las6292.bib

@article{aanensenEpiCollectLinkingSmartphones2009,
  title = {{{EpiCollect}}: {{Linking Smartphones}} to {{Web Applications}} for {{Epidemiology}}, {{Ecology}} and {{Community Data Collection}}},
  shorttitle = {{{EpiCollect}}},
  author = {Aanensen, David M. and Huntley, Derek M. and Feil, Edward J. and {al-Own}, Fada'a and Spratt, Brian G.},
  editor = {Hay, Simon I.},
  year = 2009,
  month = sep,
  journal = {PLoS ONE},
  volume = {4},
  number = {9},
  pages = {e6968},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0006968},
  urldate = {2026-01-15},
  abstract = {Background:Epidemiologists and ecologists often collect data in the field and, on returning to their laboratory, enter their data into a database for further analysis. The recent introduction of mobile phones that utilise the open source Android operating system, and which include (among other features) both GPS and Google Maps, provide new opportunities for developing mobile phone applications, which in conjunction with web applications, allow two-way communication between field workers and their project databases. Methodology: Here we describe a generic framework, consisting of mobile phone software, EpiCollect, and a web application located within www.spatialepidemiology.net. Data collected by multiple field workers can be submitted by phone, together with GPS data, to a common web database and can be displayed and analysed, along with previously collected data, using Google Maps (or Google Earth). Similarly, data from the web database can be requested and displayed on the mobile phone, again using Google Maps. Data filtering options allow the display of data submitted by the individual field workers or, for example, those data within certain values of a measured variable or a time period. Conclusions: Data collection frameworks utilising mobile phones with data submission to and from central databases are widely applicable and can give a field worker similar display and analysis tools on their mobile phone that they would have if viewing the data in their laboratory via the web. We demonstrate their utility for epidemiological data collection and display, and briefly discuss their application in ecological and community data collection. Furthermore, such frameworks offer great potential for recruiting `citizen scientists' to contribute data easily to central databases through their mobile phone.},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/VP2TRLJS/Aanensen et al. - 2009 - EpiCollect Linking Smartphones to Web Applications for Epidemiology, Ecology and Community Data Col.PDF}
}

@article{alexanderQualitativeDataSharing2019,
  title = {Qualitative Data Sharing and Synthesis for Sustainability Science},
  author = {Alexander, Steven M. and Jones, Kristal and Bennett, Nathan J. and Budden, Amber and Cox, Michael and Crosas, Merc{\`e} and Game, Edward T. and Geary, Janis and Hardy, R. Dean and Johnson, Jay T. and Karcher, Sebastian and Motzer, Nicole and Pittman, Jeremy and Randell, Heather and Silva, Julie A. and Da Silva, Patricia Pinto and Strasser, Carly and Strawhacker, Colleen and Stuhl, Andrew and Weber, Nic},
  year = 2019,
  month = nov,
  journal = {Nature Sustainability},
  volume = {3},
  number = {2},
  pages = {81--88},
  issn = {2398-9629},
  doi = {10.1038/s41893-019-0434-8},
  urldate = {2026-01-15},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/2C97AIPN/Alexander et al. - 2019 - Qualitative data sharing and synthesis for sustainability science.pdf}
}

@article{alstonBeginnersGuideConducting2021,
  title = {A {{Beginner}}'s {{Guide}} to {{Conducting Reproducible Research}}},
  author = {Alston, Jesse M. and Rick, Jessica A.},
  year = 2021,
  month = apr,
  journal = {The Bulletin of the Ecological Society of America},
  volume = {102},
  number = {2},
  pages = {e01801},
  issn = {0012-9623, 2327-6096},
  doi = {10.1002/bes2.1801},
  urldate = {2026-01-15},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/DTRQZYPB/Alston and Rick - 2021 - A Beginner's Guide to Conducting Reproducible Research.pdf}
}

@article{bakkerTranscriptionToolsSoftware2017,
  title = {Transcription {{Tools}} and {{Software}}},
  author = {Bakker, Rebecca},
  year = 2017,
  journal = {Works of the FIU Libraries},
  volume = {62},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/IWFIHWCA/Bakker - Transcription Tools and Software.pdf}
}

@article{barbinTranslatingArticlesHumanities2014,
  title = {Translating {{Articles}} in the {{Humanities}} and {{Social Sciences}}.},
  author = {Barbin, Franck},
  year = 2014,
  journal = {EspacesTemps.net},
  pages = {hal-02089141},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/9HVGT5SS/Barbin - Translating Articles in the Humanities and Social Sciences..pdf}
}

@article{bokhoveAutomatedGenerationGood2018,
  title = {Automated Generation of `Good Enough' Transcripts as a First Step to Transcription of Audio-Recorded Data},
  author = {Bokhove, Christian and Downey, Christopher},
  year = 2018,
  journal = {Methodological Innovations},
  volume = {May-August 2018},
  pages = {1--14},
  abstract = {In the last decade, automated captioning services have appeared in mainstream technology use. Until now, the focus of these services have been on the technical aspects, supporting pupils with special educational needs and supporting teaching and learning of second language students. Only limited explorations have been attempted regarding its use for research purposes: transcription of audio recordings. This article presents a proof-of-concept exploration utilising three examples of automated transcription of audio recordings from different contexts; an interview, a public hearing and a classroom setting, and compares them against `manual' transcription techniques in each case. It begins with an overview of literature on automated captioning and the use of voice recognition tools for the purposes of transcription. An account is provided of the specific processes and tools used for the generation of the automated captions followed by some basic processing of the captions to produce automated transcripts. Originality checking software was used to determine a percentage match between the automated transcript and a manual version as a basic measure of the potential usability of each of the automated transcripts. Some analysis of the more common and persistent mismatches observed between automated and manual transcripts is provided, revealing that the majority of mismatches would be easily identified and rectified in a review and edit of the automated transcript. Finally, some of the challenges and limitations of the approach are considered. These limitations notwithstanding, we conclude that this form of automated transcription provides `good enough' transcription for first versions of transcripts. The time and cost advantages of this could be considerable, even for the production of summary or gisted transcripts.},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/4SA7VPAV/Bokhove and Downey - Automated generation of ‘good enough’ transcripts as a first step to transcription of audio-recorded.pdf}
}

@article{bromanDataOrganizationSpreadsheets2018,
  title = {Data {{Organization}} in {{Spreadsheets}}},
  author = {Broman, Karl W and Woo, Kara H},
  year = 2018,
  journal = {The American Statistician},
  volume = {72},
  number = {1},
  pages = {2--10},
  abstract = {Spreadsheets are widely used software tools for data entry, storage, analysis, and visualization. Focusing on the data entry and storage aspects, this article offers practical recommendations for organizing spreadsheet data to reduce errors and ease later analyses. The basic principles are: be consistent, write dates like YYYYMM-DD, do not leave any cells empty, put just one thing in a cell, organize the data as a single rectangle (with subjects as rows and variables as columns, and with a single header row), create a data dictionary, do not include calculations in the raw data files, do not use font color or highlighting as data, choose good names for things, make backups, use data validation to avoid data entry errors, and save the data in plain text files.},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/B77H7GDZ/Broman and Woo - Data Organization in Spreadsheets.pdf}
}

@article{dekoningGuidelinesAnthropologicalResearch2019,
  title = {Guidelines for Anthropological Research: {{Data}} Management, Ethics, and Integrity},
  shorttitle = {Guidelines for Anthropological Research},
  author = {De Koning, Martijn and Meyer, Birgit and Moors, Annelies and Pels, Peter},
  year = 2019,
  month = jun,
  journal = {Ethnography},
  volume = {20},
  number = {2},
  pages = {170--174},
  issn = {1466-1381, 1741-2714},
  doi = {10.1177/1466138119843312},
  urldate = {2026-01-15},
  abstract = {As anthropologists we are increasingly confronted with attempts -- be it by employers, the media, or policy makers -- to regulate our work in ways that are both epistemologically and ethically counterproductive and threaten our scientific integrity. This document is written out of concern about the problems that occur when protocols for data management, integrity, and ethics, developed for sciences that employ a positivistic, hypothesistesting and replicable style of research, are applied to different scientific practices, such as social and cultural anthropology, that are more explorative, intersubjective and interpretative. In social and cultural anthropology, issues of scientific governance and its ethics are strongly case-specific. Still, concerns about the imposition of scientific protocols from other disciplines require anthropologists to develop some general guidelines for data management, integrity and ethics of anthropological research. Rather than fixed rules, these are broad principles to guide work and adapt it to specific cases.},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/A4XCRH87/De Koning et al. - 2019 - Guidelines for anthropological research Data management, ethics, and integrity.pdf}
}

@article{drinkwaterUseOpticalCharacter2014,
  title = {The Use of {{Optical Character Recognition}} ({{OCR}}) in the Digitisation of Herbarium Specimen Labels},
  author = {Drinkwater, Robyn and Cubey, Robert and Haston, Elspeth},
  year = 2014,
  month = may,
  journal = {PhytoKeys},
  volume = {38},
  pages = {15--30},
  issn = {1314-2003, 1314-2011},
  doi = {10.3897/phytokeys.38.7168},
  urldate = {2026-01-15},
  abstract = {At the Royal Botanic Garden Edinburgh (RBGE) the use of Optical Character Recognition (OCR) to aid the digitisation process has been investigated. This was tested using a herbarium specimen digitisation process with two stages of data entry. Records were initially batch-processed to add data extracted from the OCR text prior to being sorted based on Collector and/or Country. Using images of the specimens, a team of six digitisers then added data to the specimen records. To investigate whether the data from OCR aid the digitisation process, they completed a series of trials which compared the efficiency of data entry between sorted and unsorted batches of specimens. A survey was carried out to explore the opinion of the digitisation staff to the different sorting options. In total 7,200 specimens were processed.},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/PLXX3G4X/Drinkwater et al. - 2014 - The use of Optical Character Recognition (OCR) in the digitisation of herbarium specimen labels.pdf}
}

@article{dukeEthicsDataSharing2013,
  title = {The {{Ethics}} of {{Data Sharing}} and {{Reuse}} in {{Biology}}},
  author = {Duke, Clifford S. and Porter, John H.},
  year = 2013,
  month = jun,
  journal = {BioScience},
  volume = {63},
  number = {6},
  pages = {483--489},
  issn = {1525-3244, 0006-3568},
  doi = {10.1525/bio.2013.63.6.10},
  urldate = {2026-01-15},
  abstract = {Recent increases in capabilities for gathering, storing, accessing, and sharing data are creating corresponding opportunities for scientists to use data generated by others in their own research. Although sharing data and crediting sources are among the most basic of scientific ethical principles, formal ethical guidelines for data reuse have not been articulated in the biological sciences community. This article offers a framework for developing ethical principles on data reuse, addressing issues such as citation and coauthorship, with the aim of stimulating a conversation in the science community and with the goal of having professional societies formally incorporate considerations of data reuse into their codes of ethics.},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/2AVVN639/Duke and Porter - 2013 - The Ethics of Data Sharing and Reuse in Biology.pdf}
}

@article{goodmanTenSimpleRules2014,
  title = {Ten {{Simple Rules}} for the {{Care}} and {{Feeding}} of {{Scientific Data}}},
  author = {Goodman, Alyssa and Pepe, Alberto and Blocker, Alexander W. and Borgman, Christine L. and Cranmer, Kyle and Crosas, Merce and Di Stefano, Rosanne and Gil, Yolanda and Groth, Paul and Hedstrom, Margaret and Hogg, David W. and Kashyap, Vinay and Mahabal, Ashish and Siemiginowska, Aneta and Slavkovic, Aleksandra},
  editor = {Bourne, Philip E.},
  year = 2014,
  month = apr,
  journal = {PLoS Computational Biology},
  volume = {10},
  number = {4},
  pages = {e1003542},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1003542},
  urldate = {2026-01-15},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/LJRUQQGA/Goodman et al. - 2014 - Ten Simple Rules for the Care and Feeding of Scientific Data.pdf}
}

@article{hartTenSimpleRules2016,
  title = {Ten {{Simple Rules}} for {{Digital Data Storage}}},
  author = {Hart, Edmund M. and Barmby, Pauline and LeBauer, David and Michonneau, Fran{\c c}ois and Mount, Sarah and Mulrooney, Patrick and Poisot, Timoth{\'e}e and Woo, Kara H. and Zimmerman, Naupaka B. and Hollister, Jeffrey W.},
  editor = {Markel, Scott},
  year = 2016,
  month = oct,
  journal = {PLOS Computational Biology},
  volume = {12},
  number = {10},
  pages = {e1005097},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1005097},
  urldate = {2026-01-15},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/XBN2JJEA/Hart et al. - 2016 - Ten Simple Rules for Digital Data Storage.PDF}
}

@book{heimGuidelinesTranslationSocial2006,
  title = {Guidelines for the Translation of Social Science Texts},
  author = {Heim, Michael Henry and Tymowski, Andrzej W.},
  year = 2006,
  publisher = {American Council of Learned Societies},
  address = {New York},
  collaborator = {Wallerstein, Immanuel Maurice},
  isbn = {978-0-9788780-0-9},
  langid = {english},
  annotation = {OCLC: 156794370},
  file = {/Users/emiliobruna/Zotero/storage/Z9JWI4NN/Heim and Tymowski - 2006 - Guidelines for the translation of social science texts.pdf}
}

@article{johnsonOrganizingMountainsWords2010,
  title = {Organizing ``{{Mountains}} of {{Words}}'' for {{Data Analysis}}, Both {{Qualitative}} and {{Quantitative}}},
  author = {Johnson, Bruce D. and Dunlap, Eloise and Benoit, Ellen},
  year = 2010,
  month = mar,
  journal = {Substance Use \& Misuse},
  volume = {45},
  number = {5},
  pages = {648--670},
  issn = {1082-6084, 1532-2491},
  doi = {10.3109/10826081003594757},
  urldate = {2026-01-15},
  abstract = {Qualitative research creates mountains of words. U.S. federal funding supports mostly structured qualitative research, which is designed to test hypotheses using semi-quantitative coding and analysis. The authors have 30 years of experience in designing and completing major qualitative research projects, mainly funded by the US National Institute on Drug Abuse [NIDA]. This article reports on strategies for planning, organizing, collecting, managing, storing, retrieving, analyzing, and writing about qualitative data so as to most efficiently manage the mountains of words collected in large-scale ethnographic projects. Multiple benefits accrue from this approach. Several different staff members can contribute to the data collection, even when working from remote locations. Field expenditures are linked to units of work so productivity is measured, many staff in various locations have access to use and analyze the data, quantitative data can be derived from data that is primarily qualitative, and improved efficiencies of resources are developed. The major difficulties involve a need for staff who can program and manage large databases, and who can be skillful analysts of both qualitative and quantitative data.},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/MGDYTUQV/Johnson et al. - 2010 - Organizing “Mountains of Words” for Data Analysis, both Qualitative and Quantitative.pdf}
}

@article{jooImageDataAutomated2019,
  title = {Image as Data: {{Automated}} Visual Content Analysis for Social Science},
  author = {Joo, Jungseock and {Steinert-Threlkeld}, Zachary C},
  year = 2019,
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/D62XEB56/Joo and Steinert-Threlkeld - Image as data Automated visual content analysis for social science.pdf}
}

@article{katsnelsonFiveTipsDigitizing2024,
  title = {Five Tips for Digitizing Handwritten Data},
  author = {Katsnelson, Alla},
  year = 2024,
  month = mar,
  journal = {Nature},
  volume = {627},
  number = {8002},
  pages = {233--234},
  publisher = {Nature Publishing Group},
  doi = {10.1038/d41586-024-00646-z},
  urldate = {2024-07-11},
  abstract = {Need to digitize field notes or historical documents? Researchers share their best practices.},
  copyright = {2024 Springer Nature Limited},
  langid = {english},
  keywords = {Ecology,Environmental sciences,Software,Technology},
  annotation = {Bandiera\_abtest: a\\
Cg\_type: Technology Feature\\
Subject\_term: Technology, Ecology, Environmental sciences, Software},
  file = {/Users/emiliobruna/Zotero/storage/BETNPU9V/d41586-024-00646-z.html}
}

@article{lewisWildlifeBiologyBig2018,
  title = {Wildlife Biology, Big Data, and Reproducible Research},
  author = {Lewis, Keith P. and Vander Wal, Eric and Fifield, David A.},
  year = 2018,
  month = mar,
  journal = {Wildlife Society Bulletin},
  volume = {42},
  number = {1},
  pages = {172--179},
  issn = {2328-5540, 2328-5540},
  doi = {10.1002/wsb.847},
  urldate = {2026-01-15},
  abstract = {Changes in technology have made it possible to gather vast amounts of data, often of high quality, that in turn can improve the quality of wildlife biology. However, with this growth in data, practices such as data management, exploratory data analysis, data-sharing, and reproducibility of an analysis have become increasingly complex. These practices often depend heavily on computer scripting languages, and are often hidden from the peer-review process despite their influence on the final results. Although these issues have been discussed in the literature, they are generally dealt with in a piecemeal fashion, preventing synthesis, and thereby slowing progress. We offer a conceptual framework to illustrate relationships among these practices, and show where wildlife biology as a field has embraced these changes, where awareness is growing, and where it lags behind other fields. We then present several case studies to emphasize the importance of adopting these practices. Any of these case studies could have been conducted with little attention to these practices or employing scripting languages, but there are many disadvantages to this approach including increased chance of errors, inefficiency, and lack of reproducibility. We suggest that a change in the culture of how wildlife biology is conducted is required and that this change will be fostered by integrating these practices into wildlife biology education, implementation, and embracing the idea of open data and open computer code. \'O 2018 The Wildlife Society.},
  copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/2TG7U3I4/Lewis et al. - 2018 - Wildlife biology, big data, and reproducible research.pdf}
}

@article{mauthnerQualitativeDataPreservation2009,
  title = {Qualitative Data Preservation and Sharing in the Social Sciences: {{On}} Whose Philosophical Terms?},
  shorttitle = {Qualitative Data Preservation and Sharing in the Social Sciences},
  author = {Mauthner, Natasha S. and Parry, Odette},
  year = 2009,
  month = mar,
  journal = {Australian Journal of Social Issues},
  volume = {44},
  number = {3},
  pages = {291--307},
  issn = {0157-6321, 1839-4655},
  doi = {10.1002/j.1839-4655.2009.tb00147.x},
  urldate = {2026-01-15},
  abstract = {Over the past decade, an academic debate has developed surrounding qualitative data preservation and sharing in the social sciences, and has been characterised as one between supporters and opponents of this movement. We reframe the debate by suggesting that so-called `opponents' are not resistant to the principle of data preservation and sharing, but ambivalent about how this principle is being put into practice. Specifically, qualitative researchers are uneasy about the foundational assumptions underpinning current data preservation and sharing policies and practices. Efforts to address these concerns argue that the inclusion of the `contexts' of data generation, preservation and reuse will adequately resolve the epistemological concerns held by the qualitative research community. However, these `solutions' reproduce foundational assumptions by treating `context' as ontologically separate from, rather than constitutive of, data. The future of qualitative data preservation and sharing in the social sciences is dependent on shedding its implicit unitary foundational model of qualitative research, and embracing `epistemic pluralism' and the diversity of philosophical perspectives representing the qualitative researcher community.},
  copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/N59742ZK/Mauthner and Parry - 2009 - Qualitative data preservation and sharing in the social sciences On whose philosophical terms.pdf}
}

@article{michenerNongeospatialMetadataEcological1997b,
  title = {Nongeospatial Metadata for the Ecological Sciences},
  author = {Michener, William K and Brunt, James W and Helly, John J and Kirchner, Thomas B and Stafford, Susan G},
  year = 1997,
  journal = {Ecology},
  volume = {7},
  number = {1},
  pages = {330--342},
  abstract = {Issues related to data preservation and sharing are receiving increased attention from scientific societies, funding agencies, and the broad scientific community. Ecologists, for example, are increasingly using data collected by other scientists to address questions at broader spatial, temporal, and thematic scales (e.g., global change, biodiversity, sustainability). No data set is perfect and self-explanatory. Ecologists must, therefore, rely upon a set of instructions or documentation to acquire a specific data set, determine its suitability for meeting specific research objectives, and accurately interpret results from subsequent processing, analysis, and modeling.},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/4JSHRJQ9/Michener et al. - NONGEOSPATIAL METADATA FOR THE ECOLOGICAL SCIENCES.pdf}
}

@article{michenerTenSimpleRules2015,
  title = {Ten {{Simple Rules}} for {{Creating}} a {{Good Data Management Plan}}},
  author = {Michener, William K.},
  editor = {Bourne, Philip E.},
  year = 2015,
  month = oct,
  journal = {PLOS Computational Biology},
  volume = {11},
  number = {10},
  pages = {e1004525},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1004525},
  urldate = {2026-01-15},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/RW86EB3R/Michener - 2015 - Ten Simple Rules for Creating a Good Data Management Plan.PDF}
}

@article{moylanIncreasinglyMobileHow2015,
  title = {Increasingly Mobile: {{How}} New Technologies Can Enhance Qualitative Research},
  shorttitle = {Increasingly Mobile},
  author = {Moylan, Carrie Ann and Derr, Amelia Seraphia and Lindhorst, Taryn},
  year = 2015,
  month = jan,
  journal = {Qualitative Social Work},
  volume = {14},
  number = {1},
  pages = {36--47},
  issn = {1473-3250, 1741-3117},
  doi = {10.1177/1473325013516988},
  urldate = {2026-01-15},
  abstract = {Advances in technology, such as the growth of smart phones, tablet computing, and improved access to the internet have resulted in many new tools and applications designed to increase efficiency and improve workflow. Some of these tools will assist scholars using qualitative methods with their research processes. We describe emerging technologies for use in data collection, analysis, and dissemination that each offer enhancements to existing research processes. Suggestions for keeping pace with the ever-evolving technological landscape are also offered.},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/EWTBLIWI/Moylan et al. - 2015 - Increasingly mobile How new technologies can enhance qualitative research.pdf}
}

@article{oliverConstraintsOpportunitiesInterview2005,
  title = {Constraints and {{Opportunities}} with {{Interview Transcription}}: {{Towards Reflection}} in {{Qualitative Research}}},
  shorttitle = {Constraints and {{Opportunities}} with {{Interview Transcription}}},
  author = {Oliver, D. G. and Serovich, J. M. and Mason, T. L.},
  year = 2005,
  month = dec,
  journal = {Social Forces},
  volume = {84},
  number = {2},
  pages = {1273--1289},
  issn = {0037-7732, 1534-7605},
  doi = {10.1353/sof.2006.0023},
  urldate = {2026-01-15},
  abstract = {In this paper we discuss the complexities of interview transcription. While often seen as a behindthe-scenes task, we suggest that transcription is a powerful act of representation. Transcription is practiced in multiple ways, often using naturalism, in which every utterance is captured in as much detail as possible, and/or denaturalism, in which grammar is corrected, interview noise (e.g., stutters, pauses, etc.) is removed and nonstandard accents (i.e., non-majority) are standardized. In this article, we discuss the constraints and opportunities of our transcription decisions and point to an intermediate, reflective step. We suggest that researchers incorporate reflection into their research design by interrogating their transcription decisions and the possible impact these decisions may have on participants and research outcomes.},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/FZHGSQVG/Oliver et al. - 2005 - Constraints and Opportunities with Interview Transcription Towards Reflection in Qualitative Resear.pdf}
}

@article{peeplesLessonsCOVIDData2022,
  title = {Lessons from the {{COVID}} Data Wizards},
  author = {Peeples, Lynne},
  year = 2022,
  month = mar,
  journal = {Nature},
  volume = {603},
  number = {7902},
  pages = {564--567},
  publisher = {Nature Publishing Group},
  doi = {10.1038/d41586-022-00792-2},
  urldate = {2024-07-11},
  abstract = {Data dashboards have been an important part of pandemic response and planning. What have their developers learnt about communicating science in a crisis?},
  copyright = {2022 Nature},
  langid = {english},
  keywords = {Communication,Epidemiology,SARS-CoV-2},
  annotation = {Bandiera\_abtest: a\\
Cg\_type: News Feature\\
Subject\_term: Epidemiology, SARS-CoV-2, Communication},
  file = {/Users/emiliobruna/Zotero/storage/8IVGUP6X/Peeples - 2022 - Lessons from the COVID data wizards.pdf;/Users/emiliobruna/Zotero/storage/YP7W2345/d41586-022-00792-2.html}
}

@article{renautManagementArchivingSharing2018,
  title = {Management, {{Archiving}}, and {{Sharing}} for {{Biologists}} and the {{Role}} of {{Research Institutions}} in the {{Technology-Oriented Age}}},
  author = {Renaut, S{\'e}bastien and Budden, Amber E and Gravel, Dominique and Poisot, Timoth{\'e}e and {Peres-Neto}, Pedro},
  year = 2018,
  month = jun,
  journal = {BioScience},
  volume = {68},
  number = {6},
  pages = {400--411},
  issn = {0006-3568, 1525-3244},
  doi = {10.1093/biosci/biy038},
  urldate = {2026-01-15},
  abstract = {Data are one of the primary outputs of science. Although certain subdisciplines of biology have pioneered efforts to ensure their long-term preservation and facilitate collaborations, data continue to disappear, owing mostly to technological, regulatory, and ideological hurdles. In this article, we describe the important steps toward proper data management and archiving and provide a critical discussion on the importance of long-term data conservation. We then illustrate the rise in data archiving through the Joint Data Archiving Policy and the Dryad Digital Repository. In particular, we discuss data integration and how the limited availability of large-scale data sets can hinder new discoveries. Finally, we propose solutions to increase the rate of data preservation, for example by generating mechanisms insuring proper data management and archiving, by providing training in data management, and by transforming the traditional role of research institutions and libraries as data generators toward managers and archivers.},
  copyright = {http://academic.oup.com/journals/pages/about\_us/legal/notices},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/Q2ARZ68N/Renaut et al. - 2018 - Management, Archiving, and Sharing for Biologists and the Role of Research Institutions in the Techn.pdf}
}

@misc{SpiderBiologistDenies2021,
  title = {Spider Biologist Denies Suspicions of Widespread Data Fraud in His Animal Personality Research},
  year = 2021,
  month = mar,
  doi = {10.1126/science.abb1258},
  urldate = {2026-01-15},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/Y7AZTMPB/2021 - Spider biologist denies suspicions of widespread data fraud in his animal personality research.pdf}
}

@incollection{taftiOCRServiceExperimental2016,
  title = {{{OCR}} as a {{Service}}: {{An Experimental Evaluation}} of {{Google Docs OCR}}, {{Tesseract}}, {{ABBYY FineReader}}, and {{Transym}}},
  shorttitle = {{{OCR}} as a {{Service}}},
  booktitle = {Advances in {{Visual Computing}}},
  author = {Tafti, Ahmad P. and Baghaie, Ahmadreza and Assefi, Mehdi and Arabnia, Hamid R. and Yu, Zeyun and Peissig, Peggy},
  editor = {Bebis, George and Boyle, Richard and Parvin, Bahram and Koracin, Darko and Porikli, Fatih and Skaff, Sandra and Entezari, Alireza and Min, Jianyuan and Iwai, Daisuke and Sadagic, Amela and Scheidegger, Carlos and Isenberg, Tobias},
  year = 2016,
  volume = {10072},
  pages = {735--746},
  publisher = {Springer International Publishing},
  address = {Cham},
  doi = {10.1007/978-3-319-50835-1_66},
  urldate = {2026-01-15},
  abstract = {Optical character recognition (OCR) as a classic machine learning challenge has been a longstanding topic in a variety of applications in healthcare, education, insurance, and legal industries to convert different types of electronic documents, such as scanned documents, digital images, and PDF files into fully editable and searchable text data. The rapid generation of digital images on a daily basis prioritizes OCR as an imperative and foundational tool for data analysis. With the help of OCR systems, we have been able to save a reasonable amount of effort in creating, processing, and saving electronic documents, adapting them to different purposes. A set of different OCR platforms are now available which, aside from lending theoretical contributions to other practical fields, have demonstrated successful applications in real-world problems. In this work, several qualitative and quantitative experimental evaluations have been performed using four well-know OCR services, including Google Docs OCR, Tesseract, ABBYY FineReader, and Transym. We analyze the accuracy and reliability of the OCR packages employing a dataset including 1227 images from 15 different categories. Furthermore, we review the state-of-the-art OCR applications in healtcare informatics. The present evaluation is expected to advance OCR research, providing new insights and consideration to the research area, and assist researchers to determine which service is ideal for optical character recognition in an accurate and efficient manner.},
  isbn = {978-3-319-50834-4 978-3-319-50835-1},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/EZ6RA269/Tafti et al. - 2016 - OCR as a Service An Experimental Evaluation of Google Docs OCR, Tesseract, ABBYY FineReader, and Tr.pdf}
}

@article{teacherSmartphonesEcologyEvolution2013,
  title = {Smartphones in Ecology and Evolution: A Guide for the App-rehensive},
  shorttitle = {Smartphones in Ecology and Evolution},
  author = {Teacher, Amber G. F. and Griffiths, David J. and Hodgson, David J. and Inger, Richard},
  year = 2013,
  month = dec,
  journal = {Ecology and Evolution},
  volume = {3},
  number = {16},
  pages = {5268--5278},
  issn = {2045-7758, 2045-7758},
  doi = {10.1002/ece3.888},
  urldate = {2026-01-15},
  abstract = {Smartphones and their apps (application software) are now used by millions of people worldwide and represent a powerful combination of sensors, information transfer, and computing power that deserves better exploitation by ecological and evolutionary researchers. We outline the development process for research apps, provide contrasting case studies for two new research apps, and scan the research horizon to suggest how apps can contribute to the rapid collection, interpretation, and dissemination of data in ecology and evolutionary biology. We emphasize that the usefulness of an app relies heavily on the development process, recommend that app developers are engaged with the process at the earliest possible stage, and commend efforts to create open-source software scaffolds on which customized apps can be built by nonexperts. We conclude that smartphones and their apps could replace many traditional handheld sensors, calculators, and data storage devices in ecological and evolutionary research. We identify their potential use in the high-throughput collection, analysis, and storage of complex ecological information.},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/MBHBZN9X/Teacher et al. - 2013 - Smartphones in ecology and evolution a guide for the app‐rehensive.pdf}
}

@article{templeQualitativeResearchTranslation2004,
  title = {Qualitative {{Research}} and {{Translation Dilemmas}}},
  author = {Temple, Bogusia and Young, Alys},
  year = 2004,
  month = aug,
  journal = {Qualitative Research},
  volume = {4},
  number = {2},
  pages = {161--178},
  issn = {1468-7941, 1741-3109},
  doi = {10.1177/1468794104044430},
  urldate = {2026-01-15},
  abstract = {The focus of this article is an examination of translation dilemmas in qualitative research. Specifically it explores three questions: whether methodologically it matters if the act of translation is identified or not; the epistemological implications of who does translation; and the consequences for the final product of how far the researcher chooses to involve a translator in research. Some of the ways in which researchers have tackled language difference are discussed. The medium of spoken and written language is itself critically challenged by considering the implications of similar `problems of method' but in situations where the translation and interpretation issues are those associated with a visual spatial medium, in this case Sign Language. The authors argue that centring translation and how it is dealt with raises issues of representation that should be of concern to all researchers.},
  copyright = {https://journals.sagepub.com/page/policies/text-and-data-mining-license},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/7Z7Z4TH9/Temple and Young - 2004 - Qualitative Research and Translation Dilemmas.pdf}
}

@article{tenopirChangesDataSharing2015,
  title = {Changes in {{Data Sharing}} and {{Data Reuse Practices}} and {{Perceptions}} among {{Scientists Worldwide}}},
  author = {Tenopir, Carol and Dalton, Elizabeth D. and Allard, Suzie and Frame, Mike and Pjesivac, Ivanka and Birch, Ben and Pollock, Danielle and Dorsett, Kristina},
  editor = {Van Den Besselaar, Peter},
  year = 2015,
  month = aug,
  journal = {PLOS ONE},
  volume = {10},
  number = {8},
  pages = {e0134826},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0134826},
  urldate = {2026-01-15},
  abstract = {The incorporation of data sharing into the research lifecycle is an important part of modern scholarly debate. In this study, the DataONE Usability and Assessment working group addresses two primary goals: To examine the current state of data sharing and reuse perceptions and practices among research scientists as they compare to the 2009/2010 baseline study, and to examine differences in practices and perceptions across age groups, geographic regions, and subject disciplines. We distributed surveys to a multinational sample of scientific researchers at two different time periods (October 2009 to July 2010 and October 2013 to March 2014) to observe current states of data sharing and to see what, if any, changes have occurred in the past 3--4 years. We also looked at differences across age, geographic, and discipline-based groups as they currently exist in the 2013/2014 survey. Results point to increased acceptance of and willingness to engage in data sharing, as well as an increase in actual data sharing behaviors. However, there is also increased perceived risk associated with data sharing, and specific barriers to data sharing persist. There are also differences across age groups, with younger respondents feeling more favorably toward data sharing and reuse, yet making less of their data available than older respondents. Geographic differences exist as well, which can in part be understood in terms of collectivist and individualist cultural differences. An examination of subject disciplines shows that the constraints and enablers of data sharing and reuse manifest differently across disciplines. Implications of these findings include the continued need to build infrastructure that promotes data sharing while recognizing the needs of different research communities. Moving into the future, organizations such as DataONE will continue to assess, monitor, educate, and provide the infrastructure necessary to support such complex grand science challenges.},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/UCR83YX2/Tenopir et al. - 2015 - Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide.PDF}
}

@article{tesiOutdatedVersionExcel2020,
  title = {An {{Outdated Version}} of {{Excel Led}} the {{U}}.{{K}}. to {{Undercount COVID-19 Cases}}},
  author = {Tesi, Whitney},
  year = 2020,
  journal = {Slate},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/SKSFPDYZ/Tesi - An Outdated Version of Excel Led the U.K. to Undercount COVID-19 Cases.pdf}
}

@article{wilsonGoodEnoughPractices2017,
  title = {Good Enough Practices in Scientific Computing},
  author = {Wilson, Greg and Bryan, Jennifer and Cranston, Karen and Kitzes, Justin and Nederbragt, Lex and Teal, Tracy K.},
  editor = {Ouellette, Francis},
  year = 2017,
  month = jun,
  journal = {PLOS Computational Biology},
  volume = {13},
  number = {6},
  pages = {e1005510},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1005510},
  urldate = {2026-01-15},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/T97GF9FE/Wilson et al. - 2017 - Good enough practices in scientific computing.pdf}
}

@article{woodburyDataManagementHealthRelated2019,
  title = {Data {{Management}} in {{Health-Related Research Involving Indigenous Communities}} in the {{United States}} and {{Canada}}: {{A Scoping Review}}},
  shorttitle = {Data {{Management}} in {{Health-Related Research Involving Indigenous Communities}} in the {{United States}} and {{Canada}}},
  author = {Woodbury, R. Brian and Beans, Julie A. and Hiratsuka, Vanessa Y. and Burke, Wylie},
  year = 2019,
  month = oct,
  journal = {Frontiers in Genetics},
  volume = {10},
  pages = {942},
  issn = {1664-8021},
  doi = {10.3389/fgene.2019.00942},
  urldate = {2026-01-15},
  abstract = {Background: Multiple factors, including experiences with unethical research practices, have made some Indigenous groups in the United States and Canada reticent to participate in potentially beneficial health-related research. Yet, Indigenous peoples have also expressed a willingness to participate in research when certain conditions related to the components of data management---including data collection, analysis, security and storage, sharing, dissemination, and withdrawal---are met. A scoping review was conducted to better understand the terms of data management employed in healthrelated research involving Indigenous communities in the United States and Canada. Methods: PubMed, Embase, PsychINFO, and Web of Science were searched using terms related to the populations and topics of interest. Results were screened and articles deemed eligible for inclusion were extracted for content on data management, community engagement, and community-level research governance. Results: The search strategy returned 734 articles. 31 total articles were extracted, of which nine contained in-depth information on data management and underwent detailed extraction. All nine articles reported the development and implementation of data management tools, including research ethics codes, data-sharing agreements, and biobank access policies. These articles reported that communities were involved in activities and decisions related to data collection (n=7), data analysis (n=5), data-sharing (n=9), dissemination (n=7), withdrawal (n=4), and development of data management tools (n=9). The articles also reported that communities had full or shared ownership of (n=5), control over (n=9), access to (n=1), and possession of data (n=5). All nine articles discussed the role of community engagement in research and communitylevel research governance as means for aligning the terms of data management with the values, needs, and interests of communities. Conclusions: There is need for more research and improved reporting on data management in health-related research involving Indigenous peoples in the United States Edited by:},
  langid = {english},
  file = {/Users/emiliobruna/Zotero/storage/AEUFDIGY/Woodbury et al. - 2019 - Data Management in Health-Related Research Involving Indigenous Communities in the United States and.pdf}
}