33cqilwPI/ONnrzTHo/UKyQFseHO5Zpc0QSB6UhYeaK60Is6LPX9NMzryGehHR68z5JTEJHuk4IJAZbd8w54MM2/XrC5zY9x/+JyCJyyi+jUrUP9uLSz0kDynL9+wj67feUhzx/VjicfKZYvRyIH+TDwI4sOuQ6Zzm49SVFm0vbZg5Q7C9xvKFuAfgWwg2paStkLJ42l1FMEDeJ/VaTWILh5YwvtdgXRw/7E47tvWhzxSPWjYA6pfnesJUYN4O7nf6ItFZUUN3m17QxyRid1SdmxAszbuPEo37z3mgB0GRvWSRfOxJBRaQaB7rZppHkAQsS06RudJBfO6UepU39CNOw+dqiKBd+DTf0jtBMcAoZQze5ZETOamedp2RyNZVrfVYIqaO5w+/fUXdRsynUDkBImrW/ces5SeGAudvJR2Hzqj91CQQAktWWKuZ+tjLJVWRBKtUYdhXywD7Quxu8IZtWHMpOKCQDIFbXHahkBxyfQgfk8qwZau3cNqBSAVRDA1OyyQKpYpqpk6EA6HT12kFbNDlLAczRyFJMua8BH8rRICxTlLt/AxYkgUFbVgBU0WrRrtAydQXPQiTv4L9wbfRLTnqffGvJup+tU8vyn1LDWIl/nOScGqK/OUnWo4R2Rit5QdW/sBQAUs4cMvFveuOtVDpS7WYg9cv/2Q3id8YPIwJRk2VE07j6Trx1Yw7B3M+6MGBXDAtHXvCVFw63c//swEfwfWT2fiJ8GUKsVkLWlFMNz7BU5gZIJ3vSqyPiZg3L959zGjAaBYgQQnAhOl2JGYSzRj8UbasmQszVy0iVEivfz/ZaJfuWk/9y2PC+6ilCXxPEEw6Bc4ns7vW8z/LgSK4B3adeC0RW0XinKEHU4WibhK3r1pz6rJzL2De1O/ZnlWekBbhzbHhB1O326npPrVbm+NVSamBvFWcav+i+pi1X31+j3DuyYM60otvGvJPCN1OEf2gBTs2I7sH1uuDQmRa7cfGJ1CsULuTkXGdPXWA9p7+CxBAUCw56/eEpAL+XK7MCxW7iDN6E3ScwACS6+OITRhaFeqVLYY9xo3qV+VA75nL9/Q9FG9jF4askD12wZ9oauu1CDemtKKq7ceoktX74pSMcDv7/yVz6z4Sc0lW2bmqHn24jVVq1jS4D1yBElS+KJ3yCw6c/EmoXUFiAb0wQuGpBNk84J6tzX6vNrTAUB8eLYNYvQLCO0QKK4NH0nTIzaQR9Uy1NrHw56m63RzWRi1k+WVfepXo8Dhc8g9twvVr1WeUROqme8B1a/m+05pZ6pBvB3cMUDpvToOpznj+qrZRxvfD1Ti0a+qy0oWyUcZ0qdhOaTe/r42nqm44aVgxxY3knqUqR4QKpLGzhMqFcaOc4S/I5jwaDWIN9jQpU72TzUz7sptevsugRrXq0KlSxTQScRoj+sHZLt2i8+s8ujnh/4xDNWSZTOCjQaIOBYtBrfuPmZ/ILAUDBV6vK+kJMuT04eWSCuCvAnr1zbwKYAUK7dbdgrt38HoUvD7Q6uDLkPAioBi9ZaDtDtqst5rGVP/GDmwo9F52MsB8ClaPMCzgOcqqYEPpXSx/PYyXVHzwG/Hs00QK1w0bVidg3h8EyuXK8b9/2obmCg3qgcp0AP4lj57+ZrwjnJ1yaqiJxV4D8VMWQ3ixXhJhmNGTImkHNky8cdGNdt5AEiJEVOW6ZwAKjLZsmQkQA+BmlCSPX/5hi5eu0s/f/iFN6c1KpZSZI+xknxuylyROd996DQz/DszazIguyDWunkiKpH7lFx1xn3VNqgXAF0B3gOxhn74hVE76M8/P1FYUCduKzh2Op6yZc5IpRQWWOlas6nSivqCZ0BwJ4f2YFZ1Sw1BLf6HNgh9pkuS9Pn3b2jo+EW0Zek4vaSWls5NPV+8B4AiwD3MnjUjXbhyhwq4uzLaQDXbewDveii5oCgCedb5y7dT4fy5aNrIXlwwUc08D0AeMmTyEsK+TzCgfMOGdEqUCDbv6upZ9uQBNYi3k7sBWZt0aVMzrEg1+/OAkuWckHQYOCac9X6Fl3rxwu60cu5wVf7MgkcNMFpUWjNnTJeo/xUZcLA6izVsLDsPmkq4J+9+/MAs7LhXzmioUA+buJgJ37T1nCEbBmgsoPSOYGLUIYR1Cj2OA7q1pE+fPlHU5gN0dk8Eha/YTtApV6IWOZKlIISD1a1ejlKnMk2NBcE1gjNtA4mcuZXVP/78n+ZSx09fppc/vKUOLepTiuTJzJLw6zl0JtWpXpb8mtVTxOOKNRfKn4ty5TT+3sGxhQvkJjeXrHa/NryL4+Jv6ZxnjmyZnUYS1R5vlMCifnbPZznDqj59qVv7xhR/7R4jJfp3VYayg735Fu+yem2GUJ2qZZgU1yV7Zrpy8z6Nm72KurT1oq7tvOxtyup8LPCAGsRb4DxzT8WHBeQd5y7dZCKcPG45uN9MiuqBuXNSz0vsAUeRc8KzVtUnkD+I/q0aMJwQ5EWjpy+n0sULqB9KCx589PC9evOetkWOZ9g3SJSmLljHfaWQiENF0Bg79dHYeBowaj5LUaEaAdbk7ftP0Zr5I5z6feBIyBFL1SEELXKBHK9h+6E0f0J/evDkFe3cf4qWzgi24Cm2zakg+HvyjwpB1fIluId95NRIRqFArk2fIXiXkjAOkmNrth5i0jZdhh5wcza9WEv6tKkJqgFKMFRBJ89fS2ODOlOjupU1bSzac0fScsmaz5KjhzbOVEQQr6tlAkkxGALGIT1bK+H2OOQcHz55SZ0GTaVTO+azpFzQuAhOTh6KuaCSDlpwx6Em1LzrKGb9T5smleZKq5D8vXRLJXO0wLf2eKoaxNvgroyevoK274thSCQqbufib3EAsHXZOIZZqmZbDziSnBN6ohq1H0bXj6/kjZnAznvq/HXFBgC2fTo+j44KfIVGvfiDWLNyaf5v/UfOo+/fvKfWTTxo8Zo91Nvfx6A8nFBhHTXIP1HFbvbSLfTbb/8lJfXTSnlPHA05Yqk6BKrWYKffvGQsJ3bwnLXy8aCX37+lc5duiSJxk/L+WHotbN59Oo2gUzvDGa6OteGfD528QGcv3jS4HrRVoL/TmEoBCAWREGvWqKbeaqvwG0ayDRDeZMk+s8lHH42j5y9fMzs72hWMQa91oQqQBE6ZMrkoqTtL/SnV+QikwmZ+bmNp7lWDORyADgTa49rNB7RtXwxVr1iSRg8OSKSSINX4cl0H96ZljzG0aXGYYvkk5PKVNcfBb7RKk0CKmhtKa7Ye5ILWwsmDOKmGyrFY+U1rzlGJ1xaIUJNy6YyfvYo+/vq7KDJVJa7bWeesBvEy33l8ED1aDqL1EaMTkdghCwn4qBKhkTK70KrDOZqcE6pd3h1DNMzWQhC/eM1uhtL36dTUqv501ItDx9arQwhLF6HaLkADI2cNJVQWUdk6dOICRUzVTZwFv2Dzf+z0ZfKuW/kLN92694Th9c5mjoYckUIdAiRjqFyn+uZrqlKuOMPQwZ9y484jGtKrjeJaDECc1qBdMMUfXEpff/0Vdew3iblg3v/0gbZFnzSILEDCG8gVqBN0b9/4C/g3kgJQeoFMGqqwCBDQ7qLLhHejpfwL5qIK7PG3LbyT7t5/SkCAIClZKF8uKlIgN3Mv4N3mCAaVCBBF9uz4r4yeI6xLaWuI2nSAZizayNNeNS+UypcuTE0CQqlji/rk11wZrSj25nO8A/sMn0OoyDeqU4lyZs9Ml67fo8MnL5KwP7G3OavzMd8DahBvvu/MOlOARl4+HElfpUyhuQYqDMdi40XpBps1sHqSKA84mpwTZLnKeHanlXNCWOIKQTxgnoCPJtWdFuUg9SD2gKD/feVIJKVMkYLOXLhBPYbOpLh9izg5AmKZnsNmavSJdbnNkMSV9vFlShTkAM4ZzNGQI1KoQyCwGjcrMdEfnjm0w7RoXEsn9NnenxUE7r4NqpG3ZxUaNW05Va9Qks5eusmVa1R6DRkS4WCh33csjhPh6OXOkD4t98hfvHqXfnjznmHSAW0a8m9Tn+HduH77EU6CIJkgGL7RP/38UVTAagmqwN7vkSPPb8Kc1ZQpYzqDrRuOvH57Wht+z19/ldJsPgt7Wou9zIUTmJsOcJsC3mUF8riSf+sGot5p9rIGdR7iPKAG8eL8JNlR2JCVb9iTPx7tmtXlzcf9h88peMIiql+zAvfGqmY7DziinBNIwdKk+oYqfFuE+xnzu+ekahVKOpX2uNRPFPpDK3r1otXzR3D1YHrERjp59gpFr5nKQ6ESv2rLQdqxfILeoQ1JXGmfJMCopV6DPV7PUZEjSNi8+P4NoT8+t2t2p96w6mOWB2wdpI6AcYsxBO1gtUfF6e37BG41KJTPjSvHgIGLMVSt9h8/T7Fx1ynhw0cqkNeN/JrWFa0QYQmqQMz81GMs9wCSPenSpOK2p4Sff6GYuKvMQ4QEkGryewBJsvzurhy4GzMg3tKmSa3KoxlzlPp3p/WAGsTb4NYDDjlwdHiikcuWLERLpgcZJcKywXSdbkjIsM1dtpUOn7rEmrKANfcOaEr1apRTtC+wgQHrMnSUUS1WzTIPTJq3lqAqUbd6WYbFjxvahVo1rs19vv79J1PBfG5MFKWaeA84InIEQebgsAWJyNMCWjekoYHtRFXRAadftm6vUSe2b1bPaP+20YvIcAASpU//IbUThkuRIjnlzJ7FJFUHKaa6YOUOWrRqF1fjQXKH7zDg+PvXTeNAT4xZgioQc331GPM9IBRNANVGEjtgwGS6c/8pgY9Em8/E/BHUM031AAjWQOg6MaSbwd8Yqsgjpkay7GqBvK6mDuN0x1+6do9iz183uO7SxfKzaoZqjuMBNYi30b38MeED3bz7mMk8cufMRiWL5pOUdddGy3KIYQeFLSDAJNEvHjZjJbVv7snBGhiUldgTiKTRig376OqtB5r7gyAiuHdb2TfNDvGA/LOI//3vE1fbr995yM9FS+9arMGKKj1IuhAQuOcyHAhgk6nLUiRPTv/79Imvpa+n15F8qb0WR0KOIKHTovsYrg4H9W5DrXqE8eYVcPBOrRsaJD4UfIJnZMSUZUZvd0hfP4JsllLt+u2H9D7hg1HSOqnWJwR4a8JHULlShTWknyDEg40Z0snoUFKhCowOpB5glgfwHW/XZzwzdX8H1u5uo+nIplncinH3wTOV5Mssr1p2EgglF6/+rHKAb6ZXvcrknsuF0RJA1Ny4+4h27D/FnB9jg7uQV51K6t5YhMuBBNx7+Cwfiee7WoUSlDF9Wv73T3/9RQdPXKBRA/1VrgERvlTSIWoQb6O7BSgtMmcvXr2hvLlcqHK54mpAZaN7oT0sqvDQK8WHHnJHAhHcuu1HuJI2dWRPO5il+CkI64GEIapNGdKnofNX7hB6AgXZOfFXU4805gFT9L/1BQAYA4mjIgVz08qN+zUQfWNjO8rfHQn+KhAeXjywhHkNhPfJ/mNx3K8YPmmgo9w2k9aBhCI2nHcfPNWc9/zVWwISI19uF35XgbzOmgZFGL/A8RreCuHexF64Llriyp5QBdb0lVKvjWJJQ7+hdC56EW3efZyT8bujJnOQs/vgaVVuy4Y3FgiljTuP0s17jzlgh4EkFgUtJNUCWjVw6rYjc28NEJfVfPvSpYNLE7VMgjwblfhObRqZe2n1PDv0gBrE2+CmzFq8iVZs3M8vLJCrQBMZkO3ls4apLy0b3A/tIfFh6dhv4hcbO8C/Ys5dpdlj+9p4hqYNj01yi25j6OyehYmeLVSb0JumRI1p0zxgvaMt1f9GAHD/0QudE0QG/T//IXr97icqUTiv9RZhZ1d2NPgrCBDbB07gSiD0zYVAcVt0DKHfU0y1V9ct+uPP/9HR2EsMC0YLh5IM/AAerQaRR9UyzBAO6UtY3JXb9PZdAjWuV4VKlyhg9ef+1Q/vyLNtkGazi3uzNnwkTY/YwHNr7eNhtlvlRhWYPVEnOBG/v2xZMlL8je8ILSeMsJu5kgOc0P4dnMAD9r9EvBMSPvzidKgza9wZQTnn+Na5lD1rRs0QQD6A+BNtJKo5jgfUIF7meylsHMYFd9EwC4Mcp+uQadSgVgUK6t1W5hmpw2l7QGCTPrVjPveXYmM3tE87ily/lwb1aEV1qyurL17QmIakYcG8bpqlbtlzgtmglZaUsKen1VL9b3tai73MxdHgrwiyK3n3JkGzF++T+jXL094jZ1mJxBC5FjZjeAdp81eAeR0JgKjNBziAnzCsK7XwrmUvt0/UPIBoauw/nCyVdhM1mIGDkETzbBPE8nZNG1bndz3e/5XLFaO54/qJTqjbA6rAUl848vlAw+D3kjJFcurRoQkXT1ZtPshEd2qvtSPfeedc219//UW1Ww6iWpVLUzc/b+YdAAEo2kQrlC6iylg72GOhBvEy31BBkkbQyBWGX73lIJ04e4VWzA6ReUbqcEk90HnQVKpboxzDubCxy5QhLTXxrEpd2nkZlCyyR0/+979/cE9u6lRf8wtcsJi4a5QhXRpNENHVz5urFaqJ84AU+t/oDcSzpssaelSiru28xE3GgY5yRPjrwqidlMctO/nUr0aBw+eQe24Xql+rPENGDRm+CSBea9esHisg7DoQy32NhQvkpg4tPMmrTmVFEqEi+TBs4mKaFRaYCO4JLgQkuQGll8vAcJ8sWTKuWF24cocKuLuaRA5oL6gCufyljqN6QPWA/XsAicXBYQtZblMwcPRMH92bXHNksf8FqDMU7QE1iBftKmkOxEfft/MIGjmgI1WrWFJzUcDrnzz/nlChV81+PIBKNmB3SjUE8WBRN2b9urZIBL0ydryz/10K/W/0/+7cH5vIlfhvuF8gUfSsWd4p3azCXz/fdnwrTp67Qmu3Haa4+Nv838Bj0cvfR/FET/ZE6GgJP429oAqc8kWhLlr1gOoBvR5AyxXa9dD2V6JIXkqbRlUkcsTHRQ3iZb6rYJv2aPmZzMgl+79MwqgIaP+3BZMGipa4kXkJDj8cAl+QDp6Lv0XQrQYMHf2R2vfL4Z2gLtCoBxCIgPkfhjYLoB2kMATx+OAC5uuM5mjwV0DHfRpUo97+vrTzQCzNX76dCufPRdNG9mKiSTEGro5Nu46xDBogwP4tGzDxG6DBSjN7InS0lJ/GnlAFSnsO1PmqHlA9YB0PIM4wZt9885UoiVNj11H/blsPqEG8zP6HLNX+43FGRwWxTrq0qY0epx4grQfQTxQwYArde/iMalQqxRIdJ85eZVjSrqhJifrKpR1ZvZrSPNC6ZxgneWCQmEP1fOTUSFY16NeludnLAYNyzLlrTkVAAyk2EL85mgns9CCW5OfEpy91a9+Y4q/d495rVNZNMahN7Dx4mqI2HeB3EuTqmnvVNOUSNj/WXggdpeSnwXvg0rW7XPXK7Zad+61Tpkhhc1+rE1A9oHrAuTxgKEmq7QmoNKicEMp/NtQgXvn3UF2BhB6A7BDaHVbPH8F9qIJ17DeJNb8nDe8u4WjqpZTqAYHb4tTOcEIAWqv5AMI/Qx/+7MWbHNAbs6Rwelznx4SPtHb7YQoMaOpUeq4bdh4lV5esRjXCkWSDskKzRjUVsQHBc9Jp0FQCUSYk5SDzc3ZPBB2KuSBaxkzXcwSofez5a/TTz78wKZsSDWt49vI1YdOJe585YzpZlyEVPw1IQsfOiuK5583twlKkuVyz0YaIMbJXKSxtAAAgAElEQVSvSVYHqoOpHlA9YHceQJL00ZNXieb1x59/UqeBU2jc0C5UOF9u/huSjV+lVBONdncDTZyQGsSb6DApDkc1JfbCDYIMDTalgoHoqKFHRSmGUK9hpgeSyg4Jl9m2L4aij5xViQfN9KujnQZFiQbtgkkgqESSB/D39z99oG3RJ0VJ9+kitsuSMT3Vqvot+TaoLhk8Xwm+R+vKgFHzGSLevX1jypUzW6JpI8Fx+cZ3NHPRJkIPc9TcUEUESHi/V2kSyPNds/Ug/fzxV1o4eRCt2XqIrty8z+Ru+gzPktjAVugxVwp/x5Ub9ylk8hKWVxUMLPthQzpRihTJZXlkpeCnwbNY2bsPSwW29K7FcwdfRp/QOUxcGNLXT5a1qIPo9wD2Ww+fvGKG7jfvfuJEC1rkQDD59VcpVdepHnAKDyxatYt+ePsjjQ3q7BTrdZZFqkG8De40YLjYzJUvVYRSpvx3w1KxTFFmQVfNdh5AsODffzInU6BXLNiy9dGEXnkBJp0hfVpKnjyZ7SaqjmxzDyBw921Qjbw9q9CoacupeoWSLNuXKUM6Gj04wObzU9oEsMGevnAD7TsWx6oJhfLnIvzOwBcCfVvAx4f0bE0BbRoqCqoM6PuMRRv5dqyaF8oInyYBodSxRX2DaItVmw/Qu58+UP+uzQ2uF35DhT9sSGdFoBNAuFSvzRCqU7UMtfGtw1wjSGiMm72KurT1kk2VQQp+mtvfPaFWPcLo2tEVib4Hm3cfp8MxF1lGUDXbeADB+7zIbcwjgcC9SIHcLBv4+s2PdPHaXZ5UcO+21KpJbVYoUE31gCN7AEH8rXuPKXzSZ04u1RzDA2oQL/N9fPT0FW/g4vYtSqT/K/M01OH0eMAR+4nuPnhKz168obo1ymo2K6jc3X3wjArly+VUFV+pHnx9zwl0vVfPC6V8eXKKHuq/f/xJqEzef/yCYbjVKpRQVJAqeqEiD0TQjqoZyNzevk/gDXihfG78rCqVJwSBNqp+CCLEGmDZI6Yso4QPv1DvAF8mT9QmsgMaBOigyPXRnPwd1tdPEUoauK/Nu46iuOhFiRiTkbQ4e+mWbFwQUvDTQBKxRtP+PGf0wcOAvggMnctSTqjQqya/B+Kv36PeIbP5nvTq6MOyjNqGe3/64g0CsWGqb76mpdODRZNMyr8adUTVA+I9gL3d6GnLNScAXo/3FBRO5o3vT561nFP1RrwHlXWkGsTLfL9++vkjVfftR7G7wrlip5p9eQCV+Jc/vDM6qRxZM8kG+zQ6GSMHAPnhmiOrpk8bvbnY4MAQdG5cNIbcXLJaOoxTnY8P49N/SO2EhQNKmzN7FqMIDUDHl6+P5ioddL+HjP1Meib004JsBpwMIFVUTZkeQIU2v7urKLju0xc/UNo0qXVC5xEQbouOocgN0Qw9z5EtM2XJlI4JFcGMDu1fVBPLlCyoGEfh/Vq/bRDtWTWZfSTY+Nmr6OOvv9P0Ub0UsxZMdO6yrbRs3V4OFHO5ZKW4y5/lALcuG6cqzNjoTq7bfoQyZUxH3nUrG5wBgvmJ89aw2oNK8mWjm6UOK6kHUBRYvHp3omtmSJeGJa2hiqKaY3lADeJlvp/YlPUaNovy5s75BSERAipk71VTPSCVBxJ+/oWq+falzUvHUonCeZmErUX3MVTQ3ZV6BfjS5PlrqUq54tSzo49UQzr1dcBz8T7hg0GCtuUb9tH1Ow9p7rh+BPmxymWL0ciB/hz8AzLedch08qhWhob2aefUvlTy4lFVPhobz+zxedxy6F0KEmojpkZS1JzhRoMIoD8ePHnJ6IQ8btk56aNEBnS8g/oMn8NIi0Z1KlHO7Jnp0vV7dPjkRYqcNZSVHpRmaKNBpYvZ6V2zk2/D6qL5DJS2ViXMF3wHeJ9ivwUki6GCCZ5H/E+F1CvhzqpzNNcDKDwAEYYClGqO4wE1iJf5XhrTyB05sKPMM1KHc2QPAKLdtPNIunIkkjf8qOB5dwzhKlGxQu4caCxdu4c2LQ5zZDdYZW1Xbz2gvYfPEtoVBHv+6i2BdT5fbhdq17QuE7UlNfgbkHFAbWs2H0CzwwIJfBiCbdhxlA6fuqiSKFrlrslzUZAWohoCqDsIz7zqVSb3XC6ULk0qDsJv3H1EO/afoht3HtHY4C7kVaeSQ0rs6fM2kxNuOsCM/UCnFcjjSv6tGygygJfniVJHMccDwn6rbvWy1LRhDapZuRR9/fVX5lxKPUf1gCI9gMQi9ilQvalWoSSpMYYib6PeSatBvA3uJzJiugwqyY6olWwDF6tD/uMBQHW9OoRo+k+PxFyigWPC6fLhSJYXuXDlDg0ZF8ESWKqJ9wAqPR6tBpFH1TJUtGAeSvaPxnncldv09l0CkyKWLlGA0Q9JDfdgxuKNtGXJWGZbd8uZjXr5/4uEWLlpP8tUjQvuIn5C6pF26QFUmzfuPEo37z3mgB2GvvaSRfMxe3lAqwYm9cnb5SKdeFK6FCYEdzT0qCQbSZ8T3wKDS0eFHaSY+4/F0abdx/m351O/GjWpX5W+LVFQ895W/ad6QOke2H3oDEFdqbWPB6OA7j14Rpv2HKeNO49x26R/y/rU3KsmZcuSUelLVeev5QE1iLfR44CKKMhXAHcGLBIELCrbuY1uhgMPi6pwdd9+NKRXG2rj40GDwxYyvDBq7nBeNaSusMFZHzHagb0g/dIQZAMKf/PEZ31owaB3DjkjQ9luJAB6h8yiMxdv8scVklTa/Zio0rf1rUNBvdtKP3H1ijbzAO47fntiZeNsNlErD6yrZ1MYsnSx/FSnelkrz0C6y+P9unN/bKILPnv1hjkvoERQ4dsi0g2mXskiD4TNXMnv2pQpUzAXCfglWjepTY09q6jcBRZ5Vj3ZHjwActxxc1Zx8F68sDvduveESXL9mtej2lXKqPGFPdwkK8xBDeKt4FRjl9x18DSzDsPARg3CIvzooCWszT5s7Drq31UPiPHAlj0naOysf4NNoe8Um2lU6Zs3qkH9u7UQcyn1mH88AFKxYRMXs863tjb3yXNXORsOKL0hQ4UIci9gGMd9SGqAXiOgUU31gKN5ICl7MtYH2bkjpy5xiwkSWHLZvqPnGCUR1KsN90QDJXft5gPu0a9SrhiVKJLPrKmATLRHhybUoHZFs85XT5LeAwjiwU/Rzc+bID93+NQlmrZgPRXOn5vWLhgp/YDqFVUPyOwB7CsuXbtH63cc4URV5XLFqH1zT0YMgnhXNcfzgBrEy3xPsWEv16AHDezekjq1acTsxdjI9xw6k2FekBJSzXYewAZz5NRIgxOAfJigF2+7mZo2Mvq3b919TBXKFGGpLhiYeV/+8JayZMqgJo9Mc6fmaCTgoDmMTeH/2bsL6CiybQ3AP+4OIQQJzuA+uHtwCO4OwTXIQJAhgwR396CDBw0OA0GCwyABAiEQ3F3e2mdu5xEIxLqruzp/rXXXmgvddXZ9p0l6V52zt31aW5QsnJt3vMNpybdFboHuQ6aoPZvy5EiLQxL2ig37qqXVfTo2UEMuXbdLJXZyM11u1MkKpbw5MoU5nGHjFiJ+/LgY4MTilGHGM9EbDEm8rHrcue841mzdr57MS5LDfcImQudpzSYgK/rWbt2vVltKHYjm9SqhngOX05ttQkw0MJN4E8H+7LSGPvEnd8xR/UkNh/xDk9ZTM1x7aRwRh/tW4P37D3CbvSbwj+SOpmx3kC+XhiNNquTqBoyej9BUUdfz9WkRu6G+gGE1jYwpK2oWTR6I+HHjaBECx6CA1QhIQUcpdDdzTG9NrkkqNZet3ws73cerFXGyGqBCwz7qy26HZtUxespy9eU3PIm4VKuPFzcOV9NoMpMhDyIJzUDXuaoGjOHndL1qpVGpTCEkT5oo5BPwFRTQqYB8p9154CSWrduJfDmz8IaVTufxZ2Ezidd4Qh88eoZyjr3gtW1WkC/6s5dthu+d+/hrcEeNI+JwvxKYtmA9ZG4mDu+KKmX1uTQyvFXU+cn4uYDsby5W0wnd29ZDC8fKqsq8FKobOm4B8uTIpP6cBwUo8KOAPAG/6Xsv8C++4quqDTNp7lrVblGrrT2yd7Ruu6E4v2+RKnB2+ZovHDu4YPuKsWrZtdxUl6e3kuT/7JDCdh36uQX71xVLFUSqlMmwacdhzW5M8PMWVEASmE7OE1XyLvVHGtYoqzpFZE6fmlQUiHQCT569jPQ1Waxt0pnEm2FGW/cag8plCqllXHLIEu767YehW9u6qFauiBki4pDBCSxctR0TZq9WrWk27TyMEf3bwLF6GV1hRaSKuq4uVONg7/g/QNWmAwITAEnity0bg0PHz2Pj9kOYO76fxhFxOAroQ+BnbVblJukApyawtUmqyYUEPHqK8o69sXfdJNU7WVr+/TVtBY55zFJJvawKcHFbjL1rJ/40Hils97fHwWD/XhLFuHFi48K/N9BQw33+muDpZBC5yfLX1BWoWbk4CubJyl7wOpk3hhkxAdepy9WNKvm5c/HKTazetA/p06VCqwZVuN0vYrQW924m8RYyJVzebCETAeDLly8YO2Mllv+9G6MHtkedqiVx/PRltOk9VlUMb9u4muUEG0IkEamirpuLNEOg0l3Cobkzzu1ZqH4pGpJ4WbUhS+m7tKpthqg4JAX0IfDm7fsggUptGK27s8iKgGrNBqBIvuxoXKcChoydr+qFjPujk4ptyvy/VQI+z62/PlAZ5S8F5CmkFP4yHFKQlIWE+aGxNgGpz1OsZle1osjWJpl62JAutQ18fP3h1Ko2mtTRpuaItbla6vUwiTfDzHB5sxnQQzmkPLnuP2qWquw5b3w/FC/8/3vhZd469BuPto0ddFOAMKJV1EPJFuleJk/g8lVsj0WTnPF7/uwqiU8YP67q777DfRzS2tmEykR+4UpLums3/SB7dKX+gtxBlyJ5ktjwoIA1CkhtmChRoqjPuxxS3DVxwviaJ1XS5rWz80RVxE4SujVzhquY5P/Xaz9U/awPqVq+/BuePG+dqnYuhdKkLkbnlrVRoWQBa5w63V2T9IdfuGqb6gL07cGCdrqbSgYcCgH5LtG2zzgc2jAVpy9cQ/Nuo3F822zsPngS+46cxpRR3UNxFr5ELwJM4jWeKS5v1hg8jMPJUs9qzZ0xd1xfZM9i/8O7r/jcxtQF63VVgFC2a0j7JjnKlyiAuHH+v6BiGHn48m8EpJ1cvDixVS/o+e4eyGifShVA/Lbl3M/A5Iu/POlbtWmvShqyZUqLhAni4cHDp6ravRz9OjeCY40yXALKT51VCRjqSQzp2QK1q5TAqElL1b8DSaJXzhyGTOntNL1eWRXg5/8AmdKnVqsB5Ak9vn4N9b+7Xi7TccPXX62+cRm/SG2Tk4Ko8mW5WMGcml4LBwsqIDdjfnfoDJc+rVC0YA5Ejx498AXysztRwngko4BVCUgdCHmocGD9FCxevQNHTlxQLRRlu9Dew96YNrqnVV1vZL8YJvEafwK4vFlj8DAOJ23X5ImoFCT62fHqzVtdVR+XnsWy/FsO+VIpXy6ljZ5co95a5YVxOk368vA+TTQ8/ZNWR52a10TWTGmDxCmfwSMnL6h6DNLBYu64fvyyadKZ5Mm1FJAnolWa9sdZzwXqZ23FRn3VTdE9h70RM0Z0DO3dUstwIMVmb935/0J7csNTVgbI/tE0djaw+8XvAsPSVc/VE9TPU8O2mhXrPdWqnDFDWKhW08n8bjCfW/6o1Xowzu9dGOqbMuaMl2NTwBgC/UfOwrHTl9XKIMOWUGljnS9nZji1rmOMIXgOCxFgEq/xRHB5s8bgYRxOnhIdP3M52HfZpkiK6NGj4c7dB0GW2YdxCE1fLk+IarYajEMbp6n9gKXr9lD/vevACRw9eZFLq8I5GxF5mihf8JMkTgCH8r8uYinJ/J9TlqFF/cqaP50MJwvfRoEQBfzuPUQTp1Fquee2vV5wm7VaFY+TLUx/exzQtCjkINe52Lzrn5/G7NytKVo6Vv7p31+94Yfm3f5Uy1XlMCTxckPi4LGzqqsJD/MJyM9phxbOGNW/rdr2xIMCkUFAVhMd+OeM+r5aonAutXXp7EUftVpQVvzxsB4BJvFmmEv54pIgXhzIkzhprXPQ66xqaZM3RyYzRMMhvxWQp+xSsTi4Q/ZGyl7lpWt3YvNiV13AyROlyo37wXvnXNXzWPZH9WxfH1LkR+svzLoAC2WQEXmaKF8sQ1vES268yP+iRo0aysj4MgpYvoA8iZd+7JK4y/L5Ef3aYPqiDbgX8Fg9OdLiePz0hbqp+X0Ni5Ub96g6FUN6Ng8xDHnSJYm73JCQFmby3/27NMZ8963o1cFRbV/iYT4B2UpWu80QyJaJ3NkyBAmkdLG8aFy7vPmC48gUMJGAfO6DO6JHi4ZPnz+rfw9JEycw0eg8rZYCTOK11P5fO7mCVTpiyZRBai9tyx6u+Pf6bVVIZ/bYPiqx52G5AnpMqiRxr1W5OBwqFsUfYxegRKFcOHrqIpIkSqD50lXLndmwRWZJTxPDFjlfTQHzC8jPn3EzV6nl827DuiBNqhToN3IW6lYrhZK/59YkQP+Ax6jUqG9ghwnDoGFJ4uU90jK2fMkC6om9JPFJEsVHjYrF0KZxNcT4Zg+2JhfFQYIIfPj4CUvW7AhWRWreaPVZ47RQQCuBn7XwlPGlXke2zGmxaNV2eCwbo1VIHMeEAkziTYgb3KlleXPjLiPh5TEL1274oW67oZD9dPJ0/orPncD2NhqHxeG+Efi+/ZHhr2Rpknzp1NPxsx/o8tRo6ZRByJAulZ4ux6JijejTRFn1UcShyy+vqUiB7Fg40dmirpvBUMAaBGTJ6aUrt/Bb5nRq2anhkCf07z98/OVe+J9dvzwBC01hS2vw4zVQgAKWJyA/167fvBtsYNIBJEoU4MHjZ8iZNb3lBc+IwizAJD7MZBF7w9PnL1GlSX8c85iFNZv3qSq2sjRbkvjNO4+op/E8zCcQ0l3M0CyxNF/0P44sP9Bv/6+o3bc3I1LZJAv1km5Luh5LiiWiTxMNn7Xxw7ogXjAdA05fuI6TZ6+oyrI8KEAB4wvIfvgZizfg48fPcOnbCmWK5sXeI95IkTQxcmfPGKoBXacuV20hG9Yqh4tXbmL1pn1Iny6VKowX2m0zoRqILwqXQPUWA1GzcnF0blELG3ccVt1lsmZMg7FDOrFgaLhE+SY9CMi2PVkxKMU306a2US08eVifAJN4M8xpU6dRSJEsMbwvXEPTOhX+a03jtkjdwR/UvZkZIuKQBgFJem/6/n+lYvnzDx8/olXPv3TfMkiWFhoO6RfqH/AIzepVQvRoUbnn2gz/BN6+e49CVTvhyObpwf6CleKD7hv2YPHkgWaIjkNSwLoFDO3HerSrj8+fP2Pxmh04umUmpi1cr6rmh2ZvvqE6/fYVY2FrkwxVmw5AutQ28PH1h1Or2mhSp4J1I1r41UnngXKOvXB0ywwVabGaXdGuaXV4n7sKWeXUvW09C78ChkeBsAtc+Pcm+o6cCandYzhaNqiC/k6NEVUexfOwGgEm8WaYSvnFIl8YYkSPhg7Naqj+uEvW7FT74bXukWuGy9flkLOWbELAo6cY3re17uKXHszL1u1SLY+CO/p2boS2javp7rrMEbDc/MiSMY3awxvSIa+V9nGpbZP/9KU5y7bG1qV/BbutYeWGPapn/AQXp5CG4t9TgAJhFLh8zReOHVwC24/J9pipo7rDx/ceNm4/FKoq+ddu+qFtn3GqsN3pC9dU4VCpVL/74EnIv39p58nDfAKyfbFVrzFqfg55nUPfETPVjZpdB09g0w6ufDTfzHBkUwl8+fIF1Zo7q+XyLR2rwM42ueq4NHLiEvWQUOqO8LAeASbx1jOXvBITCkgSf+nqLUwb3dOEoxj/1Ianva6DOqglhFGj/ncX1mOPF/z8H6BTi5pq6ajskecRsoAsx5Tls3Izp2r5IsHe1ZaaCnOWbcZ8dw/sWuX2yyS+UecRqFL29x9uokgBxTa9x6Jwvt/QlX1dQ54YvoICYRSQ/etSnX7NnOFIn9YW3YdMgWPNsvC//wjHTl0KVQL+/v0HVczuwPopWLx6B46cuKC2v2zYfgh7D3vr7vdFGAkt/uWS0BSt4YTFkwdh2bqdePHqDWa49lI3tc9cvM4bpBY/gwwwrAKyhF627O5bNxk2yRMHvn3agvW45Xefn/mwglr465nEW/gEMTxtBeSL3dCxCwIHleX1UsfAy/sypozsjoqlC2obUARH8/ULgENzZ1zcvzjImcJagTmCYVjV2+WJjovbf551q5VUT9ETxI+rluCeu+iDv7cdVL1Zh/ZuibR2Nr+89q2eR+H85xy4uXRB5dKF1R5aKXg3Zd7fql6GLNOV9pM8KGBtAp8+fYb8fHr45BnsU6eErU1S1c9Yq0OK1zXo6II4sWOhaIEc8Dx0CilTJIEsRe3TqWGo24/1HzkLx05fhrSbkyX4daqWRMf+bsiXMzOceANOq+n86Thyc2X8rFXq76UrUME8WVGj5SDV4rBJXW53MPsEMQCjCty+G4BqzZyD7brhdfoyJo/oZtTxeDLzCjCJN68/R7cwAfliN3vp5iBRJUoQD8UL51JPsvV2fPz0Ce7rPdUXUukTbzhkKemzF69QrGBOvV2SRcQrN3v2HjmNK9dvQyzvP3yCLBnSIFumtKogVlhcZbvDqElL1XWlTJEUAQ+fqC02c8f1Q75cmS3iehkEBYwlsH2fFxau3IZLV33V5zx+vLiBn/lCebKp/uqyDcXUh/wbHjEh6M1NaQmXJ0cm1KteOtR7R+VG74F/zqgK93LzTm5EnL3og4z2qZAwQTxTXwbPHwoBucEaK2aMwPmQOcPXr6wFEwo7vkRfAlLQrqxjLwzq1hQOFYqq4OXP6rb7A/UdSqNVw6r6uiBG+0sBJvH8gFDAygVkafb2fcdx2Os8nr98hUzpU6NJ7fJIlTKZlV+5fi5P6mScu+SDO/ceqJZXubJlUE/3eVDAWgTu3n+kkubrt/zRoVl1VCpdCMmTJlKXp9oi3fDD1j3HsMDdQxUfk20kknhpfTDB01rc9OPJz9dbd/6/YK2supDPo3QQSGNnE652gqaPmiNQIHwC8nmXw7CcXpL4O/4P1GontsAMn6mlvotJvKXODOPSVODJs5eqPZAUF5SKw2u3HlCFiaRYUY6s9mjbxAHVyhXRNCZjDTZ90QbInn55Gi9PffPnyqKui0u1jSUcsfPIL9i79x/i5as3avk9n95FzJPvtkyBJWt2qOXzUiFZlrD/7JCKyr2HT8eYwZ00KfTKBM8yPy/GimqQ61xIK8GfHc7dmqKlY2VjDcfzUMDsAmyraPYp0CwAJvGaUf83kCzXnrdia7CjytO3RAnjQfatSE9THtoJrN2yH+s8DmD1bBdVwVZ6dLdqUFm1DfLyvoTVm/dh2ugeKF+igHZBGWEkWTJasEpHLJs2GAVyZ1VFmLYtG4NJc9eqsw/r08oIo/AU4RW4esMPvV2mB+kcwFYw4dXk+yxZQLp7pEyeJFQhSl2IKIiiltub8mCCZ0pd85/78dMXqnjhDvdxQeqTsCaM+eeGEZhGgG0VTeNqqWdlEq/xzEhSNfivecGOKvuupX+858FTGDWgrcaRRe7h5Em17Gse0a8NfnforJLbGhWLBaK4uC3Cs+evQlWx2JIkfW75o4nTSNX2SA5DEn/4xHm22DHzRMk2h3rth6m99H07N1Ttrv50bodxM1aqZZ4Na5Uzc4QcngKmFZB/Ay9evsHnL1+CDBQ9WlSTr0hhgmfaubWEs/sHPEalRn2DLfJ1w/cehvRsbglhMgYKGE2AbRWNRqmLEzGJ18U0MUhTC8ideUNbIalY3LV1XZQtni9w2C27/4GH5zHMHtvH1KEY9fz3Ah6jYqO+OLVzrtoLJUn88mlDMG7mSpQtlg8NapY16ng8WegFDHfMT+6Yo5YXG26wbN/rpXoa662dYeivnK+M7AKyH3nMtBWqOGRwh2z5kVZtpjyY4JlS1zLOLfUNLl25peqMSOFBwyE3cGRVpB3rwljGRDEKowmwraLRKHVxIibxGk+T7H+V5dnBHVKZOlN6O40j4nAiYGjFJi2B7tx9AGnTIfvgDcd6j4PIljkderavrysw+RJTsWFfFXftKiVUoiitkIoUyK5ajXD/tfmm8/qtu2jqNApeHrNURWtDEv+3x0H1+eNWB/PNDUc2nYD8TGrUaThixYyJTi1qquJ20aJGDTJg7NgxTd5a0RgJnnT/+OfkRVy74ac6Vdx78ASZM6RGtoxpkeu3DKpTBQ/zCQTXbcYQTZ7sGVGuRH7zBceRKWAiAbZVNBGsBZ6WSbzGkyJ7/co79v5h1Ndv3qmKvH06NtA4Ig5nEDhx5l8sXrND7U9+++7DDzD1HEqhW5u6ugO7/+CJaqUjlUrlGjPZ2yFpkoS6uw5rC1j+zcvWjS1LXJHR3k4l8ZVKFYT0jp/n1h95c2Sytkvm9VAAhifg/2yeoWrAmPOQIqaT563D7kOn1M1NKWLauWVtVCgZcu2TMxeuY5jbQsjPV+kNnyFdKkg7Ullhc+bSdew+cBJVy/0O565NA6tEm/NaI+PYsn1x6NgFQS5dbrRIYVfZsljPoXRkZOE1RwKB79sqRoJLjpSXyCTeAqZdns47dnRR/XHLFM1rARExBGsS2LbXCwnixVGV95+/eI2DXmfVUy4mieaf5RmLNyJdahvUrFQcTgMnwT6tLSqVLqiKEPKggDUKSH/41r3+CqzTYc5r7OUyHbKHtEur2nAZvwhN61aE+wZPVfukWMGcPw1t255jGD5hMfp2bqR6L3+7VNvwpqfPX2L6wg2QrVibF7uq9k48zC/w/v0HVGs+EJNGdOXvQPNPByOgAAUiIMAkPgJ4xnyrtN85d/kGJrg4GfO0PFc4BWTFxKlzV3H33kOkT2OLIgVyIFq0oEs+w3lqTd9mqE6/ZMogFDmxCsQAACAASURBVMqbDS17uOLf67chT4Flf78k9jwoQAEKaCUgNxKL1+qq9rzL3ndzHfIUvljNrvBcPQGpUiYL3M6yYr2nWo01ZkjHn4a2c/8JZMmQWq2gCemQJD5fzsxBqqOH9B7+vWkFBv81HylTJNHd9jjTqvDsFKCA3gSYxFvAjH34+AnD3RZDKvKOZFV6s8/IhNmrsXDVdtXeKEniBJC+xbLMcsGEAbrbQy5PmRp3Gan2Xcu+zbrthqovrfJ0/orPHYz7o5PZvSNrANyvGVlnntc9dro7lq7bheoVi6kn1FGjRAmCIkl1IxN3Z5D2js27/flD5449h71x8NhZTBzelRNlpQKy2iJB/LhqBRQPClCAAnoVYBKv8cwFtydenopKwrhworMqhsPDfAKGau7Saq5e9dLqy6VUUm7bZywqly6klk/q6ZAlnVWa9Mcxj1lYs3mfWioqSzslid+884juqu3ryT6kWIPbryk39DwPnVJF7UydxIQUH/+eAqYSkNZy8jmXjiDS2lO2lH17SIHX/l0am2p4dV7ZAy91KA5tmKpqhMh/y5jz3beqrW3lS4S8L94Q4Nt379Wy/IePn//QLk/2ycsqKB7aC8jn6viZy8EObJsiqdoGIYVspb0vDwpQgAJ6E2ASr/GMBVedXu4I58iaXpfLtTXmM/lw8kWsZqvB8N45F7FixQwcb+nandh/9Iy60aK3Qyqgp0iWGN4XrqFpnQr/7f90W6Razg3q3kxvl2P18XYfMgXFC+VCk7oVrP5aeYEUMKdA615jUL5kAbR0rKyS+CSJ4qNGxWJo07gaYkSPHqrQjnlfQv9Rs9VNATnkhvy3h9QekUKVPLQX+FkhYYlEbpJKDRL53S43tnlQwBoF5GGBfNfjYZ0CTOKtc155VeEUkJsstVoPxpAezYPcnZfl9b5+9yFP6PV2SLVkqbofI3o0dGhWQ33JXLJmp9oPz5aGljebKzfsUX3iZ475sYuF5UXLiCgQdoE3b98H+yZ5MhozRuiS57CP+ut3hOfLrrSYK9+gj0r8u7aug/jx4hg7LJ7PhAKyIkT+J91beFDAGgTu+D/A2q0HcPnqLZy95KPqH0n76nw5MyFP9kxoXKc8k3prmOj/XQOTeCuaTF5KxAXky2XZ+j3Vib6tJixthL79s+mje5q8j3FErka+mEjvcR6WKyB9qm/63gsM8Cu+qu4Bk+auRZH82dG9XT3LDZ6RUSCcAq9ev0WR6l2CfbdUhx/Ss3k4zxz2t7lOXY7M6VOjYa1yuHjlJlZv2of06VKhVYMqoVoZJwXwqrcYiLOeC4KtUB/2iPgOYwvIzRnZuiGHbJGIGyeWsYfg+ShgdgG5oSgPZ+T7Q5WyhVGtfBHYp06paj88evIcF/69ifXbD+Lx05cY0a81ixqbfcaMEwCTeOM48ixWIvDp02ds3+cV4tWULZZP/XC01GPlxj2ws00eYsvCL1++qB/6daqW4lN5jSfzZ8mM/AIe4NSELak0ng8Op43A9zevZNQPHz+iVc+/QmztZswIDdXpt68YC1ubZKjadIBq9+jj6w+nVrXRpE7I21mkOF7dtn/g/L5FPxTnM2asPFf4BRp0dIGvX4A6gbQNlPaBQ8bMVx0JurWpG/4T850UsCABKRS6ZdcRjHZuj6yZ0gYbmTzc2bH/uGqnuXq2CzKkS2VBV8BQwiPAJD48anwPBSxcQPZp9vhjKhwqFEX7ptWRJlWKIBHLD/PTF67BbdZqyL7BxZMHIWniBBZ+VdYX3vfLimPFjBGqJ4DWJ8EriuwCs5ZsQsCjpxjet7UmFNdu+qFtn3GqsJ38LGzebbSqVL/74EnsO3JaJXshHYYWnoN7NFc1LL6vsh/S+/n3phUw1Lg5tHGaWjZfWgoZbpyGXQdO4OjJi6GaY9NGyLNTwDgCsnQ+S4Y0oVppIj/7kiRKgORJExlncJ7FbAJM4s1Gz4EtVeDh42eYumC9KmQnxYpk33j7pjVQq7K+2tHIdYybsVJVopfiSlkypkGihPEhWwNOnr2CgIdP0KdjA7RsWCXURZwsdc70Gpcl7g3WqyXj1reAJPGXrt7CtNH/bWcy9fH+/QdVzO7A+ilYvHoHjpy4oHrXb9h+CHsPe4c6DukDP3D0XFXh3jZFkh/2V2fLmJatY009mT85v3SWqdy4X2ChWrlR07N9fTx59hJ/exzA3PH9zBQZh6UABSgQcQEm8RE3DNMZZN/Kms37f/keuTsmS2p5aC8gSz2bdBkJWWYuxYpkSbr3uauqp/GkEV1RuYz+5kWSdrnzKks/ZW9U+rS2yJIhtbpra8lbArSffW1HtKS9wdpeOUeLzALft1aUn7nSCtPL+zKmjOyOiqULasbTf+QsHDt9Wd2sHT2wPepULYmO/d2QL2dmOLWuE+o4pDXpmUvX8fDRsx9azElnEPldwsM8ApK4yw14h4pF8cfYBShRKBeOnrqonkQO7d3SPEFxVAqYWODi1Vs4JQ9rHj3Fly9fg4xmlzIZWjhWNnEEPL0WAkzitVD+ZgzDF3d5uhstWrQfRpfCVvIPTJ4I8NBe4PbdAFRr5oxdq9yQ2jZ5YAATZq9W/eInDu+qfVAc0SoFfrU3WD5nJX/PbZXXzYuK3ALvP3zE7KWbgyBIL3Xp1Z01YxpNceTf4IF/zqiidCUK51LFQM9e9EFG+1RImCCeprFwMOML/OxGqayaWDplEPcEG5+cZ7QAgSVrdmDczFXIkdUedimT/7BFzz6NrVqRwkP/AkziNZ5Dwy8VL49Zwbaj2XvEGwtXbmcSr/G8GIYz7KE7vGmaulNvOOYs24Jzl30ww7WXmSLjsJFFQD5rfvceYtSAtpHlknmdFNBMQJZSh7b+h6wakONnfZaDuyFhuJA82TOiXIn8ml0XB/pRQG7S3P5fUTvD38oNm1Q2yVh7hB8YqxSQlnK/O3RWD5y4otcqpzjIRTGJ13iOpQ95ngptsXftRNW78ftj865/sGOvF3tEazwvhuFkfhxaOKu2Q47Vy6gK4WcuXMekeWvRr3Mj1YqIBwVMKTBxzhpcv3mXPwNMicxzm01AEt95K7YGO36ubBmQKGE8eJ2+jM4tapkkRnlK9fjZS3RvW/eXtUCkpkjfETPh0qf1Tzt3fL81QAL+8PGTamk2rE8rNOLvC5PMYUgnZYvVkIT499YqcMXnNuq1G4YL+xaxzbC1TvI318Uk3gyTLHfJpPKttDv5/hg/axVevnzDQjhmmBfDkD63/OHitkhVLDYcsj+yS6varD5sxnmxtqGlqF2zbn8GuSypWSD7c3kX3dpmm9djEJDEd/Bf84IFkSX1sofc8+Apk61Ekd7uMv7zl6/RuWUt1Ts8XtzYgfHItikPz6OY7+6h9rIP6Nrkp0/ifzar3YdMQfFCuVTFeh7aC7DFqvbmHNEyBAxbQk/tnBvmn1uWcQWMIiwCTOLDomWk146cuAQ3bt/D7LF9gvwju37rLpo6jcKoAe24DMZI1hE5jSxLkvZryZMk4tK7iEDyvcEKfPr0GVs9jwb5O/mzcTNXYsvSv5AyeRLKUSDSCMjSZ3z9+kN1d1MASOHSvz0OYv5KD/j5P1Sr4pIlSaD6icvP/fy5sqiVV/lyZQ7X8Cs37MEhr3NcTRMuvYi/iS1WI27IM+hTwLCaNG+OzOjSspa6KSq1Pr49okWLygRfn9P7Q9RM4s0wkVItsm3vsaqfo/Txli/r127exYr1u1Uxq3F/dFaFdnhoLyA/AL28LwU7sHzRk4KEPChgSgEpSCPLQZ27NjHlMDw3Bcwq8ODRM9y6cy8wBlmCLk/BWzWogjR2NqrAqxaH1Knx8fVXnTvSpbZR3TtiRI8eqqG/L075FV8hxWknzV2LIvmzo3u7eqE6D19kfAG2WDW+Kc+oD4FLV30x0HUOZFVpcIfcpGTxbH3MZUhRMokPSchEfy8tdaQvrvSmleV90se7dLG86NC0Bp/6msg8NKeVJ+/lHXv/8FJ5OtOuaXXVV50HBUwpID2rj5++zKd4pkTmuc0qMMh1LqT+y88O525N0VIHLZB+Vv1cCkoNcGqiaqrwMK8AW6ya15+jm0dAbjDKjaxHj6XtZdAWc/IAUeo+8dC/AJN4C5hD+ccW9bvlLhYQFkP4n4A8nW/cZQScWtVhtWF+Kowm8P79B0yYsybI+V68fIMtu//BmCEdUbNScaONxRNRwFIEHj99gdJ1e2CH+ziktbMJDEv2Md/wvYchPZtbSqihikNqW3x7xIoZgzfiQyXHF1GAAhSgQEQEmMRHRC+c773qcwfuG/eoJbNtGzvAPk1K3PF/gHhxYkP6l/KwPIGl63bB+/xVTB7RzfKCY0S6FJAk/q/p7kFil97URfNnx+/5s3NLjS5nlUGHJOAf8BiVGvXFuT0LgyS7ek3iDW3ovr/u6NGi4dPnz5AkP7Qt7UKy499TgAIU+JXA23fv0brXmGBfUqXs72jbuBoBrUiASbzGkylPdcs69kLubBnw8dMnyP74zYtdMXzCYrUPT29PITTm02S4Fy9fB44jtZZevnqDsTPcVSGQ8cO6aBIDB6EABShgjQKy8uzSlVv4LXO6IDeq5Am9tJ/Tai+8MWx/tpxezt20bkVky5wWi1Zth8ey4L9UGyMGnoMCFKCAQUDyio3bDwcBkT8bPWW56opVsVRBYlmRAJN4jSfz8jVfOHZwgffOuUCUKChVpzvWzRsB7/PXsGv/Ce6D1Xg+vh/uV1/K1s4dgRxZ7c0cIYe3JgGuyrGm2eS1hFZAbpROnrcOuw+dUi0V5edq55a1UaFkgdCewiJeJzckrt+8G2wsiRPGl1/xePD4GXJmTW8R8TIIClAgcgpIEh8/Xhz0bF8/cgJY6VUzidd4YqWgXcna3XFk83TIL/mO/d3QumFVPH72Atv3eDGJ13g+vh9OvpT5+T8I8sfS9qt9PzdMGtFVFSDkQQFjCHBVjjEUeQ49CvRymY4bvv7o0qo2XMYvUk+t3Td4qidFxQrm1OMlBYn5/OUbePL8JcoUzav7a+EFUIAC+heQn68Hj51Tra15WI8Ak3iN51K+uLfoPlpVhqxUphDmrdiK7FnscfzMvyhfIj+6t2VLGo2nJFTDzV2+BbfuBMB1UPtQvZ4vokBIAlyVE5IQ/94aBeQpfLGaXeG5egJSpUyGUnV7YNuyMVix3lN1apGijno6zl7ywdbdR3HF53Zg2H73HqntchnS2qJx7fKqlSwPClCAAqYW+H45vdTeevr8FZav3w2nlrXRpG4FU4fA82sowCReQ2wZSlqVVW02IMioshe+UN5s+KNnc0hhKx6WJzB2xko8ePQUE1ycLC84RqRLAa7K0eW0MegICly94Yfm3f7E8W2z1ZkMSfyew944eOwsJg7vGsERtHu7YTVN2WL51B5/Q5cZrzOX8ejxc1SvUBR5cmbicnrtpoQjUSBSCwRX2C5Z4oSqhXWtyiUg7eV4WI8Ak3jrmUteiREEpJJwg44uQc4kvTbl5suiSc6qajgPChhDgKtyjKHIc+hNQPbAS+J+aMNU1Y1F/rt/l8aY774VvTo4onwJ/eyLl5UD1VsMxMX9i4NMg14r7evts8R4KUABCkRmASbxZpz9Dx8/BY6+78hp+Ac8QrN6lRA9WlREjRrVjJFF3qFl//uuAyeCACSIHxf5c2dB/LhxIi8Mr9zoAlyVY3RSnlAnAtICqXzJAmjpWFkl8UkSxUeNisXQpnE11aVFL4f8Gx7w52y1Qku6lxiOA8fO4l7AY7WUngcFKEABrQTk4cDxM5eDHc42RVLVEeTO3QcoXjiXViFxHBMKMIk3Ie7PTr1q014sW7dL7f8L7ujbuRF7OZphXjgkBShAAQpoKyB91r9NgLUd3Tij+fk/xMlzVyD7/e3T2qJk4dyIFo034o2jy7NQgAKhFXj15i3KO/YO9uWNapVTP5+Wrt2pWlvz0L8Ak3iN51D2qxSq2gmugzoga8Y0iBo1iorAY4+XqoreqUVNpEiaWC0z5KGdgKyEyJIxDdKkShHioPLarJnSIrVt8hBfyxdQ4FcCsn0juEPulseMoZ8nkpxlCoRF4P37Dzh17iqOeV+Cr1+AKvTaoGZZ2NokDctpLOK1ngdPoeewaUhjlwKSzMshLfMWTR7I1VsWMUMMggIUMAhIoTv5H1f7Wsdngkm8xvMoX1gcmjtzD53G7iENt3HHYbhOXY7hfVujavkigQWKvn2fJFxzlm3GfHcP7FrlxiQ+JFT+/S8FXr1+iyLVuwT7Gmm5NaRncwpSwOoEvnz5gpY9/sLVG3dQ8vfcqtXq/qNnEfDwCTYt/q9zi14OWbparKaT6irT4n9bA9bOGY6h4xYgT45M7Dajl4lknBSwQgFu2bXCSf3ukpjEazzH0v7Bfb2n2isX65s9dNJu6tmLV1bRI1djUqMNd8jrHFzc/itQVLdaSWRIlwqyH14K25276IO/tx1EicK5MLR3S6S1szHauDxR5BT48vUrbvreC3Lxfvcfov/IWVg7dwTs06SMnDC8aqsW8Lnlj1qtB2Pp1MEomCdr4LU27zZafeZHD9RPG887/g9QtekAnN+3SN34NVTaP3T8PDZuP4S54/tZ9Vzy4ihAAcsT4JZdy5sTU0XEJN5Usr8471WfO3DfuEctaWnb2EF9cZEvA/HixOYyejPMx7dDyv7MvUdO48r125AbK/cfPkGWDGmQLVNa5M6ekTdZzDw/kWH4jv3dUK5EfjSpw36ukWG+I9s1SsG3io364tTOuUH2wstNUg/Po1g40Vk3JIaVdef2LFR74A1J/Oxlm9VS+i6tauvmWhgoBSigfwFu2dX/HIblCpjEh0XLCK819JXNnS0D5Kl8wKOnqsDE8AmLVVVeLqE1AjJPQQEdCwwZMx8J48eFc7emOr4Khk6B4AXk5nWL7q6oUraw6qNuOOa5e0D2yndrU1f9UaKE8S2+OJz8Ds9XsX1g+1FJ4uXfrhSt3eE+jiu2+I+AAhTQVIBbdjXlNvtgTOI1ngJ5uuvYwQXeO+cCsvyuTnesmzcC3uevYdf+E5g5JviqkhqHyeEoQAENBGRJ/ffHg0fPECNGNCRJlED9lSzT5UEBaxH4VS2Ib69Rbm5nSm9n8Zct7eRkFV2hvNlUvZSM9qlQvFAu3Vfct3h4BkgBCvwgwC27ketDwSRe4/l++vwlStbujiObp6uCPrJ0tnXDqnj87AW27/FiEq/xfHA4CphLwNqSGXM5clx9CciTeP+AxyEGnTJ5EtXTWC/H8xev4R/wCGlT27AqvV4mjXFSwAoF5Gfs9n3HcdjrPJ6/fIVM6VOjSe3ySJUymRVebeS+JCbxGs+/LKdv0f2/CryVyhTCvBVbkT2LPY6f+RflS+RnNVuN54PDUcBcAlKl+9JVXzX8yo17cPTUJYwf2hmxYsYIElKWDKmDFME0V7wclwIU+FHA89ApLFy5DWcv+QT+ZcsGVdCvcyOL3w7A+aQABaxPYPqiDZi1ZJMqoC1F7vLnyoLTF65h+4qxSJeaBXOtacaZxGs8m6/fvEPVZgOCjCp74WUp3h89myNhgngaR8ThKEABcwrI8vn2/cZBqnaXKpIHk0d241Jcc04Ix6ZAKAVevHyNYjW7olGtcuoLc6KE8dQN+VGTlga2nQvlqfgyClCAAhEWkOLMBat0xLJpg1Egd9bAYpuT5q5V5x7Wp1WEx+AJLEeASbzlzAUjoQAFIpnA7bsBaN1rLLJmTAPXge3Rb9QsJTDDtRfixI4VyTR4uRTQl8AVn9uo124Yjm6ZEeQGvHxhlvo3bDGnr/lktBTQu4A8DGjiNBLHt81Wl2LomHH4xHls2nEEs8f20fslMv5vBJjE8+NAAQpQwAwCUolbfsGWKZoXroM7qO4UchfdadAk5MqWAX06NTRDVBySAhQIrYD8ey1dtwfcZw5VW+QMx9ot+3H01EVMHN41tKfi6yhAAQpEWOD7Fp7yHWP5tCEYN3MlyhbLhwY1y0Z4DJ7AcgSYxFvOXDASClAgEglIYTt5YidtJaNGjRp45dLndf/RM6hWrkgk0uClRiaBN2/fQz7nSRMnQJRvui9IzRjpt66XQ27E1Ws/DHHjxEKhPNkCwz7odQ6JEsRD3hyZ1J+1beKAFMkS6+WyGCcFKKBTAel4U7FhX/RsXx+1q5RQDwqePH2BIgWyY/KIbtyyq9N5/VnYTOKtbEJ5ORSggGULSOXYbxMXy46W0VHA+AJOAyfh3sMn+Hv+SNVCUZaAjpm+Av+cvIiKpQrCdVAHxIsb2/gDG/mMksSPnrI8xLN2a1sPNsmZxIcIxRdQgAIRFrj/4Il6MCA/c06c+ReZ7O2QNEnCCJ+XJ7A8ASbxljcnjIgCFLBiAalEb2ebXC2j/9Uh1evlSX2dqqV00S/biqeMl2ZEAXkCX6hqJ7U3Uwo5ytF9yBTcf/gEDWqUxexlW9C5RU00rFXOiKPyVBSgAAUij8CrN29x6txV3L33EOnT2KJIgRy6WuUUeWYqYlfKJD5ifnw3BShAgTAJHPO+hB5/TIVDhaJo37Q60qRKEeT98qRe2sG4zVoN+UW8ePIgteyYBwWsQUCKOVZr5qwKL8nTdunOUM6xF+ZP6I9iBXNi447D2LX/BGaO6W0Nl8troAAFKKCpwITZq7Fw1Xb18zVJ4gTw83+IHFntsWDCAC6n13QmTD8Yk3jTGwcZQQrh9B/5XwXqnx0Z7O3Qp2MDjSPjcBSggFYCDx8/w7gZK7Ftr5faN5slYxokShgfsgzu5NkrCHj4RP0MaNmwiip4x4MC1iJw/dZd1G49BGc856vP9j8nLqBDfzd4bZuF+HHj4MyF6+g4wC2wurK1XDevgwIUoICpBQyF7Ub0a4N61Uur7Up37z9C2z5jUbl0IfTt3MjUIfD8GgowidcQW4Z6/+Ejpi9cHzjq6s371NOHdKlt1J/duH0Pz1+8xvLpQzSOjMNRgAJaC0jSfu2mH67e8MOjJ8+RPq0tsmRIjSwZ0iBB/Lhah8PxKGByASlqV7haJyydOhgF82TFuJmrcODoGXgsG6PGlifxS9buxIYFo0weCwegAAUoYE0CN3z9UbPVYHjvnItYsWIGXtrStTtVwdyFE52t6XIj/bUwiTfzR6BR5xFwalUbZYrlU5F4HjqFxat3MIk387xweApQgAIUMI2AFINz3+CJ8iXyY++R0xjRvw0cq5eBbCVp0d0VmTOkxvC+rU0zOM9KAQpQwEoFpMNHrdaDMaRHcxQvnCvwKmV5va/ffcgTeh7WI8Ak3sxz2XPoNBTMmw0tHSurSFZu2IPDJ85jhmsvM0fG4SlAAQpQgALGF/j06bN62n7+3xtqJVp9h9KIHj0a5Cn9rgMnkD9XFtinSWn8gU1wxis+t3Hn7kOUL5k/sFWkbJu74nNHraiR9nM8KEABCmghID9Dy9bvqYaytUkaOKSs+vv2z6aP7ol0qfXxM1YLN72OwSTezDO3ZvM+uM1ejVED2qkvMcMnLEbrhlXRromDmSPj8BSgAAUoQAFtBAIePcW7dx90k7wbVBp0dIFdyuSYMqq7+qNDXufQ2Xmi+m9p67Rq1jCktk2uDSJHoQAFIrWA3CDdvs8rRIOyxfJxy16ISpb/AibxZp4jWfoyesoyyN54OWR54bihnREnNu/em3lqODwFKEABCphA4OWrNzh66qLqD284zl7yweOnz1G+RAEULZhDPY239EPq1xSv1RVr5g5Hzqzp1XaAeu2HIbO9HTq1rAXXqctRtEAOdGxe09IvhfFRgAIUoIDOBJjEW8iEvX//AV++fmXybiHzwTAoQAEKUMA0AnXbDcWbt++QNUOawCXoN+/cw6vX75D7twyoWbk4KpYqaJrBjXjW7yvt+/oFwKG5M9bNG4HsWeyx57A35i7fgtWzXYw4Kk9FAQpQgAIUAJjEa/wpkOr0s5duDnbUPNkzolyJ/BpHxOEoQAEKUIAC2gj43XuIKk36w3vXPMSKGSNw0JUb9+CG7z0M6dlcm0CMMIqh572XxyzEjxcHngdPoeewaTi9ez5ixoiOE2f+RZ8RM3Fow1QjjMZTUIACFKAABf5fgEm8xp8GKXgzdOyCIKPee/AEpy9cw6gBbVHPobTGEXE4ClCAAhSggDYCT56+QKm6PXBq51zE/qYFktSHuXn7Hpy7NdUmECOM8vHTJ5So1Q19OjVEw5pl0dtlBp6/fI3Fkweqsy9btwvb93rBfeZQI4zGU1CAAhSgAAWYxFvUZ0CW0ldrPhCTRnRF3hyZLCo2BkMBClCAAhQwtsDd+48QJUoU2KVMZuxTa3q+tVv2q4K0hmP+hP6q4r6suqvWzBl1q5ZE93b1NI2Jg1GAAhSggPUL8Em8hczx4L/mI2WKJOjZvr6FRMQwKEABClCAAsYXGDV5KVZt3KtO3LV1HTi1roN5K7YidaoUcChfxPgDmviMUpTv0pVbKJQvm2orJ4dUifYPeIRkSRIhXtzYJo6Ap6cABSjwn8C2Pcdw8eot9O3UUNUckXpb5y764NT5qyhaIDtyZstAKisRYBJvIRPpvsFTtXuoWam4hUTEMChAAQpQgALGFZBWcuUde6sl55+/fEG7PuPU0voV6z1x6eotTHBxMu6AGp3tw8dPgSPtO3JaJfDN6lVC9GhRA4v3aRQKh6EABSKpgCTsFRv2RY1KxdCnYwOlsHTdLoyd7q5uJr5+805t7+GqX+v4gDCJN8M8Pnz8DFMXrMf+o2cg+wMzpbdD+6Y1UKsyE3gzTAeHpAAFKEABjQQMFd3P712oklvps/5Hr5a4/+AJ1m3dj3lu/TWKxDjDrNq0V+19v3XnfrAn7Nu5Edo2rmacwXgWClCAAr8QkPyibP1e2Ok+HmnsUkBuLlZo2AfN61VCh2bVMXrKcsSKFRMDnBrT0QoEmMRrPIlyl6xJl5H48uULalQsBjvb5PA+d1XdKZM98ZXLFNY4Ig5HAQpQgAIUypO/owAAIABJREFU0EZAfvdVa+6MUf3b4vf82dFn+Az11OjilVu44/8Q4/7opE0gRhjl7bv3KFS1E1wHdUDWjNIuL4o6q8ceL/j5P0CnFjWRImliJE2S0Aij8RQUoAAFfi1w1ecOpIXn+X2LEDVKFFy+5gvHDi7YvmIs0qVOiWPel+Ditkgl+Tz0L8AkXuM5NLSk2bXKDaltkweOPmH2akihn4nDu2ocEYejAAUoQAEKaCMgiW+Zej3VYBnSpVIV6eWQZZ7zxvdD8cK5tAnECKMY+sJf3P//he3ktHpsl2cEDp6CAhQws4Bhu9LedZOQMnkSbNh+CH9NW4FjHrNUUn/I6xxc3BZj79qJZo6UwxtDgEm8MRTDcI4bvv6o2WowDm+ahiSJEgS+c86yLTh32QczXHuF4Wx8KQUoQAEKUEA/AlLwbfOuI0ECjhEjOrJnsUfm9Kn1cyEApMWc+3pPNK5dXi1RNRzy9OvZi1eqSj0PClCAAloJyGrfas0GoEi+7GhcpwKGjJ2vim0aVjhNmf83Lvx7Q3fblrTy09s4TOI1nrHPn7/AoYWz+rLiWL0MbG2S4syF65g0by36dW6EhrXKaRwRh6MABShAAQqYV0CeIL179wH2aVKaN5Awji7LV9037sHXr1/RtrGDiv+O/wPEixOby+jDaMmXU4ACERfwPn8VnZ0nqtVNUsxuzZzhSJ/WVv3/eu2Hqp9TjZhrRBzaAs7AJN4Mk+Bzy1/tSTl94Vrg6NJip0ur2mq5Cw8KUIACFKCAtQq8fPUGR09dhPwuNBzSpu3x0+coX6IAihbMgfy5slj85ctN+bKOvZA7Wwb1VF5uRGxe7Kr6xseIHh1Deja3+GtggBSggPUJvHn7XtXlyJQ+NaJF+6/NHL5+ZacMK5tqJvFmnFC5K/bqzVskT5JI/SPjQQEKUIACFLB2ASm89ObtO2TNIMXg/vvdd/POPbx6/Q65f8uAmpWLo2KpghbPYCga5b1zLhAlCkrV6Y5180bA+/w17Np/AjPH9Lb4a2CAFKCA9Qk8ePQMt+78V29EDs9Dp1TdrVYNqiCNnQ3sUiazvouOhFfEJN4Mky53xLbuPoqjJy/g+YvXyGCfSvWT5T8qM0wGh6QABShAAc0E/O49RJUm/eG9ax5ixYwROK4ei8E9ff4SJWt3x5HN05E4YXx07O+G1g2r4vGzF9i+x4tJvGafKg5EAQoYBAa5zsXmXf/8FMS5W1O0dKxMMCsQYBJvhkl0nbocK9Z7okThXEib2gb7jpxBwMMngX0dzRASh6QABShAAQqYXODJ0xcoVbcHTu2ci9jfFINbs3mfqlQvXzD1cshy+hbdR6saN5XKFMK8FVtVgb7jZ/5F+RL50b1tPb1cCuOkAAWsQODx0xcoXbcHdriPQ1o7G13fJLWC6TD5JTCJNzlx0AFevX6LItW7YPbYPihVJE/gX7btMxbZMqbV1RcYjek4HAUoQAEKWImAJMB37z+E7I+XL5sJE8TT3ZXJlriqzQYEiVv2whfKmw1/9Gyuy2vS3SQwYApQIFDAP+AxKjXqi3N7FgbZpqvHlU6c1pAFmMSHbGTUV1y/dRdNnUbh2NaZQQpM/L3tIPYcPMXld0bV5skoQAEKUMDSBK7e8ENvl+m4ded+YGgtG1RBf6fGLO5qaZPFeChAAd0IyHbdS1du4bfM6RA9erTAuOUJ/fsPH7ltVzczGbpAmcSHzslor3rx8jWK1eyK3asnBPnHNHrKcnz58gVDe7c02lg8EQUoQAEKUMCSBKQVW732w1Tv4r6dG8Kxgwv+dG6HcTNWqqJLem2z+uHjp0DmfUdOwz/gkap1Ez1aVFaEtqQPIGOhgJULyH74GYs34OPHz3Dp2wpliubF3iPeSJE0MXJnz2jlVx+5Lo9JvBnme9aSTaqXrEOFomr0d+8/YNSkpWhcuzz/gZlhPjgkBShAAQpoIyBVk8s59sLJHXMQJ3YstT9+27Ix2L7XC4e8zmHa6J7aBGKkUVZt2otl63YFWVXw7an7dm6Eto2rGWk0noYCFKDAzwVki8/vDp3Ro119fP78GYvX7MDRLTMxbeF6PHz8DKMHtiefFQkwiTfTZPr5P8TJc1cgT+bt09qiZOHcbDNnprngsBSgAAUooI2AYUuZl8csRJG2bP9L4v/2OIjbdwMwrE8rbQIxwihv371Hoaqd4DqoA7JmlHZ5UdRZPfZ4qR7NnVrUVE+/kiZJaITReAoKUIACvxYwtL08v3ehWgFUpWl/TB3VHT6+97Bx+yHMHd+PhFYkwCTeDJPpefAUeg6bhjR2KSDJvBw5stpj0eSBiB83jhki4pAUoAAFKEAB0wsYnhRtWeKKjPZ2KomvVKogtnoexTy3/sibI5PpgzDSCL5+AXBo7oyL+xcHOSOLSBkJmKehAAXCJCAre6U6/Zo5w5E+rS26D5kCx5pl4X//EY6duoQpo7qH6Xx8sWULMInXeH6kIm+xmk6q9UwLx8rqC8zaOcMxdNwC5MmRiS1pNJ4PDkcBClCAAtoKzFi8EelS26BmpeJwGjhJrUarVLogCuTOqm0gERzt46dPcF/vqbbCxfqmXZ48DXv24hWKFcwZwRH4dgpQgAKhF5DidQ06uqitSkUL5IDnoVNImSIJLvx7E306NVQ/q3hYjwCTeI3n8o7/A1RtOgDn9y1SVXgNSwkPHT/PpS4azwWHowAFKEAB7QWkH7wspZcnRXLcvf8IiRPGR7y4sbUPJoIjSqG+7fuO47DXeTx/+QqZ0qdGk9rlkSplsgiemW+nAAUoEDYBeRI/YkLQlUHS9lIeEtarXprdP8LGafGvZhKv8RQZlt8ZejgakvjZyzarpfRdWtXWOCIORwEKUIACFNBGwLAabUjPFqhdpYQq6irF4SSBXzlzGDKlt9MmECONMn3RBkixWnnCJdeRP1cWnL5wDdtXjEW61CmNNApPQwEKUCD8AtJ6Dl+/slNG+Akt8p1M4jWeFll+l69ieyya5Izf82dXT+ITxo+rKtvucB+HtHY2GkfE4ShAAQpQgALaCEgdGCm2dNZzgaqWXLFRX8xw7YU9h70RM0Z0XbVZladeBat0xLJpg9VWAMNN+Ulz1ypMPRXp02b2OQoFKKCFgHQBuXXnXuBQsqxeVjxJG880djbsF6/FJGgwBpN4DZC/H+LAsbOIFyc2CuXNhvnuHshonwrFC+VC7G/21JkhLA5JAQpQgAIUMKmA372HaOI0Coc2TMW2vV5wm7Uae9dOxM79J/C3xwFdVU/2ueWPJk4jcXzbbGVmSOIPnziPTTuOYPbYPia15MkpQAEKfC8wyHUupFf8zw7nbk3R0rEy4axAgEm8GSbRmvYDmoGPQ1KAAhSggI4F5El883qVVOIuy+dH9GsDWZZ+L+CxrvoYS7yykuDUzrnqJrwk8cunDcG4mStRtlg+NKhZVsezxNApQAG9CTx++kJVp/9+ZS87ZuhtJkMXL5P40DkZ7VXWth/QaDA8EQUoQAEKRAqBo6cuYtzMVWr5vNuwLkiTKgX6jZyFutVKoeTvuXVjIPtMKzbsi57t66v9/ZLEP3n6AkUKZMfkEd2QMEE83VwLA6UABfQv4B/wGJUa9YWh7pbhipjE639ug7sCJvEaz6s17QfUmI7DUYACFKAABSxK4P6DJ6pYlE3yxDhx5l9ksrdD0iQJLSpGBkMBCkQOAbmxeOnKLfyWOR2iR48WeNHyhF7az9mxa4ZVfRCYxGs8nda0H1BjOg5HAQpQgAI6Fdh35DSyZEyjnrqHdMhrs2ZKi9S2yUN6qdn/Xvb1J4gXB6WK5MHzF69x0OusqkqfN0cms8fGAChAgcgn8OLla0yetw67D51SK4NyZLVH55a1UaFkgciHYeVXzCTeDBNsLfsBzUDHISlAAQpQQIcCG3cchuvU5RjetzWqli8SbL/iN2/fY86yzarg665VbhafxBuq0y+ZMkgVqm3ZwxX/Xr+N12/eqaJ2ktjzoAAFKKClQC+X6bjh669aVruMX4SmdSvCfYMnpozqjmIFc2oZCscysQCTeBMDB3d6a9kPaAY6DkkBClCAAjoVOOR1Di5ui1X0dauVRIZ0qZAgflzVau7cRR/8ve0gShTOpdrM6aHdqnxRbtxlJLw8ZuHaDT/UbTcUnqsnqKr7V3zuYNwfnXQ6UwybAhTQo4A8hS9Ws6v6OZQqZbLAjhkr1nuqVtZjhnTU42Ux5p8IMInnR4MCFKAABShAAU0E5On13iOnceX6bVy+5ov7D58gS4Y0yJYpLXJnz6irJ0VPn79ElSb9ccxjFtZs3qeedm1e7KqS+M072WJOkw8UB6EABQIFrt7wQ/Nuf/7Q9nLPYW8cPHYWE4d3pZYVCTCJN8NkVm8xEDUrF0fnFrUgSwynLliPrBnTYOyQTkiUkNVszTAlHJICFKAABSgQZoGmTqOQIllieF+4hqZ1Kvy3hNVtkWo5N6h7szCfj2+gAAUoEF4B2QMvXTIObZiqCmzKf/fv0hjz3beiVwdHlC/BffHhtbXE9zGJ13hWHjx6hnKOvXB0yww1six7ade0OrzPXVVtabq3radxRByOAhSgAAUoQIHwCMjv9MVrdiBG9Gjo0KwG4sWNjSVrdqr98JnS24XnlHwPBShAgXALtO41BuVLFkBLx8oqiU+SKD5qVCyGNo2rIUb06OE+L99oeQJM4jWeE9lD16rXGHWXTPYH9h0xE0e3zMSugyewaQeX32k8HRyOAhSgAAUoECaBr1+/IkqUKGF6D19MAQpQQGsB2b4kq4J4WKcAk3iN5/XLly8oWsMJiycPwrJ1O/Hi1RvMcO2FZet24czF65jg4qRxRByOAhSgAAUoQIHQCqzcuAd2tslRpmjeX75Fft9PmrsWdaqW4lP50OLydRSgQIQEpAtI5vSp0bBWOVy8chOrN+1D+nSp0KpBFUSLFjVC5+abLUuASbwZ5mPx6h0YP2uVGlla0xTMkxU1Wg5C83qV0KRuBTNExCEpQAEKUIACFAiNwDHvS+jxx1Q4VCiK9k2rI02qFEHeJk/qT1+4BrdZq/HqzVt10z5p4gShOTVfQwEKUCDcAobq9NtXjIWtTTJUbToA6VLbwMfXH06taqNJHeYY4ca1wDcyiTfTpEhLnVgxYyBhAhayM9MUcFgKUIACFKBAuATkd/i4GStVJfq8OTIhS8Y0SJQwPu4/eIKTZ68g4OET9OnYAC0bVuE+1HAJ800UoEBYBa7d9EPbPuPUll25kdi822hVqX73wZPYd+S06hXPw3oEmMRrPJefP3/B8TOXgx3VNkVSRI8eDXfuPkDxwrk0jozDUYACFKAABSgQFgFJ2uWLs7R2evTkOdKntUWWDKlV27wE8eOG5VR8LQUoQIEICbx//0EVszuwfgpk1e+RExewfPoQbNh+CHsPe2Pa6J4ROj/fbFkCTOI1ng9ZWlfesXewozaqVQ72aW2xdO1O1WuWBwUoQAEKUIACFKAABShAgdAI9B85C8dOX4a0mxs9sD3qVC2Jjv3dkC9nZji1rhOaU/A1OhFgEm9hEyV76eR/UaOy+ISFTQ3DoQAFKEABClCAAhSggMUKfPn6FQf+OaNW9pYonEt10jh70QcZ7VNxC6/Fzlr4AmMSHz63CL3rzdv3wb5f/sHFjMEejhHC5ZspQAEKUIACFKAABSgQSQSePHsZ6uKZ0nZODrae0/+Hg0m8xnP46vVbFKneJdhRm9atiCE9m2scEYejAAUoQAEKUIACFKAABfQosGTNDjx+9hLd29b9ZSFNKcjZd8RMuPRpzbaXepzo72JmEq/xJMoyl5u+94KM+uHjR7Tq+ZeqGlmsYE6NI+JwFKAABShAAQpQgAIUoIAeBW7duY/Bf83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"Sweden", + "Sweden", "Italy", - "United Kingdom", - "Germany", - "Germany", - "Germany", "Italy", "Italy", - "Germany", - "United Kingdom", - "United Kingdom", - "United Kingdom", - "United Kingdom", - "Germany", - "Spain", - "Germany", - "Germany", - "Germany", - "Germany", - "United Kingdom", - "Germany", - "Germany", - "Germany", "Italy", - "Germany", - "United Kingdom", - "United Kingdom", + "Italy", + "Italy", + "Italy", + "Italy", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", "United Kingdom", + "Japan", + "Japan", + "Japan", + "Japan", + "Japan", + "Japan", + "Italy", + "Italy", + "United States", + "Italy", + "Italy", + "Sweden", + "Italy", + "Italy", + "Finland", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "Switzerland", + "Switzerland", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", "Germany", - "Ireland", - "Ireland", + "United States", "France", - "Ireland", - "Spain", - "Spain", - "Ireland", "Italy", "Italy", "Italy", - "Spain", "Italy", - "Spain", - "Germany", + "France", "Italy", - "United Kingdom", - "Germany", - "Germany", "Italy", "Italy", + "United States", + "United States", + "Italy", + "Italy", + "Italy", + "Italy", + "United States", + "United States", + "Switzerland", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", + "United States", 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aff_name=%{x}
count=%{y}", - "legendgroup": "India", + "hovertemplate": "aff_country=Japan
aff_name=%{x}
count=%{y}", + "legendgroup": "Japan", "marker": { "color": "rgb(102,194,165)", "pattern": { "shape": "" } }, - "name": "India", - "offsetgroup": "India", + "name": "Japan", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Venus Remedies (India)", - "Tata Elxsi (India)", - "Samsung (India)", - "IBM (India)" + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd", + "Takeda Pharmaceutical Co Ltd" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=South Korea
aff_name=%{x}
count=%{y}", - "legendgroup": "South Korea", + "hovertemplate": "aff_country=Finland
aff_name=%{x}
count=%{y}", + "legendgroup": "Finland", "marker": { "color": "rgb(50,136,189)", "pattern": { "shape": "" } }, - "name": "South Korea", - "offsetgroup": "South Korea", + "name": "Finland", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Amorepacific (South Korea)" + "Nokia Research Center" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=Slovenia
aff_name=%{x}
count=%{y}", - "legendgroup": "Slovenia", + "hovertemplate": "aff_country=Switzerland
aff_name=%{x}
count=%{y}", + "legendgroup": "Switzerland", "marker": { "color": "rgb(94,79,162)", "pattern": { "shape": "" } }, - "name": "Slovenia", - "offsetgroup": "Slovenia", + "name": "Switzerland", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "EN-FIST Centre of Excellence (Slovenia)" + "Novartis Forschungsstiftung Zweigniederlassung Friedrich Miescher Institute for Biomedical Research", + "Novartis Forschungsstiftung Zweigniederlassung Friedrich Miescher Institute for Biomedical Research", + "Novartis Forschungsstiftung Zweigniederlassung Friedrich Miescher Institute for Biomedical Research" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=Russia
aff_name=%{x}
count=%{y}", - "legendgroup": "Russia", + "hovertemplate": "aff_country=Germany
aff_name=%{x}
count=%{y}", + "legendgroup": "Germany", "marker": { "color": "rgb(158,1,66)", "pattern": { "shape": "" } }, - "name": "Russia", - "offsetgroup": "Russia", + "name": "Germany", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Surface Phenomena Researches Group (Russia)", - "Surface Phenomena Researches Group (Russia)", - "Surface Phenomena Researches Group (Russia)", - "Surface Phenomena Researches Group (Russia)" + "Nokia Solutions and Networks GmbH and Co KG", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "Airbus DS GmbH", + "MacDermid Enthone GmbH" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", - "hovertemplate": "aff_country=Greece
aff_name=%{x}
count=%{y}", - "legendgroup": "Greece", + "hovertemplate": "aff_country=South Korea
aff_name=%{x}
count=%{y}", + "legendgroup": "South Korea", "marker": { "color": "rgb(213,62,79)", "pattern": { "shape": "" } }, - "name": "Greece", - "offsetgroup": "Greece", - "orientation": "v", - "showlegend": true, - "type": "histogram", - "x": [ - "Advanced Microwave Systems (Greece)", - "Advanced Microwave Systems (Greece)", - "Advanced Microwave Systems (Greece)", - "Athens Technology Center (Greece)", - "Athens Technology Center (Greece)", - "Athens Technology Center (Greece)" - ], - "xaxis": "x", - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "bingroup": "x", - "hovertemplate": "aff_country=Austria
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count=%{y}", - "legendgroup": "Austria", - "marker": { - "color": "rgb(244,109,67)", - "pattern": { - "shape": "" - } - }, - "name": "Austria", - "offsetgroup": "Austria", + "name": "South Korea", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Vienna Consulting Engineers (Austria)", - "Siemens (Austria)", - "Siemens (Austria)", - "Böhler Edelstahl (Austria)", - "Böhler Edelstahl (Austria)" + "Korea Hydro and Nuclear Power Co Ltd" ], "xaxis": "x", "yaxis": "y" }, { - "alignmentgroup": "True", "bingroup": "x", "hovertemplate": "aff_country=Belgium
aff_name=%{x}
count=%{y}", "legendgroup": "Belgium", "marker": { - "color": "rgb(253,174,97)", + "color": "rgb(244,109,67)", "pattern": { "shape": "" } }, "name": "Belgium", - "offsetgroup": "Belgium", - "orientation": "v", - "showlegend": true, - "type": "histogram", - "x": [ - "Aquaplus (Belgium)", - "Aquaplus (Belgium)", - "Aquaplus (Belgium)", - "Aquaplus (Belgium)" - ], - "xaxis": "x", - "yaxis": "y" - }, - { - "alignmentgroup": "True", - "bingroup": "x", - "hovertemplate": "aff_country=Liechtenstein
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count=%{y}", - "legendgroup": "Liechtenstein", - "marker": { - "color": "rgb(254,224,139)", - "pattern": { - "shape": "" - } - }, - "name": "Liechtenstein", - "offsetgroup": "Liechtenstein", "orientation": "v", "showlegend": true, "type": "histogram", "x": [ - "Ivoclar Vivadent (Liechtenstein)", - "Ivoclar Vivadent (Liechtenstein)" + "Vesuvius Group SA" ], "xaxis": "x", "yaxis": "y" } ], "layout": { - "autosize": true, "barmode": "relative", + "height": 900, "legend": { "title": { "text": "aff_country" @@ -8358,57 +5723,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 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oDa2jQ3zfjg3VOSnnet6y/7a0n9w+11+d1t9HXVrWQrO65QztnytRgAIU8EQgzIV4+UGs3ry/usCUL/7YMaNDxjOprnx2Y571xbvGlBAlF2XyIy/L0B7NbEHZFXilxr3Vxd6vaycj/n8TbLkrHBl/1bHfFLWKhIZXr96oY5Nl7fyhtknVNu48DJ/BMzCmf2uUK/b/Y0zlveQ97X9cJ8xehbn/dQGT/cjF4+Mnz1UXRfmR27h4lOpapi+WZB0JHnqRccByMSHjgiX4ysWsHucqf5cw6TjeWH7E2/aaqI5d3IoX+gL7jpxW28mYUrkzLcFZLmalZVkWuUiRmwIn/7iojm1kH2+33dD/uX4bZev3UMckQVQmzpHwJ+UoP5jywymLs3KUiwU99s7+feS4W3Yfp7rByWfLnjEN7j96YlvXbIg3WhZy3BKmGnf6dyy9DtRHT/2pPp92l9c8qQsyrEOCnZh9mTMjbty6p8aJy793rhqPRau3qhCvA22sGNHU+zevVwEF82VzWQekC6G0tDm2ksiNnCI1OkFC696fpri9mJL3rOE9UJW9XESnSpYYJ89cVMeiW1mMnh9GQ/w3VTuoYxNf6dp7+s9LKmxIHV02rZ86LwL6HrCvE3KhXL/dUFVf5DzLlC4Fjp/+S+3TvkeQ3qer88nIcbk7X+WGn26hkrpSKG82/P3PTXVcctPux7mDbeGsdqtB6u/yme3Ho8o8DgtWbHJbX1x9l+l6pr9vIoT3QubPU2Fw96ZqjKmR7zh9rkjZyA1NvQzp3vSDGyf6tY8J8frz632IgwQQOc9kTKscsyxycyZPaW+U+CYPJg1prya8krot9fPo5ln+hgnosen64t2+bst3bs4sn6vWZVnsv6t0q6R8/+rfGVlHuhi3bFgpwO9K+f5r3nWMbU4Ad+ez3BSR43RsHdc92KQL8ZRhHd39XBkqy1Xrd2Py/DUffKdILyUzId5dvZXvLf1dYsRbf0g5H2XiM/kdlWXzstGqa7c7R/mdNnquucUE1DAmuYEvv5lyo+rNm7c48N8QQfvvD3f1RLqK29dTec/A+l3Txy83i2q0GGBoTPzSNdvVEAlnIV4aHUrU7qqGtkwc3E6ddzIsL2GCOEifxv8EsqXr+ajvUWc9e7IWaayuZbYsG6MOceOOQ/AZMlP9bsiNMrkhLC3r8pvyWeL4ARWD09elZf3PC//Au/s4VZf3r5uqbnJLA0LxWl3Ub4jUaftFO0mjQv8u333U+3IjClCAAu4EwlyI1xe/Y/q1QrniXysbaeGp2rSv+u+dqyeoWdb1hbZcBA/u1sQ2M7WEHgmt8qOxackol7OzykVejuJN1Q+yTPgW0CLj5CWUSoiRC2jdQi5BuE3PCeqHTsZyqh+pjwjxcvHZq319JEkUT12AtvAZo1qk7bt8u+qeax9kZD8yw7dMJCN3vOXGhmOI1xNMyeRF0hIlMyXLew4at1C14ku3OZnpW41PXbROXSjLBbMs8mO5Y+8xJIgXG7mzpXfJJhfVMonP56mT2daR1q7yjXqpYKbN9bFLOchcCDJGTW4gyGdv2mWUP9ete46o1ma5oSAXsHreBH1RHlghPqCyEKuqzfqqi0n7+RLk7+Ua9lB3/3VLvNG6oFsM5EJVLk717NV37j3C+q371QWZLK66jbqrA1JW+cq1UjcXdq2aYOsFoSc1kpbL9s2quT0FdPCTnhoy5lHXhXVb9qswXapwXsPnh9EQLxdZGdKmsI2PlLrXse9k9SSCnxcOU3XL1ffAydMXVGBX3wNLR6s6JS12A8YsUD1BpAVZ/ib1VLqLyk0Zfa4FdD4ZOS7xcXW+6pt5EgYXTOhpa03WXUylRbxpnX+7nuowJDdO5GZMtoxp8frNG8SMER0FK7VVN3jc1RdXhSqTWUn527dOefIdp0O8nLcyjvrrPFlUDxRpVdM9RBzf20yIb9WokpoQTs55uUgvU7+7CvL/2zlffcc7hnh5b/395XjDUb5X5Ptl1+qJSJQgDoZMXIQfftqpbhJLIJehTHI+1vQeoG5k7lkzSX3f6XAm+5bWfZlMMXGCuOppFNKjIqDvSscQ7+58lhb0hu2HfzBho+62HNA8LZ6UpW69XDixp8teFK7qkauWeFf1VurrqGnLPPKWuj+kezPb0CfpSSO9MuzPE1ffi56ca+6+ACXAVmjUS/3uyzkr9UYWaXQoU6+7v5uA7upJnNgxPgjxgfW7po8/sEK8vpaS3w35zZbzTS9yM23CoHZqlndZOg2Yim17jmL++B4fPNVAB/z/7VqgvnPlRvTEOauVpf2EdrIfOf8G+TQNcGI7+7KS+iu/KXJ88p3as1191UNSFrlB8lX51uq/t/0wDrFj/f9s+bqXgP21m9sfQb5IAQpQwEOBMBXidbCWH23pZmW/6B9ufcfY3azUclEuP6Q7Vo5XodjZIuGoaI1O6kt/1exBARaLDgUNqpdUYdt+0SH50IbpqrXaaHCTfeiL4Z8WDPV3d3v5TzswdOJiNRmdBCRZAgrxrn6MHEO83HSQmw9blo+x/QjL/g8dO4MWPmPVmDZptdUXRhLy5N+ejluTfcqP/4XL13Hr7kN1F1+68MuF1cH101Trqqty1D++0vVSzx4vXQTlBsTkoR1U7wG9BNaYeKNloWe5d1ZPHcfEG60LOsTLBdOyaX1VC5OzJaAQ76oOSGuLtLrY2+nzZOsPY9XEQK4WfV7a9wxxXNeT88NoiJf3kOB+6cpNXLt1V9UfaaGU1uLpIzqhcP5cLuuPbKs/n/RikPDg7TNW9eJw7HmjbxjpHjK6Trq7uAvouNydr9J9VQL7pMHtUeLbPDZK/b7230k6DDnO2Gy0vrgqU2ch3pMy1OeKJ+NJPzbES4ByvNGqvwt2/zgR8h3hLMTr3kDyqDQJF7LIDeFSdbrZWuylHLMXbWLr9WT/lC0JihLMdbjV4cxxfK/s18h3pSchXr7/JDTa30SQz/ht1Q6IGyem7caUq/L1pCyDMsQ71tuP8ZZWUvtHdurvX3nCgTzpwN7fccJPT841uYEtjyCzX/Jkz6BubMjM7COnLoN944KsJxPVSZk4a4l3Vk+c1dPA+l3Txx1YIV7/dsmNurpViiNLhtTqLX7evE9dP0hvph+m91M373Truvx+9WpXT/XWksnu5Ptafndk0RPyDh7/PeTJHRLia1QorIYiPn7yDAtXblbXBp62jM9bvhFrNv5quyEgwxrkxqJ+/J3MmC89TqQ1Xq5t5PyR7wF5HKzcyNS9d1z+APIFClCAAh8pEKZCvL7AkruxI3r7n3hFBzU91sldiJexkPN/2OR24jLpdp6rRHN/XZ/dlZFu7XXWXVSPrV8zbzAypksZKCFe/4CO6ttSPeLOXSgI6DFbjiFe3xl39Xnlh1XG0p//+5qtB4T8kEuo+Sp3ZlQqXTDAsZgyA648MkoCm7NFd3dzd+xynG/fvlddyWWRgCwXtfoGgN5vUId4x7LQ7+dszOHHhnj5LHqyMflvueDIlS09KpbMb3sMm7uL1YDqgL7w1WMT9aOOjHTL1eelu5l8PTk/jIZ4CeuDxn/v7zFousx1bxEj3wM6aMqjFuW8d3z8n74Q1xdzAVkaOS5356sMbZDAYN+jR38uXcf1Ba+EeGch1mh9cXWOOwvxnpShqxte7r5DAwrxOgjbz07v6vPrILBtxTh1I9JZOJJj0a3ucsNS5oPQrYD6PXR3W3fHrb+DdYh3DJWyrZHvSk9CvOxT38jV3f51+chEYPKIycD6vQqqEO+s3gaGt96H/p1y973oybmmJ0Czd5UeIO2bVoOub9K4IAFVL+5CvLN64qyeBtbvmj6mwArxEnwlAMu1gFjrRW7qyiMJpffS0ql9kSvb5+ol3fPF3k9CvfScsb/hLUMN5Td1/ffD/T16TizL/tfD5vft8zxuNJAbm3ItJoHefpy7NCR07D/VNp+GPj65ppHWewn9Vn2ihduTnC9SgAKWFwhTIV6P97Mf66hLSHf91Xe33V1o6zFwAT2Gy9WPp7NaoR9F52wsuO7iqMOC0dZXeR9XF8O6y3tQhHjpWi0/XoN8mjg9AVInT6LGZMsik+VMW7AW2349autOJz/Miyf3tg0pcLYTHdTkx1u6wWZImxwJ4sVRM8fKeNOPCfFy3PaTxun3DeoQ71gWejZneaqC4/NozYR4uTiSbrk//rLH35hb6VqtJx0LqCXe3fPS9c0cGZv405Z9qvXQyIzUukuqs/NSl4En54eREK8Dj1xoSetJjszpkDRJAuzYd0z1UPEkxOvJlqT+yGSB+qaQPnYZ5pG/Ylvb0A133y1Gj0v27arnjO6C7KwHhA4Sp3ctUN263YV4I/XF1S+csxDvSRl+TIiXZ3nb9/RxPDb9PWo/hMjV59dd4AMK8frGgQwXadmgouqGLxf7u1dPVC11+jdHuge7mgw1X65MqrXQXYg38l3paYjX9VJ6kWxfMRaNOoxQc2Q43sQ0+3v1KUN8YHjrXnRGQrwn59pfl66peW7sF+k2L/69R8xVE8uumDlAzU8RmCE+sH7XAjvE6/ouNzHkZob9Iq3f42etVNcQMlmtXuTm8B9/XsaLl69U7y7pCSnDEO1vFuthQ/aPgdPb695SAU2k5+p7Td/gke70iyb39rfaH39eUnOPyJI25We4cv2OmrvIyE0xyycFHiAFKGBJgTAV4vVdaum+Jl0Y7RfdKjF+YFs1qZy7C219gaqDoquS1S2fzn6k9DbSrdHX1w+/nTyrJiWSi0HpXm6/6O5weoylvnB0bLV3N7GdY3f6oAzxOsw5Tvjk7gzQM9ouWLFZ3ekOaBy1njTQ8YJTXwx9TIjXXYsdZ5n91CFeX9zYjw/Xdo4h3pO6YO+vxtUfPoWhkxarlgztqEO84+PxAmo9ln3LTNQ9hs5Sk3Wt3bwP0aJGDrBbrmynz0uZA2HJ1D5Oq4kOKEbODyMhXj9BYNbormrWf73IrOh9Rs41FOL1eSmT9sWLExP12gxRk7Ad3zrH3yR+jt1z3VkaPS45XlchXrdYfT+pl+1mmawvvVdk1mb7ISTuQryR+uLqnHYW4j0pw48J8TrAOetpJcepj0nmPZGx5rKYDfHSCicTEcrNG+kKLWWihwvJ/vXjttzVbW0YUIi3/82QVmjH70p3Id7V4y71XBTSsiihyWhXY0/K8lOG+MDwdhfiHR09Odfc/f7J0AoZ0qd7Aup1A6MlPrB+1/QxBVZLvLvzVfdMCGiCW+kRKT0j7dfTNwudTT6sewnqnjOeXpXrxwYbGSaph1vpCRI9fS+uTwEKUCAggTAV4gVDJhOSmX/tu1pJi1MN7wHqWZ/Siihjc11daMtjfqo1629orLv97OvOJgqSVuiBYxeoxyZl/DylGvsmd+Xlmbj6sSgy0Z1005W/71g5TrWeyWPFGnccCZk4TiZrk0VCsEyeJKHM2ez0RkK8zBotXXn1zQJdeQIKcI7d6eUxRzMXr3M6e618Zhmfljl9KhUi5XEx9hNV6QuEgLph69b+w7/MsE3cJRPtyezyEqQ+JsTrC1r7Wf9ljOXkuT+qbrKBNbFdQGWhu5dLL4PVcwbbJuHREzA6PmLOSF2Q8YNnzl/2F1ilfHWZr54zSJXJ8rU7VB3SN7OM1gEdxqUO6wmKZDypjCs1sugWYvvuk7Kd3GC4duueanUxen4YCfG6y6X9REnyPSCTYskYy4Ba4mWmZLkglHGXek4FCUHSguTYeqTnC9AXmu7OJ6PHZV92juerngyzYskC6uJWL9LbpVP/qf5mXXcVYo3WF09CvA4kRr7jPibE60At9W/jklFq0k296O9t3eosk9UFRoiXfegbL/K7IfVCt97r99Y3d+y78evXZGx5ymSJ/h129d8zs511kzbyXeksxLs6n/X7yxMJpA7oRX8PBHTOelKWnzLEy3Gb9XYW4l05enKuuTM9dPwMmnUZrYY5zRnroybElUc9zlj0s3o+urMx8Ua703vyuya/MUtWb0W0qFFcTkYaWCGLpIxDAAAgAElEQVRehh6VrttdTeYr1zz6MbryPZy/Yhv1O+LYJd7eUH7npazlnN66fIyte7y+OSDfzfJbq5+CoIekiK1cN8i1lD5/4eeH5vUr2J4iJJ9RrsFkAl/7RZ7YMXbGCtX9X4YBuFp0falcuhCG92oe0OnE1ylAAQp8lECYC/G6y6VcNMmzgeULXSYgkcmn6lQphn6d/g3F9jNIyxe2zGIts43KRDayGJ1pV98plm1kTKyM73rz5h3OXriiZluVRQd8HX7lh7x25WJqfO30hT+rHzn7UCXdIOXRLGq8VY1S6nnOvx763fYopo8N8Xo8p/RUkN4It+8+VDcKYsSIqh4v5qortWOIFzvp4iYBTJ6jKzPRy2eRZ8uv23rA9ggkCSxyESTdTCVAyueRSW3kx3ne+O74+ossLit110HT1TPopYVL9i93yKUlWD/+7mNCvG4xlTdtXLsMokeNgl0HTti6ngcU4uXZ9Zt3/gZpHZdniUvZZMmY2vaoPE+GNuhWXvl88sznf67dVuOcZbF/xJzRuqB7acgkXEXy51IXPnKhImUuXQMXTuqlZvbVMwbL601ql1GPOZLPIDdb3NUBXVA6yMq/jXTL1dvpG1Py7zaNq6hgI+UhExTJpIfy7Haj54eREC/7ldYeCV4SduWCTm5gyXvK4hji5XtCzoX0aZPj7v3H6vFrUtfsH50k/5ZWWVnkuyVd6mQ4fPyM+gxyQ2btvKH+Hlvn7Hwyelzqe+O/WZgdz9fECeOiftuh6jySVulv8+dUz8GW+ieLfTd7VyHeaH1xdYI6a4mXdY2W4ceEeNm/Hooi5SVP0ZBx6vKIKbGSxX4iT/m32ZZ42Yf994azyQrlud61vAeq95ffmOyZ0uLOvYc4+vufaiJEHZzdhXgj35XOQryr8zl/nqy2otM3to30FrAvb6Nl+alDvFlvZyHelaP8Rhk911z+mP33QotuY2yPlHOcWd1MiPfkd00HY2dP1ZGbgPfuP8aN2/fUnEDqe+e/SXGrlftGDUWTRUL4ynW71H8fOHpaPe1DrqEypUupZm/XTwWS1/UjJ+X3pnWjSuoaYPnPO9TNMMdeIXIzwyu8l7RYqN8uuZaQ45w2ohPy5szkj3fU1GXq0YBSp+tVLQ6pEzIGX/bv+GheeUSdLPY3DJat3Y5hk5ao65ccWdMhbuyY+O3EWXXNIYvjTcIhExapp5k8f/lKfWa5npSJ+WaO7Kx+a7lQgAIUCAqBUBvi9SNb7H/8NKB0j+49co6/R5rIuOD2zarbWj3tHy0l2+lwKF/Ig32afNCi6a5wpLVl9PTl/p53LOvLl3zNCoXVs0yjRomsfvxmL/E/WZt+NJoEDftFd/HWf5MwUr9aSciPlzyeqLN3TfXSpLk/qn3qR2bp9XV3evsfNPnMk+b8qMbm6dZUmWhHxp1Jy7fREC/vIRdCEuhksiT7RUJkpxY1kSNzWvWaBC75wdaLfN6OzWsEOLGS7L9930n+nm0tE6Pde/BI/YAeWDdNXTDI53B17NI1/d3797bny6of5/8e36ePRy6mpLv1kh+3qckQJRS5Wuwvluy31y21npTF4yfP0XngVPVZ9CI3ZzZsO/jB5GlG6sKjJ89UaJVyt1/kcXoDuvz7nF29SED94eedtnKRFoeyxb5yWwf0tvommdFuufbHItsOm7zE32OBZHxor3b11c0vo+eH7pq6dt4QdY7J4nijSfbVf8x8SPd5vcjNs0yfp1She9rwTurmia4/zh6DZD+fhN6HtPb0GDbLdjNA/i7BTrp2ymPEZHFXJ40el+zH1fkqNwyk/gwav9BfeUsZj+vfxt+YW1ch1pP64ux80CHecQIpo2Xo6lxxefL994L0nJGbsvI9aP/IKj2rtdRj3QInm7j6/HIBLxfy+gkkeshHycJfYuKgdh8chu6yLM+Ql5u1jouMlx0xZZm6SWa/yHdWz7Z11Y05fe4M6PIdalUq6m89I9+VujXXsUu2s/PZfiIxHcYdw01A1kbLUsKUlIfj8I6A9i+v6zHnjl2SAxoGYsZbh3jH7zBXjkbPtYA+rzxKcP7yjZByfPfuvertV7pIPjVEw37eEnf1xFU9Nfq75i7E6+9QZ59j389TVNCVRR4/+EWpFk4/rrMnrkjgHztzhb/zVcbIy6NGdY9E2ZnuNSb/LdcJMgmujDd39ux3eS689KCTuqcX2WZg18b+biLIazrE24+Tl/NUnhYgPVXsF/k9kvNLbmrbL3of8jc5l8sWzacmv9OPqQ2o7Pk6BShAgY8RCLUhPiAMudi7fvMuXr56o7pd2v9YyLb2XV5lrOPdB49VS6WMJ/3YRS56rt+6q577myh+HPUINGeLtFr/c+2O6h6WImlCl8+ilx+qq9fvqMfOOfsh+9jjlPe/efu+mihOxjWbWeQzy77e+/oiScK4trv19vuU1mR5RJz8yMpYVaOPmpMylM8vk9wkTZzA3zNazRyzlM/lf26q/QX3XXS5oHz0+ClSpUjyQR21/4xG64KUx627D9QFk5SHqzoowzOk9TZ69Ci2izMjpjKeXIKx0W65zvYp9UG6c8ePG0vVbcfF6Plh5HjF9+6DR0gQN1aAZS0Xp/JYMa9wXqobvX7EkLP3kZ4h8jgpaQl29hkCOjZPjsvd+SrfY3KOxI8b2/bs6YDe2/51o/XFk33KuoFZhs7eW74b7t57iHsPn6ibJ/K9Ld/fwb1Il38ZUiQX94kSxHVbh1ydGx/zXenqfJbyLVmnG549f4F9P0/16PnZ+viCuizNlJlZb8f3dve9aPZcc/Y59/32PzVErG+nhuoxbGYWK/2uOX4OqYdXb9xRN9Xl8aeO12OyvtxYlO7+MvxOnhhhfzPOlYvc1Pjn+h1EiRxRfRfrYTRGHeV3VXokyv9LY4a+UeG4vZzTDx89UZOjulrH6HtyPQpQgAJGBcJsiA8IKKBx4AFtz9cpENYEZLhJsRqdVRdGVxPUhTUTfl4KWFlAz5MgQ1ZkQj4uwScg46hl6Jp0y5Ybfxev3IDMpyE91eSJF8F9Qzn4ZPjOFKAABSjgTIAh3kW9YIjnCUMBzwQ+tluuZ+/CtSlAgcAS0JPAbV8xLlB7cwXW8YWl/bTpOUHNEeO4OBu2E5Zc+FkpQAEKUMC5AEO8i5oh3btkbHiyJAkh47i5UIAC7gV27DuunjxQoWSBj+qWS18KUODTCUj36g3bDqghNc7G8X+6I+E7iYB0yZbJX2Uojgw/S/FZQuTIks42YzqVKEABClCAAvYCDPGsDxSgAAUoQAEKUIACFKAABShAgRAiwBAfQgqKh0kBClCAAhSgAAUoQAEKUIACFGCIZx2gAAUoQAEKUIACFKAABShAAQqEEAGG+BBSUDxMClCAAhSgAAUoQAEKUIACFKAAQzzrAAUoQAEKUIACFKAABShAAQpQIIQIMMSHkILiYVKAAhSgAAUoQAEKUIACFKAABRjiWQcoQAEKUIACFKAABShAAQpQgAIhRIAhPoQUFA+TAhSgAAUoQAEKUIACFKAABSjAEM86QAEKUIACFKAABShAAQpQgAIUCCECDPEhpKB4mBSgAAUoQAEKUIACFKAABShAAYZ41gEKUIACFKAABShAAQpQgAIUoEAIEWCIDyEFxcOkAAUoQAEKUIACFKAABShAAQowxLMOUIACFKAABShAAQpQgAIUoAAFQogAQ3wIKSgeJgUoQAEKUIACFKAABShAAQpQgCGedYACFKAABShAAQpQgAIUoAAFKBBCBBjiQ0hB8TApQAEKUIACFKAABShAAQpQgAIM8awDFKAABShAAQpQgAIUoAAFKECBECLAEB9CCoqHSQEKUIACFKAABShAAQpQgAIUYIhnHaAABShAAQpQgAIUoAAFKEABCoQQAYb4EFJQPEwKUIACFKAABShAAQpQgAIUoABDPOsABShAAQpQgAIUoAAFKEABClAghAgwxIeQguJhUoACFKAABShAAQpQgAIUoAAFGOJZByhAAQpQgAIUoAAFKEABClCAAiFEgCE+hBQUD5MCFKAABShAAQpQgAIUoAAFKMAQzzpAAQpQgAIUoAAFKEABClCAAhQIIQIM8SGkoHiYFKAABShAAQpQgAIUoAAFKEABhnjWAQpQgAIUoAAFKEABClCAAhSgQAgRYIgPIQXFw6QABShAAQpQgAIUoAAFKEABCjDEsw5QgAIUoAAFKEABClCAAhSgAAVCiABDfAgpKB4mBShAAQpQgAIUoAAFKEABClCAIZ51gAIUoAAFKEABClCAAhSgAAUoEEIEGOJDSEHxMClAAQpQgAIUoAAFKEABClCAAgzxrAMUoAAFKEABClCAAhSgAAUoQIEQIsAQH0IKiodJAQpQgAIUoAAFKEABClCAAhRgiGcdoAAFKEABClCAAhSgAAUoQAEKhBABhvgQUlA8TApQgAIUoAAFKEABClCAAhSgAEM86wAFKEABClCAAhSgAAUoQAEKUCCECDDEh5CC4mFSgAIUoAAFKEABClCAAhSgAAUY4lkHKEABClCAAhSgAAUoQAEKUIACIUSAIT6EFBQPkwIUoAAFKEABClCAAhSgAAUoEKZCvK+fH+DnBy8vrw9KXl67e+8hEsSLg/DhP3z92fOXePvuHeLGjslaQwEKUIACFKAABShAAQpQgAIUCBaBMBPi/fz8MHDcQoU8qFsTf9h7Dv0On8Ez8PzFK/X3gV0bo2bFIuq/X7x8jR5DZ2Ln/hPq3zmzpMPkoR2QIF7sYCkwvikFKEABClCAAhSgAAUoQAEKhF2BMBHit+w+gqGTFuPBwyeoUaGwvxD/6vUbfFu1A9o1qYr61Upi14ET6NhvCrYsH4PknyXEvGW/YOWG3Vg8uQ+iRY2MVj3GI23KzzC4e9OwW2v4ySlAAQpQgAIUoAAFKEABClAgWATCRIh/+eo1njx9jgmzVyNy5Ij+Qry0wrfpOQEnts1FpIgRVCGUb9gT9aqWQP1qJVDTewBKF8mH5vXKq9fkhkCXgdNwetcChAsXLlgKjW9KAQpQgAIUoAAFKEABClCAAmFTIEyEeF20QyYswrv37/2F+FXrd2Phys34ZfFIWw1o32cS0qT8DF1a1kK+cq0wtEczlCqcV71+5vwVFewPrp+GWDGjh81aw09NAQpQgAIUoAAFKEABClCAAsEiEOZDvHSX37z7N6yaPchWADI+Pnr0qBjQ5TtkK9oE00d2RuGvc6rXL16+gUqNe2P7inH4LHH8YCk0vikFKEABClCAAhSgAAUoQAEKhE2BMB/ijbTED+vZHCW//VLVEMeW+Bv3X4bNmsNPTQEKUIACFKAABShAgVAokDR+1FD4qfiRQpNAmA/xekz8ye1zETHCv2PiS9fzQaMapW1j4ssUyYdmLsbEM8SHptOBn4UCFKAABShAAQpQIKwLMMSH9Rpg/c8fJkK8r68v3vv6YujExXj37j0GdmuM8OHDwytcOMikd1+WaYke7eqhftUSH8xOP3fZL1ilZ6ePFhmtuvufnZ4h3vqVnEdIAQpQgAIUoAAFKEABowIM8UaluF5wCYSJEL9y3S4MGv+9P+Mh3ZuiWrlv1d927T+Bdn0m2V7v26kh6lYprv4tz46XMfLSYi9LtkxpMGVoRyRKEEf9myE+uKou35cCFKAABShAAQpQgAKBL8AQH/im3GPgCoSJEG+ETFrrb955oMK57lZvv508ou7N23dIEC+2v90xxBvR5ToUoAAFKEABClCAAhQIGQIM8SGjnMLyUTLEmyx9hniTgNycAhSgAAUoQAEKUIACFhJgiLdQYfBQnAowxJusGAzxJgG5OQUoQAEKUIACFKAABSwkwBBvocLgoTDEB0UdYIgPClXukwIUoAAFKEABClCAAsEj8ClD/PVb9/Dz5n3YtvcY4saOgfnje8DZ34JHgu9qVQG2xJssGYZ4k4DcnAIUoAAFKEABClCAAhYS+JQhvlGH4bj/8Amqlv0G79/7omXDinD2NwvxuDyU12/e4otSLTC8VwtULl0wJBxyiD1GhniTRccQbxKQm1OAAhSgAAUoQAEKUMBCAp8qxN978BiFq3XE9BGdUDh/LiXg7G8WonF7KK9fv8EXpb0xrGdzVClTKKQcdog8ToZ4k8XGEG8SkJtTgAIUoAAFKEABClDAQgKBFeIPHvsD42etxJVrt9VjqzOkS4EmtcuiUqkCePTkmWpxv3j5BrJkSIXIkSKharlC+H7lFn9/q1+9BMoW/cqQjrzfnKUbcPrcJSSMHwdf58mCdk2qIm7smOrmwOjpP0DWkbBdrOAX8GlTB/HjxlL7XrhiMy5euQF5DLdefpJu/r8exbThndSf+o+ej/jxYuO9ry82bDuIiBHDo27l4qhXrQQiRYyAtr0nYveBk0ieNCESxvv3cdxzx/ngz4tXMWb6Dxjs0wS/7DiEU2cuIlO6lDjxxwV0b1sXOTKntb3npl2HsXztDvWeMWNEM/S5w+JKDPEmS50h3iQgN6cABShAAQpQgAIUoICFBAIrxG/ZfQSHj59BzqyfI2qUSNi57wTWbzuAJVP7IHP6VJg090csWrUFzeuVR5JE8dTfZBv7v+XK+rn6e0DLnkO/o03PCepGQZ3KxfDs2QssXLUFU4d1RNYMqVG5SR/cvf9I3USQZcGKTSro/7xgGCJECI8hExbh1NmLWDV7kO2tZi5eh/nLN+K3jTPV32p6D8CZ81eQO1t6lCqSF1ev38Gytdsxa3RXFMqXHas37MGAsQtQvkR+5M72+b/bVCiibhy06jFe/Ttd6qTIkj41smVMg1lL1yN/nqwY3beles3Pz08dZ6pkiTFlWMeAPnKYfp0h3mTxM8SbBOTmFKAABShAAQpQgAIUsJBAYIV4/ZEknD55+gIPHj1BhUa90K11bRWmfz9zEfXaDMHaeUNU+JbF2d+M0FRq3BsyJn3LsjG21V+8fK2C8f4j/0PnAdMwfWRnFP46p3p9z8GTaNNrIiYOboeS335pOMQn+ywhJgxsi3Dhwqn9yPt+lTsL+nRsoFr4nXWn33v4lArxI3p7q14IepGbBFPmrcGeNZOQIF5sHDt1XvVOmDe+O77+IouRjx1m12GIN1n0DPEmAbk5BShAAQpQgAIUoAAFLCQQWCH+4eOnGDtjheqSLt3p9SJd3Ft/VznQQryeUO67WmXQvU2dDyRnfP8zpi5Yi0Mbptu6qD95+hz5K7ZV3e3lWIy2xGfPlBb9u3xnew9p/ZdFbhAEFOJ3rByvehzo5c69RyhaoxM6e9dUvRF8Bs/Amb+uYMOiEbabBBaqFpY6FIZ4k8XBEG8SkJtTgAIUoAAFKEABClDAQgKBFeKllf3qzbvo1a4esmVKo7qvl6rrg3pVigdqiH/24iW+Ktca7ZtWQ6tGlT6QnDhntRorf3zrHESOFFG9rgO3rC/bSYg/fvov1StAL8660zuG+PZ9JqlZ9T8mxMv7SHA/dOIsVswcgJK1u2JAl+9Qq1JRC9UGax4KQ7zJcmGINwnIzSlAAQpQgAIUoAAFKGAhgcAI8TpYd/GuiWb1yts+3TdVOwR6iJedy36TJo6vwrD94uvnp55D33fUPCyc1BN5c2ZSLx85eQ6NO420zSQ/Zf4arFy/G3vXTrZtLi34Mnbefky8uxAvYT5H8aaqpb62XRDX3ekdW+LljXQXehlOcP3mXexaPRHRo0WxUG2w5qEwxJssF4Z4k4DcnAIUoAAFKEABClCAAhYSCIwQLx9HJoLz8vJCt1a18e79e6z55Vds3HnY1oU9MMfEz1u+Uc2EX7NiEdQoXxivXr/B9ys3o3n9Ckib8jMUr9UFqZInVu+NcOEwdf4aNWu+BGuZBf7k6Quo324oerarBwnqMhmddMGXQG00xMtnlrHvz56/VGPkZR6AL3NmxIGjp9XfnYV4PZmdzNLfuHYZ+LT+cDiAhaqGZQ6FId5kUTDEmwTk5hSgAAUoQAEKUIACFLCQQGCF+ANHTmPQhO9x7cZd9ekqliygZqfX3d5tIX7+UGRIm1yt4+xvRmjevXsP6f4ured6kS78Ewa1Uy30sl+Z3O723Qfq5cQJ46lJ7fTj3aQVfeC4hViz8Vf1urSMy3bSYq9DfO1Wg9RM9/Zj4jv2m6JuUOjH0En4HzFlqXpMnixHNs3CsVN/qhC/c9V49b6Oi57gbtPSUUiZLLGRjxvm12GIN1kFGOJNAnJzClCAAhSgAAUoQAEKWEggsEK8fCRpaZYW73hxYiJWzOhB/il9fX1x++5DxIge9YPnrMuxyGv/hvi4TiePe/rsBV68eo3ECeKaOlaZtC5WzGiIEjlSgPup2qwfPksYT42r52JMgCHemJPLtRjiTQJycwpQgAIUoAAFKEABClhIIDBDvNmPNWH2Kiz/aYfb3UjLfsMapcy+VbBsr8fmzxnTDQXyZguWYwiJb8oQb7LUGOJNAnJzClCAAhSgAAUoQAEKWEjASiH+7bt3avZ3d0vECBEQPryXhQSNH4qMuz997pJqhff679nzxrcOu2syxJsse4Z4k4DcnAIUoAAFKEABClCAAhYSsFKItxALD8VCAgzxJguDId4kIDenAAUoQAEKUIACFKCAhQQY4i1UGDwUpwIM8SYrxrZGo3B3+1G1l1QtKiFNhxom98jNKUABClCAAhSgAAUoQIHgEmCIDy55vq9RAYZ4o1Iu1pMQf23JVvVqhn6NGeJNenJzClCAAhSgAAUoQAEKBKcAQ3xw6vO9jQgwxBtRcrMOQ7xJQG5OAQpQgAIUoAAFKEABCwkwxFuoMHgoTgUY4k1WDIZ4k4DcnAIUoAAFKEABClCAAhYSCI4Qf3XDQY8Eon0WH/HzZPBoG64cegQY4k2WJUO8SUBuTgEKUIACFKAABShAAQsJBEeIX5e3Ne4fO29YodKRGYES4uXxdfcePkbsmNERJXIkw+/PFYNXgCHepD9DvElAbk4BClCAAhSgAAUoQAELCYSFEH/m/BWMnLoUx079/42DDOlSoFndcqhQIr+FSoOH4kyAId5kvWCINwnIzSlAAQpQgAIUoAAFKGAhgdAe4iXA1/QegEY1SqFx7TJIEC8Obt19gI07DuHkHxcwbXgnC5UGD4UhPgjqAEN8EKBylxSgAAUoQAEKUIACFAgmgdAe4ht1GK66z08Z1vED4Vev36hu9X5+fli5fjcWrdqCJ89eoFrZb1CvanEkThgPFy5fR5+Rc9GrXX0sWr0Fd+49wsBujdFr+GyULfY1lvy4DW/fvUMX75qIHCkiZi5eh4ePn6Fh9ZLwblBRvefR3//E4Anf49adB+rfRQrkRt+ODRArZnS1f9mX9AhY9tMO9XqzOuVQq1JRHDl5DpPm/og5Y7shapTI6rU9h37HktVbMWtMN3iFCxdMtebTvi1b4k16M8SbBOTmFKAABShAAQpQgAIUsJBAaA7xMgY+R/GmmDSkPUp8k8elurTKDxy3EAO7NUGaFEkwc9E6FfwHd2+K/539G3VaD1aBvnr5b1Xoz5crk/pbycJfolaFIvj9zEVMXbAWWTKkUsH97bv38Bk8A78sHonUKZLgjz8v4a9L15E5fUq8fPkG/cfOV0Fegr/ef7GCuVVwv3bjLoZOWoyD66epmwLfVO2APh0bonLpgur4m3YZhWyZ0qptw8rCEG+ypBniTQJycwpQgAIUoAAFKEABClhIIDSHeGk1L1qjE5ZN74ecWdIp9VmL1+P+w8fqv1MlT4L61UqgQbthSJU8MRpUL6n+fvYvGUO/DAfXT8eZ85dVYP9t40xEjxZFva6D9+ldCxAuXDi8ePkaecu2xMrZA5E1Q2q1TtVm/VQX/qplv1H/vnv/EU6c/gu37z7Etl+PImaMaKorv+O+ZF0J7kN8mqJIgVwYP3sVDh8/gxUzB+DSPzdRoVEvbF42GimSJrJQLQraQ2GIN+nLEG8SkJtTgAIUoAAFKEABClDAQgKhOcTrlvjxA9uidJG8Sn3Bik14+Oip6uIeJUokzB/fQ4XmaFEjI2G8OP5KRlrwb9y6p0K8DuzOQryvry+yF2uKlbMGIGvGNGofcmOgfPGvUbdqcWzadRjdBs1AnhwZkOnzlDj/9zXVoj9zVBenIb58w55o26QqyhX7Cv9cv42y9Xtg9ZxB2LD9IC5fvRXmxvEzxJv8wmCINwnIzSlAAQpQgAIUoAAFKGAhgdAc4nWYjholEuaM9fGnLi3yh0+cUSFeJr6rVKogGtYo9UHJOGspd/ybr58fshdt4jLEV2rcG2WLfoXW31VW+5//wyb8duKsoRAv63v7jEX8uLGwY99xyA2JQvmyW6gGBf2hMMSbNGaINwnIzSlAAQpQgAIUoAAFKGAhgdAe4k+d/Rt1Ww9GlTKF0KpRJSRNnAAPHz/FuJkrcPveQxXiZy9Zj8U/bsP04Z2QNWNqXL91D6s27PE3Zt1tS3wAIV5a5TOkTY4uLWvh6o07avx93NgxDYf4nfuPo32fyUieNCE2LR0dZia006cJQ7zJLwyGeJOA3JwCFKAABShAAQpQgAIWEgjtIV6o5fnwo6cvx+lzl2zy2TKlwXe1yqgu62/evsOE2avU7PR6yZsrExZO7Km2qd1qkL/u9I5/c9YSL7Pily32FepWKY4DR06jx/DZePDwiRpXnyFtCsSKEQ3TR3Z2un/pTt+uaVXVei/Lu3fvkbNEM/RsV89pbwELVacgORSGeJOsDPEmAbk5BShAAQpQgAIUoAAFLCQQFkK85n795i3uPXiMBPFiq5nfHRcZQ3/v4WM1M72MWQ/MRfZ98/Z9JEkUDxEihPdo19L1vknnUWrGenksXVhbGOJNljhDvElAbk4BClCAAhSgAAUoQAELCQRHiN9eqa9HArkHNEL8PBk82iY0rdy290QkThAX/bt8F5o+luHPwhBvmMr5igzxJgG5OQUoQAEKUIACFKAABSwkEBwh3kIf3/KHIjPfr9t6APlyZ0bSxL4lpGoAACAASURBVPEtf7xBcYAM8SZVGeJNAnJzClCAAhSgAAUoQAEKWEiAId5ChcFDcSrAEG+yYjDEmwTk5hSgAAUoQAEKUIACFLCQAEO8hQqDh8IQHxR1gCE+KFS5TwpQgAIUoAAFKEABCgSPAEN88LjzXY0LsCXeuJXTNRniTQJycwpQgAIUoAAFKEABClhIgCHeQoXBQ3EqwBBvsmIwxJsE5OYUoAAFKEABClCAAhSwkABDvIUKg4fCEB8UdYAhPihUuU8KUIACFKAABShAAQoEj0BwhPinB4549GEjJoiHKBnSebQNVw49AmyJN1mWDPEmAbk5BShAAQpQgAIUoAAFLCQQHCH+7xZd8er8RcMKaeeMC9YQf+zUecSOFR2fp05m+Ji5YuAJMMSbtGSINwnIzSlAAQpQgAIUoAAFKGAhgdAc4t+/90WO4k1t2s3rlUdn75qYt3wjkn+WEKWL5DVUEm17T0SOzOnQsmFFQ+tzpcAVYIg36ckQbxKQm1OAAhSgAAUoQAEKUMBCAqE5xAvz23fvUKFRLzStUw41KxSGl5cXOvabgkyfp0Tr7yobKgmGeENMQbYSQ7xJWoZ4k4DcnAIUoAAFKEABClCAAhYSCO0hXqhL1/OBd4OKqF7uW2zZfQT9Rs9D5MiRkDRxfGRImwJDujdFz2Gzsf/oaTx4+ATpUidFuyZVUarwvy31OsTXrVIMLXzGom/HhsieOa167c69R2jfdxLG9m+NFEkTWahkQ8+hMMSbLEuGeJOA3JwCFKAABShAAQpQgAIWEghrIf72vYfoOnA6UiZLjKrlCiFGtKjInD4Vlq7ZjvRpkiFe3FjYc+Akxs9ehQPrpqmx8PYt8W16TkDcODExrGdzVYqzFq/H9r1HsWr2IAuVaug6FIZ4k+XJEG8SkJtTgAIUoAAFKEABClDAQgJhLcQLvbPu9L6+vjh34SrOXbiCO/cfYcq8NVg5awCyZkzjL8TvOfQ7JMhLwI8RPSqK1OgEn9Z1UKlUAQuVaug6FIZ4k+XJEG8SkJtTgAIUoAAFKEABClDAQgIM8cDzF6/Quud4nLvwD4oV/AJJEsXDnKUbsHxGf+TInNZfiJfJ8krW6Ybm9cohaeIE6D50Jvaunay653MJGgGGeJOuDPEmAbk5BShAAQpQgAIUoAAFLCQQVkN8xnQp0KZxFVUSO/YdR4e+k3Fw/TTEihld/S1rkcZOQ7y8Nm/ZL1izaS+SJUmALBlSo1OLGhYq0dB3KAzxJsuUId4kIDenAAUoQAEKUIACFKCAhQRCe4h3Njv97CXrcfT3PzFlWEfVCn/+76to1mU01s4bgsQJ42HjjkMYOmmxyxB/78FjFK7WUZXiluVj1OPquASdAEO8SVuGeJOA3JwCFKAABShAAQpQgAIWEgjNId7Vc+IvX72FzgOn4fzFq8idLT0WTemNLgOnYdueo6pkihXMjZ37T+CHGf3VLPTt+0xS/y8z3OvF22csIkeKqG4EcAlaAYZ4k74M8SYBuTkFKEABClCAAhSgAAUsJBCaQ3xAzPI4Oek+HyFCeLXq/YdP4OUVDnFjx3S76dNnL/B1hTaYO84H+fNkDeht+LpJAYZ4k4AM8SYBuTkFKEABClCAAhSgAAUsJBAcIf5qr2EeCSRsUgdRMqTzaJugXHnRqi1YunY7Ni0dDa9w4YLyrbhvAAzxJqsBQ7xJQG5OAQpQgAIUoAAFKEABCwkER4i30Mf/qEPZe/gUYseKoWau5xL0AgzxJo0Z4k0CcnMKUIACFKAABShAAQpYSIAh3kKFwUNxKsAQb7JiMMSbBOTmFKAABShAAQpQgAIUsJAAQ7yFCoOHwhAfFHWAIT4oVLlPClCAAhSgAAUoQAEKBI8AQ3zwuPNdjQuwJd64ldM1GeJNAnJzClCAAhSgAAUoQAEKWEiAId5ChcFDcSrAEG+yYjDEmwTk5hSgAAUoQAEKUIACFLCQAEO8hQqDh8IQHxR1gCE+KFS5TwpQgAIUoAAFKEABCgSPQHCE+PenD3r0YcPFTgCvFOk92oYrhx4BtsSbLEuGeJOA3JwCFKAABShAAQpQgAIWEgiOEP9yTGv4Xv3LsEJUnxnBFuLfvXuP9+/fI3LkSIaP19WKvn5+2LL7NxT8MhtixYzu8f58fX3x+s1bRI0S2eNtA9rg8PGzSJwwLlKnSBLQqp/8dYZ4k+QM8SYBuTkFKEABClCAAhSgAAUsJBDaQ3zjTiORP09WtGxY0ab+v7N/o07rwTiyaRaiRXUfiKcuWIsd+45j7bwhuHbzLsbPWokx/VojfHgvj0vx7bt3yFWiOVbPGYTM6VN9sP2W3UfQb/Q8/LZxpu211Rv2YMDYBVg2vR9evHyF5l3HYP+6qYgTK4bH7+9ug0YdhqNM0XyoV7VEoO43MHbGEG9SkSHeJCA3pwAFKEABClCAAhSggIUEwkKI/zpPFrRqWMmmfurs36jberAKy9GjRXFbGnfuPcLTZy+QLnVSnP3rCmq0GICT2+ciYoQIHpeipyF++6/H0LH/FMwc1QXffJUDz168xJVrt5EpXcqPuonAEO9xkYWODRjiQ0c58lNQgAIUoAAFKEABClBABBjio6BemyEoUiAXtv16VIXkOpWLoU3jKogSORI27jiEY6fOo1/nRqjpPQBnzl9Blgyp4OXlhd4dGiBH5rRYuX43Fq3agifPXqBa2W9Qr2pxJE4YT1Wwg8f+wIgpS3Hx8g3kzJIOv5+5aKglXrq3N+0yCuMHtkXpInnVvv6+cgO9RszB8un91Pu7O25Zf+/hUxgz4wf13nlyZFBd8Uf1aam6zF+9cQdDJizC/iOn1b/v3n+ETi1qqJZ46bY/b/lGLP9pJ549f4Hihb5Az3b1ETvWv0MA5H2//Tontuw5gus376JKmUKoWLIAxs1aiTPnL6v/bt+sWqD1FmBLvMnvKoZ4k4DcnAIUoAAFKEABClCAAhYSYIiPgqxFGquW9laNKiN61MjwGTIT4wa0Ua3fi1Zvxe4DJzB/fA+s3bQXfUfNw9xxPogQITwypk2Bfb/9DwPHLcTAbk2QJkUSzFy0DrFjRsfg7k1x7cZdlK7ng8qlC6FmxcK4eecBfAbPCDDEL5jQA7VaDlL7qF7uW1tt+eP8ZdTyHohTO+arlnh3x33pn5uo0KiXuiFRuUwhNRRAv3eGtClQqXFvxI0dEy0aVECkiBHQZ+Q8NK9XToV46cI/evpy+LSugySJ4mHyvB+RNHECTBrSXh2LvG+GdCnQulEl+PkBXQZOUz0auraqjZTJEql9tWtSBdXsjt1MlWeIN6MHgCHeJCA3pwAFKEABClCAAhSggIUEGOL/DfFLp/ZFrmyfq5LpOWw24seLpUKsfYh31p2+QbthSJU8MRpUL6m2lXVGTl2Gg+unY+6yX7BkzTb8umYSwoULByPd6XUgln1tXjoa8eLGchviXR33jO9/xrKfdmDv2slqe/v3fvHyNWQM/C+LR9omsrMfEy8t7Zk+T4n+Xb5T227fewwd+03BgXXTVGu8o1ftVoNQvkR+NKpRSq0vrf8PHj7BiN7egVLTGeJNMjLEmwTk5hSgAAUoQAEKUIACFLCQQGgP8S26jUGubOnRtnEVm/rJ0xdQv91QHN8yW8067xhKh01aomaklxAbUIj/pmoHNTlewnhx/JWqtFpPnLNadWEf3bflB0Ha1cR2EuL7dGyApWu2qxb9+RN6qG79sjhribcP8fbH3W/0fLx9+w4j+/wbpO1D/F+XrmPoxEX+JtCzD/Hymbp410TVst+obW/evo8Stbuqyf2kBd7RS7r9F8mfC41qllbrT1/4E85fuoaJg9oFSk1niDfJyBBvEpCbU4ACFKAABShAAQpQwEICoT3E9x89H+/e+2J4r+Y29Y07D2Pg2AW2EGs0xP958R9Ua9Yfx7fOQeRIEdX+ZJx8pVIF0fC/Vmj7opVx8lv3HMWSqX0Mh3g9O710xa/WvB8K5cuOsf1bqzHwnoT4Vet3Y+X6XVg1e9AH7y1j3qW7vv3s/PYhvmqzfiiUN5vqHi+LjOuXWfF3rZ6IRAnifBDi5UaJDD1giLfQiW1/KAzxFi0YHhYFKEABClCAAhSgAAU+QiC0h/ite46g84BpmDW6q3rUnIwNb993EnJnS49B3ZooMaMh/tXrN8hT2hsyZj1HlnTw8/PD4tVbsfjHbZg+vBOyZkyN67fuYdWGPaolW4f+Mf1bI1+uTNiw7aDqam70EXOnz12CdFVvVrccurSs5VGIl4nrytTrjvrVSiBfrszYtOswNu/6Tb23jInPX7GNGv8u//vfub/Ra/hs28R28li9NRv3YuLgdurZ8UMnLMKtuw+wctZANSzA0Ysh/iNOvE+5CUP8p9Tme1GAAhSgAAUoQAEKUCBoBUJ7iJegPWnuj5izdIMNsvDXOTG0Z3PEixPTZYiX1mqZkV5C+q7/JraTlSXgynhzWWSCuzw5MmLC7FVqdnq95M2VCQsn9oSvnx96DJkJafmXRWbA333gJNbMG4yM6VJ+ULDOnhMv67ftPRGDfJogS/rUquXffmI7x+70+rhl57o1XlrxixbIjSnz12DDohFIk/IzLF+7A0MnLVbHIF3kHz95jhb1yqNu1eKQMfO9R87Btj1H1esye/3kIR3U5H/Obno4hnjx+evSNTWzfmAs7E5vUpEh3iQgN6cABShAAQpQgAIUoICFBEJ7iNfUMib89p2HiBMnBmJEi2qqBKRF/s2bt4gV899Hrsny/r0v7j18rMax6zHs+rX7D58gYoTw/tY3dQAGN5ZjklnsZTlx+i/IJHzHtsy2HZ+E9afPXyBxgrhO9/jk6XO8fP3G5esGD8P0agzxJgkZ4k0CcnMKUIACFKAABShAAQpYSCA4Qvyr2f08EohUthG8UqT3aBuuDOQr1wq5sn6uxu/v3H9CPbu9VcNKIY6GId5kkTHEmwTk5hSgAAUoQAEKUIACFLCQQHCEeAt9/FB9KAeOnMbVm3fVM+3lkXFZM6QOkZ+XId5ksTHEmwTk5hSgAAUoQAEKUIACFLCQAEO8hQqDh+JUgCHeZMVgiDcJyM0pQAEKUIACFKAABShgIQGGeAsVBg+FIT4o6gBDfFCocp8UoAAFKEABClCAAhQIHgGG+OBx57saF2BLvHErp2syxJsE5OYUoAAFKEABClCAAhSwkABDvIUKg4fiVIAh3mTFYIg3CcjNKUABClCAAhSgAAUoYCEBhngLFQYPhSE+KOoAQ3xQqHKfFKAABShAAQpQgAIUCB6B4AjxfrdOevRhw0WJA8QJmTOre/RBuTJDfFDUAYb4oFDlPilAAQpQgAIUoAAFKBA8AsER4n13DQIeXTH8gb2K9jcd4v38/HD3/mPEjhkNkSNHMvze7lZ8+eo1IkWMiPDhvQJlf9yJcwF2pzdZMxjiTQJycwpQgAIUoAAFKEABClhIILSH+AePnmLagrXY+utRPHj4RMmnTpEEfTs1RP48WT+6JF69foM8pb0xdVhHFC2Y+6P3ww0DFmCID9jI7RoM8SYBuTkFKEABClCAAhSgAAUsJBDaQ3yXgdNw4fJ1jOjtjXSpkuL6zXvYsP0gkiSKh9qVin50Sfj6+eHcX/8gRdKEiBkj2kfvhxsGLMAQb2d0684DJEoYF17hwn0g9+z5S7x99w5xY8f09xpDfMCVjGtQgAIUoAAFKEABClAgpAiE9hCfr1wrtGxYCc3qlnNaJOu3HcDuAycRPWoUbN79G+LGiYm+HRvim69yqPV7DpuN/UdPq1b8dKmTol2TqihVOK96rUG7YejTsQEyp08F2c+vB39HrFjRsX7rAWT6PKVaN1/uzCGlKlj2OBniASxavRVL12zD27fvVVCvWvYbdPGuqQrtxcvX6DF0JnbuP6H+nTNLOkwe2gEJ4sVW/2aIt2zd5oFRgAIUoAAFKEABClDAY4HQHuIHj/9etby3alQZX+bMiPRpkiFqlMg2p4UrNmPMjB/QqlEl5MiSDivX7cKps39j79rJap2la7arbeLFjYU9B05i/OxVOLBuGmLHio6sRRpj0eTeyJMjA/R+mtYpi0L5cmDjzkM4c/4yVs0e5HGZcAP/AmE+xP9x/jJqeQ/Ewok9kTdXJlz65yYqNOqFZdP7qcA+b9kvWLlhNxZP7oNoUSOjVY/xSJvyMwzu3pQhnmcTBShAAQpQgAIUoAAFQplAaA/xT54+x+Ift+H7lZvx/MUrVXr1qpZA2yZVECdWDBW+9x/5H+aM9VGv3bn3CEVrdMLGJaOQKnli+Pr64tyFqzh34Qru3H+EKfPWYOWsAciaMc0HId5+P5ev3kL5hj1tgT+UVZtP+nHCfIj/7cRZNOk8CpuWjkLKZIkV/jdVO6B7mzqoWLIAanoPQOki+dC8Xnn12pbdRyDjSE7vWoBw4cKxJf6TVle+GQUoQAEKUIACFKAABYJWILSHeK0nY9iv3biD306cw+jpy1G/Wkl0bF79gxAv60sX/CHdm6FQvuxo3XM8zl34B8UKfqHG0c9ZugHLZ/RHjsxp3YZ4fTNg56rxSJwwXtAWYijfe5gP8W/evkPzrqNVRWzftJq6G7VlzxEsmtRLTcggFXZoj2a2cR5nzl9Rwf7g+mmIFTM6tn83GlcXb1HVJGP/xkjX8d9u+FwoQAEKUIACFKAABShAgZAnkDhulE9+0J/yEXMyi3wUh0fK9Rs9H1dv3FG9kx1b4q/fuodSdbph4aSeePL0BTr0nWzLQgIlXegZ4j9tlQnzIV645y77Beu27ldjQU6fu6Ra3Ts0qw4vr3DIVrQJpo/sjMJf51Qlc/HyDVRq3BvbV4zDZ4njY1/zsfhr/ib12hfDmyNb9zqftgT5bhSgAAUoQAEKUIACFKBAoAmE9/pwkutA27mLHX2qEC8NmMVrdUGnFjXw9RdZECtGNPx+5iJadh+nJp1r/V1lFeJ/3rofs0Z1xZs3bzFt4VrsO3Ia234Yi5N/XECzLqOxdt4Q1Zq+ccchDJ20mCE+qCuIw/7DfIjfe/iUGud+aMN01fJ+4MhpdBowFV1b1VaPWJCW+GE9m6Pkt18qOseWeE5s94lrLN+OAhSgAAUoQAEKUIACQSgQmrvTv3/vi0HjFqpZ5/V4eKH8rlYZdPaugYgRItgmpNPEyZMmxJh+rVV3eemCL0OLt+05ql4uVjC3mgD8hxn9kf2/7vSLp/TGF9kzqDH3+4+cxuwx3dS6d+8/QpHqnbBz9QQkThA3CEsw9O86zIf4iXNWY+f+41i3cLittNv2nojo0aJidN+Wqut8mSL50Ixj4kP/2cBPSAEKUIACFKAABSgQ5gVCc4jXhevn54eHj5/h7du3SBg/Dry8vGzlrrvTS2/kp89eIl4c/4/YlhXvP3yiei07Pn47zFeeTwQQ5kP8xp2H4TN4BmaO6qImarh28y7K1OsOn9Z10Lh2GdXVfpWenT5aZLTqztnpP1Hd5NtQgAIUoAAFKEABClDgkwsER4j3OzjJo88ZLnNlIE5qj7YxurLjmHij23G9TycQ5kO8dAmZvXg91m7ei4ePniJG9GioXLog2jaugggRwqtuJhLy9xz6XZVKtkxpMGVoRyRKEEf9m93pP11l5TtRgAIUoAAFKEABClAgqAWCI8QH9WfyZP/n/76GO/ceqgZOLtYUCPMh3r5Ybty+rx6T4BXuw8ks5HmKMhFEgnix/ZUkQ7w1KzaPigIUoAAFKEABClCAAh8jENZD/MeYcZtPK8AQb9KbId4kIDenAAUoQAEKUIACFKCAhQQY4i1UGDwUpwIM8SYrBkO8SUBuTgEKUIACFKAABShAAQsJMMRbqDB4KAzxQVEHGOKDQpX7pAAFKEABClCAAhSgQPAIMMQHjzvf1bgAW+KNWzldkyHeJCA3pwAFKEABClCAAhSggIUEGOItVBg8FKcCDPEmKwZDvElAbk4BClCAAhSgAAUoQAELCQRLiH950TOB8DGASIk924ZrhxoBhniTRckQbxKQm1OAAhSgAAUoQAEKUMBCAsER4v1uLgbe3DasEO6zhsEa4i9fvaUeQ5cvd2bDx2xkxfsPn+D4/86j5LdfGlk9zK7DEG+y6BniTQJycwpQgAIUoAAFKEABClhIIDSH+PfvfZGjeFObdvN65dHZu6bH+otWb8XuAycwf3wPj7d1t8GRk+fQuNNI/LF7YaDuN7TtjCHeZIkyxJsE5OYUoAAFKEABClCAAhSwkEBoDvHC/PbdO1Ro1AtN65RDzQqF4eXl5bE+Q7zHZIG6AUO8SU6GeJOA3JwCFKAABShAAQpQgAIWEgjtIV6oS9fzgXeDiqhe7lslP2rqMqRMlhiPnz7HgaOnUadKcRQrmBuT5qzGhh2HEDd2DNSuVAzVy3+LKJEjwT7EP3ryDG16TsCFy9fVvrJkSI3e7esjQ7oU6t/12gxBkQK5sO3Xo7hy7TbqVC6GNo2rqP34+flh8eqtWLhyC27ffaC2OX/xKlviAzgfGOJNfmEwxJsE5OYUoAAFKEABClCAAhSwkEBYDPESwvcc+h1liuZDzizpkD1zWvy8ZT/O/nUFXbxrIVw4YND479GqUSVULFnAX4h/8vQ51m7ai9zZMyBypAiYt3wTLv1zA6tmD1KlmrVIY6RLnRStGlVG9KiR4TNkJsYNaINvvsqBjTsPw2fwDLRtXAWF8+fE1j1HMXfZLwzxDPFB+43AEB+0vtw7BShAAQpQgAIUoAAFPqVAWA3x0greqUUNRf3q9RvkKe2Nvh0bIle2z9Xf1mzcqyazmzSkvb8Qr9c/deYiLl29hdPnLmHNxl9tQVxC/NKpfW376TlsNuLHiwWf1nXQtMsoJE4QFyN6e6v34Jh4YzWdLfHGnFyuxRBvEpCbU4ACFKAABShAAQpQwEICYTXEf5EjA2SiO1lk9vnyDXsiS4ZUiBwpkq10EiWIg/ED2/oL8dKNvknnUYgVIxq+zJkRb968xbqtB1yG+GGTluD9+/fo3+U7fFO1Azo2r44a5QszxHtwDjDEe4DlbFWGeJOA3JwCFKAABShAAQpQgAIWEmCIB6SLfP6KbVWXeAnyjov9mPhR05arbvfzx3dXk+T9fuaiGgevZ5h3bIm3D/Fte09E5vSp0K5JVYZ4D84BhngPsBjiTWJxcwpQgAIUoAAFKEABClhcILSHeGez08uYePuWeCki6er+7t17jO7bCgnixcafF//BsVPn0ahmaX8t8dMW/qQeNzd9RGe1/vTvf3bbnd4+xC9fuwMLV23GyN7eSBgvDqYuWIv12/6/Fd/iVSXYDo8h3iQ9W+JNAnJzClCAAhSgAAUoQAEKWEggNId4V8+JlxCfJ0cGNPuvO70Ux+17DzFo7EI14Z1eWjWshPbNqqkZ5Xf995z4W3ceoH3fSThz/opaTSas23v4lNuWeF9fX/Tr3Aj3HjxGC5+xakZ6WQrmzYb9R05zYrsAzgeGeJNfGAzxJgG5OQUoQAEKUIACFKAABSwkEJpD/Mcwv379Bo+ePkeCuLERPrzrZ8rfuH1fPYouapTIHr2NPGZObgTEixMTkSP///h7j3YSxlZmiDdZ4AzxJgG5OQUoQAEKUIACFKAABSwkECwh/s5ajwTCxSkARErs0TZcOfQIMMSbLEuGeJOA3JwCFKAABShAAQpQgAIWEgiOEG+hj89DCQECDPEmC4kh3iQgN6cABShAAQpQgAIUoICFBBjiLVQYPBSnAgzxJisGQ7xJQG5OAQpQgAIUoAAFKEABCwkwxFuoMHgoDPFBUQcY4oNClfukAAUoQAEKUIACFKBA8AgwxAePO9/VuABb4o1bOV2TId4kIDenAAUoQAEKUIACFKCAhQQY4i1UGDwUpwIM8SYrBkO8SUBuTgEKUIACFKAABShAAQsJMMRbqDB4KAzxQVEHGOKDQpX7pAAFKEABClCAAhSgQPAIBE+Iv+/hh5Xnqcf0cBuuHloE2BJvsiQZ4k0CcnMKUIACFKAABShAAQpYSCB4QvwxAE89UMjDEO+BVmhblSHeZIkyxJsE5OYUoAAFKEABClCAAhSwkEBoD/H9R8/Hjxt//UD8t40zET1aFAuVBA/FlQBDvMm6wRBvEpCbU4ACFKAABShAAQpQwEICYSHEP376HJ29a/pTT5k8MbzChbNQSfBQGOKDqA4wxAcRLHdLAQpQgAIUoAAFKECBYBAICyHeD8CQ7k0/0K3XZgi8G1TE3sOncPavKxjaoxlmL9mA/UdP48HDJ0iXOinaNamKUoXzqm1HTV2GCBHC4+LlGzh66k8UKZAbHZpWQ/KkCdXrx/93HhPnrMa5C/8g2WcJ0bB6SVQr9y1u3L6PEZOX4PCJs8iZJR1qVixi22cwFHmIe0u2xJssMoZ4k4DcnAIUoAAFKEABClCAAhYSCAsh/uyFK6hUqqBNPV/uTMiYLiWyFmms/tagekkkTRwfpYvkw459x5E+TTLEixsLew6cxPjZq3Bg3TTEjhUdbXpOUOG9Y/Maap1xs1biqy+yoIt3Tfxz/TbK1u+B6uW+VcH98rVbOHH6L/Tr1AiVm/RBziyfo2GNkrh09RZ8Bs/A1h/GIlmSBBaqCdY9FIZ4k2XDEG8SkJtTgAIUoAAFKEABClDAQgJhIcTvOngSOTKntanXqVwM33yVQ4X4WaO7olC+7LbXfH19ce7CVZy7cAV37j/ClHlrsHLWAGTNmEaF+C9yZEDzeuXV+jLWfsmP27B23hBMXbAWK9btwq9rJiGcXTf9w8fPommXUfh+Ui/bGPyB4xaiSulCqFu1uIVqgnUPhSHeZNkwxJsE5OYUoAAFKEABClCAAhSwkEBYCPGuutNLiF86tS9yZftclcjzF6/Quud41R2+WMEvkCRRPMxZ+n/s3QdUFUffBvAHUFBRECyosRfsBXuJsfdCsEvUqCixFxQ19t7FYBcb9tgTC/aGr1iwS+xdY68oKE3eM5NARFDYu15YQBalIAAAIABJREFU7n32nPd8wp3/3rm/WXK+587s7HasnT9SfgnweYjffcgfHl7rsXvNNAyZ4CXPMXmYa4zR3ezjixFTl8KhWIEYv69RxQEubRtq6ErQblcY4lWODUO8SkCWU4ACFKAABShAAQpQQEMCDPH/hXixlL7P8Fk4tm0urNJZylESQT8hIX7GgnU4fPw8tnpPjDG64ndi+fyxbfNgZmaqoZFPPl1hiFc5VgzxKgFZTgEKUIACFKAABShAAQ0JMMT/F+KPn7kEF7epcnm8XSZb+Ow/jvGeKxMU4qNqR7r9jKZ1K+PRkxc46h8Ax3pVULv1ADg1qCrvpReH//krCAsLR+2qZTR0JWi3KwzxKseGIV4lIMspQAEKUIACFKAABSigIQGG+P9C/MfISLiNnou9h0/JEapZxQEHjp7F7/NHovi/y+nLlLCHy7/3xO857C83txPL6cWxfP0uTJ33e/ToduvQFL07N8O5gBsYNmUx7tx/LF8Tz6efNNQVtb4vraErQbtdYYhXOTYM8SoBWU4BClCAAhSgAAUoQAENCRh6iNeF+sWrQJiamsDGOp3icrEx3vOXgUhvnRbmKVPEqA98G4Sw8AjYpk8XY/M7xW9iZAUM8SoHnCFeJSDLKUABClCAAhSgAAUooCGBpAnxFxUK5AagPFArfBM216gAQ7zKgWGIVwnIcgpQgAIUoAAFKEABCmhIIGlCvIYA2BXNCzDEqxwihniVgCynAAUoQAEKUIACFKCAhgQY4jU0GOxKnAIM8SovDIZ4lYAspwAFKEABClCAAhSggIYEGOI1NBjsCkO8Pq4Bhnh9qPKcFKAABShAAQpQgAIUSBoBhvikcee7JlyAM/EJt4qzJUO8SkCWU4ACFKAABShAAQpQQEMCDPEaGgx2JU4BhniVFwZDvEpAllOAAhSgAAUoQAEKUEBDAgzxGhoMdoUhXh/XAEO8PlR5TgpQgAIUoAAFKEABCiSNQFKE+JCIu4o+rKlJGqQ0zaSoho0NR4Az8SrHkiFeJSDLKUABClCAAhSgAAUooCGBpAjxzz9sRtjHZwlWyJiqmV5C/PsPITBPmRJmZqbx9mXPYX+ULVEQtjZW8bZV0uDEmcuwy2SD3DmyKCkzqrYM8SqHmyFeJSDLKUABClCAAhSgAAUooCEBQw/xI6cuxSYf31jivltm4QenPpgzoS9qVHGId0SKVu+IFbOGokwJ+3jbKmnQoc9E1K9RHs5OtZWUGVVbhniVw80QrxKQ5RSgAAUoQAEKUIACFNCQgDGE+Ddvg9DftWUM9ezfZca1G/eRI1smpEubJt4RYYiPl0hvDRjiVdIyxKsEZDkFKEABClCAAhSgAAU0JGAMIT4SwLhBnWOpt+s1AcP6tkPhArmwba8ffI+dh5WVJbbt8UOh/DnRq5MTyjsUlnWfhviVG/dg2bpdePLspVxe39axJrr/7AgTE5N4z3P/4VOMm7kCR/0D5BL6Zy9eo1/XFpyJ/8rfBEO8yv9gMMSrBGQ5BShAAQpQgAIUoAAFNCRgDCH+8o27aFq3SrR6eYdCKJgvZ4xg7r1uF6bN/x2d2zTA9+VLwOfAcVy6dgcbvMbECvF7fU8hhZkZsmfLBBHKew+bhXmT+6NaxZL42nkiIj6iacehsLFOh67tGsM8ZQoMm7wEXZwbMsQzxOvvvwoM8fqz5ZkpQAEKUIACFKAABSiQ2ALGEOIPHjuHEoXzRtO2cayJqhVKxArxR/0vYtF0d9nuzv3HaNR+CPy2zoW1lWWMtuL1m3ce4tL1O3j24g2WrduJrs6N0KFlPRniv3SeG3f+hrgHfsfKydEb2fGe+PiveM7Ex2/01RYM8SoBWU4BClCAAhSgAAUoQAENCRhDiP/ScvpPl8h/Hr6fPn+NGi364cAGD9hlso0R4qfMWYMVG/egZhUHGcZ37D+B9i3qoFPrBrFC/KfnOXH2Csb/tgInfRZEXwEM8fH/MTDEx2/EEK/SiOUUoAAFKEABClCAAhRILgIM8f/sOJ/QEJ8nRxZUdeqDZTMHR98v322wByqULhxviH/+8g1a/TIG/jsXIk1qC3mJMMTH/5fCEB+/EUO8SiOWU4ACFKAABShAAQpQILkIMMQrC/H2ebOjYuMemDCkC+r8UBanLlyF+9j5cmO7+GbiM9qmR6UmPeT97+J/F6/cwq8TvbixXTx/LAzxKv9rwuX0KgFZTgEKUIACFKAABShAAQ0JGHuIXzl7KEoXt8fy9eJe9gB4TRsoR0fsGl+9eT8c2DgTdhlt5HL6qLZL1vrAY+F62S5f7mwICQ1DW8da6Ni6frznWbtlP8Z7rpS19vly4E1gkLyfvq1TLQ1dFdrqCkO8yvFgiFcJyHIKUIACFKAABShAAQpoSMDQQ7y+qIOCP+Dtu2BkyWyr+C2C34fgbVCw/HKAR/wCDPHxG321BUO8SkCWU4ACFKAABShAAQpQQEMCSRHiX4bsUiSQLmUZpDTNpKiGjQ1HgCFe5VgyxKsEZDkFKEABClCAAhSgAAU0JJAUIV5DH59dSQYCDPEqB4khXiUgyylAAQpQgAIUoAAFKKAhAYZ4DQ0GuxKnAEO8yguDIV4lIMspQAEKUIACFKAABSigIQGGeA0NBrvCEK+Pa4AhXh+qPCcFKEABClCAAhSgAAWSRoAhPmnc+a4JF+BMfMKt4mzJEK8SkOUUoAAFKEABClCAAhTQkABDvIYGg12JU4AhXuWFwRCvEpDlFKAABShAAQpQgAIU0JAAQ7yGBoNdYYjXxzXAEK8PVZ6TAhSgAAUoQAEKUIACSSOQFCH+UfDfij5sKrPUsLFQ/jx2RW/CxpoV4Ey8yqFhiFcJyHIKUIACFKAABShAAQpoSCApQvz+v3fiVcjLBCvU+q6BpkL87kP+KO9QCDbW6RL8Gb7WcM9hf5QtURC2Nlbf5HxRJzlx5jLsMtkgd44s3/S8iX0yhniV4gzxKgFZTgEKUIACFKAABShAAQ0JGHqIHzl1KTb5+KJbh6bo3bmZlH/7LhgVG/fA7rXTkD1rpq+OxpK1PrJNverlotsVrd4Rq+cMR6li+b/JSIrzrZg1FGVK2H+T80WdpEOfiahfozycnWp/0/Mm9skY4lWKM8SrBGQ5BShAAQpQgAIUoAAFNCRgDCH+4LFzePkqEL5bZiGDjRUC3wahUpOeCQrxfUfMRqH8OdH9Z0eG+CS6bhniVcIzxKsEZDkFKEABClCAAhSgAAU0JGAMId7ayhInz11B6eL2GNyzbawQ/yEkFJ6LNmL7/uOwsU6L1k1ronmjH3D42HmMmLoEFhbmyGaXAfZ5c2DcoM4QM+ddnBvB71QA7j54gjaONdGj449IZWEuR/bU+auYNv933L73CLWrloWzUy0UK5QHN+78jWGTF+PXXj9hxcbdePr8NVbNGSbPFzUTv3LjHixbtwtPnr2Uy+vbOtaUXyCYmJhg214/+B47DysrS2zb4ye/XOjVyQnlHQrL973/8CnGzVyBo/4Bcgn9sxev0a9rCzkT/+DhM0yasxr+567Iz1OpTFGMH+wC85QpNHQ1xt0VhniVQ8QQrxKQ5RSgAAUoQAEKUIACFNCQgDGE+PTWaVG5XDG4uE3FvnUzYJkmVYyZ+NEzvHH5+l24ubaCiQkwxmO5XH4vwvGA0fOQ8zs7ODX8HmnTpEbhArlk6M6XOxu6dXCEZWoLuI9bgBmjeqBqhRIySNd3HoQB3VqjaoXiEPfPb/Y5gv3rZyDgym206T4Wdpls5ZcEIvS7tG0YI8Tv9T2FFGZmyJ4tkzxX72GzMG9yf1SrWBLe63bJLwc6t2mA78uXgM+B47h07Q42eI1BRMRHNO04VN6n37VdYxnOh01egi7ODWWId3WfLs8rQv2bd0HYtN0XI/p3kBZaPxjiVY4QQ7xKQJZTgAIUoAAFKEABClBAQwLGEuLdfmmFjv0myxlqN9eW0SE+o601ytRzxfC+7aPvcReh++nzV/Ac1xsJWU4/ZIIXMthawb17G8zz/gM79h/H9JHd5SiHh0fI4L55yViEhobLf5/0WRAjPH9+T/zNOw9x6fodPHvxBsvW7URX50bo0LKeDPFH/S9i0XR3ee479x+jUfsh8Ns6V87yi3vgd6z85zOK49N74tv1moCMGawxtHc7ZM6YXkNXYPxdYYiP3+irLRjiVQKynAIUoAAFKEABClCAAhoSMKYQfy7gBn7qNR5r549E2+5j5T3xImSLIFzEPhcszP9ZDi8OEXQ9RvdMUIif4LkKERERGOn2M36d6IX9/zsjl95/evT42RHp0qaRIT7g4DK5PD7q+DTET5mzBis27kHNKg4yjO/YfwLtW9RBp9YNYoV4sRy/Rot+OLDBAyfOXsH431bILwiijk9DvFhGP3iCl1ymL2b5uzo3RovG1TR0JX65KwzxKoeJIV4lIMspQAEKUIACFKAABSigIQFjCvGCvefQ3xAWFi7vGxch3iptGjkrL5akiyD/+SFm4gvmyyHvef80dH+6O/2nId5j4Xp5L/zsCX1jnevi5VtfDfF5cmRBVac+WDZzcPR97t0Ge6BC6cLxhvjnL9+g1S9j4L9zIdKktpDv/fnu9B8/fsSte4+wz/c0Zi/dHGPWXkOXZKyuMMSrHB2GeJWALKcABShAAQpQgAIUoICGBIwtxIt731t0HSVHIOoRc53dpsgZ+anDu0Esr7968x5OX7gml7B7rdomN6oToTwo+ANs06eT97B/KcSfuXgN7XtPxORhrmhYsyLeBL7DHt9TKFuyIN6/D/lqiLfPm10++m7CkC6o80NZnLpwFe5j58uN7eKbic9omx6VmvSQ97+L/128ckuuCoja2E58udC8UTXk/C4zrty4Jw02Lhoj7/HX+sEQr3KEGOJVArKcAhSgAAUoQAEKUIACGhIwihCfPp28Dz7qGDBmHnYdPBkd4p88f4Ux071x+Pj56Dbd2jdFb5dm8r7z/qPn4trN+3AoViB6N/nPQ7yY5RYbxYljs48vJs9ZI0O/OMSy+PmT+yPwbTBadxsT53L6lbOHyt3zxXPpReAWh9g8LyQ0DG0da6Fj6/pYvl7cEx8Ar2kD5eti9/nqzfvhwMaZsMtog7Vb9mO850r5mn2+HHgTGCTvp2/rVAu9h3niwNGz8jWxsd5PTrXg4txIQ1fil7vCEK9ymBjiVQKynAIUoAAFKEABClCAAhoSMPQQr4Q6JCQUr98GIaONNczMTGOUiufMW6WzRIoUZgk6ZWRkJF68CpS7xIs6JYcI/2/fBSNLZlslZbJt8PsQvA0KlqH+80N8vjdvg7mxnWLVZF7AEJ/MB5DdpwAFKEABClCAAhSgwCcCSRHijz4+pGgMitiUgI2F8kCr6E3YWLMCnIlXOTQM8SoBWU4BClCAAhSgAAUoQAENCSRFiNfQx2dXkoEAQ7zKQWKIVwnIcgpQgAIUoAAFKEABCmhIgCFeQ4PBrsQpwBCv8sJgiFcJyHIKUIACFKAABShAAQpoSIAhXkODwa4wxOvjGmCI14cqz0kBClCAAhSgAAUoQIGkEWCITxp3vmvCBTgTn3CrOFsyxKsEZDkFKEABClCAAhSgAAU0JMAQr6HBYFfiFGCIV3lhMMSrBGQ5BShAAQpQgAIUoAAFNCTAEK+hwWBXGOL1cQ0wxOtDleekAAUoQAEKUIACFKBA0ggkRYi/8PJvRR/W2jw1cqXlI+YUoRlQY87EfzaYL18Fyt/Y2ljFeOVd0HuEhYfDxjpdjN8zxBvQXwM/CgUoQAEKUIACFKCA0QskRYifcHYX7r17mWD7oQ71kyzEh4dHICIiAhYW5gnu75cafoyMxO5DJ1GlbDFYpbP86vlETjM3T4m0lqlVv29yPwFDPABx8SxZswMrNu6BuDgs06TCSZ8FcmyD34dg8PgFOHD0rPy5ZJF8mDW+DzLaWsufGeKT+58A+08BClCAAhSgAAUoQIH/BAw9xHfsNxmVyhTFL+2bRH/oi5dvoU33sfDfuRBpUlt89XKYs2wL9v/vDLYsGYcHj57BY+F6TBvRHWZmpoovIzFJWqp2F2xcNAaFC+SKVS9ymtfKbfA5cBw37zyUr4us1uPnH9GxdX3F73/4+HmIz9qrk5PivmqpgCEekBfell3/Q/cOTVG/RgWEhYXBLtM/y1NEuF+//RBWzhomL+hugz2QN2dWjB3UmSFeS1cy+0IBClCAAhSgAAUoQIFvIGAMIb5imSLo1r5ptNaFy7fQtvtYOZEpQvLXjqfPX+Ptu2Dky50Nl6/fRYuuo3Bu32KkTJFCsX58If73Pw9g3MwVWDh1AByKFcDrwHc4duovXLp2ByPdflb8/mu27MOugyexYtZQxX3VUoHRh/hnL16jevN+GD/YBU4NqsYam5auo1Cvenl0cW4kX9t9yB9uo+ci4OAymJiYcCZeS1cz+0IBClCAAhSgAAUoQAGVAgzxqeDcYxyqVy6Fvb6ncPfBE7RxrIkeHX9EKgtz+Ow/jtMXrmFE/w4QWenStbsoYp8LpqamGNqnHUoUzov12w5hxYbdCHwXjGYNqsLZqVb0JOmx039h0uzVcmZdrHI+f+nmF2fiB41fiNdv3sJr2sA4RzWu979w6SaWrduFJ89eyluk2zrWRPefHXHv76do13uCXHldrFAeeb4Vnr8iEoDnoo3Yvv84bKzTonXTmmje6Af5WR88fIZJc1bD/9wVefuAWMEgcqN5SuVfWKi8LGOUG32IF0tB+gyfhTY/1sT1Ww9gYZ4STepWQdO6lSVU+Ybd5EDVrVZO/iwuUnGxHNs2V963weX03/Jy5LkoQAEKUIACFKAABSiQtAIM8alQtHpHOdPerYMjLFNbwH3cAswY1QNVK5SQtyAf8juLpR6DsWXnEQyfsgSLZ7gjRQozFMybA/87eRGjZ3hj9MBOyJMjCxas2ArrdJZyJbMIxfWc3eFY73u0bFINj56+hPvY+V8M8T4HTsjXO7Soi2qVS8E+bw7Ypv9vj7K43v/E2ctIYWaG7Nky4f7Dp+g9bBbmTe6P8qUKwWPhBpw4e0l+ASGOMsXtMXbmCjmj7+baCiYmwBiP5ejWoSma1KkMV/fp8lz9urbAm3dB2LTdV9bGt1pB31ew0Yd4saRigucq9O7cDPb5suPazQeYvXQzpo3ohgY1K6BYjU5y0KtVLCnHQnxj1LTjUOxbNwNZ7TLA12U6bi7bKV8rOd4FRQa21veY8fwUoAAFKEABClCAAhSggJ4EUpmb6enMXz5tYm5sJ+6Jj285vQjxq+cMR6li+WWnh0zwQgZbK7h3bxMjxMe1nL5drwnIld0O7ZrXkbWizeQ5a3Bs2zwsXrMDqzbvhe9mT7mqOb7l9BERH7Ftrx/mr/hTfgEgjirlisG9RxsUyJP9i8vpRWa7dP0Onr14g2XrdqKrcyN0aFkPny+n/xASijL1XDG8b/voz7rZ5wiePn8Fz3G9IT5LxgzWGNq7HTJnTJ/o18WX3pAhfss+iHsttnpPjDb6daIX3oeE4rcxveRM/IQhXVDnh7Ly9c9n4g90nIq7K3bL14qM7oQC/VtpZnDZEQpQgAIUoAAFKEABClBAmYBtOvW7rit7RyAxQ3zXgdNQqlgB9Oz4Y3Q3zwXcwE+9xuPMbi+5bPzzEC8mPcWO9OI+9E9n4uMK8VWd+si9xDLZxgy9IhT/tmgjQkLDMHX4L/K94wvxnzqK26ADrtzGrKWb5XL2dQtGxRnip8xZI/tYs4oDcufIgh37T6B9izro1LpBrBB/5/5jNGo/RN4OYGH+37iLwO4xuqdcRj94gpdcmi9m9rs6N0aLxtWUDu83b2/0IV7sUNhjyEyc37dELgERh1iyEfwhBHMn9pNL5+tXLw8X3hP/zS8+npACFKAABShAAQpQgAJaEzD05fQjpy5FeMRHTPy1SzS9WLY+evqy6Cd0JTTEX715D81cRuLMnkXytmRxiPzUtG4VtG9RN9bQivvk9xw+hVVzhiUoxL//EILUqWLulr91jx/EpKvYTO/W3Ycx3l/c7y6+RFg2czDKOxSW7yE2Jq9QurAM8Wu37MeO/cej3z/wbRAqNemJDV5jZJCP6/j48SNu3XuEfb6n5YrtHSsnyy8HkvIw+hAvdlas1cpNLq/o8bMjLl65LTdyEEsq2jrVkks+NkTtTp/GAt0GcXf6pLxg+d4UoAAFKEABClCAAhTQp4Chh/g9h/3Rf9RcueO72KhNPCau93BPufv7mIGdJG1CQ3zUcnQRmksUyYfIyEis3LgHKzftxbyJ/VC0YG78/fg5Nmw/DDfXlogK/dNGdpf3qG/fewzT5v/+xXvixaqBEoXzoWGtisiWJQOu3/4bEzxXyi8MxA7zn79/4Lsg1GjeP3ol9akLV+UErdjYToT4MxevyVC/c/VUmJqaIL1VWrgMmIrw8AhMHd5NPkZc9FFs3CfyoXiKWfNG1ZDzu8y4cuOe3In/S4/D0+c1+fm5jT7ECxCxQ2LfEbMRFPxB+jg71caQXs7yWYfid2LgxYy9OMROhrPH942+J4Ib2yXm5cr3ogAFKEABClCAAhSggH4FDD3Ei6DtuXgTFq3eHg0p9v8aP6RL9KZxcYV4MSMtNnUTIf3gvxvbiROI58bPX/6nPJfY4K5MiYKY6bVB7k4fdZQrVQjevw2BeO774HELIGb+xSF2wD/kdw6bl4xFwXw5Yw3s6s37sGStj1zOHnWIGrGs3y6jjfzV5+8vbn8W4VscYnM+sXy/rWMt+Vx5cY99z6G/4ciJC/L107u98OZtEMZM947Oe+L34vF7vV2aofcwTxw4ela2FY8g/8mpVvQKbf1ehV8/O0P8vz5iQB8/ewkb63TyHo7PD7HUIjQsXH478+nBEJ+Uly/fmwIUoAAFKEABClCAAt9WwNBDfJSWuB/9ydNXSJ8+LdKmSa0KUcyIh4aGyad3RR0iXz1/9UbuTC8e1/bp8eJVIFKmMIvR/msdeBf0XoZtkcWilu1/2v7z9xcTsWLFdZbMtnGeVmQ7c/OUMfoVEhKK1+I9bKzlZG7UIX7/5m0wN7ZTdYVorJghXmMDwu5QgAIUoAAFKEABClBAhUBShPi5fx1W1OPGuYojV9q4A6qiE7FxshTgTLzKYWOIVwnIcgpQgAIUoAAFKEABCmhIIClCvIY+PruSDAQY4lUOEkO8SkCWU4ACFKAABShAAQpQQEMCDPEaGgx2JU4BhniVFwZDvEpAllOAAhSgAAUoQAEKUEBDAgzxGhoMdoUhXh/XAEO8PlR5TgpQgAIUoAAFKEABCiSNAEN80rjzXRMuwJn4hFvF2ZIhXiUgyylAAQpQgAIUoAAFKKAhAYZ4DQ0GuxKnAEO8yguDIV4lIMspQAEKUIACFKAABSigIQGGeA0NBrvCEK+Pa4AhXh+qPCcFKEABClCAAhSgAAWSRiApQrzvjeeKPmymtBYonCWdoho2NhwBzsSrHEuGeJWALKcABShAAQpQgAIUoICGBJIixLdb7o/Lj98mWGHVz+VUh/jIyEg8e/EG1unSwMLCPMHvrUvD67cf4O27YJQubq9LOWs+E2CIV3lJMMSrBGQ5BShAAQpQgAIUoAAFNCRg6CH+5eu3mLtsC/b4nsLLV4FSPneOLBjerz0qlSmql5GYv/xPXLlxD57jeuvl/MZ2UoZ4lSPOEK8SkOUUoAAFKEABClCAAhTQkIChh3i30XNx487fmDTUFflyZcPfj55j+75jyJLZFq2b1tDLSDDEf1tWhniVngzxKgFZTgEKUIACFKAABShAAQ0JGHqIL9+wG35p3xQubRvGUhfL3odPWYJlM4cgTWoL+Bw4gf1HTmPGqB6yrYfXBmTNZIu2TrXw8MkLTJq1CifOXkbJIvnQskl11K1WTrYLfh+CqfPWwmf/cblUX5yrUL6c0TPxp85fxbT5v+P2vUeoXbUsnJ1qoVihPLLWucc4VK9cCnt9T+Hugydo41gTPTr+iFR6XvKvoUsw3q4wxMdL9PUGDPEqAVlOAQpQgAIUoAAFKEABDQkYeogf67Fczrx36+CIsiULokCe75A6lYUcgZCQUJSu5wpvzyEoV7IQXN2n46h/APb8Ph3fZcmIqk59MHZgJ1StUAKOnYahZJH8aN+iDm7ffwz3sfOj24n3OHTsPHp2+hH5c3+HhSu2ImXKFDLE33/4FPWdB2FAt9aoWqE4dh/yx2afI9i/fgZMTExQtHpH5MudTfbPMrUF3MctkF8iiPfk8Y8AQ7zKK4EhXiUgyylAAQpQgAIUoAAFKKAhAUMP8YFvg7By014sX78LQcEfpLyzU20ZuNNbpUXXgdNQ3qGwXFpfqUlPVClXTM6MixnzGi36wW/rXHl/e2e3KVju+Sss06SS5xg9wxs/1vseLZpUQ6naXTBuUGc0a/iDfO3T5fTzvP/Ajv3HMX1kd/laeHgE2nQfi81LxqJgvpwyxK+eMxyliuWXrw+Z4IUMtlZw795GQ1dJ0naFIV6lP0O8SkCWU4ACFKAABShAAQpQQEMChh7io6g/RkbiwcOnOHn2ilz6/lOzOujbpTmWrPWB/7kraFqvCvb5nkKdamWxetM+ufx+3vI/sMFrDDb7+GLE1KVwKFYgxsjVqOKAetXKoZ6zO7avmIQ8ObPGCvG/TvTC/v+dgX3eHDFqe/zsiMrlisUK8RM8VyEiIgIj3X7W0FWStF1hiFfpzxCvEpDlFKAABShAAQpQgAIU0JCAoYf4DyGhse4vF4FcLHP3/m0ILly+hbbdx6J21TKoX7M8qpYvgQqNuqNp3crIlNEGbq4tcfj4ebl8/ti2eTAzM40xemJmvWRtFyye4R692/2nM/EeC9fLe+FnT+gb56h/PhPPEB+biSFe5X8wGOJVArKcAhSgAAUoQAEKUIACGhIw5BAfGhaOWq3c0K9rC1QsXQRWadPg/KWb+GXQDPTq5ITuPzvK5e0ihIvjxI75SGuZGr2HeeLA0bPNeF1IAAAgAElEQVRYMMVN3psuluTXbj0ATg2qom+XFrKt//krCAsLl+G/74jZCI+IwOCebfEmMAhjZy5HNruM8p74MxevoX3viZg8zBUNa1bEm8B38nF34v58cf88Q3z8fwwM8fEbfbUFQ7xKQJZTgAIUoAAFKEABClBAQwKGHOIjIj5izAxv7Dp0Mvp+eEH/c6v66O/aAilTpJAjIUK7OKJmy7ft9ZP3pkeFevHauYAbGDZlMe7cfyzbinvjxWPran1fWr7mOmi6fA/xexHOM2VIH707vViOP3nOmug+iOfUz5/cHzm/s4szxH/8+BEj+nfQ0FWStF1hiFfpzxCvEpDlFKAABShAAQpQgAIU0JCAIYf4KObIyEi8evMOYWFhMlybmsZcEq9kOMSsfFh4BGzTp5O7y0cdYkb/yfNXyJrZNs7ziz68eBUI85QpYJXOUslbGn1bhniVlwBDvEpAllOAAhSgAAUoQAEKUEBDAkkR4vtvuqBIwLVKHhTOkk5RDRsbjgBDvMqxZIhXCchyClCAAhSgAAUoQAEKaEggKUK8hj4+u5IMBPQa4sWjCazTWcI+X8zHBzx78RrHz1ySGxl8vpthMjCL0UWG+OQ2YuwvBShAAQpQgAIUoAAFvizAEM+rQ+sCeg3xYkOEogXzoFuHpjEcHj55gTqtB2DHyskQmxgk54MhPjmPHvtOAQpQgAIUoAAFKECBmAIM8bwitC6QJCH+0rW7aOk6CjtXT5E7ECbngyE+OY8e+04BClCAAhSgAAUoQAGGeF4DyUtALyH+14lecrdD8QxAm/TpkCdH1miV0LAwnDhzGUXsc2GD15jkpRVHbxnik/0Q8gNQgAIUoAAFKEABClAgWoAz8bwYtC6glxA/cupSvHkbhDMB12GVNo18LmDUYWFhjvKlCuGHiiWROWN6rfvE2z+G+HiJ2IACFKAABShAAQpQgALJRoAhPtkMldF2VC8hPkrzj13/g10mG1QqU9RggRniDXZo+cEoQAEKUIACFKAABYxQIClC/Dn/B4qk09umRu58GRTVsLHhCOg1xBsO05c/CUO8MYwyPyMFKEABClCAAhSggLEIJEWIHzVgB+7cfJlg4jEzGhpsiD91/ipsrNMhX+5sCfYwtoZ6DfEhIaE4dOw8Dvmdxa17j2LZLvEYhLRpUidrc4b4ZD187DwFKEABClCAAhSgAAViCBh6iBe3Potj7KDOmhz5HkNmonQJe3RxbqTJ/mmhU3oN8cvW7cT0+etQpoQ9cmTLjJQpU8T4zEN6OSOVhbkWHHTuA0O8znQspAAFKEABClCAAhSggOYEjCHERwIYxxCvuWsvoR3Sa4iv5+yOCqUKa/ZbnoQifa0dQ/y3UOQ5KEABClCAAhSgAAUooA0BYw3x4eER+KnXeEwd3g25sv/zGPB53n8gXdo0aNmkOvoMn4WyJQvCtV0T+dohv3NYvGYHfhvbCxlsrLB+2yGs2LAbge+C0axBVTg71YJdJlts2+sH32Pn5Xm27zuGLJltMXpARxw7fQm//3kAGW2t0auTE2p9X1qeV8zEp0pljrfvguF36i84FCuAyUNdkT1bpuj39fBaj5t3HsrJ4uH9OsA+b3Z8rf/tW9SV/Tj31w2ULJIf2/f6oUDe7HDv3gZHTlzAtPm/R58vJDQMU4b9gtw5smjjgoyjF3oN8c49xqG8Q2H069pCswBqO8YQr1aQ9RSgAAUoQAEKUIACFNCOgLGG+NCwcDjU6YLNS8aiYL6cckCGTlqMDDbpMKBbaxl2uw32wOIZ7vIR4k07DsWwvu3hWK8KfPYfx+gZ3hg9sBPy5MiCBSu2wjqdpZzM9V63S4ZkF+dG+L5cMazevA/7jpxG/Rrl0bzhDzh94RrWbz8E382eMDExkSH+4tXbMtint0qLWUs2oUxxe3muG3f+hmPHYej6U2P8ULEEVm3aC//zV7Fn7TSYmZl9tf9R/ShZJB9qVS2DrHYZUDh/TjTu8CvaONaEY/3v8eDRM7iPnY+Ni8agcIFc2rkoP+uJXkP82i374b1hF7Z6T4SFeUrNIqjpGEO8Gj3WUoACFKAABShAAQpQQFsCDPFxh3gxSp6LN2HjjsPIZpcBxQrmwYj+HeTgtes1Qc7et2teR/58+fpdTJ6zBse2zcPKjXtw1P8iFk13l6/5+Qegq/t0/HXIW/78JjAIlZv2hM+qKfIcn98Tv9f3FMbOXIEjW2Zh9pLN2L7/GHavmSZrX74KRFWnPpg7sR8qlysWb4jfc9gfq+YOh6mJiayfv/xPrPljvzy3OMLCw1GqdhfjDvHiG5jZSzdDfNuRKUPsZ8JPGuqKNKkttPVXq7A3DPEKwdicAhSgAAUoQAEKUIACGhZgiP9yiI+I+IjqLfrJ8Hx6t1f0/mYiSItcl8k2ZubzHNcb2/b4xQjxZy5eQ/veE6NDvFi+XrpuV2xZMg72+XLECvHXbj2AU+fhOLjxN8z0Wi+vHJEjo46aLd3Q9adGaN6oWrwh/tMvE0T9iKlLERYWjsnD/jkfQzwgl1FcuHTzi3+i00f1YIjX8H/A2DUKUIACFKAABShAAQoYm4CxhnhxT3nJ2i74ff5IFC+cVw770E+W04uft+7xw/jfVsjXOrdtiG7tm8p/t3QdhaZ1q0Dce/75IZaxfxqezwZclzP3UTPx8YV4nwMn5BL3M3sWyaX14j55EfjFERT8AeUbdoPH6J7ynvqv9f/zfoj6DdsOYf22g9jgNYYh3pj+0DkTb0yjzc9KAQpQgAIUoAAFKGDoAsYQ4t+8DUJ/15YxhlJs5Nahz0SULm4Pl7YNcSbgOoZPWYIf61WR98Rfu3kfTi4jsGjaQFknlsSLR4ZXLF0EXqu2YeWmvZg3sR+KFsyNvx8/x4bth+Hm2lLeE680xH+XNRMG9WyDuw+eYKzHcrkpnlgyf+z0X+gyYJoM7VXKFcPyDbvl5nuHNv0mV35/rf9xhfj7D5+ivvMg/NSsNsqXKoydB09g18GTxr2cPjJSPLzgy4fYuCC5HwzxyX0E2X8KUIACFKAABShAAQr8J2AMIX6Tj2+sIb94cBkO+Z3FqOnecrm8CPXm5ilRtXxxdOvgCCeX4ahXvbwM5uLw8NqA3//YL+9lt0pniZleG+Tu9FFHuVKF4P3bECxfL0J8ALz+Df+fz8RHbaj36XL6Uxeuyll2cRSxz4VZ4/rIjejEEXXLtvi3ZZpUcml91M72B46eibP/br+0itWPqH5GzcabmpqiRmUHeTv49hWTkCdnVs3+Weh1Y7u+I2bLnQe/dBzbNlcOeHI+GOKT8+ix7xSgAAUoQAEKUIACFIgpYOghPr7xFve9P3/1BnYZbeJrGuv1qFqxM30qC3PF9Z8WvHrzFiEhYfKRdJ8fISGhePbyDbJmzgAzM9MYLyvtv2gfdY6oLxg+vd9f1YfQU7FeQ7x4DMGjJy9idX32si0oap8bs8b3gXnKFHr6aIlzWob4xHHmu1CAAhSgAAUoQAEKUCAxBJIixM+ccFDRR3NqUwK58/0zM81DnYC4p75U0fzyaWoHjp5Fb5dm0ff6qzuz/qr1GuK/1O0tO49g0uzV+N+fcxji9Te2PDMFKEABClCAAhSgAAUooFAgKUK8wi6y+TcUEI+8u//oGVKkMEOh/DnlZLPWjyQJ8ff+foIGPw3G5iX/Pb5A61Bf6h9n4pPryLHfFKAABShAAQpQgAIUiC3AEM+rQusCiR7iP0ZGYt0fBzDecyUOb/ZERltrrRt9tX8M8cl6+Nh5ClCAAhSgAAUoQAEKxBBgiOcFoXUBvYb4kVOX4uCxczEMxE6H4mhUuxKmDv9F6z7x9o8hPl4iNqAABShAAQpQgAIUoECyEWCITzZDZbQd1WuI99l/HLfvP46Ba5k6FSqXKwb7vNkNAp0h3iCGkR+CAhSgAAUoQAEKUIACUoAhnheC1gX0GuK1/uG/Rf8Y4r+FIs9BAQpQgAIUoAAFKEABbQgwxGtjHNiLLwvoPcSHhIZBzMhfu3kfwR9CkCNbZtSvXh7Zs2UyiHFhiDeIYeSHoAAFKEABClCAAhSggBRIihB/f/sxRfppsmZAhjL2imrY2HAE9Brin798g596jceDh8+kmGWaVAgK/iD/PXNMT9StVi7ZSzLEJ/sh5AegAAUoQAEKUIACFKBAtEBShPit5brjxelrCR6Fpv7zjTbEnwu4gTSpLWCfL0eCvQytoV5DvNjYbtehk5g7qR9KFM4HC/OUuH3vEaYvWIdDfudwercXUlmYJ2tThvhkPXzsPAUoQAEKUIACFKAABWIIGHqIFxltk49v9GfOlzsbHOt9j3bN68i8pvVjwJh5yJ09C3q7NNN6V/XWP72G+Jot3dC4TiW4ubaM8QGu3ryHZi4jsX7hKBQtmEdvHy4xTswQnxjKfA8KUIACFKAABShAAQokjoAxhPh3we8xoFtrvAsKxvm/bmL2si0oXawAPEb3hJmZaeJA6/guDPGAXkO8k8sIlCySD6MHdIwxRP7nrqBjv8kM8TpeuCyjAAUoQAEKUIACFKAABfQjYAwhPhLAuEGdowFv3X2INt3HYnAvZzRv+AMiIyOxftshrNiwG4HvgtGsQVU4O9WCXSZbbNvrB99j52FlZYlte/xQKH9O9OrkhPIOheX5psxZA1MzU9y88xBHTlxA5bJF5XkXr96BA0fPoFypQujr0lwuh38d+A49hszEjTt/y9oi9rkxtPdP0UvlnXuMg2u7JvI8l6/fxfjBLpjr/Uf0THxExEdM8FyJ9x9CMH5wF81/AfGtrli9hngPrw1YsmYHxgzsJAfLxjotTl+4hgUrt+Lhkxc4uGEmUqQw+1afJUnOw5n4JGHnm1KAAhSgAAUoQAEKUEAvAsYY4gWk2+i5SJ3KAhOGdJEbk4+e4Y3RAzshT44sWLBiK6zTWWLsoM7wXrcL0+b/js5tGuD78iXgc+A4Ll27gw1eY+R4iFB+6sJV9HdtiTw5s2LU9GVyjzQRxiuVKYLlG3bDKm0aTBrqisC3Qdiy8wgcitvDwjwFlqzdidv3Hkafq2j1fyaDxVL/bHYZUK96efneYjl9z85OGD1tGU5fvIblnr8io621Xq4HLZ5UryH+Q0go+gyfhaP+ATE+u62NFWaN6w2HYgW0aKKoTwzxirjYmAIUoAAFKEABClCAApoWMNYQ/9uijTh2+i+sWzAK7XpNQK7sdjI8i0PMgk+eswbHts3Dyo17cNT/IhZNd5ev3bn/GI3aD4Hf1rmwtrKUIb50CXt0cW4kX/dcvAnXbt3H3In95M9ib7QR05biyJZZ8meRGS9cuonb9x8j4MptbPbxxV+HvOVrIsQvnDoA35cvHn3NiOX0Ob+zw4cPITjgdxYrZw1D5ozpNX1NfevO6TXER3VW7CAoBi74fYh8tFzlssXkjoKGcDDEG8Io8jNQgAIUoAAFKEABClDgHwFjDfEiHFumTiVn26s69ZF5LZNtzHDsOa63XEL/aYh/+vw1arTohwMbPORy+89DvNeqbTh/6WZ0iD9x5jI6u02RQV0so+/Uf4qcmS9bsiBCQ8OwdY9fjBC/es5wlCqWP0aIF8vrxVPPxKqBH+t/b3SXrl5DvPjGZufBk2jZuJp8PnzUIQYyc0YbgwBniDe6vxl+YApQgAIUoAAFKEABAxYwxhAvniDWutsYjOjfAU3qVEZL11FoWrcK2reoG2ukxXJ6JSF+0ertOPfXjThD/JS5a+Us/1KPQTA1NZVhX9wH/+lMfFwh/sGjZ3J2XizzFysHihVK3pulK/1z0muIHzZ5MS5dv4tNi8bIQYk61m7Zj/GeK/mIOaWjxfYUoAAFKEABClCAAhSggF4FjCHER+1OL+5Jv3j5ltydvlKZopg8zBWmJiYQk64rN+3FvIn9ULRgbvz9+Dk2bD8snzr2LUO82KTukN9ZzJvUH+HhEZi3/M9Yy+njCvFRj5gTm+iJx+WtXzgauXNk0et1oaWT6zXEN+04FI51q8Dl3/shoj74sxevUb15P2xZMi5650EtoSjpC2filWixLQUoQAEKUIACFKAABbQtYAwhPuo58ZZpUsl73xvVqoSfmtdGyhQp5OCEhoVjptcGuTt91CE2Kvf+bQiWrxcz8QHwmjZQvhSV7Q5snAm7jDZyOX2ZEvbRGfDzmfiTZy+j1zBPnPRZgMdPX6L3cE9cunZXnqtqhRJyJ/qvzcS7j52PnNnt0LtzM3z8+BGDxy/E6YvX5ZPPjGVzO72GeLEko6h9box0+znGX6rYob5Dn4nYvmKS3LEwOR8M8cl59Nh3ClCAAhSgAAUoQAEKxBQw9BCvZLzFI9yev3ojd6ZPZWGupFRRW/HkMvEkM7E7Po/4BfQa4sU9DuLbmzXzRqB4oTxySb3Y+GDktKU4c/Eajm6dE/1tT/xd1WYLhnhtjgt7RQEKUIACFKAABShAAV0EkiLE72s6XFFXHUZ1QIYy9opq2NhwBPQa4t8EBsHJZQSePHsJsVTju6yZcO3mfakn7rcQmyYk94MhPrmPIPtPAQpQgAIUoAAFKECB/wSSIsTTnwJKBPQa4kVHxGPl1m09IJ/59/59CHLlyILGdSrJZfaGcDDEG8Io8jNQgAIUoAAFKEABClDgHwGGeF4JWhfQe4jXOoDa/jHEqxVkPQUoQAEKUIACFKAABbQjwBCvnbFgT+IWYIhXeWUwxKsEZDkFKEABClCAAhSgAAU0JMAQr6HBYFfiFGCIV3lhMMSrBGQ5BShAAQpQgAIUoAAFNCTAEK+hwWBXGOL1cQ0wxOtDleekAAUoQAEKUIACFKBA0ggwxCeNO9814QKciU+4VZwtGeJVArKcAhSgAAUoQAEKUIACGhJIihD/1s9fkUDKjLZIZZ9PUQ0bG44AQ7zKsWSIVwnIcgpQgAIUoAAFKEABCmhIIClC/K2uA/Dh2s0EK+RdNMPgQ/z7DyEwT5kSZmamCXYxloYM8SpHmiFeJSDLKUABClCAAhSgAAUooCEBQw/xI6cuxSYf32hxh2IFMLBba5Qqll8zo/AhJBRl6rlizoS+qFHFQTP90kpHGOJVjgRDvEpAllOAAhSgAAUoQAEKUEBDAsYQ4t8Fv4d79zZ4+y4Yi9f6YMe+Yzi7dzHMU6bQxEh8jIzElev3kCNbJqRLm0YTfdJSJxjiVY4GQ7xKQJZTQIFAihtnYrQOz19aQTWbUoACFKAABShAgfgFjCHERwIYN6izxLh28z6cXEZg5+opyPmdHQLfBmHK3LXY63sKaS3ToGXjanBt10Qua9+21w++x87LYL193zFkyWyL0QM64tjpS/j9zwPIaGuNXp2cUOv7f/5/tJUb92DZul148uwlbG2s0NaxJrr/7AgTE5Poc1lZWWLbHj8Uyp9T1pZ3KCxr2/WagGF926FwgVxfPU/8I2p4LRjiVY4pQ7xKQJZTQIGA5epRiAry73/sj9CSNRVUsykFKEABClCAAhSIX8DYQvz6rQcxe9kWHN7sCVMTEwwavxBXbtzFgF9a4cWrQEyeswb9uraAs1NteK/bhWnzf4eLcyN8X64YVm/eh31HTqN+jfJo3vAHnL5wDeu3H4LvZk8Z1MUXASnMzJA9Wybcf/gUvYfNwrzJ/VGtYsnoc3Vu0wDfly8BnwPHcenaHWzwGiMHqWj1jlgxayjKlLD/6nniH1HDa8EQr3JMGeJVArKcAgoEGOIVYLEpBShAAQpQgAI6CRhDiP+ffwAqli6Cx89eIODKbUwZ9ou89zz4fQjKNfgF00Z2R8OaFaTflDlrcPzsZWxZMk4G76P+F7Fourt8zc8/AF3dp+OvQ97y5zeBQajctCd8Vk1Brux28nc37zzEpet38OzFGyxbtxNdnRuhQ8t6sc515/5jNGo/BH5b58LayjJGiP/aeXQa5GRexBCvcgAZ4lUCspwCCgQY4hVgsSkFKEABClCAAjoJGEOIv3zjLprUrSLvhQ8NC8faeSOQysIcUUH60xAuls2P9ViOkz4LYgXvMxevoX3vidEhPiQ0DKXrdpWB3z5fDvkFwIqNe1CzigNy58iCHftPoH2LOujUukGscz19/ho1WvTDgQ0esMtkGyPEf+08Og1yMi9iiFc5gAzxKgFZTgEFAgzxCrDYlAIUoAAFKEABnQSMIcRH3RP//OUbeT+8WLLuMbon3r4NljPp8yb1Q7VKpaTfnGVbsPPACexYOTlW8D4bcF3eux41E/9piBf3x1d16oNlMwdH3+febbAHKpQurCjE58mR5avn0WmQk3kRQ7zKAWSIVwnIcgooEGCIV4DFphSgAAUoQAEK6CRgTCFeAF2+fhctuo5C158ay3vfRShPa5kao9x+xqs379B/9BzUq1YObr+0UhTis9plQMXGPTBhSBfU+aEsTl24Cvex8+XGdkpm4u3zZv/qeXQa5GRexBCvcgAZ4lUCspwCCgQY4hVgsSkFKEABClCAAjoJGFuIF0j7fE+j78jZmDzMFcUL5UWfEbPkveziqF65lLxnXgT75evFPfEB8Jo2UL72+Uy8WJrvUKdL9HL6JWt94LFwvWybL3c2iJn6to610LF1/VjnevbiNao374cDG2fCLqONXE6/cvZQlC5uj6+dR6dBTuZFDPEqB5AhXiUgyymgQIAhXgEWm1KAAhSgAAUooJOAoYf4hKKIe9RTWaSEVTrLhJbE2S4o+IN8Hr14HJ2a41udR00ftFLLEK9yJBjiVQKynAIKBBjiFWCxKQUoQAEKUIACOgkkRYi//+sERX3N1KkNUtnnU1TDxoYjwBCvciwZ4lUCspwCCgQY4hVgsSkFKEABClCAAjoJJEWI16mjLDJaAYZ4lUPPEK8SkOUUUCDAEK8Ai00pQAEKUIACFNBJgCFeJzYWJaIAQ7xKbIZ4lYAsp4ACAYZ4BVhsSgEKUIACFKCATgIM8TqxsSgRBRjiVWIzxKsEZDkFFAgwxCvAYlMKUIACFKAABXQSYIjXiY1FiSjAEJ9A7HdB7xEWHg4b63QxKhjiEwjIZhT4BgIM8d8AkaegAAUoQAEKUOCrAgzxvEC0LsAQ/8kI/f34OZw6D0ebH2vBzbWlfCX4fQgGj1+AA0fPyp9LFsmHWeP7IKOttfyZIV7rlzj7Z0gCDPGGNJr8LBSgAAUoQAFtCjDEa3Nc2Kv/BBji/7UQM+3OPcfh5p2HcHFuFB3il6zZgfXbD2HlrGFIk9oC3QZ7IG/OrBg7qDNDPP+SKJDIAgzxiQzOt6MABShAAQoYoUBShPiIgGOKpE2sM8I0RwFFNWxsOAIM8QAiIj6i59DfkCWzLd6+C8Z3WTNFh/iWrqNQr3p5dHFuJEd99yF/uI2ei4CDy2BiYsKZeMP5W+AnSQYCDPHJYJDYRQpQgAIUoEAyF0iKEP9+Wnd8vH89wXKp3eerDvGRkZF49uINrNOlgYWFeYLfO6rh+w8hME+ZEmZmpoprlRQEvg3C0VMBqF+9vMxf3+r4GBmJ3YdOokrZYrBKZ/mtTpso52GIBzBp9mpcv/0AC6cOwJAJXjFCfPmG3TB+sAvqVisnB+TStbsQwf7YtrlysLmcPlGuU74JBaQAQzwvBApQgAIUoAAF9C1g6CH+5eu3mLtsC/b4nsLLV4GSM3eOLBjerz0qlSmaIN4PIaEoU88Vcyb0RY0qDjh8/DwuXr6FXp2cElSvpNFf1+6gletoXNi/9Jt+YSD2OytVuws2LhqDwgVyKelSkrc1+hC/9o/98F6/C+sXjIa1lSUGjJkXHeLFt1PFanTCvMn9Ua1iSTlYYrl9045DsW/dDGS1y4ADHafi7ord8rUiozuhQP9WST6o7AAFDFUgpfcImF4/LT9eeAs3RDjUNtSPys9FAQpQgAIUoEASCdimUz4rrbariTkTL1YV37jzNyYNdUW+XNnw96Pn2L7vmFyV3LppjQR9FDGLfeX6PeTIlgnp0qbBmi37sOvgSayYNTRB9UoaMcTH1jL6EF/P2R25vrND/jzZpc7+/52GVdo0cua960+NIWbiJwzpgjo/lJWvfz4T7+syHTeX7ZSvlRzvgiIDWyu5JtmWAhRQIPBx0TBEXj0lK0zbDIRJ2ToKqtmUAhSgAAUoQAEKxC+Qytws/kbfuEVihniRb35p3xQubRvG+hRidfLwKUuwbOYQuR+Yz4ET2H/kNGaM6iHbenhtQNZMtmjrVAvtek3AsL7tkCZ1KrTrPUHO6hcrlEe2mzikC4ZOXhzr/CLki4lSz0UbsX3/cdhYp0XrpjXRvNEPSGVhjm17/XDurxsoWSQ/tu/1Q4G82dGwVsUYM/Fi5bRYXi/eL1/ubHL2P2rV9JQ5a5AihZmceD114SqqV3ZAn87NkD1bJtmXY6f/kquwxetiw/Lzl25yJv4bX8uJcrp1Ww/iTeC76Pf6c/dR+Ri5JnUry2+ixNJ5cf+F2OxOHLwnPlGGhW9CgTgFuJyeFwYFKEABClCAAvoWMPTl9GM9lsuZ924dHFG2ZEEUyPMdUqeykKwhIaEoXc8V3p5DUK5kIbi6T8dR/wDs+X06vsuSEVWd+mDswE5yCX3R6h3lzHsR+1zwWLgBJ85ewoj+HeR5itrnxuXr9+S/P378iMETvJA/dzYsmDoA4v0vX78LN9dWELe4j/FYjm4dmqJJncrwXrcL0+b/LgN2rapl5MrnXNntYoT41Zv3yT7b2ljhsN85+cWC39a5clV1jyEzZXjv26WFbDNj4XpUKF1E7nf24OEziAlcx3rfo2WTanj09CXcx85niNf3H1RinP/T5fTi/Rav2YENUbvTp7FAt0HcnT4xxoHvQYG4BBjieV1QgAIUoAAFKKBvAUMP8WKjuJWb9mL5+l0ICv4gOZ2daqNnpx+R3iotug6chvIOheWEZqUmPVGlXDFUr1wKtauWRY0W/aIDc1SIL1PC/qvL6Res2Ir12w5h0+Ix8ssCcS/98L7tUapYfvnem32O4OnzV/Ac11uG+D2H/U+o8KgAACAASURBVLFq7nCY/ruJ3efL6cWXAldu3MeVG3fx9MVrzF6yGesXjkLRgnlkiC9dwj56U/JNPr5YtWkvtiwZh4Urt2HV5r3w3ewpN8jjPfH6/ktKxPN/HuLFhS2+oRGbNYhDLBGZPb4vMmdML3/mxnaJODh8K6MXYIg3+kuAABSgAAUoQAG9Cxh6iI8CFPe1P3j4FCfPXsHUeWvxU7M66NulOZas9YH/uStoWq8K9vmeQp1qZbF60z65/H7e8j+wwWuMPEVCQryffwC6uk/HugWjZI66c/8xGrUfImfvLcz/23tAZCuP0T1liD/qfxGLprtHj/OnIV5sqNd9iAeu3LiHmlVKy/v4F63ejrXzR6JE4byxQrxYRe3htR6710zDiKlLERIahqnDf5HnZojX+59S0r+B+MYqNCwcGW2tY3SGIT7px4Y9MB4BhnjjGWt+UgpQgAIUoEBSCRh6iBdBWNx//ukhAu79h0/h/dsQXLh8C227j0XtqmVQv2Z5VC1fAhUadUfTupWRKaNN9KO4Pw3xa7fsx479x7FqzrDo0z588gI/dhoG9+5t0LJJdfl7kanE7L74IkAE+c+P+EL8oWPn0Gf4rOgnhUV9mZCQEL9iw27sOXwquo8M8Un1F6aB92WI18AgsAtGI8AQbzRDzQ9KAQpQgAIUSDIBQw7xYlKyVis39OvaAhVLF5EbeovN3X4ZNENuENf9Z0eEh0egZG0X6X9ix3yktUyN3sM8ceDoWSyY4oaqFUrI1z4N8WcuXkO3wR7YuXoqTE1N5LL5n/tOQuYM6THx167RY2lpmRpdBkyV7zF1eDc5QXr15j2cvnANHVrWi3cm3v/8Fbi4TZXL4+0y2cJn/3GM91yZoJl48T7NXEZi2sjuKF+qELbvPSbvv+cj5pLsTy3p3pghPuns+c7GJ8AQb3xjzk9MAQpQgAIUSGwBQw7xEREfMWaGN3YdOhl9P7zw/blVffR3bYGUKVJIbhHaxTF7Ql/5f8Wu8WJX+KhQHxXiV84eitLF7SHO23Pobzhy4oJsL5bDi3vrPz92r52GlClTYMx07+jblUWbbu2bordLM3mfvthIz2vawOjSqKeDiefEm5iaQDwib+/hf55WVLOKg/xy4ff5I1H83+X04h79qE3Jxf31YnM7sZxe3D4weNwCueO+OMR9/of8zmHzkrEomC9nYl9mqt7P6B8xp0qP98Sr5WM9BRQJMMQr4mJjClCAAhSgAAV0EDDkEB/FIR7z9urNO4SFhSFThvQwNTXVQSp2iVgub26eMtZy/bhOLnbCf/02CBltrGFmpuz9X7wKlDP+4qliSg9RmzKFGazSWSot1Ux7hniVQ8GZeJWALKeAAgGGeAVYbEoBClCAAhSggE4CSRHiP3iNUNRX8wYdYJqjgKIaNjYcAYZ4lWPJEK8SkOUUUCDAEK8Ai00pQAEKUIACFNBJIClCvE4dZZHRCjDEqxx6hniVgCyngAIBhngFWGxKAQpQgAIUoIBOAgzxOrGxKBEFGOJVYjPEqwRkOQUUCDDEK8BiUwpQgAIUoAAFdBJgiNeJjUWJKMAQrxKbIV4lIMspoECAIV4BFptSgAIUoAAFKKCTAEO8TmwsSkQBhniV2AzxKgFZTgEFAgzxCrDYlAIUoAAFKEABnQQY4nViY1EiCjDEq8RmiFcJyHIKKBBgiFeAxaYUoAAFKEABCugkwBCvExuLElGAIV4lNkO8SkCWU0CBAEO8Aiw2pQAFKEABClBAJ4GkCPGRj88p6qtJqvRA+tyKatjYcAQY4lWOJUO8SkCWU0CBAEO8Aiw2pQAFKEABClBAJ4GkCPEfD44BXt9NcH9Na4xkiE+wluE1ZIhXOaYM8SoBWU4BBQIM8Qqw2JQCFKAABShAAZ0EDD3Er/1jP8b/tlLaNKxZAdNGdtfJiUVJJ8AQr9KeIV4lIMspoECAIV4BFptSgAIUoAAFKKCTgKGH+PDwCHwIDcX0+evw9l0wZozqoZMTi5JOgCFepT1DvEpAllNAgQBDvAIsNqUABShAAQpQQCcBQw/xUSgTZ63Ci1eBMsS/DnyHHkNm4sadv+XLRexzY2jvn2CfL4f83a8TvVC7alms23oQ74KC0fWnxvJ/4li5cQ+WrduFJ89ewtbGCm0da6L7z44wMTHBtr1+8D12HlZWlti2xw+F8udEr05OKO9QWKexYdE/AgzxKq8EhniVgCyngAIBhngFWGxKAQpQgAIUoIBOAsYY4gPfBmHLziNwKG4PC/MUWLJ2J27fe4gNXmNw8fIttOk+Fo1qV0KTOpVw8uxlLP19J3aunoKc39lhr+8ppDAzQ/ZsmXD/4VP0HjYL8yb3R7WKJeG9bhemzf8dnds0wPflS8DnwHFcunZHnpeH7gIM8brbyUqGeJWALKeAAgGGeAVYbEoBClCAAhSggE4CxhjiBdSHkFBcuHQTt+8/RsCV29js44u/DnlHh/iAg8vk7Lo4GrUfgi7OjeDUoKr8+eadh7h0/Q6evXiDZet2oqtzI3RoWU+G+KP+F7Fourtsd+f+Y1nrt3UurK0sdRofFnEmXvU1wBCvmpAnoECCBRjiE0zFhhSgAAUoQAEK6ChgjCFeLJnv1H8KrNKmQdmSBREaGoate/y+GOLdRs+FTfp0GNGvA6bMWYMVG/egZhUH5M6RBTv2n0D7FnXQqXWDWCH+6fPXqNGiHw5s8IBdJlsdR4hlnIlXeQ0wxKsEZDkFFAgwxCvAYlMKUIACFKAABXQSMJYQP9ZjOd68DZL3xE+ZuxaXr9/FUo9BMDU1xflLN+HcY9wXQ3zNlm5o1aS6/F9Vpz5YNnNw9H3u3QZ7oELpwgzxOl19CStiiE+Y0xdbMcSrBGQ5BRQIMMQrwGJTClCAAhSgAAV0EjDkEP/oyQtYpkmFwHfBcBkwFS5tGqJV0xqY6/0HDvmdxbxJ/SF2r5+3/M9Yy+m3LB2PzBnTY7PPEcxYsA5bloxDVrsMqNi4ByYM6YI6P5TFqQtX4T52vtzYjjPxOl1+CSpiiE8Q05cbMcSrBGQ5BRQIMMQrwGJTClCAAhSgAAV0EjDkEO+5eBO8Vm2TLo71vseI/u2ROpUFHj99id7DPXHp2l35WtUKJXDkxIUYM/Fi5/mXrwLl6+MGdUazhj/Ify9Z6wOPhevlv/PlzoaQ0DC0dayFjq3rY/l6cU98ALymDZSvP3vxGtWb98OBjTNhl9FGp/FhEe+JV30NMMSrJuQJKJBgAYb4BFOxIQUoQAEKUIACOgoYcoiPjIyUS+hFcLcwTxlL6OGTF7CxTitfjzqidqe/sH8p3gS+Q3rrtHLJ/adHUPAH+cz5LJl5n7uOl52iMs7EK+KK3ZghXiUgyymgQIAhXgEWm1KAAhSgAAUooJNAUoT4yGOeivpqUtgRSJ9bUY2ujaNC/Ke70+t6LtZ9GwGGeJWODPEqAVlOAQUCDPEKsNiUAhSgAAUoQAGdBJIixOvU0UQqevn6LXyPn8eP9b9PpHfk28QnwBAfn1A8rzPEqwRkOQUUCDDEK8BiUwpQgAIUoAAFdBJgiNeJjUWJKMAQrxKbIV4lIMspoECAIV4BFptSgAIUoAAFKKCTAEO8TmwsSkQBhniV2AzxKgFZTgEFAgzxCrDYlAIUoAAFKEABnQQY4nViY1EiCjDEq8RmiFcJyHIKKBBgiFeAxaYUoAAFKEABCugkwBCvExuLElGAIV4lNkO8SkCWU0CBAEO8Aiw2pQAFKEABClBAJwGGeJ3YWJSIAgzxKrEZ4lUCspwCCgQY4hVgsSkFKEABClCAAjoJJEmIf39TWV/N0gLmdspq2NpgBBjiVQ4lQ7xKQJZTQIEAQ7wCLDalAAUoQAEKUEAngaQI8ZGPVgKhTxLcX5Os7Q0ixF+//QBv3wWjdHH7BH92NgQY4lVeBQzxKgFZTgEFAgzxCrDYlAIUoAAFKEABnQQMPcR37DcZlcoUxS/tm0T7XLx8C226j4X/zoVIk9pCJzddiuYv/xNXbtyD57jeupQbbQ1DvMqhZ4hXCchyCigQYIhXgMWmFKAABShAAQroJGAMIb5imSLo1r5ptM+Fy7fQtvtYnPRZAMs0qXRy06WIIV4XNc7E66b2SRVDvGpCnoACCRZgiE8wFRtSgAIUoAAFKKCjAEN8Kty5/xgTPFfC79RfyJ0jCzJlSI/WjjXQoEYFnDp/FWNnLsfjpy+lcPXKDhjetx2s0lnixp2/8etELzSuXQlr/tgvX3dp0xCtmtaQ/w5+H4Kp89bCZ/9xWFiYy1n/Qvlyypn414Hv0GPITHkOcRSxz42hvX+Cfb4cOo6k4ZZxJl7l2DLEqwRkOQUUCDDEK8BiUwpQgAIUoAAFdBIw9hCfIoUZmnYciu+yZETXnxpLw2GTl6CLc0M4O9XGX1dv4/rtv1G4QE68fx+KkdOXyiDv5toSUcvya1ZxkMH9wcNnGO+5Ese2zZUhf6zHchw6dh49O/2I/Lm/w8IVW5EyZQoZ4gPfBmHLziNwKG4PC/MUWLJ2J27fe4gNXmN0GkdDLmKIVzm6DPEqAVlOAQUCDPEKsNiUAhSgAAUoQAGdBIw9xIuQ3qn/FOxaMxU5smWWhh36TET9GuVliBfHsxevcTbgOp48e4W9vqeQLm0azJ3YLzrEBxxcBhMTE9m2qlMfjHPvjCrli6FU7S4YN6gzmjX8Qb72+XL6DyGhuHDpJm7ff4yAK7ex2ccXfx3y1mkcDbmIIV7l6DLEqwRkOQUUCDDEK8BiUwpQgAIUoAAFdBIw9BDfdeA0lCpWAD07/hjtcy7gBn7qNR5ndnth9+FTGP/bCnl/fNTxaYjfefAEBo6ZjzIl7FEof05cu/UAqSzMsWCKW5whvlH7IejZyQklCuVFPWd3bF8xCXlyZo0V4sUyevHlgVXaNChbsiBCQ8OwdY8fQ3wcVzFDvE5/2v8VMcSrBGQ5BRQIMMQrwGJTClCAAhSgAAV0EjD0ED9y6lKER3zExF+7RPv4HDiB0dOXyeB+885DuZw+agm8aPRpiBeviXvju//sKOuX/r4TJ89ejjfE1/2hLErWdsHiGe5yd3xxfDoTP2XuWly+fhdLPQbB1NQU5y/dhHOPcQzxDPE6/R1/tYgh/tub8owU+JIAQzyvDQpQgAIUoAAF9C1g6CF+z2F/9B81FwunDpBh+sGjZ+g93BMOxQpgzMBOiIyMxA/N+qKofW40a1gVAVfvYMmaHRjWt51cTt+u1wTY580Ot19a4f7Dpxg9wxs21uniDfENa1ZA3xGzER4RgcE92+JNYJDcIC+bXUZ5T/xc7z9wyO8s5k3qj/DwCMxb/ieX03/hYudMvMr/CjDEqwRkOQUUCDDEK8BiUwpQgAIUoAAFdBIw9BAvQrrn4k1YtHp7tE+1iiUxfkgX2KZPJ38n7ncXbcS971XKFcNR/wB0atMALRpVg59/AAZP9MLLV4HycXT2eXPIJfDzJveX97G37jYGn94TL5bT9+rsJGfvxbJ910HTERT8QdaKze3EzvcixIvd7sWXCZeu3ZV9qFqhBI6cuMCZ+DiuYoZ4nf60/ytiiFcJyHIKKBBgiFeAxaYUoAAFKEABCugkYOghPgolLDwcT56+Qvr0aZE2TeoYVhERH2FmZip/9y7oPWq27C83ritXqpD8nXj90ZMXyJLZFmI3eyWHmGV/8vwVsma2lcvmPz8ePnkBG+u0SJ3KQslpjaotQ7zK4WaIVwnIcgooEGCIV4DFphSgAAUoQAEK6CSQJCH+6RZFfTVJXxkwt1NUo6SxeF574LtgZM6YHv7nr6JQvhxYOG0gTP/dcV7Judj22wswxKs0ZYhXCchyCigQYIhXgMWmFKAABShAAQroJJAUIV6njuqx6OrNe/jr6h2EhoUjZ7bMqFimSJyz5nrsAk/9FQGGeJWXB0O8SkCWU0CBAEO8Aiw2pQAFKEABClBAJwGGeJ3YWJSIAgzxKrEZ4lUCspwCCgQY4hVgsSkFKEABClCAAjoJMMTrxMaiRBRgiFeJzRCvEpDlFFAgwBCvAItNKUABClCAAhTQSYAhXic2FiWiAEO8SmyGeJWALKeAAgGGeAVYbEoBClCAAhSggE4CDPE6sbEoEQUY4lViM8SrBGQ5BRQIMMQrwGJTClCAAhSgAAV0EmCI14mNRYkowBCvEpshXiUgyymgQIAhXgEWm1KAAhSgAAUooJNA0oT4Fwr7ag4gncIaNjcUAYZ4lSPJEK8SkOUUUCDAEK8Ai00pQAEKUIACFNBJIGlC/GkAbxX0twxDvAItQ2vKEK9yRBniVQKynAIKBBjiFWCxKQUoQAEKUIACOgkYeogfOXWpdBk7qHO0z+OnL1GrlRt2rp6CnN/Z6eTGosQTYIhXac0QrxKQ5RRQIMAQrwCLTSlAAQpQgAIU0EnAGEJ8JIBxn4T4R09eoHbrAfBZNQW5sjPE63ThJGIRQ7xKbIZ4lYAsp4ACAYZ4BVhsSgEKUIACFKCATgIM8XYYPcMbFUoXRoMaFaThIb9z2HXwJCYPc8WNO3/j14leaFy7Etb8sV++7tKmIVo1rSH//TEyEt7rdmHZup14+SoQlcsWRUhoGFbMGipfHzLBC0dPBcjX8uXOhl6dnFC3Wjn5mnOPcXBt1wRHTlzA5et30dqxBjZsO4xF0wcidSoL2ebw8fNYtXEPFk4bCFMTE53GOLkXMcSrHEGGeJWALKeAAgGGeAVYbEoBClCAAhSggE4CxhDiz126ER2cBVLg2yCs3rwveia+Q5+JMsC3daolDf/Y9T8s37AbW5aMw8XLt9Cm+1jUrOIgg/uDh88w3nMljm2bC6t0lrLtsMmL4fZLKxngdx48iSVrduCvQ97yXOJ9CuT5DrY2Vjjsdw4eXhvgt3UurK0sUbR6R9mmXfM6yGaXAdUrO6Cl6ygM69sejvWqyNc6u01BsUJ54ebaUqfxNYQihniVo8gQrxKQ5RRQIMAQrwCLTSlAAQpQgAIU0EnAGEL8iXOXUbZEwWif9x9CsPuQv6IQH3BwGUz+nQmv6tQH49w7o3rlUhBfAOTIlhkThnSR5/c/dwUd+02ODvEfP37ElRv3ceXGXTx98Rqzl2zG+oWjULRgHhniF04dgO/LF4/umwj5J85cwroFo3D73iM07vArdq2ZKt/DWA+GeJUjzxCvEpDlFFAgwBD/f/bOOj6Ko43jv3hycSO4BYJ7cXcr+kIpWtydIsWtFG9xdy3a4l6keIDgXoJbEkLc835mlrvkYrd7e0nuLs/+0x73jOx3Zvfy3ZGVAItCiQARIAJEgAgQAa0IZAWJ17QmXsxIfGKJb951HAb1aINm9SqDCf3wPu3wv2a1kkl8aFgEBoxbiEfPXqFe9fLIns0Fa7Ydwo4Vk1G6WEEu8duWTkTZkoVUbffq7Uc07TwWe9ZMw6FTl+H7+gOWzRquVdsaSyKSeJktSRIvEyAlJwISCJDES4BFoUSACBABIkAEiIBWBEjiPfiU9VqVy6B7hyacYUrT6VOT+NHTVyCHhyufTs+OxCPxp/+9iaETF6um3rPvmbinJfEspu/o+XB1dgBLv3DqILWReq0a2cATkcTLbECSeJkAKTkRkECAJF4CLAolAkSACBABIkAEtCJAEu+BFZv+xtVbD7F4xhC8ee+H2Uu3ITg0XG1NfGoS//fxi/h10Rb079YK7q6O2Lz7OB48ecmn01+5+QC9Rs7l+Xi4u+DI6St8Pb0miT9z8SaGTFiM3DndcXTb3Cy7oZ2yQ5PEa3VpJyQiiZcJkJITAQkESOIlwKJQIkAEiAARIAJEQCsCWVHik74nnk1ZHzF1GZ48fw1bhTXKl/KC/5ev2L16Gu49eoEO/achqcQP7tmGb4bHdqJfvG4v39He3dUJRTzzYP/RC7h2ZCXfuX7k1GU4ec6btw3bHO/MxVvYuWIySqUynZ7FxcTEokyDXhg3uBO6tmukVbsaUyKSeJmtaawSH/fZT42MqbubTFKUnAjIJ0ASL58h5UAEiAARIAJEgAikTcDYJV5K+3/8HAA3FyeYmZmKTsY2rmMb3ik3vWNr3tkr45SvmGMZ+X8JgqmpCZwd7UXle+3WQ/QYMUdtGr6ohEYaRBIvs2GNVeLDVqxF1EnhvY/WnTrAul1rmaQoORGQT4AkXj5DyoEIEAEiQASIABHQR4m/K7FZ8gMQJ8ASM5Yd7nPvGUZOW44SXvkQERmFS973sXbBaFStUELrvAeN/wMebs6YPPInrfMwpoQk8TJbkyReJkBKTgQkECCJlwCLQokAESACRIAIEAGtCGTOSLxWVdXLRGHhkbh0/R4++wfC3l6BciULI1d27Wf1spH9AycuoVK5Yvzd8XQAJPEyewFJvEyAlJwISCBAEi8BFoUSASJABIgAESACWhEgidcKGyXKQAIk8TJhk8TLBEjJiYAEAiTxEmBRKBEgAkSACBABIqAVAZJ4rbBRogwkQBIvEzZJvEyAlJwISCBAEi8BFoUSASJABIgAESACWhEgidcKGyXKQAIk8TJhk8TLBEjJiYAEAiTxEmBRKBEgAkSACBABIqAVAZJ4rbBRogwkQBIvEzZJvEyAlJwISCBAEi8BFoUSASJABIgAESACWhEgidcKGyXKQAIk8TJhk8TLBEjJiYAEAiTxEmBRKBEgAkSACBABIqAVgcyQ+MjYl5LqamqigIWpu6Q0FGw8BEjiZbYlSbxMgJScCEggQBIvARaFEgEiQASIABEgAloRyAyJ94vYh+i4z6Lr62bdliReNC3jCySJl9mmJPEyAVJyIiCBAEm8BFgUSgSIABEgAkSACGhFwNglfvLc9dh75Dz6d2uJIT3bckbBIWGo8v1AHN8xD7lz0Ai/Vh0nAxORxMuETRIvEyAlJwISCJDES4BFoUSACBABIkAEiIBWBLKCxP9z2QcBX4Jwfv9iuDo7ICg4FFVbDCKJ16rHZHwikniZzEniZQKk5ERAAgGSeAmwKJQIEAEiQASIABHQikBWkHhHB1tc83mE8qW8MHZQx2QSz6R+zrIdOHneG3a2CrT/vjb6dmkBMzNTHDx5CT73n6FM8UI4dPISChXIhTsP/sOo/j+gXMnC8H39AWN/XYU1836Gg70tjp+9jvNXbuPXcb2xZc8JbPjzGD5+DoCLswM6tqqHAT+14jMB+oyej4nDuqJUsYK83T75BWLIxEWYP3kA8uTMplVbGmsikniZLUsSLxMgJScCEgiQxEuARaFEgAgQASJABIiAVgSygsQ7OdqhWsWS6DVyLk79uQC2Cmu1kfgxM1fh0bOXGNXvB/h/CcLspdsxvE87dGrTABv/PIZ5K3aiTHFP1K9ZATk8XLHn0FlUKluMT9Fft+MIFq7ahdkT+qJFw2qYNHc97O0UGDPwR/5QwNzMDLlzuuP1u08YMmExls8egdpVymDguN/h7GTPZZ8dq7YcxKkL3ti9eppW7WjMiUjiZbYuSbxMgJScCEggQBIvARaFEgEiQASIABEgAloRyCoSP7LfD+g+fDby58mOkX3bqyTexckBFZv2w7zJA9CsXmXOcM7S7bhy6yH2r5vBJf7EuevYumwiTE1M+Pdrtx/G1ZsPsGb+aHToPw1uLo5c1hfNGILGnUZj/JDOqF21LI997vsOD5764rP/V2z48yj6dGqObu0b49yV21zkLx1YBjtbG9RpNxyjB/yIlo2qadWOxpyIJF5m65LEywRIyYmABAIk8RJgUSgRIAJEgAgQASKgFYGsJPE+956h8+CZ2LFiMjoOmM7XxMfExKJ513E4snUO8uX24AwPnbqM6Qs34dqRlVziL16/y4Vdedx+8BydBs7A8e3z0Lb3JOxfPxONfvyZf2YSf/ngMj61nj0M2LznBOpVL8cfHhw+fRVd2zVEjw5NERsbh4Y//ozenZohp4cbxsxciQv7F8PKylKrdjTmRCTxMluXJF4mQEpOBCQQIImXAItCiQARIAJEgAgQAa0IZCWJZ4AGjf8D0dExuHj9Hpd4e1sFqrUchOW/DVeNni/dsB9Hz1zF4S2zU5R4Jv5lGvRCq8Y1YGNtiUkjuqHL4F+RJ6c7nvm+5VPi2UZ6NdsMxYbfx6JSuWK8bfqPXYjK5YtxiWfHuu2Hse/oBeTK7obiXvn5FH46khMgiZfZK0jiZQKk5ERAAgGSeAmwKJQIEAEiQASIABHQikBWk/iHT1+iXZ8pnJXyFXNMwNmU9ikjf8KXryEYMXUpGteuCDYFP6WReJaWTYVnU+LXLhiNqhVK8E3s2Fr63p2aY0Tf9qrX2LE17w1rfQfvO48xevoKvrGdUuL9Ar6idtthanXRqhGNPBFJvMwGJomXCZCSEwEJBEjiJcCiUCJABIgAESACREArAllC4p3s+Tp45TFq2nIc++eaSuLZDvNDJy3m69fZUadaWcyZ0I+L/aZdbDr9Paye97MaXybtS9bvw8UDS2Fhbo63H/z4lPpVc0ehRqVSPFa56R37f8/8OREZFY2Oreqje4cmqrz6jp4PK0sLLPlVkHk6khMgiZfZK0jiZQKk5ERAAgGSeAmwKJQIEAEiQASIABHQioCxS7wUKOw1b9ZWFnw9u66O0LAIPiqfPZtLsizZv1f5fqBqNF9XZRpbPiTxMluUJF4mQEpOBCQQIImXAItCiQARIAJEgAgQAa0IZIbEB0Qek1RXe4sKsDB1l5TGEII37z6ObftP4ei2uaqd7w2h3hldR5J4mcRJ4mUCpOREQAIBkngJsCiUCBABIkAEiAAR0IpAZki8VhU1wkQXrt6Bo4MdShcraIRnp7tTIomXyZIkXiZASk4EJBAgiZcAi0KJABEgAkSACBABrQiQxGuFjRJlIAGS+G+wg4JDEREZjWxuTiniDwkNR3RMDJwd7dW+1yeJj4+OVaubiYWZ1l0pbMVaRJ08zdNbd+oA63attc6LEhIBXREgidcVScqHCBABIqA9gdj4OLXEZiam2mdGKYmAgRx8qwAAIABJREFUHhIgidfDRqEqqXtefHx8fFZmwl5j8NOw38B2YGQH2yWxT+fv0aJhNf45LDwSY2euxJmLt/jnMsU9sXjmULi5OPLP+iTx58p0R8Q7P16vqmcWw6GU9tNQSOKz8lWhv+dOEq+/bUM1IwJEIOsQWPviLN6Gf+En3ClvNRS288g6J09nmiUIkMRniWY26JPM8iPxbMfFv45dQKvG1WGrsAHbTGHjrmM4v38xrK0ssW77Yew6dBZbFk+AwsYK/ccuRMG8OTB9TE+SeIPu+lR5QyRAEm+IrUZ1JgJEwNgIkMQbW4vS+SQlQBJPfULfCWR5iU/aQG/ef0bjjqOxZcl4lC/lhfZ9p6BxnUro3ak5Dz1+9jpGTl2Ge/9sgImJCY3E63sPp/oZFQGSeKNqTjoZIkAEDJQASbyBNhxVWzQBknjRqCgwkwiQxCcBv//oBUycsw4X/loCFyd7VGrWHzPH9kKj2hV55IMnL7nYXz64jL8vkabTZ1LPpWKzJAGS+CzZ7HTSRIAI6BkBkng9axCqjs4JZIbEvw97K+k8rM1s4GyV/D3rkjKhYIMlQBKfqOmevniDzoNmolv7xhjcow3YdgEl6/bA8tkjULtKGR753PcdWnYfj1N/LkAOD1f823s+nq4/yr8rP6s3So75MdM6w578HRH25jMv/3vvlXApW0jrunxYsAJfD53g6d16dYZrl3Za50UJiYCuCEStHI+4R9d5dhadx8CsYkNdZU35EAEiQASIgEgCc2+fgG+IP48eVLw2SjjnFJmSwoiAYRAwMzXJ8IqefnsUXyIDRJdbP1dTnUh8RGQUgoLD4OrsADMzYZPKF6/eg+0bVrFs0RTrExkZBVMzU1iYm4uuLwXqlgBJ/Deebz/4oeuQWahUtihm/dIbpqZCJ2Yj8b+O642Gtb7jn5OOxJ/6aS5ebznOvysyuTs8h7XXbQtJyO1MqZ9UG9vVOMs2tvOUkFo9NHT5GkScEHanV3TuAJv2bbTOixISAV0RsNkyBWbPbvDsItqMQEzZ+rrKmvIhAkSACBABkQRWPf9HtbFdl3zV4GWfXWRKCiMChkHAw9k6wyua0RJ/5eYDzFq8lQ9QKo+Wjaph9IAfceDEJVy8fhdr5o9OkUOXwb+idHFPjBmYeYOXGd5AelYgSTyAZ75v0WPEHNSrXg6TR/ykegrF2opNnW9SpxJ60Zp4Peu6VJ2sSICm02fFVqdzJgJEQN8I0HR6fWsRqo+uCWTGdPqMlPibd5/wwcuB3VujY+v6fDPvB098MW/FTkwe0Q3XfR6nKfHsrV421pbwcKfp/Lrue2Lzy/IS/+T5a7TpNQnNG1TF0J5tYfJt+gzbiZ69E37t9sPYrdydXmGF/mNod3qxnYviiICuCZDE65oo5UcEiAARkE6AJF46M0phWASMXeI7DZzBlwUvmDJQrWHi4uIQGxeHbXtP4fDpy3y0/eCJSyhaKC9falypXDEeP3f5ThTKnxNtm9XCwZOXcP7ybTg42KYYO+7X1bjofQ8BX4L4q7xZPsq9xgyrV+hXbbO8xB/95yp+nrYiWauw6SS/je+L0LAIjJ6+Aueu3OYxJYsWwJKZw5DNzYl/po3t9KtDU22MmwBJvHG3L50dESAChkGAJN4w2olqqT0BY5b42Ng4lK7fE4umD0GDWhVShLTxz2N8VL7nj01Ro1JpHDlzhY/U7149jccPGv8HShfzRL+uLaApdtu+UyhcIBdcnB1w7pIPFq7ejUsHlsHRwVb7BqKUyPISL7YPBAWHIio6Bm4ujmpJsqrEB4+brOKg6NMDZp4FxKKkOCKgNQGSeK3RUUIiQASIgM4IkMTrDCVlpKcEjFniP/kFom674di+fBLKFE95/ywm5onXxLPp8827jlPJd1KJTyuWje4/evYaj569xCf/QCxZtw+7Vk1BiSLkDnK6P0m8HHpZeCQ+qM8gxPkLO2jaz5tFEi+zH1FycQRI4sVxoigiQASIQHoSIIlPT7qUtz4QMGaJV47EL5w6CI3rCK/QTnoklXil+J/ZvZCvg09L4hPH2tkqMGDcQjx69gr1qpdH9mwuWLPtEHasmIzSxQrqQ1MbbB1I4mU2XVYdiSeJl9lxKLlWBEjitcJGiYgAESACOiVAEq9TnJSZHhIwZolnuDv0n4bs7i5YNGOIGn0m+GxN/PZ9p9RG4rWV+HuPfTF04mJcPrgMDvbC9PkSdbqTxOugz5PEy4RIEk8j8TK7ECWXQIAkXgIsCiUCRIAIpBMBkvh0AkvZ6g0BY5d49nq5XiPnolfHZujarhFsrK3w8OlLzF/5Z4q702sr8S9ef+Dl7F83g4/gHzl9BTMXbSGJ10FPJ4mXCZEkniReZhei5BIIkMRLgEWhRIAIEIF0IkASn05gKVu9IWDsEs9An73kg9+WbsObd59V3JvVq4xfhnbBwRMXcfH6Paye9zP/7rN/IOr8bzjO7PkdHm7OGDJhEUoVK4i+XVpg0y62fj7lWHdXJ4ycugwnz3nzfNjrvM9cvIWdKybz9HRoT4AkXnt2PCVJPEm8zC5EySUQIImXAItCiQARIALpRIAkPp3AUrZ6QyArSLwSNnsTF9vA283VERbm5unSBv5fgmBqasJf302HbgiQxMvkSBJPEi+zC1FyCQRI4iXAolAiQASIQDoRIIlPJ7CUrd4QyAyJv/jhrKTzL+5cGs5WLpLSULDxECCJl9mWJPEk8TK7ECWXQIAkXgIsCiUCRIAIpBMBkvh0AkvZ6g2BzJB4vTl5qohBECCJl9lMJPEk8TK7ECWXQIAkXgIsCiUCRIAIpBMBkvh0AkvZ6g0Bkni9aQqqSCoESOJldg2SeJJ4mV2IkksgQBIvARaFEgEiQATSiQBJfDqBpWz1hgBJvN40BVWEJD59+gBJPEl8+vQsyjUlAiTx1C+IABEgAplPgCQ+89uAapC+BEji05cv5S6fAI3Ey2RIEk8SL7MLUXIJBEjiJcCiUCJABIhAOhEgiU8nsJSt3hAgidebpqCK0Eh8+vQBkniS+PTpWZQrjcRTHyACRIAI6CcBknj9bBeqle4IkMTrjiXllD4EaCReJleSeJJ4mV2IkksgQCPxEmBRKBEgAkQgnQiQxKcTWMpWbwhkhsTfCXgr6fwdLW2Qz45eMScJmhEFk8TLbMysIvExt+8i9vVrTsumTw9EbNyKOP8A/tl+3iyYeRaQSZKSEwHNBEjiNTMylggriyA42z/lpxMbZ4nPgaWM5dToPAyYgE/gS5z8eI+fQS4bZ3TKW82Az0b7qpPEa2Zndf5PWF09wAOji1dHePOBmhNRhN4QyAyJ//XWMbwKEf62FnOML9ck0yQ+Lj4ex89eQ/XvSsLB3haRkVEwNTOFhbm5mKpTjA4IkMTLhJiVJD7m/gNOSzFqKEm8zH5DybUjQBKvHTdDTMUk3tXhofAHcKyCJN4QG9EI68wk/u93N/mZFbbzIIkHOAPGgg51Akzirf/Zyv8x6rumJPEG1kGMWeJjY+NQun5PVYv07tQcI/q2l9RC0TExKNugN/asmYZihfOhy+BfUbq4J8YM/FFSPhSsPQGSeO3Z8ZQk8TQSL7MLUXIJBEjiJcAy8FCSeANvQCOtPkm80LA0Eq+5g5PEa2akzxHGLPH84XhMDL7v9gt6/tgM7b+vDVNTU0nNkVTifV9/gI21JTzcaXq/JJAygkniZcAjiafp9DK7DyWXSIAkXiIwAw4niTfgxjPiqpPEk8SL7d4k8WJJ6WecsUs8o96402j07dIC/2tWizfCnKXbYW5uhue+7+B95zHqVCuHoT3bIndOd/795Rv38duSbfz7MsU9cfvBc9VI/NzlO1Eof060bVYL3rcfY/rvm/Dhk+AJLJ+Jw7rwafd06I4ASbxMljQSTyPxMrsQJZdAgCReAiwDDyWJN/AGNNLqk8STxIvt2iTxYknpZ1xWlPiB437n8j6sdzsULpALC1btQuXyxTGyb3u8efeZS3+rxjXQvkVtvP8UgNHTV6gkftD4P1C6mCf6dW2B+49f4OmLtyhWOC/Cw6Mwef56LvIsHzp0R4AkXiZLkniSeJldiJJLIEASLwGWgYeSxBt4Axpp9UniSeLFdm2SeLGk9DMuq0p8+dJeYGvk2bH3yHls3XsS+9fNwKotB7F130mc37cIJiYmfDp+4jXxiSWepf3sH4hb957i4+cvOHneG/Z2CiybNVw/G9tAa0USL7PhSOJJ4mV2IUougQBJvARYBh5KEm/gDWik1SeJJ4kX27VJ4sWS0s84knjg+NnrWLh6F45vn4dJc9cjMioacyf24w2WlsQf/ecqfp62AhVKe6Foobx48t8bWFtZYuWckfrZ2AZaK5J4mQ1HEk8SL7MLUXIJBEjiJcAy8FCSeANvQCOtPkk8SbzYrk0SL5aUfsaRxKtL/Obdx3HinDe2Lp2gUeJbdh+PpnUrY8BPrXjs+p1Hce3WQ5J4HXd1kniZQEniSeJldiFKLoEASbwEWAYeShJv4A1opNUniSeJF9u1SeLFktLPOGOX+JR2p2dr4hNPp088Ev/4+Su07TUZ8yYPQKWyRXHo5GXMW7EzxTXx7HVzXgVzY2S/H/D63SdMXbARzo72JPE67uok8TKBksSTxMvsQpRcAgGSeAmwDDyUJN7AG9BIq08STxIvtmvLkXiTiDDYHPhDVVRY258Bc0uxRVOcDggYs8Sn9p54JvFsCnyvb2viT5y7zje3Y9Pp4+LjMXbGShw5c5XTrVOtLM5e8sG+ddNRxDMvhkxYhFLFCvLd7i9dv4exs1Yj4EsQbBXW8CqYBw52CiyfPUIHLUNZKAmQxMvsCyTxJPEyuxAll0CAJF4CLAMPJYk38AY00uqTxJPEi+3aciXeYU4HVVFfJ+wliRcLXkdxxizxchD5fwmChbmZxtfFsQcF7z/6I3s2F/7aOjp0T4AkXiZTkniSeJldiJJLIEASLwGWgYeSxBt4Axpp9UniSeLFdm2SeLGk9DMuMyR+2f1zkmB8n68U8tm5SEpDwcZDgCReZluSxJPEy+xClFwCAZJ4CbAMPJQk3sAb0EirTxJPEi+2a5PEiyWln3GZIfH6SYJqpa8ESOJltgxJPEm8zC5EySUQIImXAMvAQ0niDbwBjbT6+iTxkbHRapStzCwyjPraF2fxNvwLL69T3moobOeRYWUbSkEk8YbSUinXkyTesNsvK9SeJF5mK5PEk8TL7EKUXAIBkngJsAw8lCTewBvQSKuvTxK/7+113P36hpNukr00Krt4Zhh1knjNqEniNTPS5wiSeH1uHaobI0ASL7MfkMSTxMvsQpRcAgGSeAmwDDyUJN7AG9BIq08SLzQsSbzmDk4Sr5mRPkeQxOtz61DdSOJ10AdI4kniddCNKAuRBEjiRYIygjCSeCNoRCM8BZJ4knix3ZokXiwp/YwjidfPdqFaJRCgkXiZvYEkniReZhei5BIIkMRLgGXgoSTxBt6ARlp9kniSeLFdmyReLCn9jCOJ1892oVqRxOusD5DEk8TrrDNRRhoJkMRrRGQ0ASTxRtOURnUiJPEk8WI7NEm8WFL6GZcZEn/+mZ8kGO52ViiW3V5SGgo2HgI0Ei+zLUniSeJldiFKLoEASbwEWAYeShJv4A1opNUniSeJF9u1SeLFktLPuMyQ+C6bruPhh2DRQLb+VFG2xMfHx+Oz/1c42itgZWUpumypgcfPXkelckXh7EgPHaSySy2eJF4mSZJ4kniZXYiSSyBAEi8BloGHksQbeAMaafVJ4knixXZtknixpPQzztglPiAwGMs27MeJ894I+BLEGyF/nuyYOLwrqlYoIatR1u04gtw53NG4TkVVPiXqdMe2pRNRtmQhjXl/8gtE3XbDcWjzbyiQNwePf/DkJdr3nYJxgzuha7tGGvPICgEk8TJbmSSeJF5mF6LkEgiQxEuAZeChJPEG3oBGWn2SeJJ4sV2bJF4sKf2MM3aJHzl1GZ75vsVv4/vCM19OvH3vh0OnLiN7Nhd0aFlXVqMMm7QERQvlxYCfWsmS+IObZqFgvpzwff0BP/SbyuV9SM+2supmTIlJ4mW2Jkk8SbzMLkTJJRAgiZcAy8BDSeINvAGNtPok8STxYrs2SbxYUvoZZ+wSX6lZf/Tr2hK9OjZLsQHi4uLARtR3/HUGIaFhqF+jPMYN7gxHB1vcf/wCc5btwObF41Vp+49diD6dv4dfwFdMmruOT83P6eEKr4J5MGNMT7CR+N6dmuOS9z28fPMRP7aqh4HdW8M6hSn8ypF4JvG2tjboOGAGGtQsj/FDu6jKO3vJBwtX78Jz33eoUNoLE4d3g1fB3Pz7TgNnoG+XFrhw9Q4ePn2JmWN7IWd2NyxasweHTl+Bs6MdOrSsh/81r8XL37LnBDb8eQwfPwfAxdkBHVvV4w8gTExM9LNzfqsVSbzM5iGJJ4mX2YUouQQCJPESYBl4KEm8gTegkVafJJ4kXmzXJokXS0o/44xd4qcv3MRH3vt3a4XvyhRB4QK5YGNtpWqMPYfOYe7yHRg94Ec+Or943V7k9HDDohlDcO3WQ/QYMQf3z25UxddsMxQzx/RE0cL5MGrqcuTN5YE2zWrATmGDYoXzcYn3zJ+Tl2drY4XRM1ZiwZSBqFm5dLIOoJR49pBg2sKNKFmkIGaO6wXTb1LNZhC06j6BPzSoVaU0tu49ieu3H+PEjnn8HFhZ7Ojyv4b8QULjOpWwcssBLvQj+/4Als20hZvQv1tLtGhYDSfPe8PczAy5c7rj9btPGDJhMZbPHoHaVcroZ+ckiddNu5DEk8TrpidRLmIIkMSLoWQcMSTxxtGOxnYWJPEk8WL7NEm8WFL6GWfsEh8UHIote09i065jCA2L4I3QqU0DDOrRGk4Odnw0m02JnzzyJ/7dqQs3wKbJXzqwDI+fv0pV4mtXLcvjNE2nH/frari6OPCHBEkPpcSzUXG2Xn/jonGoWKaoKmzJun04dPoyjm+fx/+NxbCHCMtmDUedamW5xK+aOwo1KpXi30dERqFC476YOKyrak3+viMX8MnvC38owQ42ov/gqS/f5G/Dn0fRp1NzdGvfWD87J0m8btqFJJ4kXjc9iXIRQ4AkXgwl44ghiTeOdjS2syCJJ4kX26dJ4sWS0s84Y5d4JfW4+Hi8efcJ12494iPvnds2xLDe/+NSPLJve7RpWpOHvv/ojwYdRmH/uhkIDAqRLfG/LtqK2NhY1UOCxL1AKfHtW9RBcEgYnxa/Z800PrrPjl9mreb/Zev5lUe99iPRp3NzdGxdn0t84k302Jr65l3HobhXPlhZJuzAn83NCQunDsKcpduxec8J1Ktejm/ud/j0VXRt1xA9OjTVz85JEq+bdiGJJ4nXTU+iXMQQIIkXQ8k4YkjijaMdje0sSOJJ4sX2aZJ4saT0M87YJZ6NTiddjz5p7no+nXzjH+PQptck1KhYEqP6d+ANdPnGffQeNQ//7PmDx3QbOivF6fTKkfginnn4mnflkVSsxUg8WxOfK7sb+oyej49+X/Dnyil8lsC8FTtxyfs+f6DADjaTgK3xZ0LOdsRPWhabdVC1xSDsXj2Ni3ziQzmKv+H3sahUrhj/iq3vr1y+GEm8fl6auqsVSTxJvLI3xT5/odaxzDwL6K6jUU6cAEl81ukIJPFZp60N6UxJ4knixfZXknixpPQzzpglPio6BvV/GInhfdqhSvnicLBT4PaD5+g3ZgEG92jDN3VbumE/2JTzP6YPhoe7M2b+vhkfPgdg16qpCI+IQsWm/bD8t+EoXdwTx85cw8xFW/hnJvGrtx6E9+3HWPLrMC7YLk72ycRarMSz3enZyH/nQTP5O+bXLRyDm3ef8AcKTNqrVyyJTbuPY/nGv3B27x9wd3VKVhbrYT1HzkFMTCzmTuwPNxdHviTgxp0nfKZBle8H4tdxvdGw1nfwvvMYo6ev4AxoJF4/r02d1YokniRe2ZkiDxxG+Mat/KNl7RpQDBuks35GGQkESOKzTk8gic86bW1IZ0oSTxIvtr+SxIslpZ9xxizxsbFxmLZgI46dvaZaD89a4acfmmBE33awMDdHWHgkxs9eg5PnvHkDsWnmi2cM5ZvTsYNJ87KNf/H/Z+vQ2W7xys3g2PT1EVOX4cnz1yhXsjC2Lp2QosSzHfAnjeiWrAOk9J74N+8+o23vSahTrRzmTuyHlZsPYMn6fcLfhgprPrWe7aDPjpTeSc9G8qfN34hzV26ryuvftSWG9GrLd+FfuGoX/3d2fpFR0ejYqj66d2iin53zW61od3qZzUMSTxKv7EIk8TIvJhHJSeJFQDKSEJJ4I2lIIzsNkniSeLFdmiReLCn9jDNmiVcSj4+Px5evIYiOjuYj2Kampskag01FD4+Mgoebc7Lv2Cg7G91mr51L6WBT1R3sbWFubpYujRwZGYXPAV+RI5srzMyS1z2lQlmawOBQuDk7qqVh58LW37Od+A3lIImX2VIk8STxJPEyLyIJyUniJcAy8FCSeANvQCOtPkk8SbzYrk0SL5aUfsZlhsSP2HtHEoy+1QugWHZ7SWko2HgIkMTLbEuSeJJ4kniZF5GE5CTxEmAZeChJvIE3oJFWnySeJF5s1yaJF0tKP+MyQ+L1kwTVSl8JkMTLbBldSvzHw5cQcCHhKVyx2f0l1e5cme6IeOfH01Q9sxgOpQpKSp84OGzFWkSdPM3/ybpTB8TcvouY+w/4Z8WooYjYuBVx/gH8s/28WaBN3ACaTq91dxOdkCReNKpUAx2eCeu+2BFtlxvh2avJzzQdciCJTweolKVsAiTxAsK1L87ibfgX/v+d8lbDs5APKraVXDzhamknm7WhZ0ASb9gtSBJv2O2XFWpPEi+zlXUt8T7dZ/EaOVcpgUoH50iqHUm8JFw6DyaJ1znSZBmSxMtn7Hx/Jaz87/KMvnp1JomXj5RyyEIESOJTlvj9b70RHhvFvxxcqCFJPACSeMO+MZDEG3b7ZYXak8TLbGWSeBqJV3YhkniZF5OI5CTxIiBpCCGJl8+Qcsi6BEjiSeLF9n6SeLGk9DOOJF4/24VqlUCAJF5mbyCJJ4kniZd5EUlIThIvAVYqoSTx8hlSDlmXAEk8SbzY3k8SL5aUfsaRxOtnu1CtSOJ11gdI4kniSeJ1djlpzIgkXiMijQEk8RoRUQARSJUASTxJvNjLgyReLCn9jCOJ1892oVqRxOusD5DEy5d4tole9PkLvE2s2raCdfu2OmufjMyIptOnP22SePmMM1Pi3cO2wywumJ9EgM33iDLLleoJ0cZ28tuactA9AZJ4knixvYokXiwp/YzLDIn3uf5GEgwnFxvk93SVlIaCjYcATaeX2ZYk8bqReNVO+B3bk8TL7JPGnJwkXn7rZrbEm8cJO1r727QmiZffnJRDBhMgiSeJF9vlSOLFktLPuMyQ+CmjDsP3ufDmJzHHtAXNZEt8fHw8Pvt/haO9AlZWlqpifV9/wCe/L6hUrpiYqug8Ji4uDpFR0bCxttJ53saSIUm8zJYkiSeJV3YhGomXeTGJSE4SLwKShhCSePkMKYesS4AkniRebO8niRdLSj/jjF3iAwKDsWzDfpw4742AL0G8EfLnyY6Jw7uiaoUS2LznBM5euoX1C8dmSgNdvnEfvUfNw8UDS+HkQK+sTKkRSOJldk2SeJJ4kniZF5GE5CTxEmClEkoSL58h5ZB1CZDEk8SL7f0k8WJJ6WecsUv8yKnL8Mz3LX4b3xee+XLi7Xs/HDp1GdmzuaBDy7qZLvEhYeF4+eYjinrmhZmZqX52kkyuFUm8zAYgidcviY++5o346GhVq1pWryqzhcUnp5F48ay0jTRWibe4L+wJoTyiS9TUFpHGdCTxGhHJDvCLTljXaG/mAitThew8DT2D+0Fv1U6hhEPq+yHIPdf0LIskniRe2T/N3j6BaeDHVO/bJPFyr+TMTW/sEl+pWX/069oSvTo2SxF00pH4s5d8sHD1Ljz3fYcKpb0wcXg3eBXMjY1/HoPvmw+YOqq7Kp9VWw4iNDwCI/u2x7uP/vht8VZcvfUQZYp7on2LOmhUuyKP7TRwBupUK4uT5725sP/Yqh4Gdm8NaytL/PfyHX75bQ12LJ8EU1NTjPt1NS563+OzBjzz58TgHm1U+WRuT8m80kniZbInidc/iQ+dvYC3qplXYdjPni6zhcUnJ4kXz0rbSGOVeNvt02D+1JtjCW81DFFlG2iLSGM6kniNiGQHXPn6F+IQx/OpYN+EJB7AqU/3cdHvCWdSxaUQGmcvJZtzahmc+fQAF/wep0tZJPEk8Ykl3m7tKP4xzjEbgoevU+uSJPHpdolnSMbGLvHTF27iI+/9u7XCd2WKoHCBXGrrzxNLPBuxb9V9Avp0/h61qpTG1r0ncf32Y5zYMQ9P/nvDZfzM7oXwcHfh69hrth6CWb/0QZ2qZdGqxwSUKV4IXds1xIvXHzB6+gqc2DkfubK7oUSd7lzIWR1sbawwesZKLJgyEDUrl8b9J774oe9U3Dm9no/Eb9t3itfRxdkB5/gDhd24dGAZHB1sM6Q/6GMhJPEyW4UkniRe2YVI4mVeTCKSk8SLgKQhhCRePkNNOZDEJydEEq+p10j/ft/b67j7VZj10SR7aVR28ZSeiZYp1r44i7fhwiaVnfJWw/633giPjeKfBxdqCFdL41/DykbiSeK17EAGkMzYJT4oOBRb9p7Epl3HEBoWIVzLbRpgUI/WfA16Yolfsm4fDp2+jOPb5/E4Nhpes81QLJs1nI+kN+86Dm2b1kSvTs1x6vwNjJ+9hq9lv3nnKXqOnINNi36BrcKap526YCNaN66Bjm3qc4nftnQiypYsxL9jo+2uLg4YPeDHZBLPNrp79Ow1Hj17iU/+gWB12rVqCkoUKWAAvSl9qkgSL5MrSTxJPEm8zItIQnKSeAmwUgkliZfPUFMOJPEk8Zr6iC6+J4nXBUXt8yC07inaAAAgAElEQVSJ156dIaQ0dolXtkFcfDzevPuEa7ceYe7yHejctiGG9f6fmsT/Mms1D2fr55VHvfYj0adzc3RsXR/b95/Cpt3HcWzbXAyesIivYx/Sqy32HTmPSXPXo1zJwmpNXrd6OT6NP6nE/7poK2JjYzF55E9qEh8RGYUB4xbi0bNXqFe9PF+3v2bbIexYMRmlixU0hO6ULnUkiZeJlSSeJJ4kXuZFJCE5SbwEWCTx8mFpmQNJPEm8ll1HUjKSeEm4dB5MEq9zpHqVobFLPBNjtvY88cGE+/W7T9j4xzg1iZ+3Yicued/H/nUzeDgbuWdr6hdOHYTGdSoiMCgE1VsOxrzJA/h0+aPb5iBvLg+cu3Kbf758cHmKm9OJlfizl30wdOJiXD64DA72wvR5lpYknr0gkA6tCZDEp7/Eh8yYrWofmx/+h/Bde1WfbUcNhYkiYdMotrEdrYnXujune8K4d+8Rtm6Tqhy7SePUyow8fAzRN334v5na2UExYrDa9xkp8WydOuKFdc1so7mk69RZXZRHWKsRiLdz0pofrYlPjs7KIgiuDg8F/rEKfA5MvzXUWjdcKgmlSPzHqBfwj34n9HkTUxRVZNxmnLo+77Tyo+n0uqdNEq97plJyzEyJt/7kDZuPV1XV/VJqkJSqU6wIAsYs8VHRMaj/w0gM79MOVcoXh4OdArcfPEe/MQv4hnEDfmqlJvHK170xaa9esSQfdV++8S+c3fsH3F2Fv33YVPiDJy+h2nclsGb+aP5vbMp+gw6j0KZpTQzr3Y7/2/XbjxAdHYMGNSuIHolnaXqNnMsfIrB190dOX8HMRVtI4uNJ4kVcyqmHkMSnv8QH9RuCuM9+vBHsf5uO4F8mqxrEces6knhZPThjEzOJDxo8khfKHr6w9kt8MIkP/yb5ljWrZ7rEp7XZnOOMVkCcIPlBo7aQxIvoSu5h22EeJ6yj9bdpjSiz1Hcoz0oS/zz8FmfiYpGDJF5EP9IUQhvbaSIk/3taEw9ktsQ7PdrAGzLKqQgCSg+V36iUgxoBY5b42Ng4TFuwEcfOXlOth2cn/9MPTTCibztYmJtjy54T+CfRe+JXbj6AJev3cUZsfTubWl+/RnkVs+s+j9B9+GzV6LzyC597zzBhzlr4vv6QLG1KI/Fs7fukEd3w4MlLtO87hW9sZ2JqAvZKvJPnhA2A61UvhzMXb2HniskoRdPp6crVlgBJPEm8su/QxnaaryKS+JQZ0Uh8ci4k8ZqvJ0OKoJF43bcWjcTrnqmUHEnipdAyvFhjlnhla7Bx3C9fQxAdHc1H1Nmr3NI6IiOj8DngK3Jkc5X87nY2Kh8dEwsXJ3uYmJho1SH8vwTB1NQEzo72WqU3tkS0Jl5mi5LEk8STxIu/iEjiSeJpJF69D7Dp9DQSL/4eIiaSRuLFUJIXQyPxNBIvrwfpf+rMkPjff/1HEpg2P5ZGfk9XSWko2HgIkMTLbMukEp/zh3pqOVpldxFdwsfDl+DTfRaPd65SApUOzhGdlgWeK9MdEe+EaedVzyyGQyntd2wMW7EWUSdP87ysO3VAzO27iLn/gH9WjBqKiI1bEecfwD/bz5sFM0/tX/GgVlbH9rBu31btvGk6PRAXIExBTu0wdXGW1FcyK5gkPmXyuhyJN436qlZInKWj2mfanT79e7/UNfH6KvHBMcJrh5SHvbnwiiBtDhqJ14Za2mloJF4z04Aw4bV3ysNFob6Rl+YcUo9IOhIf0kt4/ZbysLx1Etb/bOUfo75rivDmA0UXZxIRBoc5HVTxXyfsBcwT6s7WxGfUdPqoOPX7gKWp9vcB0QD0IDAzJF4PTpuqYEAESOJlNlZSif988jq+XLnPcy27/hd4tKguugSSeMCaJD7F/hLtfROhs4Q/EMwKe8KyRjWEb9jCP1vWrgHFMMPY1IYkPuXbgS4lXvHuHBye7eIFRbiVQ2Dx3mqFksSLviVrHWgsEr/yvzP4GCE8FOqarzoK2mbTmglJvNboUk1IEq+Z6dlnn7Hg7DPhb7JcjpjRtLjmRCIjkkp8RP1uUOybz1PHFCiDmPyljELi/aLf4EnYNX5ejubuKGFbUyQhww4jiTfs9ssKtSeJl9nKJPE0Eq/sQum5Jp4kXqCc0bvTG+LGdiTxMm/qOkhOEp8cIkm8DjpWkixI4jUzJYnXzEhTBEm8JkL0PRHIHAIk8TK5k8STxJPEi7+IaCQ+ZVY0Ep+cC21sJ/66Sq9IGolPTtYn8CX+fneTf1HYzgOd8lZLL/wa8yWJ14gIJPGaGWmKIInXRIi+JwKZQ4AkXiZ3kniSeJJ48RcRSTxJPG1sp94H9HljO5J4kvjU7u6GsrEdSbz43+fUIkni5TOkHIhAehAgiZdJVZPEhz57oyrBrUHFNDebS7om3r1hRcTHxqrSFxyRsMlJStXWl43tYl+9RvS1G6oqWrdrnSZl2thOcyfMyOn00Td9EPufr+j201z7hAiSeIGF1QVhzbryMH/9EGlN3ZfCWOp0etOoIFX20Y6FEeXoKbo4xfsLMIkOVcWH5m2SZlp6T3xyPJkp8Rf8HqtVqKZbEbXPciT+ZqAvQmMiVflFxsXgot8T/rmKSyE0zl5KdD9jG+yxEXDlkbSeSTOi3elFoxUdmLivWJiY4V7QG7wNFzZcZbMR9r/1RnissInc4EIN4Wppp1XeLJGm9k0r4yefQ+DzNmFzz2x2lnqzJj7OwS3RTd8SiEnYdC8uWz5EF6ms+l7qxnaRTl5qWDTdi0U3DoDMlPj/fv9Traqa/g6Wcl7WfrdgFvYx1d8vWhMvhSbFZgYBkniZ1DVJ/MPxqxD5QdjFXdOO8SlJ/JMZG3naXB0boOTi4WnWVp8kPnj4GF5XEydHOK5fmWa9SeI1d8KMlvjQmcKbEdgmevZzZmquoMgIkngBlPXxtbC68jf//8iaP8Dsw3+ZJvG2b07DPOwDr0tAmZGSJd7h6U6eNsKtLAKL90mzJ5DEJ8eTmRKfeJ16VddCaOShLtZyJf7gu1v8hL3ss8PdykGWxC98clT4TYEJJhdP+8EwSbzIG7KEsMRT9xt7lNKpxJ/9/BDnPj/itankUhBNs5eRUDP1UCbxo/6+y//Rw94KXSrk0RuJR3QkLG+fEe6XjXrB4sG/MHsjPEgL+3GiLImPcioMO99DQl45qiOocCetGSZNmNkS/3SWsIlv7i6NUOL3oTo7LybxTg/W8vzYw2v2+5f4yAyJf33osqTzU+RwhWsF9Qc4kjKgYIMmQBIvs/lI4pNPp2cj8STxut0xniReuFCNZWM7knjA36Y1osxypXoHpjXxMn+cRCQniRcBKUlIVl0TTxKfvK9I3Z2eJF769cZG4rOSxB+oOAD+N4QZS2KOltdXGIzEx8TEIjY2FlZWunvNoxhGxhxDEi+zdUniSeKVXchYdqdn0+lpJB7QtNmc44xWQFwcb/6gUVsQb+ck+m5CEk8Sr+wsNBKv+bJh0+lpJD45p4zc2I4kniSeEcjoV8yRxKd9f5Qj8ZPnrsfeI+dVBZQrWRg/9++AsiULab4paxGxdMN+nP73Jvavm6FFakqSEgGSeJn9giSeJJ4kXvxFRNPpBVYk8STxJPHS1sSTxJPEi/mloen0NJ1eTD/hv8N6OJ0+I0fimcSHhIVj9IAfERwShrU7juDwqcu4dXItLC3MxWIUHffJL5CX45k/p+g0FJg2AZJ4mT1EjsQHP/CFT/dfVTXwmtIDPt1n8c/OVUqAbWyXUWviXyzdizebj6nKLljMDlEnTwvC0akDYm7fRcz9B/yzYtRQRGzcijh/Ya2//bxZMPMsoDoPfZ5OH75qHaJvC+vlLOvWgnX7tqn3gNhYBA0Zpfrefu5MmNilvlmPLkfiY9+8ReiseaqybXp2U31m69Qta1RD+AZhnZhl7eRT90OmzULcx09C+3Vox2PEHlJH4kNmzEbce2FNNePJuKZ2GLLEW/27B4gXRt7DWo+A3cZxNBL//gIyak18WEQ22FoL/SwmVoGA4MJiu3SGx+nyPfGPw64gNFbYqCunVWH89cYX0XEx/PP3OcqhgK271udH0+mlo6Pp9ICmNfG9C9QBG7lXHoM8G8LUxCRV2EnXxEfERuNNuPD3RUXngqjiKn5kUNcSf6FSwj4f5Vf/jNjVK1Tn4fxLf9itH80/xzlmQ0T9blDsmy/cowqUQUz+UrD+Zyv/HPVdUxjLdHpH82z4FCVsfsv+39OmnPQLSWSKpCPxBTwtEfNA2D/BskE9xNy4hbgvwgaLNt06waJKJZE5k8QziY8HMGNMT87syfPXaNNrEo5um4O8uTyw59A5vHr7ESP7/cC///ApAMMmL8G6hWNgp7DBjv2nsXXfSXz2D0S+3B4Y3KMNalctiys3H+D31bvx4tV7uLs6oXWTGujT+XscOX0FN+48waQR3RAYFIKB437HM9+3PO/iXvkxfkhneHnmEd1+FAiQxMvsBXIl/lLtwbwGVtldUGx2/0yV+CfTNvC6sE30jFniI4+f4udp3bG9RokPbN9F1UMcN6/JUIkPHvozL5s9OFAMHSBZ4tmDF3Yohg1Kd4mPuXVbKGvIAKOWeFN/4QcnpOdcknjW3hks8Y62wh+OEVEuWUri/aPf8fMuaFOWS/zHCEHqu+WrQRKfwm84bWwn8w+bFJJLmU7PJJ69gk55TCrWWrLE3/n6midnDwwyU+LPFOmE6ADhDR41Ti5A9PSpqvNyXTQly0r8q4j7nIOHZYEMl/ioM+eEv+G6dOQSH/NQkHrbMSNI4iVc+kklfteBf7Bkw36c27eIX68rNv2NR89eYdGMITxXJvRNO4/F5YPL8N/L9+g8eCYWTh2EgvlywOfeM7A1722b1UT5xn3Rv2tLNKtfBb5vPuDKjQeYMKwLNu85gbOXbmH9wrEICg7F/qMXUK6UF6wszbFux1G8ePUOu1dPk3AGFEoSL7MPkMQb3kg8SXzanV6bkXiSeFoTn96707OReJJ4knjanT7h/q2va+JJ4o1/JJ4kXqY8pJI8o6fT/3v9HqqUL44Pn/1x79ELzJnQD3WrCzMr0pL4+0980XvUPKyaOwpVK5SAmZkpT8Om51duNgBDe/0PXds1gsLGSnWmiSWe/WNEZBTuPHiOF68/8LL3HTmP+2eFN3LRIY4ASbw4TqlGkcSTxCs7h66n09NIfHUoRggzVZRHZu5Oz6bT00i8+q2QRuJT/mnQ9XR6Gomnje1S6mkk8cmp6Ho6PY3EJ39PPJtCTxIvUx70ROIfPnuJFo2q87XwUdEx2LF8Eqy/7R6flsTb2Fhh9pLt2Pm38MrEJnUrYUTf9sidw51Ps5+5SFjuyTbLG96nHb4rU0RtJJ5No+8xYg4c7BT8u6ioaBw4cYkkXmK3IomXCCxpuKFK/JcrwlQo5RHo/QjpNZ3e9mf199ubFy+qVrac98TbzZ7OFsiq8osPCUHo7AX8s5lXYdiz7xMdbE18WiPxyrVWLImJqSmCx09Rpdb1dPqkZcV/2+mcl21uhuBxk3nZuphOb+rulkDB3AzmXqmvJ046Em/zU8KSgpQul4i9fyHVkfi4OMQ8Snhdiom5OYLHTRLOS6GA49Z1allGHj6G8HWb+L9Z1pQu8eavEvp1vKk5TL6tHWb5xeQtoVZW4tiUvk+6O70ciU9alvnDy2m+Jz7OJUeqd6ZY5xyIt3dJ9XvFu3NweLaLfx/hVg5hueqqxdq+OQkrf2GpxVevzkjrPfFmEf4wiwxMtSzzsHcZuiY+rZF4S/NgtXpGxdinWu/0/iKxxJeyq4P4b3spsHIdzBNdiwA07U7P1sSnJfFmJsIICDscLWzgaKEQfXqGuia+e/6aaueYV+Gq9lmX0+lfh/nzdaPKIyAqBH+/u8k/FrbzQKe81dLk/SrMP9W6hsZEwj8qRPV90vNImnHSvLy//Ie7X9/wsCbZSyOHtfobMjTlJ7qjAHyNu7IsMWviE0+n75m/lhrDpPVKaU08TacPg8OcDqom+jphL2Ce8Gou60/ecHokLIGMcioCTe+JT/v+GA2YCuvKhYPtX5DQ6/0io/Ak7JpwjzF35+vgDUHiTSLDYfbxv4TTMjUD4hL+XjRXRKm9Jz6kZEe1S8ItW0Epl4hOYjN6JF65Jt4v4CtfD1+htBefIs+m06/achC37z/D8tkj+Lklnk7vYG/L/+1rUCjuPHyOhat3o6hnHvw2vi//98jIKDx6/hqbdx/HNZ9HOLf3D2zdd0o1nX7Osh14+PQl1i8cA1NTU9x+8BydBs4giZfYi0jiJQJLGm6oEv/x8CXV+nunSsWQrWmVdJN4Rf/eKrE2L+oFu1nqa17kSnyIUnYd7KEY2FeWxAf1G4K4z368me1nTUtXiQ/qPxRxnz6nWBZ7+KBLiY/YsUtVFnvwIUXi1TbRq1kdJna2iDx6gtfbun0bxDz7L02JD2zXWXXZ2M+eka4S7zithaqskF7zYbdO2Fcg3soWQeN2ql2+llcPwubYav5v0SVqIqzdGLXvdSnxltcOweboKlVZcfYuaUp84gcGoT1mw3bjeNWmekEjN0mS+FgrZ9i+FZ6Wh+ZpBPOw95Ik3v2a8DAp3tQSX0r0g8vdJfxzjG0uhOWsqTcS72L/BNaWwh+igaEFwKbfZ9aRVOLvhgjrg01hiiqOrdWqJVfid7y+jOhvf5gOL9w4y0j8Bl/h1UiulnYYXKihGlNdS/z6RGXVcPOSJPGr//sH7yOEB2Gd81ZDITsPVV2ZxM9/ckT1eUrxNml22X8+PcR5P2H9b2UXT4TFRqpJ/O3AV6qy2MMF9pBBV4dciVcydLJQYFjhxmrVIokHwn6ciOgilVVcTCJ0K/Fujg+gFPmAYC9ERDknaoNomNjsUH2Oj2wKE6ujwud4BT4HVzFYiXeYLWzKxo7QXvNgu+7bRoT2LojuNlxN4qOK1YF91GUeG2ZRHLY5m+nq8hGdT2ZJPKsgk+p2fabwTejY6Pl1n0cYNP4P7F07nYv2uu2H8eeBf/iaeCbdbKf5ejXK8+8mzlkHe1sb9O7UHH+fuIgOLevC0cEOf/51Br+v2Y2LB5Zix19nVBK/bONf/P+X/zaCr6Vfvulvmk4vupckBJLEawEtcRKSeM3T6UniU+5kJPG6H4kniQeSjsSTxMu8yWuRnCQeuBnoi4PvbnF6XvbZ4W7lgIt+wqycKi6F0Di79q+YYyPxJPEk8SldmjSdPuVXzJHEk8QnvV6SbmzHvj91/gbfgX72hL5oWrcy//+zl3x40sZ1KuL42etc4h88fYmhExcjNCyCf1e9YklM/bkHLMzN0H34bPi+Ft4kU9wrHwb3bIvaVcpgy54T+OfbxnZsp/shExfhwZOXPK5m5dK4cPUOjcRL/HuDJF4isKThJPEk8co+IXVNPEk8STzrO5E1f4DZh/9g/tSbd6XwVsNAI/GAlUUQXB0ecibRsQo+sp7WdHoaiQdoJF64G9NIPPg0fxqJt0KXCnmw4Owz3i/K5nLEjKbFJf3VZyxr4kniSeIldfxEwQFfgqBQWKvWyiu/io+PR0BgMN+8zsY6YQM79j0bpY+JjYWzY9rL2t599Iezo12y9NrWNaulI4kX2eIhoeGIjolJ1iFJ4kniSeJpOn3QqLR3p6fp9MJV4h62HeZxwpR3f5vWiDLLleodmCReQKNpTTxNp6fp9KyfsDXxNJ0eoJF4GolX/qiwNfGGNp3+VMuJIq1ECCs3pRtcK3hJSkPBxkOAJF5DW4aFR2LszJU4c1GYFlimuCcWzxwKNxdH/lmKxFc+Oh9vNh9TlZi7WxNcbSqs2RXznngTM1PEx8bxeEWBHAh78V6Vl1u9Cng8ZR0i3gnruVMqK3HZ7o0r6W5N/G/TEXnytKoulo3qQ7VO3ckRSafTW1T6DrFvhPdt88PMHFHf0rN3t8d9/KT6yrxMKURs3ZmwTv236Qj+RVijyw62vjutNfGW1Soj9pXwvll2mFhYqG1sl2ZZSdbEs3XqkScSztOiSiVEXxE2e+GnkTcPwjdu5f9vWbsGTKytER8dzT+zjeWUa+3ZZ/NSJZF4nXrS9fdS18SzTeLiIyNVZbHN5BK/J15tTfysaYg6JayRZodVowaIPHFK9dmiWhWEzpwjnFNhT0hdEx9z/4EqL8sG9RCSaHPApGviFSOHIPrSlQSGBfKrbWxn4mCP+PBw4bxcXeAadx/mz4RNpcJbj4CZr7BBGzuiyjeC3fqEde1J18SzNe8W9y+o4mOzF1RbEx9n5wT2o88OtnFcWqPjSd8Tz8qyvJFwbbM19mpl5fDU2Zr4ZGWVqg2Lu8J7c9lhUrGs2sZ2UqfT23y4pMqLrXl3vTVPYCJiTXystStMo0N5fJylA0yjhPcr8/Zx9ISdg6+axCuiHyeUZVMOCtuEzZXCI93SHImPibWCqWmMUFacOczNItTWxFuaJ2wYFhbpBoWVcG9kBxvVV1gn3GdYWTaJvo+KsUPi9IGRX2BiIlxf8fFWqv/nn+Pc8Sw44TzdLfLgYeglxEG4V7ON7RKviS9mWx2fo1+p6mJv5oLn4cLvi4tFDtiY2iM6XijL3MQSkXGhaW5sl1jiexeoDe8vvqq8yzjmxe2vCWXlUbjgdViA6ntbcyvVFPeqroUQHivcr9iRy8YZ3l9eqN5J3zVfddX6a/b9d04F4B34QhVfzikfbgUKUyPZwcpKazp9RFxCWWxDNuW6cc7MMXeystb6CvsKsFfMJZ1Oz9apv0y0gZy9uTUu+Al9i03dT6usEg65cD8o4feognMB3PiScF7lnfIhrTXx2a2dEBwjTCm1MjVHZKLNNLNbO6qJNVsTn7SsdYnep842gLuZiGF+hRt8wxL6rYO5jaQ18Q8Sndd3zgV4eyoPdl6aygqKEe6HlqZmCI+NkrWxXeI18XWzFceLUGE/GHawDRnPfRbW+ldyKYiI2Ggk3tjuY2TC9VXMPiceBr9Tpf3OKT+8AxP6fBHzQhh94Ns7zO2Tj8TXjgIC/cN4enMLU8REC9cpOzxyOcD5cMIeBR7Nq+Hu0D8y7D3x5o8SfgujKzRWrd9mdQsbtgiKjxdVdY10LpbmxnamebKrYqNNnWHjbqW2Jl65fwgLCgl3Q6zlX6p4qWvi731J2ATPzcoefpEJm4yypTRPgoWp1eyo4JQfNxK1VzGLwjj6MOFeXNfGEk/ufkzop1+f4+ksYafz3F0aoYCnJdJ6T7zDR2FNOzuiKzSB7bd9cdhnQ1gTr6o8/Q8REEGAJF4DJLaRw65DZ7Fl8QQ+ZaT/2IUomDcHpo/pyVNKlXiltLO0TLSlSvybrcKGYoXHd4X/OR8EXBQkpsyasckkPrWyLN2dUHzeQJ1KfKpinYrEh2/ezuttWbcW+yVVk/iY+w8Rc+ce/569YkyuxKvEum4tmFhaqkl8mmWlIPGqzebYJnqD+yN0liA4bCd89sAgqcSrdsLv8D++S3tqYq0LiVdtNqehLLaxYKpibWcHxfBBsiQ+Yvc+xH0QfoTTLEuhAJN41QMDzwKwrFMrmcSzHevZwTbRSyrxVud3wjRAeJjFxVqDxLNX1LEjNkchRJWpl0zira4e5N9rmuKeksSrNtGztEFY+3FQlZW9IKLKNdSpxKuV1WE8bLcIO/6zBxNxLdrKkniXO4uAeGEHX/9yoyVLPNvtnh0pbaKXVOJdww8wBRfKsu8ENxdhzXR8vCkCgotolHg7m29tH54jmcTbWX+AuZkgIX5BxeBq/xgmJkJZfl9LwM1R+EM/aVkxsTYIicgOJ1tBeNgGUAERwTAx/xYfUwImJkGAmfBwMD66Kny+vERYnCAaJWxraJT4+6HCwySFqQNyWHkmk/i3kQKHnFaFJUv82hfCAx1TE1N0zVsdm14KZbE/rpmopybW7LvAqDCVIDXPUTaZxO98fUW1iV7v/HWgFGtW1k/5aqjWqWsqi4l1UEwYHgQJMtY0exncCvTFh4iv/DN7YPDn66uI+ibEictKTeIT7xjPxDqxxAfHhKvkOWlZTKx3v7mmKqtXgTpQijUrq0f+mholXlkW22wuJCZCVVbS0XFW1p431xH57QFG4rLYeTOJV8qui6UtarkVxV/vbnAmbEO8nNbOkiR+b6Kykr67XUpZTKx1LfH73wrLhwraZuMPfNKS+HtBb/A2XHjA1zFPVc6E1YcdvfPXxlrfhIeYHdzqa5T40weEB82V6xREZEQMfK4ID7qa/VAaFr+vwtcbwgOg8lsnZ6jE2xxYDJMw4T6SWDjZZybxrneEN+/EWrkguEArDRLvAZsY4cFIkGW1ZBLP7m+mpsKDtE+BxWRLvM+3h08NPEricfB7sLc6sOOHPJVx+L0P2CaO7OiVvzbWJWqvju4NMOpv4e89N1tLjCiUDTtWXhX6RhF31Lf4IEniXS4tg2mw8KAytNd8knhOgg5jJUASr6Fl2/edgsZ1KvEdF9nBNnUYOXUZ7v2zASYmJiTxbBf3tEbHSeJ5v7EmieccUhqJJ4mXtiZebYYBe2BAEo+QzJT4qKpgf8CSxJPEk8R/E+sCdZD0FXNJR+JJ4nvB4sG/MHsjPDBgu9OTxJPEG6ts0nmlDwGSeA1cKzXrj5lje6FR7Yo8ku2kyMSe7c7I3pNII/Ek8axf0Ej8bX59KIYMAI3E00g8HwVJ4XV2RjkSTxLPR/1pJF547RuNxANpjfqzV8yx6fQk8STx7HeCRuLTR+4o16xBgCQ+jXZmOy+WrNsDy2eP4K9HYMdz33do2X08Tv25ADk8XHGxzwI8WSesoarwW2+8OXIVHy8IU9zr7p6Cq8OWIuydMK2o+cUlOFx9iKrE5peW4HA14bMipyuqLBmCM/+byj971CyF3M0q48Yva/nnwt2bgK2JV5ZVfmZPvDt5Ax/OCfJUZ+ckXP95JULfCOvM0irLxsMZVZcPU5WVrXpJ5G1ZDd5jhXdms7I8i9viy0Fh6n623p0R4n0bYT7ClKfcU+tJygQAACAASURBVH7Gx+UbEP1ZOK8Cy+fgxcCxqvMqsGIuXgwQ1iabOzshx88D8XrCLOE8SxaDfY1K+LhyE//s2KQuTC0sVGW59+qEsFv3EHrzDv8+16SR+LR6C6I/CueVVllmjg7IOWawqiyb4l5wqFOd11VVlpUVvvwtTM/WVFb+ZbPhO2hcwnktn4sXA4Xz4mWNG4LXv/zKPycrq2FtmCpsEsrq8SPC7j5EqLfQXrkmjsCnddsR/V6Ydp5mWQ52yPnLsISyihWGQ/2a+Lh0vXBeDWvDzM4WAfuFfuiuoawCy2bjReLzWjkXL/p/Oy8HO+SaMAKvxs4QzitpWQ1qwczeTlWWW7cfEPHoKUKuCWt6WT39Nu9C1FthmnNaZZnaKpB78ihVWdZennBqUhcfFgt93pGV5eiAgL2H+GdWlkPUXcQ+vM4/W3UZg+hjWxHnJ0zJtRm+COF/DFO1l83IJQhfKFxfJja2sOo+ERErfuGfTfMUhnmlRojau4x/Ni9fByb2zog+t59/tmjUCXFvniH2gbDngVXn0Yg+uQNxn94klLV4BFuMLXxOXJaVAlY9JyWUlbsQzKs0RdQe4f3qvCwHV0Sf3SuqLOthvyNiyajUy+o9BRHLhOvPNHchWLdrj/jb24TzzvkdoHBB/DPhWjbxagYEvUX8B6EfmpTrjvhnx4Fgob1Ma41D3L/zgG/vHTetMxFxZ2cKTM0sYVplCOIuClM64ZgbJgXqIt5HWK9okrMCoHAT8kulLFg+B6KF+4aJRwfEf9ytmk5vkrsHYKbc48CUrYwGINQTsGVXDQBhmjngxqgDUO53kYftksAmyn/7nm30w9Y5C+vzgbIA2D1Fuf61PABhbwX29nZNZQVHhyAkWqiLnUUZxMR9RUSssA7X0bIW/v3wH4KihHeB18pRHxc/nEXstyUJ9XI1xpm3AhMzEzPUyFEX594Je1A4WDqhsEMR3PATpo/mVOSGvaUDHgcK0329HIshNCYEb0OF8yzvVglbnz3F21ChrBGl6mHZ/XOI+tZeY8s2whwfoa3NTEwxvFRdLLgjLG/IrnBAw1zFsOWpUFZpl1zIaeuIY6+FshrmKgq/yFDc8hPK6lSoIs69TyhreKl6WJ6orHFlG2F2orJGlqqPeXdOiiqrQa6iCIgMxc1vZXX0/A7/fnyO1yHClGlW1soH5xERK+x5kLgsUxMTjCrVQFWWh409muQpgU1PhPXEJV1yIq+tM468FpY/sLLYMgHvz8KU6aRlDS1ZF6sfXlCVNbZMI8y5LTBkZf1cugHm3hbOS1NZ9XIWQVB0uKqsDp4VcPnjC7wKEab3srLWPPxXtfdA4rLY92PKNFSV5W5tj+Z5S2LjE2GNbwnnHMhv74rDr4TfYVZWSHQkrn0W+mHSsoaUqIO1jy6qyhpXtjFm+wj9UCirEeZ+O09NZdXN6YXQ6ChVWT8ULI9rn1/CN1i4lllZ6x9fQmiMMBKfVllu1nZoma80j2dHMafsKOTgjoOvvv3dlNMLYTFRuPpJOK+kZQ0qURsbH19WlTW2TGPMuZ1wXoM8m6HHFuHazulojQE1C2LSoW/T5/O7oGG8Cf7aKfx9Ua+JFyLCo3HpnLB0pmOPCoiZsxyfrwhvxWhwYCYu9JyHSD9hmUera8vwYVTC3wRFNsxCxB9D+XcmLh6wbNELkZuEv3XMvMrBrHAZRB3eyD+b12gBREUi5prQtyxb90eMzznE+QplWfeZjsjtCxAfKpSV+DeFfbadvBJxF4S8oXCFSfH/Id5b+JvNxL0Y4FYU8Q+F3y+T/LWBPNmAUOEaMHGuBThYABDyBkp+u5cK7RUfXxYfwv/89h3gat0a/hHCGnkzE1tExtbE1U//8s/u1h7wUGTHvQDhfljAvhDYmvhLH//jn/9XoCx8/N/ieZDwN1v/YjWx/dl1BEUL+0Ykvkexz0MKN8dPm4QlI9nsrTC1dC4sny8sASpa0gPNFJ9xc6Lwt45Xr2Z8TXzgUeGelq1vV4RcuYGwO0L75pkxDmYnf0f8V6Ff2oxcjPCFQvvwzyMWI/z3b+3l6Aqb/qMRf2258KWrF0zL1EN84Hnhs10pmLg2VqWl/yEC+kiAJF5Dq7CR+F/H9UbDWt/xyKQj8frYqFQnIkAEiAARIAJEgAgQASJABIgAETBOAiTxGtqVTZ1vUqcSeqWyJt44uwWdFREgAkSACBABIkAEiAARIAJEgAjoIwGSeA2tsnb7YexW7k6vsEL/Meq70+tjo1KdiAARIAJEgAgQASJABIgAESACRMA4CZDEa2jX0LAIjJ6+AueuCOt/ShYtgCUzhyGbm5Nx9gg6KyJABIgAESACRIAIEAEiQASIABHQWwIk8SKbJig4FFHRMXBzcRSZgsKIABEgAkSACBABIkAEiAARIAJEgAjolgBJvG55Um5EgAgQASJABIgAESACRIAIEAEiQATSjQBJfLqhpYwzgkBEZBRCQsNphkRGwAZAvKWDzkxmmVm2dFKUgggQASJABPSJgNzfELnp9YkF1YUI6BsBkngJLRISFo6uQ2ahXfPaaFT7O7i7Zty6+MwsmyF6/PwVXr/9jHo1ysHUlL1XWRC6x89fo3CB3FDYWGkk6fv6A575vsWXwGDkyu6GIoXywtXZQWO6tAKOnLmK7ftOYevSCbLyocTiCEjh/fLNRzx/+RYBX4KRK4cbihXOBycHO1EFyUkrqoA0gnRdthRmcuueNL2msmNj4/Dv9buoXaVMikWzZUTsmi1fir13Xf3Q1T1J1/eFJ89f49DpK3jz7hMWTh2UrN5yzlnX7ZOR+bG2bN9vKrYumZChv11Jz1HX15cYhrr4/RJTTloxb9595teSX8BXfj8s4ZUfDva2orLVJm1mnrOcsjPz+pRbtpzzFtURMiFI02+IpirJTa8p/9S+p98BbclROkMiQBIvobViYmKxZtsh/HX8X7Af1ZqVS6NVk+qoU7UsbKzTllj2B2/lZgPSLK1y+WJYv3BsijGZWTarEHvVXk4PNyyaMYTX78LVO+g/diH/fxdnB+xcMZmLeUpHcEgYpi7YiGP/XONf2yqswTYMZMfwPu3Qp/P3ElpBPTStHwg5zOWkZTWUk15OWrlla2oIMT/IrG1nLd6Kv479m6y9p47qjvYt6qRajLZp5TJjFdK2bLnMdFH31Oqgqb3YLJbKzQfgrw0zU8zi6X9vsH3/6RQfksm5J7HCdHlf+OQXiGNnr2H/0Qtgf7zlz5MdPX5syh+4Jj3knLPc60tuW8tJzx6WNO86DvfPbkyxrZmA7Dl0HhOGdUnOTObvl5zrS845K09E298vXZQdGRmFBat2Ydu+U2r3Q/Y7OPXnHmhWr3KqtxA5abU9Z7l9nKWXVbaMe5Lc9pJ7b9D2vGXXWwfXp7a/IXJ+/+Sed9KyM/J3QNN50/dEICMIkMRrQTk+Ph5jf12NR89e4sOnAP7H//+a1cL3DaviuzJFVCPVibPmT3iv3VH9059//wNHB1s0qVuJ/9ute8/gffuxxhFlXZTNyps4dz06tqqHEkXy45rPI9y+/zzVsr8GhaJay0HYtXoqHz1gdWjbezIK5cuJft1acmGrUr44+nZpkSLN8b+txY27jzHt5x6oVLYoj3nw5CX+vXYXS9bvw7xJ/dGsfpVUW4KxS/0H5goYy5RG4pU/yL+N7wtHewXPYtaSbWhQswKvR1rnrUw7b/IA2KYwy0BTeyVtbynM5fYVXaTXhrcyzW9LtoHJ4/Sfe6B6pVKwtDDnDzX2HDyHeSt2Yt3CMby/pHRom1ZOW+ui3tr2UVZ2XFwc7jz4L0UePvefcWblShZO9fqUU7aSW1q3wbTKZum0uSexdHLvC2HhkTh72Qd/Hb2Ai9fv8YeJbZrWRNO6lfisj9QOuecsp71S6qdJ68lGZxnzlI7Uyg6PjMSeQ+f4g1I2q2L57BHJkrOHLuw+vmb+aJQp7pnse/b788eaPWneS+dPGQDFtwfW0xZuxg8t6qBY4bzwuf8c130epfn7lVnXtpzfLzm8lYBXbj6A9TuPcGFnvz3K++G2vaeweN1e7FkzLdX+qm1aOefM6i2nn+qqbG3uSSn97ib+zWd5pnV9ybk3yDlvOfeUtH5DxNwXWHo5vyFy0uvidzuzfgfS6p/0HRHIKAIk8VqSnjJ/A/Lm8kD3H5rg6s0HOHTqCv4+/i8fnV85Z6TGXGf8sRnuLk7o360ljz114QY2/nlMo8SzWLllszwadxqNKSN+QrWKJblwpTUlnU0BbNV9AnxOrYWFuTnYdMhmXcaq/vg4/e9NrN56EH+unJLsvNkNtmLTfqn+ocLO+czFm9i8eHyKzOT8qCrTXti/mP+Bz45OA2ega/tGaFq3cprnHR4Rie+a9MPFA0tTnAJ+4tx1PkK58Y9xGttaGSCFeeJM5fQVlo+U9HJ4s7LYGxzKNeyNtQtGo2qFEsnYLN2wHw+fvsSyWcOTfScnrZy2lltvucxS6kAvXr3nD7iOn72O1k1qYMBPrZA7h3uyULllK9Of3784xX784IkvVm05qPG+JPWeJPe+oEzPKt3u+9poXr9Kqg9Qk56Yrs45cb5S2yvxPUn0DSSFQDabYfehc1i5+W9kz+bC+0mjWhVhZiYseUp6rNxyAEvW7eOzoVI6vArmSVPirx5ZATuFDU/aptckjBvUCWwG2T8Xb2HdjiOp9pPMvLbl/H4lZSSVN5Oj0vV74vdpg9CodsVkyOcs3c6n17OHxUkPOWnlnnNK91Ox/VRXZcu9Jynrm/g3X9M5yLk3yD3vpHUTe09J6Zyk9FNd/YakxTa1B8Fyf7f17XdAU/+i74mArgmQxGtJNOkfrYdPX+HThyuWLapR7NiUz4lz1vGn8srp6Zt2HeMjwynJTdIqyilbmZcUoXz19iOadh6Lq4dXwM7WBqfO38CwyUtw6+RaPqrARmBGTlsO9odp0oMJW7s+U1Kdwnnv0Qv0HDkH146sTLEl2B8yN+89SbWVrng/wNVbD9P8w1MbiWcFlqjTHYc2/4YCeXMkK3/H/9l7D2griqX9uxTBnAUBRQQUTCgoBgQBiZIl5yCZQ845BwGJkjOI5AySM5IlR0FJgqAiKuZXr9dv/Vr7vHP2mdz7iO//27XWXRfPnunuqenp7qp66qnFG2Tv4ZMypEec7xkUROe6UdO5EvR+E30zZg4eJWp2kiMbp9giUg4e/VTNlY3z/0rFsIrJvaaHAZO+TXVm1QHInrHvLVVR1fy5skvzeuUU54STmPZNBOjYyXOS9cmMtl1wSPr8iyuuY+DGoGuS6boA+umlYo3kwZT3SaVSr0vR/C8pp6ofidYz01fQ92ViHEU+28KVW6X7oCnyzBMZJK5WaXntlefkxhtu8FQBRuP+I6fkzz8TXwo6zA4lA6z7+SINZMuiEfEkokDzm9ctJ0XyvSjL1+2QZWu2qyi/nZh8X6bftsn+ZX2WMPq+ePmKFKnSLn6v/PLrb+XWm1PE58J/dOhj6Tl4mqyYMSCR2kzuNX1mk3lq2nc0v0+UGsSIN+nb9Ln1BAi6ppiuC6Z7iMn9pt/2v2Uf8Fx0YxfENJBEGogZ8SEUawcfxSAvVSSX5H4pq9ycIrltq2wQwBWJWLRtXElmLFgnL2d/Qh5Kk1LGTl8qHZtWlRrlC7uOKGzfkY0GMSh//89/JFepptK6YUUFn2zVY7Rc++GneGfFjAVrZRXR/DHdEo39ytXvJF+5lo4R7XVb98qY6Utl8eQ+Id6EuEbTTTeISo16SZF8L0mdykUTjI138Fargcph06T2m77HHUTnpnPF9H6nh/JCbejD+kerxtuSHcKl0GPwNFcjPsy9pu/aZNxeE8BLZ9z/7bUfZMrslTJlzio1r1o1qGALefbqK/J3P31zzZ2336pQREBCt+4+pAxiO8i1Xf9h1qRorAvffPeDgIjBUYUzkPGyBhfOkyMeeRNUX36vD/u+TIyjyLGBYOo7/H1JnfJelcqUJ6c/I97vM0Zeh9Okf6f6yvkMSV7Okk0k06NppW6V4jJr8XrlTOjWsqarEX89vm2T/cv6MGH0ferMRanetG+8k7p1z9Hy4nNPSJUyBVTTh0+ckSadh9s6wE3uNX1mk3lq2nfY+el0XxAjnjZwXNrJTTclU0ELJzF97rBrSlKvC372ELd35spb9Df/QdhAC/1Gcx+ArBlU7K+//qYQXjGJaeDfroGYER/gDZFTOGnWClm8+kNFbMehEahrobw55N6773RtCW9ly+4jZeP2AzJ+UBtl7EPANH3+Gjny8RnJ92p2aVG3nCMM0qRvu4Fd/fZ7ufOO2+Ij6Zt2HJT2cZUdn2H+8s2KnE6Lhkv/z2+/qyh9mTdyS7O6ZW3vL1W7s4rk9e1QN54AkIP/x59+Ji17jFLQdgjuwojbBoGX9o1q7WXZtH7x7yeu4zApXzKv5M/1vJAGsGjlVkf0wwfrd0qHvuOFPFANUSW3e8TEherQumrmQN/RP57Nr85N54rp/WE3ZO7jIJOtYD0Z1a+FvJ4re6KmgPZ/ffVaPALFeoHJvabv2qRv/Qykmew7fFIRtqV7KJUyikk/8ToE6dQN2iGqaac31hfWDCdhPQIZgnGVPl1qyf1iVrWWePXNoeWFIg1k+ohOCo5es3l/9V2iT9KCeAYnMV2TorkuQNq2csMuWbBiq3x55RuVF16p9OuSN2e2RMM3JVMyeV/ou8uASYofBFSTqfCtr9m8R4DJ829IQuEXcTI2lq3dIaOnLZbff/9DerSppfSEcUpqlxMaQ4+RPnA0Fcj9vDI+n30yozzzREYZMWmBckTzzTuRm5p8X6bfNuM32b+s7yiovjXcd/PC4aoiQKQRP2/ZJoXgs3OAm9xr+sym89RE3ybfJ/MM7hWrTJy1QnK+8JQ8kyWD+vMD998thfLksP303KDlVcsUtCV9tDYU9rlN1hS7Bwk6T2kj7B7Cvdr45d+crazVipLyjBb57EH2Aeu9p89dEpA2Cz7YrP7cuXl1dbaPSUwD/3YNxIz4AG+IBb5U7S5SoURedVBK/7A/+CZdcG/R6h1k8uB2kjlTugC9/nWpSd/cH9a7bB3ooeOn5fjJc5IjW5Z4eC0H+Utffi3333u3Y54lzorqzfqppjAKbrk5hWzdfVi++fZ7Fb3BgOBvYQTIIQu3m4ETpl19z5ylG6XPsPfUfwLdxUAgn3TCoLaS7ZnHXJtmY/MSUBs3REBgozFXws41DhO1WyaGdvIcoBIoreil7yHj5sq+w6dUioMuR8j9wA2rNe2nnDlOJc1M7vXStdfvJn1bD28wo6Ojh9OmlNljusvPv/zqqjO+zVot+rsOj1zlfh3r2V6j01voj4MY8lTm9DJ1eEf57tqPrn2fOX9JKjfurVJlYKInz3n93CHK+Kd85KCuDR3HZbomJcW68N8//5QDRz6RFet3yqUvr9ryk+iDeliSNpP3xeF6z8ETtjpNnfI+Idp34fOvFFeJnXA/Rnek/Pe/fypUAsR2r+Z42hbWrqGnQOD/+OMPmTZvtexcPkZxL4CMcJpfui90i9G558AJeTpLBilb7DVP57V1nCbfl7UdHMCRa6bXt83vYfYvE33rMbGefvb5V4rIljnftUUNFYmHJ6BMna7KWHCq0GJyb9hn1uNGz6s27ZFtu4/ItR9+lEyPPqTIcNM8eL8fdYfSNw2bfJ98m5Ub93IdH+S8kN3aCXP87PnLCX767fffpVaLt5Xj2Y7nJbKdMPPMZE2hf9N5arKH0D+s/DixEXSErnBWMleY315nBl8TKsBFfvYB0IqrN38kc5ZsUOcVuD3Kl8gnBXJll5tDnkcDDDF2aUwDUdFAzIgPqMawBwiMXRY54IdOhzMMKLfoTNi+Tb3LXiravOOgKrs3vFdTx0sZw8IVWwSIIAdGIpUvZM2s2Pmthl5kA0T6Yej1kurlCiWqOe92L1Eku4inXT+ULTl8/LRcuPyVPPHYI8qjD4rBTfyQxXD/smn9E82JvxwjV+XwidOqi0jPtpcuuB+I2W+//W4bnXW7n0jGklV/lYbTcuHyFZk8a0V8tNar/+vxO++a8o9eUvXNAlGHWuvIUffWtVSVCgwxHFSNOw1T9dU7NKniNazQv3N4y1kyTprVKatScV4r01zmj+8p3QZNlmefyqT+7iZAOMnZ3bVirDLQQJgwJzHiyXH2IunUaxIGiRZIznDsVStbSG5KdqPr9x12XaAvUAekJ637cJ/SN46LRjVLq0gx4rRe6m8zLElb6Jf1d+nJ/OUTM8fTJrn9oCjem79GvQM7Ya453a+vZ87ZvTfNQ6D5KkjvebdPMzl9/rJi+J/wTlvPR2O+Xbj0lTK00qZ+QO67xx2B5tmgzwt4v8MmLlDVCPg3jnBSmYD2+5XPv/haGf9pfRqhtGuibz2uYyfPypGPz8YP8/lnHlfjZ40nvQjuC6L0dmJyb2R7fKMbtu1TKBu70ouR10NCSppf5dL5BWc25GQHjn7iG4HGXMHJznfKfn/PXXf4elXX8/t0GiB6gM+AEqn/RjGZp6Z7CI7gkrU6y4dLRqo1N0+Z5urfOBV37j1mi7rTOuS8MW/ZX9FvJ3ngvrsVQi2seO0DBGX8OmjCjiF2X0wDSaWBmBEfQLMY2fXbDra9g8MEXselq7fZlvfRG5NTjd4jJ87IwNGzHdl9ieq26z3WdbQZ0qeV1g0qJLrGzrt88Ysrqr35E3oFQhTYDcALshtAxYku5bm7DZyc4O8cRshb0uV6+BGOASLlVnG7F4OLQ7OXsMFB7KUg0mlTxRMSed2Hzj89+7nXZaqetR301c2z3fStMp7tunnWNbu0ZyOWCxgP0SI7huXIdoaOnyeURoO5HwcNh7h5yzerCF6+V7NJlTcLOEbSvAwzpzHzrju/PdHzkTCoI+eJviks1FgbR4c3TEmQDoNRDOeDE9lX5FxlTiNBnDYYVG9UbS9HNk1VpGYY8StnDJAP9xzxbZiRM4oRsf/oJ4KTA4ZziOpAx3RqVs1Vpxzu4cQg0mInbRpVSsQp4fmSfF5AKg4HSDXed6YKcFecEF4HMlOSNuYaDo4KJfPZzmMcSqTquNX/dnpEDpz8z82x6VM9tmspB+x543uqdadZlxFSvmQ+ufTF17Jr33HXwzaNQUrZof/4eLQHfytbLI/0aF1LOa7cxNTJBvnpl1e+lbJFX5OH06aSHXuPKvLHqcM6yEvZn/RUCWk8c5ZsVNdh/MfVflM5/UgDCPOePDuM4gVBHCegnajEYl3jQY8tXLFVIS8w4Pu0r6Pem5voNJsZIzsrR6ReV4ZNmK9uY/90Ezgq2vQek2Cu1KxQRNrFVfYkXzT9PhmXU2pT2NeCEU+ljpH9Wjg24YVii+TWsWvICdIedtz6Pt4nZ1AM4kgx3UNwjhWu3Fb2r5mgItjVm/aTFvXKqUACgRs35yDjYh1ykh17j7mWVzU5k3NGw4nGPk0gKteLz0i54nnVGcWJ18r0PcTuj2kg2hqIGfEBNIrXkM3QTh579CG57dZb5OjHZ6SijXGojXin0j5srm71mDFcidDYCZsLpai86jlH3tug3WAVjcaoMhE3Iz5a0fDI8bHwv5rjmXiCoCDj93svqIFWPUYlMFD8HkT0eMJEf0w82/Rr6lm30yUs2HfccZsrbwL36VrU7RpXVkYOgnOKzZyUB+bp8N5NHXMSwxpmQd6/3bUmUGOi2blLN0uQQw5UL67TcBX18zrwMp6wThtd7lE7EPRhm/xlDvIYuF4C0oQDfvKbkilHDWvU9HlrVOqLE3KINnUeJ2RnmTM+LDfe+Bcz+ooNu+Xipa+kYY2SKtdal3e0jsN0XdDEakD/cZ7q5565aL36Xgd0sYfL6jGYkLRx8MQYzvFsFunV7q0EUVQcOh36jVeH5SlDOziq3sTIcMs/dXvX6Jx5dustNysGepxGD6a8V5ECQlpKxNVJ2H8KVGwtr+fMpvY3ytnhqOs1dLq8Vamop6PGxMkGm37esi1k0eTekiXTI/FDBK6b7MYbpXf7Oq5TnAgq6AWcin/8979St/Ug2bdmgjBX2Du9Kow4pUXdlCyZ/OePP1SqmhMiwcTI4KGCOk44IxBBr/xmAXnh2cwqqMCaS+S/WtmCin/G6QxiVSI5wlXiescT8unva9tHR2TpaneEDmsf6VzA1muWL6IQG6SQ9B46XTkFyxR9zWtJUpUnwpIouqU2cYaD/d2JtDPS8Y+hx/q+e/8JGdG7mRTM44z8sEOx8bd+I9533fO0MqLteLcq2e2MFo09BMO9VOFXpVjBV1TlpVw5npGd+46pdJturezJLt0mAWknIyYvVMY1DjcnAmGTM7m1f85pcFPglMaJFMuJ9/xEYxf8SzQQM+L/oRehjXg2Ajs5e+GybNl5yLMes/Vecq8mzfxAkeVxAKtd6Q0VLfYrHILuuuM26dC0qustHEQadxzmeA3weBZrcqAjxS4afvmrbxQsz09EwKlTSrzhRR0zwB6a6vZAfu4lGla2XneV+9+mUUVVJo9c7kGjZ0utCkVsHTWRfYaN/ph4thmDqWedNiIRCMdOnZPbb7tVkVm5CRDRQpXaKLZljDc97zGqShZ6VSilePL0RenfKXF+t6lhZnLYNoUaA+smssdh+eHUD6iyh8iCib08yQ9NnDaaMExHJDls801jyK6eNcj3ehAk2qffvz78RaKLZi/ZIGfOX3YlgTJdFyKZu7WRQQR8665DMrRnE9d5akLSRsOseRB9Ul4TYyNfzmyK9JT88tJFcqsUCnKg7cTNyPADTw/r8EHnvSzkpIwN4kXSLsoWz+MaIUXf5G/rMqP6ufied+477pl24XdPsrtO8zZEliGFlBTDx2sP0PW7dRoB+uvasqYy5iCyckPKeKWiZXksnUyds8q2RBzPYmJkhHGc8B1v2XVQ3l+4ThmeCCk1ONSC8Ahc/vKqFKzURjk7QOTwfb0/sosMGjNbzXXtoLV7ymUyfgAAIABJREFUX7o03qYFwyXVA/+bJjBy8iI5d/ELT6cJbYb9Pr1Sm3BKuqWr2DkX777zdsVRgaMyjAwcNUuSp0hui5LU7SWF4906Vjcj3nQPcfpG2P/fG9HJtkSvkx45u4yZtkRAxYGQaVqnrDFSNMg7IwhByg7PFCO2C6K52LXXSwMxIz6A5k2iR2zIePlqOpSQIxq2+8BxZeh4Cd754ZMWqAMkEK3q5QvLgw/c63VbIvbRzBnSyS23JPckJ2LssBI7CQYQzOt2RrzdPXg6i1bvKMN6NfEsYxWZCvCn/KnKYAHrezn7k46M+PRrci/v4/XyLWXv6vEqcqWNBErp4Txwg9XRt2n0x8SzbepZN0Eg4EGHHE3Du3HW8CyanZn/bt5tpK+SSkEMM9PDto6uhoUa886JPHBw1qkXlDvzY5SZOm227Dokt996i2KXx5DMmD6NQqn4JYsMGu3T3zSHv1mL1isHopUIiPXgu+9/9EUCZV0fgqwL5EUzP7SziH+D/pg06wNV6YKUBDcxJWnTbRO9wRmq0QZupI3c42VkePEnmDh8PDcIlwu0c2759P6SMf3/8roQXf3x519dCRCthgoVP3btOybf//izcm6R0gS03000vHrmqK4JyEQpzYaT1auyiY4M92lXR0HvYYgvUSinHDt5Ti5cuuI6dre0KHK84ST96up3KuocbTF1nHD/3KUb1bkD47VGucKKkNdPJJ7nLlixjYJFly6SS31rfHOQf8F9c9ed9g4qdKDrpUemF+Hcw7npxp2jdRj2+/RKbQLanVTpKk7vH6fBtj1HXGHl0XK8O41h5cZdMnfpJsczmskewrv67G9SO90/6TVpUt3vWG0pcpw4RXGCg44BAca8e/Lx9L4+qbApeLpxE1SUrwHGLoppIAk1EDPiAyjXNHoUoCvXS1nsiPxBotW8XjnbPKfIBq4XVMvpQTq/PUlBOVms3cTJOIPopH1cFQXrdBKTe4nekCtM5IkIhjYoSafgkOIFkU6K6I9fz7aJZ90UgYDxlqtUU9GH/alzV6na5xhbCGQ3IyYttI1cmRhmpodtE6hxNL5rE6eNSf9hon3W/ogghTHMTNcF7oe5O3/u55VjlO/z3rvvkBIFc8pblYuqCPM/Icxn4KOkY0CsN7h7nGvkyMvI8OJPMHH48K527z9uqxZ4ItxSJ1gXQGNhGEJGmibVfbLvyClZt2Wv6HKjXvruNmiKKulJZQoqKezaf1yAbYNW8Tqw4zi95ZYUqsY6wl68YsMuBdt12wO4FiQZcHwkwyNp5Oxnf7GP884mvtPWsRKA3fMEJZc1cfxHw3HCM2DkLFmzXabNXa2qq+Bo8gNpB6kANwPRdAIGmdKn9UUKyjzLV76ldGpaVTkNEP5Wpm5XdWapVfENr6kS+nev1KaXn3/KkbdId9r/3feF9EjSRiAWxPh99JE0CoFH2U4n4V2PmrIo/mf2pO9/+FmVgKxTpZg0qlHK8V5Tx7sfIt2g6ZahX8LfN8Lz9M21Hxwr0eh5MWb6EkVeTIpDq4YV4r9xv/2bpOCZoqL8jjF2XUwDSaWBmBFvqNkg0SMTwq/IYbLoYyCxCBEJI1fbqeTd9YRqOal3/IxlkiJFcpVP6SWR5fEgHXHbTK3thb1X50hrYxQjodBrLyiDhYO2U06d7ts0+mPq2Q7rWTdFIPD89du+oyKxpQrnkgkzP1AHNx0t69R/gvzyP785RmOul2FmAjXmmYvX6CglC7+qDmpEZ9+dvEjBLwd2aegIq9ZzxQSO6JZza/0OSGd4OE3KRJ+aabTPxDBjMJGcERDTUfnBDyLJ+jC8P7/IAxOSUN0nyKN+I2aofGMQAOVL5FXoIKKelBCr9GZ+W3i6l5Hh5Ryk/7AOHycGa9a6ulWLu8J96Zf7MQQxqPm+Mz2SVmpUKOwLcUGkLV+5lqoeunXtbNNrjHpvXuXtTJxFQGSXrd2eYO4nT36TchxgrHmJCTO+CblqNBwn1mdDh9v28O5+UtF1NzEthcg+gmg4vU7XweHi9Z2afp9uqU2///6H4GB3YjvXKV2rZg6U1KnuV6ShjzyUSk6fvyRxtUq78gcxbl2OVuv2d+DZOw7I5oUjEtROj9S9ieOdttDv/qOnHF/prr3HFQrCDi1puofQKamdH6zbKSdPfxY/houXv1bpJBnSpVZnVO3QsQ7SuvcRpLBzvmZ9IoMj6aZJCp4pKspr3Yj9HtPAP6GBmBEfBS139hFVNiX8YkPn4BgpeMsx5qkPjCFvB8eMBlTLTU2HT5xRER6nWrfAi4EaE3XRwqJ/9dtrCvb6ygtPKVI+JwlL5GRtD2P+/MUv5KHUD/hmmB89bYnawDEo4joOU+WfCuV5QbH1ekk0oz+6L941Ze50NMptDESbQBBoqCrGEvBPLyilKQKBMZFKQEUBiK+AxsFeDfwSY4EoB1BWP/V2gxhmXu8jKX/Xjo+dy0erbnKWbKKMov1/1571KvNmAkd0y7m1PjP5hXYQWJNon6lhFpYzQj9XWMPOlCRUp17gNKXetNUQxHlG1Q/K3DnVojbhTzBx+Nh9A+iwfIMeyslGhDypRCMQDqyblKAaBxDrjdv2e1ZwMHUWmTyXKTO+Xd9+yVVNHCcmz+xWssyrFKJJWVjGbPp90kbY1KZPzl6UOq0HKeSYTgWDi4EqI5TOpPJFUMFRhcOoXtXirreGdbz7GY9XTrwTYbOfPUQjL+BKoAQvVVKQ3QdPyNdXr0nxAq/Is09nsk05UY6lj464PgIcL07nQxNuFFNUlB+9x66JaSCpNRAz4qOgYT/RIxPCL4ZoApcyhWqZ1vIkR/rnX36VzBlgsP4LjgaR348//Sp4WYlgutX7DUrkhLNg1abdQik2crQHj5sr2z86Gv+m8QrDPuo3mh9mioSJ/nD4iSxtgvNmz4GPZfbSDQq66idippEXXVrUUBEXogNECDHgZ4/p7gqbNUUghNGVvscEemrSL/ea9E2ecq2WA9TBjyglh7ady8fI2q0feTI5u43bDxzR9LlNon0mhpkpZwTPHU3DLghJKA462Ps5lNuVViNyu3jVh+pbdZKwRoaJw8dpLJDT4Yj1Ymk3yT3VJctYkyu/mV/uvusO+fTMRWnbZ6wUei2HK7eJqbPI5Ns2ZcZ30rkfclWTbzsa5c7c+ncrhWhSFtapzyDfp24jjOMeZCWouy2LRijUCecGotd8zzibvLhw7MbPu8ZAHzewtesrDet49zNPkrIMMCSqINHCEJz6GbvbNSYpeNFARZmOP3Z/TAOmGogZ8QE0aOJhNiH8Yogc3jAW3OSWFClUrmGkmEK13Gp5/vLrb8pj7ZRvpZlq96+dmMBA9cNgzXOEIXKibjUM1cDegZ5y0G7bqJKkeuBe2Xv4pPQcPFUZ+CAX/k3CYZrNnhQDIKdsvLoGd/GCOaVy6deVnr1Yhqk1W6RqOzm0frJi0YZleHT/lqp2NTXpvUq+mCAQTBw+JtBT0/do0jepE6+UiJNpwzvJjAVrFGkX+ubdgUbwMo4Ye1g4IveaGFfcHzbaZ2KYmXBGMGZTw07Pl7AkoabzLRr3hzFSIvsl6tlz8DS5KZl3qTaT3FP6paRdi24jEwyB9Wz8oDauCCETZxGdmXzbpsz4JuSqep6TmgNjNgYLvAX1qpZQ5bzcxK3cGdFkN6e5tV3Whn2HT8nnl6/Iow+nFnLKwzq//aIPrP37/T6j6bgHSbPrwAmlb9I8YCmnHG+2px9T5c6cBOf5xu37E/zMXgApcNmir7k69Uwc737WEs5hGNuUebUTkz0Ex3/7vuPUPmdNleAsQ5UDt9KVkWMZMm6uQulRDcCvmKTgmaCi/I4vdl1MA0mpgZgRH0C7+tCa68VnhLIjCHVnyYvMnyt7/ALWtnElgSjIKiaEX3ZDZIP59tqPitTHCyLN/dGGanGAxDtNKRjgTq3qV7Ctoao9pbpUjX6Wecs2KYIhr/J2YYicxk5fqg755JdSb5bNBVi3FkqYYFzBVJtUEib6w4b33oK1qgSOFqJ5EHdRd9qvsGFXieujIsM4AgaPnSsb5w9V85R67Un53MyLak37xg8V5xXfgi63RWUBaqf7rWRAQ2EOf3515XUdhssLWTNLDYeqEvp+ojbvjJ2j/nP6iE6qRnOJmp2ketlCUqVMAdduTOCINGxqXNkNjggk651dHr31+rCGmQlnBP2bGnb6GcKQhJrk7IaNkEbDSLGDSHMAZ/+gpv0zT2RwnKcmuafWRol+wQqPcZMuTUrVp5dT0sRZ5Pbh+VlXTJnxTchVcQBUadxb+FYgbKTeOik67BFUdimc90Wv5SvR79Qsv+P2Wz0JZbkRgwpSUubHvffcqarbQN44eUh73ylp1gGEQR/4/T6j6bhH71t2HFTOf856zM9Dx06rqh9urPx8SyVrdU6g8/vvvVPyvPycChg4lZzkBlPHu4nzPBp7SDRSHhlHzeb9FemiH+JFu8kfJgUvLCoq8McXuyGmgSTQQMyID6BUnftKhNMKowQ+m/WJjKpOu5uYEH7RLpv5gg+2KHg4G4YWNtYOTaqqElNOEi2oFs6IOUs2ypQ5KxWRXsPqpSR/7uzxMPkA6vR9aVAiJ4jFcBLMHN1VGrYfoghVrDU/F6zYIjs+OupZS9r3AG0uNIn+cNiGJEZH4THiy5fI5wqDjxwCkXgMSAx3oje92r4lo6YuVp5xNxKpMM4HNz2RCtG8brl4JwqRiimzVwUy4v0c/kwPMU7P4BcKyf04jkiHcDvo2fVjAkc0Na6IxDLPImX/kVPy7Xc/SKsGFRQHhFv0LYxhZsoZEU3DLghJKHqKZKGeu2yTih7Bn4Gc+eyyKoNp56gKGyGNhpFix04PieBTmR/1jK6a5J5Gzq1vvvtBlfnSQvTOyxEd1lnktjb5WVe434QZn/vDkqvqUm1r5wxWXC5aMK5xbg/t2STwFkXq39Zdhz2h3bpOPPtG2eJ5VJ4zfcIPUDhPDmnTqJJj36bog8iG/XyfSeG4Z23UQj78pS+/lmplCynUik4LDPwCHG4wdby7oSXpcsfeY45oSdM9hPaDpjw66S2oEW9ScUOPgTbQP3pI91AqxR0Uk5gG/q9oIGbEB3hTOvqz64MxikFZC7WZ9x466bkxmhJ+QWBHVBVIF7AooNF7DpyQTTsOqhIw8yb0dCQPyVkyTsLkSEeqp/ugKbJw5VapWqagdGpezZaB2XqPW+TJeh3RcruoXxgiJxbj4jU7SfqHHlTOFphlS/xd6oY+t+4+rKIYbuXtOGxj6APrima5Kr+RXcbJYQjCwPcXrpPNOw6qGr2kAfgh1sO7PGjMHDVHBndvrHTbtvdY5eF2gtTRp4nzwe5TatRhqCpJpZ0oOFhWbthliwYwOfzpeYLDIlmyZLZf9VOPp3d0YGjmZn0jNgbRQuqOQyToloLgdpCwDiTbM4/bMhSbwBFNjSved8vuoxLpCzJMnp9IJNFSIrV2AtLjzttvVU4aDNetuw+p+t9e1RvCcEZE9h/WsDMhCbXTQaVGvRRzdd6c2dTPjAtkRhC0iVeENFpGSlhnrknuqdYZzg6cv0QdrcJe0qVFdc+dOIyzSK+jZ8//VVYO+VP+VHOVagIvZ3/SNR8/GkaC54M5XKBTybYtHSn33n1n/FXjZyyXwydOq5QdJ4l0FjHnQe69v2idxNUs7YkO0n3vXzNBbr45RXw3oMSA9jutB1xogj5Q7ycEiW80Hffwx2gHup1+cWDUqWxfVcckIh3W8e42v0DCjZi8UJ0fODc2sUkHMN1DwqQ8Oo05qBFvWnHj6MdnpU3vMQnWJFAT7eIqe55tw37XsftiGoimBmJGfABtalbi0kVyS7WyBZVhdPr859K443AFu+rTvo7rpmpiFOq8dicYXe+h0xUruN3GbgrVsj4U3mmMsLHvLZXbbr1FGlQvIYXzvOgYyXGLPDWrW1buv+cu1TwlX+wimGGJnDjwLV+7Qy5cuqIMkUh5MfsTrmWsNMEbESIMX3LSIeHzgn56TacgkV1rW0RlKCd40003+YJCeo0j6O9+oKd2bY6cskg2bj8g4we2keTJk0mTTsMle9bHVVmuSDE5/Ol7SSGgVE1Q4X2/Ua19otswXiIP0YnG/fMvkr98q0T3aqiy/mHe+J7x1QIiL2ZtsZObkiWT//zxh4rm3XfP/x7k9bXRMK7s+tWcFa0bVpAcbzSUdXOHqDQIq+hoOOkDoIA4gH386WcKJQSJkzWFJej78Ht9GMPOhCTUblzkeb/A8/+dcsE3DuOym5EV2Y5XhDQaRopp3q1J7qleT6lUQTUS1jEtt996i2cZRqfvw6pHUDB267PRumLzbWsUnB+SUa8IqR5/r3Z1En1fvK9iNTqo6gfli+dVqXPkiA+bOF9xvFDH3EnsnOfstXlyPqdKf952682unxh9l6rdWbo0r54gPxl4PVVeiNC7SVj0AW2G+T6j4binb/TGete/U31VIvTGG/9iWl+xYbdcvPSVNKxRUlLed4/jHmMSkQ7reLd7DzhhSRtctnaHUJmkaZ2yjiWITfeQMCmPTnMHlBN7XljeBdr1W3FDp3Q9nflRqVm+iEpX2XPwhHCW7tSsWmhIv9+9K3ZdTAPR0EDMiA+oRSLerXuNUaQnWl7M9oQM6NxAbbJOYmoUaiKowxum2C5wkMs17zZS5UFHiilUy+6ZWAAhSpvw/nK5+i2w2/KBajt7RZ4CvpYkuZx3BpfA6KmLFSkMpIEcpoq+/rItgaB1ECaRXaeHCcJWbkJUY9e/X+hp5L2UFyRHXpcXxMCeN66HpIkwCPV9YQ9/pka80xyHW6BWhSK2NW7dJp3fg4TXoZUoZZbH0snUOatkxYwBtl2aGFdOz7BtzxHlFCxXLI/ilXh/ZBfJnCldgss16dfuFWPlkzMXhSoU6+cOUTwMJ09fkEFdGzqqKBppG2ENOxOSULsHInWHFKc+7esq5E/PIdOkdsU3pG6VYokuDxshjYaREk1nbtDcU75/jMIjG6cEhiL7MepQ9LJp/R1TjsKuK3bvm7S6cvW7y9xxPRIZ3pHX4/SevXi9+jPfxZWr16RWhcKJmi39Rm5bGC966zF4qiKP1UJEtXGt0kkaKURf+cq1UF1azzWUObX+bVS/Fgp5Eylhv03aCft9mjru6VtX8gnDtB7NiLTWJ2vwr7/+5miAR+qdlC54BGYuWq+cqKANKW/nJaZ7SNCUR6/xmP7up+KGJl3etGC4pHrgnvguR05eJOcufuGLkNZ0nLH7Yxow1UDMiA+hQSK7n56/JD/99Is8nSWDZz6f7sLEKNT5cZF1dnXbeHE5PK6Z9Y7tE5lAtdjQ8TA7CQauEzu90z1ekSd9H57x+m0He76lAV0aJILjY0jhWbWT1CnvUwfuC59/5cmEyiEK1AEcAJD5AcGCuKx6uUKO5EImkV3Ga8JWzv1hyc5MIO1OL4mD7MGjnygSSLgjIFbykqD5iElhxDNGSKT2HToZqj6wn4OEPrR+evZzW5WQn0fZ3a+ufmebKhN5U1Djivu96q1f/fZ7uffuOxIZXxyai1RpJ7tWjFUcFHzTGFIYK8vWbHdNLzJN24iGYec1B/3+jv76jZghwMURSE4HdWskt96SONppEiE1NVJMnLmmKSM6qtynXR15KfuTflWrrmNNcvo+rA09mi51ghr0kZ3gOCKSjUMch+yrOZ4OnSrVuudoVRO7QfWSvp6F9JHOAybJivU7VUoURngQYT8BOvzAvXf7jlJ6fddu/TNeyrR6CbXBramFXP9v+ja9xh/5O062WYvWK0Z1axoBqZTwAcF94SSmEWkc3pzjtMNbnwOufntN8ud6XiFYnOql867HTF8i495bplKZWjWsoNLAwkjQPSRMyqMeF3P65WKNXYdJGqFb+kbkzX4rbugzdWRgDBTa7gMnZHivpmHUF7snpoF/VAMxIz6kusOQ8+iuwhiFuoxVo5qlE+VjseHGdRom9997l7zduYHtE5lAtdjYFq/80FVTRFjtytaEjTzpzrh/4Yqtnm8JyFgkHN8pX4rGKpV6XdKnS62Y4DE83IT3RbRBR9aIzlOGJnPGdIHyXnmHfiK7pmzlJkQ1JtBTz5fk44Kw+YimRrxdHiZtDho9W5HdAK8LIn4PEtY2ee/AIOkXaJ8dfD5yDMypIx+fTTS0tVs+UiSGfTvU84TOmtRbrxrXR1Lef4/sP/qJVH2zgDJO+F4gKwuqMx7Cb9pGtAy7IO/U61qcu4zLznj3uvef+j2sM9ct99RKSueUMoJhUPqtLiotJGuWhCz4QLyDlKHyq6td+4/L5FkrVKlRCD4xvBGMfRzP8Ge8927nUERWfYa9pxjbMci9hGeH/PbEJ59Jjza1pOvAyQrR5cTJwjqAswdnp/6+t+85oioyZEifViqUyCvp0v5FougmJt+1V9tuv1/vb5M88FlLNqjc+jqViynnO+sqaRteqVamjn+TiDRIpp9/+VUyZwDGf6NS8dkLl+XHn35VqXwlC7/qWBrQum/zjHY8PrRBecFIMeV9CJvyyDgiEYv8reugKVKldH55Osujsufgx6oygBO/iEnFDX3O6tS0ajzSjr+VqdtVIdBqVXQnqjb5RmL3xjQQLQ3EjPiAmjQl56G7sEbhuq17FQlVvlezSd5XnpMUKZIr4jNg7cjSqf0cYcoBHzNql5tEnqI2CIeG2OT5nxfTrNXpQoSRKDkHQA4IVtZ7P+P1E9k1YStnDKZENdGEnvrRib7GJB+RgwSHXGoZR+bTeeWV07+T84KoBvmRHPydxOQgodskQtih//gEBDtli+URcoitlTAix+An6lW/WglpWb+87fBN660DLZ42b7UkvymZ0A8G3fR5axSUk28kqIRN2wjaz/W63tRIMD1wmzhz7Q7/5Rv0UHOL/chNcGqBTLET4L5uZJvcQ27v6GmL5fff/1CGMP1R6YL85KxPZrRtd/LslXLk4zMqola8RkdFYge5K+vDl1e+kTqtB6m91I6fw9qgaUQbqDIoiilD26tym0QAqzXtp74XzaNg7e+D9TsFLhFQdfw/0VWe96E0KRVxJGkRi6f0VTnbTmL6XdMubYSpUX+9vi361YYZjiICAMDRcdKDUsSw9SJQNHH8m0SkNbR7/9qJqsKJFs1N4jVuZQx/dMRV9ZQCtovkm5LDRft942js0aqWQkiC6gIZ4WTEm1TcYNzsX4iG02tHOikk1pr30X7GWHsxDURLAzEjPoAmTcl5dFcmRiEG5Pj3lsmRk2dVXj6wQCBeRMAefOBex6cxyZE2PXgGULHtpSZjjzRIdQcYRjC3uwn3wsSrc+LxcCuSuwIvS5ZMj7jeaxLZNWErZ1CmRDVhSdZM37NJPqKbMesnr5z3pfM99XNgkPopF2d6kMDAKVCxtbyeM5siq1IEVsc+lV5Dp8tblYo6MiG76ZvDH7DhskVfk4oNe8nO5aNtnyVa9daDvnvTtA23NBsQQXAuLF29TcYMSEw4GHSs0bzexEhgHP+2A7fflBETHep9l1KVf/zxh3Ia7Vw+Rhm4GJpOJTPha+Gb7t66lrxWprkM7REn8NfEG0iLN8i6D/d6QnVNItqsS3XbDFKpJVaGeda6VRt3S6OapRKpBvb5cxcuK1Qd445cAxq0G6zQL26lQk2/a9Ma9ZxT+P72Hj6pDGstRJMb1Uj8zCbzw3qvfm5Y9clDeu3NZrJgYi/Zf+QTWbv5I+P1wM3xbxKR1nv2vjUTEhiPpClRUaJD06rRUpGvdoJwuvhqMMBFQYz4AM0mupT0GvgDvASEmReCw6uN2O8xDSSVBmJGfADNmpDz0I2JUWg3TDYN6rf6kbA50m4HR37zC0vn0LLv8Ekh7wt4MpE6v6Xbwo7dy7Bz827zrl4s+hcxV6nCryrSvpeff8p3PqJJZFfPFa/3esstKRzff1iiGi+deZGseY3Z7XeTfEQ3CGfQvHKTZwhzL8iJMnW6CgRxVr4ADKSd+457lq6065MykOc+u6zqOWMIYETAwhsp0ay3HuTZTdM23NJsYPOGw+Lox2dcGbyDjPefutYvOsg6niAHbiLS2pCC8Z5IKxHdgV0aejLER+ogaMoICCMqhhBNPn7qvMrdheG9QO7nXdWrDTNNisch/90+zeT0+cuyZNWHtuUqaXD91n3yzrg5Mn98T1WalUg27OJaKNnKmNyY1k0j2rwbvjEcguwpMLtT8x3nIO/ajk2f8oSfnL2ojHSeNZJHYOma7bJ0zTZX54Ppd21So15HwwkuwDtgRRJR9tUJORGNbwzEQ+7SzWT7slEqTQKHBySTV7/7XlZt2B3IiA/Ky+K2b/sJGpiQAUZDd5Ft/BMOOrtxBzHi3UoYF8n3kqsDHH13fnuip+o6NKmiEDQxiWng36iBmBEf4K2YkPOYGoUBhpnoUpMcaT/9eh08KY0GnA3R+YggCGaP6e6Z92sy9shoH/3/9vvvUqvF2yo3zI2khg18zeY9ilDGmvPpRx9cYxLZ9QORpg83NmbrOIMQ1ZgawxrW5wSv5X0SIbardW8KFfb7buyuczsMWK8f0iMuEYGiaVT40pdXpVClNrJ8en/JmP5/IeiUuvnx519dWd712JycZMzDDR/uV9BEp7JSYeutm+jb7sALjNSktFCQ8TAPz5y/rIwlDDXWJYx/eDKscNYgbfq5FucDNcsjmf6t9+IsDpqK4OfADXT09fItFSoDyVmyiTKi9x8+JZBHNatT1vERTFNG4AsoWLmtpEuTUjkR7r7rDtm195jgbBrzdkvJmzObY9+6tKvOt4c3oXzJfHLpi69l177jjqSTrCeNOgyRHXuPqUga0U6rXonS44TG0eUkYSPavEOI4ciZJ0eb6gXbPzoa3w0cAJ2bV7ed7/sOn5LGHYfKu32by8Zt+1XJxjYNK8bfO3HWCpW+0tryN7vxm3zXJjXqeV7yu8NUItDPEfb75J3XaNZPfcuF8uZQkVbSNcjMi0XXAAAgAElEQVSthnDSbY7rvk15WezeBWgwt6BBtPd8P2uR2zVBHXSm/Vnvh0gVskSQklSD2rTjoLSPS1ySlnvcShhzxrPjaYrmWGNtxTRwvTUQM+IDvAETch5TozDAMBNdapojbTUU9h85Jde+/0kdeommex26NfsokEbIQvBIc5hq3GmYMuTwcrpJtMZu7WPs9KUqV65nm9qJuo4WqZBumPxFIIUcSjAQcr+Y1VNnbs6HXu3ekswZ/ir1BaLBKSUgTA6p23vwW+pGH0aWTO1r2xylyGYt3mCb4+ZkKNCQV01mkyoGtM/3mb1QPWlWt6xQTxmhPnvf4TMSpKoUyfdiIli6aVQYQ7txx2GKy+CN11+SNKnuk31HTsm6LXtl0pB2rs4mxmniJNMvKUy9dZM1yXpvZITSb7thCKz4DkdMWigc1FnDsmRKp97nV1e+Vd8pQg3u8iXyenJlQCiK8+TKN98JEUbSIOwiq9bn0eshhmPtikUS9MFvRIxhYnfKAbXTjd8DN4ZZrZYDVBnSD3cfVmRrwNLXbv1Ilq52ryZgmjJy8vRnUrZud1k1c2CCkmTt+44XDHw7wi39rMBeqY4CYeArzz8lGKcPprxXVQnBkHUjxePbOn7qnMAcTjuRkv7h1PKsQ04914aNaM9YsFa27jqkSPUgO2PfY16leuBeNc96Dp6qDPyaFYrYTneMz+ETFzh+Cl5Goel3rQMWYWrUa51REtONS8Tu4Uy/T7uqMCD+cjyXRbq2qO6ZHmXKy4KDzioXv7iiSHDnT+jlWibuepIBmjro/K7XdteZOP3d+vVbwpi5ynqCELBxcnSbPGPs3pgGklIDMSM+gHZNyXkCdBXVS01zpBkMED4NPSKKjnH6VOb0Mm14J9dItY5kRJbxINcLoj4OOW4SjbFHto8Rz8FuZL+/6uBaJRqkQro94Jwtuo9UvAXoC0FnU4d3lDtu8y6zZjduJ+eD9dqwOaS6DZNSN34iCkHKEfqtyWxSxYDnBlZbsWFP2bNyXAK1Dxw9W2688QZP8ivTD5aDFDBajCtKGWV6JK3UqFDY04A3dZJZxx0GPhr0uU0ilNa+whBY4YBs1GGocj42rF4yUTQco3z73qMyZNxcZTBOGNTWFmJOhHXK7JUKEg5K547bb1NEafw7x7NZFNGbW6R9x0dHpUP/CZIhXWqV9wy8+qNDH0uHvhMkefJkMrBzQ8n2zGO2qjU5cOsKJ6zZMxaske9//FlG928pGJxwMIAySSrB8ftqqSbKgWDNLwXSv3rTHs9yhL3+RnLp8WGYPftUJilbPI/vlLKwzxYmos0eA8ID5/VLxRop3TLvtIyZtkTpfMI7bR2HhWF39ZtrysEYKewfd991e6K/M4ftrrde6AfazfVha9SzFtdq/rZyXESmSvBdOCHgovV9hn3P3GfCy+LUL5D+13NllypvFjAZWpLda+qgwxDGUeEmVFVo3aBCoktMnP5u/fktYYxzkHeOMC9xJnYZMElxqvipPJFkLyXWcEwDPjUQM+J9Kur/+mVhc6R5biIYzxeur0riUHYDuCmRDTYncsXtyHm0vnSOGjm5+hDDYTKu03BJ++D96pDjJWHHHlmLmkMR49m9/4SM6N1MCuZ5IVHX0SAVolE2xpwl4xR8r0b5wiovmdzMboMmq8OnH1hf5ODcnA/Wa8PmkOo2olHqZuvid21fK84TdBwk0hi0JrPXfLL7HYh/6dpdFCM0ThctwybMVw4fN2cT3wcM0l5SvVwhVQYymmLqJGMsYeGj3BsU9moaodS6C0NgNXPRelUWjHKUboIh1HfEDKlRrnAC+DVrHsbkp+cuSf1qxaVQnhzywH13q6ZUJO3MRflgwy5V1gzkSJPabzpC89EbDiKMWMYDCzMlLONqv+nKimx64MZR9M7YOWrM00d0kheezSwlanaS6mULSZUyzkaGSe6pXg+rNukjGR9JIwVe+991d9GKrZI8+U0KYo9QCzuMg9PrmzJFJgVFqvBecVTPHN1VGrYfokpYWSuZLFixRXDmDO3ZxGvogX4nrWLQmL/er5P4jeLr+4PWqGffJeXBTjBm6d9OTL9P3aYJma0JL4uTvjEKYYb3IqczIfDVfYdBB+l7NTN7kBKn3KsNcVJTdOrhkjXbFQfL4xkekk/Ofq6usdvzTZ3+JiWMdcrIh0tGqvTHPGWaC/+mPOvOvcdc0UGBPsrYxTENJKEGYkZ8QOX2f/d9lW8Fi/Sxk2dl7tJN8ugjaaRWhSKeMOmAXbleHiTPObKhoPfCjspBb+/q8QlqIHMgpxYv0Rw3ARYIPBAv/MOpH5DdB06oy2GNpf56EAkydjvj6u47b1f5wU7leaJBKsTzUJf2jart5cimqSpShBG/csYA+XDPEVcyJu4N43yw6jBsDiltmJa6wUFz7OQ5R/IiDliff3FFHs/gXB4pcj74qcnsVkHB2l62px+zreOtycGoJVy22GtqXgL9x5hrUL2kY01n/b5eKNJAcr34jDC/kD/++19Vm5o8TF2qpm3jSrYEOW5OAKC+HHydxNRJFhY+Ghb2Go0IJboIQ2DFO/ZK/9F6tuP5wEAiatMurrJrLXhQN616jpIBnRu65rbzrdVs/raK4sMhMax30yTNx9fPRoQYR6yfygv6Hrvc0wuXryiHBc4A4MpugiFYslZnz6V+6rAOiWDHpk4yU2QSDpY7b79VOaFBFEDMx/oAMZ+T8H0Ur9lJpVkQkT59/pKUKPBK/OVbdx+WwnlfdF1XwqQIgeK5+s33isUfZ+zEd9rGrzn8rW7bdxRRHqX1vCTMc3u16fa76fdJ217ErF6l2vT4+CaPnjwnf/73z/ghP5TmAZVfH1S27DykqkoUt7x/uzbCEvjSlik6KGyJU6vOrSibqnF9FJKs6Ot/OSidysTp9xXW6W9SwhinbOHKbYVKBjffnEKlvRCo+ua7H2Thii2uKJmgcyB2fUwDSaWBmBEfQLOaZI28vtSp7ldG2iMPpVIbdFyt0kkKl8IonP/BFjlx6pyqU87BBMbMbE9nkmefzCSV38zvGMExMRJQjyZE2r1ybIIoybgZy+T8hS8UJNRLqE9MBFyx06dNJaWK5PIktdNt/pOOk2iRCmlYnk4j0EY8OiPSRElAJwnjfLC2ZZJDGo1SNyaREJ4jTCUDt9JdVt1ociw73UN0NWjMbGV8IxzS87zynLxV6Q21yTuJ/j4OrZ+cgImZnOOsT2SU2pXecP08Ip02XAwkFhgvSBUiHG5i4iQLAx81gb1GK0IZDQIrrzUr8ndSWdzKeFqvZz7eIDfYphoRtSdKi3MK453DLtFTjCwMf1Ju3MQkYodzYtWmPbJt9xG59sOPkunRh6RK6fwKPhpGgKNS7xyDNKlE51iHdZKZIJN039pRUbN5f/n408/U/mtFl9k9O44m2PgvXLqi8v4j5cXsTygkm5OYpAhpdnntRNZ9gIL6/PIV6d2+jueahGMyzHPTsOkeoAfHfP3+h5+VU9QqNyW70dYJZUJmq9vv/PYkxf5P2oe1gg7OXS+INWezD9btFDggtFy8/LUiYCN9Bv4GUBmR4kXgW69acUX2Frn+RAMdZFriVBviYYz4pHD6B1mHMNypPlSs4CvSdeBkyZXjGeGsSjnIbq1qBmkqdm1MA9dFAzEjPoDaYTKu03qQyus7cPQT5bkjh5bc7k3bD3jCb1jYL3x+RfLnzh5PaMQh4eTpCyoyaUeqweI/fd4aAdYLqVbR/C8r7z4L+tffXFPkPotWbZWr3/4gvdrWTpB3px/N1EigHSDthfPmiIfC0Wa5et2laZ0yytsaVMgn5RDpxhBPm6aOkzAH3miQCvHeshWsJ0SWXsr+pIrEA6kj93r1rEHKkZFUwrsxySE1IXsxjYREg6TNVK8cBH/77XdXWLO1D20k7PpgjPoutUyatUL2HjoZqkQcbQBJfTXHM64wZ91XWCdZGPioCew1GhFKntmEwErzCLjNE9japwztYDqVEt2Pvuu3HaxYl/t2qKsg1pDh4XgbPW2JimxD9ulEeEaDJhG7UVMXC2gIjAnSKOCnYC+LJJzz++DdB02RO+64zZE9OrKdMESf0XCSAZUNym7P2IHcVm7cW5V/BJlDdHv93CEqusi+PajrX2VI/22iOQgiHQ2d+k8Q2L/dcvFNn9t0D6B/jNMBI2fKxu0HbFUbhFeFBtzIbK0d4KjLX76VQMwaBC1GG5qnI1/ObPLEY4/EczXsPnhCvr56TUXin306k22pTy8C3/LF80r9doNVyTRrNYVooINMS5yaGPHoLcy52PrOwhCccr/TPMV5896ITpLhkTT/ts86Np6YBhJpIGbEB5gUeNMxxrYsGqFIqCgZQ57P4lUfqlIwdkRp1uaJWqR98IF4Yx8SK4iWEBaOOWO7K5Ijq7y3YK0sX7td+nWo50iWhLd69eY90uOdqTJ3XA/fi08QIyGAmhJcikHYvGvi/Gg2aYiVqr5ZQDkmnFhsTR0nYQ+8YUiFInW0ZdchAZ4N1BSDLmP6NMoo0/BqN51GO2IW5P2ZkL2YREKiSdLG8xJh2LBtnzL4OAS5iUl5O52+ULpIbqlWtqAqQXf6/OfSuONwBbHv4xH1chrX7MUbFNHdmAGtgry+wNfy7BA67tp3TH2TQIWJ/jt9k6awV9MIZeAHjLhBMSLvOZzgr10HTVER6aezPKrKUR06dtqRt8HECcDBsXm3d6VP+7qJ1noGBMHdnCUbHUnmvCJ2A7o4o6J0VHnGyM6qMohGB+EgRvzwk0TqHufR7bfd6srwru8JS/Rp6iQzQSYxV4tUaSe7VoxV6AkIsyjtiRG/bI07o79+7qDfl1XHJvsAZV1Xbtgl+V7NrrgISP/DKKYqS4WS+Vw/I5PndtsD4ADI/VJW1765v1LDnnJzihTSsEZJxTuR7MYbE9xzyy0pAqXi+eWT0eU+NcQ6yFqDg754jY5ybPNf5XS1zF6yQZWzdIPy+yHw5f0NGT8vgXMxGugg0xKnpkZ8mHOx9dvKV76lZM2SQaEd0AffJ3MfFIWbzplnn/1NaqfbI/UlTar7faddBZkfsWtjGkgKDcSM+IBahYVz14ETqkxav471VCQFgjdybSElchLtGZ83oafyxLI5l63XXR5Ln1Ya1iwlQMYpn0P+rVWAZzlF6SP7wuAFBqTJlrweza+RYEJqhCH1/sJ1iYay7/BJhSRInfI+VTf8g/feti3PZOI4MTnwWjcJUhmCkr146d7rd5OImYlBmlRkL34iIWFI2oCN4gCzkmGRz7hwxVaZNm+1MuAxossWy+OqcpPydjRMZLV1rzFqXdDyYrYnZEDnBqr0mJtEHnr/lD9V7i3G1cvZn1Rl75zE5NvUbXYbNEUWrdyqoN2Q+sFzATM1nBVeOaCmURSv7+Cf+r1I1XbSo1UtxZfhlsPJeICAHj5+xnZoMI5DHOcWKWTtdytFx3yAR8NOvCJ2bkRpvNMqcb3jKzBoI37bR0c8S8y55WdTixk4/tLV2xwdTiZEn6ZOMlNkEvm9Ke+/R/Yf/UQ5nUmF6jF4qnLGdmpWzXOKmnxfpvsAsHBQgt9e+1FYjzhjgDLxI6bPHdkHUH54ILycmtqo3LFstC0Dv9vYTflkaLt+23cU5L1M0df8qCn+Gvaa9n3HKQec1VGPM//yl1ddSyHSiB8CX4jrMDSjKaYlTrUxTGrpjX87W3heODcgukMvpFHa7YNhz8X6+cMQnFp1F620j2i+j1hbMQ0E0UDMiA+irb9ZiLfsOKgWUqJsHMaI2hBldSMK0uzXB9dPUh5CnYuqD8obtu2XCe8vV5H0aIuJkcBYTEmN7J5He6c7Nq2qWNxh8XWCr4V1nJgceBmzCdmLyTs0jZiZGKRJRfbiJxIShqTtvflrhINu5TcLKKZtDAly2iFRJCpOqodmzA36TvyWt9Pt4nD69Pwl+emnX+TpLBl89+sE6yN9pn1cFVcngN23yd+ok0u5HAwsN4HkLF+5ljJrTLcERF3k83MQxVHpJiZRFJMoY9B36XV9ECPeri3IP0dOWaTmHo5dDD0QGdEWPxE7pz45WBes1Eb2rZmg3i1G/PsjuygeCCDAbtFZt/xsiF5vu/UWOfrxGUX4aicmRJ+0Z+IkM30HrAM4BJPflEzl/7OekOIG0V2mR9Mm2fdlug9cz+e263vo+Hny6dnPPZFFpNrVbvl2onKffp7HlE/GDWVTs3xhT4Z5xhgmZSTy2YIQ+Op7+UZB8eCExRECx4ZVCB458ReFLXGq22e8XgKZZqTz0vRcHIbgVI8zGmkfXs8c+z2mgaTWQMyIT2oN/92+Jpoht+6O228VDS08sG6SpEh+U/whhXx7N8GTPWHGcjly8qwiqMmcMZ06TECexSJpJyZGgttYTEiNMLCJlGN4UZ8b77UTUz1OiDCOE5MDrynZi8m0MomYOfUbxCA1IXsxjYQEJWkjwrdl10GF9oA4EaF8HzBMt2in3/fjt7ydCY8AY4mMCPAt+2VSt3sWjHjWGdh23URHMvQ6pK/FyUaKkFtpPdMoikmU0e/783tdWCMeMsSx7y2VBR9sUZUImtcrFziX1u8Y9XV+InZ2bbKOFqzYRs2J0kVyKSOeNZLI7PBeTQOx1QcdswnRp+4rrJPMBJmk+w5bfsvk+zLdB4B2U7qvUY1SqpThu5MXqaosA7s0DBzhDvK+WcuqNe2b4BZQd8w1kCI4J91EryukKoJo+SeF93z4xGnbLikP6lVNJ2zKSDSesX3f8bJ5xwHl0E6d8t5E+x8ITcpiRlv8lImjT2DukU4v03OxCcGpXdrHxS+uqJr38yf0SlQlI9p6i7UX00A0NBAz4qOhRR9t4CXNVaqptG5YUSqWzCeteoyWaz/8JNOGd1R3U65tFaU4xnRzbA0Su0qNeqlcVVhtWZSJcHDgfihNShXFxyFgJ9E2EujDD6kRnnGI4uzkmSwZ1GGCknMcNJJCwh54TcleTJ7FJGLm1q8fg9SU7MU0EsL4w5K08c7mLt2oCLs4LFDnG1hk2Eg8Y/FT3o7rTHgEuN/UCRD53snd3brrsCepno72wbpMhYu777pD1Ttv22esFHothyuU3ySKcr2jjJH6guwLUkLtUN2046ArWRsRoCmzV8qUOasUTLlVgwquJcdM1gO3e3XEjlzQX3/9zfPgidMByGuqB+5RjuNM6dOqdBQvMTWEryfRp1PlCmC+dasWl9YNKrg+vgkiy+T7MtkHNBngzuWj1bPlLNlEPev+w6eU0wZHZ1IJkG84NqzC30B8LH/vbV8VHgaOmiXwARUvmFMhkSLTS0jfcKrasWztDhk9bbH8/vsf0qNNLZUmtHH7fkl53z2OpU+9dHHkxBn55toPqi0nMUkZ8erf63cdsFg8pa9j+VynNkyrF2EMg7DwEs6tkedT03OxCcGp03hJj6Wsa5U3C3g9Uuz3mAauuwZiRvw/+AqszNt0O2lIO8XOziJatFoHKfNGbtdDMzlqbGiDujZKkBeF57pio55SsUQ+tVH/U+KH1IhDTOe3J9oOifxTcg3xXnvlyUXjmYJA1EzJXkzGm1QRMz8G6f8LZC8gPJas2a7IJ8mPhwXcK7/Rrc48vA2kz1z4/CuVMx0p0eARCOsEiITTA1En//X9ReskrmZpX8z2lLJr0W1kgsciAjZ+UBtXB4hJFMU0yhj5DoJ829xrkgtJfniON/5iJSeqyIEvUuAm8SLwCrtGkF/K2osOtcCdcvXba5I/1/PyygtPOUYwwz63qSHMOE2IPsPqys3gqty4l8TVetP2/en7ooHICvt9mewDrEm1Wg5QlXQgxyQ9ZufyMbJ260ee/AfR1rVujzKKrE9UX/ASrkNvu/Ydly+ufKOY362Ck7Zd48qJmsGoe6lYI2let5yCk5MGwXOT6kLqUIHXnpfVmz5yrSoQtkycacqIl07cfj926py81XJAqBQEu+pFl7/6RlWt8MMnwzeCER9ZGhPukBOffmbLxm99FtNzsYne7O7tMmCSqiTUoWnVaDcday+mgahrIGbER12l7g2yQRw/eU5yZMsSD7vES33py6/l/nvvdjw0c3B7pUScbJg31DZHFjIm2HN1ZN86ClNSItpiAwSOt3nnQQWLYxOtV7WEqrH5b5awebemZC/ohBxAiK54J0S/MC7nLd8sew6ckHyvZlOeXifId9iImfVdAGGlpjdOHrzgpF2YQLT9vGfTiJ2fPvxeo1nIv/v+JwUfdhO3OvNEfNKnSy3k3wMJjBRTHgETJ4Adsd3999wleXI+J6UK57ItW2mnByLLx06eU+z06dKklGeeyOCZjmASRTGJMjJ+DszzP9giJ06dE9ZUDu8PprxPsj2dSZ59MpNCFThVgTDNhcQQrtUi8Tyw6pU0Jyc+AdYB2KohImVd5dskr5w55pQSZW2bMmc///KrZM7wcDyR1NkLl+XHn36VrE9kUBBqOy4E0+eOnDd8X+Ub9JCW9cu7Rij9fq//9HVEelkfSSVwkmghssJ8X4wp7D6AAcV5YdrwTjJjwRr1XY/u31Ih/tiTSF/7pwWnKntfUlbb0OkLRzZOUd8GaTLv9mkmp89fliWrPlSIGQxep2olJmXiopEyEvad4KzOX6G1bJw/VK2DpkLqStHqHWVYryaeCCPNI2A1+JnvHfqOl/QPp3ZliNfjDHsuNn1Ou/tBsSRPnkyRRMckpoF/uwZiRvw/+IacyD9uSpZM/vPHHyo6dN899gsHLNA1mvWP97SyGQOF1JFBNibK1dnl1JuSEhERqNK4t2JlLlEwp6RN/YCC5XEIYpEvnNc5x42DY/d3pqiSSlZYM/WzgXNCAJWUYpJ3a0L2gmPm1VJNVLRAE0YNHD1bFq7YoiJ0EGAN793UMUfNpCwR+ly6Zns8AgLGcch28JRzqHODl7s5fKzviVJWdsRd0YjYJeV8CNs2Th3+p9l3I9sx4REwdQKEfSZ9X9joLPeHjaKEjTKylkEqBnM/UXDKU6Z/6EEFhSfvlpSjRau2ytVvf5BebWsrx1WkXK9cSIz3EZMWqnQPDPcsmdKpPPSvrnwrew+fVMNs26iSlC+R13GewYlCybP9aycmMPj9lLEyKf/oNMeoU334xBlPozAaVRRM5jm61/Lnn6LYsgeOnqUcPe90b+zY9PVEZJk8L/diNFMpAZk+opPinylRs5NUL1vIF0InbP8YgJRCs8r3P/wsy9ftEPYNUgHdxMQRrCsZzBvfU31jlNEtXzKfXPriaxXVb9Ooopy98IWj08mkTFw0UkY4N+AMuPLNd2pdA3nph9uFvQkYOGk1NSsUUSkLkfexRj73VCbfr7Xz25PkwZT3evKq0OC2PUeEdD3S14oXfEVa9xwjaR+8X4Z0j1MVT8LK5h0HZcmaba6OtrBtx+6LaeD/BQ3EjPgAb5FFesfeY/LJmYty8tPPBMjRYxkekiwZ06nIVdYnMzq25hUFyfJYOpk6Z5WsmDHAtg3taT28YYqKprJgvvjcE/GbMYvo2yNnOt4f4DETXaphs2vnDE5Q23jIuLmCAeJW1gidvdVyoOCZHdWvhdpYgbgNHjtXuraoEfgwEQQ2ez3zbvXhD6cKOaf6/etDDIffk6cvSv9O9uzfJmWJSM94vnB9tfnWqvgX4SHviU2eA1Sjms78A24OH+vEKJb/Zd9EWP/XInYgF0DGpHsoVYLSdU7fkCmPAO2GdQKYpAHQr9e65FZnV+sjbBQlTJQRx+HytdulX4d6qgKBnXCgXb15j/R4Z6riCcnwSBpfy1/QXMggLNREfHGy4lRoWL1korFzeN++96iwpt56y80yYVBbW/IxnfuqGeb1g4HCgiE/DATUT/lHOwUCo+05eJrclOxG6d2+jquOk6LCia+X6jHHIbCKhAFb240GIsvvOJPiOpAerP9ulXPs+jU562DEvz1qVoJm6f+V7E/KS9mf9CyRZuIIZu8jNYlviHJ6QPIxRHHuwUeEA3vu0k0CaZ6dmJaJC5sysmrTbsWxATM/Y7zj9ttUKhj/zvFsFoV2cVrv9HN89/2PMmbaEpW2wtoUKThx3nu3s+9pBq8Khr+X00U3iAOhfP0eCqlJhY4ebWo7cjT5HYRXuU+/7cSui2ng/1UNxIx4n28Wcpvug6coeBsLFAfDu4mifP2dHDz+qazbslfeeP0l6dCkqiINihQ38o977rpDKAv81dXvHPOHuP+V4o2laZ2yCu4KWRyLa5Uyf5Fv9B46XX78+VfHXC8iEds+OioQtBBR1/J81syebLEa7rtt6cgEECPqvsLkCkzPTTA02FzGzVimSKCOnzqnSp0UyP28p/ZNYLOmebcm7L6nTl8QYK9HNk1VpDzkl2GobV44XPEA8N/Nu420RU6Ylv3iME/EZe/q8eowowX0BuVnvN6X50sJcYHfiF2IpqN2Cwc+DlIYpVqIahAddUtDMOURMHECmKQB8IxJEZ2N2guxaYh3QynK227933nt1B9wdSCREID6kSC5kEFZqGcuWi/33nOn4PxyE4z5viNmKFJGr/Jlfp7JzzV+yj86la3EyJgytINyYocRPxVOTAxKPccvXvoqwfDQc722g33DhYlqk1uOoZTpkbRSo0JhxWfzf0nYk+Yu26S+nTaNKjkO3fSskxQ68esIxnHfa8i0BEOgpO+zT2WSssXzJCLIcxsrTiotm7YfUI7damULKaeVEyKL64OkseFcZ7yfnrsk9asVV8g8vV6pM+OZi/LBhl0yedYKxXfUpPabvlJukuIduLXJHsZzYHSTyrP/6CcyrGcTyfFcFqOhxIx4I/XFbv7/gQZiRryPl7xywy7pOWSa2vjKFctj60lWkeYpixVkjLxZYFBugoffD0zK2sbQCfNlzpIN8X+ihjTQSyBgukybHXyUGzgskRf3QtYsKt9HC0Y1EHk3YQMtVqODytskl4xnY6MfNnG+MnCcagNb2yQdgEMTXtrKpfOrPCm3jTAasFmTvMw8AL4AACAASURBVFtTdl8Oe7lKNZXl0/tLxvRpZercVYrJWqc7rN3ykYLW2iEvTMoSoXM99t0rxyaIJONEOX/hC8dasdybFNUEgkTsTHgEfHzKjpfg5ILBmfx35idVE/Yc/Fix08PkXKN8YZPmXe81dQJ4rTNuaQBO97pFZ2GexiHkJkXyvSR1KhdNMp2ZNoxBM2vJBpUiUadyMcXqzpp21123ScZH3Gt/h2Gh5h6/fBRuqRsm/CYm5R/tIM5E6Z7K/Kjv57J7Z14VTpLSoJzw/nI5d+FLRzSU2xwjdQN92qUUmc5Nk/tJUWNfJeqKsI9u3HZAfa84jmGmpxIFzns7icZZx43t3Npn9XKFhLJtfuWfcgST7oK+OFfZCedAp7UtaBobz4TR3y6ucgKHe2S/RNZb9RwlAzo3dHXuoXu+GSqHAGN/NcfTghMjKYU+S9XurJwLI/u2UGvpwpVbVfWi/p3qu/LRsJ417jjMcXgENHDEOiEnIm/kHP7tdz+olE8nThS/ugCRd+zkWVsyW79txK6LaSCpNRAz4n1omPzlxzM8pIwxL8GIz/b0Y5IubapEl2LADpu4IJ4cDngUnlU7EiKvfoL8riOzkUZdkDaIavcYPFUdBLTE1X5T5bRHln+xtsuBFO8/xhBkU5VKvi6teo5WsPwBnRs4ljeKBmw2bN4t448Gu2/9tu+oyA0EYxNmfqAcQMDikE79J8gv//Obba6XSVkirXtK6xXOm0Oqlimo/kSb5ep1l6Z1ykjR152jgabVBEwidqY8AkHmc+S1OJnK1u0ulGSyQk/JvcapMuGdtibNJ/m9QaI/fgbjFp1FV4ePn1HN4NxkDSAHE4Hbo+/wGTK4R2PXeeZnDG7XYJzMW7bZtRkiWnY1qTV5VdYsGZSRAwwUxyvPwoHXK4XgerJQm/CbRKP8IwqHQ+H8xS/UGh4Epm1XRtGtwkk0DEq3CQJHyVdff+uaz48D0s5hRXoExgJEaRjEfh00JvPeTyoZkUvqXJMiAFJg8eptAry9XPG8UqFEXs8zTDTOOnr/yvXiMwqtiPzx3/8qHpj8ubLHG1dtG1fyTcIWxBHc/933VcCB4AJGGPD5Rx9JI7UqFPF8T7ryBMZn5owQR96gxr9iw24BzdGwRklVqs6uLGOYNDbWHr12es0N9tYb5IYEnDYg64jSTxzcTumXNEuEtEWcEKB5gNCD9kwqYVz9R8yU7q1rJjCc4crYufeY0pmT8F5BvzkJey/lP92MeM55y9ZsVzwMnLG1UGGFtfzJx9P7fnTGs33PERWMQ59BUxB8dxS7MKaBKGkgZsRHSZF+mqnTeqB8eeVbKVv0NXk4bSrZsfeoLPhgi0wd1kHlinlJWKZ1HRWOhMN79Wf3OzljLNoP3Hu354bI/ZG54OpvP/8iXd6eJKkeuNfxwBwt2GyYvFvGGA12XzbobgMnKzZgEBI9WtdSh150yEGjRKGcjpDMsGWJwrzTaN5jErEz4REwhdxqQqRZY7qpA6AWiNswNNx4H0z1Z0omGDT6Yx2vSXRW83RoJmjdLqWkgJySg5pUgiFZrWnfBM0TXYeZGRQFURSIlewOfxrpsn/NBCGP6bU3m8mCib1k/5FPZO3mjzzZs01YqDWTs5teiJYCUfcjYRBdftrV1+C8JV+X6C36HTxurmz/6Gh8E6BWOjev7msvCFpGMRoGJQNlrtC3VYjwsQ577b18Hy27j0qkMhw5INswkEklcHpf3I9R7SYZ0qe1rVUfNpWMZ1u08kNVZhKjpm6VYlK9XGHbNL8gc8HvtRoJdmj95ASoRcrcZX0io9Su9IZrUyaOYI2oWjVzoKROdb+8UbW9PPJQKjl9/pLE1SrtWftbf9vHNieE5PshjjRNY2OeshdAbmxFabqheCbPXilHPj6jggGk/72c/Unp0qKG+h5Zn+u0HqQq4diV4/P7Pv1cx7MzZpwHCGkCOA7cSHT9tOsHTg9ygiAR/D+v58omVGeh/9mLNyh+AObCIw896NgdaygOhw/W7ZSla7apdYHAGuczzm2mEX0/zxm7JqaBsBqIGfEhNAdD8IQZy+XIybPy+eUrQjkhPnY2J6cSQcDv8pZtIYsm95YsmR6J75U8zGQ3ehMDcUNYpnUM0obth8ij6dIkgjbhUeaw6yVhHQh4NonakMdqFbynkAMG8ZJ6jTHydxOWW9q6Xuy++jnCliVyY4K26ogyQ3ZwULuIWRjdcwjFiQO0zanqgrXdsDwC0YDcchgvW6+7yheFSEjL1t2HVTRJs/rWqVJMcRpEU0zIBMNEf6xjN4nO4iArULG1LJ7cJwHpEugFeC+IDvkVP1FGr7YoJ9WjVS0Ff3Q7/PFd5S7dTLYvG6UOmhDa1a74hlz97ntZtWG3pxFvwkLNWqxRDJHPg7MPNnEiSG6Rp38S0UUUeuuuQ+pdwulx003JVAoVDlgY9XsOnqoMfLgj3MSkjKLXe/f6HYQP6UtWIRUge9bHfRFX2rWvjbrWDStIjjcayrq5Q2z3Ue3Erla2YPzZAGQacxRumzOfXVYOJ+v7jkYqGWOmHZzBM+avVTwfxQvmVOlCz2d93HcqH3sJ7+7K1Wsqmm4V1kW7nGftJNv1wRhFjKZl0qwVQlWacQNbu74yE0cwPBgYrqStaR6aPSvHybqte4W89hF9mrn2jc5mLVqvUqpuvjlF/LU8E8EQNx4EkzQ2OorrOEwuX/lGFk7qrdCNONAGjJqpyJQxKkEHRBrFpISwDndvXUteK9NchvaIU7xDWjBk132417dTkPuCrsU6vQjnAaVcMagxrBnr7DHdjbg9vIx4HWwhxYG5HSk4jm5OkcI2ZYZxMyepZkK6AlH30m/kloK5X7AlFfVaZ2K/xzRwPTQQM+IDah2W00qNeimPI8RyQDYxVtjUH0qTUrEhp0ieOAeJjbBy497xJeJ0t4tWbhVIkrxqp5owrUeDgTqsAyGgehNdjne6XptBQoTUTti0p85ZbbtIO3n0aQeSmNYNKngOL5LdF+eDuJQaw8Hz2cUvXdslWuhFWmVS9suOCZq/9RvxvjSrW1Z5qhGgxnZw2KARs8iHxaju0H98AobcssXyKCQCRoCThOERiBbkFiMe/XgJxJJ2xJVe9yXV76bRH9NxYdj9z2+/SaVS+SV9ugfl1OmLMmLSAhXxAmrsJGGjjG7j9WvEc3ir0ayfQlwUyptDkYTiTIQDAbgvHAheEpaF2q5d3uHIKYsUfBPSVNIT3HKtTRFdXs9m/Z20CtZAjISXijVS0HMr7wqEpTgfvNJNolFGMYxBaX0W3ntQx6KTriC5++Lrb6VC8bxKL++P7GLLHq73XgxJbYBBeNqpaVWFvsOwJJpqNeKjkUoWOW5K0M5ZslE4b/jd+4Bqt+szLh6iHGlA4ti0c9RpVFPpIrkF5wVz+fT5z6Vxx+ECxJ564kklrOMYs1sWjVBOeFAj6Hbxqg9l47b9MrJfC8+uwwYsaDhsGpuG8ePg0N8X5fG+uPKNVCiRT8bNWC6NapRMxD/E2fGdcXNk/viequIPZ1ArfB0uHmD1vdq+lWRrMQYway/IC9aKgpXaKPLcDdv2q7Nwt1Y1PXXudAER8t37j0v9aiVsL4lfVyJKbuqLmcM4Fey4h/S3yVmMdS0y0BR60LEbYxr4BzUQM+IDKrtqXB9F7Daoa6MEBgne9IqNekrFEvnUJhkpbC7PF2kgM0d1lWzPPBb/c5POw9XioXOlnYZjyrSujE8bIePLi2DPxIEQUL2JLtcLLcz/dkJU7bff/uOb+ARvebn63ZWzxQ8CgevPXbgc3zWRDTYO8utIiYhsA2cOkUgEWBZiPfzwN+CNblDjaDhd7HSFkXrH7be61n01jZiBvCA6+3rObOrAoUgQj30qvYZOl7cqFfUkOwvKIxAtyK3pPAXGeeb8ZcGpxEEGJx9GYvp0qZOUTdg0+mP63CCMKIu2bO0O1RTQ4ryvPCcgFuxgiNGKMtqN268Rzzf4RrX2CZogF56oYtcW1QPleVsbofLHN9d+cKxBHTlmImhj31uqUqpwHjSvV87zIBkNRFeQd75k9TahfN3M0V0Vmos60DgatCxYsUV2fHTUV7pJ2DKK9BXWoNTjDOtY1Pe77QO3336rPPnYI7ZErThjXyzaUKypbJCAETnkO2H9Ys+YNrxjvE6jlUpm955BcXz2+VcJziB21/Gd5q/QWpHewtvDvhFEINhr3WtMghxlIsTw4HiR/pqSq5K+sOvACdV3v4711HwFbQNXETw+XnI9Aha6jK929uh1fdKQdir6z3dol+qDY6pRhyEqWg+qkme2BghYY4hQ21UjiNZaTOCiSlwfhX4gco4zYeP8oWpuL1yxxdPB5/U+3H7X6L3I1A19D8gPOJg0obC1LRA6OJxBxrAnME+KF3hFXn7+KV/pQSbjjt0b00C0NBAz4gNo8ocff5ZXSsTJhnlDbTciFjAOPNYN2do83vtbbkmh6rsjGMcrNuySXDme8dzYTJjW6Yux3Xn7rcrLi8Nh6+5DKk9Iw4Td1GDqQAig4kSXaoMWJls7wWBiw/PLXkobkL888dgj0qC6M+EK10E+p40Tu76pyVzTgbVcOz62Ln43noEXR0r1Jn1V2kXhvC86qiWpyn5R93XrrsOucEbTiNmpMxelTJ2usnvF2AQHP1h4d+477gmlNOERCDPPOAQRBeGQCjTvyMdnFbEN8ElyVSGDsiOp1H1hvFNlAPgghnuWTOmUEfjVlW8V3BgBfkwVCbdqDGHGru8JG/0x6TPyXubsb7/97pk/mBRRRj0WosKw4nOI9YJhmj47hhY5lJD8abl4+WsFYc6QLrWC42Lw2gmOR8icqFaBYQNiwc86TFumiK6gz838Ll6zk6R/6EHltCa3uITluUg3YS1rUa+ca9MmZRRNDUpTx6LJPoBSns5XWyHJeMcaekzucvsmVWTklMVKb4O6Ngz6ajyvNyF4I3pLjrWTceTZOZVO/uc3+fT8Jfnpp1/k6SwZfOdHm5KrshZt2XFQzVci/wQpDh07LRnTp/F00F2vgAVs8qVrd5GD6ycpck0cY/XbDRZNRowTqkH7wYmQnLwHkAOkL7F34wCJlPQPp5Znn8yY6O/RXItxoFYvW0gZ7qy/RP5xhnBuxZGSVKI5RuaN76HmWKQAl9+254jjmZzr+SbhvWE9h9COoAtEkBj0T2d51DPIlVTPFms3pgE/GogZ8X609Pc1HNhqNOsfv5CSL5gpfdr4EhRA1hp1GGrr9XPLz7YOIdszj9vWQDZhWtcb0/QRnVS0qWbz/vLxp58p76MVvuWkClMHQgAVJ7pUEwM5weA4bOBJ7dCkiu9ugFdRs5lcTieBETVPmeayetagBEacH4Ib2tSHoH1rJiQwbIC3EbELQ5TmVvbL+hyRcHo2+W+v/aiIjuJqlpYqZQq46sokYqbJ6XRpPd1R76HT5ceffw11WGXu46zxw+IbFHJLuTQFZZ71jvr/ce8tUxEyYIk4uoAKLp7SVzEVRwoM1XzvOMYaVi+ZCE6Lp3/73qMqUn3rLTfLhEFtkyzXjgMch1U/6BLfH4qPC+1KtQFZvv3WW2wZnJMyymgdLqkZzBkniKRJuopmt8+XM5tyBurqHLsPnpCvr15Th79nn84kT2d+NJEGNWyWH0hneT1X9kTXUFIp90tZbbVviuiiUeYlBF5XvvlOGedERt3QWDgdlq/dIRcuXVGGWaS8mP0JlVrmJiZlFE0NShPHouk+gE7gvTn+yXnl8N1z4ITSPbwaoLoQ2MN1STg7HXKu2HfopKqi8N//JkTU8b3blb80JXjTOjuyaWqg2urW8bN24shkLCCScr+Y1SjC6ZXK5mO58rzkegUsNGJDzwXIQbfsPBgPAycSP33+GsU/Ei2J5lqMEcyYgc8P7t5YpVC07T1WyhR9zXEti9ZzgGYlwMb51rqOsf7jiPJbBpnx4GzcuP2ALFn9oezef0IhpPykYETrWWLtxDQQVAMxIz6AxjQb8+ENU9RmRESXqLo2ivD4vT1ypn3+zc+/SP7yrRL1hiFthVvPG98znuEz8uKwTOs6ekN09BMipXW7yfq5Q1S06uTpC56GlYkDIfIZgpKmOL0eDjS//vqbqknqJvRnJzclS6bKYbF52pGuaWNUv2vdhl8jXh+2idaDIuCgT6SXqCmHdC+CHbsxu5X9sl5vR2xHHnyenM+pcneQtzmJScSMNnEYUPeVQyApEGlS3Sf7jpySdVv2ioYG+v3kOEBjPMxeukHyvPycZ+mvMJDb8TOoFX1Z3u7cQOVSRkL+gWFy4LaLJsxctF45g4rldy7Zx7NiNPUdMUNqlCvsyYXgVzfW6/oMf0/luyJAX4GMkueNI8JrbGH60/eYlmoz6dutFjVRJzvjWPdnmq6ijcowDNasN7Va9Hd9dIhS3aJXYRFdsMyDADh+6rzac+64/TbFYM2/IXMkpYuyp/82MTUoTRyLpvsAusShhBOT8wGQboyKxzM+LHAh3H3XHa610kEwYRxRMi7tgw8kMoKJstqhIEwJ3rTjn8oDnG/cysjazRdytVt0H6lqlWPMIzzD1OEdfZMJBk1li8a8vZ4BC9LdQMthOGJI9mr3lpQvnlftqQSPHsvwkPRsU9vxMUENjp62WH7//Q/p0aaWckZv3L5flcTLahOJj4a+/g1t4CTiGyO90TpPOQdhyFOlwI6nymvszIVjJ89JwTwveF0a+z2mgeumgZgRH0D1GLOvFG8skFvBLMtBmQiENuKDRhs5BJdv0EMdnlhwvSRs9IhISpEq7WTXirEK7s9GQV1kjHjqa3qxxTKusA6EaBBY4WXF04uXXAte5KvfXpP8uZ6XV154SjE6R4rXYT3LY+lk6pxVtk4X3vXxk+dUpM1KxoZhiQHhJ+K5cOVW6T5oijokP5X5UQV5w2lDuoWVQTZy3CZlv7zmkNfvJhGzeCPp518UqRDGBptrpkfSSo0KhV2ZffW99L/34MdCaTfmJ4fAKqULqHIvkEg6SVjILePkwIvRBCSwT7s6Cco9Ur6NsjN2JaTcSv9EjpODGP9zg9Rj7GNogZKBDZrSSORDukXpcGbhHGROwR5dt/UgAf2Bg4H5BmFPEAniZDMp1WZS553nifxG+Nvlr75RjNQQZ0Gk6CSm6Sp8w+37jlO6teb9Q3bHwQ8o/b9JQGn0GjJNPj13SepXKy6F8uSI/5bQxadnLsoHG3apetPwueAIsquyYkL4ZaIPU4PSxLEYjX0g7LMzzyDNA7UFaiOIRIPgDWhxx34TFKImdcp7E61dWTKmk942JHU6ZQCCSFACOEchXus2aLI8+1QmX8SRpikMQXRlvTaaAYugY2D9J9pOyTjW/XLF8qizB+c+qitwxnEKWui50rxuOfnjjz9kGulry8codBmGrJtT0KQMIs9oymGg2vif32TVpj0qqMQZi70P1Ogrzz/lqUbmG4i6XfuOqbKPpInCA6DL3Tk1wH17Dp6w/Tl1yvuU7i98/lU80tZzILELYhq4DhqIGfEBlT50wnyZs2RD/F3t46qofFeiMxUb9kzE3uvVPJ52GDi9DtteBmmXFtVdu4KQj4ji/qOfSNU3CygG5B6Dp6pDaKdm1VzvdVvsrDcSZQA2jESLNIW2QA78/MuvkjnDw/EHibMXLsuPP/0qWZ/IICULv6pKsESKOqCe/dz22SgtdcMNIl9d/c4W9hqNjYmOQUHAkMvmkv3px8QpXcI6SJOyX17z7Xr97gcSz2Gi9FtdVOQGkplyxfOog4sX8SLPFBZyu+/wKWnccai827e5Yi/mMNTGUt984qwVkvymZL5rnmMwfP/Dz4nKMVE33a4SgH4fRJ0adhiianHj4KGEF6UZqYZR+c380q2lPcOvzqXUtdqpLNC1ZU3ldFvwwWbPMm8mTjaTUm0mdd6d5jAHwaLVO8qwXk1855hb2/KbrsI9Tg5Va3vwnzhFMKMNNXb7rtljgHC3i6scvz7bXc+YWvUcJQM6N7RFjFwPwi89zrAGpb6f3NmwjkWTNdNkHyF9r2zd7nJ001Rfa2DkOE0J3mgPp9TB45/Kla+/S7SmcZ6A+C5SWFOoz66h+BjxK2cMkA/3HJElqz70JDqLRgqDyTuzC1jcc8+drlVpdH/RJjgFKQOHkRdCRjtU9T6AQ/rdPs3k9PnLnjqHM+K9+WtsVca5k2CVW9lLUw4D5ku9tu+ofT9y7wNJNrBrQ1fnd7dBU1TVBQJhOP1B5BHwWTCxl2sJY7vqRVoJOAFIAUEvBLxiEtPAv1UDMSP+Or4ZFs+eg6cJB3w7j7Z1aHbRo4tfXBE26vkTennCyjES8M5ikFCug+jw9HlrVD6vV7kzt8XOOkZrKkC0SFNgPgVFsD+ihIhfWHvY12u6Mbkd9PHwhoF3hX2W63lfEEg8Rlj1Zv0U3BfHWJk3XpPnns7k6wBrArnlkDJ84gJHNVUtU9ATyk+0c8DImQoGaSdetb+JeB3/5JwqzWMl0sPJAIeFUzSOFI2i1TvEIwhI8QG1AAyQHGYnwqxoONmiUarNqiu/7PJu87nz25PkwZT3ehKt2bXhN13FzaFqbZfDn93aGg2ocZBvGrSGHz4J2mStv0FuSERCdr0Iv6zPGcagDKKnpLjWZB/RjOWRvCp+x2lC8Oa3D7vrcBgVq95BdCqaNuLHzVimoPQEENwkGikMJuPn3qBQ/mgRnGKww7WCIUr6Bc7pmhWKeHL+6LJ++gxGebryJfPJpS++ll37jgdO3+NsCrM8lXZIGYNzCNRjUggpazhFh/SMS7BOwR1Ut80gxaxvVwOesYAyyFeuZTxxpB4f9eEJUJmQ6vlBzyWFPmJtxjQQRAMxIz6Itv6+NkwUxalmOcY0UF1KM4URFkDyP6nL/G+SaJGmUDKFQ0DkQYa0AHIKyTl3ErvccH0tLNZ1KhdNMpWZIieSbGD/QMNhIfF6aESfKV0FpB5IHBt48YI5XXNHTSG3jPnqN9eEw0ukcPC8+67bHTXHvZUa9pSbU6RQNXqB/Se78cYE1xOVBeZnJ5o/gbKHdusADrFde4/JmAGJOTWY43nL/lX7OMMjadQ3gYAomPhOW0coYDScbNEu1RbGiI8k9CNV6M47bnMlWjNNV3GD45PHmjnDX3nl6R5KlchZFw2o8T/wCSfq4noRfl2PZ/239MlcKVajgzz31GPSuGYphaSLRCXBzWNXytH0GUw4J3AQZitYT6YO66BSk9i/77rjNmWQRhLF2o3TJIWBvim3BvfPyU8/Uyk25JID/Wdt9ZMbHhTKHw2CU9akWi3eVsgroskvZ3tSXngui+JQAFruhUbjfYHCAgkJBB3SRJyZtEc5W78pPjiFSWMbOn6+QlM2r1NW3iyaWzHmJ4VodnnquNvB3zkHwBrvVPFJIxAOrJuUYK0lyAOybuLgdoGGzXtAd/AtEUiISUwD/3YNxIz4gG8obBTFjp2ewya50mzEYQXmWzZIN2M2bNuR94VxXkSr7zDs25Es7YyFv0EgA7GcHQRfj9etmsCDKe/zRC+YIieipbd/uh0TSHzkWOFC4EAxc9E6eTn7U57RcFPILf2HmeM6crRj2ehQ7PMYSNSO1lBI+B9wBGhmddJtWnYfpWrvRgp5lMvWbk/w5+TJb1IwQurUO0m0nGzRnF9BjfiwhH5Jla7iB45vCjU21TfrHySIRPqYt+TPWgVGfUgeIyUahF9hocYmBqWpvqJxf9icXfoGldSx//gEfDDWMXkhfMKO345zAgcnBk731rUcI6O6P/ghqFBBXjNlvijv9mqOZ3w7HJgroKPWfbhP1T6HFK9RzdJSILdzNJgybN0HT1GpRKRj4dS8m3KfX3+nUgIgV4VstUOTqpLqgXtsVRMGyh8NglPmOLDwFet3Sr5Xs6myjXleeVaR4PoR3he8F1bB8IaDoGzxPJ7EhESdt+46JEPGz1P6Q9dVyxTw/b78jNHuGl3nPZIkVF9LND6u83Dbik9co533VBoi7QyiSDg+2vYZK4VeyyHN6pb1NTT2YDiMSEFDIHRkDsUkpoF/uwZiRnyANxSNKAptfP7FFVUSA9isW55s5NDs8jCBE916SwqVQ5uUEtZ5wZjrtRmk4E52AqnY1DmrpX8n51qiYQ/rTvrAiKcuuFttY6cUAqKPkD+1blAhlLr/KeSE9igzSGBwboz0oR7E4SYTSLzbODjIQbDkJSaQ27BznIN27ZZv29bw9Rovv2v4qT7IRFa9AFLfrs84WyPeT/te1/CNEtGnSoM14uOHuC9s9Qe7MQWp8x5tQj8vHfn53Q8c3xRqrCNXbuN5+fknbYkYuad93/GyeccBqfxmAUVWFhnhw3kE8V2kmBB+mUKNTQ1KP+/O65qgZfms7YXN2dVtaD6Rr6+Sl56wxBzrupuzzuu5gv4OTBtj3KtMadB2I69v2WOU4pNR3D3vTBVSmkDZ4HyH9C1SVm7YJT2HTFPQa00IF3kNHB6jpixWtcBJdaG0YqSEgfL7WSd1P14Qbeb6jr1HlcNhw7b9ituDiimvvviMqUod78eBACEqpKAQKHK+wflhlRTJkzs6PkwGptMlP1wy0rZKEIR+IyYttCUf1v3iWGrRbWSCYeDcGj+oTaK0IOtFoA5Wb/5IcVyxx7Juli+RTwrkyi4335zC5LFi98Y08I9pIGbEB1C1aRSFvN1WPUYpWJkW8p0gHPIq4XI94dkmzgs9bjzgdsLG+ttv/5H3R3Wx/T0pDuscBrbuOuyLld86qKDVBOweKBrICTZ69OrG1g60DoMB4dDD4Ye+0zx4v+C1dpJowBFpOwwk3oQEKsBnbHupyRwnj/HVUk3UHLarkuA1Ng0/nT22u4JO8p6oqawPyhiHHLAmvNPWq6lQv8d1HCaXr3wjCyf1VusQUYkBo2YqWCpolf6d6tsehrzWJLfqD14D9arzbkroh2EETHPn3qOKOCpD+jRSrWwhX1UnTOD4plBj5um2kEUSFgAAIABJREFUPYfj1Td36SaF/tDr64Gjn8reQydt11OdmrR4Sl/JnPFhr1eQ6PcwFUqiATV2Gug/YVCaluVLypzdwC8wCjfMXvz/sXce0FZUyRquGdQxYEAQCQICKiqogCASBCTnDAKSJGeQnKOSJCkISFYEQZAoWTJIkCRJwSwojpgYfRN0GN/6NrOvfc/tvPsAjqfWeus53O7du6v36d5V9df/b1KqI3atPXp40zY2rXGPDC7fK91TT8WbvdOo/imRIuu3viN3Z88sObJl8rxLgnhg6lbuEX2SCZSfMSAjPP3FOSlVLF8SGRvvC1jXQVb5Saazz9y046AsfnOrut8BXRp7Jk1M0Cp+OD7ihfggqVG8VhfF09SkTrkUz65Vj+fUcwL94WbsI+GBgUAY5ShaJ7xaEPR909LqlBzyXEyJAxIeuMweSATxAR6ASRWFl1WtloPUi7x723pSp9VgeaZ3Cxnz4mvStG55qVftcdeZuPVhQnpV7JEHAtxJsENNkhf6RYlWup2xyQHu5hTEm2zWY+H0PIPvz/8kry7dKO2bVPf8MNrN16+aAOfaVcO516uvTuUbJmc3ByDmC5a+5egzKhhVm/YTstvqI1mzs/pvstq79x93JLmJAo4YO98gkHgTEiiTTQxzNlnjnD968gKhz5zefSo8sUk5NqNO5DycT4UUKKU2vXH75rvziiSqT8eGrrJpwX7Rvx3NhrtAhTYqoQXJJUZw9NW576RulZIybd4qadu4qu37yUT9wXSjH5bQT9/5iBdeVTJ8RQvmUb3rW3YdVprp6xc8p3pS3cwUjm8KNbbODZQSOtBtm1RT/0xVChZ2u/fp8VOfylNdR4VGjISROI0Cauz0LPwElJzLWiGIcjN6ia09uVHJ8pn07Jr+RsK+E5TPfv1VPvnsIr8G9qv8qpJdEJ0VynefK0zZro3t9NlzSsLw5ef7Koi9m1HsaNTxmaR1qoN4KtNAvtnvXKlG8jzT7emSvrEkPNr2vtgGBZJs4dRBkjlDuhTTJzk3b8kGWb1pt2qh4DtCVZz3kx/OA1O0CokTN0uVKpVrVdt6bhCZUs5DHu7Q0Q9Uu5xVghVix5fmrVL7Yzd2/jDvJb3GeT7wK219+7Dyde3KJVQ7g53E5pW65hLz+mN7IBHEB3j+JlUUgrfH63SV/eteUuQj+sO0dvNeldme9OxFcqqgxksOSBLayE5mqslskrzQGqRO90emedHKLY7sqyabdbtNUNpbbpLihR+SauWK+sqKW30aRE2A88JWw73WgFcQzwa0XP0ecnD9dAULa9TxWdU68N0PPyrGWbuKblRwRD13Ow4Dv5B4r/u3+7vpJsZkjatN7q+/qgAKJmACYDZlVoOlvGe7+o63RiUBbXht9Gwigwh8mhaBrJnSxwXipxmw962ZpjZp+j01c1xPheBYvm6nbNj6jmvlLczzMt3ohyX0Y646sWhNXPDvzbuNVgRYl4JfJNZn9H5+d/5HJZPk15at3SEDRs9SaAkqSRhJxn2H31cqB7FGkqJU3W6qLQNejyDmhbpwkjiNAmpsElBan7fb/cZWGqOS5TPp2TXhdDFFVDk9bwJLZHXtoOhe64nvIRVX+r3dTCNGdix7QQW+7JV4d85c8KZ0bVXHkyWdbxkJK6RCCQpZP0eOfyQHjp6SR/PfJ7lzhSMR9ro/jch6ffoQJVurCzd3ZcskbZpUExKHkM61blQ1xVBa550/0A7waIHcSj73joy3eVaU3eYVBK3Cb5VkNs8+U4Z0tvB2u2uZyJTq8cK2mYZ9L8XeB/sVvnULV2xWmvWJnniv1Z74+5XigUQQH/BJhK2iUFFGq33v6qnqpayD+DdWbxc20l5wIadpjn/pdaWF7gZvYyPBy9zO/vHPnxVU1w0uZZK8cJo3MHkYQLPdYc/Yrc8z2awHfLTJDjdVEwhbDbd+1Jzmv2bzHgFG64Re4DwC92rlikilMo+qjX7RAnkEwjSIcgY+nVJzPCo4Itc24TBw6rG2+oIsuRdUTh/vdxMTjzUeZP2FrSYEuYbdsRrpcvitmYqB+O13jkmrnmNl75qpSg4KdEbrXmNtq7fxqBT63eiHJfTDB/pdvOfNKckqPxAbbdp+wFfCIgzxlvY/pIJA+YHeajtz9htFupk9SwbFJF2p9KOOj5bEJqRfs15bIz3aPSHzlkD8eK9kznib0HoBaqOxDTSVoAJODt69tHEhOxf7O4JslT7cWIuSqJN5/O3Hv6fQHUdq1Y4jxjSg1OdvXDROUl9/rbo1pCy7tKwjBR/KJXzTY9+nUcjyaR+G7dl1WgBenC5RIapi30m8d01IeAeNmS2pU18vvdo7JzP1PTfrOkpKFcuvINbsldLcnFpp0j9Vv6IrU7rmbkBmU/PWgJACKUWSkmAZfh67NR71u1QnhrVeOUiC6a+uElRI7AyED8m8dw6/L7veOab2Zcz56VZ1Q6EGuYZftAprpveIlxSxq7ZalYrL4G5NBUlcO4tCppRxTdpM3RCqYSDyfFe27j6sEhkJYjvTX0Ti/EvhgUQQH9DLYTfbOtO66uURqm+LD1PZxx5WUCJkMLw+Klz3yY7PJJstUFuy1k4a0m63xnhUctBuhd2eD0WZ4g87nhI2ecGAQKoJIOm1tW5kv/3+vMqqP/rw/Y69xGE262R1CTAgr2PDe/T9T2TXvqMCtDF7tkxSt0oJ2344682bqgmEqYbr65v2qDmdT1Xjlef7KtbeeJkJh4Gf+2beTvrbdvfkdxPDuWHXuKmSgUk1IQxU2Oon3gMFK7aRV17oJw8/eI+MmbJQtu0+nEQkRHXi5cXrZdms4Snca1IpdFp/QTb6Ydew7rklqMt0e9qkYQiO8Kddkiv2WkGJt/T5rJWSdbpKycJ55d67sia1Xew9/J588+15qVz6UXkwd05VybMzzu86aJJs3nVIETfRRgXDM8/o6PsfS8ki+aRLi9qOwRZcAxAIstatG3Z9LdYAa8GvBSHq5J04atJ8NXc7c0skmwSU+vdFAp1vAlazxUAVTII2Ibias3Cta1LUrz+cjgvTs+s0lhunS9SIKtP75rl9duYrBSGnOn7D9dfJg/flCDRsEHi25iDQbTGg50rX6yaNapWVVk9WVso0oNP8JBICTVJEFWMqPtlbFWpYZ5ooVcufEZx3GzrFkWk99noUEoCZY7rVyWlOJmgV7aPHC+dVbVOgLA4f/1CGjn9Zkeo5SfFGIVNq2mbq5A8/SiGcy7tzwfJNCjXRvH4lVVQCWYCygh8i3aBrJHF8wgNReyARxAfwqMlmm8u8OHe5Iq6qWraIQCaVLUsGKVv8Ycn/wD2esyCYJeC3Gv82ZsprsuqVkaqq4sfYxCExNHvhGvXCatOoWjISFrcx2EAC3WcTTB8pcF8/xobp7//4p9yT/Y6kytcnp8/KT//3TwUZq1quiKvcm59rWI/BT5NmL1X9rfz/aa+sVDBVKlXb917cvIYldwoyl6DVcD02fj547JTjpfbsPyF7D73nuOnkg/75f0nt9CBk0zOmT+tZSaEawAcQ2KKdAe3e8vYhxRZsZyYcBm491tZr0bt6zdXJdWtNNjFBnqndsaZKBiZVTj+JDy9SIja2BAaliuZTARZa53Uql1Abm8adRiid5SHdm/l2k1el0DpQbNsFyT4/G31TDgTWOO8/XfEmUBg+4RVVBffSkg5DvKXvmfahyo37SKykErrGH3921lNGkeddsVFvmTW2p2ufqO+HZXigX6JO1vgTbYbIX665Rto0rqpIOVP9Obm06rXXXiNZM7sjs8JMl/fpg6WbiyZKYwzkDFs1rKK0oGnn2rLrkCPRaVhZPq+5suaotkIYWLPiYykOD8PpEhWiiiR4qx5jbW+B9g04Plas25kMtUKSHiJAiFMJjsZOW6Qqytr4bQFT9lPN53eyasPb6ntNjzhFDljT3STmuI6WLDu6ZY5KkGlOgrXzR6u1hbTi4LFz1N7AznTSP/Zv8Mnw7Xumd0vHNjyeV9FqHZUue72qJeXpwS/K+R//L0njnJ532iedlHpMlD5M0CpUwms2H5CUfND3TkvJ7gMnHH8XUciUxqvN1I9SiE6oPpAru0JBUYCgQIDKAag0pxYhr9914u8JD1xKDySC+ADeNtlsB7hMoEOpnLHZ7t2hga/zqHQBGyUI69v5SU9WfD0obOPdh01JVr3xw6yvJUQObpiRjCzE76bVjbHcesMNa5ROypzCE/Dp6bNK7xjEQ2w2merRbWlvkWf7OMvamTKlx7Ma7tUT72shOByk5+3U3vHF2XNy8OgHzgmE//xHBRnDezaXR/LdJ8ilAW2EOfb0l+dkzIA2JtNzPNdkExOPCUWhZOC3ymklJRo/fbGkTXOTIgPC7KDCsfdLMlBXcqlMaokmqmhsXkkCeLW9WMf0q/5g2nYxcPSsZLcSRMeaE0nm7T9yUiUlSaiiCOAnwDAh3gKR1euZaTJucPtkhFU8J4IEAh0341nBbfHzz7+EmntYJJnTnPwSdWrprrdXvqjY9INYmIAydnwSJ7UqPqYCQR3oUWkrXuhB2bh9v/pGIGdmZ2Fl+WLH4p1w4tSnsu/Qe7Jr/zHZe/A9dQiwXbtvUdScLkF8TlBDq5+dIWl3/XXXyrH3P05GeEmQCvEcyEIS2CSOe7R9Qsnf8jsbMnaOCvDZN7gZPcll6vdQLOMk+NH+3rP/uNq3TBnZVUoUzut4ukaCbV4yQRU2QBuOnDRf9qyeqvY68A8NHjvXUa7TT1KUvn6nJPfiVVtVEKhNc4uwn6BKX7NCMVtSQK8CkR+lj7BoFf3b1ChRPfdh41+Wn/7+z7h9s7mOaZupiVKITvDAHSS0uNboJLQ+sL+JBw9MkN9f4tiEB/x6IBHE+/WUy3F+NtumlSOny8NEzKbArSfeei4bXSB3U19ZoT7ErRtVkXLFC7puXjW5HBDPJnXKK9KTfYffE17yfTs9aVtF0NfUJDUH1k9PtmmFEfSTz896kki5MZZb74skhiZrwifoz7MxouKiA0p9/Ir1u2TF+p2OWsocZ8KUzvkm1XA9T6cgwyuIN+lV1psJK1Oz1c9wKABBdurHN+UwILtPhYdNn5Ucjs1c28YXGbidLOwmJoJXgO0QQZQM7AbwW+XU55LM6z50imrX0TKCBOGvvrExEETarz/CVAr12CZtF27z88uBoKGuMNFrWPn992STORP7KC4ANzMl3nIKpK3XpCrtJDsadu5egUI8K09UU5t1HRmKGT9MQBn7/LTPCNx5fvw+aON6bflmhQro3KKWLQu4qSwf77Alq7cprglQJiRxkL8iWQZLe948d3muN7+/R7vjeB/DzwJp5oX/JCfbRAvciyk+dkw3Lhuqn8DZSQA/UqmtSlRZYeC0cQDT9pLKhCuiVotBoqvneg4kUwjwNYmj3f3y3a34ZC8plPc+qV+jtPQfPVMpAunkMZrjJB9INAQxig4EnCSC6rUZKrtXvWjL38CYfMNOnPxUCuTNpa6NkXz78q/fSNo0N9uyvJsofcTeBwR7XAu0pNe7jHP5brTrM0H1piNVmTH9rYoEEK16nYTw+u627D7GEWHAXmzOwnUyom/Koolpm6mJUggtLsWqd5JdKycrVCn7+Gb1Ksi3P/xN1m7a63tPHWQdJY5NeCBqDySC+Ag86mezbceeffbr7xScDmZ5SETcjI/XuJdeT3YI5EBonqKbCkQ/iBGYa6KVb7//UZ5uXcdxDF1N37JkosCarW3SrKXy6Zmv1Mfay+zYyr3OCfv3A0dOSbs+4+WFZzrL5p0H1cYJplptMxaslquvSqVgb5fagE6ePvu1FHzoXtdLu23Ufzj/k9KPdZIVNOlV1hv9WLivniyIjFGTFzgG8WE4DPTYGt7GBrdIgdzJCHWyZb7dE+ZsAkeMeh0EVTKwu77fKifnspkZOu5llZwiGAWyCSQQpA6s5LG/UX4jO/cddb1telcfL5rP8RiTSqFJ24XbpP1wILDOCldtL52a11IEcKB1Fr80RAaOmSUP3p9T/buXhSXe8lPp49pOvA8mc4+aBMrLR9a/a+Zukn8gO6Iwv+So+loEh4eOfSj33Z1NvUuckiTWuZnK8lmfNxV3mMn9olp41iTL7SzDbbeq9+PpL76WIgXz2B4DdLzn8GkqaYFBkmY1IOpuwWxQLhv4M0jOz39xgLTpNU61qljJwXQyw0siTq8VzU6v58z467bsc4R36+MOHj2lZN347nPPr780RMkH8r9rtRyoep/d5D7tnAkK4NPPz0r3tk+o9wXKFk68FdbzeYeTkGa/UbZ4gcDLPsgah0Bx9mtrVBJBG6gH0BBeCCNawih+gFSg5TJn1kzSuG45lWzyMr3GSQDYGcHyzz//23HPYNJm6jU3t7/z+2rc6VkBWVK2RAGZMf9N9W5A3YPWMj/fAZPrJ85NeCAKDySC+Ai8GGSzbb0cgXnFRn1kwtAOnsR2HDty8oJks4XJ99F89ynYshODKCdQ+YH12ckICN36ZjVhy5FNs5N9DMhO05s9cWhHVy+awGbtBgYOSR/j9df9RX1U7YwXMgzOTkY7QTwqTwRTsRqjZLr3HXpfXluxSWW3gXRq5ly7+Zls1N0eRJBe5dhxCADk11+TsXnbXYt7pQ8wli+Bf8fcWOWT+hk3z/a8Tuy1vaqMfuCIYV8FpkoGpnBh4NWdB7wgn33xVyUthmLF5198rRATJAn5N7RvrQZ5HQzpGMgOkib6mVGxo7dWa9WH9YvbeSbSkYxrwoEAcVGFhr1E981qpZAd+47K8rU7PCuFsfcVhHjLLZCGi+Ce7FnU8FTRYnkf+Peo586YfkmgTNcBDOGQYaGBDXlWbBBNn7VTcBU0oLR7RnbzvypVKvn3hQvqG3nrLTemOMRElk+vU8jJDhw5qVjH395/XJCbLFrwASmYN5fkzX23o5SXE9cG4+InWkBeWbxeJXxijUQucoIwundoViOJ0C/IMwzKZUNbSuUmfYWEK/uRjz77UqpYVBa27z2i5OWQO3Uzvn8NOwyXHFkzSunHfiPbXbp6u1x99VUKYo+BpHCqNPM8z3z5teS8M7NnAGs3F5jleWasO36LIApIivId27TjoEqcsP+INY4/eOwD1SrBO5a9FegPnoFXq4zJGtc8HawLrkPLCsEoPB86WRnk2Qc5Vn97G9Uua3sa6Az2yG5qOkGuF3ssrTIaqUei54VZS+WeHHfI6P5tXFt3SOpUeLJXsuF4xqBTBnRp5Ii0MJlr4tyEB6L2QCKID+BRE5iy02X6jZwpt9+WxvPDFmCath/0ZWt2uA7BhwbCGjvTFdK+HRsmEUHxbzVbDFD9s03rVXAcOyxslg0PGq+wJWNsSjbvPCT03RGYFMp/n4JEupECsmH+9rvzQlU01vj4B+3N9PMMgFDT30qPJZUOAiTmrAityhSW+tUfVwkTt2A2Hht15u63V5lj+ejCK6CNLD9oCnqt78iUPhmrt9Uv+oP+ZK0y0rN9/SQ5IJIuSCG6JU60pvLqeaNU5SSIRQVHpMVjxvzVcuzkx4pw6p4cWaRwgdzqedpt2pijqZKBCVxY91qmvuFaeWl0d0U4xWYQgrr3P/xcbXTdtMd11Su23QVYPpV4u982vYRA9mOTVXbPiwRg6huuTxGomLZdmHAgaOknnZTUQTxKHbwXnHqj9f2ZqhHY+clvIG06d6dr06s96dkurj85AgU7CTj9G9CKIE6DEADxHtlz4IR8de67ZO0ynENwix64nQUNKO3eSXbjksx1S/CZyPLZXY/f63unPlMB4obt+wVkExVMP2i22PGYG//HdzLWNIHiu2/Nck3wOz2rsFw2VF0hpIP/hMJDrBXMd68napDgqmpTb4WEORN6e6Iavv3+b7L/3ZOy9+AJ9W6kn93LrH3tfIfwJW03r00Z5KqdTtKlUKV2angQCLx78+a+y1MJR8/HZI3rFoRYmP+E6YsVuZ9bC4Npqyff7Z7Dpjq+P/Af33873ibTPbUmxuO+scJVO6giycEjp9QeMVFN91rtib//3j2QCOIDPEE3mPLEYR1DwaUIrNDm9QOHv5xyGLwsMQ2nZyNLfxtzJ+vpZGFhswS/fBiABgPpWrZup9oU1K5cQknEEURciQY5FdUmKiTa+Kigd0vvpR8z3aib9Cozv74jpsvKDW87TrV3x4bqfuxMB1dsyG9KfYPwu+C+/QTxzLtp55Fq0xnLQnxPziy+oH2xcwoCR6R60rzbaLXmShd7WM2bhMrStTtUAP/GzGG+egz9POOojsHfkAcCUdXyWUHG1iib2FaZmQtWq80vsNFYI1FFK84zvVu4sokDzew3aqbMndBHBWhWM2m70OOE5UBgneUt01IIAkAxEcQjs8lmc92CMZ6bbifkBfPyQtk4PRs/bMqcazJ3ExIorzYb9K1Hv/haXKptYQNK7WvTBF9UsnwELECd+V3RI89/EyQS8PkJLvX94Ovvzv/ompzTjOMabRLkncCxplw2Qa8X1fEgfGDEpwgAsoY9EwWKYgXzSHUC6/z3u15KB+L09WuCT3zRru8EVTBwIxBmT7Rs3Q5Zv2WferbA5/k/gvlr/3KN63VN1zi/7eI1O6s2KuDh2khIwMXg1sJg2upp8uxMWv+4LnwPTbuOUrJ9fG9IPu9eNUU2bH9HVqzb5dl6wRg8c9rLIO29844MUij//aHQGyZ+SJyb8EBYDySC+LCes5wHTPDqa652hUi7ZTutUwCSBLt0rJnKYZCxX7tln+zce1TO//iTgpk1qF5KZaf9WFiolwlsFhjW0jU75NWlG9WmokWDStKodrlkffl+5n45juHDAFxZV+EJeutUKZkimLGbm8lGnfFMepWpXLAZiA1m/KoJ6M3+vjXTZNBzsxWkb/KzXVRV2E8lHmIyO6M320nWTh8fdo3q8yFBJGE06OkmyapbPEuk1oBU2rVBmMLhuX5YSSV+17wbqOqF+W3z+yxRu6ti6eb3hQwTRETooBd4MJcjazayjQT6bHQrli4k2e7IIDfecJ188915OXbyE8UKTZVxSI+npOLjj7giT0x+nyS86IEFUUBAxDPy6v/keqBl0AIGOsl95MiWUYoUyOO52XaaK0nO2q0GyaJpgx1RKpxrEkjra4eduwkJlP5dx/ZV6zlRPXVryTJBL/xeA0p8w7t85vzVKpAiUGCNss5Yd/jLyjFjt7YIBvmOUGnVdubsN2rc7FkyKOi0lkm0nq9RTUi6NahZ2lf/v931eW5ffHXuIqw8U3rfEGPT/UbYd4IVoQMajJ7wOzLe5ns4zVge2zpIrz8qBn4J8VizW3cflnlvbFSJBBBobt8v0zVOgaNWy0Eq2cx7WxstDBAYggrEmjeopJR5vCxQq+fPvygZXzvz4lVxmoff1j++X49WaS9zJ/aVeUvWy99++rtqH2PfRZHJC+Eybtoimb1wreJOSHPLjYrklCT+rHG9fK91L18m/p7wQDw9kAjiI/AuVVdIotwgS3bZTi2JBIxd9z/2aPdEEsu6dWqmchiT5yxTvY989Beu2Kw2EMDSYxlgndwRFuplCpvVGyFgmPMWb1AZbmDp9H7lf8Adlh7Bo00aIkjfq/W6VIGA8sEQvvXtw77aADg/7Ebd9J613Iwd/4FfHetCldspHWw2cnMWrRM+lKw3SGPiwUOg7znsGuV8nbzQTLWxfty866AgXUiQFmsmcHjGMpFU4nzT3za/KTSN6f/VxvMaM7Cta0BKtW/h8k0C+RcBO8ZmCPZtqlYkrpzg16YQTq6FykS/kTPUdTXLPBswNnR2wSbcAXZ9z3a/GU2S6FVBs54LIuLeu7Iq8jInMwmk9Zi0fNCOo1tOaHOBz8ApwDZ9J3C+Do6eH9bJdrhPTp+VbbvfdazEO/V3E/z7RS+EDSiZMAlhemUJrAiYQIa0bFhFqv23v9rNRyayfNagkm8W3648ue6Uv3hUZpmPTtyXLJxXrSvNIbD38HvyzbfnpXLpR+XB3DkdCdYgve3z7HRVic5wW5oUsPtcObLIsF7NHW+d3/fTgyerBKM2P7KyUbyTTNYsvw/2C9z/9+d/Us+YPdaD9+X0TPBpxnIQSJpZn0Cxfd+J6l3oJL1qnS8JRdYZFXm+48C6Wzao7EhAqJ818nxwk1jXBsnBf/7rX66IJ/0NIfD1so7Na3kmjvQY/Xy2errtbfFXUBJBrh+k9Q9CvuemLlTTfvn5vqoFs0qTvtKoVlmVwHIyUJNlnuguQ3s8JbUqF1e/L96loPHKFS/gyLfk5ePE3xMeuJQeSATxAbzNBmzy7KVJZxCgwRC/fus+leH0ksCyu5RfSSQTOQydlZ83qZ/aXOseUPqlMK8PkwnUKwrYrNVvBAwLl2+WpWu2+978BXjESYcCo1785jZ579SnKnHAZhMJu7y5c6rNQP0apQJX7YAuA2+76qqrfHEgsInD9/SiQq4TSxbndl8EhlR+YCimWgnErm7VkopQys1Y08jjsGm0kiUS5LL+2ci4mR3slipUl4GTpHr5YrZBPPf4+Zm/uo6L72Mh2dYTTNYo4+iewmNb5thWjVkDrXo8F0giyy+U30RSyfS3rX1IQhGkBBW33LnuDAzNZ61CaBgkSDbReWct5i/XSv2O6NunP58NGDJBtCa1bZJSjpA2gG9/+FE6Na+ZxNVgt+gI+IBlDu7WzBdyRo8BiRTVHC3vF+a943WOJr3s36WxVC9fVBFXkZQlgKdn1+03gs8g/LSzPLmyK44QiErtvmOsD67j1EZDsLH30AlfbWH6+txLndaDlea2G28Dx5sElLzTGrQbJgRjEL0hkUrPLG1PkMpCtuZkXoSZXklJEpkffHxG3gFCv/+YSuRiBfPeq2DWVE1Jetkli3Rfe6xSiF9UFNchUDl84kM5980PKSTmqMjiDztj3lR2kUjr3rae1Gk1WLXPjHnxNcWLUq/a444+i+qd5PVb8PN33q0DRs8SJA5Zu7Qe3cK6AAAgAElEQVSCeRmEuPxOaOG6I0M69ZvA0BAHqeRkfHNJhhK4E4yXf/wRebxIPltkZewYep2xFiAK1ghJP21oXvcT9u9BWj3truFnb2va+qevyzubb4BT0thufkDx4V5AJ96aOKEoRxJm9vjeYV2XOC/hgUvmgUQQH8DVfJzYNFntl39fkK1vH5KtbzzvSHzldgk/kkicbyKH8dGnX0qD9sOSAhAdxO9856ivviFTqFcAF/s+lDnBwI3ObpTGR+Xl19cLCY7yJQtKxVKFFNsuvf8KKvz+J7J07XZBlm9oj2bJdHCjnAfX6T5sSpKGNWP7rYKwWW3SeaSc+vi0kqEj+N+6+11VaV0x96KkipvRD//i3GXyyy8XZHD3pmqDTSX6tltv8ZR5Y52yrmI35W5JADalOqFEsgSzVhVVta5BJVdJQNM1yrMtUauLrF/wnKrqxhpJI+a5ePpQW9eZQPlNJJVMf9vcDO81KldYqaL5Q73Holr7fjZ+XItqG9WW/etekuuu/Y0lGhgliSsglbFGUETlnmQDQT73al1nJAFWv7VbwesJbnp1aGAbXLlJf1mvCamVdW76byTlCBQ27jigqsKgB9o2qZ6CB8LOp8A9afuAsIyNK5Uk7hWOAtBcA59u4vgoeM4auRB7EGzbBHVIWyJ5eqmMxMqR9z52hb2aBJTch+Z92LBwrGTOkC7p1kAI8czd+oWjluUjqU0i+iJT/THFYu7UhsB7r9cz05RvrEE+QSLBuRfbuckz1IRh+vel9wxrN+9VvcduJIhRvJNM5k6yinfAhq3vKAg8iWt+z3zLaQnwYySeeTa6jaBa+aKeCUp+X7y/UCAAJVMo333yUO675IF7s8udWTO6tjToIB5+BObMb5pET5AgPirOpDBkgHY+9bO3NWn9s16Tvc2xk5/Kr/+5qIKDZc6YTqH/nIz3eLVm/aR/50bJUBLA6z8785Wq0Ccs4YEr3QOJID6CJ0TVhpdFy4aVHUczkURiUBM5DA0b0gzUfJBfndRfxkx5TYDqUaGNl7lVf6zXbFijtIL9aYsK+sqGGRg4fb5sevm4EsQiz2PHrk11ZtWGXfJs75YqE29nbCrXbd0ng5+bo+DV2bNmjNR9mkcAHdomdcqryhF6wcPGvyx9Oz0pNSs+5no9NlF8nF55oV8Suz8nNOr4rGLzfbZPS8fzWWePVGornVvUlgsXLsjc19cpophJs5cq/7mdy6Am2sa6grN92QtJ1Qt+N406PCPNnqjgWjHTN0SQgy4vz51nXKzgA54QSn0uMowP3JsjBTKFCku1p/pLjfLFHJnLTaD8JpJKUfy2uW/QGhicAM8P7yT9R81U1aB4VpXtFqGfjR/n6SBj75qpycgGYZj/7PRXMrJfa9s1zm/rjdXbZeZrq1WCDIRH2jQ3qvvXfd3oKrslB92kv6wX1frUsROBb4AqECz4vEPolaXqhd+9dJlBnDRoP1wROUH+OXbqItm8eLySBHxj9bbA8njMzY98pCmLtN3DoLo/ZOxcuSrVn11h3SYBJdfVFbedKyZJmpt/k5GjPebIex/ZJny8Xuh+1QT0OPzGQXcRrPE+14gV3nkko53IYcP2lpu2q0BI27D9cNm7eqpCJukgnt8OSRE39F4U7yRaGFhz+Mmq5IIf3TgvOK9k7S7qt8zvqmHN0pF/n73WBs8U8kH4YHbtO6qQfHAd8e12MiuCDcQLxSJI9K655mrhe+6F+DDhTDIlAzTd23r50+vv/UbOlBXrd6r9IxJx2mpVesz1+6XXCsdbEYqo0lj/DU4fNwSG1/wSf094IJ4eSATxEXiXjSfZcTsmZz28iSSS6RR5yZap111BT4Fg8kGmAkSvFtAtLwiSyQbOrfpjvS8+WGyotZlCXwninp/5hoKAErjnyplF3efX575XQR7GZr1OlRLJegX54AIhdJITs86ZxACbQr+s836fo4aGxzKGT5q1VD4985UnWUvsJkpf940121Wl0Q0mprkXjv5Xq52q3wvDO8lHn531paFtom2s4aOxcmdzFq1VmyK3ihn3SBWxy6BJSf3R/BtVzjkT+/hilefeT350WvVQWmWbgMXv3n9coRrsnrUplN9EUsn0t60DnB3LJykOA0gN+e8N295R90xgGQ+LYuPXrOsoKVeiQBJhFO+a2i0HScfmNaXi44U8p807GS1rUBhZM6dX7wnrJtBzgBAHaD3ntxaNU0kSHRzNX/qW6j0e1d8++WC9FL9J+j0J3IHPUzGCF4HfvVeSjXHCyEeaskg7MfqDhOB9BIzYyUwCSsYkwKnUuLdK3tapXEJt2A8f+1AmzFisvgFu0HCnOflVE+B8rtV7xEvJUFW1KhWXwd2aesq/heW7MO1T1sncVS+PUEowrNOyjz0sb761W5G7aaI0O/+YvpMYs32fCXL23HdKEYReZQLZUZPnqyo3/e0j+ray5YAA6cD3QjPUg6qiZQG0A/sdr0q8yV7Haa1Qzacd0i0QjG1DY810GviC4ktxakOzXs+EM8mUDDCqva1VCnjLrkPy5V+/kSdrlVVJPjsZRe5fSxgvn/OM2rcFMdbK2i17PU+h0AUSM2EJD1yJHkgE8QGeCpsBYMVWgw1z9mtrpFbFx1SPtpuFlUTSY5oQ7JBd5EUIGy5QvpzZMiWrfLvN224Dd/rsOZm1YLUiEoFpN2ozgb7CVt2293gFdW/TqGqKijov7137jynCNeCu08f0iItmfFifaPinHbkc/XkkXtyMYAw2ddoBID/SNmPBarUp0NXVm29KnaKqoaVqdBUReGCdqiXly6++UfrOpkGdm7Yxc8tfvrXqW6RyweaNKgHBGskSt2vrXmF0YRvXKac2nYtfGiIDx8ySB+/P6UsvljHYpO45cFyx3LLpgpTHS7PeFMofdp3o80x+28CJy9XvkdQXCFqDZB9ImLCVXT/3E9XGz4TszM88nY4J24JAb3ejjs+kaG0CDg+xlVeiivkA9R0zZaGCz48d1E6xb/cYNlUhdEg0uZmJfKTduH5ZpO3Y6dkY33/PnZ5IGZOAUs+ZIHDw2DmKzFVb+2Y1FBpCE8bZ3Z+pmgCBSel63eTxwnlVskAlEI5/KEPHvyxPPVFRmtev6Pi44tFb7rddhUm9OHe5Sm7BMUFQDbKpbPGHFa+Ol5m8kwikC1Roo4oimlyOeX917jupW6WkTJu3Sto2ruqZfMF/+PqdQ++rFi8SD7D1x3OvowkxY69xVapU8u8LF4Q9nB1viB2XDMk2yDKB43v18ptwJjFXEzJAzjfZ21Jo0So+ds+me9snHH8nmog3tq/da30m/p7wwP+KBxJBfIAnaVcxA4pZvNBDql8ZYiAvCwv3NSXYCUt05nY/wHDRt3UjBrI7n94ter2odvOCdrKw0FeqWhBMVSrlXo0jmH/m+XnSuHa5FIRQfJRadh+jdFftjCr8nIXrZERfZ2i611pw+ruGxvXt2DBJPoh/q9ligJL0gsjLzdzWivW8lXNHpLhvYJg8V5IbaOrSK337bWkUF0C3NvV892E6wUe9fAJaYNCY2arKwgb/xKlPFTRy7sQ+qkfQyYCqVmjYS7Qusq5wohW8fO0OXzDjgWNmK8JE+vmp4NBXyeYfQiO33jo9p8sVUAKrRt6NDS/99dv3vqsSEG7VMqsfCdxBH1Qq86gigSpaII8KFEmcuPVYez1Lr7+bbPwY24TszGtuXn8P24KgEz7A4YF/sk57tqsvMxe8qQje6NOPl5nKR9rNKwiLtMl9mQSU1uvyLgEVkC7NzZ7JA84zVRPQeu3A0lPfcF3SVECa7T5wwhW9F4/ecr/tKlaf8U6hKgq5aurrf7sHt+dpUnDQSWxkSvkO6HaKmeMuSoAuX7dT9btPGfV0oCXFfsKpous1kJ+9jtceLdddWWTOwrWyet4o28uB0vFCRbrtGRp3ush3U7ZEAUXMxzcLSH+povl8JbH12GHIAPW5QdeKTtiArKCl5M9//pMaavWmvXLmy6+lTeOqiovH2moZ6wMIZ5FZ9GoztPOdCT+J15pJ/D3hgUvhgUQQfym8/N9rmMB9TQh2TInOnFxEsJU69fXSq339FIdQ7eeDidwHRjV/885DKuNKNQRoGxVhP1l9zg8CffXqm7NO1qkyrD/IFR5/xPb2yXz//PO/HSWVOCloP771QmxcMK0jrINiKjleslfcExlqL7s9XZoUcE4qCUPHzU12KhBjqtlahsVrXBP4KGMD8QYOSTU8X+67JG+euz3bG+hprtSot2j0gg7i6ZFm40nFzc3o9y9Zu6tK2liDX/gu8LcXTPlyBZS6WqcRMU06j5D3P/xcJT6slSyne3faeLJpeuX5vnHvJw2r825Kdua1ht3+btqCALKkVLH8ii2bdZrm5tSKeOup+hV9wfnDyqWZyEeaskizWW/VY6ytW4FH01qwYt1Oz8AsaJCgL7hm0x5FKNe9TT31XeJ7euT4R3Lg6Cl5NP99kjuXM5zfZK1on2tYuh4LfpOf/v5PGTOgjePwJr3lUbSrkMAFZUiLmTaKFbQguPWlewWzXv3dtE9Ub9ZfDr81U/0e3n7nmLTqOVY0/wXfl9a9xjoqhcQjMHPb62jf4HMUPuwMctk//Unk629/cJQENJm3CWcS8zUlAwy7VvR3O6wCAwm5QpXa2frcjxqBCT+JyXshcW7CA1F5IBHEB/RkWAbQKOC+dlP1Q7BjQnRmvWZsdZWM7Q3XXycP3pcjxdSoDvYcNlX1JJM9X7Zup4Jy165cQupWKaH67OJtTkzp+rp8NNkw2CUS9EYEWLedsZEm0H51cv8Ufw7bj68HciMlsl6MuaW1kAHG259+xjeBj/oZ3+kYgoy8ZVrKnAm95ZF896ng6KbU16s+43ULxnj2QuqewkMbZyqYsjYY6TfvPKj6QJ3scgeU9dsNUwRUSFlBsEe/Nb8/+vvdggTuh41nrLwf0oIZ06f1Vak0eWZBdd6t1zIlOzOZd5QtCCRhvJJy1rmayKWZyEeaskjz+4QUzc6oHl5/3bVy7P2PHSHSYYMEvcbhhKlStrB0a11XTQEC09GTF6hKLwFQbPLO+i4OK8vHGLwb2vWZoFAjJIQzpr9VJQ42btsvurLstBZNestN21U0dwPtRLDggzKkqgvhmm5Zcpv3J5+dTfbnM1+dU/sB1D0gV3UzqvgFK7ZJImaldWTb7sNJFWwq8S8vXi/LZg23HSYegRnIJKe9jte7BD6X787/6CmjGI95e82Nv5uSAZqsFd4LC5a+pdaYVeaN7/EPf/vJk+iTPR7klHbG3siNhyAKfhI//k0ck/BAPD2QCOIDeNeEATQKuK/dVP0Q7JgQnelrhqmuEuguXbNDXl26URHpIRPWqHa5pOpyANeHOlRvZCA9sTOCngXLNtkG4myu2XQ4SekQHNISACGf1aLox3cjJaJqpYPMHu2eSEYGGMpJMSeZymeFgY/Shw5Cw83Kl3zEtX+UcyGXvOG6axVHAzJhObJllCIF8vgKknRFG3RI/RpsWlPLhx+fkR7Dp0rZxwpIpxa1HKd3OQNKECHlG/SUPaunyusrtyiWc9okCOJXrt/lCteNYr2EHSOMzrv1WqZkZ2Hnrc8L24Jg1xuux4TY003nneNM5NI436TaZ+ozu/P9MOObBAlcU6NstHykTjRCDtjqycpCXz8BhB2iLApZPqqFcxetU9JsBCY5s2aSxnXLeQYozN2kt9ykXYUEfa0Wg2T3qheTQbyRAiXAmv5cj8DLoXXPsfJ40XzSoEZpz3N5JrzLgIJv3nVIhvZ8SpESar6Xu7JnliHdm6UY53IHZqAW3ty4W/CftjNnv1FIxOxZMqhgFeh3rIWddxQqPqZkgCZrxfR96LmQXA6Igp/E5PqJcxMeiMIDiSA+gBdNGEBN4b4mBDsmRGe4x7S6ygeMSsq8xRsUNK9ymcKKMCz/A3cnk48J8Ch8HeqnN9xJo9fpArCh/vOfPztWE6Lox3e6dhBSIl8OsjnIVD4rDHyUTcCREx+r2QwZN1dB34H6Y5ABPTNxnowd3M4X4zjnhIXcska7DJyUzCusj5fGdLdlQtYHXu6AEikoNL4PHvtAkGpU0mVj56jkhZusEfOPCuYcdL2F0Xm3XiMKsrOgc9bHm7QgOLG0MzbEqLpS7DQ3U7m0oNU+NtmsEfq5acs6+v4nSjaLb2H2bJkUqsqL8dt6L2GY8U2CBK4Neg6EiubL0N/xtfNHq0od3Bf8Xgjyr0RjzRw4ckq+OHtO7rwjgxTKf3/cUTKa4BSEAkgJbYtXbVV8GX4IGGN9iWwl6Cgvkjb13v/3BVVtP/r+xyrZARcMCCESEyhn8F62q+hfzsBMF3lgM7/3rqxJhIl7D78n33x7XpHMPpg7py2cPuy8TVV8Yp9RGDJAk7Vi+j50+35Z7w3VDwhArXY5+UmuxPdMYk6/Tw8kgvgAz82EAdQU7mtCsOMnmMUNdkRnahMETLf5AAXZDUrOE+te+hIXLt+sCMT8bFoDPJ4Uh+r7RnfcziBNQyvYDhLP8UjDsGGhHUEbSYhvvz+vCKgeffh+tZmwWhT9+E73HIaUyMR/Yc41gY/+9dx3UqpuN9Hydvr6wCmRmYFYz81MILd6XH7jx09+qvrxs2S8TUlfWXWK7a5vGlDybkA6CWTIyQ8/l7NffydUmnLlyKKu/4BNu4p1HgRGc19fJ1dflUoRTQIRfvn19YrozquyawpzDrNGOCeszrv1elGRnQW9h6hbEPBF7VaDZNG0wZLp9rSu0zGRSwtT7QMlM2n2UhXg8v+nvbJSwYIzZ7xNEShC1Lps9jOOOufWmwnLjG8SJHB9LUO1eckElRxctnaHjJw0X6FXYKanQj547FzZvHi8q+/DSGCZVhpRUJm9cK36TUPWir9pUZs1rpcrCZppco7Wt1otBykuEiTatG3fe0RuvvGGJN6Q5g0qqQSiH2OdX311KkWYGca8EuiMeTkDMy2RGqa/O+y8TVR8/DwDP2SAUa+VIO9Dt++X9f4gObYjDTTlJ/Hjw8QxCQ/E0wOJID6Ad/kgmzCAmsB9naYJ3O702a+l4EPOzN0mRGdcN0x11cutfLQ+/+JryZvnLq9DQ/+dDxABmVMQRFb/i6/OOeqLUr35+z/+KfdkhzX1z2oen5w+Kz/93z+V7EvVckWUZq2dmfTjR0FKFNppNieyed2084DqHQXS6GVh4aOsZeSY6HW8J2eWpMsA4STh4taXbgq5td4T6+KzM19J5gzpfLMFhw0oaVMZNHa2gs3WqFBMEcmxSWYjc/jEh6p3ll7a3h0aurahXC5mfK+14PZ3U513k2tfaeciJUX1rnWjqp5TCyuXFqbaR5Lz09NnZWS/1opnIlYWDYg0QZwX8aMJM75pkMD7tOKTvaRQ3vukfo3S0n/0TPXO13wRz898Q/XjO71fTCSwTCqNug1uaI+nkkhF4WNo3m20lCtewFXZxTQ5h8+BtHtZx+a14tIeFyaBrud6uQIzvo+9npkm4wa3T9bCxb6PZwmU3ut9GIbwMqyKj3UuKze8LS/OXSa//HJBBndvqhJ1yCnDDO+VRI7HWgnyPrTzqZ82ndjzgvKTeP02En9PeOBSeCARxAfwsikDaIBLJR1KBf4v11yd7FSC8n2H3pfXVmxSm/x4V7TDVFeBLEJeFzt3Ox/Q55n6hutt9VPD+MzunKBVlDNnz6le44MbZiS7B4jOPv7srHgx7Jr045uSErGBe33lVlfXpbv1ZqUjr41nACO5VUKIyjhkVFR5WfvDezWXWpWKR/VIbMeh1/hfP/8sT1QrJdmy3C6nPjojz89covoon/4vKZXdiWEhtwREa7fsVUoJwG7HTlukmPG1sfFCW9iNjTmsQ2DMpn0AmUUNF40dC2TA5NnLZNXGtxVSBnWCWDNhxncjUYSwkh7WeFrQ5MOWXYfk7hx3pIBG2s2RY0kGkYyJ0uxI3vT4frgb7OYCYRiVVtahXwsqlxam2kcvN3KaBOnlG/aU4T2bK+JIbZATrli/U2aP7+06bRNm/CiCBM1Vgs+oar/+0hC5M0sG9V6r1XKgNK9fSbV4xVoUElixY/qtNOrWiVgN7FcWr5etuw97+tzugfipZvtdf/E8ziSBbp1X0MCMgBhCUDdDfpW1E2us0+dnLZX61R9PRqgG+lD99go96NtlQeetBw6i4qPP0Uiyzi1qy4ULF9T3fveqKQp5A5+EV4LO900FODDo+zBMm87l/vYFcEfi0IQHHD2QCOIv0eIIC2+j54lMLhUQpK8grIIETEG3yhRWHwwg3W6Q37DXtromaHWVeW/aeVCe6d3ClSEUKGO/UTNl7oQ+ntDfMI8qbBVFb3gPrJ+eLKsOeRj9vF59fX5aGNz68U1IifS1gVKnSpVKuY0AFeIsWIbpGQe2a20jYGM4ec4yValCFhC5p/Vb31FB0JO1yqh+dDa/Xmb6Yfzmu/MChJTKAAacnKoAsE03Fu+wkFt+S9v3vKuqcCQQ6LtEQil9ujSy/8hJGTJ2jgqskFZyM36Pqza8rSDGJ059pn6rJNdKF3PW/ca/d2fP7EupgSA+b+67UvQfmzLju5EoDurW1Daw8VoDfv8eJvkAM/WIF15VpFYVShVK6ju1XpPfzkvzVipyww0Lx0YexMfKrXFt/o3K5fPDOzmiczjOlDjSFJ4dtEpJL3a7PuPlhWc6K5UGNvzItGmbsWC1auPwanXRzPhUwP/6zXeqVYle+l/+fUHJW3m1EfhdU27HsS7Qns55Z2bfSTlTCSyn+fipNPKsqzXrJ/07N5IiBfMkDQW8HqQQFXo3M6lmR+HvsGOYJtAJplm3cB3w/Ojpr1u1pG0CNHaOJt9tfS7fSarxOmiHABfpObfEv+l3M6yvOU9zROg2NpJ1LwzvJB99dlaWr93hSWLo9k7LcNut6pt6+ouvk61h63z5hq3dsk927j0q53/8Sf0+G1QvpWQn/ZhJm87A0bOSXYJiD2158f72+bmvxDEJD/jxQCKI9+MlyzGxwZX+Ey8qqzRV7LBh4W3AsJDDIcjSRnCABibVVD8W9tp+xnY6hsQB/ZNspKkyVixdSLLdkUFuvOE6IVA7dvIT1Zt47P1PZEiPp6Ti44949h4HnU8UVRTgiyRIgm4yTfvxw64zfKSvvWPZC6q6jkF+BiMywTiJIGRdrEE8H+Jtew7Lq29slL0H31PnICXUpnHVQM8lqqCQTf/PP//ii1meuYaF3KLuQLWBj/Yjldom23wx7pS5y+Xw8Q9dNzJcu0z9HqqHnhYLmO337D8ub6zZLlNGdpUShfMGXbq+j48XM368SRRNkg+6hxkn1ax4sQXhxtTXq+eI9jd+L1owjwx8ukkg0jXfTnc4kCAezpAuLWs7DmVKHOl0PsF1UESW32ofEmsTZyxxvKeGNct4IpM4OUzSxvSZ6PN5n8Aavnv/MZXEzJ4tozxZq6zne91UAstp/n4qjVr6izGsCBxab6z/NvnZLraJ8qiq2VE9A7/jmCTQqaQ36TxSTn18Woo98oCgz75197sCqmzF3GeTkfQ5zYfWLG1Nu46S1o2qSNECF5MoFFQWrdhiy6Ojv7sj+raSfiNnKEUT2mMgA/QK4u2+m3CjHDr2QdwRcDoBrtEpvPvrVC0pX371jew5cEIlJt3M7Z0GuiVblgxq/wqSzM4oHvAdBvVG0YXiBvetiSfdrm3SpuM0bry/fX5/B4njEh7w44FEEO/HS/89xi1L63cjE3s5v/A2XpRsQnQVniC+TpWSoarXbKC9yLqs8zSp/rBxW7h8kwApI2DHyFRTYUWfnfuwIxwJ8FgcDzWtogyf+Ioi4cM6NKsh7ZvVEDa0kDpBlOJmJv34pussTBBvvRee2aIVm9UHlWp+49rllCyOn0q86YeRtQaZ1p4DxxW5HOzRbATs4IvWa4WF3FLZBV0x/8UB0qbXOHWf9KZrW7J6m7z9zjFXNmYN5Y/ddPR65iWVXPDaBOlrkXQCQnvu2/Ny4T//SeZK+uSRzou1eDHjx5tE0TT5wMYT6SmIAKkkfXXuO9XnnCtnFtXDCaP1pTYksbbvOXLJZf34zdRpPVi6tqrjqUUdtkpJEPztd+eVUkms0YIDwsfNTJI2UTxH0Bsoh5DcyZI5vWzZdVgFdlp2zu0aJrJ8bjrW1mved3e2FAlLGNpp9fEy2NBJYlnNtJrtdU23v5sSdeqxwyTQaY8CvfDKC/0UokwbKCvY7INCw0Gu1Kv2eNL3ngT4kje32rYy6O8uxHbsdzr1f0Fy35NNvY94p3u14MX6lN9qxUZ9ZMLQDklEgibPxelcUAB1Ww8W2gQezX+/qkTfflsatV8DYePVy+81J377/J/mFbIer6Vd503qp/aD8G6smTdK4MHBSK67mUmbjtO48f72efkr8feEB4J4IBHEB/BWLOEYp/78yy/StMtItVH32jhGAW9jDnsPnlAV061vH5ZC+e9TcF9egG5GdnvCjCWql47/BiZNYOpEzGYdK6rqDxua8z/+X1x7363zNqmiaEbjuRP7qICqRbcxArSejSAka8Dl/FrQfnzTdWYaxOv7YvO6fP0upXHMhpfWiJoVH/N728mO8/thHDhmtlIuAEJ/R6bbFCSSjdmSGUOFjW7Uxj1WbtJXsmW+XcH+PvrsS6li0fGFjblciYKu1VUqe0WqdRAr8oF5kiBYt2Wfr6CO++w5fJr6bWKxCRPg+XbEW6bM+JeLRDFeyYeo14fdeLFwejao35//SV5dulHaN6kuDWq662CzcWWjjKFyAQO4qdG+dOS9j13fS1FUKYNyGOj7Mk3amPhHvw+nje6WrC8ZgjgUILxao4LK8sV+OwtVauc5/VUvj/DVVuM50H8PMKlm+72G3XFREXWGTaBrMsDYNjjQOavf2h2YR2DY+Jflh7/9JGMHt1etO0C3r7rqKlUdjzVrEM/f0HDvMWyKQraFLfL0GzlTBdRu6B6T58W5vH5KGQ0AACAASURBVI+GjpubbJirr7pKHrw/ZxKhotc1wr7T+LY3aD9M9q2Zpi6hg/id7xyVFet2eX47dZsOpKB8v7VRofdq07lc3z4vXyb+nvBAEA8kgvgg3nI4FigQQR99mm4WNbwNMjKgWnxUvF7ybFj+eu57qVXxMbkjU3p5e/8xWfLmNpkzoXcyoiK/7ghS/fE7ZjyOoxd8wfJNKhMMeRHZ+NNffi03XHdtEtTc7roEGdWb9U+SOyNTPaBrE8UiTibejSldjxe2H9/JD37XWVRBvJ6HYtrfd0R++Nv/SfXyRV0fk8mHETh0ydpdBW1iglZt3YdOUVWqoFUUv+sJ8jj62U9/eU5VzmOtYL57pWrZIo7D4Z+GHYZLjqwZpbRFrWDp6u1y9dVXKYg9hiShlThQD0hQiLRelTKFVWLNKuPo5x7CMuMztimJop/52R1jmnwIe90ozrMjtkt7y01SvPBDUq1cUc+gnHcJKCGMxC8JYDS06QENQmyn74Uk4ZCxc5UM4zCb4EIfZ1qlNIHDX86kjb72njenJKsGEtht2n5Apox62nFZhJHlcxqM3+nKDbsU/4vffl/T9Ro26cJ1QRid/uKclCqWL8lvBGsQv4F6iU0+RUXUaZJA5zvfuNMIRdqKLrs2uBt4t+vfFy1PfshKtWwcSDCO5zcU+33S16D9oWmXEbJ4+tCk6+L/afNWSto0N4WqaIPuAWXh9v0xXSNO5/Mt55uMLKOXhX2nxSZdCOJfndRfxkx5TUCYwGXgZgTqoCPtLE+u7AohtPfQe9K2cbUUh1yub5+XLxN/T3ggiAcSQXwQbzkcS3BFdXbSs10cR7uc8DZ60EvU6iJLZw2TXDmzJs2RjWOqP7tv/Nzc46f6E4F7Qw/BB7Rkna7yQK7siniKzQF9WTCCk2l2g7dRtarYqHcSGzNERFXKFlaSdQR7Wp7IaXJR9OPHju1nnXEOMEw2i2xi/vKXa9QwoDaAxkMmRRID2La1Vztoi4XTfZt8GDXBzqGNM5PxS6AIAKmWn8RJ6MVicCIBadWm/TxHIGFGEinW9Ebx3bdmJasmeA5oOSAM9FSfbkKiGGSOsceaJB9Mrns5z9WM4zuWT1KJxeI1Owv/vWHbO7J7/3HP1gsnyTKQGzDE06bkZCZVSlM4vGnSJmhAafWBDsQ3LhqXrAceDgPe83AnOFkYWT67sd45/L4AzUbj/dvvf5RXnu+rkEbxNJOkC/MiMMt0e7qkNQkXRdve49WU4VpZOHVQMtLIKIg6Gdskge6HmI5rsA/ge+jHKJasfmuP/PvCBRVYekmuhUmcmBJe+rkPv8eAGKV1E2RRkQJ5PNsATN5pJArK1OuuilAUCAjiQZGAMJ04tKNnqyVJJfgH7AwySOQv39p+wBY5wTmX69vn91kkjkt4wMsDiSDey0OWv8eSj/ACoooHXOr5YZ2kTHF7zXCGCAtvi0JSiZds/Xa/QZb0LQFb5gXnVolwco/f6k8A90Z+qA4KkeiRP/1JHqvRScGyDx79QDZsfcf1vgnCSXxgEGfBSI+xGZ3xXA9HplV9Eyb9+CbrTF8/KDEegXKmDOk8e2rZ9NKvVqPCY46boLAfRt0fR7Wkfo1SiiDuw4/PSI/hU6XsYwUUUVC87HKyA7PZrtl8gBzdMseWbd3rnsNAT6NK2njN7X/57wTUsGB/cfac3HlHBimU/37P6h7JlnL1e4iWDaNXlw0s0Ns3Vm/zxQRNO5XVqNTdf8+dntc2qVJGAYc3SdoEDShj1x0JUBJocF5gvGsgl6Pf1y0oCyPLF3ttVFo6D3hBvb+oCPL+XLp2h8x7oZ8n10fY349p0kW3CL0+fYjkvudOlXCq1XKQ3JUtk7RpUk0pRNA/DXFb1GaSQGee9El7GdVlK/xaH2/C/8MYYRMnpoSXXvdr93cUYEjsUe2+9ZYblYLNolVbFA8QSZrGtcuq9jkCYTczfaeBbqRfPn26W4RkV85smVxRkmHuNcg5R9/7WL47/6PnXijImIljEx6IlwcSQXwAz9pt9CGcIuN3T447PEciEIRQTpN08fKDPdWNMCwKSSVgZPnLt5b5kwdI3jx3Jc2zQ7+JChYHIZKbmVR/PJ0SxwNIsBSr3kl2rZys/Ny651hpVq+CfPvD32Ttpr2uQbyuZlunBzSavmwka7zMpB/fdJ2FIcajJ5uNJpvclg0rp9DhZnMEY+zYqYtEyQ1O7OvKbXDmy3NKoo0qGOy0xQo+4Blk4FN6hbsMnJTMvbDVvjSmuxGxntfziopV3+s6dn/XyQv06OmnpvfSr4WFnkaZtPE7V46LIikZ5HrxOhYZRKS+eHej7856p8o6a1wvz+oRgXu1ckWkUplHZcDoWYr5eveB45Lm5htdq8LWewmDvDCpUl5OOHwUASW/MTu7KlUqVWEl8UggY2dBZfmsY2gEwoCujaVBjd+4EsZPXyz/+Me/PKucYdevadJFV8MPvzVTodZ0Ulpzk5CYmP7qKlk0bbDjFMMQdTKYaQKdMcJUwznPaa/D37zUH0wTJ2Gfddjz4C8YOuFlFbzz7kIatUiB3OobVOLRvL6+1/raUbzTwtyHadLl3RMfKdQBSB9tZ85+o5Cb2bNkUEk+nfgLM7/EOQkPxNsDiSA+Ag/76R3iZVO4anvp36Wxgg1RBaBnmk3ga1MGuUK7opBUYoxrr71GCj50r7pjNjWrN+1RG0irfI2dO+xelH6rPxG4N/QQzLtxp4uyMmVLFFC9UwTh+w6/L6WK5lMSavG0sP34pnMKS4xH/9uYF19TEnT0pN+d4w5VDSdTvv/dk4rcrlvrutKkXnm1sXMy0B1dBk1ScFGCG4xNwpyJfWx7wmPHIflC2wLs9Mi2AREOoqZg6j/r+ZdKbgYd+D7PTlcViAy3pUnB5AsBl12/c1joadRJG78+jyIp6fda8TpOw9LR6a5VubhKuhBUwztSrngB6d72CcdLOwXSPHcg1qB+vCwM8oIxTaqUpnB4rk/bCNwT2/e+qwIG3jEERqWL5Xe9ZdOA0iupmeuuLDJn4VpZPW+Ul+vVdxN+Dr+mVRTs1EzwAe9FN6PPHKbz7m3qqXcC73ZkFA8cPSWP5r9Pcueyb58wTboAIa/4ZG/Zu3qq4ujQ73Td6kTFtNvQKYrM087CEnUylmkCPWw13Ok5kBCp3WqQSli4Sc2aJk7CksP5XYt2x/FOAE1E7z3tEMDYIeGjbcAOqWA3RhTvtLD3YEK6rNstuVeI8XTyfO/h9+Sbb8+rdsQHc+dUSJSEJTxwpXogEcQbPJkgvUMEM+Ub9hT6XgmWyjzRXV4c0VXIaKMv79aXxxRNJZUuV0Bp4F7jU9l4VniyV7JxCD6R6hrQpZFrxcyNMMU6YMMapW2hXyb9+IzPGnlh1tIkNQH691o2rKIqeGHNLzEeQfsHn5xR0ED4FECO3J09s0JtxEoZxc5FJ6tIkDSuU071uC1+aYgMHDNLsd3GK3HiB46IggMM1UHML6t+kDGdjiU4PHziQzn3zQ8pJOaANEJ8F2sm0NOokjZB7z2KpGTQa0Z5vO4B1ZB4PTZayKh/0JvuZARhn/+X1E4fw2Y5Y/q0vipfYZEXUdy/CRweNFiZ+j1UUg6iR5KDe/YfF8jlpozsmoyfI3aupgElPken285AaAF8+frbHxw362Fl+fT1gJ6TSEaq7PjJT5TO+J1ZM0rTuuVdn7nuF4aLheQp9sqSDTJ68gKV/Of75kSyZpp0oRJZtFpHJTFWr2pJeXrwi0pZBrUWDKnbtZv3quvHmilRp9Na9SPHG69qOJw4BHpu7QOmiZOw5HBR/LYZg+/+4je3qmcLl06jWmWlViVvOL3pOy2q+etx/JIuay4aJAGtBkrt48/Oxg0lE/X9Jsb7Y3sgEcT7eP5R9A5BbNeg/XCVuabSCSx58+LxKvvppxfSxzQdDzENKIG3teox1nZ8JOpg2l2xbmeo3nqT+4rnuW6EKdbr9u7QQG6/7dYUUzHpx+ej2KDdMEW6ROBGr/rBI6fUBg7NWCTPwphfYrwwY+tzIM2r0LBXUn+3lozZse+oLF+7w7PnN+y1WeObdl6U7fr1V5EX5y5T/92hWU21ScfYsD+S7z7bS5iw6oedcxTnRQE9NUnahL0H06Rk2OtGcR5rDS3q/p0bJePGAF7/2ZmvhAp9vCws8iJe8/E7LnDVWi0Gydr5oyVr5t/IHXs985JiDYeh38lMAkq/83M6zlSWT5Pqcd8Z0qdV78asmdMrKcv2Tasng9jHzkGrdWgte3hoStfrpoKrVk9WFoj5CLZ6ta9vO32TpAsDonwDCay2meN6KjUFEtxU6WtWKGbLUxIFUWdYOV7TarjTOgA5SduMm3qESeLEhBzOdI3Hns/vcf22/TJvyXrJm/tux2D2SuZV8UO6zPPq9cw0JctpRdds2/Ou4goASp+whAeudA8kgngfTyiq3iEq8XyACdyprLLZmzxnmXphxEs+i9szCSg5n03UG6u323qKCsP1110rx97/WFUarjRz6oW0zvMv11wdOVzbpB9fV542LBybjP2XPlxgu+OHdHB1cxTEeGGfo+6dPLJptqoy6SAemR3k1do1rR52aN/nEWShN79i/U5pVLus0oH26jM3YdX3PTGHA01I9Uyhp6Zz/yOeT/90ydoXSS+trUgkQ6z/NvnZLskCVv5mJ0+nfVi+5CPSvH5FV5eaIC9MnpUpMkn3tZPEpnVAG+0V67bs80TIhA0o9XUIpifOWCIbdxxQJLPA2Ns2qe4J5TeV5QPR1LzbGJW8h1OE3mE0sTdu36/4IdySF6DnkKXVhJf6O64TIUDWB4+dIwT5bobvv/zrN5Ilc3pf7UzWsegZPnHyUymQN5dCYmG8cxgvbZqbbXlKTIk6uUZYOV7Tari+d75jB46cFJIJ+O2xQg+6tpDp88ImTkzJ4Ux+227nQrjpxBURJa9KWH/bzf33QLocr+eVGPeP54FEEO/zmUfROwR50ZgpCxV8fuygdoo8rMewqYoBtNgjD/icSfDDTAJKu6tdyRlY63xNiJzs7luxt67covRx3fpeOdekH19n5XeumKTIrrS9NG+VHHnvI9WG4WamxHjBV9hvZ5DwyVumpSCnRtWbIP6m1Nerfth1C8Yoibt4GgkMdOWR1CMxRktCoXz3yvBeLTx7/MKy6ptC+S8nqV48n8X/6tgEMWu37PW8PXotY9tP+H0sX7sz2bn8G1VVAjqQTW4WBfLCc+I2B5gik3gfNuwwXHJkzSilLfe4dPV2gTAUiD326MP3OwaaYQJKfStdB09WspokEQc/N0f1/dIHjM+pLjuZiSwfY1LV5B24benzMnfROtn1zjF5dXJ/WbZ2h5LNdJOl1a0Tm5dMUFrdnDNy0nzZs3qqSkrqthQQfXYGSejs19YIftPWpG556dH2CV+tG3Zj+mHuNiHq5Jomcrwm1XB9v9aEEa1kfLvgd4G7yCmgDfObij3nUpPDkRTKkS2TUMTwMgoLqW+4Ptn9R8WrYuLv3yvpspe/E39PeMCvBxJBvF9PWY4L2zsU4lKRnGISUOoJUL2YMGNJUo/2PTmzSIdmNTw3nZHcQMhB3HohrUPyoSaxgkHYA4HQww/eo/43G+zNOw+pPjEqKRC/AKmjv9rNTPrxeV6VGvdWfZR1KpdQ1T7QIBNmLFYbsLCIBz8EjCFdnew04Gg3XHet4h6YuWC15MiWUenNBiGECjMPqgYd+02U02fPyayxPYU1yka4+dOjVfBA4gz4adRmCuV3mo8fUj2TKn7UfkiMF94DBPEQiCE352a/V+QF78OqTft5OojkH1JwfsxPQMk4GtL+1qJxqvVLo4PmL31LBWij+rd2vJyJLJ8etOewqbLn0HsKAUBisUaFYkopJW/uu6R9sxqO1+Z9XfHJXlIo731Sv0Zp6T96pqqGjxnQRp3z/Mw3FAJuxtieKcbQ9/xEtccVLPjmm25QhK7AwjVfiZePTZi7wxJ1Mqewcrz6fsJWwzmfoLBQpXYyqFtTqV2puEr8Mp92fSeobz7tc05mgrK5HORwQM7hZHqmd4sUiCHrPZIs6jdqpsyd0CcFAbMpr4qJv5nj75V02eu3l/h7wgN+PZAI4v16yuY4v71D+lR66+nV/eWXCzK4e1OlQ7l510G57dZbXLVqDaaoTjUJKPW1YV7+67nvpVbFx+SOTOnl7f3HZMmb25IqrqZzvJTnk4Q5ffbrJKZ+67XhK2DTBdySCs2ydTtVNaV25RJSt0oJlbm+FAaME6gkiQNtbPioJHlBw2Pn9+33f1Os0K+t2CTFCz10xRK2UMHB926WPVumJJIn63FA6Ko27auqCtPH9EgGc2YT1qLHc8k2wLHXiIr3IQyU3+l+/ZDq2VXxz379nVo3w3s1l1qVil+K5fqHu0blxn1U9RjdbyDhID6QGR3dv40KmIIaVeHte454wsqDjht7/PiXXpfDxz9UBGUkKwn2Xl+1VfYdek9KFsmrerTtlCDcpJys18ib526FVIraTAJK4N2NOj6jYOyYDuIJYLbvede1PSkKNBfB+La3D6uAsGjBPMq/7x7/SCU3b7rRfa0cPHpK2vYer77hkNm9/tIQRTTK/67VcqA0r19JCNRjTXMQ7F71YrJroFFPBXb6cz1cH1EUzN1hiDqtkworE2ey9nTLgm4H02O9vnKLaoGwS5joY+xQNiqhvGC1vPx8X5XUdrLLQQ7HN2/aKytVop2ERcXShSTbHRnkxhuuU4S2x05+otAfx97/RIb0eEoqPv6IY9thWF4VE3/H+hIkHZwkmTOk8/xdmayRxLkJD1xJHkgE8RE9DbfeIS6hYV6dW9SWCxcuyNzX18nuVVNk0uyliok8nj3xprfIC71ErS6ydNYwyZUza9Jw/UfNlFR//rOt9JXpNU3OpzoZCxGjorLv0PsqkN24bb+r5ivPY+maHfLq0o0qC9+iQSVpVLucpE93S6BpRdGPz7ohW50uzc2BIJBsCvYffl+RE5GYAA7YoHppgek43a03B7qPS3UwgTgM33Z24tSniksCzXjgqLHGZrtj/+flheGdbD/gBCvT5q1yJIGKgvchLJQ/alI9kk4VG/VRJIjIeCUsWg9o8iwCJKxw1Q7qfQL5JEgdNwWG2I0+76Xvz/+k3jXtm1RXGs1uZoK8oIpfpFoH6dmuvtStWlJdZvSLryliVdq5+H1NHNZRyhYvkPL39fd/SKk6T6f4dx1c6j/oIDNKj5sGlLqyq/vxCeLxwcwFb0rXVnWkVFFniTsTWb6ofEBwcubLryXnnZl9fwN4FxWv2Vmxx4Po0sb3gLY+L16Vy83cHVYmzuT3gY906yEqJvTBY/BQtO87UcnLUaEParDOt3qySmhC2qDXC3o8vl64fJOSMiRgx0gYIe0K+qBJnXJxC4rD+psiBy1NoCJpcxw7bZFqVdEG+qRf50a+fy9BfZY4PuGBK8UDiSDex5Mw7R3iEjrjeHTzbFUBgeSOgOOjz87GlbVb355JQEkvYf12w5IqGXrMpWu2K/3YKaNSbu58uDVuhwATA9L91BMVVRBDEAscXm1MyhSW+tUfV8Ggl/Y4G276Cuct3qD6CjmXqkf+B7zPNangsGndd/g9T/8Ax7zu2pRVL5519af6K412oJu1Kxf3db+eF7wMB+D3mfPflM27DilYaLMnKtj21RMIkxxz068PM30/skaMawLljwepXr+RM+X229J4wrPD+OSPfg7vw6ZdRymyMqCmcDCQkN2w/R1ZsW6XazXdDnKb9pabpHjhh6RauaKeVWwT/oQv//qtlH2iu5o35HJ63QEnr1q2iPDePPnRGRnRt6WvR+xXysnXYC4HRRFQNus6SkoVy68CEoL4NDenVsofT9WvGPk7w/R+7c4nwakNQjyI5Z6sVVauSvVntZ+INRJ5tVoOUuupwIO/VYC37z0iN994Q1Jyr3mDSoJ8ZayZMHebBtImMnEmvw/tAwgQZ8x/U7Vj3ZEhnew9dPFbvGTGUFfYudNzHzRmtqROfb1jApnzTKD4Ua43ftNICcaz9z92vmH8zX4OFA3ICLgEQLnQapg+XRrZf+SkDBk7RwX4cEA4Gfu7t/cflw8+PiMnP/xcQLDdlT2z5MqRRSUwHrgvR5SuTYyV8EBcPJAI4n24NYreIZ0Z15UK+l3rVC0pX371jew5cMKVpdbHFF0PMQkoGZgNQf7yrWX+5AGSN89dSdfq0G+igihTzbiSDBgfcmzWqi6VMjZwYavQZKkXLt8sJC4YS+v2Ot13mH58PZYfojSOdap68bwadXpWTpz6TOpUKSE1KzwmD+XO6Zm0uJKeIRwAE2cuURwFMHY3qlNOkTs5WRRw+LCyRqZQfu4pLKmekz+AZ0OqRnCWsGg9QGXu0SrtZe7EvkqG6W8//V2RTbKxBKqOZNGlNj/8CbFs55otfesbE1Ugx//uPHCSCvL9mh8pJ79jOR1nElDajcm3ON78HKb3rM9fuGJzUgLabkwIVu0UDfgGwLPgZR2b13JEmOEnktgYaAW/bRKmgXQ8ZOL8/D6svgKtsPfgexfZ6TOll2rli4YObBnrhuuvkwddgkJTwkuv53yl/z2ov5HLBTEJMuKRSm3VO1cjJ7jXKXOXq3exU9sI+4tBY2cLbQAUOrJnzaiSW6y9wyc+VGjNCo8/Ir07NAyMwLzSfZ2Y3/+WBxJBvI/nGUXvENlpYFVUTh/Nf7/6OFIpA77UrU29uGpSmgSU2j1UnK699pqkPnI+1Ks37ZGiBfIk6z/24c5LdgjB8Jsbdydtggji61QpmYKcJciEgGZ+/sXXyZIZQc5368cPMo6fY1lbS1ZvU5B6+ihBEYAmSGuRePIzzuU4hkoIGXp69Tq3rO2ZfIkCDh9W1sgUyo9/nZAyV6VKJf++cEEF+U7VEX7frPPd+48JclLZs2VUVTrgnwmLjwdgGn9u6kI1OP2uEGFWadJXSYh6QuL/9bMcOHJKYHdGWgm4M/B2q1xd0Fn74U/44W8/SdFqHWXVyyMUt8ecRWsFbXsdtG/Y9o4iS1s9b5Svy19KKaewASU3YloZ9uWMOBzEvqNAhTYyom8rxbfw5z//SV1l9aa9Cl7fpnFVxadjleyLchrsV1ifGPwwMPnTQgc5oJteutMc/AbSUcnEWefh5/cRO++g0nymMox2fvNLeBnlc78SxqKFk988Ck52Bg8JPAXzXxwgbXqNk0qlH1XBuDb2PW+/c8y2bWTNpj0yZNxcpTCkyQtjrwHMf/LsZQJB48q5I4zezVeCPxNz+N/1QCKID/BsTXqHeCENHTc32dWA/j54f06pVbl4YLKyANOO5NARL7yqNpswox8/+YksWrFF7syaUZrWLX/F9x0R5Ow9eEJefWOjkh5zY5g3bZ0w7ce3Piygbae//FrBXjNlSBe6EkA1gZaC+Us3SqF898eV2C7KDTMbSAINkhBA6YHG+WWuDrroTWSNTKH8bkgZpLBy3ZVF5ixc6xhc8duEaRvSLDSNt+w6LH89953Sj4YLIWHx8QCVILg3vMjJrFenit+k80g59fFp1Yd+y02pZevud9XzWjH32WT9y3azNuVPaNXjOSGYB7o/ff6bahOrkVR9R0yXf/zrZ5k4tGOKS0ch5UQiisCUZJS1lYn3XKpUKSHh1kmYBJSmleH4rB7vUXn/VWrUW45vTb5vQJ/748/OxvU9rmVOdyyfJMDb6a/nv0n07N5/PBRy0G8gbSITZ/r74KmEleYzlWG0WxGXivDSezXG5wgSgSCYYg1Cx+9/+FGebl1X9eXHvh/guKncpK9ky3y7gtJ/9NmXUqX0o0nD0DZSrkRB23YyuD/uzp7ZF0kxQTxti/GWxo2PdxOj/hE8kAjiQz7ly9E7FHKqxqdpuZq180dLhvRppULDXpI1c3r14mzftLpiNP69GHqnBIVXXXWV7QvetHUiqn584F69R7yk+tq1wTQ+uFtTT71zt2cBkiBelRuua7JhZrMIk36sgV4gmF+3ZZ8K5O1kfkyTByayRm4VmDy5siu2cvoqYTJ3Csw+/OQL278R5P3pTyJff/uD5L7nzpTB1f/9QwpVbqf6sK1wQtQk6O3r3bHh7+WnecXP0zTBxw1CyFStWT955YV+STKW/Dt9nSSovAhOTfkT4HgYOHqWgpqyXnifkIQgcCIZBPGlnW56FFJO7ftMkLPnvpM3Zg5TSWt8MWryfNWXWuaxh1XFGUKtWItHQMk1/FaGTRamSd8t5y5Y+pZKYFqlMVmHJGLc9O1N5sy5X3z1jZSr30MOrp+urs36RP4Q7g+IEN3Y7aMIpMPKxJn+PqKQ5gvje1PCyzDXvBLOYb/QddDkFFOheEGbEm0h9KfPHt87xTFUy1HdOf3lOXVcrBXMd2+inexKeMiJOcTVA4kgPq7u/W1wN4me22+71QjiHe9b+OCTM9K82xgFu9R9lMj1ILkCyQ4wu/8VM22diKIfn+x06Xrd5PHCeRXyQenEH/9Qho5/WZH12fVAWv3vRoyX4bZbVRLg9BdfS5GCeS7ZY/OzYfbD3eDETm+SPDB1glsFBh/TbwwBJJJvQcyPDraGnu55c0oygqs31myXTVcg6WSQ+7/SjjVN8HE/vB/KPNFdDqyfnqwvm+e1+q3dtpvVWD9EzZ/gl7zR5HloaLg12cQ74atz30ndKiWVckTbxlXV+y7WTAJKtzn7rQyHleWLou8WHoMFyzepajhyciR6CHBuuO7auCZj8RuBe7VyRaRSmUdlwOhZqnWO3uU0N98oA59u4uha00CagT/5/KxCa9AGhrEGSGjaJXmi/H2YSvOFfV6mhJcmv80r8VyNNunWpq5qKdm4aFzk7WEkTl5fudX19uFQKl+y4JXoosScEh5QHkgE8ZdoITiRlVEB8UOUdommaXsZspww+m5b+rzQC4qUBzJfaIhu3nlQJj3b5XJOL8W1SSzcneMOx34q6wkcCwst2qJWVZv3igAAIABJREFUM2mdYByTfnyuXbP5ANm7eqqkvuG6pGkRROw+cMJTS9qNGI+++GxZMijSP3q9LpX52TBTwaHq5mbXXnNNIIi4n+QB14uCGM/El2F1sHXlKHaTQy8l0G23zbbJfP+I55om+PAZwVjjTiPUxrCyBf45Y8FqVU3SvcY335TaFWJOoo4WEJ4/LRQEOH4sLHkjY3MteCo27jigpDfvvyebtG1SXUoXc5Zo03MCAVXxyd5K4YRATJOXzRzXU1WU6XHdsPUdR6WTsAEl1zepDIeV5Yui71ZL6z2QK7sQcJBs4Z1NPy+teP27NPLzyEMd4xSIg+J65fm+igjMzUwSTdx34artpX+XxlK9fFEZPuEVgeCPdfPalEG2BQ8viV/rXDX/iB25oYk03+V8XqEe8hV80s59R9V6p90H4rpXJ/VX+7QoTa/xnHdmklSpUtkOff/d2TzRUVHOKTFWwgNBPZAI4oN6LMLjL5VETxRT7jlsquw59J7avAH5hESkdc+xql+ofbMaUVwisjHYEAINHdK9mVQoVciWb4BNxkvzVsrMBatlw8KxKYJ462RMWieC9OPra2opKE1Apf992PiX5ae//1PGDGhj5CsCCf7PTprIaGDDDbO+NlUXqjBRELP5SR5wXRNiPFOUjakONky9VOgg98HYiLLxBYabkMkxXdEpzzdJ8PlBm3BFgjU2l3YGYWX3YVOStdrQZtKzfX1PbpWw5I3Mo+vgySrJ1q5pdRn83ByBr4GeXZBYXtBuECPVm/WXw2/NVAEopFOteo6VvWumSurrrxOq1q17jU0hY8p1TQNKk8pwWFm+KPputSwtkHZ6ah6r0UnJnB08+oFrwkOvGarKp784J6WK5Ut61/NuOPnRaaUq48Y2z3fr8/+S2unxQHBlTJ/Wk7/A9BdHCxkSvO++NUsxkINcQf1h086Dcs3VV9kmJklwf/vDj9KpeU1XyUDGQxJycLdmtr8vE2k+0+fFtaMmvDR9FlfC+bTYIQkZ9X5Fvxe05OaVcK+JOSQ8ENQDiSA+qMciPv5SSPToKQNRmzF/tRw7+bGS1rgnRxYpXCC3gmh7ycfwUd/29mEFxYZAiyDr3eMfSY5sGQMRO0XsPsfhYNMfPPYiIVDNihclRJDc4iN+5PhHAnyV+6BSealIS7z68fXNEGC36zNBCBaQOcmY/lY5cPSUkj3RlSsvP8ZWQqwbMTZC8TKTDTNzGj7xFSXlh3VoVkMliGCqz5zxNqlUqpDjtE2qbaa+MEXZmOpgOz1r632hLEEfcsKi9UDQBB+/bQJDL0NOkXdtrIGuqNiot+JHaFKnvCK83Hf4PSHB17fTk1Kz4mOOQ5uQN2rEx1uLxil2cpBZa+aNUoSKrF+05t2MNVqwYpskLoAxUxbKtt2Hk8gaSby+vHi9LJs1POU9RxBQhq0Mx0OWz+vZ67/T81useifZtXKyQlqQNG9Wr4J8+8PfZO2mvY6oBX0+ZICZbk+X1O7GN7Ft7/Hqz1TUF04d5Jq8tpunnxYfU1QT67RB++GqfQ9C1rFTF8nmxeOFxIhTPz5rsN/IGUrjvG2TakoSzwq9JzFMuwpJ+yplCkuvDg1sZQZNpPlMnpcp4aXfNZU47jcPJIL4xGr4X/BAIoi/jE/xUkr0oHkK2RUQyNLFHlaSXfTWLV27QwXwkA1REflfMqoOm3cdkpMffi5kyem/pAKRK2cWVaH0qh6F8QWbdCvzcpgxOIfAkNYFNl6QGOXMmkka1y3na85ebOfxhGEy97AbZuBzpeo8LXMn9pEL//mPtOg2RvUOEyicOPWpq/62afKAeZPgeWHWUtm6+7BCnFAJbdmwiuoLDWpBUDYmOthRVHaD3lvieDMPmPT76kB8y5KJyfSLJ81aKp+e+cr1N2JC3khCsVHHZ5Iq5TqIpzq6fc+7tlJOsV6ixYPKfami+dR7eWjPp6RO5RJJLQZ3Zc+s0FN+zU9AaR0rTAtCVLJ8BLagGM59e16926yGPnWBh3KluG3m27jTRcWCsiUKqGTmfXdnk32H31c+7NS8lqOrkEcrUq2DvD59iEr48F2q1XKQ3JUtk7RpUk0h1ZC6bd2oquMYYVt8TFBNejJU4pFrJHDnPTy0x1Myec4yxSnhRP5IIPzG6u0y87XVCqUC11DaNDcqmTzesfCp9Gj7RGh5WK91afK8TAkvveaW+HtKDySC+MSq+F/wQCKIv0RPMQqJHpOp8lEkaB30dJNksCTmRY8mTMXdWtc1uUTiXBGBkIXqWIlHH3L1BxuOCdMXS40Kj0VOahhblWYiZ746J7RELJ4+NG4ybdYbZhO1/8hJ1UdLD36xgg94wjA15Pbo5tlqjVJJGtC1iUKNLHlzq8wY29PVp2GTBwyKzxq0G6b6yKnU8AwPHjklryzZIBOGdlByNUEtKMomjM/snvXPv/wiTbuMVEHSPdkv9hHSNx1PBEZQ3/xRjw/T72v1le4tP7JpdrLfE+8dFBDs5OFifR2mXUUnADT0lCC+Z7v6MnPBm0qijsqnl9FfTrX96Psfq2+R1mjmd4t0GUGWk4Rk2IBSz8mkBSGsLJ++9p6DJ6Tn8GkqMYjFkrM9dH9O23cbgWeFJ3slcyutCAT8A7o0ckXAxbYvaLk64PgkAki+TH91lSyaNtj2sZm2+NgNGoRAEQI90Bq8s8YOaqf4bXoMm6qQJkgzehkBGuo56I2jpANBHr7za0g+Hjv5qfz6n1+TTsmcMZ3ynZOZPK8oCC/93lviuIseSATxiZXwv+CBRBB/iZ5iFBI9YadKTxE6rxqWFzvO5l0H5aV5zh/0sNf9I57Hhq3zgBdUf3LLhpVTkOtREYHhH4igqrZP7Juk/w45z/GTn6iECsHv4je3KfZ/jgdB0bxBJan4uDOk3MvfwDEfL5ov7pKAMLF3GTRJEdBpiTzmP2diH1e0h4YKD+/ZXB7Jd590G/Kikrw6fvJTJSPjlwuAoOCzM18pqKhfDW8dHMXyI4ybtkgxI48f0sHLvcn+HhRlE9ZnTpOiT55Nc5DKZqAbTBwcygNh+n2tF9LBVd+ODZM4EPi3mi0GqKC4ab0KrvMK267CoM26jpJSxfJLkzrlFJyePlUSXk/VrxgoQLJO0E9gZxpQmrQgMNewsnycS1W6VN1uyk+0B1mJSkMtoP8mHMWD00S/zzQ5qn6/HNo4UwXG7xx+X7oNnaIg63Zm2uJjQqAY1i9Rnddv5ExZsX6najmwBv61Kj2WRDwZ1bX0OFERXkY9r3iPx28TjgY3u+7avySpFMQexx6qUKV2rucXyn+freqHJtLNniWjZ4Eh3n5IjJ/wQFgPJIL4sJ4LeV6YKkjISyWdpmVTjm2ZYwv1pspBtQHm4ISZewBY9pgXX1P9fFRZYMqHbZqK8v53TwpZflAPTeqVT7ZJQL9+yeptqjoC+c6hYx9K07rlJEP6tLL34AlZtHKLTHq2s6+ql91d9B81U25KfX1ctcN1pRGoZ+P/bvYXvzREBo6ZJQ/en9MVAgrktESti0oHcBgAO8aocMx4roetJB4wxLVb9qrNFf2rY6ctUuoJ2iB369e5kedHWmtR71wxSUkoaSO5deS9jxSxkpOZomxMfOY0J4J4WhCuNOUI81/X73uEMP2+sXcMszuWPt0t6v+zfmiNQorSjnFbn2/SrhI7B1qV3K5l95TCBnamAaVJC4LTaiMA8AqkOVfPHZI2O44DP6uZ5/3p6YvvQuytHQdUYrFp3fJyR6b0tgSgJA+KVuso3drUk3pVS8rTg19U/eK0KmHzlmyQtZv3yoIpA22nYNLiw4BhCBSjUJXx40+3Y/RvZPmcZ1TrXVALy0XzR22L8nPfTrKyPBt9/sh+reXmG69Xj2vEpPlS5rGH5ZG896rWE7ibUFOKNVPuhqBrI3F8wgPx8EAiiI+HVx3GNKmCmEwTSBnB0foFz9nKcy1ds13BwIFaJyw6DxC0f/DJGUVQxzMA0nd39sxqcwDJXqwReNG3T/8fsiqDujVVFRxtg8fOkR/O/5REVOQ1UyCUB46cFDbPwKlz5cwm1193TbIg1WuMoH8nmKjQsJcc3TJHEanp3tkd+47K8rU7ZPpzPRyHBG67csOuZH+/+uqrFISRvlA7YzNKTy5Qe2So2CjT95g+XRoF5x8ydo4K8GHvdjMCoUqNe6vr0KdLQARj9oQZi9V4dvrVejxTlI2JzwimBo6elXRrBBcQLMGB8fywTlKm+MNBH2Hi+Dh7IEy/r3VKYaXeTNpV/vXzL6on287y5MouN990g4Lzt21czdF7YQI7BjMNKKNoQQgTSDN3LReq34dBl1bfEdNl5Ya3HU/r3bGhQkbYGUlh5Oi0aVJUniVyfzUrFJNOLZz76jkvTItPWALFqFVlgvqa47UaAYoAf/nLNYGGMOGiMSW8DDTRK+hg7bM3XxkpadPcpGbWovsYeaJ6KSlXvIBs2LZfyU/aBeEcaweJb9h+uOIPArVIIWXB0rdsz4+Cu+EKcmViKn9QDySC+Ev04KOsgoSZMv3FD9ybQwWGVoONtdpT/aVG+WJKOsjOeNm9vf+4fPDxGUUSd/br7wQSolw5skiee7MnZKzCPBCbc0ik7DlwQgXpPK8OzWpKySJ5k45ctfFtWf3WHk+deE6wbuBIHlARAt6Ozu6tt/xWaY5o6knD6N5L3bOrg/hp81YqKL3TGnObhxvklsQHyAfWNYmPcYPbq3YEbVPmLpfDxz90TR7oY6nqkyihfUEb7PjM2S+zexgov4nP2JBPe2VlMvdBlFWkYB65J0fwSlLU6yExXkoPmPb7hpV6M2lXIVkE+7edsdZuS3uLANke3qu57TFhAzvrYGECSs43bUEwCaTx28PlWys0UIOapX2/R5i3boNbt2BMMgUVvhMff3bWl048KLsTJz+VAnlzJVWWSZZ++ddvJG2am1P051v9HbbFx4RA8UpQlQGVSDucm9KD3SJ34ydxkmGMigj39/qeNQnCuWfT83+vfkvMO+EB7YFEEH+J1oJJFSSKKcLOTu8RTNtWvU0CpN37jyuyGBjrY41q5KCxsxUUHG14YM4ECVQmDp/4UMmeIYPWu0PDZGzJUcz5jzaGDuYIHE9/8bVQQaIPXtvS1dsl111ZpUvL2q6u0X1iBLaaPIqNVbu+EyT/A/dI7w4N4uZaEj55y7SUORN6q752gngg/CQRYjejdpMICrklS//6yi0y/8UB0qbXOLX5Yp1qoz0BTeogPe1U/vBhujQ3u8Lwo4Lym/osbg8zMfAV5wETqbcw7SpROcAksGMOYQNKPf+wLQhRBNIkX/s8O131WGe4LU0KvWuS4cNskh+6KmxHYug3iI99fn4Z/aNo8WGML746dxEJlim9b36Sy6Eqo/3k1mMN4gHkQ1Bz4ye5Eohwg95PlMebBuGm54dFNUXpg8RYCQ+YeCARxJt4L8C5JlWQAJdxPJQPI710GEzCXrrwHLdm0x4Fx+ve9omkYDD2AkB3J89eJmxUVs4doWDICQvvAQiH5r6+TgW9//jnzykG8kOuQ8KmTqvBErv5I9jduH2/J8t7+NlfPHPbnnflhuuuVSzK6PLmyJZRihTI46uHNijklo9w5SZ9JVvm2xWUHkbiKqUfTbqF7XuPKGZ5r8SHHSReD4JUETJHsRYVlN/UZ6bPK3F+/D0QVb+vidRbmHYV7Zkwvw87r4bhhIkioAy7WY8qkIZ9nKT3uW9+SCExB4rB2jal/UZllyr6vXdlTdZPT2IBBE6m29O6LlwTRn+TFh8mxTp9evBk9R3TRktTz/b1A6ER4v/LTH4F1hocKHYG3Dtr5tsDT8mNn8SECDfwRK7AE0yDcK0IsHLus0ltgu37TJA6VUuofS4qDLSLOnHahEU1XYGuTEzpD+qBRBB/iR785ayCcIvAs6n0Ysj7AO+C6Czj7WkdGVfRaKWHO0e2lAFMrNsI4vPmvisZ7O8SuTZxmRgPkFgpVr2Tgt1raDlJpPZ9J6qNX2xLxZXiwLCQW+531Ya3FYM97SGxVjDfvVK1rLvWux05HeOwSWjRsLKt/GKUUH49Z/SdgbrCYUD7QcL+NzwQVb9vFFJvsR71wxAf5vcRe52wnDCmASXzCLtZNw2kTVdv2OSDKaO/SYuP1qSH+6V723oqofxM7xaK7BUyPjd+EVN/Xc7zw/KThCXCvZz3GtW18dnQcXOlf9fGSd87vqu06EAKTCIKJF2YNjyvOZqgmrzGTvw94YFL5YFEEH+JPG1SBTGdombe3rF8kvCBRW6O/0abFyg9AX3CriwP8HGzs6tSpZJ/X7gg9F679bb/P3tXAR7F1bUPBUpxd/fi7q6B4EGDBLfgGggSgru7a3AnQIAgQYO7u7ZQnPrX9nveQyfdTVZHdmez9zzP9/z9ycy95547O3OPve/MJVsYiCpX9oyUIU0KBp2CgCdYTjbBVutYQnw1HGPisC5R6PeUltzaqqOt1+Eg3KRLAPNgVypdKMptapbyo0pm+fq9fGiRBJkrgOrFjPmNrSqL63RsAbX6fZVQvdnbrmLJnPh9tOgeSL5tGzJ1pSVRggmjxKGETkoP6wCWm7dyO/35518UMKAtvwtAy5oyWRKreDCmMCskOxXMk82q3eQGH5Qi+itp8UHrQpUmfen8/kUEejAJFwVo+PgN6JkxA9/dXsNmWX2LBA7qEKUSQik+ib1AuFaVdIEL8A755dffTAL94ndrjSJWye9LSVWTC5hWqOgmFhBOvBtsNEoYa7YYSBLiKlC8UV4MXvKtwcesgn7hRYnoKJwZU4JSwSOnLlHLRtXdwJraL9Eaym3uHBlpxYZ9FLxmokVlAKAFlHKpJ7G+RzlNQe2gjCXEV0NlPauWMvuBlttLiSz8hat3CSWKOPgDab5pvcqKWjxWbdpPV289ZMC8yKJWKb/kZDSvX4VAiQekb1DjjJmxmin5QNUnJHpYQO1+X3up3uxtV7Fm9dVbDtDFa3dpZmBPi5cqwYRR4lBCKSWHdVTiADCzd8fG9Ndff3Gr0+nd82nO8m0MqDluSCeL646cncXFf/z5P25tQ0UUfvPmREnwQSmiP3SS2xaFvQZCODjqYxgwlGwNDmOcF71Wgkl7s377Id4SIJu/efuRaV4jS4Na5SlJogTWfh7i71YsgLNO484jac6Y3pxwgKD6Zem6PczoA0BIS6Lk96VFVZPYcGEBR1tAOPEOsrglih5DFVo2rMYAOGoLHHeA2nlWL03DJy2jcsXzE5w8cGKP6OdjcTrJqTT38X3x6g1dvHbPLA2I2muJ7uPhI3b/0QuTy8TBIUYMotdvP1C+XFminSnk9lKiXcCn9wS6+/AZgzTCTkdPX6Ef37yjnSvHmaWos2RAHLZHTV1JsWJ+YxJ4CveqUcp/58FT8uo4kk7vnmcU2JixeDMB38ASLR87KQ+eUdCOUK6y6dDCkzJnSM284cAl0OJdEu0eOjdZkNx2Fck8cColAU06goOT5gUx1sWUkd0tWlEpJoxchxJKKTmsS/gi1w4vZ0A60APOHtOLHjx5ZZUy05JBkO0FTggQ682JkuADxpSDg2OoC5zYhPHjcksW2nzCwq9wFRfKnC2JFPjYvWo8t+IhE1+jQjHac+g047FYu18PP0dUTvpPXErBh05zu6Gt5dyogEBrF2x18+4TXivasaqVL6qHZelOB5x15izbRovX7qYpI7rxt3vohCUEbCAEBlFWL0ds+X1hXCVVTXL0EvcIC6htAeHEq21RM+NZougxvAXI4QDSUlPMZXZxwF89aygjzlsS6X5QlZkSALCh19ocl6eaa3GnseRmpFHSjo+TKfGoXJI6tKitmRmVBKuU9FICKb5+O39aPdufihXMFbE+BK/g1FrLmJnq+cVhNH6872j5dD+mUtRK8G5Ai0vQ/BFGwQbQBCLQZglZX+p9LZA7K1dBoGwZAJMApIwdK5ZNNFRarUuMqy8LKGlXsVQdtHlxIOXNldniYpViwsh1KCWl5B7Wpd/mpkWjCN8/OAdN6lWmlz/8FEEHKmeX128P5dLy+RP7mb1dSfABg8rBwZGUkajxVs0aygClPr3H0+37TxkjxBBrxZzy81buoEzpUzEWCYDGMmdMQzUqFmN2FL0L1j4gcD7duveU2yeQ9GhSp5JVcFRUglVvMZAypk1J9WqWpcSJEtCZ8zdo694wmj+hL1Uq8x9drN5t4Gj90PeO1hEIzqOofMuQNqVsNWz5fUUe3N6qJtnKiRuFBVS0gHDiVTSmXodCtPPpv6B2ko5A8k6bKrlN/bbSAe7G0ZUml3j99iOaODdIOPEqPgByM9JQAc7cjn0njLTBv42btZbxD6pXKKaipsZDKQlWKemlREtH9eYD6ELIYiMUfBygkE2BI25JTKFvJ0wQj/LmymLTb0RJKT/u9eo0khkjihfMHaEmkPVB5yhlrkA3CCRrQ5EyhWiVQYlGhYa9GPcAlTEHjp6z6CRo9hCIgXVtATnBQXxDnr98bbQuZCs7DZxKMwJ7WM2uKsGEUepQKjmsIygJZxi93aWL5uUy+NQpkxK+ef27NuP2F0sSmTv8H/qHs9qosilVJA/16uhl8X65wQelODi4v0X30VwSf+/hc0IbxqGN07jEHFS1k4d3tekZdzWgTjynsDkqrJZPH8wJFbQAtOo5jjq3qkugmTMnUkXVvnWTjHBnBo9dxICrAnvI/CMD5pzA6au4cuz77Blp0rCuNlWRKf194f49B0/T6fPX+XeZNXNaauVVwyrzg00Pv7hIWMABFhBOvAOMbG4KlMFu3HWED++gcdNSAIRmSuDMfxs7lqyp8QKkf/6JwnsrazBxU4QFlGSkLZkRTnyC+HGtZhSctRVKeilhsza9xpNH5RJUx4BibklQMB+gUBIJQXZEbaA4paX80A97Y016dvCiVCmMnXiJieDkrrncQtBl0FRq16wWvf3wifaFhgsn3ppR3ezvSoKDpkyFMtjHz36k8UMt94abM7MtvOVqOJR12gzh7Gi3NvUJgJSzl22jXNkysLMADApzIqFnG/4dFS4F82YnrzoVrdKlmatgwHtqsK+3XXgd9mQKleLg4L3i4T2IzgQvIDhYQdsPcYUPnPhdISc5G29JXBWoE/vVccBkXh9aDSUBxgqA+br51De7bDiBZev3oOPbZxs5oHje9h85a9VmbvYq4uUioDhozAICExIqziqVKcRo9SfOXec++cL5c1g0i9Lf1/jZa2ndtkNUrkR+ZoQ5cvIyt+CFBE2hDOnkVwK4416KNTvHAsKJ19ju6O1BL51U4ouM6OETlwgc05eu36NSRfOwg6FlmZk1oLRhfSyDh0gmQqb08bNXERbDhxqHBdDGZEiXSkQvVXqWlGSkLamAg1jYmauaHybgUO87cpZOhF+jj5+/UPYs6cm7QVWmM7QkSnopLT3jhnPiIGqK813J1ikt5VcyNw5BbXp97fmvUak4MxLkyZmZgfGqlivCwHhCoo8FlICMahEcnDRvPb3+6b1J4MfIVpfLW67UoZTep8CcgJSp14P7lC9evcvfXzm/EVto+aT1Rw6gx/k2ts2BRDgZ+G2Dlu3GnUe0cecRypIpLX9zrQUjleDgQHeA06Hy5+L1ewSsHvSFB0xdwZVOQ3u1MvujcmWgTrxPESxBG5WhYL9//fV3bqkwJ7i3ZY8xlC1TWqpmUO22LTiMYseOxUEkSOlieQV96L9GxHe7ebdAmjO2txGVMc7HwPCw9Jwp/X1JZ4bI7SEd+k+i3Nkykl/PltHnwyFWEm0tIJx4jbcWketBoxdwzyD42bfvP8GZwcZ1KlHTupVs4mBXqmLkkiOM9/yHN6wX+hnRM2xNho5fTKDaMSd44VkqNbM2vvj7fxZQkpHGKJHL6XF4f//xC63ddpB8fRpYBFNSYx/mrtjObAYoNd2w8zAVyZ+TA1aRywxNzSW3lxJrfPnjW6vqp06RlFB9oqYoLeVXogsCH7VaDTYaAplC9LEO79PaKkWPkrnFvY63gBKQUSXBQTiiKCs3FKCz4/lbMcOPShbJY9EYSnnL5TqUUAqZ/LZ9J3KGFH3o6HcGwvyBsHO0c7/1rLKatHz2PDGSM4z3ZppUyalWy8HcZ/7gyUvybduAvBuaB8VTioMDPfG8AI0/dqyYXEoOx3bVphAGurMUCFUK1GmPjbS4Vu5+47dQr62/VZXwe7HlzGV1oGhwAarYfvv9T65GheA7DkYDCJ5hVA7aKnhHPXn+A6VPk8Km7550zjqzZ75RNSla8ELDLogqNlsNL65zqgWEE+8A8+Ows23vcXaiAFbT0duTWjeuGaU01gGqGE2B0lvw+1o6DOCGt+8/MfDW/qDJlDFdqogx1u8IpYdPXgnwLJU3TklGGqqYArZLniQRVSxTiOrXLBfxwVRZbR5O6l9dM8efq0skjmD0gEK0phdC2eOFq3e+0uqlT8UHTji1WoozS/m1XJcYW38WUAIyqiQ4iF7hA8fOGRkEmBFFCuS0KauolLdcrkMJheEolK7rSytnDqU1W0Lo05dfaN74vlwNd/nGfatVBEpo+fAu7jxwqskHCdgkqE7auf+ESYfh3qPn1KH/ZA4+IAiKzPrZvQvpYNh5OnLyksUea7k4OIZOlNynXwlQp9w51bxPyX6rqYe7jIUz8YwlW+jo6ct8PgbVXI92DS1i96D6bd+RcK5iRVvq1IUb6eS56xEmQwIB9HSWqlWkINnBjdOMqkjR2oZ3hjXWJnfZH7FOfVtAOPEO3B9kSFGCvmbzAUJpYZ3qZZgntmiBnBHRRweqQ8MmLqVECeJZLRtChrNG8wF0NXS50UtROPHa7ZbcjLR2Gtk2Mj6u3r6j+bAJkZz4E+euWc16oVR44epdJicqmCcbB5wsCdDcgcoOQdkjHAf0ta2fP5KSJfmvv9G2ldh+lTNL+aGlFngXtq9eXOlICygBGVUaHFSyTjm85Wo4lJLOKzfupykLNvD/C8R1tLdAZhriAAAgAElEQVTV9RlKrb1qWKxMUkrLh28++NFNCcrk48X9jq7ffsjl8pEFFXt4fx7bNougP5wUMMBs33ecDp+4SHPG9bF7S6xhEOCbni5NCqpUupDFseHkIDDbsFaFKFl5JUCddi9I5Rvk7Pe7D59t/r4gwAFBS4KQrxZA+fqPb96TV+0K3JZ56vx12rLnmMUKHwTgws5cYcpCBLdQXTewW3NKlSIpnb96h0ZNXcEOvk9TD4tmRsUgqiI8/8XRwf6MmbGaqwgL5MkmtkhYQPcWEE68k7boxt3HtGHHYdq2N4z78/p3aaq5JpFRiVHGFDt2TCMAF1NKIKp/885j+j5HJqNSZGTo4XiBXk6IuhZ49PQVB3akHjxgDwC4LHKvnrlZneXYRS4txyF07ZxhNHn+eqpcpjA1rVfZrKHwAR0xaZnR31+9fsdZqDGDO5CXZ0Wz94IirpRnd870N/asyM8povrdh87gigBQN1oTROZRWYIMGKpnYHsctEGPhD5Wc+LMUn618C6s2Ub8PXpYwNnBwecv3/AhG781/K7KlyhgNlumhkNpuGv4TeN3nCiheSC7yLushJbP0hNja089Wt7OXLrF7zLQZDasVZ7BKwvny0G+7RpafCjlYBCcuXiTeg+fzU5Np5Z1otB84V2H9/HUBRsJ71xUN0QOkCoB6nT2r0zOfq/atJ/efvhMvTo0slj1hecPrRwB/dupjsvibLvJnf+ndx+pklcf2rZsNOXOniliGCSYYn7zDY0e3MHk0HC+YU9870t6duNqGlTdSTJ/5Q6uslk8ZaBF1cydkwxv+u67b60CWMpdv7hPWECpBYQTr9SCCu/HR+Ppi9dWUTgVTkNKUYnRDz9v5Xb688+/mDsVkfrDJy9SymRJRMRS6eZEuh/BljL1fGlYnzbUwKMcR4bRWw4HHllla8BsznTsEPCp3mwAI+BDdzjxeMYBIDUzsKddB2iYBQfC2q2HWKWxkqjWIleLAFkZ5aeI2JsTOBSzlm5lG8Nxz509I+v5+s17djggiPI3qVvJIhMD9u3Zy9fcy4dslpbZf2ktpvAu/vjzT2rbZwKX2wKHQ0j0sYApKkRpdaDDsvZucKYlDoVdoD4j53B1DJx5CLBiVswcYrIkXw2HUq31yqHlk+aW22ON+/H7PnbqMgclgaCNwO6VGw8oW+a0Ft+lSjAI4BxNnreekehBb5kzWwZm9fjh9Ts6f+UOo3cj6eDTzEPzViW19k/LcVDx5T9hCX38/DOj11ctV9Qo2I4APGhOlwYFU93qZWhwD2+Rif93QyTmCalyT9onJLfwvpg/sZ/JrQPiP77t6+YNp66Dp3HQCQEuSbYEHyNwzwPx3pw4u4JOy2dSjO0+FhBOvAP3Gh/FhPHjcsQQdCRh4VeYT1TigdZKFaWoxFIZZu+Ojemvv/5isBsAA81Zvo2jocgQCFHPAjjgerQcRFcOLWP7gv8cPZyhJy4yHaC1Xi1Ljh0+auVLFlBPWRMj4bAHRgbQoYGdIXvmdDZxvppTyn/CUuZmRmDAnEhUa4ZIsyj59B06kytFzPXiX7x2l7r5TeffZNfW9bgfz1DQC3zy/HWatnAjc0UvnjzQJCXV5ev3yW/8ogjnBGOgciCgf1urQHrIaFVtYvqwIumCagJrtE6GeiNTgWzfqAHtNN1rMbhjLWDuWcE72paKLrlUa0pXKQUmgQTfpklNDu5tXjSKRkxexnRt5hDi9eBQKg2A29tjrUZ5tlIMAuw33uOoSsL6kTFFgDNn1vSUM2sGAh5CdBVUMACnAAFcPLeSSBSFptaNbw3aJpauD+ZvAAJqyZMmJGC04LcJcFcEgq1RpkVXm5pbF4L0RT260Lq5w41s08N/Jj9nfTs3MXkrAu91fIZS5vSp+fsKsMe6BtSyYeFXqWalEhbPDJbOSYGD2lOurF/PAsDWkUvD7G77KdbreAsIJ95BNpcAv9CPB+Ron97j6fb9p/yCj0xxobZKSlCJoYuU5bx2eDk7Z3AwZ4/pRQ+evKId+45bLVlSez3RfTz05Xn7jmFAIwR+ULp4ePN05lLdGnxMtr0XrdnNtC0oTXclATUeDo31anyl6DEnM5dsYYo1OOIZ0qSg8Eu3+NItSwI5WGZKwBGbNElC8qxayuLYcObHzlpDbRrXjJLt/OPP/1G1Zv2pSpnC3NuaJlUyLuULnL6K2jevTR1a1LY4Ng6KoScu8DX//ENc8QLp0a4R/QvUy60U1hDADSeBE3/z7mNZfbOu9GwIXb9yLTfpEsAHXku9zFpQrdlqf1SoAF392pEVXJoqYWUcP3vNpm+IsxxKpQFwOT3WapRny8EgsHUvo/N1UgUDKpjKFs9nFICFw2hLnzQyvHAqEfgAmwCCH1qDq7rynoAxAiXrJQp9z8vAWTk49AyVK56fv6XmBIH73QdO0bOXb7hiL7KUKPK91TODqbFFANyVnyb301048Q7ac6lsKDx4Ad17+JwQnT+0cRo7aXcePKPJw7tqpokSVGLppQp0+k2LRvEHqdewWdSkXmV6+cNPdObCTYsouZotKpoPjEAJQJfguKNENnBgewJ1G3rO5VY+TF+0ie4/eqFb6hTgK8AJNyX5c2flDDgc825t6pvd/dMXblD4xVtf0enTpaL6HuUslrXj0GaNb1maDAd6/A+BLENBpqpRh+GE37YhJQ4O46cv3LQrgw59RkxeTjtDTlDrxjUYdBJOjzmJjCOA7AION7DBrNG9qHrFYtH8lyKWBwvgWbt666FFpHWlVGuYB8EsZBffvPvAWTAcsiVKKEs7gXs8W/tFgKNKTvzCNbu4lB4c5HoUpQFwOT3WapVn412178hZOhF+jT5+/kLZs6Qn7wZVGRFfiGkLAOkcZzMpYSHspL0F9PacigC49nsuZlDPAsKJV8+WFkfCwdrDexCdCV7AvTzILu5aOZ6d+F0h1rlqlaipFJUYzhU4glFOXLpoXkbYR3nz9duPqH/XZozkKURdC8AZnTx/A5dxTR3ZnQGGBo5eQI1qV7BaDg+wllY9xxophKwADpQop/eoXEJdZVUaDQ4pegtNSdkS+Sll8iTcJ6d1JQEOFZ8+/0J//f1fKSV0ihXzG5N9qBJ7w+5V4ylb5nQR6o+evoq+/PKbzQE6rB/AR0dPXeZAzexl26hUke9pzOCOZkvyTSH6J04Yn2CvXNkyqLQzYhg9WwCVIKOmruTn0xwQFPRXQrUGOqfl6/fSzbtPuN83Qfx43BuN/y5eMDdXAURuRTG0GVDaC1fvFIE4DScezChwWCNTl2pta/zObEUHVxoAl9Zib0+9GuXZCPrCIcH3GXgfKOkGKB14581VJmlte72PL1VMBq+ZGAEqq3edXV0/Jc+pksC/CIC7+pMj9IcFhBPvwOegpe8YdkQuXr9HLRtW4+xDwNQVfKAY2quVppooQSXGyy7wX+ouSUmUh6GX0atORYHcqenO2T84smV7Dp02uhH/BoT43asnUOoUSe0fVOd3KKWnw/IAQDRxzjo6fPKSydXiEAyKp8gCp7/7kBncO1qrSklKmyoZXbh2lw4eO09Lpw2yCVwOfbA9/WfSs1dvaNnUQewQoae9Q79JlC1TWg7kxBG0RDp/CrVXz1RPPIK0cKaXT/ej/N9ntaiEvVRr+E3g3X//8Uvq3KoO1ahYnFIkS8xzoOrj/sPntCf0DC0LCuaefPA7m2NyOHbmCsWP+x23kwHkC+BsZYvnt9mhlmtdlPJv3nOMbt19zNSusBd6lgvny04F82SnFg2rmtVBaQAcOivtqZdTni05o2vm+DM7h1T5AFo4iDmMELk2jnzfnQdP6dmLN1S1fJGIyiXohKpD9DrHixtHralUHQfBpra9J3DQtFr5okZj450sgEJVNTeXzhfz6EJyn1MlgX8RAFd3L8VozrGAcOIdaHeU5gEULnasmNS5VV0+eK3aFMKgWnpGFXagicRUGloAmX04nLbQrclVAxkncMKb680FIA2yWzhYmhKUMwbtCGU9O7TwZA5XHMJx+E+WNJFZtUzR0yFDiaoRHFibm+BhNhwMDknzrqMozrffUtc29dhRAcWNoaBvz1wGi+mWNu4n9Pd9+PSFsmdKR22a1rTp0Ac967Udys4PgPMM+wBRPdFx4BQ++JpruUE2E72BAMpEphRAmXCoIh9C5e6puE8/FjCFTg+8iLy5stjcFmIP1RrK9FEKP8i3BVdimROAefUbNZcm+nc1+y1TSptp7y7AIcP3FY4rqo9qVy3FLQCwFyqTUEm2bV8YvX3/mQIHtjOiqDKcS0kAXGlPvb1rlq5/8PglefuOJgn1W3Li8W7euV/byj/ogMq9dKlTRLTa4b0IAFEI3uMbFoyk9GlSyF2eZvfhO4J2QVNSpVwRatmoumZzu+PAzn5OTdncVvpHd9wvsWb9WUA48Q7eE3vL6hysnsnpoPPZy19BwiwJuGstHfSs3S/+rq0F4GSevXRL0554ibZlxwrjcn5pZcCDCNoeajKjLYEKFcidlXAAx8cULSejpq1kYKBhfVrbbaC+AXOpWIFcjIhtSaSS+FO75plEn7d7YjtugM16DpvFYJGmOKwR+Fi4ZjcN9m0RZVQA+lRvMZAypk1JQE8GFdSZ8zdo694wmj+hL1UqU9gOTcSl0dECACZFm4e5DLnhmp+++JFL5SV6RPwGba3cQSArBsUwoteSxlZKmylnX1ZvOUC7D5ykcX6dzJb6w8nef/QsBUxZQRsXBlDWTGnlTGX2HqU99XKVAXYKWE0uhCzmKgM48WvnDONqrMplClPTepXlDm31PjDvlK3fgzYtHkX5cmXhgKxXp5GUI3M66upTn8bPXstteV1a17M6lrggeltAjefUHNc7qimsocoroX+M3jsjVucqFhBOvAN3SmlZnQNVNZrKFgos3CAB3zlLTzHvVwvAsZu2aJOROdDjvfvgKZo4rIssxFZbbWsL96q5snSJBeFiyGICLHuFhr0YWf7itXt04Og5WcGH9dtDCWW81ujZkMFu13dCRObK1vWyvf/4kxau3mXyloJ5shEyOJYEVQCgbpSDYIyyVa+OI6P0uQ4eu4ifA3DFC4k+FpDzrCGbDnrKsX4dLfZCI1vqP3EprZwxRPXKMKW0mXJ2EKXztpZug0otaeKEEa0ChvMpoeVTq6fe3vXjnVK92QCm2GrgUY6deFT1lCqah2YG9rTIMW/vXJGvx5obtBtGlw8t5XeaBGqId3menJn5WVy8djcHTfQoeG9euHqXzly8ybrnyJKegx6WkNL1uA5X0Enpc2rpvIGqCWuBf3vpH13BpkJH97KAcOIdtN/OKqtz0PLENDqyAA4hE+YGGWmEDG/pInmYpgwRaq1E+qiGbZ9tcgrQnoHqzlRvucT1fnLXXKZU6zJoKrVrVovefvhE+0LDLTrxXMZ/9mrEnKBq+/TlF1oatIepa0b087G4ZCl7BL0QZLBHlJby//rb79R54FSTU1avUIzRpMFbPH9iVC55SW/QERq2G+zYf4L2HzlrNXhhzzrFtc63gJxnDc8XgkzoQ2/sWZFqVytFmTOkoYTx434tK7/ziLbvO87l5aMGtqfaVUqaRJxHdcyGHYfZuUHlCgJPhoKs6wT/LiaNpBVtptY7opSWT42eerlrBC0fmDRSpUhC5y7fpuyZ01lsSZI7T+T7UM1Ru5VfBFsHwEj7jJxDlw4u5cwodOkfOJ8pVPUmABT06T2B7j58xgCy+A4dPX2FQRx3rhzHDr0QdS2g5Dm1xPWOALYlDAM59I/qrlyMJiyg3ALCiVduQ5tGcFZZnU3KybgIfbzgtsYhpUmdSjJGELdYs4BcOidr42r5dxyCbtx5bJZPF6VvL354wxmyyAJHvE2vrwelGpWKM90cMjdnL9+mquWKUK8OXmZVx3NYq9XgKH9H9unEzjmcZbMmk+YGEUpw61Qvw1mXyNRucKat9dYbzmFrKT+co63BYSbVgy3ixf2Ort9+yBz0pmzWsscYBr+rVuE/OrltwWEUO3YsLrGHlC6Wl6m8hERPC6CPFyBx3o2qmV0gKsE27AilG3cfs8MOAS4LwPCAUeHTpKbFDC2qO46eukQtGlajNCmTRnH0gSMB4DtzogVtpj27iXVfuHKH23T+/vsfo1vTpU5usuVGDVo+JT319qwv8rVgvkGgBpg7CPYBMwOYHsDM0FLwPitXvycz1zSrV5n6Bcyjj59/ppUzh/C0a7YcoH2Hwylo/ggt1ZA1Nnq067fzp9Wz/alYwf9wW1r3HMf4LHLpXWUp4wY3adWqaQvXuxz6RzfYErFEF7OAcOIdtGHOKquTloe+2odPXhHKBgFsBL53OAiZM6Yx2yuJiDqye4aHf0Sk4XAAoA+OE+i+vDwrOsiK7jGNUjonWAn7PXPJFjp4/AKXUebNlZm6+TRwONgZsuvvP3ymdGlSWEWhNuWIoxwTaNbD+7S2uwQUAQVv3zHUtqkHeVYrbfXhQbUMgPDOXLhJP7x5RzhgGArAJwd1j9qXbm5gW0v5rSpm4QLYrF5bf6tDrJjhx4dQIdHTAnjWUA5vqlrD1IrxbMOxknrfrVlFOvBuXz5WNnWhEtpMa/pZ+ztaCgDsifcgANdixjQGrURlAkrPI4sSWr7IY8GRfvnjT5QxfSrNA2oS6veqWUP5/enTezzdvv+Uv9loLYJjr6Vs3n2UsUwkkVg60A6CLH2jWuWpV0fzQVktdbM0duQebelaYIwEHzrNDBBC1LOAVq2a9nC9uyJOlXo7IEZydQsIJ95BO+issjo4c7OWbmWeWDjuubNnZGfo9Zv3dP7qHV79wG7NqUndShFUMJJJVm8OIXB4IvOCqDRKekOOnmOQoFZe1al2lVImQYwcZNJoN42adE7IAiOLxDSGU1Ywqm7Q9kPcI601TQ5K3HaFnOS+fBz+JUGZOnrUkF13lCCzjuyblr3hSkv55fQ5O8p+Yh59WSBy+eg/9A9nWYHAXqpIHs0cI2Sx2/edKAszwtkWlL6900f1YJR6e8VeWr7I4yMwuHz9Xqa3k8SnqQd/dyMHE+zVzdz1ePe36D6aS9oBJore30MbpxGy86B5M8d0odb8GAfrvXnnMRUvnDui8grVZQhkJE+aWJdnBwRy2/Qaz89JHYPA75KgYMYY6dm+EZsIAKJa7Z2aexDdx1LK9e6qOFXRfV/F+my3gHDibbeV4isdXVZ38dpdpnVB1L1r63pREHrxQT15/jpNW7iRUeVBb5U4UfyIdcI5OXbmMq3depDCL35Fp0dJMyi4YsSIodgeYgBjC6hF54TATZl6PfjQhhJwiV5o3bZDBDoygNtpKQgYjZmxmto2q0VVyhWm5EkSMQe7lJnet26SWYAtfJStCVC2Iz9/OHy9NQgYYAz050+et54zX0N7tbI4rCnqLukGcEpbooBUWsovp8/Zmo3E36OnBcwBOcHpGOzrrRn4FiqwqjbtT4c3T2eOdVvlyMlLlDNbBsqQNqXVW3AtAsRqU49J4I/Xj6yQ/d2yh5bPcKHSuxhtOC0aVOXvK9qD8H7Et9Qaa4ZVo5m5ABVQHt6D6EzwAtq06wgHcMH0ASceAVZrQJ9y57V037VbD+ndx89m6Ue1mNPeMW0BZsWYsKWgBbbXuqavl/vNx2hKuN4FTpU6+ydGca4FhBPvXPtrOjuctqRJEpJn1VIW54EzP3bWGmrTuKbZDxMilht3HuaMPj5euBYlyuipFKKOBdSic8Jete45NgpHMFCBw85cIWSktBKp/HRAt+Ym+8cHBM5nLvbxQztFUUHJAcrcvej/HD+0M1ehWBJzZX1w0MG53r9LU7tMZm8pv6nBbelztkspcXG0sEBkSiUEtbTOCuLAC6BJvKOQRQbtXORAGvjXI/dbA2ARlGKjBrSjWlVLRcGZwIZgPYvWfAXeO7BhqupOvAS0JtGt2fIQKKHlMxxfCiCc3j3PqB0IlROYY/GUgbaoI+ualr5jKGXyJHTx+j1q2bDa16qsqSu4rclaUFPWhAY3IQu/5+Bpwvolef7qJ6YOzZoxDQc0bGlxUqqHvffjOQdoozXB868lQKy1+aPL35V885XaILrhVCm1h7jfNS0gnHgH7RsQgtv1nWhyNo/KJalDi9qqa4Lsoq2HO3y88D+g2VoSZBZ2hJwklBgiOwPaoka1K6iuu7sPCDub4gyHXbCveJ4SxDcNVCb1r0qI5cjEo5cbSO19OzehquWKamZeZNxrthhIFw8sMYm1AGRrZKGC10T9LaBU+P6jF1Z1g0Memf8Vzy5Qbg0FASZzNrQ6yb92btIlgG1WqXQhW24xukZpKb+9fc52KyhucFkLgPrqwtU7BJ5jVJqg2koORaE9Bvjw6QvNX7mDKRtBGRdZ0HIFQLDIgl79gKlf+6Mb1S7PXOxw+JHdvnrjAaHfuFyJ/MwgkTFdKntUsulavC892/hRobw5qLtPfXZsIwcg8J2EcyuJWrR8yDJWbNSbQdwMkc3RMw6MAC0DqnBSgF0TO1ZM6tyqLgfcV20K4WdFyywy7F25SV/mo/8+R6aIwE345Vv009uPXKZeMF925pB3FQGS+avXb5npRIh6FlDyzZe0kIv/42ycKvWsKEZyZwsIJ95Bu48I9I59J4xmw7+Nm7WW+3VBJeUIweELLy9QC6VPm4I/pHIcHakP+MOnn5mHVoh6FpCi0zeO/gcMZDg6yhInzVtvkqZNug4Bo6rlizLiNJz4pIkTUN3qZah9i9qaHvbvPnjG/ZdXDi0zmak4f+UO9Rs1TzV6oXcfPtsMziWV7Rke1q3tGg7zV289pGkBvmYvVVrK76w+Z2trF3/XpwUMQcMQ0EKLTIZ0KWn9/JE2/xYcvTL89g6fvER37j/lDDSAI8FQAYyWAnmyaY7TcfPuExoyfhEBfdyUAK/DkPZSLVo+9FF7dRpJ8eLGoeIFc0dMHRZ+lRInjB9RudDB25ODC0oF7yJnt7rheazTZghF/n6t3xHK4LrWuLuV2kCt+3HGQeUafm8IXHVqWYf62VmRpZYuYhzzFpCL/+MsnCqxl8ICalpAOPFqWlPGWHDikVE1hYwrYzizt+AwAaAxlNhDEJXHSwz/F9zApkrutXaQ1FxfdBpLcuLNtSpg3yIfOi2tHwdoexxXJbZEWXopz+60aVEA5cudNcpQKJk9cfZaBN2Q4QVy+NLhZL/98Jl6dWhkMTiBrB9K+QP6t7M5CwUaxVFTV1KsmN/Q6MEdzJpFcSn/z79SqTrdo4yvdZ+zkn0W9zrHAtLva2T/tsz5jpJeVN50HzqDaeL8enhrpljkMn5pIugQuTJGMyUUDIxgGd4DP739QH9FopiDk22KA1wpLR++u/jGW5OeHbyYz12pwFEGE4i1yiG0+6Ckv2GtCja/D23VDd+nwWMXcuDT8LsDRxjo7yil17NAR7SBBO0I5d8W8AxQbYhgkxD1LSA3kw5NlOL/OBqnSn3riRHd3QLCiXfyEwDAmbAzVzUHmlm4ehct37CXHXZk/XHowoFw3dZDNHvZVtqyJDAKariWDpKTza7r6SWncNboXib1fPTsFR07fcVsJl4JSJsahunhP5PLfEFvZJgVwgEaGRqgMpviPJfDl46sj/+EJUyX1c2nPrcKGAY/UN4PaiAED1CJMLiHt8mAhqmeeCnIBVohcGmbEzVK+eX2OSNAM2j0AovbljVzOrt7+tV4DsQY6lsAWewmnQPoauhyo1YpgJcdDDtPS6YOUn/Sf0EiTQWaMBmYL1wlu6rEOPbS8imZS+69aFfqPXw295sjcxwZUBDvqkvX79HUBRv5+79y5lBNqjfwXgIqPwTvZARJXEGkIBm+If69W1PNSiVcRndXsK8pHeVm0jGWM/F/XNXeQu/oZQHhxDtoPyOX0+Nj+v7jF1q77SD5+jQg70bVNNMEh4+C1TrQjMAe/FGKLJPmBnF5/ZSRxtlAtRwkzRYWTQdGBhgAgiiFNyXodQy/dJPq1Shr8u9qg7TZa2ZEx9E/myFdKiMgK2TaT4RfpYdPf2CGA7UEWaWtwWG0dH0w9+oCPTt50oSEvmGpagGBg8L5c5id0lTgA327eXNlMYsroXbpKpB2L1+/z+0uKI8uWzyf1dYHKeADR+q7OLGjrO/h01dMQWZYKqyW3cU4jrcAUMfLN+hlxPWN59936ExKlzo5IUOvhURu+cAcf/z5J7XtM8EhtJVK1uQMPBol+iq9F8FSsHIAiR5Ag2AHACUaMEPQzgQsGwB1+jTzsPp+katL0y4B/P6FgNIULYPDJi5lthSJpk3u2FreB5BfJDXw/QUocLO6laluzbIM5ChEfQsozaQrwf8R1K7q76cY0fEWEE68g2xu6iAB6q2KZQpR/ZrlNI32ApQFdDOXDi7lDDwQhuPG+TaiF/7cldtcNmwSbEwFB8lBJo720yih6IGTqgSkTS3j4mAZtO2QZk4lnNoHT15yUCpT+lSMSq8V4JfS0lVkzZYFBXP2NOToOeo/ah6bWepzBvgUgMKSJEpg1vxS5igy+rV0A7JhAKEUTrxaT7Dzx5m5ZAstWbeH6dgypElB4Ze+0n+imipT+tQOVXDBqp38PQH6vF5FL3g0jrYPnPZ7j55zthLvQ7xXcmZNz1gECFBqJeCor9fWn47vmMNguQD2w38fOHaOTp+/wQ693gVtEGBzCdoeypULlcsWps4t61oMBOt9TXrUT41Mulz8H0HtqscnQuhkrwWEE2+vxVzw+sgvSjgLQFmVsv8A7kIJNNDMLYkjHSQXNLOqKmtB0WMLSJuqizAxmNZOvFz9EZWHY2RK8ufOyvzOcJa6takfcYnS0tVl6/fStdsPaWZgT24zKFUkDw3r04Yz/8iWdeg/mQ+PYBYwJ8iQFqjSnkLWTzHJxQ3k77DTV1zi4Cx379zxPiCbh1+89RWdPl0qqu9RTpOyaGu2hRN/8+5jmjOuj7VLdfd3R+HR6G7hGisUwVASspjixPmWWvccx3br0jkAACAASURBVJg/wNjZGnxMU1o9LZaGQMi24DBeC5hKhKhnASWZdFNaqIH/I6hd1dtfMZL2FhBOvPY2dvoM6LctUbsrHd06kxFwIzvx6KdkIJf5I5yuq1DgK4Wc2hQ9toK0KbU/5gH2gjm5ff8p0wzpLTOMjz96601J2RL5+XdzKOwCjYkEcKekdHXx2t1c4ooSaDAITA/wpRKF/6MwAsXcwePnCT35lsSj5SDq1cGLe/4jy9DxizmT79ezpdKtFffr2AJaU2BFzloheITSfgQSgN1RvaJj2FXU3AJH4dGoqbOrjAXHvX7NsuRZvTQNn7SMyhXPz5R6SRMnZCpBIcICkgXkZtKl+1Htse/IWToRfo0+fv5C2bOkJ+8GVbl1Q44Ialc5VhP3OMsCwol3luUdPC9elE9fvOaMImjAhvdpw5l4OF2NOgynhrXKM5esEOdbQClFjxKQNqWrR9tI9yEzzA4DpxcHOb058eYUhrNC//xD33zzjUXTyCldRVBgysINtHnRKAaaSp82pRFWwIqN+5g+LHBge4tzA7Ry0+6jtGKGH2XO8F85NaoeAHq3Zo4/I5cLiV4WcCQFlqn+UVCkIcCVK1sGXRvWmXg0MMydB0/p2Ys3VLV8kYj3CIIidx4849J2VwF9s3WTzbF1JEuaiFbPGkpZM6W1dSiHXyeHIcXhSkbjCeVk0ueu2E6oCALrAbAMwNyDFoh96yZZbC8S1K7R+EFyo6UJJ95NNvvGnUd07fajiNUWzZ+Teypf/viWjodfparliqjCU+sm5tR0mUopeuSAtGm6IIPBbSmnB7gQQJHevPtAmdOnpjSpkjmM+xiggY+fvYrQGD3lKA9t29SDgfoAHqaWYJ+6+U2jU+dvEA64KC1EH7wkCAyA3mhAt+YWp4STMmziMkbhr1CqINsLKObXbz+iIT1bUhszAIlqrUOM41gLCAos++ztTDwaaAqQt3SpU0S0tOB7281vOi8Cv/sNC0ZS+jQp7FuUjq+Gc/T0X1A7SU3QEKZNldwsSKheliOHIUUvuruiHkrB5eD0F/PoEhGoRkXb3jUTmT4RYgno01ywSVC7uuKT5L46CyfejfYeTsOLH95E9FEmShjfjVbvWkvVgqJHCTCeWtaz5MTvOxJOy9fvpZt3nzBNXIL48bg3HP9dvGBu7kdE4EkrQen5rgOnzA6PknRzjAFydUIpIHqKESjAgSayZM6QhgrawE8MhHIAMQF9GgGQfLmzMCp08UK55aom7tOhBZxFgSUHM0KH5nO4SmCGKFu/B21aPIry5crCQG9enUZSjszpqKtPfRo/ey2VLpqXurRWj63D4Yu0cUI9fH9sVFVc5iALKAWXe/D4JXn7jqazexeyxpITf+LcNdq5/6RV6ma51K4OMo+YRljAqgWEE2/VRNHjAoDb9QuYy+W5kvg09aBBvi2MaMCix2pdfxVKKXq0AMZTw6pHT12m7fuOGwGtwYENnLaS7j9+SZ1b1aEaFYtTimSJeTpkde4/fE57Qs8wknvHlnWoR7uGFOfbqHRqSvR7+/4ToyjvD5rMQGGSAIH+4ZNXbsGDrcR+4l7HWMBZFFhyMSMcYxXLsyB4ffbyVwT/yJImZTJClvjZi9fcGqC2gC6yQbthdPnQUmbJQIDNs7UfMwnkyZmZA2/Axti4MEDtqZ06nl6/P9aMgmfl6q0H1i7jvfsuzrdWrxMXyLOAreByqEqq3nwAXQhZzPsBJ37tnGE0ef56qlymMDWtV9miAkgqJIwflyvYEHALC7/CJfigZhQiLOAKFhBOvCvskkIdpeg/+u8GdGtGTToH0Fi/jswlizLhZvWrKJxB3K6mBZRS9GgBjKfm+iKPBdR8HG4RUIr7XRyzU4EDvt+ouTTRv6tR2bkauqGtpEbzAXQ1dLlRyaejnHgE13YfOMWHCFQi4BCBgEW18kWtLg9cu6AdO3j8Apfk582Vmbr5NLDpXquDiwt0aQG9UGDZihnhTCOawgiR9EG7SuaMaWj15hDatXK86mo+ffEj1W7lR+HBCyhB/LgMjtln5JwIutdzl29T/8D5VplhVFdMwwFd7ftjaAqp0sWaeXavGk/ZMv/X+mTtevF3+yxgK7gc3j/Vmw1g9oMGHuXYicc3sFTRPMz6YqnaVCrFXzVrKFes+fQeTwDeRTvjwkn92bEXIiygdwsIJ17vO6SCfujzrdKkL53fv4idJKnkaN/hcO6Hd0V6IBXMotshlFL0KAXGc7RhwDOdOkVSm6bFISsGxeASezUFh4Gbdx7T9zkycWZOEmToUUqsZi98ZL3hkFVvMZAypk1J9WqWpcSJEtCZ8zcI9HDzJ/SlSmUKW1xq34C5hMBP97YNKGDKCmrZqDoBeRt8zCipFxK9LeBICixHYkY4atcQ5Mb/rIFXytEHPdbl6vek/l2bUbN6lalfwDz6+PlnWjlzCA+3ZssBwnc4OjHDuNr3x9y+zlu5g3YdOEkrZwyRjXQu55lxp3vUAJcDdgx+u6lSJCEExbJnTsdYE9YE38wW3UdzgO3ew+fUqOMIOrRxGiE7D9DJycO7WhtC/F1YwOkWEE6807dAewVQ0tfSdwy/rGLEiBHhxG8NDiNkCiyBf2ivnZjBlAWUUPQoBcZz5o4gq2wueo4MD0CqkNHSQpyV0QZ6tVfHkVHQdAePXURw8OGMmxPoXKZeDz58gFJHCtCt23aIW2cmDuuihanEmE60gBZ4GbYsxxmYEbboJfcaR/Vob959lEZNWxmh5tJpgzi4huAgsvSNapWnXh295C5Dd/e58vdHMiacQTD6oKrp7fvPjKqfIV1K3dna1RVSCi5nCsRXsknqlMksVuyBItPDexCdCV5AoFlG4BvVOHDid4VY76d3ddsL/aOHBYQTHz320eIq8FEt6dmNpBIwHPRrVChGew6dpiVTB4n+H509A2pQ9DjroK/ElNK6bxz978BrOB4O3ZPmrdeMns5ZGW0J/Or49tlGGYQd+0/Q/iNnLYLzAOuidc+xUYB90GsbduYKTR/VQ8mWiHt1aAGleBlyluTqmBHO7tHG/Kj0KV44N9PKQYBv8PLHnyh50sSqVxbJ2WO170H70/mrdwiBRrQslC9RQPfo9LAB3p29h8/mwEq3NvUZ6XzbvuO0ZrY/ZcmYRm0zuf14SsDlzNHpwqhoR+vfpalF+yK5lTJ5Erp4/R61bFjtazXb1BXcXz+0Vyu33xthAP1bQDjx+t8jVTREaVim9KmoXo2y5DtkBn9Ua1QsJvijVbGuuoOoQdHjjIO+UitITry5UnkEo8ABqwXHvDMz2sgmtOwxhrJlSkvVKhSLMOO24DCKHTsWl9hDShfLSwniGVchoP8PQTkpAID/HtS9BS0N2sNo/lXLWe+pV7pv4n7HWUApXoZcTZ2NGSFXb9ynxx5tR1UBKLGbknul3n9kr+HMQ5DVXjFzSJR3mJJ51L5XSngM79uGvBtWixh++uLN9OuvvwuAU7UN/u94+I0+f/WGAz4Z06eiJIkSyJ4JLT+NO49ksEhrbXC4duWm/RQ7Vkzq3KouB9NWbQrhfnhDulfZyogbhQU0toBw4jU2sBheWMDRFnDWQR/rRA8oeM/RY3bn/lN69fod5ciannJny0j5v89KBSzQpUlO/KzRpsvHHz17RcdOX9HEiXdmRhsHx3pt/a0+Jitm+FHmDKmjXIeyz6rlizL9HZz4pIkTUN3qZah9i9qMiC0k+lhAKV6GXEs4EzNCrs7Sfc7u0XZ2FYBS+9l7PxyyMvV8qVcHL2rz7ztp86JRNGLyMiqYNzv/u14FFWyHT14iz6qloqgIwFEEIoSoa4Hrtx/RgNHzI4I9GF0pc1L/UfMY30YOdWN0D7Cpu3tiNGdbQDjxzt4BB8yP3ruFq3eZnAkc1FXKFXGAFmIKWy2Avu/OA6davRz9zhnSRu3Tc9ZB//L1+zRy6nIC0EzDWuUpa6a0lDhhfEK0+/LN+3Tw2HmqVaUk+fVoySA0keWPP/9HG3YeNsvFjnHCL93kahK1JbpktHEIFdRHaj8d+hpPCV6GkpU4CzNCic6415k92nqsAlBqT2v3P3v5mmq1HEzXjqxg+loJp+P42Wu0Y99xWjxloLUhnPZ3S3SEhkoVzpfDIpOK0xbgYhP//fffVLu1H+XLlYV8mnhQujQpmA5y9PRVXM7eqHYFWSsaM2M1JU2SkHq2b2TxfncLsMkyprhJ1xYQTryut0cd5XCwHzFpmdFgyJBeun6PxgzuQF6eFdWZSIyiigWQzQbooDVBtsAcCJyjD/p7Q88weNOAbs2psWdFI4R3aR0Akpm7fDvtPniKAWTSpEpmbYn8d0dFxp2d0QbN3oWrd+jzl1+4pBAlfXIy6bDXluBjlChBPN4PIdHHAmrgZci1hrMwI+Tqa3gf0Of3HTlLJ8Kv0cfPXyh7lvTk3aCq5qjjzq4CUMN29o6B95hna78Iuk7JiV+4ZheX0qPvWK9iiY7QUOdNi0aJ/ngVNhEl9ACXO7JlplFgf86ybfT4+Q80LcDX6ixyud7dMcBm1ZjiApezgHDiXW7L1FEYqNe1Ww+hGYE9BLCdOibVfBSUtL55+8EqHZszDvohR89RzqzpbeLOhROPTEbGdKmi2EwvkXFHZ7QNEawBnoTDP/pJ188fScmSJLT6bGHPmapqRyjdffCMKpUuRJ1b12UMASHRxwJq4GXIsYYzMSPk6Bv5nrkrttOCVTupRYOqXO2D3wWC2PvWTaJM6aO2qKgxJ8ZwZhWAWmuwdxwEoQtX70Ro/ylZJA9n4hFQxDttf9Bkk+99e+cQ10cPC4AdCQwNV0OXG4Eert8RSuGXbjHXuyVRwvXujgG26PHUiFUYWkA48W78PPhPWEqpUyalPp0au7EV9L90ZGb3HDxNa7cdpLLF81sF13HWQV+pJZ0dGcdHffeBUxQWfoXQ/1gob3ZGuK1WXltwOGR/Snl2Z6pHqYoB5f3dh85g4Em/Ht5mTYsD84TZ62jjriMESp0WDapQ/ZrlbK5yULpn4n59WEDrahVnYkYotbB00F8zx59/T1JmGKjjEK0pVl2RKUSpzY+duULx435HxQvlpqVBwZQtc1r+drlCqw/2y5TEihmT/vfXXwQ0dVsCq0pt6A73S9/8oT1bkme10rxk/FujjsP5W9i2WS2LZlDC9e6OATZ3eKbcbY3CiXe3HTdYL3gxEyaIp0mPsRubVfbSdx04Ra9+fEtN61XmQwIyqht3H6ENOw4z9VibxjW4RwyUKHoVHHA6DZhMQfNHmFTx3qPntGLDfho/tFOUvzszMo7KlOotBlLGtCkZDT5xogR05vwN2ro3jOZP6EuVyhTWzOS37j2hJp0DomQjwF17MOw800CaEwlNGai6w/v6UM1KxV3ioKyZMd1gYGdUq7gyZsSDxy/J23d0FBrGE+eu0c792vNBuyJTiNKfkdwSZ6XzKr3fXBUbxm3ZqDrlzpGRVmzYR8FrJiqdStz/rwWAdQORcHLgxANXAe121oI+anC9uyoVoniAhAVgAeHEu8FzIIDtXGOTAQwXOGMVO+9AwUU2uGzxfOTdqBpVKl3YZo5dpcB4SqwlHYIAYGdK8NH944//mUSYd2Zk/M6Dp+TVcWSU8trBYxcRHPxZY0wj5iuxlXQvbFK+QS/mg0cfPASAP75DZzJFjrVM4bsPn2n3gZNcSv/+w2dq4FGeGtYqR/lyZ1VDPTGGjizgzGoVZ2NGyN0GBEarNx9AF0IWs1OATPzaOcNo8vz1VLlMYQ6aaiXOZArRak3WxlVS4mxtbK3/jiq2+49emJwGtGcxYhC9fvuBgdiEqGMBpYCZSrjeXZUKUR3Li1GigwWEEx8ddtHKGkwB2wEJ/NDxC+wgNK9fxQ2s4BpLBADThat3CVUS6DMvVTQPZwBw2IwVK6ZNi1ADGM+miUxcJDnxrRvXMDkEevoReTfH9e6s0tOPn36msvV7RPCtS8rv2H+C9h85yw62ljJzyRZasm4P5cqekTKkScH9gJAtSwJt7tnFAfTspVuE/nro3KllHerXpamWaouxHWwBZ1arGC7V0ZgRSsyM30X1ZgO4bayBRzl24lFZgHcrem7NgYMqmVO611lMIWroLncMJSXOcucU97muBZQCZsrlendlKkTX3W2hudoWEE682hZ1ofHw8ixWIBdzuQrRnwVA1bZ5z1Fas+UAxYnzLbX2qkFenvoup8fhftDoBTRnXB+TBoUTgv5tc33ezio9xQe9ZY8xlC1TWqpWoViE7tuCwyh27FhcYg8pXSwvIyxrIacv3KDwi7e+otOnS0X1PcrJ7r1EsOTFq5+ocP4cWqgqxnSSBZxZrYKKFAQYz1y8SUAgz5ElPWexbWWZcJLJeFq8S7/55hsu2T13+TZlz5yOW5QcIY5mCnHEmizNoUaJs7PWgCo2VJyYEo/KJalDi9rOUi1azutMwExXpkKMlg+DWJQsCwgnXpbZosdN67eHEgBotM4yRg9rOW8VODyHHDtPa7aEUOF8Oa0C2zlPU/MzIxtG//zDB2lz4szSUzhH9dr6WzUdEJczZ9AOzdqqAiYucBZ9lhxdxT3KLeAM4C20d/j0nkB3Hz6j8iULEEqLj56+Qj++eUc7V45jh17vAgBJBCFevHpDWTKkoVJF89rcoiR3bc5gCpGrq5r3KSlxVlMPe8dCFduOfSeMbsO/jZu1lluqqhsEeO0dW1wf1QLOBMx0ZSpE8SwJC0gWEE68GzwLyDKeOHs1YqXwpz59+YWWBu2hEoW+pxH9fNzACtFjieh/dgVkXJS4PX72KsLoaN1AaWnbph6UIV0q7vWOLO5YegobKM3+OIs+K3r8olxrFc4C3gI4XP12/rR6tj8VK5grwmjIMiOoNW5IVKBKPVl22sKNtHzDPgIAZNIkCQlgVsAdWTZtsKbl9K7KFKJ07+SWOCudV6v74cQniB9XMPmobGBnAmYKKkSVN1MM5xQLCCfeKWZ37KTIMtZqNTjKpHiBntg5h5Imts5D7ViN3XM2oJRny5yO4nwb26oBwK+aIH48XTr0Q8cvJiDtmxO/ni3Jx0wLh7uVnsJGlrI/M0f3pBoVi5u1pbPps6w+qOICVS3gLOCtyOBw0qLA3hB86DQtn+6n6jrVHEzSPXBge/KqU5G+iRGDA4od+k+imhWL04BuzdWczqaxtKYEtEkJlS9CRVAMIL9FQwFGTdiZq6JqUYO9dSZgpitTIWqwFWJIF7SAcOJdcNPUUBnlkd6+YzgzKvFzqjGuGEO+BVZt2k+hJy7SWL+OFsHMjodfJf+JS2nljCGUPUs6+RNqcOfb95+oYqPetD9oMvd1S7J+Ryg9fPLKYiuAu5aemtuGSXODKPa3sam/BXA6Z9NnafAIiSFtsIBEw4TfTLo0KTQP5sFBa9NrPHlULkF1/uVzhppLgoKZvaFn+0asNagZY8Y03zJjw9JUv0Rq07kYspixRSRZvTmEjp6+rHkAwhmUgKob0YYB8Y7Hs1ipdCGLV+PsMWPxZmpYq4Luvl+RA6p47t9//EJrtx0kX58GzBQjRDsLuBJgpnZWECMLC9huAeHE226raHfl6i0H6MKVO5rSZ0U7o2m4IJRVL1y9i5YGBVNjz4pUu1opypwhDSWMH5d+eveRrt95RNv3Hafrtx/RqIHtqXaVkiYzHzgk3XnwzKKmcb+LQ1kyplF9NS9/fEs1mg+IwnluixPvrqWn5jYBTsaJs9do8ZSBZvfJmfRZqj88YkCbLAAqSr/xi7gkXBIvz4oU0L+tzQwWNk1kcJGlMn7DsXatHK87xwwBD7QCDOvdmsqWyB+hLsrrnzz/gZCh10qcSQmo1ZrMjQvAw97DZ3NSAMwYGdKmNLoUDvGl6/do6oKNBHyClTOHah58stcGplqbkidJRBXLFKL6NctRvLhx7B1SXK+xBQCOhwTBvUfPCYCuONcAoyNzxjQWqxotUfEC+yBt6uS0c/8Jmj+xn8YrEMMLC8i3gHDi5dvOZe7ExxMZUkPBoWzyvPWUMX0qGtqrlcusxR0UBdjLhh2hdOPuY3bYIejlzP99VipaIBeXoluiRbLlwF0kf06zNG9KbAxH/Oadx5QoQTyKFTtWRO87nr/f//jTZC+8kvmiw72wy9zl2yKWAht++vwLhRw9Sx28Palbm/pml+lM+qzoYHtXWwOoQas1609VyhSmZvWrMDL85Rv3KXD6KmrfvLZm6Nn4hiBAZ01Sp0iqWSDB2tzm/v7Lr79T5cZf2TIMkfSBWG/4b3PH9bGZztFWXfRCCWirvkqvgxOFc8Xew+FUKG92ypktA1dnwNbnr9xhIERUFvk086DYsWIpnU7c78YWgPM+a+lW2rDzMDvuubNn5HPR6zfv6fzVO2yZgd2aU5O6lUwC6lqi4kUQIF7c7+j67Yf8nhUiLKBXCwgnXq87o6Je5pw6fGTHD+2sSUZWRfXdeihkcj5+/tmujIW03wc2TOUsfmRBH9jGnUc0ceIx15iZq2nDjsM8bY92Dcm3XUPmQE+fNiV5Vi1ldj8tRcYNb5o4rEuULI8rPyQoIRwzY7XREv7831909NQlOrp1ltXsjzPps1zZ7q6oOwJ8jToMp/DgBQy0JQlacU5fuCl6dk1s6v/+9xftOxJudbsrlylMCRPEs3qdPRc4kxLQHj3VvhbvJGRG8byiigxOVs6s6Sln1gyq21ht3TEeAquoeLn/+AVlSJeSyhbPJ4IOWhha5pgXr92lbn7TqUKpgtS1dT3KlT2j0Uj4zZ88f50AaImqw8WTB1LiRPFlziZuExbQrwWEE6/fvVFNM2RRpKyDNCgyu5ayuapNLgZyuAVwAClaszMd2zaLUiRLHGV+ZEm27w2jJVMHqa7bjz+9p6pN+tHKmUPor7//po79J9OFkMW0btshunn3MU0L8DU7p6XIuOFNCASo/ezCkR42calFe2TNlDai91d1w5kYcEDgfMqTMzOXppoTBHnOXr5lVZ3C+XLwYUaIa1tAalfZvWo8g2BKMnr6Kvryy280eXhX115gNNUerQ/IDiJ7iDLf8iUK6A47IJqa3qZloRVgWVAwfxNDjp6j/qPm8X0IPqCaAtgzYGYAtaIQ51sA5wmwTFhKCkBLOPNjZ62hNo1rRmnzwTkJ7YumpGCebFSlXBHnL1RoICxgxQLCiXejRwSIvECPNUXv5UZmcIul5qvcjjYtCqB8ubNGWS+y4gB7muDfRXVbIHPRoN0wunZ4OZewNe0SQMP7+nAQacueo7ICBygZR5kmSnW1EoBzTV24KWJ4oBHjAFe2+H89tBnSpqC2zWpppUKUcddvDyVUTSyc1N/snOgtRdDEmmxaNEpU3Fgzkgv8HQHZ7kNmcIazVpWSlDZVMrpw7S4dPHaelk4bRGWK5XOBVbiXiofCLlCfkXM4oyvhGIDebsXMIZQgXtRKKfeyjj5Wu2z9Xrp2+yHNDOxJddoMoVJF8tCwPm040IIWgA79J1PlsoVpUPcW+lA4mmkBZxu87W/efaDM6VNz24slpgMEr7E3wP9BpaIlhiW8M/E/nEcMBYH7EZOWGf0b2pVAhzuyf1tqLsroo9lTFj2XI5z46LmvUVYlt8TZTcwT7ZbZw38mpUyehEYNaGe0Njh9jTuNpHZNa2mCtIuPau3WfjRmUAcqWSQPZzTq1ihDN+48pmcv39iVKfz85Rfac/A0IwPDmR7Wp7XD9mnOsm20cM0umj6qByNyayk4kBw+edFoik9ffqHl6/eSV+0K1NFCJl5LvcTY+rQAg4Jt3E9gqfjw6Qtlz5SO2jStKRx4HW4Xfttl6vlSrw5e1KZJTarQqDdtXjSKRkxeRgXzZud/F+J8Cyxeu5sDzXDesEfTA3ypROHvIxRDQPXg8fOaMxk43xKO1QBtLvjO3bz7hHF/QJuLoAn+u3jB3NS3c5MopfKGGkqtg1XLFaEGHuWpQqkCRgwUclbTa9gsPm8IJgI51hP3ONoCwol3tMWdMJ+SEmcnqCumVMECV289JO/uo6lFg6rUoFZ5SpU8CWffZy3bygd/HCTVLkmH2uhrr+T1FUQK5eePnr7i/0Zv6JIpA43QoaVlglMeKOtN61Xm3v+7D57Rxt1HuK8+WdJE1KZxDWpUuwIHJRwhQK1GLx0OBTtDTlDgoPbUpE4lzaaGbeq19TcaP3nShFSxVCHyaephUy8fshgXrt4hBD4AVoleQQEcpdmWiYGFBWyywLOXr6lWy8F07cgK5qeHg7h3zUQ6fvYa7dh33CLzhE0TiItUsQCqJaYs3MDfRaDnA7+la5t6EWOv2LiPy+q1ZDJQZSEuMgiqQgOnraT7j19S51Z1qEbF4hGtf6i8u//wOe0JPcMtDghiA1snzrexo6wOGXYAJu47HE4bdx1h579ejbKcOCiULwf/5uwVBGwQIBWo9PZaTlzvDAsIJ94ZVnfwnFqUODt4CWI6GRa4dushDRm/mA8fkiC7gL705EkTyRjR+i0oi9t14KTRhbFjx+LebiC+mhIACAXOWMXOO8pMEZUHkBAi4ZVKF3ZY7yiqCCbNW09rtx6kcUM6UcNa5enspVvUvt8kGtCtuWbI39atavmKzbuP0qhpK/kiqYcTpbvr54+0CxBRqR7ifu0t4Mw+zumLNjESPvAuUJqK/u5Nu4/ybwSlxt4Nq1ksgdXeOvqbAcE1z9Z+EZSbkhOPKh+U0ndv20B/SruhRqiY6OY3jU6dv8GB43fvPxn1UCNLj/JqfAeEKLcAgDjx2xjk28IiVgvaT/qNmksT/btapa4MmLqC9w3nDeAapE6ZjJrWrUR1qpc2yTqBYMGjJ1+TDJB/6B/6+OlnmrF4M7dT9OooqmSU77QYQWsLCCdeawvrYHw1S5x1sByhgp0WAL3b81dvGBlYrzy3iKhfuHqX0IuOD3CponmoZaPqBMToWLFi2rli+y/HIW7QmAU8d+SKgSs3H1DngVOoQwtP6uZjnu7N3lnfUcCGOQAAIABJREFUffhss5ON/j3Id3G+NZoGpdWlPLtzGWhjz4psKxxkug+dwXSEfj287VVLXK9jCzirjxPBubL1e3BPMCpmIAh4bQ0+RuVLFuDfzczRPTmjpkfZG3qGKTsHdG3GAQgc4K/eeMB4AqWL5jGJHaLGOgDWWbh6J1oxw4/bi+DEg34TgdX9QZMpY7pUakwjxlDBAvgGAXwVWWIEyyJL5gxpCIBnQpRbANWhtmLc4BsXg2Jwlt2SwInPlD41dfT25ADjweMXaNLcIMqVLaNJJh5zrE1onxvs621ER6l8xWIEYQFtLCCceG3sqqtR5ZQ462oBQhlZFnAGIjIOPwDOsyYtG1bjjIcpQdZj856jtGbLAe5va+1Vg7w8tS2nxwcdvfyLJw/gqoHIcufBU5q9bBvNG9/X2tJs/juyEW8/fKZeHRpZLH0HqB+Q6gP6t4uSjbh17wk16RwQkemTJt+06wgdDDsvC0jQ5gWIC3VjAa37OCVU/OPbZ/PvVjoAg+4R5at4lu88eE7jh3bSjU0kReCwV282gEtswVEOWb3lAB/w4RignSVo/gjmNddCAE4ZP+53VLxQbloaFEzZMqflntvIATkt5hZjCgvo3QK//Po7t+Ghlc4QzE4Cr7NVf8mJRytZyJGztGnPUQ5oIxlgDk8HcxsKSvYBmCdEWMBVLCCceFfZKQV6yilxVjCduFUHFnAWIjIyhf4Tlli1ADLEKHezJECMDzl2ntZsCaHC+XJqCmyH3wic5bSpk5tVCRkBNdGkkY2DrYCuiwx/1XJFjbINyAgFHzrNB/+61cvQ4B7eUQ7+7z9+pvINejGCPQ4vEFTe+A6dySwUyNALif4W0LqPE60ujTqOiOjtvnT9HrXuOY6Obv0KoIn/v/eIOQQnX2+C33Xlxn0pJGgKI8QDgbpas/4cHEQ/7rhZazlYONhXG+RxUHomjB+Xf58o1w0Lv8IZQ62CBnqzv9BHWMCSBXyHzKBXb97R1qWjuYf9weOXNHHuOm5tqF6hGI0f2tlqFh6Bf7QOnrt8m6dCW55X7YpUo9J/vfaSDmpUwIkdFRbQiwWEE6+XnRB6CAuoZIHoiIhsz4dXjhktVRDkz52VweXCL92ibm3UK6eXHO6twWG0dH0w008hsAFQO/QLIkNYJH9OGtitORXOn8PssmYu2cLVD7myZ6QMaVKwnpAtSwJN9gLKsY+4Rx8WcFYfJ8Awy9XvSRI/PYC+AAApOe0Hjp2jWUu3UvCaifowlIEWkQMQUvXKvnWT+PcBjnBk8eDkqy0Iahbz6EKrZg3lTLxP7/F0+/5T/m0bBt7UnleMJyzgChZABr54ra5GvwVUFf3w5h01rVuZFq7ZTd3a1KNmZujeEOjv6jednXdUCDWrW5lqVytlFn8HNlGjAs4VbCt0dA8LCCfeDfbZmWBIbmBe3S1Rb4jIjDa/6wj345sCBsKhOlvmdCbRZyMb9+mLH5mGBqV3aoqlCoKyJfJzthHVDWMGd1BzWqOxUKL84MlL+undR8qUPhWD1NmKMH/6wg0Kv3jrKzp9ulRU36Oc6jbSbOFiYJst4Mw+TuBCwJmvX7McLV63hzEYQAEFGTp+Mf36+x/Ms603kdhZDm+ZwX242/cdpwlz1tGZ4AWc+QMSdcDUlXR483TVVQcjSIvuoyk8eAHde/icqxkObZxGyM7fefDMLspN1ZUTAwoLONkC+J7XbuVHZ/cu5Gz7658+UJUmfWnptEFMmblj/wk6cPScWaR4BAEmzF5H9WqWpWIFc0Xhgje1PDUq4JxsNjG9sECEBYQT7wYPg7PAkNzAtLpcojMQkREJB2AUPqQQADodPnGJ+9pRagugup7tGzHYWmRBZDz0xEUa69fRYuYYh23/iUtp5YwhVpFqdbkxQilhARUs4Kw+TjjDIyYtY4R6lIYH9G/LNJXIKo+fvZZ7znHw1pugeqF2q8FUqnAeatGwGg2btJRBPicP78qqooLg+u2HmuBHoN3Fw3sQBwyAUwHgzl0rx7MTvyvkJGcghQgLuKsFJOaky4eWcsD61Lnr1HnQVArfu4Bb18Bc02XwVHbyrQmq9QBOKAkwJ8yB4aHlTGkFnDV9xN+FBRxhAeHEO8LKOp1DazAknS472qvlDERkHEoHjV7AvWg4yG/ff4JQ6ta4TiWmeUGm3Zwgmr5w9S7u/UZ2D+VwQAJGHymy0tfvPOLs2fXbj2jUwPZUu0pJTaisIjtHkr5AfP82dixdPTd7Dp3mAIkl8ahcUre0eLoyposp8+jpK37+UakBAXZCkkQJrPaNarFMOPa//fYHZc6QWovhVRvz4rW71M1vOgcccLDftGgU2w//v1enEcw8AQoxLaSl7xiu5Ll4/R4B0BO0cijfh5MxtFcrLaYUYyqwAJDNHz55RfcePWecFDwnoEfNnDGNTdViCqZ2u1vxzS1Ruyutnu3PCYDJ8zfQsdOXI9pykIlftTmEti8bY9Y2qPJbvmEvt6MZiiVAO8PrlFTAud2GiQXrzgLCidfdljhOIa3BkBy3EjFTZAs4AxEZB55te4/T2m0HGRUWVC+tG9ekVCmS2LRBdx8+pw07QpkKCg47BAfu/N9n5Qy+T5OanPnTQsyVKWMuWw8DWuhlbkyg5V+9+ZD/DI54OAYSZc///vqLxs5cQ1MDulPtKqUcqZaYS2MLSHgXw/q0oQYe5WjMjNW0Yedh/p2snz9S0woVtGqgbQPAU5KAfvHt+48Myli6WF7GcNCrwGF4/vI1Zc+S3qEI1CgRXrlpP8WOFZM6t6rLe7VqUwhXM2TPYj64qVc7Rle94LyjKgO/JzjuubNn5O/N6zfv6fzVO7xs4JM0qVvJprLt6GontdcFYElUqFQtV4QOn7xEgYPaU5M6lTir3qbXeMqRNT2NGtDO5LQIwpX07MZVQXj/xIr1X7AdjBDAshEiLBCdLSCc+Oi8u/+uzVlgSG5gWpdaIrjiX71+SyUKfa+p3qgEOHT8Aq3ZfIBwyK9TvQxnuYoWyGlzBh3OClDb1e59N7fwyL8RXPfHn39S2z4TaNaYXrosE4aOP755R1Wb9qdrh5cbHSyR0YgV8xvq37WZpnstBnesBZBt8mg5iK4cWsZZwurNBzDtIdpRUC0yop+PZgqhn/uXX3+jXFkzRDxrj569oi8//0YFvs/KfalAk9azAJlekiMnL9HLH3+iVl41+LeCdiC1BA6IIV2WWuOKcbSxgFSpgcBK19b1GCTUUMBecvL8dZq2cCPF/S4OLZ48UDiIKm0FbIts+7XbD/k7i2o8VL8h6AbATAQGzVX6IKBYv51/lO+fSqqJYYQFdG8B4cTrfouUK+hMMCTl2osRlFgAznDYmSu0efdRQna+U8s61O9frmQl49p6L7LqG3Ycpm17w6hjyzoRPM223u/s6xas2kkoGTaXCXC2fqDWAV0Wyg0ND54zFm+mm3cfa9Ln6+w1u/P8CMR5+45hVHi0sExdsJEB2UKOnqOtwcdo8ZSBmpgH86K3++KBJUYlxet3hHLpsTkeZk2UkTEosqtoPwGolSkB4GaHFrVljGz6FtglXZoUVKl0IYtjojcXv9WGtSqIrLxq1rd/oHXbDlHSJAnJs6rlyiU4nGNnraE2jWuK/bLfzKrfgfONZxs/GjOoA5Uskkf18cWAwgJ6t4Bw4vW+Qyrp5ywwJJXUF8PYaYFXP75lZNegHaFc2o5MeKPaFahAnmx2jqTO5dDh6YvXFqnS1JlJ3VHgxMMZnjOuj7oDqzga+Lp//+MPal6/KmXOmJruPnhOs5ZuIe+G1RwasFFxSWIoCxZAJh4c53DcUY4dOLA9zV2xnfCbHzekkya2w++3QqPedCFkMfdySwKwNvTo+/Vsqcm8agwq0ViBbzpXNlQRxOBhg0PDuby+a5t6lDJZEqaoUktAW9d7+GzyrFaaA6cZ0qY0GhqZegB+Igjz5ZdfaeXMoQ6rOlJrjdFpHDiDMWPaVomBvcP/1KzciE62tGctlqhdDccBloSp3ydAmxu0H8ZZ+wK5sxpNXbFMIWrRoKo96ohrhQVczgLCiXe5LVNHYZQVhp64wMA+6D8SEn0sgENhKc/u3Hvp37s11axUgundtBR7eNzx4YUYOgNa6mbL2JEZHFBeD2Rp0LbNGt2LqlfUb5kwwP9Q5rnrwCleKjAEkAHs4O2pKxvbsg/iGusWQF862iVQPj91ZHd2EAeOXsBBuvIlC1gfQMEVANFDmXi61MkVjOLYWyW2jhtHVxpNrHUVAdodJs9bzxUThfJmp5zZMlDiRAkI1TPnr9zhVpj+XZqSTzMPm6kkHWs5MZuwgLYWsETtajizXw9vSp0yWRRlcI4Fu40pyZMzs+bvQ22tI0YXFrBuAeHEW7eRy14BDk5EL0HVIQkODqDWANAOHHjwXnt5VnTZNQrFo1oAJX+zl30F6EGJYLO6laluzbIRwGda2Awf0rcfPlOvDo0sHkhxsB0QOJ8C+rfTVTkiMgJAyDeUxAnjEzjikb1zBUHg4Y8//hSOuytslgvqOGbmam6NgfRo15B82zWkJev2UPq0Ka2WITtzucDoCNp2iLNycQyqCG7de8K891rT4sFpB9I5gDsRcANoWs6s6ZnmLmGCeM40jZg7kgWkALglw4Audfl0P2E7YQFhAWEBp1tAOPFO3wLtFFi9OYTLLMGNC/qOnftPcAkmemdbeVVn5GpzPJraaSVGdpQFQPEGwKug7aFculm5bGHq3LKuJiXt6DX1n7CEwei6+dRntGrDZwsZvOBDp5lGrm71MjS4h7dwNlV4EFAGGn7xpsmRkLkQ6NcqGFkMwbgQVZv0o5Uzh9Bff/9NHftP5tJ69BKj3WRagK+urXT3wTNuLUIZNOjkAJT17OVrAoK1mmX0ujaCUM6qBST8oCkju1N8E9Vrl67f5yqKtXOHWR1LXGCbBfANA2XqmQs36NOXXyhT+tTc/idRaFobpU6bIQyq2a1NfW4hnL1sGwfeJw3rKsAHrRlP/N3lLSCceJffQvMLwMvx2JnLtHbrQS4LhvTq4MU9gAI5NxpvvImlIRO0LTiMM1F9OzfRZPEAaUKVx9L1wczZCicyedKEhHJWVH0AZRYUPYXz59BkfjmDog3gxp1HTPcEiqHNe44RUKsR9ADnPUrS9UzThswRnKvIAnu7IpCgnD0U92hvgfuPX1CDdsMiUKCbdgmg4X19uDR8y56jugZQxHewcpO+3DOLrDwCErtWjmdqxtixYukelE/73RUzSBaQ8BNO7ppLSRIliGIYoKUjKI5glhB1LDBi8nIGvkULWIZ0KQl4EkCd37IkkFASb0lA31ilSV86vXseX1amXg/+7l28epdQMYHzrhBhgehsAeHER+fdNVgbSvk27jzMJdbIzgFdFaA7IhPvJg+Ag5eJjMaDJy+5fDRT+lQcVceBWW8C1P4twcdo48IALvNHpqVt05qUJlVyznBv3HWE5ozrzZUFriJwWlp0DyTftg2pSrkirqK20FPHFkCArnbr/1Cg+4+aR3VrlKEbdx7Ts5dvaPLwrrrVHmXzTToH0MWQxUQxYlCFhr3YQbh47R4dOHqO5k+MGgTT7WKEYppbIF/ldrRn9QTKmiltlLnWbw9lzni9V55obiSVJkB7XeXGfSlo/gjGjZAE32Jg5lgD6nz45CW17TuR2TqOh1/lb/jp3fPpQNg52rn/JC2c1F8lTcUwwgL6tIBw4vW5L5pphWzjjpCTtHLjfgbWGevXkQGRhEQfCyCb0HngVJMLAo9z2tTJubVCHF6JgD7/w5t3jPBd0rMbjezflsv9JQmYuoI+fPzCXPGuJKu3HCBwH88M7OlKagtddWoBvFMqeX1laIBzA0R6CCo+lkwZyNgRehUAVJZv0Iuk7GqXQVOpXbNa9PbDJ9oXGi7eg3rdOCfp1bxbIHlULhmFchCtGO37TaIShb9nTAghyi0gBdguHVzKQJ2SAHTy8ImLVit8EFwsXdeX2R3WbAnhcvx54/syneTlG/dFsEX5FokRdG4B4cTrfIO0Ug/ZuhNnr9KHTz9TA49yWk0jxnWCBVAyirJ2U5IjS3qKF/c7un77ITWrX8UJ2ulrShwWzly4yU46SoR7tGvE2AGS7D54ioIPndF1RB+BOUn++Yfo85dfaNK8IM5koLdTSPSzAMAr0aby5t0Hypw+NaVJlUzTFinMt+vASSNDxo4di8td8U7Rs+Bb16bXONazRqXiDMYHvc9evk1VyxURJbd63jwn6Ib+bL+xi2hqQHeqWbEEU8+hbWnWkq0UtP0Q7Vs3ifu2hSi3ANDpi3l0oZ7tG1GLhlWZveH+w+c0cMwCqlGhOPXqaL0cHgmpKQs2sDKrZg1l/Ke6PkOZhtO7UTXlSooRhAV0bAHhxOt4c5Sq5uq0X0rXL+7/agFkEAQGgumnQaKfAtL2sxevCYwO6IOXBDgCuXNkoj6dGuvycZKAmEwpt3lxIPf1C4k+Fth3JJyWr99LN+8+4VaoBPHjcUUV/rt4wdyMdwHgUkcJ+st/++0PBorTq6BaoFarwUbqobWneKHcNLxPa0qUML5eVRd6OckCaDscM2M1zw5sF+k3tnjyQF1hujjJPKpOe+j4BeozYo7RmMDPWTR5gM3tnijLj/Nt7IjfMpha6J9/6JtvvlFVVzGYsIDeLCCceL3tiIr6uDrtl4qmcLuh3r3/RDOWbKGjpy8T/hsHe5QAopxeiLEFzl2+zZSLQNj/9bevHPaG4uVZgTMFehQcVp6/fG2kGrKmnQZOpRmBPYz6DPWov9DJNguA3SFw2kq6//gldW5Vh2pULE4pkiXmm/EMIHu1J/QMLQsKZmAn/NZxqFVTUOEBjnqATkly5eYDevv+I2NGlC6Wl8ErXUXEQd9Vdso5egI07erNB/Ts1Wv6Pkcmyp87q6AE1Ggr0PICfA2Uw2dMm5Lyf5/VrsQD9urxs68tPhAEBvDObNvUgzKkS0XpUifXSHMxrLCAcy0gnHjn2l/T2QXtl6bm1fXgHfpPoh/fvCev2hX4I3bq/HXasucYrZjhRyWL5NG17kI55RZYvHY3PX72I40f2kn5YGIEp1sAAVlUjQzybUFxv4tjVh+wQvQbNZcm+ndVnV6wUccR9Muvv1GurBkiMlyPnr2iLz//RgW+z8o0T3oOEoqDvtMfY5dRAC0YL354w61JGdOlEtUaDtw5lNjDCUeFT5O6lazOPHT8Ytp14JTZ6/x6tiSfJjWtjiMuEBZwRQsIJ94Vd80OnV2R9suO5YlLTVgAiPAAodq2bDTlzp4p4ophE5dSzG++odGDOwi7mbEAeh8vXL1LL169oSwZ0lCponm5J9LVZNK89fT6p/cC2MfVNs6MvihbT50iqU2rwTMcg2LYXIpqy6DPX70hD+9BdPHAEqMMPzAlHj55pXuaNnHQt2WXxTWwAJh8+gXM5cosSXyaenAA7ZsYMYSRNLIAKny27g1jykqIf+/W1LBWeYuzvX3/iSo26k37gyZzsEUSV3kvaWRKMawbWUA48W602a5C++VGW6LJUkG70qL7aDq7d6HR+OBiPRR2QaAxm7H6tIUbafmGfez8JE2SkLnu0VO+bNpg3WZifvn1dwbkMxT0B6IPWFRdaPLz0s2gwLr49PkX+uvvv410ihXzG9WfV7TkVGjUmy6ELGbAREk27TrCSPXIdulVxEFfrzujP73wm/LqNJJyZs1AA7o1Y2pCMPhMnreeS7MFGKy6e4Yk0/6j52jDjlAOnoPbvUndylStXBGKY/CeMTfryx/fUo3mA+hq6HKjYLtw4tXdJzGafi0gnHj97o3QTFhAlgV+//0PKurRhdbNHW4EwtPDfyYfTgB+JcTYAq9+fEvVmw9gqjmvOhU544KeOrQl1KxYnAZ0a65Lk6H//cCxc0a6JUwQj4oUyEkJ4sXVpc5CKWUWwHM5cc46OnzyksmB0Je+du4wZZOYudsVy4zFQV+TRyFaDoqWiypN+tL5/Yu4bQWBq71rJtK+w+HMQz5n3FeaRSHqWEACZkXgHAwxZYrls2tg4FrcvPOYMQtixYoZcS8Cd7//8afohbfLmuJiV7SAcOJdcdeEzsICViyAA8d3331LJQp9z1eizyw49AyVK56f6aiEGFsA1Qv12vrTxZDFRhmA1ZtDGBxw+XQ/YTJhAadbAIfW5l1HUZxvv6WubeoxuB1aZAwFv3stKLBctcxYHPSd/ti6jAL3H7+glr5jKDx4AQOrSU48KFvBXDKyf1uXWYsrKIrfJs4qqOg5euoylSuRnxrXqcQ0r7YAc8JRX7h6l8mlFsyTjaqUK+IKZhA6CgvItoBw4mWbTtwoLKBfC6AscN+Rs3Qi/Bp9/PyFsmdJT94NqlJagdJqctOQYazfzp+G9W5NZUvkj7gG5fVPnv/AGXq9CnjiZy7ZQgePX2AmArQAdPNpQNXKF9WrykIvmRaQssqnds2jxIkcR43m6mXG4jci84Fzs9vQhlTSsxvtXjWesmVOx058jQrFCNzxS6YOEmwfGj4PqDDasf8Egd4P1YS29MQjOTFi0jIjrV69fkeXrt+jMYM7kJdnRQ01FkMLCzjfAsKJd/4eCA2EBVS3wNwV22nBqp3UokFV/iiixBYftn3rJmmSpVN9AQ4eEL3llRt/LZU0rFT44fU7o3+bO66P7uzXN2AuoZKge9sGFDBlBbVsVJ2Cth+SVZ7oYLOL6ey0APjh2/WdEAXvws5h7L7c1cuMxW/E7i132xvmrdxBmdKnono1ypLvkBmUOWMaqlGxGBUtkMttbeLIhaNFDNVvKLW3BmxnSi8EAGq3HiIoVh25aWIup1lAOPFOM72YWFhAGwsgOl3MowutmePPBw+pJHDG4s08oSgJjGp3HBz2HQm3uiGVyxTWFVcwMoxl6vWgQxuncZWFtNfrth1idOWJw7pYXZO4wHUs8PHTz1S2fg/ueXckJ7srlxmL34jrPN9CU/e0AEBkz1+9Q/itImhSvkQBRaww/hOWUuqUSalPp8buaVCxarexgHDi3WarxULdxQKgavH2/Q+dXnLsTpy7Rjv3n6SFk/q7iymi/TrRp9y659iIzKy016EnLlLYmSs0fVSPaG8Dd1vgpLlBtHrLAapTvQxXjUSmvUIwp3n9KqqaxZXLjMVv5P/t3QmcT9X/x/E32XeyjpEtikhICD+yL9kK2akkyZYliUJK0pQlW6hEWUKWEEKEkF2W7NtYh0FkX/6Pc2rmb2qY8Z2v73K/r/N4/B6/HjP33nM+z3O/5vu59yxuvRUcfTHmWHu+e82OOR3f/UzBQRnsjjCmmClhXw1+y+XFWc1INLPAqxlNQUHAyQIk8U7uXWILSIGIldYjtoMyid03n/XUwBGTZN4k169ZLiBdYgrabM029Ivv7VA+M7c8d44gtWr8rGpV9t0vAhFbfy2fMVTp0qayb+K7vdZQYyfOsbsQlC/FvPiY+t3ffm/mpy9avl6r12/X8bBwmfUcbi/mvjX3gLuLvw4z5jPi7jvBudeLbo711WvX7efNjGBz98Mx50rGLjLzb1fJmm3V/qXn1KxeZfv3a+rnffTOwC/0eP7c9ud3K+b83zbtiPaQzBnS2RXrDx85GWWdm9i1jKMQ8A8Bknj/6CdaiUCsBcyKrxUbdLFDyWpXKWX/MJovsmYP1sF927l9D+lYN8yHDzRmjV57T2bf2mcrllRQ5vTasGWXfeM5qO/rqly2mM+2vmWnASpfuoia//MlKG3qFDaGFxtWU8IECXy23TQMAU8J8BnxlLQz62nfc4iefrKAGtWt4MwAvRTV4aMnVbXxm/r956/siKKIkWTLf/tdM39crtEfd71ryy5cvKTy9d6I9hjzwMUMzTc7zMwe199LEVItAvdXgCT+/vpydQS8ImAWZIsfP74ypk+jtZv+UO7sQfZNLSV6AbN9ULUm3bVwcoiyZk4fedAno6bY/eL9ZVi6eZOUJHEiutnBAmYRxuiKeeuUKOH9e2hTo9lbqln5abVpVsuuIm1GreTNFayPer7q0ZXy49q1fEbiKhh450+asdhuhTZiQPQJY+CJuCfig6EnVL1pd21Z/KWdAx+RxI+aMNsOpTeLtcalmFFL5n/muxAFAScKkMQ7sVeJKeAF5i1Zo5TJk6pM8cdlFsP6Zc1mu6p6ofy5A94mOoCIfeJXzPpMaVOnjDzk8wk/aMuOvRrev5NPupnhhGs2bI+2bZkypLNTAijOETArNhev8Vq0AZldCXp2bHpfgo1YnX7VD8Pt9c1iii83rmFHq5gRPjENe70vjbqHi/Yf+o0ezpFVDWo9o20792vKrJ+V46EsalG/SpwW0LqHJnCoHwiYEVn7Dx6LbOkt3bJ/P82isMUL51P7l+8+vNsPQvSpJl67fl1PVGylrwZ111OF89kkPlWKZHZR1vkTBypbUMYY22seypnpDqaY6WPJkiaO8RwOQMApAiTxTulJ4kDgH4GI1em/HtJDTxZ6RM079Ncfew7JLE5lFrUziT0lqoBJhqs3626/6NerUdYuGLZp6x4NGjNVXdu8YL/8+2K503BC09cmyercur4vNps2uSjw7yTDXObqtWtq0fHD+7qloHnI1aLTAJm1F8wbyS59R2jVDyO08Je1Pr9YZsTq9GZ7zcwZH7TDd80WYnsPHlXbFrXVqA5DpF28HR132p0eklUpV0xvtm0UZftRxwXvpYCWrd6s5EmT2O8qYyfOVa7sWezUhdiOKKvfurfMG31TShZ9zP472HPAWLtbS7sX63opKqpFwDMCJPGecaYWBDwmYL5wN3ztPa2ZO1K794Wq7svv2C3IzNv5nXsPa2CvVz3WFn+qyKzq3zvkK23cujuy2W1b1rFD+v69Argvx2UeSNRr3dsubFe2RCFfbiptc5PAyK9n6cSpM+rTpaWbrhj1MmatiBLPttW4wT00YdoC/Xnhoh2dMmHaQm3atkef9G57X+p1x0V37w/VS53JMTKmAAAgAElEQVQH2gcQ5rPdtN0HdjeHn35Zp59XbrRf+ikIRAj8e7pK4kQJGa1xH2+PuEwPihhBt3zmZ3bY/P/qdpD574XL1mrVum18tu9jv3Fp3xAgifeNfqAVCLhN4My586rSqJtWzx2p72b/LLPdilnYxSTxsxewxVxM0OYttnnDnT5tar/98vb1d/O1Zcc+n06uYuoHfh97AZPEb991QJ990DH2J93jkeOmzNfHIyfbs8won6KP59WzzXuo6XOVfHrBrytXrtphusu+HyITw8q1W/XNsJ6a8eNyLVmx4b6a3SMxh/uAQFySSh9ovl81Ia7Tg8x6NZUbdtWGBaOVOHEi+4DOLOgbfva8ps9dFuPCeH6FRWMRiEaAJJ7bAgEHCjRu208ZHkyjDVt3q3GdCvZtsnnLbIao9WjfxIERxy0kJ80tN1si9QkZpwQPxNd7b74UNxjO9imBf2+BZYbXm4d2azbs0JD32qvi/4re1/aabRjNm8lUKZPf13rcffFu743U6o077C4dH7zVSnWqllbrbiF64rGHZUbbUBAwAnFNKlG8NwF3TA8yibvZBrZ6xRLq9dEXKvVkAa1av82ubfPOG83vrUEcjYCfCZDE+1mH0VwEYiNgFqIa9918JUzwgF5p8qySJ0uir79bYOfDs9jZfwX9dW55dO02IwlMf3/5aXcVeDRnbG4XjvETgStXr2nU+NlRWps6ZXK7D7JZKd7dZcfug8qVPcgm7jEVs8NDiuTJlC7N/y8MGdM5nvq9SRaW/brJ7htdqlgBxYsXT5u37bXzb/3tgYSnzAKxnrsllWaHktJPFQxEFo/HHNvpQXd66GJ24hk/pIdyPpTF422nQgQ8KUAS70lt6kIAAb8RMG/nG77WV21b1NEzpQr7ZLujG0GQMkUy5c+bw2+nAvgkdIA2ykzLWLxig97v/rLd3eJOxSx29/aAsRo36C2feUhohtTG9oGCGeFgSmwX0wrQ2yFgwza7lIQeC1M/RjZ55B6I7fQg89Dl0D+L2kU0zDyoy5LxQf7+eaSnqMTbAiTx3u4B6kcAAZ8VGD9toTb8vkuD+7bz2TZG17Dfd+xT+LnzLGznV70Wc2PNm/gx386J9sACj+S0+7Wv2bjD7uXujnLp8hX75t+sGv189f+pWoXiyh6c2W5feSr8nLbu3G/nlm/9Y7/6dH1R1Z55yr7l9oViHkCcPnte7V+qq4QJEtyxSWaKgFltv3fnlj7zAMIX/GjD/wt8+vl32rP/CPvEu/mmcHV6kFnEzlf+nXEzCZdD4J4ESOLviYuDEUDAqQJmK6qIcuuWdP7CRX00fKJ9O/fxu9Hvze0LFpu379Wcn1Zp595Dkc0JPXZKZg/enNkyq2Ht8qpeoYQvNJU2xFHAfOl9+8Mx0V7FDKk362As+mW9298Y7toXqskzF2vbrgM2YTfFTNkw0zWKFMyr5vUq+9ywdLPXtLE6d/4vtWley+4hbdocUcyiWHMXrbIPKJ6tWFJvvt6IN/FxvD/9/XSzqF2Tdu9HCcM8rDJrKZjh9GarOYr7BFydHjRp5mIFZU4f40Nqs6vGoNFTVadqGR7Qua/buJIPCZDE+1Bn0BQEEPCOwN0WNJo6uq/y583unYbFUKsZTl+uXieVK/mEHn34ocit8NZs2qFTp8+pRoUSevyx3Hosbw6fbD+Nco+AGVaqW7cUP35891zwLlcx95xJjGM7VP2+N+guFZgv8dPn/qKxk+Yq9GiYMmVIpwfTprT7Spu1IwoXyKOubV7QEwUe9mYzqdtHBK5fv6E5i1ZFaY352cARk/TD+A+VKX1aH2lpYDdj9Ybt6tBrqH043apxDQVnyRAFxLypN9tJhoycYneaMVtj+sO/V4Hdq0TvigBJvCtqnIMAAo4SMElQ6NGT//ny1qpriAb1fV2F8uf2yXjN28Yazd7StqXjorTPvKnYd/CYenZs6pPtplFxEzALVx44fCzyIouWr5d5s9yifhUFB2VUUKYH41aBA882D+r2HjxqpwE8lDWjcmTLfNdh9g4kICQXBQaOmGz3Ie/+eiMXr8Bp0QmYB4K/bdoRLU7mDOnsQpSHj5y0C3f+u5hpMAOHT7Jb55q/z3lyBSt1qhQ6fjJc6zbv1ImwcHVuXV/NG1Thc87t51gBknjHdi2BBbKAGRpukrjd+0Nl/tiZL6wP58iq7Nkyx2ql6UC2uz320d/8oAOHT6h/j1Y+SWLeJr75/ii7H/zti3ItW71Zx06ctkPpKc4S6NF/tGYv/PWOQXVv19gOb6cggIB7BMZNma/fNu5gTrx7OCOvcqddYcwBL9R6xn5fGT91gWaP63/Hmk3Sbr7nmCk/5gGd+a6TJ2dW5ckZLLPIKwUBJwuQxDu5d4kt4ARM8j5k7HRNnrXE/jF7JHc2O1f1ZNgZrduy03qY4aP1ni3rkaG3/t4BHw2fpJOnztgkmYKAtwVOn/lT/6vbQfMnDlS2oIyRzWHkhbd7hvqdIHDlylV98vl3UUL58/xF/fDTrxrQs7VqVnraCWH6TQxm9IP5nyemCfkNCg1F4DYBknhuBwQcImBWUW/T/VO7F/yrTWsqb+5sUSIzc/tWrtuqT0ZNUdIkiTV6YFe7mjVFMgsa1W/dOwqFGcFg3nR/Nai7niqcDyYEvC5w9MRpVXqhi7Ys/jLKFkok8V7vGhrgAAGTxH84bGKUSMxD8BKF89m/AWZ4N8W9AmaxTjMdyBSz+GSypIndWwFXQ8DBAiTxDu5cQgssgW+/X6S0aVKqevnidw3cJPPvD5mgZs9XZsXWf6SMycJla6O4maF4hQvmUYpkSQPrRiJanxUwazds33nALmJ4e0Jh3tCblZ6ZC++zXUfDEEAgGgHz8NwsNGlKyaKPaUi/9uo5YKyyZHpQ7V6sixkCCNxFgCSe2wMBhwiYRWIeeCB2q1MzTM0hnU4YASdgpswMHjNNPy1fb7e+MjsntGleWxVKFwk4CwJGwN0Cu/Ye1sSZi+0w7pcaVlf24Ew6fPSkkidNonRpU7m7uoC+3r6DR1WzxdtaPvMz622mCpn/Ng/UV63bZhN6CgII3FmAJJ67AwGHCZhkfsXa3++4h6pJAvYcOGL3dw708vPKjXZV239vUROdiznWTFHImjl9oLMRvxcFOvUeJvPl97UWtdX746/UuG5FTZyxyH7hNW+yKAgg4JpAxJadBR/JqWvXr+vEqTN2UbU+n4yzK5yz24drrnc6y+yoUblhV21YMFqJEydS03YfqGOr5xV+9rymz12m0R93dW+FXA0BhwmQxDusQwkHgYg9z2d+9X60GLv3hWrijMX6ZljPgMeaOX+F+g/9Rn26tFTV8sUj91m/HcbMl/98wmyNnThXCyeHkMQH/F3jPQDzAK5kzde1aMondrhpmbodNG/CAJmpNGa7QbP4FgUBBFwT2LH7oOq90tsmlYoXT2XqtNe0MX214ffdWrh0LavTu8Z617NM4l6r8tOqXrGEen30hUo9WUCr1m9T2tQp9c4bze9DjVwSAecIkMQ7py+JBAErEJHE342jcIE8JPH/AC1fs0W9Q/7eZ71utdLK+VAWuzWNWdhuy7a9mj7vF5UqVsB+obh9RXBuNwQ8LWC2UWra7n39Nm+UrToiiV+8YoN+Wb1Zn/Z53dNNoj4EHCNw5tx5la7dXitnD1OaVCnUuluIWjaoqtNn/9SPi9eQxLu5p+/0XcVMWxg/pIf9W0xBAIE7C5DEc3cg4DCBiD+Mv8wYGm1k23cd0OcTfiCJv03HrJC7ZOVG7dxzSOZtzPGwcLvPrNmir2C+XAxTdthnxF/DMXPgTeK+fMZQOz/X/He31xpq7MQ56vRKPbu6MwUBBFwTMMPpm7X/QA/nyKpKZZ/UmG/nKF+e7Ppt0x8qX6qw2r/0nGsX5qxoBcxCnYf+WdQu4gCzYGeWjA/Gen0faBEIZAGS+EDufWJ3pMDNmze1becBm3xGV8zw8CPHw2ySSkEAAf8SaNlpgMqXLqLm9SrbJD5t6hR6tmJJvdiwmp23S0EAAdcEzJaiVZu8GeVk85l6stAj6tWxqcx2c5S4C5hF7OLFixf3C3EFBAJcgCQ+wG8Awne2wNVr1yMDNAuzHT1xSk2eq6QED8RX/PixW8ne2UJEh4D/CpgRJEkSJ/LfAGg5AggEnMCkmYsVlDn9HRffjQAxLyQGjZ6qOlXLsB1uwN0lBBwbAZL42ChxDAJ+JjB51hJNmLbQLnYVXenS5gW91LCan0VFcxFA4MqVq1q/ZZdWb9hu91c2Q3/r1yynzBnTgYMAAnEUMCPVoitmmHeihIx0iSOvPd3829Wh11BVr1BCrRrX+M/uMOZN/catuxUycoouXLykcYN7KF2alO6ommsg4CgBknhHdSfBICBdunxFT1Z9Vf17vKK8uYIVP/7fw9bmLl6j0KMn9WqzmsqQLg173nKzIOBnAubNVPMOH2rXvsMq/VRBu/jW0lWbdSIsXLPG/T2Xl4IAAq4J3G1RWLOVI1vMueYa3Vlm4diBwydp3pI1KpQ/t93qNXWqFDp+MlzrNu+0/6Z1bl1fzRtUYZqQ+9i5ksMESOId1qGEg4B5O1e9aXdtW/r3iusRxQxh23fwGF9EuEUQ8FOBvQeOqlbLtzV+6Nsq+njeyCjMNk3ZgzPpg7da+WlkNBsB7wuYhdb2HzwWpSGhx8PU7b2Rmjq6r/2MUdwrYJL23ftDZXbeOBV+TjmyZVaenFntmj1mlxgKAgjcWYAknrsDAYcJXLt+XRO/X6SGtcsr8W3zZc2q62f/vMBK6w7rb8IJHIFjJ06r4gtdtH7B6Chz4c02iHMXrdKXn3YPHAwiRcBDAmaruWdKFVajOhU8VCPVIIAAAjELkMTHbMQRCPidwK69hzVx5mKZuWUvNaxu3yAcPnpSyZMmYRi93/UmDUbgbwHzeW7Wvr+qlCumGhVKRLKMmThXZq58uxfr2p+ZYakPPMDCldw3CLhDoOeAsUqVIpm6t2vsjstxDQQQQMAtAiTxbmHkIgj4joDZ67ZcvU4q+EhOmbfyJ06d0exx/dXnk3F2bhnz+nynr2gJAvcicLc5u7dfx3zec+cIupdLcywCCEgyQ+r/XU6eOquECR9Q2tR/L64Wn+3RuFcQQMAHBEjifaATaAIC7hQww+brvdJbGxaMluLFU5k67TVtTF9t+H23Fi5dqxED3nBndVwLAQQ8JGDexB89cTrG2jKlTyuzmjYFAQRiL8BDsthbcSQCCHhfgCTe+31ACxBwq8CZc+dVunZ7rZw9zK5ebebztWxQVafP/qkfF68hiXerNhdDAAEEEHCCgNn9YfuugzYUsxDsqvXb9fE7bZQ4UcIo4ZmF125fb8YJsRMDAgj4nwBJvP/1GS1G4K4CZjh9s/Z/bzdVqeyTGvPtHOXLk12/bfpD5UsVVvuXnkMQAQQQQAABBKIRMMPnW3UdKLMbRJnij2vwe+2iLCQJGgIIIOALAiTxvtALtAEBNwr8dfGyqjZ5M8oVzVz4Jws9ol4dmypVyuRurI1LIYAAAggg4AyBQ0dOqGWnj5Q3V7D6v9VKXfuNtIEN799JSZMkdkaQRIEAAo4QIIl3RDcSBAIIIIAAAggggICrAmaHhzJ1O6hsiULq//YrdiHYy1euqm2PQSrwSE51frWBq5fmPAQQQMDtAiTxbiflggh4X8B88YipmHl+8VhlNyYmfo+ATwlcvHRFly5fUbo0KaN8fs00GraV86muojF+JmAWths0eqrdwSV+/P/fotF83pau2qRqzxT3s4hoLgIIOFmAJN7JvUtsASnACrsB2e0EHSACbd8apGNh4Zo+9j271ZWZtztg2Lf6dd02VSxTVP17vKLkyZIEiAZhIhB3AbPrAw+04+7IFRBAwLMCJPGe9aY2BO67gNnnds/+IzHWkyNbZiVKmCDG4zgAAQR8Q8C8EXyy6qsa9VFnu+CWKe17DtHxsHDVf7acRk34QW2a1VSDWs/4RoNpBQJ+IGBWog/KnN4Oo79bMavXmzf1daqWUe4cQX4QGU1EAAEnC5DEO7l3iQ2B2wSOnwzX4WMnVazQo7gggIAfCphFt6o16a7f5o2yb9vNKtrP1OuksZ90U8mij2nm/BVauHQt20j6Yd/SZO8JrN6wXR16DVX1CiXUqnENBWfJEKUx5k39xq27FTJyii5cvKRxg3vY6SwUBBBAwJsCJPHe1KduBNwocOXqtf/sZ2u+fPy28Q9NmrVYPy1bp5cb11Dn1vXdWCuXQgABTwnsOXBEtVv21KZFY+2iW7+u3apXuoVozbyRSpEsqTZt3aPWb4bYJJ+CAAKxFwg7fVYDh0/SvCVrVCh/buXJFazUqVLIPPxet3mnToSF27+dzRtUsZ89CgIIIOBtAZJ4b/cA9SPgJoGvv5uvZas368UXqtkvIebLyIRpC3Xg8HHVqFhSDWs/o8IF8jD3z03eXAYBTwuYRe2KVXtV44e+raKP59XAEZO1bNUmzZ0wwDbFvIn/euoCzfiin6ebRn0IOELAJO2794dq175QnQo/JzPtLE/OrMqTM1gpUyRzRIwEgQACzhAgiXdGPxIFAjp24rTGT1uo8VMXRGqYN+/N61VW+nSpEUIAAQcIfDDkG02csUjlSxXWkpUb1bfbi6pXo6zMqJtm7fvr4ZxZ1adLSwdESggIIIAAAgggcCcBknjuDQQcJmDm7M35aVXkW3iTxNd7thwL8TisnwknMAWuX79h37b//sc+Ow/++er/U4IED8i8pV+4bK0dbZM9OFNg4hA1AggggAACASJAEh8gHU2YgSdgVqlfs2G7vpn+k5b+uknFi+RTuxfrqkjBvIGHQcQIOFjgxKkzunz5Ksm7g/uY0BBAAAEEELhdgCSe+wGBABAwq1pP/WGpEiRIoI6tng+AiAkRAWcKnL9wUavWb7P7w0eUzdv36vSZcypfqohKFM1v38ZTEEAAAQQQQMC5AiTxzu1bIgswATMnNl68eAEWNeEiEFgCdV9+RxcvXVbenMGKHz++DX7/4WO68NdlFXw0p2pWfloVyxQNLBSiRQABBBBAIMAESOIDrMMJ17kCk2YuVlDm9CpbotBdg7x586YGjZ6qOlXLME/eubcDkTlQIPRYmKo06qYNC8dE2U7SfPb3HTymnh2bOjBqQkIAAQQQQACBfwuQxHNPIOAQgdUbtqtDr6GqXqGEWjWuoeAsGaJEZt7Ub9y6WyEjp8gsfjducA+lS5PSIdETBgLOFwg/86fK1O2g9QtGK0niRJEBfzf7Z+0/dEzd2zV2PgIRIoAAAggggIBI4rkJEHCQQNjpsxo4fJLdI97sFZ8nV7BSp0ohs/ftus07dSIsXJ1b11fzBlWUMEECB0VOKAgEjsCR46fs1JmgTA8GTtBEigACCCCAAAKRAiTx3AwIOFDAJO2794dq175QnQo/pxzZMitPzqzKkzNYKVMkc2DEhIRAYAj0Gzxek2cuscG+3rKO2rasozHfzlHWLBlUvXzxwEAgSgQQQAABBAJcgCQ+wG8AwkcAAQQQ8A8Bs5Vc+XpvaNzgt3Tj5k293HmgHVr/7feLtH3XAX3Su61/BEIrEUAAAQQQQCBOAiTxceLjZAQQQAABBDwjsOfAEdVu2VO/L/nSrkxfv3Vv9erU3E6XmTZnqcaEdPNMQ6gFAQQQQAABBLwqQBLvVX4qRwABBBBAIHYCZmeJak27q1+3l/RU4Xzq3Ge4nq1UUtt2HtDho2Ea2OvV2F2IoxBAAAEEEEDArwVI4v26+2g8AggggECgCFy6fEVln+tow835UBa7Ir0pf128rDEfd9XTxQoECgVxIoAAAgggENACJPEB3f0EjwACCCDgLwLXr9/Q7IUrozQ3YcIEypcnux7OkdVfwqCdCCCAAAIIIBBHAZL4OAJyOgIIIIAAAt4UMAveXb58VdmDM3mzGdSNAAIIIIAAAh4SIIn3EDTVIIAAAgggEFeB8xcuatX6bdp74GjkpTZv36vTZ86pfKkiKlE0vwoXyBPXajgfAQQQQAABBHxYgCTehzuHpiGAAAIIIHC7QN2X39HFS5eVN2ewXaHelP2Hj+nCX5dV8NGcqln5aVUsUxQ0BBBAAAEEEHCwAEm8gzuX0BBAAAEEnCMQeixMVRp104aFY5Q4UcLIwCbNXKx9B4+pZ8emzgmWSBBAAAEEEEDgjgIk8dwcCCCAAAII+IFA+Jk/VaZuB61fMFpJEieKbPF3s3+2K9V3b9fYD6KgiQgggAACCCAQVwGS+LgKcj4CCCCAAAIeFLhx46aOHA+TmR+fLSijUqVM7sHaqQoBBBBAAAEEvC1AEu/tHqB+BBBAAAEEYimwa1+o3ug9TAcOH488o3n9KurWtqHix4sXy6twGAIIIIAAAgj4swBJvD/3Hm1HAAEEEAgYgVu3bum5Vu8qT85gdWnTQPVe6a33u7+sgcMnqUX9KmpQ65mAsSBQBBBAAAEEAlmAJD6Qe5/YEUAAAQT8RuDkqbN6pl4nrZv/uZImSWznx8+bMEA/Llmj5Wu26LMPOvpNLDQUAQQQQAABBFwXIIl33Y4zEUAAAQQQ8JjAngNH1LhtP62ZO1Lx4sWLTOKnz/1Fh46c0LudW3isLVSEAAIIIIAAAt4TIIn3nj01I4AAAgggEGuBvy5e1lPV2+iHr/srV/Ygm8RXKlNUcxat0piQbiqUP3esr8WBCCCAAAIIIOC/AiTx/tt3tBwBBBBAIMAEho+bqYeyZlTNSk+r7VuDlD1bZlX6X1EVKZg3wCQIFwEEEEAAgcAVIIkP3L4ncgQQQAABPxMw+8GbofQ5smW2LT9y/JTSpEqh5MmS+FkkNBcBBBBAAAEEXBUgiXdVjvMQQAABBBDwoIDZH75kzbbq2bGZalcppX6DxmvyrCU2gZ804l3lzhHkwdZQFQIIIIAAAgh4S4Ak3lvy1IsAAggggMA9CIQeDVOVxt20edEXCjt9VhVf6KLh/Ttp8YoNSpQwgd55o/k9XI1DEUAAAQQQQMBfBUji/bXnaDcCCCCAQEAJhB4LU6O2/bR8xlDNW7JGISOnaMnUT7Vg6VpNn7tMoz/uGlAeBIsAAggggECgCpDEB2rPEzcCCCCAgN8JmDfxTZ+rZBN3M3y+b9cXNeyrGTp24rQ+eKuV38VDgxFAAAEEEEDg3gVI4u/djDMQQAABBBDwisCq9ds0cMRkO3w+5N3XFJwlg7q+N1J1q5VR6acKeqVNVIoAAggggAACnhUgifesN7UhgAACCCCAAAIIIIAAAggg4LIASbzLdJyIAAIIIIDA/Rf4eeVG5ckVbN+6x1TMsXlzZ1PWzOljOpTfI4AAAggggICfCpDE+2nH0WwEEEAAgcAQmDl/hfoP/UZ9urRU1fLFFT9evP8EfvHSFX0+YbbGTpyrhZNDSOID49YgSgQQQACBABUgiQ/QjidsBBBAAAH/EVi+Zot6h4yzDa5brbRyPpRFKVMks1vNbdm2V9Pn/aJSxQrYbeayBWX0n8BoKQIIIIAAAgjcswBJ/D2TcQICCCCAAAKeF7h85aqWrNyonXsOacfugzoeFq48OYP1SO5sKpgvl0oWfczzjaJGBBBAAAEEEPC4AEm8x8mpEAEEEEAAAQQQQAABBBBAAAHXBEjiXXPjLAQQQAABBBBAAAEEEEAAAQQ8LkAS73FyKkQAAQQQQAABBBBAAAEEEEDANQGSeNfcOAsBBBBAAAEEEEAAAQQQQAABjwuQxHucnAoRQAABBBBAAAEEEEAAAQQQcE2AJN41N85CAAEEEEAAAQQQQAABBBBAwOMCJPEeJ6dCBBBAAAEEEEAAAQQQQAABBFwTIIl3zY2zEEAAAQQQQAABBBBAAAEEEPC4AEm8x8mpEAEEEEAAAQQQQAABBBBAAAHXBEjiXXPjLAQQQAABBBBAAAEEEEAAAQQ8LkAS73FyKkQAAQQQQAABBBBAAAEEEEDANQGSeNfcOAsBBBBAAAEEEEAAAQQQQAABjwuQxHucnAoRQAABBBBAAAEEEEAAAQQQcE2AJN41N85CAAEEEEAAAQQQQAABBBBAwOMCJPEeJ6dCBBBAAAEEEEAAAQQQQAABBFwTIIl3zY2zEEAAAQR8UGDz9r2aNX+F1mzcoarlnlL7l59TdD/zwabTJAQQQAABBBBAIFYCJPGxYuIgBBBAAAFfF7h46YqKVXtVTz/5mMoUf1xpUqdUxTJF//OzWpWf9vVQaB8CCCCAAAIIIHBHAZJ4bg4EEEAAAUcILFq+Xh3f+UwrZw9TmlQpbEzR/cwRwRIEAggggAACCASsAEl8wHY9gSOAAAL+JdCl7wht3blfoUfDlC5tKpUuVkCdWtdXpvRptWbDDvX4cIxOhIWrcIE8NrD6NctpyNjpUX72fveXlSNb5hgDf3fgl3owXWrduHlTc35apYQJH1Cj2hXU+LmKSpQwgT3/bu0xv58y+2etWr9NJYrk18QZi3T8ZLgqlC6iHu2b6NvvF2nmghW6du2GmtStoCbPV1KSxInsdS/8dcm2e/GKDbbtxYvkU/fXG+mR3A/F2G4OQAABBBBAAAHnC5DEO7+PiRABBBBwhIB5y17osYeVLSiDws+e17CvZujR3Nk0JqSbDh05oY+GT9LSXzepV6dmNt7H8ubQ59/8EOVnVcoWsw8AYir1W/fW9l0H7QOByuWK6fCRkzYR/3xgF5V+qqA9/W7tMb//dPRUfTFxroKDMqhejbK6eu26Roybac/NnSNIdaqW0dlz5/XFpHka0q+9Hfp/8+ZNNWrbT2f/vKAmdSsqbZqU+mb6T9p/6JiWTB2kFMmTxtR0fo8AAggggAACDhcgiXd4BxMeAggg4DSBK1ev2eR3/LSFGjdlvn5f8qXix49vk+HPJ8zWb/NGRYYc3c9i42GS+KxZMmhQn9cVL148e0qtlm+reOH86tmxaZRL3Kk9Jomf8eNyLZocosT/vGVv0/1THT1xStPHvtL+7cwAAAPJSURBVKeECf5+o/9Cm772gcO7nVvYBw6vvz1YE0e8o0L5c9vf79oXqrov9YpM9GPTfo5BAAEEEEAAAecKkMQ7t2+JDAEEEHCUwIKlazVqwmzt2ns4SlybFo21CbG7k/iCj+ayiXVEafvWIPufIwa8Yf8/pvaYJH7B0t+0YOLHkdd4Z+CX2rXvsKaM6h35s/Y9h+ja9Rsa9VFnG99nX3yv/HmzR/7++o2bNubu7Rqreb3KjupTgkEAAQQQQACBexcgib93M85AAAEEEPCwwK9rt+qVbiGqU7W0XqhdXsFZMmjJig3qHfKVPJXEm2T7xo2bNomPTXuiS+JNe//YcyhKEm+G5Zu3+SaJHzxmmsZ8O8f+97+LmcufLSijh+WpDgEEEEAAAQR8TYAk3td6hPYggAACCPxHICK53bzoCyVI8ID9/cz5K9RzwFivJPGxaY8rSfysBSv19odjNGvcB3o4R9YoDrdu3Yoc2s8tggACCCCAAAKBK0ASH7h9T+QIIICA3wgsW71ZZjh7t9caqtgTj2jbzgP67KsZCj/zp1eS+Ni0x5Uk/q+Ll1WzxdtKmiSR3ny9kXIEZ9aB0OOaNX+FalUupXJPP+E3fUZDEUAAAQQQQOD+CJDE3x9XrooAAggg4EYBM4zdbCE3d9Eqe1WzwvwT+XNrycqNkUn8l5N/1Kjxs6IsbBfdz2LTrNsXm4s43gx7v37jhob372SH1cfUnkGjp2r+v+bE9/lknHbsPhhlOH2n3sN09cq1yLn2ZiX6foPH223zIoqZI/9B91bKmztbbJrPMQgggAACCCDgYAGSeAd3LqEhgAACThM49+dfOnf+gp0Tb1ak93a5n+25cuWqwsLPKW3qlEqeLIm3Q6V+BBBAAAEEEPARAZJ4H+kImoEAAgggcH8FzND7qk3ejLGSNXNHMvc8RiUOQAABBBBAAAFvCZDEe0ueehFAAAEEPC5w+crVGOtM8s+e7jEeyAEIIIAAAggggIAXBEjivYBOlQgggAACCCCAAAIIIIAAAgi4IkAS74oa5yCAAAIIIIAAAggggAACCCDgBQGSeC+gUyUCCCCAAAIIIIAAAggggAACrgiQxLuixjkIIIAAAggggAACCCCAAAIIeEGAJN4L6FSJAAIIIIAAAggggAACCCCAgCs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"text/html": [ - "
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" + " }) }; " ] }, "metadata": {}, @@ -9070,7 +6383,7 @@ " x=\"aff_name\", \n", " height=900, \n", " color=\"aff_country\",\n", - " title=f\"Top Countries and Industry collaborators for {gridname}-{GRIDID}\",\n", + " title=f\"Top Countries and Industry collaborators for {orgname}-{ORGID}\",\n", " color_discrete_sequence=px.colors.diverging.Spectral)" ] } @@ -9099,7 +6412,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" }, "nteract": { "version": "0.15.0" diff --git a/docs/_sources/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb.txt b/docs/_sources/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb.txt index 9dc9cbcf..ce8f4f27 100644 --- a/docs/_sources/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb.txt +++ b/docs/_sources/cookbooks/8-organizations/3-Organizations-Collaboration-Network.ipynb.txt @@ -28,7 +28,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Aug 22, 2023\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -58,33 +58,14 @@ "Collapsed": "false" }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" - ] - }, { "data": { "text/html": [ " \n", + " \n", " " ] }, @@ -104,8 +85,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v1.1)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.7\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -159,9 +140,9 @@ "id": "L6uIjSVnGRQV" }, "source": [ - "For the purpose of this exercise, we will use [grid.412125.1](https://grid.ac/institutes/grid.412125.1) (King Abdulaziz University, Saudi Arabia). \n", + "For the purpose of this exercise, we will use King Abdulaziz University, Saudi Arabia (grid.412125.1). \n", "\n", - "> You can try using a different GRID ID to see how results change, e.g. by [browsing for another GRID organization](https://grid.ac/institutes).\n" + "> You can try using a different organization ID to see how results change." ] }, { @@ -174,7 +155,7 @@ { "data": { "text/html": [ - "GRID: grid.412125.1 - King Abdulaziz University ⧉" + "Organization: grid.412125.1 - King Abdulaziz University ⧉" ], "text/plain": [ "" @@ -209,7 +190,7 @@ } ], "source": [ - "GRIDID = \"grid.412125.1\" #@param {type:\"string\"}\n", + "ORGID = \"grid.412125.1\" #@param {type:\"string\"}\n", " \n", "#@markdown The start/end year of publications used to extract patents\n", "YEAR_START = 2000 #@param {type: \"slider\", min: 1950, max: 2020}\n", @@ -226,11 +207,11 @@ "# gen link to Dimensions\n", "#\n", "try:\n", - " gridname = dsl.query(f\"\"\"search organizations where id=\"{GRIDID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", + " orgname = dsl.query(f\"\"\"search organizations where id=\"{ORGID}\" return organizations[name]\"\"\", verbose=False).organizations[0]['name']\n", "except:\n", - " gridname = \"\"\n", + " orgname = \"\"\n", "from IPython.display import display, HTML\n", - "display(HTML('GRID: {} - {} ⧉'.format(dimensions_url(GRIDID), GRIDID, gridname)))\n", + "display(HTML('Organization: {} - {} ⧉'.format(dimensions_url(ORGID), ORGID, orgname)))\n", "display(HTML('Time period: {} to {}'.format(YEAR_START, YEAR_END)))\n", "display(HTML('Topic: \"{}\"

'.format(TOPIC)))\n" ] @@ -292,10 +273,10 @@ "Note: \n", "\n", "* **Extra columns**. The resulting dataframe contains two extra columns: a) `id_from`, which is the 'seed' institution we start from; b) `level`, an optional parameter representing the network depth of the query (we'll see later how it is used with recursive querying).\n", - "* **Self-collaboration**. The query returns 11 records - that's because the first one is normally the seed GRID (due to internal collaborations) which we will omit from the results.\n", + "* **Self-collaboration**. The query returns 11 records - that's because the first one is normally the seed organization (due to internal collaborations) which we will omit from the results.\n", "* **Custom changes**. Lastly, it's important to remember that this step can be easily customised by changing the `query_template` sttructure. For example, we could focus on specific research areas (using FOR codes), or set a threshold based on citation counts. The possibilities are endless! \n", "\n", - "For example, let's try it out with our GRID ID:" + "For example, let's try it out with our organization ID:" ] }, { @@ -341,6 +322,7 @@ " acronym\n", " city_name\n", " count\n", + " country_code\n", " country_name\n", " latitude\n", " linkout\n", @@ -358,7 +340,8 @@ " King Abdulaziz University\n", " KAU\n", " Jeddah\n", - " 1444\n", + " 1435\n", + " SA\n", " Saudi Arabia\n", " 21.493889\n", " [http://www.kau.edu.sa/home_english.aspx]\n", @@ -375,6 +358,7 @@ " NU\n", " Boston\n", " 106\n", + " US\n", " United States\n", " 42.339830\n", " [http://www.northeastern.edu/]\n", @@ -391,6 +375,7 @@ " NaN\n", " Cambridge\n", " 98\n", + " US\n", " United States\n", " 42.377052\n", " [http://www.harvard.edu/]\n", @@ -407,6 +392,7 @@ " MIT\n", " Cambridge\n", " 73\n", + " US\n", " United States\n", " 42.359820\n", " [http://web.mit.edu/]\n", @@ -423,6 +409,7 @@ " NU\n", " Evanston\n", " 59\n", + " US\n", " United States\n", " 42.054850\n", " [http://www.northwestern.edu/]\n", @@ -434,27 +421,12 @@ " \n", " \n", " 5\n", - " grid.413735.7\n", - " Harvard–MIT Division of Health Sciences and Te...\n", - " HST\n", - " Cambridge\n", - " 58\n", - " United States\n", - " 42.361780\n", - " [http://hst.mit.edu/]\n", - " -71.086914\n", - " [Education]\n", - " Massachusetts\n", - " grid.412125.1\n", - " 1\n", - " \n", - " \n", - " 6\n", " grid.411340.3\n", " Aligarh Muslim University\n", " AMU\n", " Aligarh\n", - " 47\n", + " 46\n", + " IN\n", " India\n", " 27.917370\n", " [http://www.amu.ac.in/]\n", @@ -465,12 +437,13 @@ " 1\n", " \n", " \n", - " 7\n", + " 6\n", " grid.412621.2\n", " Quaid-i-Azam University\n", " QAU\n", " Islamabad\n", - " 47\n", + " 46\n", + " PK\n", " Pakistan\n", " 33.747223\n", " [http://www.qau.edu.pk/]\n", @@ -481,12 +454,47 @@ " 1\n", " \n", " \n", + " 7\n", + " grid.411818.5\n", + " Jamia Millia Islamia\n", + " JMI\n", + " New Delhi\n", + " 40\n", + " IN\n", + " India\n", + " 28.561607\n", + " [http://jmi.ac.in/]\n", + " 77.280150\n", + " [Education]\n", + " NaN\n", + " grid.412125.1\n", + " 1\n", + " \n", + " \n", " 8\n", + " grid.62560.37\n", + " Brigham and Womens Hospital Inc\n", + " BWH\n", + " Boston\n", + " 40\n", + " US\n", + " United States\n", + " NaN\n", + " [http://www.brighamandwomens.org/]\n", + " NaN\n", + " [Healthcare]\n", + " Massachusetts\n", + " grid.412125.1\n", + " 1\n", + " \n", + " \n", + " 9\n", " grid.33003.33\n", " Suez Canal University\n", " NaN\n", " Ismailia\n", - " 42\n", + " 39\n", + " EG\n", " Egypt\n", " 30.622778\n", " [http://scuegypt.edu.eg/ar/]\n", @@ -497,28 +505,13 @@ " 1\n", " \n", " \n", - " 9\n", - " grid.411818.5\n", - " Jamia Millia Islamia\n", - " JMI\n", - " New Delhi\n", - " 42\n", - " India\n", - " 28.561607\n", - " [http://jmi.ac.in/]\n", - " 77.280150\n", - " [Education]\n", - " NaN\n", - " grid.412125.1\n", - " 1\n", - " \n", - " \n", " 10\n", " grid.56302.32\n", " King Saud University\n", " KSU\n", " Riyadh\n", - " 42\n", + " 39\n", + " SA\n", " Saudi Arabia\n", " 24.723982\n", " [http://ksu.edu.sa/en/]\n", @@ -533,44 +526,44 @@ "" ], "text/plain": [ - " id name acronym \\\n", - "0 grid.412125.1 King Abdulaziz University KAU \n", - "1 grid.261112.7 Northeastern University NU \n", - "2 grid.38142.3c Harvard University NaN \n", - "3 grid.116068.8 Massachusetts Institute of Technology MIT \n", - "4 grid.16753.36 Northwestern University NU \n", - "5 grid.413735.7 Harvard–MIT Division of Health Sciences and Te... HST \n", - "6 grid.411340.3 Aligarh Muslim University AMU \n", - "7 grid.412621.2 Quaid-i-Azam University QAU \n", - "8 grid.33003.33 Suez Canal University NaN \n", - "9 grid.411818.5 Jamia Millia Islamia JMI \n", - "10 grid.56302.32 King Saud University KSU \n", + " id name acronym city_name \\\n", + "0 grid.412125.1 King Abdulaziz University KAU Jeddah \n", + "1 grid.261112.7 Northeastern University NU Boston \n", + "2 grid.38142.3c Harvard University NaN Cambridge \n", + "3 grid.116068.8 Massachusetts Institute of Technology MIT Cambridge \n", + "4 grid.16753.36 Northwestern University NU Evanston \n", + "5 grid.411340.3 Aligarh Muslim University AMU Aligarh \n", + "6 grid.412621.2 Quaid-i-Azam University QAU Islamabad \n", + "7 grid.411818.5 Jamia Millia Islamia JMI New Delhi \n", + "8 grid.62560.37 Brigham and Womens Hospital Inc BWH Boston \n", + "9 grid.33003.33 Suez Canal University NaN Ismailia \n", + "10 grid.56302.32 King Saud University KSU Riyadh \n", "\n", - " city_name count country_name latitude \\\n", - "0 Jeddah 1444 Saudi Arabia 21.493889 \n", - "1 Boston 106 United States 42.339830 \n", - "2 Cambridge 98 United States 42.377052 \n", - "3 Cambridge 73 United States 42.359820 \n", - "4 Evanston 59 United States 42.054850 \n", - "5 Cambridge 58 United States 42.361780 \n", - "6 Aligarh 47 India 27.917370 \n", - "7 Islamabad 47 Pakistan 33.747223 \n", - "8 Ismailia 42 Egypt 30.622778 \n", - "9 New Delhi 42 India 28.561607 \n", - "10 Riyadh 42 Saudi Arabia 24.723982 \n", + " count country_code country_name latitude \\\n", + "0 1435 SA Saudi Arabia 21.493889 \n", + "1 106 US United States 42.339830 \n", + "2 98 US United States 42.377052 \n", + "3 73 US United States 42.359820 \n", + "4 59 US United States 42.054850 \n", + "5 46 IN India 27.917370 \n", + "6 46 PK Pakistan 33.747223 \n", + "7 40 IN India 28.561607 \n", + "8 40 US United States NaN \n", + "9 39 EG Egypt 30.622778 \n", + "10 39 SA Saudi Arabia 24.723982 \n", "\n", - " linkout longitude types \\\n", - "0 [http://www.kau.edu.sa/home_english.aspx] 39.250280 [Education] \n", - "1 [http://www.northeastern.edu/] -71.089180 [Education] \n", - "2 [http://www.harvard.edu/] -71.116650 [Education] \n", - "3 [http://web.mit.edu/] -71.092110 [Education] \n", - "4 [http://www.northwestern.edu/] -87.673940 [Education] \n", - "5 [http://hst.mit.edu/] -71.086914 [Education] \n", - "6 [http://www.amu.ac.in/] 78.077850 [Education] \n", - "7 [http://www.qau.edu.pk/] 73.138885 [Education] \n", - "8 [http://scuegypt.edu.eg/ar/] 32.275000 [Education] \n", - "9 [http://jmi.ac.in/] 77.280150 [Education] \n", - "10 [http://ksu.edu.sa/en/] 46.645840 [Education] \n", + " linkout longitude types \\\n", + "0 [http://www.kau.edu.sa/home_english.aspx] 39.250280 [Education] \n", + "1 [http://www.northeastern.edu/] -71.089180 [Education] \n", + "2 [http://www.harvard.edu/] -71.116650 [Education] \n", + "3 [http://web.mit.edu/] -71.092110 [Education] \n", + "4 [http://www.northwestern.edu/] -87.673940 [Education] \n", + "5 [http://www.amu.ac.in/] 78.077850 [Education] \n", + "6 [http://www.qau.edu.pk/] 73.138885 [Education] \n", + "7 [http://jmi.ac.in/] 77.280150 [Education] \n", + "8 [http://www.brighamandwomens.org/] NaN [Healthcare] \n", + "9 [http://scuegypt.edu.eg/ar/] 32.275000 [Education] \n", + "10 [http://ksu.edu.sa/en/] 46.645840 [Education] \n", "\n", " state_name id_from level \n", "0 NaN grid.412125.1 1 \n", @@ -578,10 +571,10 @@ "2 Massachusetts grid.412125.1 1 \n", "3 Massachusetts grid.412125.1 1 \n", "4 Illinois grid.412125.1 1 \n", - "5 Massachusetts grid.412125.1 1 \n", - "6 Uttar Pradesh grid.412125.1 1 \n", + "5 Uttar Pradesh grid.412125.1 1 \n", + "6 NaN grid.412125.1 1 \n", "7 NaN grid.412125.1 1 \n", - "8 NaN grid.412125.1 1 \n", + "8 Massachusetts grid.412125.1 1 \n", "9 NaN grid.412125.1 1 \n", "10 NaN grid.412125.1 1 " ] @@ -592,7 +585,7 @@ } ], "source": [ - "get_collaborators(GRIDID, printquery=True)" + "get_collaborators(ORGID, printquery=True)" ] }, { @@ -605,9 +598,9 @@ "\n", "What if we want to retrieve the collaborators of the collaborators? In other words, what if we want to generate a larger network?\n", "\n", - "If we think of our collaboration data as a [graph structure](https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)) with nodes and edges, we can see that the `get_collaborators` function defined above is limited. That's because it allows to obtain only the objects *directly* linked to the 'seed' GRID organization. \n", + "If we think of our collaboration data as a [graph structure](https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)) with nodes and edges, we can see that the `get_collaborators` function defined above is limited. That's because it allows to obtain only the objects *directly* linked to the 'seed' organization. \n", "\n", - "We would like to run the same collaborators-extraction step **iteratively** for any GRID ID in our results, so to generate an N-degrees network where N is chosen by us. \n", + "We would like to run the same collaborators-extraction step **iteratively** for any ID in our results, so to generate an N-degrees network where N is chosen by us. \n", "\n", "To this purpose, we can set up a [recursive](https://en.wikipedia.org/wiki/Recursion_(computer_science)) function. This function essentially repeats the `get_collaborators` function as many times as needed. Here's what it looks like:" ] @@ -627,8 +620,8 @@ " print(\"--\" * thislevel, seed, \" :: level =\", thislevel)\n", " if thislevel < maxlevel:\n", " # remove the originating grid-id\n", - " gridslist = list(results[results['id'] != GRIDID]['id'])\n", - " next_level_results = [recursive_network(x, maxlevel, thislevel+1) for x in gridslist]\n", + " orgslist = list(results[results['id'] != ORGID]['id'])\n", + " next_level_results = [recursive_network(x, maxlevel, thislevel+1) for x in orgslist]\n", " next_level_results = pd.concat(next_level_results)\n", " results = pd.concat([results, next_level_results])\n", " return results\n", @@ -671,11 +664,11 @@ "---- grid.38142.3c :: level = 2\n", "---- grid.116068.8 :: level = 2\n", "---- grid.16753.36 :: level = 2\n", - "---- grid.413735.7 :: level = 2\n", "---- grid.411340.3 :: level = 2\n", "---- grid.412621.2 :: level = 2\n", - "---- grid.33003.33 :: level = 2\n", "---- grid.411818.5 :: level = 2\n", + "---- grid.62560.37 :: level = 2\n", + "---- grid.33003.33 :: level = 2\n", "---- grid.56302.32 :: level = 2\n" ] }, @@ -721,7 +714,7 @@ " grid.412125.1\n", " grid.412125.1\n", " 1\n", - " 1444\n", + " 1435\n", " King Abdulaziz University\n", " KAU\n", " Jeddah\n", @@ -802,7 +795,7 @@ ], "text/plain": [ " id_from id_to level count \\\n", - "0 grid.412125.1 grid.412125.1 1 1444 \n", + "0 grid.412125.1 grid.412125.1 1 1435 \n", "1 grid.412125.1 grid.261112.7 1 106 \n", "2 grid.412125.1 grid.38142.3c 1 98 \n", "3 grid.412125.1 grid.116068.8 1 73 \n", @@ -836,7 +829,7 @@ } ], "source": [ - "collaborators = recursive_network(GRIDID, maxlevel=2)\n", + "collaborators = recursive_network(ORGID, maxlevel=2)\n", "# change column order for readability purposes\n", "collaborators.rename(columns={\"id\": \"id_to\"}, inplace=True)\n", "collaborators = collaborators[['id_from', 'id_to', 'level', 'count', 'name', 'acronym', 'city_name', 'state_name', 'country_name', 'latitude', 'longitude', 'linkout', 'types' ]]\n", @@ -896,7 +889,7 @@ " " ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -945,13 +938,13 @@ "\n", " # calc size based on level\n", " maxsize = int(nodes['level'].max()) + 1\n", - " if row['id_to'] == GRIDID:\n", + " if row['id_to'] == ORGID:\n", " size = maxsize\n", " else:\n", " size = maxsize - row['level']\n", "\n", " # calc color based on level\n", - " if row['id_to'] == GRIDID:\n", + " if row['id_to'] == ORGID:\n", " color = palette[0]\n", " else:\n", " color = palette[row['level'] * 2]\n", @@ -990,10 +983,10 @@ " return g\n", "\n", "#\n", - "# finall, run the viz builder\n", + "# finally, run the viz builder\n", "#\n", "g = build_visualization(collaborators)\n", - "g.show(f\"network_{GRIDID}.html\")" + "g.show(f\"network_{ORGID}.html\")" ] }, { @@ -1006,7 +999,7 @@ "\n", "What if we want to show a collaboration network focusing only on 'government' organizations? \n", "\n", - "That's pretty easy to do, since the GRID database includes information about **organization types**. We can easily see what types are available using the API and a `facet` query:" + "That's pretty easy to do, since the organization data set includes information about **organization types**. We can easily see what types are available using the API and a `facet` query:" ] }, { @@ -1020,8 +1013,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Types: 9\n", - "\u001b[2mTime: 1.00s\u001b[0m\n" + "Returned Types: 8\n", + "\u001b[2mTime: 2.54s\u001b[0m\n" ] }, { @@ -1052,64 +1045,58 @@ " \n", " \n", " 0\n", - " Company\n", - " 30742\n", + " Other\n", + " 165766\n", " \n", " \n", " 1\n", - " Education\n", - " 20761\n", + " Company\n", + " 158994\n", " \n", " \n", " 2\n", - " Nonprofit\n", - " 17573\n", + " Education\n", + " 22805\n", " \n", " \n", " 3\n", - " Healthcare\n", - " 13926\n", + " Nonprofit\n", + " 18361\n", " \n", " \n", " 4\n", - " Facility\n", - " 10168\n", + " Healthcare\n", + " 14865\n", " \n", " \n", " 5\n", " Government\n", - " 6580\n", + " 11545\n", " \n", " \n", " 6\n", - " Other\n", - " 4017\n", + " Facility\n", + " 10692\n", " \n", " \n", " 7\n", " Archive\n", - " 2926\n", - " \n", - " \n", - " 8\n", - " Education,Company\n", - " 1\n", + " 3059\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id count\n", - "0 Company 30742\n", - "1 Education 20761\n", - "2 Nonprofit 17573\n", - "3 Healthcare 13926\n", - "4 Facility 10168\n", - "5 Government 6580\n", - "6 Other 4017\n", - "7 Archive 2926\n", - "8 Education,Company 1" + " id count\n", + "0 Other 165766\n", + "1 Company 158994\n", + "2 Education 22805\n", + "3 Nonprofit 18361\n", + "4 Healthcare 14865\n", + "5 Government 11545\n", + "6 Facility 10692\n", + "7 Archive 3059" ] }, "execution_count": 9, @@ -1133,7 +1120,7 @@ "* **Get more results**. We increase the number of results returned: `..return research_orgs limit 50`. This is to ensure we still have enough results after removing the ones that don't have the chosen 'type'\n", "* **Remove unwanted data**. The new query filter `research_orgs.types in [\"{}\"]` will return also publications with multiple authors/affiliations, even though only one of them has the desired 'type'. So an extra step is required and this is achieved via the `keep_type` function below. This function simply filters out all unwanted organizations data after they're retrieved from the API. \n", "\n", - "That's it! Run the cell below to generate a new visualization showing only \"Government\" collaborators. Or try changing the value of `GRID_TYPE` to see different results. \n" + "That's it! Run the cell below to generate a new visualization showing only \"Government\" collaborators. Or try changing the value of `ORG_TYPE` to see different results. \n" ] }, { @@ -1150,7 +1137,6 @@ "-- grid.412125.1 :: level = 1\n", "---- grid.7327.1 :: level = 2\n", "---- grid.9227.e :: level = 2\n", - "---- grid.20256.33 :: level = 2\n", "---- grid.1089.0 :: level = 2\n", "---- grid.14467.30 :: level = 2\n", "network_grid.412125.1_Government.html\n" @@ -1171,7 +1157,7 @@ " " ], "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -1182,7 +1168,7 @@ "source": [ "#@markdown Try using one of the organization types from the list above\n", "\n", - "GRID_TYPE = \"Government\" #@param {type:\"string\"}\n", + "ORG_TYPE = \"Government\" #@param {type:\"string\"}\n", "\n", "query = \"\"\"search publications {}\n", " where year in [{}:{}] \n", @@ -1193,7 +1179,7 @@ "def keep_only_type(data, a_type, orgid):\n", " clean_list = []\n", " for x in data.research_orgs:\n", - " # include also originating GRID to ensure chart is complete\n", + " # include also originating org to ensure chart is complete\n", " if x['id'] == orgid or a_type in x['types']:\n", " clean_list.append(x)\n", " data.json['research_orgs'] = clean_list\n", @@ -1206,8 +1192,8 @@ " TOPIC_CLAUSE = f\"\"\"for \"{TOPIC}\" \"\"\"\n", " else:\n", " TOPIC_CLAUSE = \"\"\n", - " # include also the GRID_TYPE\n", - " query_full = query.format(TOPIC_CLAUSE, YEAR_START, YEAR_END, orgid, GRID_TYPE)\n", + " # include also the ORG_TYPE\n", + " query_full = query.format(TOPIC_CLAUSE, YEAR_START, YEAR_END, orgid, ORG_TYPE)\n", " if printquery: print(query_full)\n", " data = dsl.query(query_full, verbose=False)\n", " # remove results with unwanted types \n", @@ -1221,7 +1207,7 @@ "#\n", "# RUN THE RECURSIVE QUERY (same code as above)\n", "#\n", - "collaborators = recursive_network(GRIDID, maxlevel=2)\n", + "collaborators = recursive_network(ORGID, maxlevel=2)\n", "collaborators.rename(columns={\"id\": \"id_to\"}, inplace=True)\n", "collaborators = collaborators[['id_from', 'id_to', 'level', 'count', 'name', 'acronym', 'city_name', 'country_name', 'latitude', 'longitude', 'linkout', 'types' ]]\n", "\n", @@ -1229,7 +1215,7 @@ "# BUILD VIZ\n", "#\n", "g = build_visualization(collaborators)\n", - "g.show(f\"network_{GRIDID}_{GRID_TYPE}.html\")\n", + "g.show(f\"network_{ORGID}_{ORG_TYPE}.html\")\n", "\n" ] }, @@ -1272,7 +1258,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.1" + "version": "3.12.8" }, "nteract": { "version": "0.15.0" diff --git a/docs/_sources/cookbooks/8-organizations/4-international-collaboration-by-year.ipynb.txt b/docs/_sources/cookbooks/8-organizations/4-international-collaboration-by-year.ipynb.txt index cea374f0..40286910 100644 --- a/docs/_sources/cookbooks/8-organizations/4-international-collaboration-by-year.ipynb.txt +++ b/docs/_sources/cookbooks/8-organizations/4-international-collaboration-by-year.ipynb.txt @@ -24,7 +24,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Jan 25, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": { "Collapsed": "false" }, @@ -59,19 +59,9 @@ "text/html": [ " \n", + " \n", " " ] }, @@ -91,8 +81,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -137,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": { "Collapsed": "false" }, @@ -146,8 +136,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Organizations: 16 (total = 16)\n", - "\u001b[2mTime: 0.57s\u001b[0m\n" + "Returned Organizations: 20 (total = 23)\n", + "\u001b[2mTime: 0.53s\u001b[0m\n" ] }, { @@ -171,225 +161,298 @@ " \n", " \n", " \n", + " id\n", + " name\n", " city_name\n", + " country_code\n", " country_name\n", - " id\n", + " types\n", + " state_name\n", " latitude\n", " linkout\n", " longitude\n", - " name\n", - " state_name\n", - " types\n", " acronym\n", " \n", " \n", " \n", " \n", " 0\n", + " grid.772384.d\n", + " Trelleborg Marine Systems Melbourne Pty Ltd\n", + " Victoria\n", + " AU\n", + " Australia\n", + " [Company]\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 1\n", + " grid.746611.3\n", + " Noyes Bros Melbourne Pty Ltd\n", + " NaN\n", + " AU\n", + " Australia\n", + " [Other]\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 2\n", + " grid.631568.f\n", + " CityLink Melbourne Ltd\n", + " NaN\n", + " AU\n", + " Australia\n", + " [Other]\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 3\n", + " grid.530408.a\n", + " Melbourne Institute of Technology\n", " Melbourne\n", + " AU\n", " Australia\n", + " [Nonprofit]\n", + " Victoria\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 4\n", " grid.511296.8\n", + " Melbourne Genomics Health Alliance\n", + " Melbourne\n", + " AU\n", + " Australia\n", + " [Nonprofit]\n", + " Victoria\n", " -37.797960\n", " [https://www.melbournegenomics.org.au/]\n", " 144.953870\n", - " Melbourne Genomics Health Alliance\n", - " Victoria\n", - " [Nonprofit]\n", " NaN\n", " \n", " \n", - " 1\n", + " 5\n", + " grid.493437.e\n", + " RMIT Europe\n", " Barcelona\n", + " ES\n", " Spain\n", - " grid.493437.e\n", + " [Education]\n", + " NaN\n", " 41.402576\n", " [https://www.rmit.eu]\n", " 2.194333\n", - " RMIT Europe\n", - " NaN\n", - " [Education]\n", " RMIT\n", " \n", " \n", - " 2\n", + " 6\n", + " grid.490309.7\n", + " Melbourne Sexual Health Centre\n", " Carlton\n", + " AU\n", " Australia\n", - " grid.490309.7\n", + " [Healthcare]\n", + " Victoria\n", " -37.803123\n", " [https://www.mshc.org.au/]\n", " 144.963840\n", - " Melbourne Sexual Health Centre\n", - " Victoria\n", - " [Healthcare]\n", " MSHC\n", " \n", " \n", - " 3\n", + " 7\n", + " grid.477970.a\n", + " Melbourne Clinic\n", " Richmond\n", + " AU\n", " Australia\n", - " grid.477970.a\n", + " [Healthcare]\n", + " Victoria\n", " -37.815063\n", " [http://www.themelbourneclinic.com.au/]\n", " 144.999650\n", - " Melbourne Clinic\n", - " Victoria\n", - " [Healthcare]\n", " NaN\n", " \n", " \n", - " 4\n", - " Melbourne\n", + " 8\n", + " grid.474755.0\n", + " Leica Biosystems Melbourne Pty Ltd\n", + " Mt. Waverley\n", + " AU\n", " Australia\n", + " [Company]\n", + " NaN\n", + " NaN\n", + " [http://www.danaher.com/]\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 9\n", " grid.469061.c\n", + " Ridley College\n", + " Melbourne\n", + " AU\n", + " Australia\n", + " [Education]\n", + " Victoria\n", " -37.783780\n", " [https://www.ridley.edu.au/]\n", " 144.957660\n", - " Ridley College\n", - " Victoria\n", - " [Education]\n", " NaN\n", " \n", " \n", - " 5\n", + " 10\n", + " grid.469026.f\n", + " Melbourne School of Theology\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.469026.f\n", + " [Education]\n", + " Victoria\n", " -37.859700\n", " [http://www.mst.edu.au/]\n", " 145.209410\n", - " Melbourne School of Theology\n", - " Victoria\n", - " [Education]\n", " MBI\n", " \n", " \n", - " 6\n", + " 11\n", + " grid.468079.4\n", + " Port of Melbourne Corporation\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.468079.4\n", + " [Government]\n", + " Victoria\n", " -37.824028\n", " [http://www.portofmelbourne.com/]\n", " 144.907070\n", - " Port of Melbourne Corporation\n", - " Victoria\n", - " [Government]\n", " PoMC\n", " \n", " \n", - " 7\n", + " 12\n", + " grid.468069.5\n", + " Melbourne Water\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.468069.5\n", + " [Government]\n", + " Victoria\n", " -37.814007\n", " [http://www.melbournewater.com.au/Pages/home.a...\n", " 144.946700\n", - " Melbourne Water\n", - " Victoria\n", - " [Government]\n", " NaN\n", " \n", " \n", - " 8\n", + " 13\n", + " grid.452643.2\n", + " Melbourne Bioinformatics\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.452643.2\n", + " [Education]\n", + " Victoria\n", " -37.799847\n", - " [https://www.vlsci.org.au/]\n", + " [https://www.melbournebioinformatics.org.au]\n", " 144.964460\n", - " Victorian Life Sciences Computation Initiative\n", - " Victoria\n", - " [Education]\n", - " NaN\n", + " VLSCI\n", " \n", " \n", - " 9\n", + " 14\n", + " grid.449135.e\n", + " Melbourne Free University\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.449135.e\n", + " [Education]\n", + " Victoria\n", " NaN\n", " NaN\n", " NaN\n", - " Melbourne Free University\n", - " Victoria\n", - " [Education]\n", " NaN\n", " \n", " \n", - " 10\n", + " 15\n", + " grid.440113.3\n", + " Royal Dental Hospital of Melbourne\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.440113.3\n", + " [Healthcare]\n", + " Victoria\n", " -37.799260\n", " [https://www.dhsv.org.au]\n", " 144.964630\n", - " Royal Dental Hospital of Melbourne\n", - " Victoria\n", - " [Healthcare]\n", " RDHM\n", " \n", " \n", - " 11\n", + " 16\n", + " grid.438527.f\n", + " Royal Melbourne Institute of Technology Univer...\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.429299.d\n", - " -37.798940\n", - " [http://www.mh.org.au/]\n", - " 144.955930\n", - " Melbourne Health\n", + " [Other]\n", " Victoria\n", - " [Healthcare]\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " \n", " \n", - " 12\n", + " 17\n", + " grid.429299.d\n", + " Melbourne Health\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.416153.4\n", - " -37.798756\n", - " [http://www.rmh.mh.org.au/]\n", - " 144.955930\n", - " Royal Melbourne Hospital\n", - " Victoria\n", " [Healthcare]\n", - " RMH\n", - " \n", - " \n", - " 13\n", - " Clayton\n", - " Australia\n", - " grid.410660.5\n", - " -37.915775\n", - " [http://nanomelbourne.com/]\n", - " 145.143660\n", - " Melbourne Centre for Nanofabrication\n", " Victoria\n", - " [Facility]\n", - " MCN\n", + " -37.798940\n", + " [http://www.mh.org.au/]\n", + " 144.955930\n", + " NaN\n", " \n", " \n", - " 14\n", + " 18\n", + " grid.416153.4\n", + " Royal Melbourne Hospital\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.1017.7\n", - " -37.806747\n", - " [https://www.rmit.edu.au/]\n", - " 144.962570\n", - " RMIT University\n", + " [Healthcare]\n", " Victoria\n", - " [Education]\n", - " RMIT\n", + " -37.798756\n", + " [http://www.rmh.mh.org.au/]\n", + " 144.955930\n", + " RMH\n", " \n", " \n", - " 15\n", + " 19\n", + " grid.413105.2\n", + " St Vincent's Hospital\n", " Melbourne\n", + " AU\n", " Australia\n", - " grid.1008.9\n", - " -37.797115\n", - " [http://www.unimelb.edu.au/]\n", - " 144.959980\n", - " University of Melbourne\n", + " [Healthcare]\n", " Victoria\n", - " [Education]\n", + " -37.807000\n", + " [http://www.svhm.org.au/Pages/Home.aspx]\n", + " 144.975000\n", " NaN\n", " \n", " \n", @@ -397,80 +460,96 @@ "" ], "text/plain": [ - " city_name country_name id latitude \\\n", - "0 Melbourne Australia grid.511296.8 -37.797960 \n", - "1 Barcelona Spain grid.493437.e 41.402576 \n", - "2 Carlton Australia grid.490309.7 -37.803123 \n", - "3 Richmond Australia grid.477970.a -37.815063 \n", - "4 Melbourne Australia grid.469061.c -37.783780 \n", - "5 Melbourne Australia grid.469026.f -37.859700 \n", - "6 Melbourne Australia grid.468079.4 -37.824028 \n", - "7 Melbourne Australia grid.468069.5 -37.814007 \n", - "8 Melbourne Australia grid.452643.2 -37.799847 \n", - "9 Melbourne Australia grid.449135.e NaN \n", - "10 Melbourne Australia grid.440113.3 -37.799260 \n", - "11 Melbourne Australia grid.429299.d -37.798940 \n", - "12 Melbourne Australia grid.416153.4 -37.798756 \n", - "13 Clayton Australia grid.410660.5 -37.915775 \n", - "14 Melbourne Australia grid.1017.7 -37.806747 \n", - "15 Melbourne Australia grid.1008.9 -37.797115 \n", + " id name \\\n", + "0 grid.772384.d Trelleborg Marine Systems Melbourne Pty Ltd \n", + "1 grid.746611.3 Noyes Bros Melbourne Pty Ltd \n", + "2 grid.631568.f CityLink Melbourne Ltd \n", + "3 grid.530408.a Melbourne Institute of Technology \n", + "4 grid.511296.8 Melbourne Genomics Health Alliance \n", + "5 grid.493437.e RMIT Europe \n", + "6 grid.490309.7 Melbourne Sexual Health Centre \n", + "7 grid.477970.a Melbourne Clinic \n", + "8 grid.474755.0 Leica Biosystems Melbourne Pty Ltd \n", + "9 grid.469061.c Ridley College \n", + "10 grid.469026.f Melbourne School of Theology \n", + "11 grid.468079.4 Port of Melbourne Corporation \n", + "12 grid.468069.5 Melbourne Water \n", + "13 grid.452643.2 Melbourne Bioinformatics \n", + "14 grid.449135.e Melbourne Free University \n", + "15 grid.440113.3 Royal Dental Hospital of Melbourne \n", + "16 grid.438527.f Royal Melbourne Institute of Technology Univer... \n", + "17 grid.429299.d Melbourne Health \n", + "18 grid.416153.4 Royal Melbourne Hospital \n", + "19 grid.413105.2 St Vincent's Hospital \n", "\n", - " linkout longitude \\\n", - "0 [https://www.melbournegenomics.org.au/] 144.953870 \n", - "1 [https://www.rmit.eu] 2.194333 \n", - "2 [https://www.mshc.org.au/] 144.963840 \n", - "3 [http://www.themelbourneclinic.com.au/] 144.999650 \n", - "4 [https://www.ridley.edu.au/] 144.957660 \n", - "5 [http://www.mst.edu.au/] 145.209410 \n", - "6 [http://www.portofmelbourne.com/] 144.907070 \n", - "7 [http://www.melbournewater.com.au/Pages/home.a... 144.946700 \n", - "8 [https://www.vlsci.org.au/] 144.964460 \n", - "9 NaN NaN \n", - "10 [https://www.dhsv.org.au] 144.964630 \n", - "11 [http://www.mh.org.au/] 144.955930 \n", - "12 [http://www.rmh.mh.org.au/] 144.955930 \n", - "13 [http://nanomelbourne.com/] 145.143660 \n", - "14 [https://www.rmit.edu.au/] 144.962570 \n", - "15 [http://www.unimelb.edu.au/] 144.959980 \n", + " city_name country_code country_name types state_name \\\n", + "0 Victoria AU Australia [Company] NaN \n", + "1 NaN AU Australia [Other] NaN \n", + "2 NaN AU Australia [Other] NaN \n", + "3 Melbourne AU Australia [Nonprofit] Victoria \n", + "4 Melbourne AU Australia [Nonprofit] Victoria \n", + "5 Barcelona ES Spain [Education] NaN \n", + "6 Carlton AU Australia [Healthcare] Victoria \n", + "7 Richmond AU Australia [Healthcare] Victoria \n", + "8 Mt. Waverley AU Australia [Company] NaN \n", + "9 Melbourne AU Australia [Education] Victoria \n", + "10 Melbourne AU Australia [Education] Victoria \n", + "11 Melbourne AU Australia [Government] Victoria \n", + "12 Melbourne AU Australia [Government] Victoria \n", + "13 Melbourne AU Australia [Education] Victoria \n", + "14 Melbourne AU Australia [Education] Victoria \n", + "15 Melbourne AU Australia [Healthcare] Victoria \n", + "16 Melbourne AU Australia [Other] Victoria \n", + "17 Melbourne AU Australia [Healthcare] Victoria \n", + "18 Melbourne AU Australia [Healthcare] Victoria \n", + "19 Melbourne AU Australia [Healthcare] Victoria \n", "\n", - " name state_name types \\\n", - "0 Melbourne Genomics Health Alliance Victoria [Nonprofit] \n", - "1 RMIT Europe NaN [Education] \n", - "2 Melbourne Sexual Health Centre Victoria [Healthcare] \n", - "3 Melbourne Clinic Victoria [Healthcare] \n", - "4 Ridley College Victoria [Education] \n", - "5 Melbourne School of Theology Victoria [Education] \n", - "6 Port of Melbourne Corporation Victoria [Government] \n", - "7 Melbourne Water Victoria [Government] \n", - "8 Victorian Life Sciences Computation Initiative Victoria [Education] \n", - "9 Melbourne Free University Victoria [Education] \n", - "10 Royal Dental Hospital of Melbourne Victoria [Healthcare] \n", - "11 Melbourne Health Victoria [Healthcare] \n", - "12 Royal Melbourne Hospital Victoria [Healthcare] \n", - "13 Melbourne Centre for Nanofabrication Victoria [Facility] \n", - "14 RMIT University Victoria [Education] \n", - "15 University of Melbourne Victoria [Education] \n", + " latitude linkout longitude \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 -37.797960 [https://www.melbournegenomics.org.au/] 144.953870 \n", + "5 41.402576 [https://www.rmit.eu] 2.194333 \n", + "6 -37.803123 [https://www.mshc.org.au/] 144.963840 \n", + "7 -37.815063 [http://www.themelbourneclinic.com.au/] 144.999650 \n", + "8 NaN [http://www.danaher.com/] NaN \n", + "9 -37.783780 [https://www.ridley.edu.au/] 144.957660 \n", + "10 -37.859700 [http://www.mst.edu.au/] 145.209410 \n", + "11 -37.824028 [http://www.portofmelbourne.com/] 144.907070 \n", + "12 -37.814007 [http://www.melbournewater.com.au/Pages/home.a... 144.946700 \n", + "13 -37.799847 [https://www.melbournebioinformatics.org.au] 144.964460 \n", + "14 NaN NaN NaN \n", + "15 -37.799260 [https://www.dhsv.org.au] 144.964630 \n", + "16 NaN NaN NaN \n", + "17 -37.798940 [http://www.mh.org.au/] 144.955930 \n", + "18 -37.798756 [http://www.rmh.mh.org.au/] 144.955930 \n", + "19 -37.807000 [http://www.svhm.org.au/Pages/Home.aspx] 144.975000 \n", "\n", " acronym \n", "0 NaN \n", - "1 RMIT \n", - "2 MSHC \n", + "1 NaN \n", + "2 NaN \n", "3 NaN \n", "4 NaN \n", - "5 MBI \n", - "6 PoMC \n", + "5 RMIT \n", + "6 MSHC \n", "7 NaN \n", "8 NaN \n", "9 NaN \n", - "10 RDHM \n", - "11 NaN \n", - "12 RMH \n", - "13 MCN \n", - "14 RMIT \n", - "15 NaN " + "10 MBI \n", + "11 PoMC \n", + "12 NaN \n", + "13 VLSCI \n", + "14 NaN \n", + "15 RDHM \n", + "16 NaN \n", + "17 NaN \n", + "18 RMH \n", + "19 NaN " ] }, - "execution_count": 17, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -483,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": { "Collapsed": "false" }, @@ -503,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": { "Collapsed": "false" }, @@ -512,8 +591,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Year: 12\n", - "\u001b[2mTime: 0.57s\u001b[0m\n" + "Returned Year: 16\n", + "\u001b[2mTime: 0.55s\u001b[0m\n" ] }, { @@ -524,7 +603,6 @@ }, "data": [ { - "alignmentgroup": "True", "hovertemplate": "year=%{x}
pubs=%{y}", "legendgroup": "", "marker": { @@ -534,45 +612,23 @@ } }, "name": "", - "offsetgroup": "", "orientation": "v", "showlegend": false, "textposition": "auto", "type": "bar", - "x": [ - 2021, - 2020, - 2019, - 2018, - 2017, - 2016, - 2015, - 2014, - 2013, - 2012, - 2011, - 2022 - ], + "x": { + "bdata": "5QfoB+cH5gfkB+MH4gfhB+AH6QffB94H3QfcB9sH6gc=", + "dtype": "i2" + }, "xaxis": "x", - "y": [ - 13015, - 12183, - 11039, - 9954, - 9198, - 8281, - 7720, - 7184, - 6779, - 6030, - 5742, - 816 - ], + "y": { + "bdata": "9kygSotJ9UjhSOlA+T0ZOm42PjZDNBkx3y99K2koDwA=", + "dtype": "i2" + }, "yaxis": "y" } ], "layout": { - "autosize": true, "barmode": "relative", "legend": { "tracegroupgap": 0 @@ -759,57 +815,6 @@ "type": "heatmap" } ], - "heatmapgl": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 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], "title": { "text": "pubs" - }, - "type": "linear" + } } } }, - "image/png": 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", "text/html": [ - "
\n", + "
" + " }) }; " ] }, "metadata": {}, @@ -6849,7 +6435,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "auallpubs.columns = ['all_count', 'year', ]\n", + "auallpubs.columns = ['year', 'all_count']\n", "\n", "auintpubs = dsl.query(\"\"\"\n", " \n", @@ -6862,7 +6448,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "auintpubs.columns = ['all_int_count', 'year', ]\n", + "auintpubs.columns = ['year', 'all_int_count']\n", "\n", "\n", "audompubs = dsl.query(\"\"\"\n", @@ -6876,7 +6462,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "audompubs.columns = [ 'all_dom_count', 'year',]\n", + "audompubs.columns = ['year', 'all_dom_count']\n", "\n", "auinternalpubs = dsl.query(\"\"\"\n", " \n", @@ -6890,12 +6476,12 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "auinternalpubs.columns = ['all_internal_count', 'year', ]\n", + "auinternalpubs.columns = ['year', 'all_internal_count']\n", "\n", "audf = auallpubs.set_index('year'). \\\n", - " join(auintpubs.set_index('year')). \\\n", - " join(audompubs.set_index('year')). \\\n", - " join(auinternalpubs.set_index('year')). \\\n", + " merge(auintpubs, how='left', on='year'). \\\n", + " merge(audompubs, how='left', on='year'). \\\n", + " merge(auinternalpubs, how='left', on='year'). \\\n", " sort_values(by=['year'])\n", "\n", "px.bar(audf, title=\"Australia: publications collaboration\")" @@ -6912,7 +6498,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "Collapsed": "false" }, @@ -6921,14 +6507,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Returned Year: 12\n", - "\u001b[2mTime: 0.56s\u001b[0m\n", - "Returned Year: 12\n", - "\u001b[2mTime: 0.59s\u001b[0m\n", - "Returned Year: 12\n", - "\u001b[2mTime: 0.61s\u001b[0m\n", - "Returned Year: 12\n", - "\u001b[2mTime: 0.56s\u001b[0m\n" + "Returned Year: 16\n", + "\u001b[2mTime: 0.49s\u001b[0m\n", + "Returned Year: 16\n", + "\u001b[2mTime: 6.01s\u001b[0m\n", + "Returned Year: 16\n", + "\u001b[2mTime: 0.63s\u001b[0m\n", + "Returned Year: 16\n", + "\u001b[2mTime: 0.49s\u001b[0m\n" ] }, { @@ -6939,196 +6525,132 @@ }, "data": [ { - "alignmentgroup": "True", - "hovertemplate": "variable=pubs
year=%{x}
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index=%{x}
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", 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" + " }) }; " ] }, "metadata": {}, @@ -8037,7 +7506,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "allpubs.columns = ['pubs', 'year', ]\n", + "allpubs.columns = ['year', 'pubs']\n", "\n", "\n", "\n", @@ -8053,7 +7522,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "international.columns = ['international_count', 'year', ]\n", + "international.columns = ['year', 'international_count']\n", "\n", "\n", "domestic = dsl.query(f\"\"\"\n", @@ -8068,7 +7537,7 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "domestic.columns = ['domestic_count', 'year', ]\n", + "domestic.columns = ['year', 'domestic_count']\n", "\n", "internal = dsl.query(f\"\"\"\n", " \n", @@ -8082,13 +7551,13 @@ " \n", " \"\"\").as_dataframe()\n", "\n", - "internal.columns = ['internal_count', 'year', ]\n", + "internal.columns = ['year', 'internal_count']\n", "\n", "\n", "jdf = allpubs.set_index('year'). \\\n", - " join(international.set_index('year')). \\\n", - " join(domestic.set_index('year')). \\\n", - " join(internal.set_index('year')) \n", + " merge(international, how='left', on='year'). \\\n", + " merge(domestic, how='left', on='year'). \\\n", + " merge(internal, how='left', on='year')\n", "\n", "px.bar(jdf, title=\"Univ. of Toronto: publications collaboration\")\n" ] @@ -8125,7 +7594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" }, "nteract": { "version": "0.15.0" diff --git a/docs/_sources/cookbooks/8-organizations/5-mapping-organization-ids-to-organization-data.ipynb.txt b/docs/_sources/cookbooks/8-organizations/5-mapping-organization-ids-to-organization-data.ipynb.txt new file mode 100644 index 00000000..04fae69d --- /dev/null +++ b/docs/_sources/cookbooks/8-organizations/5-mapping-organization-ids-to-organization-data.ipynb.txt @@ -0,0 +1,922 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "hu34Y6_eo_8c" + }, + "source": [ + "# Mapping Organization IDs to Organization Data\n", + "\n", + "In this tutorial, we show how to use the [Dimensions Analytics API](https://www.dimensions.ai/dimensions-apis/) and organization data to extract organization IDs.\n", + "\n", + "**Use case scenarios:**\n", + "\n", + "* An analyst has a list of organizations of interest, and wants to get details of their publications from Dimensions. To do this, they they need to map them to organization IDs so they can extract information from the Dimensions database. The organization data can be run through the Dimensions API [extract_affiliations](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations) function in order to extract IDs, which can then be utilized to get publication data statistics.\n", + "\n", + "* A second use case is to standardize messy organization data for \n", + "analysis. For example, an analyst might have a set of affiliation data containing many variants of organization names (\"University of Cambridge\", \"Cambridge University\"). By mapping to IDs, the analyst can standardize the data so it's easier to analyse." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==\n", + "CHANGELOG\n", + "This notebook was last run on Sep 10, 2025\n", + "==\n" + ] + } + ], + "source": [ + "import datetime\n", + "print(\"==\\nCHANGELOG\\nThis notebook was last run on %s\\n==\" % datetime.date.today().strftime('%b %d, %Y'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "lnk_dWT3pINN" + }, + "source": [ + "## Prerequisites \n", + "\n", + "This notebook assumes you have installed the [Dimcli](https://pypi.org/project/dimcli/) library and are familiar with the ['Getting Started' tutorial](https://api-lab.dimensions.ai/cookbooks/1-getting-started/1-Using-the-Dimcli-library-to-query-the-API.html).\n", + "\n", + "To generate an API key from the Dimensions webapp, go to \"My Account\". Under \"General Settings\" there is an \"API key\" section where there is a \"Create API key\" button. More information on this can be found [here](https://dimensions.freshdesk.com/support/solutions/articles/23000018791).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 18999, + "status": "ok", + "timestamp": 1624636934872, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 300 + }, + "id": "p0v3SdNwpDLn", + "outputId": "8c654b2a-5fbe-4c73-feca-89ed1a0987f6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[2mSearching config file credentials for 'https://app.dimensions.ai' endpoint..\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==\n", + "Logging in..\n", + "==\n", + "Logging in..\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", + "\u001b[2mMethod: dsl.ini file\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install dimcli --quiet\n", + "\n", + "import dimcli\n", + "from dimcli.utils import *\n", + "from dimcli.functions import extract_affiliations\n", + "\n", + "import json\n", + "import sys\n", + "import pandas as pd\n", + "import re\n", + "import time\n", + "\n", + "print(\"==\\nLogging in..\")\n", + "# https://digital-science.github.io/dimcli/getting-started.html#authentication\n", + "ENDPOINT = \"https://app.dimensions.ai\"\n", + "\n", + "print(\"==\\nLogging in..\")\n", + "# https://digital-science.github.io/dimcli/getting-started.html#authentication\n", + "ENDPOINT = \"https://app.dimensions.ai\"\n", + "if 'google.colab' in sys.modules:\n", + " import getpass\n", + " KEY = getpass.getpass(prompt='API Key: ') \n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "else:\n", + " KEY = \"\"\n", + " dimcli.login(key=KEY, endpoint=ENDPOINT)\n", + "dsl = dimcli.Dsl()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "RlPdopEbpeyE" + }, + "source": [ + "## 1. Importing Organization Data\n", + "\n", + "There are several ways to obtain organization data. Below we show examples for 2 different ways to obtain organization data that can be used to run through the Dimensions API for ID mapping. *For purposes of this demostration, we will be using method 1*. Please uncomment the other sections if you wish to use those methods instead.\n", + "\n", + "\n", + "1. Manually Generate Organization Data\n", + "2. Load Organization Data from Local Machine\n", + "\n", + "*Note* - To map organizational data to IDs, the data must conform to mapping specifications and contain data (if available) for the following 4 columns (with column headers being lowercase):\n", + "* name - name of the organization\n", + "* city - city of the organization\n", + "* state - state of the organization (use the full name of the state, not acronym)\n", + "* country - country of the organization\n", + "\n", + "\n", + "The user may use structured or unstructured organization data for mapping to IDs like the following:\n", + "\n", + "* Structured Data e.g., \n", + "`[{\"name\":\"Southwestern University\",\n", + " \"city\":\"Georgetown\",\n", + " \"state\":\"Texas\",\n", + " \"country\":\"USA\"}]`\n", + "* Unstructured Data\n", + "e.g., `[{\"affiliation\": \"university of oxford, uk\"}]`\n", + "\n", + "*For purposes of this notebook, we will be utilizing structured data in a pandas dataframe. Therefore, please ensure your organization dataset resembles the format observed under method 1, below.*\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "X_t-RnDWv3BB" + }, + "source": [ + "### 1.1 Manually Generate Organization Data\n", + "\n", + "The following cell builds an example organization dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 320 + }, + "executionInfo": { + "elapsed": 207, + "status": "ok", + "timestamp": 1624636951400, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 300 + }, + "id": "YtckPfuTpXNi", + "outputId": "70d674be-a82a-4f49-fd52-48f1655e6a73" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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namecitystatecountry
0Augusta UniveristyAugustaGeorgiaUnited States
1Baylor College of MedicineHoustonTexasUnited States
2Brown UniversityProvidenceRhode IslandUnited States
3California Institute of TechnologyPasadenaCaliforniaUnited States
4Duke UniverisityDurhamNorth CarolinaUnited States
5Emory UniversityAtlantaGeorgiaUnited States
6Florida State UniversityTallahasseeFloridaUnited States
7Harvard Medical SchoolBostonMassachusettsUnited States
8Kent State UniversityKentOhioUnited States
9New York UniversityNew YorkNew YorkUnited States
10Mayo ClinicNaNNaNUnited States
\n", + "
" + ], + "text/plain": [ + " name city state \\\n", + "0 Augusta Univeristy Augusta Georgia \n", + "1 Baylor College of Medicine Houston Texas \n", + "2 Brown University Providence Rhode Island \n", + "3 California Institute of Technology Pasadena California \n", + "4 Duke Univerisity Durham North Carolina \n", + "5 Emory University Atlanta Georgia \n", + "6 Florida State University Tallahassee Florida \n", + "7 Harvard Medical School Boston Massachusetts \n", + "8 Kent State University Kent Ohio \n", + "9 New York University New York New York \n", + "10 Mayo Clinic NaN NaN \n", + "\n", + " country \n", + "0 United States \n", + "1 United States \n", + "2 United States \n", + "3 United States \n", + "4 United States \n", + "5 United States \n", + "6 United States \n", + "7 United States \n", + "8 United States \n", + "9 United States \n", + "10 United States " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The following generates a table of organization data with 4 columns\n", + "organization_names = pd.Series(['Augusta Univeristy', 'Baylor College of Medicine', 'Brown University', 'California Institute of Technology', 'Duke Univerisity',\n", + " 'Emory University', 'Florida State University', 'Harvard Medical School', 'Kent State University', 'New York University', 'Mayo Clinic'])\n", + "organization_cities = pd.Series(['Augusta', 'Houston', 'Providence', 'Pasadena', 'Durham',\n", + " 'Atlanta', 'Tallahassee', 'Boston', 'Kent', 'New York'])\n", + "organization_states = pd.Series(['Georgia', 'Texas', 'Rhode Island', 'California', 'North Carolina',\n", + " 'Georgia', 'Florida', 'Massachusetts', 'Ohio', 'New York'])\n", + "organization_countries = pd.Series(['United States', 'United States', 'United States', 'United States', 'United States',\n", + " 'United States', 'United States', 'United States', 'United States', 'United States', 'United States'])\n", + "\n", + "orgs = pd.DataFrame({'name':organization_names, 'city':organization_cities, 'state':organization_states, 'country':organization_countries})\n", + "\n", + "# Preview Dataset\n", + "orgs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "FK2-EG8czdg_" + }, + "source": [ + "### 1.2 Load Organization Data from Local Machine\n", + "\n", + "The following cells can be utilized to import an excel file of organization data from a local machine.\n", + "\n", + "This method is useful for when you need to map hundreds or thousands of organizations to IDs, as the bulk process using the API will be much faster than any individual mapping.\n", + "\n", + "\n", + "*Please uncomment the cells below if to be utilized*" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "Collapsed": "false", + "id": "-OHw5k8Yzcfe" + }, + "outputs": [], + "source": [ + "# # Upload the organization dataset from local machine\n", + "\n", + "# from google.colab import files\n", + "# uploaded = files.upload()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "Collapsed": "false", + "id": "0oEG2QB1xqgH" + }, + "outputs": [], + "source": [ + "# # Load and preview the organization dataset into a pandas dataframe\n", + "\n", + "# import io\n", + "# import pandas as pd\n", + "\n", + "# orgs = pd.read_excel(io.BytesIO(uploaded['dataset_name.xlsx']))\n", + "\n", + "# orgs.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "rOeRQ6S7244b" + }, + "source": [ + "## 2. Utilizing Dimensions API to Extract IDs\n", + "\n", + " The following cells will take our organization data and run it through the Dimensions API to pull back IDs mapped to each organization.\n", + "\n", + "Here, we utilize the \"[extract_affiliations](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations)\" API function which can be used to enrich private datasets including non-disambiguated organizations data with Dimensions organization IDs.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "Collapsed": "false", + "id": "s3QexS3m4OsV" + }, + "outputs": [], + "source": [ + "# First, we replace empty data with 'null' to satisfy mapping specifications\n", + "\n", + "orgs = orgs.fillna('null')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "Collapsed": "false", + "id": "rZqY2QTD26y5" + }, + "outputs": [], + "source": [ + "# Second, we will convert organization data from a dataframe to a dictionary (json) for ID mapping\n", + "\n", + "recs = orgs.to_dict(orient='records')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 3106, + "status": "ok", + "timestamp": 1624636962862, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 300 + }, + "id": "W_AkE-i231b8", + "outputId": "af752023-c2cd-45dc-948d-04b080a91b99" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 records complete!\n" + ] + } + ], + "source": [ + "# Then we will take the organization data, run it through the API and return organization IDs\n", + "\n", + "# Chunk records to batches, API takes up to 200 records at a time.\n", + "def chunk_records(l, n):\n", + " for i in range(0, len(l), n):\n", + " yield l[i : i + n]\n", + "\n", + "# Use dimcli's from extract_affiliations API wrapper to process data\n", + "\n", + "chunksize = 200\n", + "org_data = pd.DataFrame()\n", + "for k,chunk in enumerate(chunk_records(recs, chunksize)):\n", + " output = extract_affiliations(chunk, as_json=False)\n", + " org_data = pd.concat([org_data, output])\n", + " # Pause to avoid overloading API with too many calls too quickly\n", + " time.sleep(1)\n", + " print(f\"{(k+1)*chunksize} records complete!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 321 + }, + "executionInfo": { + "elapsed": 177, + "status": "ok", + "timestamp": 1624636964136, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 300 + }, + "id": "yFRkUViz34hf", + "outputId": "9c2d920b-93e5-4ad1-f84b-0871d9023801" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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input.cityinput.countryinput.nameinput.stategrid_idgrid_namegrid_citygrid_stategrid_countryrequires_reviewgeo_country_idgeo_country_namegeo_country_codegeo_state_idgeo_state_namegeo_state_codegeo_city_idgeo_city_name
0AugustaUnited StatesAugusta UniveristyGeorgiagrid.410427.4Augusta UniversityAugustaGeorgiaUnited StatesFalse6252001United StatesUS4197000GeorgiaUS-GA4180531Augusta
1HoustonUnited StatesBaylor College of MedicineTexasgrid.39382.33Baylor College of MedicineHoustonTexasUnited StatesFalse6252001United StatesUS4736286TexasUS-TX4699066Houston
2ProvidenceUnited StatesBrown UniversityRhode Islandgrid.40263.33Brown UniversityProvidenceRhode IslandUnited StatesFalse6252001United StatesUS5224323Rhode IslandUS-RI5224151Providence
3PasadenaUnited StatesCalifornia Institute of TechnologyCaliforniagrid.20861.3dCalifornia Institute of TechnologyPasadenaCaliforniaUnited StatesFalse6252001United StatesUS5332921CaliforniaUS-CA5381396Pasadena
4DurhamUnited StatesDuke UniverisityNorth Carolinagrid.26009.3dDuke UniversityDurhamNorth CarolinaUnited StatesFalse6252001United StatesUS4482348North CarolinaUS-NC4464368Durham
\n", + "
" + ], + "text/plain": [ + " input.city input.country input.name \\\n", + "0 Augusta United States Augusta Univeristy \n", + "1 Houston United States Baylor College of Medicine \n", + "2 Providence United States Brown University \n", + "3 Pasadena United States California Institute of Technology \n", + "4 Durham United States Duke Univerisity \n", + "\n", + " input.state grid_id grid_name \\\n", + "0 Georgia grid.410427.4 Augusta University \n", + "1 Texas grid.39382.33 Baylor College of Medicine \n", + "2 Rhode Island grid.40263.33 Brown University \n", + "3 California grid.20861.3d California Institute of Technology \n", + "4 North Carolina grid.26009.3d Duke University \n", + "\n", + " grid_city grid_state grid_country requires_review geo_country_id \\\n", + "0 Augusta Georgia United States False 6252001 \n", + "1 Houston Texas United States False 6252001 \n", + "2 Providence Rhode Island United States False 6252001 \n", + "3 Pasadena California United States False 6252001 \n", + "4 Durham North Carolina United States False 6252001 \n", + "\n", + " geo_country_name geo_country_code geo_state_id geo_state_name \\\n", + "0 United States US 4197000 Georgia \n", + "1 United States US 4736286 Texas \n", + "2 United States US 5224323 Rhode Island \n", + "3 United States US 5332921 California \n", + "4 United States US 4482348 North Carolina \n", + "\n", + " geo_state_code geo_city_id geo_city_name \n", + "0 US-GA 4180531 Augusta \n", + "1 US-TX 4699066 Houston \n", + "2 US-RI 5224151 Providence \n", + "3 US-CA 5381396 Pasadena \n", + "4 US-NC 4464368 Durham " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Preview the extracted organization ID dataframe\n", + "# Note: data columns labeled with \"input\" are the original organization data supplied to the API\n", + "\n", + "org_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "0xmORlDluF0e" + }, + "source": [ + "Note: Some records returned in the mapping may require manual review, as some results may give more than one organization of interest (see below). The user can utilize this information to update their original organization data that is inputted to this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 400 + }, + "executionInfo": { + "elapsed": 202, + "status": "ok", + "timestamp": 1624636975652, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 300 + }, + "id": "XhI6un5zxL7L", + "outputId": "26b38de2-f88d-4453-efc0-a174bd3dd739" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
input.cityinput.countryinput.nameinput.stategrid_idgrid_namegrid_citygrid_stategrid_countryrequires_reviewgeo_country_idgeo_country_namegeo_country_codegeo_state_idgeo_state_namegeo_state_codegeo_city_idgeo_city_name
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [input.city, input.country, input.name, input.state, grid_id, grid_name, grid_city, grid_state, grid_country, requires_review, geo_country_id, geo_country_name, geo_country_code, geo_state_id, geo_state_name, geo_state_code, geo_city_id, geo_city_name]\n", + "Index: []" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "org_data['requires_review'] = org_data['requires_review'].astype(str)\n", + "org_data_review = org_data.loc[org_data['requires_review'] == 'True']\n", + "org_data_review" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "e2YjFdSk4X6X" + }, + "source": [ + "## 3. Save the ID Dataset we created\n", + "\n", + "The following cell will export the ID-mapped organization data to a csv file that can be saved to your local machine.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "Collapsed": "false", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "executionInfo": { + "elapsed": 132, + "status": "ok", + "timestamp": 1623264999048, + "user": { + "displayName": "Derek Denning", + "photoUrl": "", + "userId": "01288319615638558065" + }, + "user_tz": 240 + }, + "id": "mvfSL5Ci38ft", + "outputId": "1585e34f-d308-4afd-e2cb-847a0e27a405" + }, + "outputs": [], + "source": [ + "# temporarily save pandas dataframe as file in colab environment\n", + "org_data.to_csv('file_name.csv')\n", + "\n", + "if 'google.colab' in sys.modules:\n", + " \n", + " from google.colab import files\n", + "\n", + " # download file to local machine\n", + " files.download('file_name.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false", + "id": "HWwI1o_q4vxv" + }, + "source": [ + "## Conclusions\n", + "\n", + "In this notebook we have shown how to use the [Dimensions Analytics API](https://www.dimensions.ai/dimensions-apis/) *extract_affiliations* function to assign identifiers to organizations data.\n", + "\n", + "For more background, see the [extract_affiliations function documentation](https://docs.dimensions.ai/dsl/functions.html#function-extract-affiliations), as well as the other functions available via the Dimensions API. \n", + "\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "[APILAB] Derek Denning - grid_mapping_api.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/_sources/cookbooks/8-organizations/6-organization-groups.ipynb.txt b/docs/_sources/cookbooks/8-organizations/6-organization-groups.ipynb.txt index 545ce5ee..6ca8faf2 100644 --- a/docs/_sources/cookbooks/8-organizations/6-organization-groups.ipynb.txt +++ b/docs/_sources/cookbooks/8-organizations/6-organization-groups.ipynb.txt @@ -11,13 +11,13 @@ "This tutorial shows how use the organization groups in Dimensions (e.g. the [funder groups](https://app.dimensions.ai/browse/facet-filter-groups/publication/funder_shared_group_facet)) in order to construct API queries. \n", "\n", "The Dimensions team maintains various organization groups definitions in the main Dimensions web application. \n", - "These groups are not available directly via the API, but since they are a simple list of GRID identifiers, they can be easily downloaded as a CSV file. \n", + "These groups are not available directly via the API, but since they are a simple list of organization identifiers, they can be easily downloaded as a CSV file. \n", "Once you have a CSV file, it is possible to parse it with Python and use its contents in an API query. \n", "\n", "Outline \n", "\n", "1. Downloading Dimensions' organization groups as a CSV file.\n", - "2. Constructing API queries using a list of GRID IDs\n", + "2. Constructing API queries using a list of organization IDs\n", " " ] }, @@ -32,7 +32,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Feb 21, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": { "Collapsed": "false" }, @@ -75,8 +75,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -118,7 +118,7 @@ "\n", "2. Use the 'Copy to my Groups' command to create a copy of that group in your personal space.\n", "\n", - "3. Go to 'My Groups', where you can select 'Export group definitions' to download a CSV file containing the groups details including GRID IDs. \n", + "3. Go to 'My Groups', where you can select 'Export group definitions' to download a CSV file containing the groups details including organization IDs. \n", "\n", "See below a screenshot of the [Dimensions' groups page](http://api-sample-data.dimensions.ai/data/funder-groups/dimensions-funder-groups-page.jpg). \n", "\n", @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -139,7 +139,7 @@ "" ] }, - "execution_count": 7, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": { "Collapsed": "false" }, @@ -432,7 +432,7 @@ "24 grid.457898.f " ] }, - "execution_count": 9, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -449,7 +449,7 @@ "Collapsed": "false" }, "source": [ - "Let's get the GRID IDs for the NSF and put them into a Python list.\n", + "Let's get the organization IDs for the NSF and put them into a Python list.\n", "\n", "Then we can generate queries programmatically using this list. \n", "\n", @@ -458,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": { "Collapsed": "false" }, @@ -493,14 +493,14 @@ " 'grid.457898.f']" ] }, - "execution_count": 11, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "nsfgrids = data['ID'].to_list()\n", - "nsfgrids" + "nsforgs = data['ID'].to_list()\n", + "nsforgs" ] }, { @@ -511,14 +511,14 @@ "source": [ "### How many grants from the NSF? \n", "\n", - "Let's try a simple API query that uses the contents of `nsfgrids`. \n", + "Let's try a simple API query that uses the contents of `nsforgs`. \n", "\n", "The total number of results should match [what you see in Dimensions](https://app.dimensions.ai/discover/publication?and_facet_funder_shared_group_facet=574603a4-0c27-4844-9f74-7e6810e25cfb).\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": { "Collapsed": "false" }, @@ -532,8 +532,10 @@ " where funders.id in [\"grid.457768.f\", \"grid.457785.c\", \"grid.457799.1\", \"grid.457810.f\", \"grid.457836.b\", \"grid.457875.c\", \"grid.457916.8\", \"grid.457789.0\", \"grid.457813.c\", \"grid.457814.b\", \"grid.457842.8\", \"grid.457821.d\", \"grid.457772.4\", \"grid.457801.f\", \"grid.457891.6\", \"grid.457892.5\", \"grid.457845.f\", \"grid.457922.f\", \"grid.457896.1\", \"grid.431093.c\", \"grid.457758.c\", \"grid.457907.8\", \"grid.473792.c\", \"grid.457846.c\", \"grid.457898.f\"]\n", "return grants[id+title]\n", "\n", - "Returned Grants: 20 (total = 601237)\n", - "\u001b[2mTime: 2.03s\u001b[0m\n" + "Returned Grants: 20 (total = 621348)\n", + "\u001b[2mTime: 0.61s\u001b[0m\n", + "WARNINGS [1]\n", + "Field 'funders' is deprecated in favor of funder_orgs. Please refer to https://docs.dimensions.ai/dsl/releasenotes.html for more details\n" ] }, { @@ -564,133 +566,133 @@ " \n", " \n", " 0\n", - " grant.9752271\n", - " NNA Planning: Developing community frameworks ...\n", + " grant.14880777\n", + " Postdoctoral Fellowship: PRFB: Mapping the Bum...\n", " \n", " \n", " 1\n", - " grant.9890102\n", - " RUI: Exciton-Phonon Interactions in Solids bas...\n", + " grant.14880767\n", + " Postdoctoral Fellowship: PRFB: Using the intro...\n", " \n", " \n", " 2\n", - " grant.9982417\n", - " CAREER: Empowering White-box Driven Analytics ...\n", + " grant.14976921\n", + " Rossbypalooza 2026: A Student-led Summer Schoo...\n", " \n", " \n", " 3\n", - " grant.9982416\n", - " CAREER: Holistic Framework for Constructing Dy...\n", + " grant.14955547\n", + " Postdoctoral Fellowship: PRFB: The Role of Pla...\n", " \n", " \n", " 4\n", - " grant.9982395\n", - " CAREER: Leveraging physical properties of mode...\n", + " grant.14880768\n", + " Postdoctoral Fellowship: PRFB: Testing a role ...\n", " \n", " \n", " 5\n", - " grant.9785674\n", - " BPC-AE Collaborative Research: Researching Equ...\n", + " grant.14973500\n", + " Postdoctoral Fellowship: EAR-PF: Reconstructin...\n", " \n", " \n", " 6\n", - " grant.9785672\n", - " BPC-AE Collaborative Research: Researching Equ...\n", + " grant.14976878\n", + " Conference: Recent Perspectives on Moments of ...\n", " \n", " \n", " 7\n", - " grant.9752397\n", - " Equitable Learning to Advance Technical Education\n", + " grant.14955637\n", + " Conference: Rutgers Gauge Theory, Low-Dimensio...\n", " \n", " \n", " 8\n", - " grant.9995499\n", - " CAREER: New imaging of mid-ocean ridge systems...\n", + " grant.14955550\n", + " Postdoctoral Fellowship: PRFB: Integrating the...\n", " \n", " \n", " 9\n", - " grant.9995464\n", - " CAREER: Reconstructing Parasite Abundance in R...\n", + " grant.14880771\n", + " Postdoctoral Fellowship: PRFB: Eco-evolutionar...\n", " \n", " \n", " 10\n", - " grant.9752334\n", - " Collaborative Research: SWIFT: Intelligent Dyn...\n", + " grant.14976854\n", + " Conference: Meeting in the Middle: Conference ...\n", " \n", " \n", " 11\n", - " grant.9752333\n", - " Collaborative Research: SWIFT: Intelligent Dyn...\n", + " grant.14976778\n", + " MCA: Eavesdropping vectors and disease transmi...\n", " \n", " \n", " 12\n", - " grant.9995542\n", - " CAREER: Learning Mechanisms from Single Cell M...\n", + " grant.14969598\n", + " Conference: Universal Statistics in Number Theory\n", " \n", " \n", " 13\n", - " grant.9995538\n", - " CAREER: A Transformative Approach for Teaching...\n", + " grant.14964639\n", + " Long term compliance observations of the evolv...\n", " \n", " \n", " 14\n", - " grant.9995527\n", - " CAREER: Interlimb Neural Coupling to Enhance G...\n", + " grant.14880779\n", + " Postdoctoral Fellowship: PRFB: Elucidating the...\n", " \n", " \n", " 15\n", - " grant.9995522\n", - " CAREER: Fossil Amber Insight Into Macroevoluti...\n", + " grant.14976745\n", + " What drives spatial variability in water-colum...\n", " \n", " \n", " 16\n", - " grant.9995520\n", - " 2022 Origins of Life GRC and GRS: Environments...\n", + " grant.14976476\n", + " IRES: Exploring New Horizons in the Observable...\n", " \n", " \n", " 17\n", - " grant.9995519\n", - " CAREER: Invariants and Entropy of Square Integ...\n", + " grant.14969702\n", + " MCA Pilot PUI: Can unhatched eggs or trash aff...\n", " \n", " \n", " 18\n", - " grant.9995488\n", - " CAREER: Statistical Learning from a Modern Per...\n", + " grant.14954673\n", + " Conference: Geometry Labs United 2025\n", " \n", " \n", " 19\n", - " grant.9995470\n", - " CAREER: CAS- Climate: Making Decarbonization o...\n", + " grant.14976899\n", + " Collaborative Research: FIRE-MODEL: Advancing ...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id title\n", - "0 grant.9752271 NNA Planning: Developing community frameworks ...\n", - "1 grant.9890102 RUI: Exciton-Phonon Interactions in Solids bas...\n", - "2 grant.9982417 CAREER: Empowering White-box Driven Analytics ...\n", - "3 grant.9982416 CAREER: Holistic Framework for Constructing Dy...\n", - "4 grant.9982395 CAREER: Leveraging physical properties of mode...\n", - "5 grant.9785674 BPC-AE Collaborative Research: Researching Equ...\n", - "6 grant.9785672 BPC-AE Collaborative Research: Researching Equ...\n", - "7 grant.9752397 Equitable Learning to Advance Technical Education\n", - "8 grant.9995499 CAREER: New imaging of mid-ocean ridge systems...\n", - "9 grant.9995464 CAREER: Reconstructing Parasite Abundance in R...\n", - "10 grant.9752334 Collaborative Research: SWIFT: Intelligent Dyn...\n", - "11 grant.9752333 Collaborative Research: SWIFT: Intelligent Dyn...\n", - "12 grant.9995542 CAREER: Learning Mechanisms from Single Cell M...\n", - "13 grant.9995538 CAREER: A Transformative Approach for Teaching...\n", - "14 grant.9995527 CAREER: Interlimb Neural Coupling to Enhance G...\n", - "15 grant.9995522 CAREER: Fossil Amber Insight Into Macroevoluti...\n", - "16 grant.9995520 2022 Origins of Life GRC and GRS: Environments...\n", - "17 grant.9995519 CAREER: Invariants and Entropy of Square Integ...\n", - "18 grant.9995488 CAREER: Statistical Learning from a Modern Per...\n", - "19 grant.9995470 CAREER: CAS- Climate: Making Decarbonization o..." + " id title\n", + "0 grant.14880777 Postdoctoral Fellowship: PRFB: Mapping the Bum...\n", + "1 grant.14880767 Postdoctoral Fellowship: PRFB: Using the intro...\n", + "2 grant.14976921 Rossbypalooza 2026: A Student-led Summer Schoo...\n", + "3 grant.14955547 Postdoctoral Fellowship: PRFB: The Role of Pla...\n", + "4 grant.14880768 Postdoctoral Fellowship: PRFB: Testing a role ...\n", + "5 grant.14973500 Postdoctoral Fellowship: EAR-PF: Reconstructin...\n", + "6 grant.14976878 Conference: Recent Perspectives on Moments of ...\n", + "7 grant.14955637 Conference: Rutgers Gauge Theory, Low-Dimensio...\n", + "8 grant.14955550 Postdoctoral Fellowship: PRFB: Integrating the...\n", + "9 grant.14880771 Postdoctoral Fellowship: PRFB: Eco-evolutionar...\n", + "10 grant.14976854 Conference: Meeting in the Middle: Conference ...\n", + "11 grant.14976778 MCA: Eavesdropping vectors and disease transmi...\n", + "12 grant.14969598 Conference: Universal Statistics in Number Theory\n", + "13 grant.14964639 Long term compliance observations of the evolv...\n", + "14 grant.14880779 Postdoctoral Fellowship: PRFB: Elucidating the...\n", + "15 grant.14976745 What drives spatial variability in water-colum...\n", + "16 grant.14976476 IRES: Exploring New Horizons in the Observable...\n", + "17 grant.14969702 MCA Pilot PUI: Can unhatched eggs or trash aff...\n", + "18 grant.14954673 Conference: Geometry Labs United 2025\n", + "19 grant.14976899 Collaborative Research: FIRE-MODEL: Advancing ..." ] }, - "execution_count": 12, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -700,7 +702,7 @@ "\n", "query = f\"\"\"\n", "search grants \n", - " where funders.id in {json.dumps(nsfgrids)}\n", + " where funders.id in {json.dumps(nsforgs)}\n", "return grants[id+title]\n", "\"\"\"\n", "\n", @@ -727,7 +729,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/docs/_sources/cookbooks/8-organizations/7-benchmarking-organizations.ipynb.txt b/docs/_sources/cookbooks/8-organizations/7-benchmarking-organizations.ipynb.txt index 4ce0f55c..b38638a8 100644 --- a/docs/_sources/cookbooks/8-organizations/7-benchmarking-organizations.ipynb.txt +++ b/docs/_sources/cookbooks/8-organizations/7-benchmarking-organizations.ipynb.txt @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -29,7 +29,7 @@ "text": [ "==\n", "CHANGELOG\n", - "This notebook was last run on Feb 21, 2022\n", + "This notebook was last run on Sep 10, 2025\n", "==\n" ] } @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -70,8 +70,8 @@ "text": [ "==\n", "Logging in..\n", - "\u001b[2mDimcli - Dimensions API Client (v0.9.6)\u001b[0m\n", - "\u001b[2mConnected to: - DSL v2.0\u001b[0m\n", + "\u001b[2mDimcli - Dimensions API Client (v1.4)\u001b[0m\n", + "\u001b[2mConnected to: - DSL v2.12\u001b[0m\n", "\u001b[2mMethod: dsl.ini file\u001b[0m\n" ] } @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "Collapsed": "false", "colab": {}, @@ -135,7 +135,7 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 20\n", - "\u001b[2mTime: 21.14s\u001b[0m\n" + "\u001b[2mTime: 12.29s\u001b[0m\n" ] }, { @@ -159,204 +159,204 @@ " \n", " \n", " \n", - " altmetric_median\n", - " count\n", " id\n", " name\n", + " altmetric_median\n", + " count\n", " \n", " \n", " \n", " \n", " 0\n", - " 5.0\n", - " 546592\n", " grid.38142.3c\n", " Harvard University\n", + " 5.292790\n", + " 715128\n", " \n", " \n", " 1\n", - " 3.0\n", - " 484017\n", " grid.26999.3d\n", - " University of Tokyo\n", + " The University of Tokyo\n", + " 3.000000\n", + " 570861\n", " \n", " \n", " 2\n", - " 4.0\n", - " 342764\n", " grid.17063.33\n", " University of Toronto\n", + " 4.019046\n", + " 435895\n", " \n", " \n", " 3\n", - " 3.0\n", - " 320966\n", " grid.214458.e\n", - " University of Michigan\n", + " University of Michigan-Ann Arbor\n", + " 3.968242\n", + " 412146\n", " \n", " \n", " 4\n", - " 3.0\n", - " 310485\n", - " grid.258799.8\n", - " Kyoto University\n", + " grid.168010.e\n", + " Stanford University\n", + " 4.939072\n", + " 393415\n", " \n", " \n", " 5\n", - " 4.0\n", - " 302094\n", - " grid.168010.e\n", - " Stanford University\n", + " grid.4991.5\n", + " University of Oxford\n", + " 5.104038\n", + " 387324\n", " \n", " \n", " 6\n", - " 4.0\n", - " 297558\n", " grid.34477.33\n", " University of Washington\n", + " 4.304326\n", + " 385718\n", " \n", " \n", " 7\n", - " 3.0\n", - " 297094\n", - " grid.19006.3e\n", - " University of California, Los Angeles\n", + " grid.21107.35\n", + " Johns Hopkins University\n", + " 4.374951\n", + " 381545\n", " \n", " \n", " 8\n", - " 5.0\n", - " 289280\n", - " grid.4991.5\n", - " University of Oxford\n", + " grid.19006.3e\n", + " University of California, Los Angeles\n", + " 3.871221\n", + " 373415\n", " \n", " \n", " 9\n", - " 4.0\n", - " 285143\n", - " grid.21107.35\n", - " Johns Hopkins University\n", + " grid.258799.8\n", + " Kyoto University\n", + " 3.000000\n", + " 370973\n", " \n", " \n", " 10\n", - " 4.0\n", - " 282170\n", - " grid.5335.0\n", - " University of Cambridge\n", + " grid.11899.38\n", + " Universidade de São Paulo\n", + " 2.778797\n", + " 367466\n", " \n", " \n", " 11\n", - " 2.0\n", - " 280405\n", - " grid.11899.38\n", - " University of São Paulo\n", + " grid.5335.0\n", + " University of Cambridge\n", + " 4.412618\n", + " 356990\n", " \n", " \n", " 12\n", - " 4.0\n", - " 271170\n", - " grid.25879.31\n", - " University of Pennsylvania\n", + " grid.47840.3f\n", + " University of California, Berkeley\n", + " 4.103148\n", + " 353011\n", " \n", " \n", " 13\n", - " 4.0\n", - " 266337\n", - " grid.83440.3b\n", - " University College London\n", + " grid.25879.31\n", + " University of Pennsylvania\n", + " 4.491342\n", + " 351125\n", " \n", " \n", " 14\n", - " 3.0\n", - " 265592\n", - " grid.136593.b\n", - " Osaka University\n", + " grid.17635.36\n", + " University of Minnesota Twin Cities\n", + " 3.252271\n", + " 324688\n", " \n", " \n", " 15\n", - " 3.0\n", - " 250749\n", - " grid.69566.3a\n", - " Tohoku University\n", + " grid.136593.b\n", + " Osaka University\n", + " 3.000000\n", + " 323974\n", " \n", " \n", " 16\n", - " 3.0\n", - " 244713\n", - " grid.5386.8\n", - " Cornell University\n", + " grid.83440.3b\n", + " University College London\n", + " 4.154059\n", + " 320344\n", " \n", " \n", " 17\n", - " 4.0\n", - " 242749\n", - " grid.47840.3f\n", - " University of California, Berkeley\n", + " grid.14003.36\n", + " University of Wisconsin-Madison\n", + " 3.220404\n", + " 316542\n", " \n", " \n", " 18\n", - " 3.0\n", - " 239283\n", - " grid.17635.36\n", - " University of Minnesota\n", + " grid.410726.6\n", + " University of Chinese Academy of Sciences\n", + " 2.287477\n", + " 313606\n", " \n", " \n", " 19\n", - " 4.0\n", - " 236142\n", - " grid.21729.3f\n", - " Columbia University\n", + " grid.47100.32\n", + " Yale University\n", + " 4.602265\n", + " 305202\n", " \n", " \n", "\n", "" ], "text/plain": [ - " altmetric_median count id \\\n", - "0 5.0 546592 grid.38142.3c \n", - "1 3.0 484017 grid.26999.3d \n", - "2 4.0 342764 grid.17063.33 \n", - "3 3.0 320966 grid.214458.e \n", - "4 3.0 310485 grid.258799.8 \n", - "5 4.0 302094 grid.168010.e \n", - "6 4.0 297558 grid.34477.33 \n", - "7 3.0 297094 grid.19006.3e \n", - "8 5.0 289280 grid.4991.5 \n", - "9 4.0 285143 grid.21107.35 \n", - "10 4.0 282170 grid.5335.0 \n", - "11 2.0 280405 grid.11899.38 \n", - "12 4.0 271170 grid.25879.31 \n", - "13 4.0 266337 grid.83440.3b \n", - "14 3.0 265592 grid.136593.b \n", - "15 3.0 250749 grid.69566.3a \n", - "16 3.0 244713 grid.5386.8 \n", - "17 4.0 242749 grid.47840.3f \n", - "18 3.0 239283 grid.17635.36 \n", - "19 4.0 236142 grid.21729.3f \n", + " id name \\\n", + "0 grid.38142.3c Harvard University \n", + "1 grid.26999.3d The University of Tokyo \n", + "2 grid.17063.33 University of Toronto \n", + "3 grid.214458.e University of Michigan-Ann Arbor \n", + "4 grid.168010.e Stanford University \n", + "5 grid.4991.5 University of Oxford \n", + "6 grid.34477.33 University of Washington \n", + "7 grid.21107.35 Johns Hopkins University \n", + "8 grid.19006.3e University of California, Los Angeles \n", + "9 grid.258799.8 Kyoto University \n", + "10 grid.11899.38 Universidade de São Paulo \n", + "11 grid.5335.0 University of Cambridge \n", + "12 grid.47840.3f University of California, Berkeley \n", + "13 grid.25879.31 University of Pennsylvania \n", + "14 grid.17635.36 University of Minnesota Twin Cities \n", + "15 grid.136593.b Osaka University \n", + "16 grid.83440.3b University College London \n", + "17 grid.14003.36 University of Wisconsin-Madison \n", + "18 grid.410726.6 University of Chinese Academy of Sciences \n", + "19 grid.47100.32 Yale University \n", "\n", - " name \n", - "0 Harvard University \n", - "1 University of Tokyo \n", - "2 University of Toronto \n", - "3 University of Michigan \n", - "4 Kyoto University \n", - "5 Stanford University \n", - "6 University of Washington \n", - "7 University of California, Los Angeles \n", - "8 University of Oxford \n", - "9 Johns Hopkins University \n", - "10 University of Cambridge \n", - "11 University of São Paulo \n", - "12 University of Pennsylvania \n", - "13 University College London \n", - "14 Osaka University \n", - "15 Tohoku University \n", - "16 Cornell University \n", - "17 University of California, Berkeley \n", - "18 University of Minnesota \n", - "19 Columbia University " + " altmetric_median count \n", + "0 5.292790 715128 \n", + "1 3.000000 570861 \n", + "2 4.019046 435895 \n", + "3 3.968242 412146 \n", + "4 4.939072 393415 \n", + "5 5.104038 387324 \n", + "6 4.304326 385718 \n", + "7 4.374951 381545 \n", + "8 3.871221 373415 \n", + "9 3.000000 370973 \n", + "10 2.778797 367466 \n", + "11 4.412618 356990 \n", + "12 4.103148 353011 \n", + "13 4.491342 351125 \n", + "14 3.252271 324688 \n", + "15 3.000000 323974 \n", + "16 4.154059 320344 \n", + "17 3.220404 316542 \n", + "18 2.287477 313606 \n", + "19 4.602265 305202 " ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "Collapsed": "false", "colab": {}, @@ -382,7 +382,7 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 20\n", - "\u001b[2mTime: 6.63s\u001b[0m\n" + "\u001b[2mTime: 4.16s\u001b[0m\n" ] }, { @@ -406,204 +406,204 @@ " \n", " \n", " \n", - " citations_total\n", - " count\n", " id\n", " name\n", + " citations_total\n", + " count\n", " \n", " \n", " \n", " \n", " 0\n", - " 28836616.0\n", - " 546592\n", " grid.38142.3c\n", " Harvard University\n", + " 43542715.0\n", + " 715128\n", " \n", " \n", " 1\n", - " 8545148.0\n", - " 484017\n", " grid.26999.3d\n", - " University of Tokyo\n", + " The University of Tokyo\n", + " 12416944.0\n", + " 570861\n", " \n", " \n", " 2\n", - " 11040840.0\n", - " 342764\n", " grid.17063.33\n", " University of Toronto\n", + " 16896263.0\n", + " 435895\n", " \n", " \n", " 3\n", - " 11710248.0\n", - " 320966\n", " grid.214458.e\n", - " University of Michigan\n", + " University of Michigan-Ann Arbor\n", + " 17899164.0\n", + " 412146\n", " \n", " \n", " 4\n", - " 5928948.0\n", - " 310485\n", - " grid.258799.8\n", - " Kyoto University\n", + " grid.168010.e\n", + " Stanford University\n", + " 22857822.0\n", + " 393415\n", " \n", " \n", " 5\n", - " 14738599.0\n", - " 302094\n", - " grid.168010.e\n", - " Stanford University\n", + " grid.4991.5\n", + " University of Oxford\n", + " 17348878.0\n", + " 387324\n", " \n", " \n", " 6\n", - " 12585381.0\n", - " 297558\n", " grid.34477.33\n", " University of Washington\n", + " 19245227.0\n", + " 385718\n", " \n", " \n", " 7\n", - " 11710928.0\n", - " 297094\n", - " grid.19006.3e\n", - " University of California, Los Angeles\n", + " grid.21107.35\n", + " Johns Hopkins University\n", + " 18542871.0\n", + " 381545\n", " \n", " \n", " 8\n", - " 10879614.0\n", - " 289280\n", - " grid.4991.5\n", - " University of Oxford\n", + " grid.19006.3e\n", + " University of California, Los Angeles\n", + " 17370426.0\n", + " 373415\n", " \n", " \n", " 9\n", - " 12084053.0\n", - " 285143\n", - " grid.21107.35\n", - " Johns Hopkins University\n", + " grid.258799.8\n", + " Kyoto University\n", + " 8426700.0\n", + " 370973\n", " \n", " \n", " 10\n", - " 10814051.0\n", - " 282170\n", - " grid.5335.0\n", - " University of Cambridge\n", + " grid.11899.38\n", + " Universidade de São Paulo\n", + " 6823063.0\n", + " 367466\n", " \n", " \n", " 11\n", - " 4105653.0\n", - " 280405\n", - " grid.11899.38\n", - " University of São Paulo\n", + " grid.5335.0\n", + " University of Cambridge\n", + " 16495121.0\n", + " 356990\n", " \n", " \n", " 12\n", - " 10450691.0\n", - " 271170\n", - " grid.25879.31\n", - " University of Pennsylvania\n", + " grid.47840.3f\n", + " University of California, Berkeley\n", + " 19445292.0\n", + " 353011\n", " \n", " \n", " 13\n", - " 9614297.0\n", - " 266337\n", - " grid.83440.3b\n", - " University College London\n", + " grid.25879.31\n", + " University of Pennsylvania\n", + " 15634591.0\n", + " 351125\n", " \n", " \n", " 14\n", - " 4653874.0\n", - " 265592\n", - " grid.136593.b\n", - " Osaka University\n", + " grid.17635.36\n", + " University of Minnesota Twin Cities\n", + " 13100152.0\n", + " 324688\n", " \n", " \n", " 15\n", - " 3694359.0\n", - " 250749\n", - " grid.69566.3a\n", - " Tohoku University\n", + " grid.136593.b\n", + " Osaka University\n", + " 6486832.0\n", + " 323974\n", " \n", " \n", " 16\n", - " 9370701.0\n", - " 244713\n", - " grid.5386.8\n", - " Cornell University\n", + " grid.83440.3b\n", + " University College London\n", + " 13014090.0\n", + " 320344\n", " \n", " \n", " 17\n", - " 11806056.0\n", - " 242749\n", - " grid.47840.3f\n", - " University of California, Berkeley\n", + " grid.14003.36\n", + " University of Wisconsin-Madison\n", + " 13060297.0\n", + " 316542\n", " \n", " \n", " 18\n", - " 8360048.0\n", - " 239283\n", - " grid.17635.36\n", - " University of Minnesota\n", + " grid.410726.6\n", + " University of Chinese Academy of Sciences\n", + " 8305318.0\n", + " 313606\n", " \n", " \n", " 19\n", - " 9400497.0\n", - " 236142\n", - " grid.21729.3f\n", - " Columbia University\n", + " grid.47100.32\n", + " Yale University\n", + " 14768834.0\n", + " 305202\n", " \n", " \n", "\n", "" ], "text/plain": [ - " citations_total count id \\\n", - "0 28836616.0 546592 grid.38142.3c \n", - "1 8545148.0 484017 grid.26999.3d \n", - "2 11040840.0 342764 grid.17063.33 \n", - "3 11710248.0 320966 grid.214458.e \n", - "4 5928948.0 310485 grid.258799.8 \n", - "5 14738599.0 302094 grid.168010.e \n", - "6 12585381.0 297558 grid.34477.33 \n", - "7 11710928.0 297094 grid.19006.3e \n", - "8 10879614.0 289280 grid.4991.5 \n", - "9 12084053.0 285143 grid.21107.35 \n", - "10 10814051.0 282170 grid.5335.0 \n", - "11 4105653.0 280405 grid.11899.38 \n", - "12 10450691.0 271170 grid.25879.31 \n", - "13 9614297.0 266337 grid.83440.3b \n", - "14 4653874.0 265592 grid.136593.b \n", - "15 3694359.0 250749 grid.69566.3a \n", - "16 9370701.0 244713 grid.5386.8 \n", - "17 11806056.0 242749 grid.47840.3f \n", - "18 8360048.0 239283 grid.17635.36 \n", - "19 9400497.0 236142 grid.21729.3f \n", + " id name citations_total \\\n", + "0 grid.38142.3c Harvard University 43542715.0 \n", + "1 grid.26999.3d The University of Tokyo 12416944.0 \n", + "2 grid.17063.33 University of Toronto 16896263.0 \n", + "3 grid.214458.e University of Michigan-Ann Arbor 17899164.0 \n", + "4 grid.168010.e Stanford University 22857822.0 \n", + "5 grid.4991.5 University of Oxford 17348878.0 \n", + "6 grid.34477.33 University of Washington 19245227.0 \n", + "7 grid.21107.35 Johns Hopkins University 18542871.0 \n", + "8 grid.19006.3e University of California, Los Angeles 17370426.0 \n", + "9 grid.258799.8 Kyoto University 8426700.0 \n", + "10 grid.11899.38 Universidade de São Paulo 6823063.0 \n", + "11 grid.5335.0 University of Cambridge 16495121.0 \n", + "12 grid.47840.3f University of California, Berkeley 19445292.0 \n", + "13 grid.25879.31 University of Pennsylvania 15634591.0 \n", + "14 grid.17635.36 University of Minnesota Twin Cities 13100152.0 \n", + "15 grid.136593.b Osaka University 6486832.0 \n", + "16 grid.83440.3b University College London 13014090.0 \n", + "17 grid.14003.36 University of Wisconsin-Madison 13060297.0 \n", + "18 grid.410726.6 University of Chinese Academy of Sciences 8305318.0 \n", + "19 grid.47100.32 Yale University 14768834.0 \n", "\n", - " name \n", - "0 Harvard University \n", - "1 University of Tokyo \n", - "2 University of Toronto \n", - "3 University of Michigan \n", - "4 Kyoto University \n", - "5 Stanford University \n", - "6 University of Washington \n", - "7 University of California, Los Angeles \n", - "8 University of Oxford \n", - "9 Johns Hopkins University \n", - "10 University of Cambridge \n", - "11 University of São Paulo \n", - "12 University of Pennsylvania \n", - "13 University College London \n", - "14 Osaka University \n", - "15 Tohoku University \n", - "16 Cornell University \n", - "17 University of California, Berkeley \n", - "18 University of Minnesota \n", - "19 Columbia University " + " count \n", + "0 715128 \n", + "1 570861 \n", + "2 435895 \n", + "3 412146 \n", + "4 393415 \n", + "5 387324 \n", + "6 385718 \n", + "7 381545 \n", + "8 373415 \n", + "9 370973 \n", + "10 367466 \n", + "11 356990 \n", + "12 353011 \n", + "13 351125 \n", + "14 324688 \n", + "15 323974 \n", + "16 320344 \n", + "17 316542 \n", + "18 313606 \n", + "19 305202 " ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -616,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "Collapsed": "false", "colab": {}, @@ -629,7 +629,7 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 20\n", - "\u001b[2mTime: 6.54s\u001b[0m\n" + "\u001b[2mTime: 5.11s\u001b[0m\n" ] }, { @@ -653,204 +653,204 @@ " \n", " \n", " \n", - " count\n", " id\n", " name\n", + " count\n", " recent_citations_total\n", " \n", " \n", " \n", " \n", " 0\n", - " 546592\n", " grid.38142.3c\n", " Harvard University\n", - " 5562378.0\n", + " 715128\n", + " 5657002.0\n", " \n", " \n", " 1\n", - " 484017\n", " grid.26999.3d\n", - " University of Tokyo\n", - " 1471000.0\n", + " The University of Tokyo\n", + " 570861\n", + " 1498274.0\n", " \n", " \n", " 2\n", - " 342764\n", " grid.17063.33\n", " University of Toronto\n", - " 2380994.0\n", + " 435895\n", + " 2557162.0\n", " \n", " \n", " 3\n", - " 320966\n", " grid.214458.e\n", - " University of Michigan\n", - " 2370219.0\n", + " University of Michigan-Ann Arbor\n", + " 412146\n", + " 2411193.0\n", " \n", " \n", " 4\n", - " 310485\n", - " grid.258799.8\n", - " Kyoto University\n", - " 1006685.0\n", + " grid.168010.e\n", + " Stanford University\n", + " 393415\n", + " 3172519.0\n", " \n", " \n", " 5\n", - " 302094\n", - " grid.168010.e\n", - " Stanford University\n", - " 2985116.0\n", + " grid.4991.5\n", + " University of Oxford\n", + " 387324\n", + " 2687354.0\n", " \n", " \n", " 6\n", - " 297558\n", " grid.34477.33\n", " University of Washington\n", - " 2411827.0\n", + " 385718\n", + " 2508430.0\n", " \n", " \n", " 7\n", - " 297094\n", - " grid.19006.3e\n", - " University of California, Los Angeles\n", - " 2137101.0\n", + " grid.21107.35\n", + " Johns Hopkins University\n", + " 381545\n", + " 2441471.0\n", " \n", " \n", " 8\n", - " 289280\n", - " grid.4991.5\n", - " University of Oxford\n", - " 2504619.0\n", + " grid.19006.3e\n", + " University of California, Los Angeles\n", + " 373415\n", + " 2151381.0\n", " \n", " \n", " 9\n", - " 285143\n", - " grid.21107.35\n", - " Johns Hopkins University\n", - " 2352686.0\n", + " grid.258799.8\n", + " Kyoto University\n", + " 370973\n", + " 966227.0\n", " \n", " \n", " 10\n", - " 282170\n", - " grid.5335.0\n", - " University of Cambridge\n", - " 2110364.0\n", + " grid.11899.38\n", + " Universidade de São Paulo\n", + " 367466\n", + " 1207947.0\n", " \n", " \n", " 11\n", - " 280405\n", - " grid.11899.38\n", - " University of São Paulo\n", - " 1124894.0\n", + " grid.5335.0\n", + " University of Cambridge\n", + " 356990\n", + " 2258714.0\n", " \n", " \n", " 12\n", - " 271170\n", - " grid.25879.31\n", - " University of Pennsylvania\n", - " 2049126.0\n", + " grid.47840.3f\n", + " University of California, Berkeley\n", + " 353011\n", + " 2404905.0\n", " \n", " \n", " 13\n", - " 266337\n", - " grid.83440.3b\n", - " University College London\n", - " 2197569.0\n", + " grid.25879.31\n", + " University of Pennsylvania\n", + " 351125\n", + " 2063182.0\n", " \n", " \n", " 14\n", - " 265592\n", - " grid.136593.b\n", - " Osaka University\n", - " 727151.0\n", + " grid.17635.36\n", + " University of Minnesota Twin Cities\n", + " 324688\n", + " 1575033.0\n", " \n", " \n", " 15\n", - " 250749\n", - " grid.69566.3a\n", - " Tohoku University\n", - " 644246.0\n", + " grid.136593.b\n", + " Osaka University\n", + " 323974\n", + " 691161.0\n", " \n", " \n", " 16\n", - " 244713\n", - " grid.5386.8\n", - " Cornell University\n", - " 1809884.0\n", + " grid.83440.3b\n", + " University College London\n", + " 320344\n", + " 2241297.0\n", " \n", " \n", " 17\n", - " 242749\n", - " grid.47840.3f\n", - " University of California, Berkeley\n", - " 2057506.0\n", + " grid.14003.36\n", + " University of Wisconsin-Madison\n", + " 316542\n", + " 1508661.0\n", " \n", " \n", " 18\n", - " 239283\n", - " grid.17635.36\n", - " University of Minnesota\n", - " 1519539.0\n", + " grid.410726.6\n", + " University of Chinese Academy of Sciences\n", + " 313606\n", + " 2620498.0\n", " \n", " \n", " 19\n", - " 236142\n", - " grid.21729.3f\n", - " Columbia University\n", - " 1754780.0\n", + " grid.47100.32\n", + " Yale University\n", + " 305202\n", + " 1861426.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " count id name \\\n", - "0 546592 grid.38142.3c Harvard University \n", - "1 484017 grid.26999.3d University of Tokyo \n", - "2 342764 grid.17063.33 University of Toronto \n", - "3 320966 grid.214458.e University of Michigan \n", - "4 310485 grid.258799.8 Kyoto University \n", - "5 302094 grid.168010.e Stanford University \n", - "6 297558 grid.34477.33 University of Washington \n", - "7 297094 grid.19006.3e University of California, Los Angeles \n", - "8 289280 grid.4991.5 University of Oxford \n", - "9 285143 grid.21107.35 Johns Hopkins University \n", - "10 282170 grid.5335.0 University of Cambridge \n", - "11 280405 grid.11899.38 University of São Paulo \n", - "12 271170 grid.25879.31 University of Pennsylvania \n", - "13 266337 grid.83440.3b University College London \n", - "14 265592 grid.136593.b Osaka University \n", - "15 250749 grid.69566.3a Tohoku University \n", - "16 244713 grid.5386.8 Cornell University \n", - "17 242749 grid.47840.3f University of California, Berkeley \n", - "18 239283 grid.17635.36 University of Minnesota \n", - "19 236142 grid.21729.3f Columbia University \n", + " id name count \\\n", + "0 grid.38142.3c Harvard University 715128 \n", + "1 grid.26999.3d The University of Tokyo 570861 \n", + "2 grid.17063.33 University of Toronto 435895 \n", + "3 grid.214458.e University of Michigan-Ann Arbor 412146 \n", + "4 grid.168010.e Stanford University 393415 \n", + "5 grid.4991.5 University of Oxford 387324 \n", + "6 grid.34477.33 University of Washington 385718 \n", + "7 grid.21107.35 Johns Hopkins University 381545 \n", + "8 grid.19006.3e University of California, Los Angeles 373415 \n", + "9 grid.258799.8 Kyoto University 370973 \n", + "10 grid.11899.38 Universidade de São Paulo 367466 \n", + "11 grid.5335.0 University of Cambridge 356990 \n", + "12 grid.47840.3f University of California, Berkeley 353011 \n", + "13 grid.25879.31 University of Pennsylvania 351125 \n", + "14 grid.17635.36 University of Minnesota Twin Cities 324688 \n", + "15 grid.136593.b Osaka University 323974 \n", + "16 grid.83440.3b University College London 320344 \n", + "17 grid.14003.36 University of Wisconsin-Madison 316542 \n", + "18 grid.410726.6 University of Chinese Academy of Sciences 313606 \n", + "19 grid.47100.32 Yale University 305202 \n", "\n", " recent_citations_total \n", - "0 5562378.0 \n", - "1 1471000.0 \n", - "2 2380994.0 \n", - "3 2370219.0 \n", - "4 1006685.0 \n", - "5 2985116.0 \n", - "6 2411827.0 \n", - "7 2137101.0 \n", - "8 2504619.0 \n", - "9 2352686.0 \n", - "10 2110364.0 \n", - "11 1124894.0 \n", - "12 2049126.0 \n", - "13 2197569.0 \n", - "14 727151.0 \n", - "15 644246.0 \n", - "16 1809884.0 \n", - "17 2057506.0 \n", - "18 1519539.0 \n", - "19 1754780.0 " + "0 5657002.0 \n", + "1 1498274.0 \n", + "2 2557162.0 \n", + "3 2411193.0 \n", + "4 3172519.0 \n", + "5 2687354.0 \n", + "6 2508430.0 \n", + "7 2441471.0 \n", + "8 2151381.0 \n", + "9 966227.0 \n", + "10 1207947.0 \n", + "11 2258714.0 \n", + "12 2404905.0 \n", + "13 2063182.0 \n", + "14 1575033.0 \n", + "15 691161.0 \n", + "16 2241297.0 \n", + "17 1508661.0 \n", + "18 2620498.0 \n", + "19 1861426.0 " ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -874,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "Collapsed": "false", "colab": {}, @@ -887,7 +887,7 @@ "output_type": "stream", "text": [ "Returned Year: 20\n", - "\u001b[2mTime: 4.06s\u001b[0m\n" + "\u001b[2mTime: 5.16s\u001b[0m\n" ] }, { @@ -911,161 +911,161 @@ " \n", " \n", " \n", - " count\n", " id\n", + " count\n", " recent_citations_total\n", " \n", " \n", " \n", " \n", " 0\n", - " 6503486\n", - " 2020\n", - " 18375337.0\n", + " 2024\n", + " 7882763\n", + " 13641369.0\n", " \n", " \n", " 1\n", - " 6391947\n", - " 2021\n", - " 4632716.0\n", + " 2023\n", + " 7755821\n", + " 24571792.0\n", " \n", " \n", " 2\n", - " 5792555\n", - " 2019\n", - " 22470145.0\n", + " 2022\n", + " 7279681\n", + " 26740821.0\n", " \n", " \n", " 3\n", - " 5369555\n", - " 2018\n", - " 23030935.0\n", + " 2021\n", + " 7016199\n", + " 27057034.0\n", " \n", " \n", " 4\n", - " 5044596\n", - " 2017\n", - " 21362603.0\n", + " 2020\n", + " 6831663\n", + " 25211581.0\n", " \n", " \n", " 5\n", - " 4598245\n", - " 2016\n", - " 19046830.0\n", + " 2019\n", + " 6004300\n", + " 20340055.0\n", " \n", " \n", " 6\n", - " 4395107\n", - " 2015\n", - " 17010283.0\n", + " 2018\n", + " 5550132\n", + " 17327713.0\n", " \n", " \n", " 7\n", - " 4244049\n", - " 2014\n", - " 15057104.0\n", + " 2017\n", + " 5171177\n", + " 15030351.0\n", " \n", " \n", " 8\n", - " 4046162\n", - " 2013\n", - " 13475978.0\n", + " 2025\n", + " 5064609\n", + " 1745079.0\n", " \n", " \n", " 9\n", - " 3762532\n", - " 2012\n", - " 11970228.0\n", + " 2016\n", + " 4775828\n", + " 13029942.0\n", " \n", " \n", " 10\n", - " 3667073\n", - " 2011\n", - " 10958039.0\n", + " 2015\n", + " 4534568\n", + " 11396769.0\n", " \n", " \n", " 11\n", - " 3430544\n", - " 2010\n", - " 9915351.0\n", + " 2014\n", + " 4382421\n", + " 9976449.0\n", " \n", " \n", " 12\n", - " 3144460\n", - " 2009\n", - " 8991871.0\n", + " 2013\n", + " 4194614\n", + " 8834241.0\n", " \n", " \n", " 13\n", - " 2937393\n", - " 2008\n", - " 7853718.0\n", + " 2012\n", + " 3898366\n", + " 7799039.0\n", " \n", " \n", " 14\n", - " 2915691\n", - " 2007\n", - " 7198101.0\n", + " 2011\n", + " 3761002\n", + " 7104082.0\n", " \n", " \n", " 15\n", - " 2610760\n", - " 2006\n", - " 6579372.0\n", + " 2010\n", + " 3327367\n", + " 6409071.0\n", " \n", " \n", " 16\n", - " 2410569\n", - " 2005\n", - " 5985630.0\n", + " 2009\n", + " 3198543\n", + " 5733537.0\n", " \n", " \n", " 17\n", - " 2246194\n", - " 2004\n", - " 5335870.0\n", + " 2008\n", + " 3001138\n", + " 5037413.0\n", " \n", " \n", " 18\n", - " 2037978\n", - " 2003\n", - " 4730168.0\n", + " 2007\n", + " 2986096\n", + " 4665167.0\n", " \n", " \n", " 19\n", - " 1892417\n", - " 2002\n", - " 4234096.0\n", + " 2006\n", + " 2688556\n", + " 4263341.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " count id recent_citations_total\n", - "0 6503486 2020 18375337.0\n", - "1 6391947 2021 4632716.0\n", - "2 5792555 2019 22470145.0\n", - "3 5369555 2018 23030935.0\n", - "4 5044596 2017 21362603.0\n", - "5 4598245 2016 19046830.0\n", - "6 4395107 2015 17010283.0\n", - "7 4244049 2014 15057104.0\n", - "8 4046162 2013 13475978.0\n", - "9 3762532 2012 11970228.0\n", - "10 3667073 2011 10958039.0\n", - "11 3430544 2010 9915351.0\n", - "12 3144460 2009 8991871.0\n", - "13 2937393 2008 7853718.0\n", - "14 2915691 2007 7198101.0\n", - "15 2610760 2006 6579372.0\n", - "16 2410569 2005 5985630.0\n", - "17 2246194 2004 5335870.0\n", - "18 2037978 2003 4730168.0\n", - "19 1892417 2002 4234096.0" + " id count recent_citations_total\n", + "0 2024 7882763 13641369.0\n", + "1 2023 7755821 24571792.0\n", + "2 2022 7279681 26740821.0\n", + "3 2021 7016199 27057034.0\n", + "4 2020 6831663 25211581.0\n", + "5 2019 6004300 20340055.0\n", + "6 2018 5550132 17327713.0\n", + "7 2017 5171177 15030351.0\n", + "8 2025 5064609 1745079.0\n", + "9 2016 4775828 13029942.0\n", + "10 2015 4534568 11396769.0\n", + "11 2014 4382421 9976449.0\n", + "12 2013 4194614 8834241.0\n", + "13 2012 3898366 7799039.0\n", + "14 2011 3761002 7104082.0\n", + "15 2010 3327367 6409071.0\n", + "16 2009 3198543 5733537.0\n", + "17 2008 3001138 5037413.0\n", + "18 2007 2986096 4665167.0\n", + "19 2006 2688556 4263341.0" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1078,7 +1078,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "Collapsed": "false", "colab": {}, @@ -1086,26 +1086,31 @@ "id": "OZVInY3lZFaJ" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib is building the font cache; this may take a moment.\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1115,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "Collapsed": "false", "colab": {}, @@ -1129,7 +1134,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "Collapsed": "false", "colab": {}, @@ -1144,7 +1149,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "Collapsed": "false", "colab": {}, @@ -1155,23 +1160,21 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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176 rows × 3 columns

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193 rows × 3 columns

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176 rows × 4 columns

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193 rows × 4 columns

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"[176 rows x 4 columns]" + "[193 rows x 4 columns]" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1560,7 +1563,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "Collapsed": "false", "colab": {}, @@ -1589,165 +1592,165 @@ " \n", " \n", " \n", - " count\n", " id\n", " name\n", + " count\n", " level\n", " \n", " \n", " \n", " \n", " 0\n", - " 1168442\n", - " 2211\n", - " 11 Medical and Health Sciences\n", + " 80003\n", + " 32 Biomedical and Clinical Sciences\n", + " 1094225\n", " 2\n", " \n", " \n", " 1\n", - " 610238\n", - " 2209\n", - " 09 Engineering\n", + " 80011\n", + " 40 Engineering\n", + " 833052\n", " 2\n", " \n", " \n", " 3\n", - " 335403\n", - " 2206\n", - " 06 Biological Sciences\n", + " 80017\n", + " 46 Information and Computing Sciences\n", + " 425860\n", " 2\n", " \n", " \n", " 4\n", - " 332128\n", - " 2208\n", - " 08 Information and Computing Sciences\n", + " 80002\n", + " 31 Biological Sciences\n", + " 365542\n", " 2\n", " \n", " \n", " 5\n", - " 304680\n", - " 2203\n", - " 03 Chemical Sciences\n", + " 80013\n", + " 42 Health Sciences\n", + " 304968\n", " 2\n", " \n", " \n", - " 7\n", - " 224973\n", - " 2202\n", - " 02 Physical Sciences\n", + " 6\n", + " 80005\n", + " 34 Chemical Sciences\n", + " 301217\n", " 2\n", " \n", " \n", - " 8\n", - " 201573\n", - " 2201\n", - " 01 Mathematical Sciences\n", + " 7\n", + " 80022\n", + " 51 Physical Sciences\n", + " 266544\n", " 2\n", " \n", " \n", - " 12\n", - " 161476\n", - " 2217\n", - " 17 Psychology and Cognitive Sciences\n", + " 8\n", + " 80015\n", + " 44 Human Society\n", + " 227080\n", " 2\n", " \n", " \n", - " 13\n", - " 151455\n", - " 2216\n", - " 16 Studies in Human Society\n", + " 9\n", + " 80006\n", + " 35 Commerce, Management, Tourism and Services\n", + " 191148\n", " 2\n", " \n", " \n", - " 18\n", - " 98630\n", - " 2215\n", - " 15 Commerce, Management, Tourism and Services\n", + " 10\n", + " 80020\n", + " 49 Mathematical Sciences\n", + " 179411\n", " 2\n", " \n", " \n", - 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"execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1804,7 +1807,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "Collapsed": "false", "colab": {}, @@ -1818,7 +1821,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "Collapsed": "false", "colab": {}, @@ -1847,9 +1850,9 @@ " \n", " \n", " \n", - " count\n", " id\n", " name\n", + " count\n", " level\n", " cutoff\n", " \n", @@ -1857,235 +1860,235 @@ " \n", " \n", " 0\n", - " 1168442\n", - " 2211\n", - " 11 Medical and Health Sciences\n", + " 80003\n", + " 32 Biomedical and Clinical Sciences\n", + " 1094225\n", " 2\n", - " 11684\n", + " 10942\n", " \n", " \n", " 1\n", - " 610238\n", - " 2209\n", - " 09 Engineering\n", + " 80011\n", + " 40 Engineering\n", + " 833052\n", " 2\n", - " 6102\n", + " 8330\n", " \n", " \n", " 3\n", - " 335403\n", - " 2206\n", - " 06 Biological Sciences\n", + " 80017\n", + " 46 Information and Computing Sciences\n", + " 425860\n", " 2\n", - " 3354\n", + " 4258\n", " \n", " \n", " 4\n", - " 332128\n", - " 2208\n", - " 08 Information and Computing Sciences\n", + " 80002\n", + " 31 Biological Sciences\n", + " 365542\n", " 2\n", - " 3321\n", + " 3655\n", " \n", " \n", " 5\n", - " 304680\n", - " 2203\n", - " 03 Chemical Sciences\n", + " 80013\n", + " 42 Health Sciences\n", + " 304968\n", " 2\n", - " 3046\n", + " 3049\n", " \n", " \n", - " 7\n", - " 224973\n", - " 2202\n", - " 02 Physical Sciences\n", + " 6\n", + " 80005\n", + " 34 Chemical Sciences\n", + " 301217\n", " 2\n", - " 2249\n", + " 3012\n", " \n", " \n", - " 8\n", - " 201573\n", - " 2201\n", - " 01 Mathematical Sciences\n", + " 7\n", + " 80022\n", + " 51 Physical Sciences\n", + " 266544\n", " 2\n", - " 2015\n", + " 2665\n", " \n", " \n", - " 12\n", - " 161476\n", - " 2217\n", - " 17 Psychology and Cognitive Sciences\n", + " 8\n", + " 80015\n", + " 44 Human Society\n", + " 227080\n", " 2\n", - " 1614\n", + " 2270\n", " \n", " \n", - " 13\n", - " 151455\n", - " 2216\n", - " 16 Studies in Human Society\n", + " 9\n", + " 80006\n", + " 35 Commerce, Management, Tourism and Services\n", + " 191148\n", " 2\n", - " 1514\n", + " 1911\n", " \n", " \n", - " 18\n", - " 98630\n", - " 2215\n", - " 15 Commerce, Management, Tourism and Services\n", + " 10\n", + " 80020\n", + " 49 Mathematical Sciences\n", + " 179411\n", " 2\n", - " 986\n", + " 1794\n", " \n", " \n", - " 20\n", - " 95061\n", - " 2210\n", - " 10 Technology\n", + " 11\n", + " 80001\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 165669\n", " 2\n", - " 950\n", + " 1656\n", " \n", " \n", - " 21\n", - " 94318\n", - " 2220\n", - " 20 Language, Communication and Culture\n", + " 13\n", + " 80008\n", + " 37 Earth Sciences\n", + " 141809\n", " 2\n", - " 943\n", + " 1418\n", " \n", " \n", - " 24\n", - " 88929\n", - " 2213\n", - " 13 Education\n", + " 14\n", + " 80018\n", + " 47 Language, Communication and Culture\n", + " 138186\n", " 2\n", - " 889\n", + " 1381\n", " \n", " \n", - " 25\n", - " 86868\n", - 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"execution_count": 20, + "execution_count": 19, "metadata": { "Collapsed": "false", "colab": {}, @@ -2218,7 +2221,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "Collapsed": "false", "colab": {}, @@ -2255,167 +2258,167 @@ " \n", " \n", " 0\n", - " 28.41\n", - " 11 Medical and Health Sciences\n", - " 2211\n", + " 37.05\n", + " 32 Biomedical and Clinical Sciences\n", + " 80003\n", " \n", " \n", " 0\n", - " 21.35\n", - " 09 Engineering\n", - " 2209\n", + " 28.18\n", + " 40 Engineering\n", + " 80011\n", " \n", " \n", " 0\n", - " 20.52\n", - " 06 Biological Sciences\n", - " 2206\n", + " 45.07\n", + " 46 Information and Computing Sciences\n", + " 80017\n", " \n", " \n", " 0\n", - " 35.44\n", - " 08 Information and Computing Sciences\n", - " 2208\n", + " 28.34\n", + " 31 Biological Sciences\n", + " 80002\n", " \n", " \n", " 0\n", - " 20.51\n", - " 03 Chemical Sciences\n", - " 2203\n", + " 33.82\n", + " 42 Health Sciences\n", + " 80013\n", " \n", " \n", " 0\n", - 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"0 25.02 10 Technology 2210\n", - "0 30.45 20 Language, Communication and Culture 2220\n", - "0 25.34 13 Education 2213\n", - "0 16.52 04 Earth Sciences 2204\n", - "0 33.18 14 Economics 2214\n", - "0 28.80 21 History and Archaeology 2221\n", - "0 20.46 05 Environmental Sciences 2205\n", - "0 15.42 07 Agricultural and Veterinary Sciences 2207\n", - "0 27.68 22 Philosophy and Religious Studies 2222\n", - "0 27.52 18 Law and Legal Studies 2218\n", - "0 16.68 12 Built Environment and Design 2212\n", - "0 27.55 19 Studies in Creative Arts and Writing 2219" + " field_citation_ratio name id\n", + "0 37.05 32 Biomedical and Clinical Sciences 80003\n", + "0 28.18 40 Engineering 80011\n", + "0 45.07 46 Information and Computing Sciences 80017\n", + "0 28.34 31 Biological Sciences 80002\n", + "0 33.82 42 Health Sciences 80013\n", + "0 25.02 34 Chemical Sciences 80005\n", + "0 38.91 51 Physical Sciences 80022\n", + "0 38.81 44 Human Society 80015\n", + "0 44.68 35 Commerce, Management, Tourism and Services 80006\n", + "0 34.54 49 Mathematical Sciences 80020\n", + "0 22.36 30 Agricultural, Veterinary and Food Sciences 80001\n", + "0 23.26 37 Earth Sciences 80008\n", + "0 39.98 47 Language, Communication and Culture 80018\n", + "0 33.78 52 Psychology 80023\n", + "0 39.12 50 Philosophy and Religious Studies 80021\n", + "0 35.81 39 Education 80010\n", + "0 29.06 41 Environmental Sciences 80012\n", + "0 37.09 48 Law and Legal Studies 80019\n", + "0 48.26 38 Economics 80009\n", + "0 31.37 43 History, Heritage and Archaeology 80014\n", + "0 38.82 33 Built Environment and Design 80004\n", + "0 37.37 36 Creative Arts and Writing 80007" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2437,7 +2440,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "Collapsed": "false", "colab": {}, @@ -2451,7 +2454,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "Collapsed": "false", "colab": {}, @@ -2488,167 +2491,167 @@ " \n", " \n", " 0\n", - " 28\n", - " 11 Medical and Health Sciences\n", - " 2211\n", + " 37\n", + " 32 Biomedical and Clinical Sciences\n", + " 80003\n", " \n", " \n", " 0\n", - " 21\n", - " 09 Engineering\n", - " 2209\n", + " 28\n", + " 40 Engineering\n", + " 80011\n", " \n", " \n", " 0\n", - " 20\n", - " 06 Biological Sciences\n", - " 2206\n", + " 45\n", + " 46 Information and Computing Sciences\n", + " 80017\n", " \n", " \n", " 0\n", - " 35\n", - " 08 Information and Computing Sciences\n", - " 2208\n", + " 28\n", + " 31 Biological Sciences\n", + " 80002\n", " \n", " \n", " 0\n", - 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"0 25 10 Technology 2210\n", - "0 30 20 Language, Communication and Culture 2220\n", - "0 25 13 Education 2213\n", - "0 16 04 Earth Sciences 2204\n", - "0 33 14 Economics 2214\n", - "0 28 21 History and Archaeology 2221\n", - "0 20 05 Environmental Sciences 2205\n", - "0 15 07 Agricultural and Veterinary Sciences 2207\n", - "0 27 22 Philosophy and Religious Studies 2222\n", - "0 27 18 Law and Legal Studies 2218\n", - "0 16 12 Built Environment and Design 2212\n", - "0 27 19 Studies in Creative Arts and Writing 2219" + " field_citation_ratio name id\n", + "0 37 32 Biomedical and Clinical Sciences 80003\n", + "0 28 40 Engineering 80011\n", + "0 45 46 Information and Computing Sciences 80017\n", + "0 28 31 Biological Sciences 80002\n", + "0 33 42 Health Sciences 80013\n", + "0 25 34 Chemical Sciences 80005\n", + "0 38 51 Physical Sciences 80022\n", + "0 38 44 Human Society 80015\n", + "0 44 35 Commerce, Management, Tourism and Services 80006\n", + "0 34 49 Mathematical Sciences 80020\n", + "0 22 30 Agricultural, Veterinary and Food Sciences 80001\n", + "0 23 37 Earth Sciences 80008\n", + "0 39 47 Language, Communication and Culture 80018\n", + "0 33 52 Psychology 80023\n", + "0 39 50 Philosophy and Religious Studies 80021\n", + "0 35 39 Education 80010\n", + "0 29 41 Environmental Sciences 80012\n", + "0 37 48 Law and Legal Studies 80019\n", + "0 48 38 Economics 80009\n", + "0 31 43 History, Heritage and Archaeology 80014\n", + "0 38 33 Built Environment and Design 80004\n", + "0 37 36 Creative Arts and Writing 80007" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2670,7 +2673,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "Collapsed": "false", "colab": {}, @@ -2683,49 +2686,49 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.21s\u001b[0m\n", + "\u001b[2mTime: 6.15s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.09s\u001b[0m\n", + "\u001b[2mTime: 1.83s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 3.14s\u001b[0m\n", + "\u001b[2mTime: 5.51s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.30s\u001b[0m\n", + "\u001b[2mTime: 5.82s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.96s\u001b[0m\n", + "\u001b[2mTime: 1.56s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.13s\u001b[0m\n", + "\u001b[2mTime: 6.63s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.06s\u001b[0m\n", + "\u001b[2mTime: 1.82s\u001b[0m\n", "Returned Research_orgs: 1000\n", - 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"execution_count": 26, + "execution_count": 25, "metadata": { "Collapsed": "false", "colab": {}, @@ -2803,7 +2806,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": { "Collapsed": "false", "colab": {}, @@ -2838,109 +2841,114 @@ " \n", " \n", " \n", - " 21\n", - " 11 Medical and Health Sciences\n", - " 22.0\n", + " 15\n", + " 32 Biomedical and Clinical Sciences\n", + " 16.0\n", + " \n", + " \n", + " 173\n", + " 40 Engineering\n", + " 177.0\n", " \n", " \n", - " 103\n", - " 09 Engineering\n", - " 107.0\n", + " 114\n", + " 46 Information and Computing Sciences\n", + " 121.0\n", " \n", " \n", - " 25\n", - " 06 Biological Sciences\n", - " 26.0\n", + " 18\n", + " 31 Biological Sciences\n", + " 19.0\n", " \n", " \n", - " 99\n", - " 08 Information and Computing Sciences\n", - " 105.0\n", + " 12\n", + " 42 Health Sciences\n", + " 12.5\n", " \n", " \n", - " 161\n", - " 03 Chemical Sciences\n", - " 170.5\n", + " 192\n", + " 34 Chemical Sciences\n", + " 212.0\n", " \n", " \n", " 142\n", - " 02 Physical Sciences\n", - " 150.0\n", + " 51 Physical Sciences\n", + " 148.0\n", " \n", " \n", - " 45\n", - " 01 Mathematical Sciences\n", - " 48.5\n", + " 10\n", + " 44 Human Society\n", + " 12.0\n", " \n", " \n", - " 9\n", - " 17 Psychology and Cognitive Sciences\n", - " 11.5\n", + " 81\n", + " 35 Commerce, Management, Tourism and Services\n", + " 90.5\n", " \n", " \n", - " 32\n", - " 16 Studies in Human Society\n", - " 36.0\n", + " 125\n", + " 49 Mathematical Sciences\n", + " 142.0\n", " \n", " \n", - " 66\n", - " 15 Commerce, Management, Tourism and Services\n", - " 88.0\n", + " 21\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 23.5\n", " \n", " \n", - " 35\n", - " 20 Language, Communication and Culture\n", - " 46.0\n", + " 96\n", + " 37 Earth Sciences\n", + " 108.5\n", " \n", " \n", - " 17\n", - " 13 Education\n", - " 22.5\n", + " 10\n", + " 47 Language, Communication and Culture\n", + " 12.0\n", " \n", " \n", - " 196\n", - " 04 Earth Sciences\n", - " 230.0\n", + " 6\n", + " 52 Psychology\n", + " 7.5\n", " \n", " \n", - " 83\n", - " 14 Economics\n", - " 110.0\n", + " 80\n", + " 50 Philosophy and Religious Studies\n", + " 106.5\n", " \n", " \n", - " 263\n", - " 21 History and Archaeology\n", - " 579.5\n", + " 8\n", + " 39 Education\n", + " 10.0\n", " \n", " \n", - " 30\n", - " 05 Environmental Sciences\n", - " 34.5\n", + " 19\n", + " 41 Environmental Sciences\n", + " 21.5\n", " \n", " \n", - " 22\n", - " 07 Agricultural and Veterinary Sciences\n", - " 26.0\n", + " 13\n", + " 48 Law and Legal Studies\n", + " 18.5\n", " \n", " \n", - " 133\n", - " 22 Philosophy and Religious Studies\n", - " 304.0\n", + " 69\n", + " 38 Economics\n", + " 87.5\n", " \n", " \n", - " 23\n", - " 18 Law and Legal Studies\n", - " 37.0\n", + " 51\n", + " 43 History, Heritage and Archaeology\n", + " 65.5\n", " \n", " \n", - " 20\n", - " 12 Built Environment and Design\n", - " 32.5\n", + " 18\n", + " 33 Built Environment and Design\n", + " 21.0\n", " \n", " \n", - " 0\n", - " 19 Studies in Creative Arts and Writing\n", - " 1.0\n", + " 6\n", + " 36 Creative Arts and Writing\n", + " 10.0\n", " \n", " \n", "\n", @@ -2948,30 +2956,31 @@ ], "text/plain": [ " for_name rank\n", - "21 11 Medical and Health Sciences 22.0\n", - "103 09 Engineering 107.0\n", - "25 06 Biological Sciences 26.0\n", - "99 08 Information and Computing Sciences 105.0\n", - "161 03 Chemical Sciences 170.5\n", - "142 02 Physical Sciences 150.0\n", - "45 01 Mathematical Sciences 48.5\n", - "9 17 Psychology and Cognitive Sciences 11.5\n", - "32 16 Studies in Human Society 36.0\n", - "66 15 Commerce, Management, Tourism and Services 88.0\n", - "35 20 Language, Communication and Culture 46.0\n", - "17 13 Education 22.5\n", - "196 04 Earth Sciences 230.0\n", - "83 14 Economics 110.0\n", - "263 21 History and Archaeology 579.5\n", - "30 05 Environmental Sciences 34.5\n", - "22 07 Agricultural and Veterinary Sciences 26.0\n", - "133 22 Philosophy and Religious Studies 304.0\n", - "23 18 Law and Legal Studies 37.0\n", - "20 12 Built Environment and Design 32.5\n", - "0 19 Studies in Creative Arts and Writing 1.0" + "15 32 Biomedical and Clinical Sciences 16.0\n", + "173 40 Engineering 177.0\n", + "114 46 Information and Computing Sciences 121.0\n", + "18 31 Biological Sciences 19.0\n", + "12 42 Health Sciences 12.5\n", + "192 34 Chemical Sciences 212.0\n", + "142 51 Physical Sciences 148.0\n", + "10 44 Human Society 12.0\n", + "81 35 Commerce, Management, Tourism and Services 90.5\n", + "125 49 Mathematical Sciences 142.0\n", + "21 30 Agricultural, Veterinary and Food Sciences 23.5\n", + "96 37 Earth Sciences 108.5\n", + "10 47 Language, Communication and Culture 12.0\n", + "6 52 Psychology 7.5\n", + "80 50 Philosophy and Religious Studies 106.5\n", + "8 39 Education 10.0\n", + "19 41 Environmental Sciences 21.5\n", + "13 48 Law and Legal Studies 18.5\n", + "69 38 Economics 87.5\n", + "51 43 History, Heritage and Archaeology 65.5\n", + "18 33 Built Environment and Design 21.0\n", + "6 36 Creative Arts and Writing 10.0" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -3004,7 +3013,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "Collapsed": "false", "colab": {}, @@ -3017,49 +3026,49 @@ "output_type": "stream", "text": [ "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.47s\u001b[0m\n", + "\u001b[2mTime: 4.39s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.12s\u001b[0m\n", + "\u001b[2mTime: 6.05s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.14s\u001b[0m\n", + "\u001b[2mTime: 1.88s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.09s\u001b[0m\n", + "\u001b[2mTime: 1.80s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.97s\u001b[0m\n", + "\u001b[2mTime: 4.84s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.17s\u001b[0m\n", + "\u001b[2mTime: 1.92s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.29s\u001b[0m\n", + "\u001b[2mTime: 3.19s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.11s\u001b[0m\n", + "\u001b[2mTime: 3.04s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.00s\u001b[0m\n", + "\u001b[2mTime: 3.34s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.02s\u001b[0m\n", + "\u001b[2mTime: 2.38s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 5.36s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 1.38s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.03s\u001b[0m\n", + "\u001b[2mTime: 1.30s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 6.38s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.98s\u001b[0m\n", + "\u001b[2mTime: 1.41s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.12s\u001b[0m\n", + "\u001b[2mTime: 4.51s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.14s\u001b[0m\n", + "\u001b[2mTime: 1.98s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.15s\u001b[0m\n", + "\u001b[2mTime: 1.47s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.15s\u001b[0m\n", + "\u001b[2mTime: 6.03s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 1.10s\u001b[0m\n", + "\u001b[2mTime: 1.30s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.97s\u001b[0m\n", + "\u001b[2mTime: 4.47s\u001b[0m\n", "Returned Research_orgs: 1000\n", - "\u001b[2mTime: 0.96s\u001b[0m\n" + "\u001b[2mTime: 1.25s\u001b[0m\n" ] } ], @@ -3086,7 +3095,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "Collapsed": "false", "colab": {}, @@ -3100,7 +3109,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "Collapsed": "false", "colab": {}, @@ -3114,7 +3123,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { "Collapsed": "false", "colab": {}, @@ -3152,38 +3161,38 @@ " \n", " \n", " 0\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " Harvard University\n", - " 845\n", - " 16932\n", + " 767\n", + " 15967\n", " \n", " \n", " 1\n", - " 11 Medical and Health Sciences\n", - " University of Toronto\n", - " 392\n", - " 10281\n", + " 32 Biomedical and Clinical Sciences\n", + " Johns Hopkins University\n", + " 356\n", + " 9182\n", " \n", " \n", " 2\n", - " 11 Medical and Health Sciences\n", - " Johns Hopkins University\n", - " 391\n", - " 10120\n", + " 32 Biomedical and Clinical Sciences\n", + " University of Toronto\n", + " 338\n", + " 8932\n", " \n", " \n", " 3\n", - " 11 Medical and Health Sciences\n", - " University of California, San Francisco\n", - " 365\n", - " 7850\n", + " 32 Biomedical and Clinical Sciences\n", + " Mayo Clinic\n", + " 380\n", + " 8507\n", " \n", " \n", " 4\n", - " 11 Medical and Health Sciences\n", - " Mayo Clinic\n", - " 321\n", - " 7659\n", + " 32 Biomedical and Clinical Sciences\n", + " University of California, San Francisco\n", + " 339\n", + " 7477\n", " \n", " \n", " ...\n", @@ -3193,76 +3202,76 @@ " ...\n", " \n", " \n", - " 12220\n", - " 19 Studies in Creative Arts and Writing\n", - " University of Bamberg\n", - " 1\n", + " 13171\n", + " 36 Creative Arts and Writing\n", + " Adobe Inc\n", " 3\n", + " 7\n", " \n", " \n", - " 12221\n", - " 19 Studies in Creative Arts and Writing\n", - " National University of Quilmes\n", + " 13172\n", + " 36 Creative Arts and Writing\n", + " Polytechnic University of Turin\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", - " 12222\n", - " 19 Studies in Creative Arts and Writing\n", - " Czech University of Life Sciences Prague\n", + " 13173\n", + " 36 Creative Arts and Writing\n", + " University of Electronic Science and Technolog...\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", - " 12223\n", - " 19 Studies in Creative Arts and Writing\n", - " University Hospitals of Cleveland\n", + " 13174\n", + " 36 Creative Arts and Writing\n", + " University of Cyprus\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", - " 12224\n", - " 19 Studies in Creative Arts and Writing\n", - " Grinnell College\n", + " 13175\n", + " 36 Creative Arts and Writing\n", + " Broad Institute\n", " 1\n", - " 2\n", + " 7\n", " \n", " \n", "\n", - "

12225 rows × 4 columns

\n", + "

13176 rows × 4 columns

\n", "" ], "text/plain": [ - " for_name \\\n", - "0 11 Medical and Health Sciences \n", - "1 11 Medical and Health Sciences \n", - "2 11 Medical and Health Sciences \n", - "3 11 Medical and Health Sciences \n", - "4 11 Medical and Health Sciences \n", - "... ... \n", - "12220 19 Studies in Creative Arts and Writing \n", - "12221 19 Studies in Creative Arts and Writing \n", - "12222 19 Studies in Creative Arts and Writing \n", - "12223 19 Studies in Creative Arts and Writing \n", - "12224 19 Studies in Creative Arts and Writing \n", + " for_name \\\n", + "0 32 Biomedical and Clinical Sciences \n", + "1 32 Biomedical and Clinical Sciences \n", + "2 32 Biomedical and Clinical Sciences \n", + "3 32 Biomedical and Clinical Sciences \n", + "4 32 Biomedical and Clinical Sciences \n", + "... ... \n", + "13171 36 Creative Arts and Writing \n", + "13172 36 Creative Arts and Writing \n", + "13173 36 Creative Arts and Writing \n", + "13174 36 Creative Arts and Writing \n", + "13175 36 Creative Arts and Writing \n", "\n", - " name count count all \n", - "0 Harvard University 845 16932 \n", - "1 University of Toronto 392 10281 \n", - "2 Johns Hopkins University 391 10120 \n", - "3 University of California, San Francisco 365 7850 \n", - "4 Mayo Clinic 321 7659 \n", - "... ... ... ... \n", - "12220 University of Bamberg 1 3 \n", - "12221 National University of Quilmes 1 2 \n", - "12222 Czech University of Life Sciences Prague 1 2 \n", - "12223 University Hospitals of Cleveland 1 2 \n", - "12224 Grinnell College 1 2 \n", + " name count count all \n", + "0 Harvard University 767 15967 \n", + "1 Johns Hopkins University 356 9182 \n", + "2 University of Toronto 338 8932 \n", + "3 Mayo Clinic 380 8507 \n", + "4 University of California, San Francisco 339 7477 \n", + "... ... ... ... \n", + "13171 Adobe Inc 3 7 \n", + "13172 Polytechnic University of Turin 1 7 \n", + "13173 University of Electronic Science and Technolog... 1 7 \n", + "13174 University of Cyprus 1 7 \n", + "13175 Broad Institute 1 7 \n", "\n", - "[12225 rows x 4 columns]" + "[13176 rows x 4 columns]" ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -3284,7 +3293,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "Collapsed": "false", "colab": {}, @@ -3298,7 +3307,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "Collapsed": "false", "colab": {}, @@ -3323,7 +3332,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "Collapsed": "false", "colab": {}, @@ -3358,109 +3367,114 @@ " \n", " \n", " \n", - " 66\n", - " 11 Medical and Health Sciences\n", - " 41.0\n", + " 73\n", + " 32 Biomedical and Clinical Sciences\n", + " 54.5\n", + " \n", + " \n", + " 842\n", + " 40 Engineering\n", + " 205.0\n", " \n", " \n", - " 840\n", - " 09 Engineering\n", - " 138.0\n", + " 1592\n", + " 46 Information and Computing Sciences\n", + " 191.5\n", " \n", " \n", - " 1498\n", - " 06 Biological Sciences\n", - " 100.0\n", + " 2167\n", + " 31 Biological Sciences\n", + " 71.0\n", " \n", " \n", - " 2294\n", - " 08 Information and Computing Sciences\n", - " 475.5\n", + " 2916\n", + " 42 Health Sciences\n", + " 31.0\n", " \n", " \n", - " 2875\n", - " 03 Chemical Sciences\n", - " 93.5\n", + " 3577\n", + " 34 Chemical Sciences\n", + " 47.0\n", " \n", " \n", - " 3512\n", - " 02 Physical Sciences\n", - " 278.5\n", + " 4211\n", + " 51 Physical Sciences\n", + " 315.0\n", " \n", " \n", - " 4277\n", - " 01 Mathematical Sciences\n", - " 117.0\n", + " 4992\n", + " 44 Human Society\n", + " 115.5\n", " \n", " \n", - " 4921\n", - " 17 Psychology and Cognitive Sciences\n", - " 52.5\n", + " 5642\n", + " 35 Commerce, Management, Tourism and Services\n", + " 241.0\n", " \n", " \n", - " 5584\n", - " 16 Studies in Human Society\n", - " 236.5\n", + " 6253\n", + " 49 Mathematical Sciences\n", + " 87.0\n", " \n", " \n", - " 6165\n", - " 15 Commerce, Management, Tourism and Services\n", - " 150.0\n", + " 7112\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 57.0\n", " \n", " \n", - " 6864\n", - " 10 Technology\n", - " 304.0\n", + " 7557\n", + " 37 Earth Sciences\n", + " 285.5\n", " \n", " \n", - " 7312\n", - " 20 Language, Communication and Culture\n", - " 398.5\n", + " 8174\n", + " 47 Language, Communication and Culture\n", + " 422.0\n", " \n", " \n", - " 7785\n", - " 13 Education\n", - " 377.0\n", + " 8696\n", + " 52 Psychology\n", + " 201.0\n", " \n", " \n", - " 8302\n", - " 04 Earth Sciences\n", - " 346.5\n", + " 9362\n", + " 50 Philosophy and Religious Studies\n", + " 422.0\n", " \n", " \n", - " 8940\n", - " 14 Economics\n", - " 310.5\n", + " 9857\n", + " 39 Education\n", + " 255.0\n", " \n", " \n", - " 9467\n", - " 21 History and Archaeology\n", - " 305.0\n", + " 10448\n", + " 41 Environmental Sciences\n", + " 190.0\n", " \n", " \n", - " 10013\n", - " 05 Environmental Sciences\n", - " 123.0\n", + " 11006\n", + " 48 Law and Legal Studies\n", + " 291.5\n", " \n", " \n", - " 10741\n", - " 07 Agricultural and Veterinary Sciences\n", - " 124.5\n", + " 11405\n", + " 38 Economics\n", + " 301.0\n", " \n", " \n", - " 11176\n", - " 22 Philosophy and Religious Studies\n", - " 311.5\n", + " 11884\n", + " 43 History, Heritage and Archaeology\n", + " 255.0\n", " \n", " \n", - " 11503\n", - " 18 Law and Legal Studies\n", - " 196.0\n", + " 12409\n", + " 33 Built Environment and Design\n", + " 428.0\n", " \n", " \n", - " 11823\n", - " 12 Built Environment and Design\n", - " 181.0\n", + " 12841\n", + " 36 Creative Arts and Writing\n", + " 285.0\n", " \n", " \n", "\n", @@ -3468,30 +3482,31 @@ ], "text/plain": [ " for_name percent rank\n", - "66 11 Medical and Health Sciences 41.0\n", - "840 09 Engineering 138.0\n", - "1498 06 Biological Sciences 100.0\n", - "2294 08 Information and Computing Sciences 475.5\n", - "2875 03 Chemical Sciences 93.5\n", - "3512 02 Physical Sciences 278.5\n", - "4277 01 Mathematical Sciences 117.0\n", - "4921 17 Psychology and Cognitive Sciences 52.5\n", - "5584 16 Studies in Human Society 236.5\n", - "6165 15 Commerce, Management, Tourism and Services 150.0\n", - "6864 10 Technology 304.0\n", - "7312 20 Language, Communication and Culture 398.5\n", - "7785 13 Education 377.0\n", - "8302 04 Earth Sciences 346.5\n", - "8940 14 Economics 310.5\n", - "9467 21 History and Archaeology 305.0\n", - "10013 05 Environmental Sciences 123.0\n", - "10741 07 Agricultural and Veterinary Sciences 124.5\n", - "11176 22 Philosophy and Religious Studies 311.5\n", - "11503 18 Law and Legal Studies 196.0\n", - "11823 12 Built Environment and Design 181.0" + "73 32 Biomedical and Clinical Sciences 54.5\n", + "842 40 Engineering 205.0\n", + "1592 46 Information and Computing Sciences 191.5\n", + "2167 31 Biological Sciences 71.0\n", + "2916 42 Health Sciences 31.0\n", + "3577 34 Chemical Sciences 47.0\n", + "4211 51 Physical Sciences 315.0\n", + "4992 44 Human Society 115.5\n", + "5642 35 Commerce, Management, Tourism and Services 241.0\n", + "6253 49 Mathematical Sciences 87.0\n", + "7112 30 Agricultural, Veterinary and Food Sciences 57.0\n", + "7557 37 Earth Sciences 285.5\n", + "8174 47 Language, Communication and Culture 422.0\n", + "8696 52 Psychology 201.0\n", + "9362 50 Philosophy and Religious Studies 422.0\n", + "9857 39 Education 255.0\n", + "10448 41 Environmental Sciences 190.0\n", + "11006 48 Law and Legal Studies 291.5\n", + "11405 38 Economics 301.0\n", + "11884 43 History, Heritage and Archaeology 255.0\n", + "12409 33 Built Environment and Design 428.0\n", + "12841 36 Creative Arts and Writing 285.0" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -3502,7 +3517,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": { "Collapsed": "false", "colab": {}, @@ -3536,84 +3551,17 @@ " \n", " \n", " \n", - " \n", - " 0\n", - " Harvard University\n", - " 61.5\n", - " \n", - " \n", - " 1\n", - " University of Toronto\n", - " 236.5\n", - " \n", - " \n", - " 2\n", - " Johns Hopkins University\n", - " 220.0\n", - " \n", - " \n", - " 3\n", - " University of California, San Francisco\n", - " 93.5\n", - " \n", - " \n", - " 4\n", - " Mayo Clinic\n", - " 152.0\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 780\n", - " University of Bath\n", - " 425.0\n", - " \n", - " \n", - " 781\n", - " Kuopio University Hospital\n", - " 250.5\n", - " \n", - " \n", - " 782\n", - " Marqués de Valdecilla University Hospital\n", - " 299.0\n", - " \n", - " \n", - " 783\n", - " Policlinico San Matteo Fondazione\n", - " 114.5\n", - " \n", - " \n", - " 784\n", - " Centre Hospitalier Universitaire de Caen\n", - " 351.5\n", - " \n", " \n", "\n", - "

785 rows × 2 columns

\n", "" ], "text/plain": [ - " name percent rank\n", - "0 Harvard University 61.5\n", - "1 University of Toronto 236.5\n", - "2 Johns Hopkins University 220.0\n", - "3 University of California, San Francisco 93.5\n", - "4 Mayo Clinic 152.0\n", - ".. ... ...\n", - "780 University of Bath 425.0\n", - "781 Kuopio University Hospital 250.5\n", - "782 Marqués de Valdecilla University Hospital 299.0\n", - "783 Policlinico San Matteo Fondazione 114.5\n", - "784 Centre Hospitalier Universitaire de Caen 351.5\n", - "\n", - "[785 rows x 2 columns]" + "Empty DataFrame\n", + "Columns: [name, percent rank]\n", + "Index: []" ] }, - "execution_count": 35, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -3646,7 +3594,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": { "Collapsed": "false", "colab": {}, @@ -3665,7 +3613,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": { "Collapsed": "false", "colab": {}, @@ -3679,7 +3627,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { "Collapsed": "false", "colab": {}, @@ -3714,13 +3662,14 @@ " reference count all\n", " id\n", " count all\n", + " name\n", " city_name\n", " count\n", - " country_name\n", + " country_code\n", + " ...\n", " latitude\n", " linkout\n", " longitude\n", - " name\n", " state_name\n", " types\n", " acronym\n", @@ -3732,119 +3681,124 @@ " \n", " \n", " \n", - " 15700\n", + " 17756\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", + " 80003\n", + " 4797\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", + " US\n", + " ...\n", " 42.377052\n", " [http://www.harvard.edu/]\n", " -71.116650\n", - " Harvard University\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", - " \n", - " \n", - " 15701\n", - " grid.1008.9\n", - " University of Melbourne\n", - " 2211\n", - " 5457\n", - " grid.17063.33\n", - " 10281\n", - " Toronto\n", - " 392\n", - " Canada\n", - " 43.661667\n", - " [http://www.utoronto.ca/]\n", - " -79.395000\n", - " University of Toronto\n", - " Ontario\n", - " [Education]\n", - " NaN\n", - " 11 Medical and Health Sciences\n", - " 2.0\n", - " 3.81\n", - " 236.5\n", + " 4.80\n", + " 63.5\n", " \n", " \n", - " 15702\n", + " 17757\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", + " 80003\n", + " 4797\n", " grid.21107.35\n", - " 10120\n", + " 9182\n", + " Johns Hopkins University\n", " Baltimore\n", - " 391\n", - " United States\n", + " 356\n", + " US\n", + " ...\n", " 39.328888\n", " [https://www.jhu.edu/]\n", " -76.620280\n", - " Johns Hopkins University\n", " Maryland\n", " [Education]\n", " JHU\n", - " 11 Medical and Health Sciences\n", - " 3.0\n", - " 3.86\n", - " 220.0\n", + " 32 Biomedical and Clinical Sciences\n", + " 4.0\n", + " 3.88\n", + " 195.5\n", " \n", " \n", - " 15703\n", + " 17758\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", - " grid.266102.1\n", - " 7850\n", - " San Francisco\n", - " 365\n", - " United States\n", - " 37.762800\n", - " [https://www.ucsf.edu/]\n", - " -122.457670\n", - " University of California, San Francisco\n", - " California\n", + " 80003\n", + " 4797\n", + " grid.17063.33\n", + " 8932\n", + " University of Toronto\n", + " Toronto\n", + " 338\n", + " CA\n", + " ...\n", + " 43.661667\n", + " [http://www.utoronto.ca/]\n", + " -79.395000\n", + " Ontario\n", " [Education]\n", - " UCSF\n", - " 11 Medical and Health Sciences\n", - " 6.0\n", - " 4.65\n", - " 93.5\n", + " NaN\n", + " 32 Biomedical and Clinical Sciences\n", + " 8.0\n", + " 3.78\n", + " 220.5\n", " \n", " \n", - " 15704\n", + " 17759\n", " grid.1008.9\n", " University of Melbourne\n", - " 2211\n", - " 5457\n", + " 80003\n", + " 4797\n", " grid.66875.3a\n", - " 7659\n", + " 8507\n", + " Mayo Clinic\n", " Rochester\n", - " 321\n", - " United States\n", + " 380\n", + " US\n", + " ...\n", " 44.024070\n", " [http://www.mayoclinic.org/patient-visitor-gui...\n", " -92.466310\n", - " Mayo Clinic\n", " Minnesota\n", " [Healthcare]\n", " NaN\n", - " 11 Medical and Health Sciences\n", - " 10.0\n", - " 4.19\n", - " 152.0\n", + " 32 Biomedical and Clinical Sciences\n", + " 3.0\n", + " 4.47\n", + " 96.5\n", + " \n", + " \n", + " 17760\n", + " grid.1008.9\n", + " University of Melbourne\n", + " 80003\n", + " 4797\n", + " grid.266102.1\n", + " 7477\n", + " University of California, San Francisco\n", + " San Francisco\n", + " 339\n", + " US\n", + " ...\n", + " 37.762800\n", + " [https://www.ucsf.edu/]\n", + " -122.457670\n", + " California\n", + " [Education]\n", + " UCSF\n", + " 32 Biomedical and Clinical Sciences\n", + " 6.5\n", + " 4.53\n", + " 89.0\n", " \n", " \n", " ...\n", @@ -3868,210 +3822,242 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", - " 7350128\n", + " 8082796\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.7359.8\n", + " 80007\n", + " 126\n", + " grid.467212.4\n", + " 7\n", + " Adobe Inc\n", + " San Jose\n", " 3\n", - " Bamberg\n", - " 1\n", - " Germany\n", - " 49.893845\n", - " [https://www.uni-bamberg.de/]\n", - " 10.886044\n", - " University of Bamberg\n", + " US\n", + " ...\n", " NaN\n", - " [Education]\n", + " [https://www.adobe.com/]\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 33.33\n", - " 8.0\n", + " California\n", + " [Company]\n", + " NaN\n", + " 36 Creative Arts and Writing\n", + " 59.0\n", + " 42.86\n", + " 1.0\n", " \n", " \n", - " 7350129\n", + " 8082797\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.11560.33\n", - " 2\n", - " Bernal\n", + " 80007\n", + " 126\n", + " grid.4800.c\n", + " 7\n", + " Polytechnic University of Turin\n", + " Turin\n", " 1\n", - " Argentina\n", - " -34.706670\n", - " [http://www.unq.edu.ar/english/sections/158-unq/]\n", - " -58.277500\n", - " National University of Quilmes\n", - " NaN\n", + " IT\n", + " ...\n", + " 45.063095\n", + " [http://www.polito.it/]\n", + " 7.661075\n", + " Piemonte\n", " [Education]\n", - " UNQ\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " NaN\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", - " 7350130\n", + " 8082798\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.15866.3c\n", - " 2\n", - " Prague\n", + " 80007\n", + " 126\n", + " grid.54549.39\n", + " 7\n", + " University of Electronic Science and Technolog...\n", + " Chengdu\n", " 1\n", - " Czechia\n", - " 50.131460\n", - " [http://www.czu.cz/en/]\n", - " 14.373258\n", - " Czech University of Life Sciences Prague\n", + " CN\n", + " ...\n", + " 30.675713\n", + " [http://en.uestc.edu.cn/]\n", + " 104.100270\n", " NaN\n", " [Education]\n", - " CULS\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " UESTC\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", - " 7350131\n", + " 8082799\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.241104.2\n", - " 2\n", - " Cleveland\n", + " 80007\n", + " 126\n", + " grid.6603.3\n", + " 7\n", + " University of Cyprus\n", + " Nicosia\n", " 1\n", - " United States\n", - " 41.506096\n", - " [http://www.uhhospitals.org/]\n", - " -81.604820\n", - " University Hospitals of Cleveland\n", - " Ohio\n", - " [Healthcare]\n", + " CY\n", + " ...\n", + " 35.160270\n", + " [http://www.ucy.ac.cy/en/]\n", + " 33.376976\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " [Education]\n", + " UCY\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", - " 7350132\n", + " 8082800\n", " grid.1008.9\n", " University of Melbourne\n", - " 2219\n", - " 85\n", - " grid.256592.f\n", - " 2\n", - " Grinnell\n", + " 80007\n", + " 126\n", + " grid.66859.34\n", + " 7\n", + " Broad Institute\n", + " Cambridge\n", " 1\n", - " United States\n", - " 41.749737\n", - " [http://www.grinnell.edu/]\n", - " -92.719505\n", - " Grinnell College\n", - " Iowa\n", - " [Education]\n", + " US\n", + " ...\n", + " 42.367890\n", + " [http://www.broadinstitute.org/]\n", + " -71.087030\n", + " Massachusetts\n", + " [Nonprofit]\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", " \n", " \n", "\n", - "

11685 rows × 20 columns

\n", + "

13176 rows × 21 columns

\n", "" ], "text/plain": [ " reference id reference name for_id reference count all \\\n", - "15700 grid.1008.9 University of Melbourne 2211 5457 \n", - "15701 grid.1008.9 University of Melbourne 2211 5457 \n", - "15702 grid.1008.9 University of Melbourne 2211 5457 \n", - "15703 grid.1008.9 University of Melbourne 2211 5457 \n", - "15704 grid.1008.9 University of Melbourne 2211 5457 \n", + "17756 grid.1008.9 University of Melbourne 80003 4797 \n", + "17757 grid.1008.9 University of Melbourne 80003 4797 \n", + "17758 grid.1008.9 University of Melbourne 80003 4797 \n", + "17759 grid.1008.9 University of Melbourne 80003 4797 \n", + "17760 grid.1008.9 University of Melbourne 80003 4797 \n", "... ... ... ... ... \n", - "7350128 grid.1008.9 University of Melbourne 2219 85 \n", - "7350129 grid.1008.9 University of Melbourne 2219 85 \n", - "7350130 grid.1008.9 University of Melbourne 2219 85 \n", - "7350131 grid.1008.9 University of Melbourne 2219 85 \n", - "7350132 grid.1008.9 University of Melbourne 2219 85 \n", + "8082796 grid.1008.9 University of Melbourne 80007 126 \n", + "8082797 grid.1008.9 University of Melbourne 80007 126 \n", + "8082798 grid.1008.9 University of Melbourne 80007 126 \n", + "8082799 grid.1008.9 University of Melbourne 80007 126 \n", + "8082800 grid.1008.9 University of Melbourne 80007 126 \n", "\n", - " id count all city_name count country_name \\\n", - "15700 grid.38142.3c 16932 Cambridge 845 United States \n", - "15701 grid.17063.33 10281 Toronto 392 Canada \n", - "15702 grid.21107.35 10120 Baltimore 391 United States \n", - "15703 grid.266102.1 7850 San Francisco 365 United States \n", - "15704 grid.66875.3a 7659 Rochester 321 United States \n", - "... ... ... ... ... ... \n", - "7350128 grid.7359.8 3 Bamberg 1 Germany \n", - "7350129 grid.11560.33 2 Bernal 1 Argentina \n", - "7350130 grid.15866.3c 2 Prague 1 Czechia \n", - "7350131 grid.241104.2 2 Cleveland 1 United States \n", - "7350132 grid.256592.f 2 Grinnell 1 United States \n", + " id count all \\\n", + "17756 grid.38142.3c 15967 \n", + "17757 grid.21107.35 9182 \n", + "17758 grid.17063.33 8932 \n", + "17759 grid.66875.3a 8507 \n", + "17760 grid.266102.1 7477 \n", + "... ... ... \n", + "8082796 grid.467212.4 7 \n", + "8082797 grid.4800.c 7 \n", + "8082798 grid.54549.39 7 \n", + "8082799 grid.6603.3 7 \n", + "8082800 grid.66859.34 7 \n", "\n", - " latitude linkout \\\n", - "15700 42.377052 [http://www.harvard.edu/] \n", - "15701 43.661667 [http://www.utoronto.ca/] \n", - "15702 39.328888 [https://www.jhu.edu/] \n", - "15703 37.762800 [https://www.ucsf.edu/] \n", - "15704 44.024070 [http://www.mayoclinic.org/patient-visitor-gui... \n", - "... ... ... \n", - "7350128 49.893845 [https://www.uni-bamberg.de/] \n", - "7350129 -34.706670 [http://www.unq.edu.ar/english/sections/158-unq/] \n", - "7350130 50.131460 [http://www.czu.cz/en/] \n", - "7350131 41.506096 [http://www.uhhospitals.org/] \n", - "7350132 41.749737 [http://www.grinnell.edu/] \n", + " name city_name \\\n", + "17756 Harvard University Cambridge \n", + "17757 Johns Hopkins University Baltimore \n", + "17758 University of Toronto Toronto \n", + "17759 Mayo Clinic Rochester \n", + "17760 University of California, San Francisco San Francisco \n", + "... ... ... \n", + "8082796 Adobe Inc San Jose \n", + "8082797 Polytechnic University of Turin Turin \n", + "8082798 University of Electronic Science and Technolog... Chengdu \n", + "8082799 University of Cyprus Nicosia \n", + "8082800 Broad Institute Cambridge \n", + "\n", + " count country_code ... latitude \\\n", + "17756 767 US ... 42.377052 \n", + "17757 356 US ... 39.328888 \n", + "17758 338 CA ... 43.661667 \n", + "17759 380 US ... 44.024070 \n", + "17760 339 US ... 37.762800 \n", + "... ... ... ... ... \n", + "8082796 3 US ... NaN \n", + "8082797 1 IT ... 45.063095 \n", + "8082798 1 CN ... 30.675713 \n", + "8082799 1 CY ... 35.160270 \n", + "8082800 1 US ... 42.367890 \n", "\n", - " longitude name state_name \\\n", - "15700 -71.116650 Harvard University Massachusetts \n", - "15701 -79.395000 University of Toronto Ontario \n", - "15702 -76.620280 Johns Hopkins University Maryland \n", - "15703 -122.457670 University of California, San Francisco California \n", - "15704 -92.466310 Mayo Clinic Minnesota \n", - "... ... ... ... \n", - "7350128 10.886044 University of Bamberg NaN \n", - "7350129 -58.277500 National University of Quilmes NaN \n", - "7350130 14.373258 Czech University of Life Sciences Prague NaN \n", - "7350131 -81.604820 University Hospitals of Cleveland Ohio \n", - "7350132 -92.719505 Grinnell College Iowa \n", + " linkout longitude \\\n", + "17756 [http://www.harvard.edu/] -71.116650 \n", + "17757 [https://www.jhu.edu/] -76.620280 \n", + "17758 [http://www.utoronto.ca/] -79.395000 \n", + "17759 [http://www.mayoclinic.org/patient-visitor-gui... -92.466310 \n", + "17760 [https://www.ucsf.edu/] -122.457670 \n", + "... ... ... \n", + "8082796 [https://www.adobe.com/] NaN \n", + "8082797 [http://www.polito.it/] 7.661075 \n", + "8082798 [http://en.uestc.edu.cn/] 104.100270 \n", + "8082799 [http://www.ucy.ac.cy/en/] 33.376976 \n", + "8082800 [http://www.broadinstitute.org/] -71.087030 \n", "\n", - " types acronym for_name rank \\\n", - "15700 [Education] NaN 11 Medical and Health Sciences 1.0 \n", - "15701 [Education] NaN 11 Medical and Health Sciences 2.0 \n", - "15702 [Education] JHU 11 Medical and Health Sciences 3.0 \n", - "15703 [Education] UCSF 11 Medical and Health Sciences 6.0 \n", - "15704 [Healthcare] NaN 11 Medical and Health Sciences 10.0 \n", - "... ... ... ... ... \n", - "7350128 [Education] NaN 19 Studies in Creative Arts and Writing 130.0 \n", - "7350129 [Education] UNQ 19 Studies in Creative Arts and Writing 130.0 \n", - "7350130 [Education] CULS 19 Studies in Creative Arts and Writing 130.0 \n", - "7350131 [Healthcare] NaN 19 Studies in Creative Arts and Writing 130.0 \n", - "7350132 [Education] NaN 19 Studies in Creative Arts and Writing 130.0 \n", + " state_name types acronym \\\n", + "17756 Massachusetts [Education] NaN \n", + "17757 Maryland [Education] JHU \n", + "17758 Ontario [Education] NaN \n", + "17759 Minnesota [Healthcare] NaN \n", + "17760 California [Education] UCSF \n", + "... ... ... ... \n", + "8082796 California [Company] NaN \n", + "8082797 Piemonte [Education] NaN \n", + "8082798 NaN [Education] UESTC \n", + "8082799 NaN [Education] UCY \n", + "8082800 Massachusetts [Nonprofit] NaN \n", "\n", - " percentage top 1 percent rank \n", - "15700 4.99 61.5 \n", - "15701 3.81 236.5 \n", - "15702 3.86 220.0 \n", - "15703 4.65 93.5 \n", - "15704 4.19 152.0 \n", - "... ... ... \n", - "7350128 33.33 8.0 \n", - "7350129 50.00 2.5 \n", - "7350130 50.00 2.5 \n", - "7350131 50.00 2.5 \n", - "7350132 50.00 2.5 \n", + " for_name rank percentage top 1 \\\n", + "17756 32 Biomedical and Clinical Sciences 1.0 4.80 \n", + "17757 32 Biomedical and Clinical Sciences 4.0 3.88 \n", + "17758 32 Biomedical and Clinical Sciences 8.0 3.78 \n", + "17759 32 Biomedical and Clinical Sciences 3.0 4.47 \n", + "17760 32 Biomedical and Clinical Sciences 6.5 4.53 \n", + "... ... ... ... \n", + "8082796 36 Creative Arts and Writing 59.0 42.86 \n", + "8082797 36 Creative Arts and Writing 350.5 14.29 \n", + "8082798 36 Creative Arts and Writing 350.5 14.29 \n", + "8082799 36 Creative Arts and Writing 350.5 14.29 \n", + "8082800 36 Creative Arts and Writing 350.5 14.29 \n", "\n", - "[11685 rows x 20 columns]" + " percent rank \n", + "17756 63.5 \n", + "17757 195.5 \n", + "17758 220.5 \n", + "17759 96.5 \n", + "17760 89.0 \n", + "... ... \n", + "8082796 1.0 \n", + "8082797 34.5 \n", + "8082798 34.5 \n", + "8082799 34.5 \n", + "8082800 34.5 \n", + "\n", + "[13176 rows x 21 columns]" ] }, - "execution_count": 38, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -4082,7 +4068,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": { "Collapsed": "false", "colab": {}, @@ -4098,7 +4084,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": { "Collapsed": "false", "colab": {}, @@ -4114,7 +4100,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": { "Collapsed": "false", "colab": {}, @@ -4152,172 +4138,180 @@ " \n", " \n", " \n", - " 15720\n", + " 17779\n", " grid.1008.9\n", - " 2211\n", + " 80003\n", " University of Melbourne\n", - " 11 Medical and Health Sciences\n", - " 15.5\n", + " 32 Biomedical and Clinical Sciences\n", + " 6.0\n", " \n", " \n", - " 738709\n", + " 733648\n", " grid.1008.9\n", - " 2209\n", + " 80011\n", " University of Melbourne\n", - " 09 Engineering\n", - " 40.0\n", + " 40 Engineering\n", + " 85.0\n", " \n", " \n", - " 1131875\n", + " 1168804\n", " grid.1008.9\n", - " 2206\n", + " 80017\n", " University of Melbourne\n", - " 06 Biological Sciences\n", - " 13.0\n", + " 46 Information and Computing Sciences\n", + " 51.0\n", " \n", " \n", - " 1643602\n", + " 1578085\n", " grid.1008.9\n", - " 2208\n", + " 80002\n", " University of Melbourne\n", - " 08 Information and Computing Sciences\n", - " 45.0\n", + " 31 Biological Sciences\n", + " 11.0\n", " \n", " \n", - " 2140491\n", + " 2046557\n", " grid.1008.9\n", - " 2203\n", + " 80013\n", " University of Melbourne\n", - " 03 Chemical Sciences\n", - " 87.5\n", + " 42 Health Sciences\n", + " 5.0\n", " \n", " \n", - " 2627450\n", + " 2644004\n", " grid.1008.9\n", - " 2202\n", + " 80005\n", " University of Melbourne\n", - " 02 Physical Sciences\n", - " 49.0\n", + " 34 Chemical Sciences\n", + " 98.0\n", " \n", " \n", - " 3094798\n", + " 3128285\n", " grid.1008.9\n", - " 2201\n", + " 80022\n", " University of Melbourne\n", - " 01 Mathematical Sciences\n", - " 18.0\n", + " 51 Physical Sciences\n", + " 46.0\n", " \n", " \n", - " 3454060\n", + " 3570941\n", " grid.1008.9\n", - " 2217\n", + " 80015\n", " University of Melbourne\n", - " 17 Psychology and Cognitive Sciences\n", + " 44 Human Society\n", " 4.0\n", " \n", " \n", - " 3909944\n", + " 3958749\n", " grid.1008.9\n", - " 2216\n", + " 80006\n", " University of Melbourne\n", - " 16 Studies in Human Society\n", - " 4.0\n", + " 35 Commerce, Management, Tourism and Services\n", + " 22.0\n", " \n", " \n", - " 4225053\n", + " 4403670\n", " grid.1008.9\n", - " 2215\n", + " 80020\n", " University of Melbourne\n", - " 15 Commerce, Management, Tourism and Services\n", - " 9.0\n", + " 49 Mathematical Sciences\n", + " 37.0\n", " \n", " \n", - " 4915245\n", + " 4832701\n", " grid.1008.9\n", - " 2220\n", + " 80001\n", " University of Melbourne\n", - " 20 Language, Communication and Culture\n", - " 3.0\n", + " 30 Agricultural, Veterinary and Food Sciences\n", + " 8.0\n", " \n", " \n", - " 5111347\n", + " 5224547\n", " grid.1008.9\n", - " 2213\n", + " 80008\n", " University of Melbourne\n", - " 13 Education\n", - " 8.0\n", + " 37 Earth Sciences\n", + " 44.0\n", " \n", " \n", - " 5429203\n", + " 5606498\n", " grid.1008.9\n", - " 2204\n", + " 80018\n", " University of Melbourne\n", - " 04 Earth Sciences\n", - " 64.0\n", + " 47 Language, Communication and Culture\n", + " 4.0\n", " \n", " \n", - " 5817193\n", + " 5864420\n", " grid.1008.9\n", - " 2214\n", + " 80023\n", " University of Melbourne\n", - " 14 Economics\n", - " 13.0\n", + " 52 Psychology\n", + " 2.0\n", " \n", " \n", - " 6111107\n", + " 6341779\n", " grid.1008.9\n", - " 2221\n", + " 80021\n", " University of Melbourne\n", - " 21 History and Archaeology\n", - " 22.0\n", + " 50 Philosophy and Religious Studies\n", + " 25.0\n", " \n", " \n", - " 6352914\n", + " 6535541\n", " grid.1008.9\n", - " 2205\n", + " 80010\n", " University of Melbourne\n", - " 05 Environmental Sciences\n", - " 6.0\n", + " 39 Education\n", + " 2.0\n", " \n", " \n", - " 6797399\n", + " 6853435\n", " grid.1008.9\n", - " 2207\n", + " 80012\n", " University of Melbourne\n", - " 07 Agricultural and Veterinary Sciences\n", - " 9.0\n", + " 41 Environmental Sciences\n", + " 5.0\n", " \n", " \n", - " 7091867\n", + " 7239785\n", " grid.1008.9\n", - " 2222\n", + " 80019\n", " University of Melbourne\n", - " 22 Philosophy and Religious Studies\n", - " 13.0\n", + " 48 Law and Legal Studies\n", + " 2.5\n", " \n", " \n", - " 7194418\n", + " 7398600\n", " grid.1008.9\n", - " 2218\n", + " 80009\n", " University of Melbourne\n", - " 18 Law and Legal Studies\n", - " 4.0\n", + " 38 Economics\n", + " 24.5\n", " \n", " \n", - " 7281134\n", + " 7643598\n", " grid.1008.9\n", - " 2212\n", + " 80014\n", " University of Melbourne\n", - " 12 Built Environment and Design\n", - " 7.0\n", + " 43 History, Heritage and Archaeology\n", + " 14.0\n", " \n", " \n", - " 7349969\n", + " 7880154\n", " grid.1008.9\n", - " 2219\n", + " 80004\n", " University of Melbourne\n", - " 19 Studies in Creative Arts and Writing\n", - " 1.0\n", + " 33 Built Environment and Design\n", + " 8.0\n", + " \n", + " \n", + " 8082459\n", + " grid.1008.9\n", + " 80007\n", + " University of Melbourne\n", + " 36 Creative Arts and Writing\n", + " 2.0\n", " \n", " \n", "\n", @@ -4325,53 +4319,55 @@ ], "text/plain": [ " id for_id name \\\n", - "15720 grid.1008.9 2211 University of Melbourne \n", - "738709 grid.1008.9 2209 University of Melbourne \n", - "1131875 grid.1008.9 2206 University of Melbourne \n", - "1643602 grid.1008.9 2208 University of Melbourne \n", - "2140491 grid.1008.9 2203 University of Melbourne \n", - "2627450 grid.1008.9 2202 University of Melbourne \n", - "3094798 grid.1008.9 2201 University of Melbourne \n", - "3454060 grid.1008.9 2217 University of Melbourne \n", - "3909944 grid.1008.9 2216 University of Melbourne \n", - "4225053 grid.1008.9 2215 University of Melbourne \n", - "4915245 grid.1008.9 2220 University of Melbourne \n", - "5111347 grid.1008.9 2213 University of Melbourne \n", - "5429203 grid.1008.9 2204 University of Melbourne \n", - "5817193 grid.1008.9 2214 University of Melbourne \n", - "6111107 grid.1008.9 2221 University of Melbourne \n", - "6352914 grid.1008.9 2205 University of Melbourne \n", - "6797399 grid.1008.9 2207 University of Melbourne \n", - "7091867 grid.1008.9 2222 University of Melbourne \n", - "7194418 grid.1008.9 2218 University of Melbourne \n", - "7281134 grid.1008.9 2212 University of Melbourne \n", - "7349969 grid.1008.9 2219 University of Melbourne \n", + "17779 grid.1008.9 80003 University of Melbourne \n", + "733648 grid.1008.9 80011 University of Melbourne \n", + "1168804 grid.1008.9 80017 University of Melbourne \n", + "1578085 grid.1008.9 80002 University of Melbourne \n", + "2046557 grid.1008.9 80013 University of Melbourne \n", + "2644004 grid.1008.9 80005 University of Melbourne \n", + "3128285 grid.1008.9 80022 University of Melbourne \n", + "3570941 grid.1008.9 80015 University of Melbourne \n", + "3958749 grid.1008.9 80006 University of Melbourne \n", + "4403670 grid.1008.9 80020 University of Melbourne \n", + "4832701 grid.1008.9 80001 University of Melbourne \n", + "5224547 grid.1008.9 80008 University of Melbourne \n", + "5606498 grid.1008.9 80018 University of Melbourne \n", + "5864420 grid.1008.9 80023 University of Melbourne \n", + "6341779 grid.1008.9 80021 University of Melbourne \n", + "6535541 grid.1008.9 80010 University of Melbourne \n", + "6853435 grid.1008.9 80012 University of Melbourne \n", + "7239785 grid.1008.9 80019 University of Melbourne \n", + "7398600 grid.1008.9 80009 University of Melbourne \n", + "7643598 grid.1008.9 80014 University of Melbourne \n", + "7880154 grid.1008.9 80004 University of Melbourne \n", + "8082459 grid.1008.9 80007 University of Melbourne \n", "\n", " for_name filtered percent rank \n", - "15720 11 Medical and Health Sciences 15.5 \n", - "738709 09 Engineering 40.0 \n", - "1131875 06 Biological Sciences 13.0 \n", - "1643602 08 Information and Computing Sciences 45.0 \n", - "2140491 03 Chemical Sciences 87.5 \n", - "2627450 02 Physical Sciences 49.0 \n", - "3094798 01 Mathematical Sciences 18.0 \n", - "3454060 17 Psychology and Cognitive Sciences 4.0 \n", - "3909944 16 Studies in Human Society 4.0 \n", - "4225053 15 Commerce, Management, Tourism and Services 9.0 \n", - "4915245 20 Language, Communication and Culture 3.0 \n", - "5111347 13 Education 8.0 \n", - "5429203 04 Earth Sciences 64.0 \n", - "5817193 14 Economics 13.0 \n", - "6111107 21 History and Archaeology 22.0 \n", - "6352914 05 Environmental Sciences 6.0 \n", - "6797399 07 Agricultural and Veterinary Sciences 9.0 \n", - "7091867 22 Philosophy and Religious Studies 13.0 \n", - "7194418 18 Law and Legal Studies 4.0 \n", - "7281134 12 Built Environment and Design 7.0 \n", - "7349969 19 Studies in Creative Arts and Writing 1.0 " + "17779 32 Biomedical and Clinical Sciences 6.0 \n", + "733648 40 Engineering 85.0 \n", + "1168804 46 Information and Computing Sciences 51.0 \n", + "1578085 31 Biological Sciences 11.0 \n", + "2046557 42 Health Sciences 5.0 \n", + "2644004 34 Chemical Sciences 98.0 \n", + "3128285 51 Physical Sciences 46.0 \n", + "3570941 44 Human Society 4.0 \n", + "3958749 35 Commerce, Management, Tourism and Services 22.0 \n", + "4403670 49 Mathematical Sciences 37.0 \n", + "4832701 30 Agricultural, Veterinary and Food Sciences 8.0 \n", + "5224547 37 Earth Sciences 44.0 \n", + "5606498 47 Language, Communication and Culture 4.0 \n", + "5864420 52 Psychology 2.0 \n", + "6341779 50 Philosophy and Religious Studies 25.0 \n", + "6535541 39 Education 2.0 \n", + "6853435 41 Environmental Sciences 5.0 \n", + "7239785 48 Law and Legal Studies 2.5 \n", + "7398600 38 Economics 24.5 \n", + "7643598 43 History, Heritage and Archaeology 14.0 \n", + "7880154 33 Built Environment and Design 8.0 \n", + "8082459 36 Creative Arts and Writing 2.0 " ] }, - "execution_count": 41, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -4412,7 +4408,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": { "Collapsed": "false", "colab": {}, @@ -4432,7 +4428,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": { "Collapsed": "false", "colab": {}, @@ -4446,7 +4442,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": { "Collapsed": "false", "colab": {}, @@ -4460,7 +4456,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": { "Collapsed": "false", "colab": {}, @@ -4494,69 +4490,17 @@ " \n", " \n", " \n", - " \n", - " 515\n", - " University of Michigan\n", - " 24.5\n", - " \n", - " \n", - " 524\n", - " Karolinska Institute\n", - " 24.5\n", - " \n", - " \n", - " 523\n", - " Emory University\n", - " 26.0\n", - " \n", - " \n", - " 528\n", - " University of Pittsburgh\n", - " 27.0\n", - " \n", - " \n", - " 521\n", - " University of Sydney\n", - " 28.0\n", - " \n", - " \n", - " 538\n", - " Monash University\n", - " 29.0\n", - " \n", - " \n", - " 533\n", - " University of British Columbia\n", - " 30.0\n", - " \n", - " \n", - " 520\n", - " University of São Paulo\n", - " 31.0\n", - " \n", - " \n", - " 534\n", - " Shanghai Jiao Tong University\n", - " 32.0\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " name filtered percent rank\n", - "515 University of Michigan 24.5\n", - "524 Karolinska Institute 24.5\n", - "523 Emory University 26.0\n", - "528 University of Pittsburgh 27.0\n", - "521 University of Sydney 28.0\n", - "538 Monash University 29.0\n", - "533 University of British Columbia 30.0\n", - "520 University of São Paulo 31.0\n", - "534 Shanghai Jiao Tong University 32.0" + "Empty DataFrame\n", + "Columns: [name, filtered percent rank]\n", + "Index: []" ] }, - "execution_count": 45, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -4578,7 +4522,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": { "Collapsed": "false", "colab": {}, @@ -4614,11 +4558,11 @@ " reference count all\n", " id\n", " count all\n", + " name\n", " city_name\n", " count\n", - " country_name\n", " ...\n", - " name\n", + " longitude\n", " state_name\n", " types\n", " acronym\n", @@ -4634,122 +4578,122 @@ " \n", " 0\n", " grid.38142.3c\n", - " 2211\n", + " 80003\n", " 1.0\n", " Harvard University\n", - " 16932\n", + " 15967\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", " ...\n", - " Harvard University\n", + " -71.116650\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", + " 4.80\n", + " 63.5\n", " 1.0\n", " 0.0\n", " \n", " \n", " 1\n", - " grid.17063.33\n", - " 2211\n", + " grid.21107.35\n", + " 80003\n", " 2.0\n", - " University of Toronto\n", - " 10281\n", + " Johns Hopkins University\n", + " 9182\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", " ...\n", - " Harvard University\n", + " -71.116650\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", + " 4.80\n", + " 63.5\n", " 1.0\n", " -1.0\n", " \n", " \n", " 2\n", - " grid.17063.33\n", - " 2211\n", + " grid.21107.35\n", + " 80003\n", " 2.0\n", - " University of Toronto\n", - " 10281\n", - " grid.17063.33\n", - " 10281\n", - " Toronto\n", - " 392\n", - " Canada\n", + " Johns Hopkins University\n", + " 9182\n", + " grid.21107.35\n", + " 9182\n", + " Johns Hopkins University\n", + " Baltimore\n", + " 356\n", " ...\n", - " University of Toronto\n", - " Ontario\n", + " -76.620280\n", + " Maryland\n", " [Education]\n", - " NaN\n", - " 11 Medical and Health Sciences\n", - " 2.0\n", - " 3.81\n", - " 236.5\n", + " JHU\n", + " 32 Biomedical and Clinical Sciences\n", + " 4.0\n", + " 3.88\n", + " 195.5\n", " 2.0\n", " 0.0\n", " \n", " \n", " 3\n", - " grid.21107.35\n", - " 2211\n", - " 2.0\n", - " Johns Hopkins University\n", - " 10120\n", + " grid.17063.33\n", + " 80003\n", + " 3.0\n", + " University of Toronto\n", + " 8932\n", " grid.38142.3c\n", - " 16932\n", + " 15967\n", + " Harvard University\n", " Cambridge\n", - " 845\n", - " United States\n", + " 767\n", " ...\n", - " Harvard University\n", + " -71.116650\n", " Massachusetts\n", " [Education]\n", " NaN\n", - " 11 Medical and Health Sciences\n", + " 32 Biomedical and Clinical Sciences\n", " 1.0\n", - " 4.99\n", - " 61.5\n", + " 4.80\n", + " 63.5\n", " 1.0\n", - " -1.0\n", + " -2.0\n", " \n", " \n", " 4\n", + " grid.17063.33\n", + " 80003\n", + " 3.0\n", + " University of Toronto\n", + " 8932\n", " grid.21107.35\n", - " 2211\n", - " 2.0\n", + " 9182\n", " Johns Hopkins University\n", - " 10120\n", - " grid.17063.33\n", - " 10281\n", - " Toronto\n", - " 392\n", - " Canada\n", + " Baltimore\n", + " 356\n", " ...\n", - " University of Toronto\n", - " Ontario\n", + " -76.620280\n", + " Maryland\n", " [Education]\n", - " NaN\n", - " 11 Medical and Health Sciences\n", + " JHU\n", + " 32 Biomedical and Clinical Sciences\n", + " 4.0\n", + " 3.88\n", + " 195.5\n", " 2.0\n", - " 3.81\n", - " 236.5\n", - " 3.0\n", - " 1.0\n", + " -1.0\n", " \n", " \n", " ...\n", @@ -4776,213 +4720,213 @@ " ...\n", " \n", " \n", - " 3721588\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.7359.8\n", + " 4134095\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.467212.4\n", + " 7\n", + " Adobe Inc\n", + " San Jose\n", " 3\n", - " Bamberg\n", - " 1\n", - " Germany\n", " ...\n", - " University of Bamberg\n", " NaN\n", - " [Education]\n", + " California\n", + " [Company]\n", " NaN\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 33.33\n", - " 8.0\n", - " 8.0\n", - " 5.5\n", + " 36 Creative Arts and Writing\n", + " 59.0\n", + " 42.86\n", + " 1.0\n", + " 1.0\n", + " -33.5\n", " \n", " \n", - " 3721589\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.11560.33\n", - " 2\n", - " Bernal\n", + " 4134096\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.4800.c\n", + " 7\n", + " Polytechnic University of Turin\n", + " Turin\n", " 1\n", - " Argentina\n", " ...\n", - " National University of Quilmes\n", - " NaN\n", + " 7.661075\n", + " Piemonte\n", " [Education]\n", - " UNQ\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", - " 2.5\n", + " NaN\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", + " 34.5\n", " 0.0\n", " \n", " \n", - " 3721590\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.15866.3c\n", - " 2\n", - " Prague\n", + " 4134097\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.54549.39\n", + " 7\n", + " University of Electronic Science and Technolog...\n", + " Chengdu\n", " 1\n", - " Czechia\n", " ...\n", - " Czech University of Life Sciences Prague\n", + " 104.100270\n", " NaN\n", " [Education]\n", - " CULS\n", - " 19 Studies in Creative Arts and Writing\n", - " 130.0\n", - " 50.00\n", - " 2.5\n", - " 2.5\n", + " UESTC\n", + " 36 Creative Arts and Writing\n", + " 350.5\n", + " 14.29\n", + " 34.5\n", + " 34.5\n", " 0.0\n", " \n", " \n", - " 3721591\n", - " grid.256592.f\n", - " 2219\n", - " 2.5\n", - " Grinnell College\n", - " 2\n", - " grid.241104.2\n", - " 2\n", - " Cleveland\n", + " 4134098\n", + " grid.66859.34\n", + " 80007\n", + " 34.5\n", + " Broad Institute\n", + " 7\n", + " grid.6603.3\n", + " 7\n", + " University of Cyprus\n", + " Nicosia\n", " 1\n", - " United States\n", " ...\n", - " University Hospitals of Cleveland\n", - " Ohio\n", - 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3721593 rows × 23 columns

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NaN California [Company] \n", + "4134096 Turin 1 ... 7.661075 Piemonte [Education] \n", + "4134097 Chengdu 1 ... 104.100270 NaN [Education] \n", + "4134098 Nicosia 1 ... 33.376976 NaN [Education] \n", + "4134099 Cambridge 1 ... -71.087030 Massachusetts [Nonprofit] \n", "\n", - " types acronym for_name rank \\\n", - "0 [Education] NaN 11 Medical and Health Sciences 1.0 \n", - "1 [Education] NaN 11 Medical and Health Sciences 1.0 \n", - "2 [Education] NaN 11 Medical and Health Sciences 2.0 \n", - "3 [Education] NaN 11 Medical and Health Sciences 1.0 \n", - "4 [Education] NaN 11 Medical and Health Sciences 2.0 \n", - "... ... ... ... ... \n", - "3721588 [Education] NaN 19 Studies in Creative Arts and Writing 130.0 \n", - "3721589 [Education] UNQ 19 Studies in Creative Arts and Writing 130.0 \n", - "3721590 [Education] CULS 19 Studies in Creative Arts and Writing 130.0 \n", - "3721591 [Healthcare] NaN 19 Studies in Creative Arts and Writing 130.0 \n", - "3721592 [Education] NaN 19 Studies in Creative Arts and Writing 130.0 \n", + " acronym for_name rank percentage top 1 \\\n", + "0 NaN 32 Biomedical and Clinical Sciences 1.0 4.80 \n", + "1 NaN 32 Biomedical and Clinical Sciences 1.0 4.80 \n", + "2 JHU 32 Biomedical and Clinical Sciences 4.0 3.88 \n", + "3 NaN 32 Biomedical and Clinical Sciences 1.0 4.80 \n", + "4 JHU 32 Biomedical and Clinical Sciences 4.0 3.88 \n", + "... ... ... ... ... \n", + "4134095 NaN 36 Creative Arts and Writing 59.0 42.86 \n", + "4134096 NaN 36 Creative Arts and Writing 350.5 14.29 \n", + "4134097 UESTC 36 Creative Arts and Writing 350.5 14.29 \n", + "4134098 UCY 36 Creative Arts and Writing 350.5 14.29 \n", + "4134099 NaN 36 Creative Arts and Writing 350.5 14.29 \n", "\n", - " percentage top 1 percent rank filtered percent rank rank_difference \n", - "0 4.99 61.5 1.0 0.0 \n", - "1 4.99 61.5 1.0 -1.0 \n", - "2 3.81 236.5 2.0 0.0 \n", - "3 4.99 61.5 1.0 -1.0 \n", - "4 3.81 236.5 3.0 1.0 \n", - "... ... ... ... ... \n", - "3721588 33.33 8.0 8.0 5.5 \n", - "3721589 50.00 2.5 2.5 0.0 \n", - "3721590 50.00 2.5 2.5 0.0 \n", - "3721591 50.00 2.5 2.5 0.0 \n", - "3721592 50.00 2.5 2.5 0.0 \n", + " percent rank filtered percent rank rank_difference \n", + "0 63.5 1.0 0.0 \n", + "1 63.5 1.0 -1.0 \n", + "2 195.5 2.0 0.0 \n", + "3 63.5 1.0 -2.0 \n", + "4 195.5 2.0 -1.0 \n", + "... ... ... ... \n", + "4134095 1.0 1.0 -33.5 \n", + "4134096 34.5 34.5 0.0 \n", + "4134097 34.5 34.5 0.0 \n", + "4134098 34.5 34.5 0.0 \n", + "4134099 34.5 34.5 0.0 \n", "\n", - "[3721593 rows x 23 columns]" + "[4134100 rows x 24 columns]" ] }, - "execution_count": 46, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -5013,7 +4957,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/docs/_static/pygments.css b/docs/_static/pygments.css index 84ab3030..6f8b210a 100644 --- a/docs/_static/pygments.css +++ b/docs/_static/pygments.css @@ -6,9 +6,9 @@ span.linenos.special { color: #000000; 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API COOKBOOKS

Organizations