From d2b6eda10e2bed1b9da1aa8afaabe5e00af51cbc Mon Sep 17 00:00:00 2001 From: crnolan Date: Tue, 21 Nov 2023 21:59:25 +0000 Subject: [PATCH] Convert issues to project pages, yay! --- content/project/project_30.md | 2 +- content/project/project_60.md | 4 +- content/project/project_61.md | 145 ++++++++++++++++++++++++++++++++++ 3 files changed, 148 insertions(+), 3 deletions(-) create mode 100644 content/project/project_61.md diff --git a/content/project/project_30.md b/content/project/project_30.md index 2e456ef..55ac5c6 100644 --- a/content/project/project_30.md +++ b/content/project/project_30.md @@ -84,7 +84,7 @@ "color": "FBCA04" } ], - "content": "### Title\r\n\r\nClustintime: a toolbox for spatio-temporal clustering of fMRI data\r\n\r\n### Leaders\r\n\r\nCris Tob\u00edas (@ctobias)\r\n\r\n### Collaborators\r\n\r\n_No response_\r\n\r\n### Brainhack Global 2023 Event \r\n\r\nBrainhack Donostia\r\n\r\n### Project Description\r\n\r\n- What are you doing, for whom, and why?\r\nThis project is a toolbox that allows researchers in neuroimage to apply clustering methods to fMRI data on the spatiotemporal domain. Conventional methods of clustering in fMRI allow to see spatial patterns but cannot describe the temporal dynamics of functional activity. \r\n\r\n- What makes your project special and exciting?\r\nProviding a tool for researchers interested in analyzing brain patterns in uncontrolled fmri experiments or clinical settings is a necessity that has not been covered yet and that could be done through clustintime. \r\n\r\n- How to get started?\r\nClustintime has been under development for a few years and has been already tried on data from individual epileptic patients, the skeletton of the project is already done, now there is need to transform it into a proper toolbox to release the first version of the software. \r\n\r\n- Where to find key resources?\r\nThe project already has its repository on GitHub (https://github.com/Cristina-Tobias/clustintime) and a public fMRI data will be used for testing.\r\n\r\n\r\n### Link to project repository/sources\r\n\r\nhttps://github.com/Cristina-Tobias/clustintime\r\n\r\n### Goals for Brainhack Global\r\n\r\nCreate integration testing suite/framework\r\nCreate CI/CD pipeline \r\nPopulate CI/CD pipeline with checks\r\n\r\n**Release version 0.1.0** \r\n\r\nCreate documentation\r\nImprove user experience of adding input\r\nStructure output messages\r\n\r\n**Release version 0.2.0**\r\n\r\nIntroduce new features\r\n- group analysis\r\n- consensus clustering\r\n\r\n**Release version 1.0.0**\r\n\r\n### Good first issues\r\n\r\nIssue 1: Create integration testing suite/framework\r\n- Find usable data\r\n- Execute each algorithm in a reproducible way (fix seed)\r\n- Integrate into CI/CD pipeline\r\n\r\nIssue 2: Create CI/CD pipeline \r\n- Find free tier provider\r\n- e.g., github actions, circleci\r\n\r\nIssue 3: Populate CI/CD pipeline with checks\r\n- Pylint, flake8, black, isort, ...\r\n\r\n\r\n### Communication channels\r\n\r\nhttps://mattermost.bcbl.eu/spin-lab/channels/clustintime_bhd\r\n\r\n### Skills\r\n\r\n- Git: intermediate\r\n- Python: advanced (only for those that want to develop new methods)\r\n- Markdown: intermediate\r\n- Bash: begginer\r\n\r\n### Onboarding documentation\r\n\r\n_No response_\r\n\r\n### What will participants learn?\r\n\r\n- Experience on collaborating with git \r\n- Experience on how to design and build an open-source library\r\n- Different methods of fMRI visualization\r\n- Unsupervised machine learning methods for fMRI\r\n\r\n### Data to use\r\n\r\n_No response_\r\n\r\n### Number of collaborators\r\n\r\nmore\r\n\r\n### Credit to collaborators\r\n\r\nProject contributors will be acknowledged as contributors in GitHub, credit will also be given in future publications (if any) to those who make a major contribution to the toolbox. \r\n\r\n### Image\r\n\r\nLeave this text if you don't have an image yet.\r\n\r\n### Type\r\n\r\ncoding_methods, documentation, method_development, pipeline_development\r\n\r\n### Development status\r\n\r\n1_basic structure\r\n\r\n### Topic\r\n\r\ndata_visualisation, machine_learning\r\n\r\n### Tools\r\n\r\nother\r\n\r\n### Programming language\r\n\r\nPython\r\n\r\n### Modalities\r\n\r\nfMRI\r\n\r\n### Git skills\r\n\r\n2_branches_PRs\r\n\r\n### Anything else?\r\n\r\n_No response_\r\n\r\n### Things to do after the project is submitted and ready to review.\r\n\r\n- [ ] Add a comment below the main post of your issue saying: `Hi @brainhackorg/project-monitors my project is ready!`\r\n- [ ] Twitter-sized summary of your project pitch.", + "content": "### Title\r\n\r\nClustintime: a toolbox for spatio-temporal clustering of fMRI data\r\n\r\n### Leaders\r\n\r\nCris Tob\u00edas (@cristobias)\r\n\r\n### Collaborators\r\n\r\n_No response_\r\n\r\n### Brainhack Global 2023 Event \r\n\r\nBrainhack Donostia\r\n\r\n### Project Description\r\n\r\n- What are you doing, for whom, and why?\r\nThis project is a toolbox that allows researchers in neuroimage to apply clustering methods to fMRI data on the spatiotemporal domain. Conventional methods of clustering in fMRI allow to see spatial patterns but cannot describe the temporal dynamics of functional activity. \r\n\r\n- What makes your project special and exciting?\r\nProviding a tool for researchers interested in analyzing brain patterns in uncontrolled fmri experiments or clinical settings is a necessity that has not been covered yet and that could be done through clustintime. \r\n\r\n- How to get started?\r\nClustintime has been under development for a few years and has been already tried on data from individual epileptic patients, the skeletton of the project is already done, now there is need to transform it into a proper toolbox to release the first version of the software. \r\n\r\n- Where to find key resources?\r\nThe project already has its repository on GitHub (https://github.com/Cristina-Tobias/clustintime) and a public fMRI data will be used for testing.\r\n\r\n\r\n### Link to project repository/sources\r\n\r\nhttps://github.com/Cristina-Tobias/clustintime\r\n\r\n### Goals for Brainhack Global\r\n\r\nCreate integration testing suite/framework\r\nCreate CI/CD pipeline \r\nPopulate CI/CD pipeline with checks\r\n\r\n**Release version 0.1.0** \r\n\r\nCreate documentation\r\nImprove user experience of adding input\r\nStructure output messages\r\n\r\n**Release version 0.2.0**\r\n\r\nIntroduce new features\r\n- group analysis\r\n- consensus clustering\r\n\r\n**Release version 1.0.0**\r\n\r\n### Good first issues\r\n\r\nIssue 1: Create integration testing suite/framework\r\n- Find usable data\r\n- Execute each algorithm in a reproducible way (fix seed)\r\n- Integrate into CI/CD pipeline\r\n\r\nIssue 2: Create CI/CD pipeline \r\n- Find free tier provider\r\n- e.g., github actions, circleci\r\n\r\nIssue 3: Populate CI/CD pipeline with checks\r\n- Pylint, flake8, black, isort, ...\r\n\r\n\r\n### Communication channels\r\n\r\nhttps://mattermost.brainhack.org/brainhack/channels/clustintime\r\n\r\n### Skills\r\n\r\n- Git: intermediate\r\n- Python: advanced (only for those that want to develop new methods)\r\n- Markdown: intermediate\r\n- Bash: begginer\r\n\r\n### Onboarding documentation\r\n\r\n_No response_\r\n\r\n### What will participants learn?\r\n\r\n- Experience on collaborating with git \r\n- Experience on how to design and build an open-source library\r\n- Different methods of fMRI visualization\r\n- Unsupervised machine learning methods for fMRI\r\n\r\n### Data to use\r\n\r\n_No response_\r\n\r\n### Number of collaborators\r\n\r\nmore\r\n\r\n### Credit to collaborators\r\n\r\nProject contributors will be acknowledged as contributors in GitHub, credit will also be given in future publications (if any) to those who make a major contribution to the toolbox. \r\n\r\n### Image\r\n\r\nLeave this text if you don't have an image yet.\r\n\r\n### Type\r\n\r\ncoding_methods, documentation, method_development, pipeline_development\r\n\r\n### Development status\r\n\r\n1_basic structure\r\n\r\n### Topic\r\n\r\ndata_visualisation, machine_learning\r\n\r\n### Tools\r\n\r\nother\r\n\r\n### Programming language\r\n\r\nPython\r\n\r\n### Modalities\r\n\r\nfMRI\r\n\r\n### Git skills\r\n\r\n2_branches_PRs\r\n\r\n### Anything else?\r\n\r\n_No response_\r\n\r\n### Things to do after the project is submitted and ready to review.\r\n\r\n- [ ] Add a comment below the main post of your issue saying: `Hi @brainhackorg/project-monitors my project is ready!`\r\n- [ ] Twitter-sized summary of your project pitch.", "project_url": "https://github.com/Cristina-Tobias/clustintime", "project_description": "\r\n\r\n- What are you doing, for whom, and why?\r\nThis project is a toolbox that allows researchers in neuroimage to apply clustering methods to fMRI data on the spatiotemporal domain. Conventional methods of clustering in fMRI allow to see spatial patterns but cannot describe the temporal dynamics of functional activity. \r\n\r\n- What makes your project special and exciting?\r\nProviding a tool for researchers interested in analyzing brain patterns in uncontrolled fmri experiments or clinical settings is a necessity that has not been covered yet and that could be done through clustintime. \r\n\r\n- How to get started?\r\nClustintime has been under development for a few years and has been already tried on data from individual epileptic patients, the skeletton of the project is already done, now there is need to transform it into a proper toolbox to release the first version of the software. \r\n\r\n- Where to find key resources?\r\nThe project already has its repository on GitHub (https://github.com/Cristina-Tobias/clustintime) and a public fMRI data will be used for testing.\r\n\r\n\r\n" } \ No newline at end of file diff --git a/content/project/project_60.md b/content/project/project_60.md index 8a866a3..41c773d 100644 --- a/content/project/project_60.md +++ b/content/project/project_60.md @@ -114,6 +114,6 @@ "color": "ededed" } ], - "content": "### Title\n\nPsy2R: Developing an R package for better inference in multivariate statistical analysis\n\n### Leaders\n\nKelly Garner Github: @kel-github Mattermost: @kels\r\n\n\n### Collaborators\n\nKevin Bird\r\nMelanie Gleitzman\r\nSonny Li\r\nChristopher Nolan Github: @crnolan Mattermost: @cnolan\n\n### Brainhack Global 2023 Event\n\nBrainhack Australasia\n\n### Project Description\n\nWe consistently use massive data sets across neuroscience and psychology. The routine gathering of big data requires that we are well equipped with tools that allow us to conduct appropriate multivariate statistics.\r\n\r\nMultivariate statistical analysis typically follows a two stage procedure, an omnibus test of the global null hypothesis followed by post-hoc tests of specific effects. It is not well known that under certain circumstances this leads to a drastically inflated rate of type 1 error. It is even less well known that this procedure can also lead to an even lessor known type IV error (incorrect interpretation of a correctly rejected hypothesis)!\r\n\r\nIt is possible to avoid these dragons by using an alternative procedure where all null hypotheses of interest are tested simultaneously. Using this approach ensures that type 1 error rate is controlled at alpha, and that only the correct null hypotheses are tested (which controls type IV error). This procedure can be hairy, and until now only one piece of software provides this method of testing ([PSY](https://www2.psy.unsw.edu.au/psy/)). However, this software is only available for use on windows and cannot be scripted into reproducible workflows.\r\n\r\nOur goal is to build an R package that implements the functions of PSY, and to make this method of statistical inference available to the masses! \n\n### Link to project repository/sources\n\nTBC\n\n### Goals for Brainhack Global\n\n**Milestones**\r\n\r\n1. Implement analyses that can be done in PSY in R [intermediate]\r\n2. Convert R implementation into generalisable functions [intermediate/advanced]\r\n3. Outline the math involved in computing critical values for STP tests under the null hypothesis [advanced]\r\n4. Interpret the PASCAL code that underpins the computation of critical values in PSY [advanced]\r\n5. Investigate the R world for a comprehensive list of packages that share some functionality with PSY [beginner]\r\n6. Test R functions and implement own analysis [beginner]\r\n7. Interpret what you think our R code does, to help us write more readable code [beginner]\r\n8. Document the PSY software functions and write documentation for the new R functions [beginner]\r\n9. Create project management board (e.g. trello) to keep us on track! [beginner]\n\n### Good first issues\n\n1. Implement analyses that can be done in PSY in R\r\n\r\n2. Document existing PSY software functions \r\n\r\n3. Investigate the R world for a comprehensive list of packages that share some functionality with PSY\r\n\r\n4. Create project management board (e.g. trello) to keep us on track!\r\n\r\n5. Create github repo for the project \n\n### Communication channels\n\nhttps://mattermost.brainhack.org/brainhack/channels/psy2r\n\n### Skills\n\n- R: all levels\r\n- stats/math: all levels\r\n- github: all levels\r\n- comp sci: intermediate/advanced\n\n### Onboarding documentation\n\n_No response_\n\n### What will participants learn?\n\nYou'll learn more about statistical analysis of big datasets, collaborative coding using R and github, and hopefully a bit about package development and coding for other users.\n\n### Data to use\n\n_No response_\n\n### Number of collaborators\n\nmore\n\n### Credit to collaborators\n\nProject contributors will be listed on the github repository's README.md\r\n\n\n### Image\n\n![DALL\u00b7E 2023-11-19 14 53 27 - draw me the greek character 'psi' with a capital R beside it in Quentin Blake style watercolour](https://github.com/brainhackorg/global2023/assets/7220723/224e6486-1a5f-4c96-a4a5-579bef546944)\r\n\n\n### Type\n\ncoding_methods, data_management, documentation, method_development\n\n### Development status\n\n0_concept_no_content\n\n### Topic\n\nreproducible_scientific_methods, statistical_modelling\n\n### Tools\n\nother\n\n### Programming language\n\nC++, documentation, `R`, other\n\n### Modalities\n\nbehavioral, DWI, ECOG, EEG, eye_tracking, fMRI, MEG, MRI, PET\n\n### Git skills\n\n1_commit_push\n\n### Anything else?\n\n_No response_\n\n### Things to do after the project is submitted and ready to review.\n\n- [X] Add a comment below the main post of your issue saying: `Hi @brainhackorg/project-monitors my project is ready!`\n- [X] Twitter-sized summary of your project pitch.", - "project_description": "\n\nWe consistently use massive data sets across neuroscience and psychology. The routine gathering of big data requires that we are well equipped with tools that allow us to conduct appropriate multivariate statistics.\r\n\r\nMultivariate statistical analysis typically follows a two stage procedure, an omnibus test of the global null hypothesis followed by post-hoc tests of specific effects. It is not well known that under certain circumstances this leads to a drastically inflated rate of type 1 error. It is even less well known that this procedure can also lead to an even lessor known type IV error (incorrect interpretation of a correctly rejected hypothesis)!\r\n\r\nIt is possible to avoid these dragons by using an alternative procedure where all null hypotheses of interest are tested simultaneously. Using this approach ensures that type 1 error rate is controlled at alpha, and that only the correct null hypotheses are tested (which controls type IV error). This procedure can be hairy, and until now only one piece of software provides this method of testing ([PSY](https://www2.psy.unsw.edu.au/psy/)). However, this software is only available for use on windows and cannot be scripted into reproducible workflows.\r\n\r\nOur goal is to build an R package that implements the functions of PSY, and to make this method of statistical inference available to the masses! \n\n" + "content": "### Title\r\n\r\nPsy2R: Developing an R package for better inference in multivariate statistical analysis\r\n\r\n### Leaders\r\n\r\nKelly Garner Github: @kel-github Mattermost: @kels\r\n\r\n\r\n### Collaborators\r\n\r\nKevin Bird, \r\nMelanie Gleitzman, \r\nSonny Li, \r\nChristopher Nolan Github: @crnolan Mattermost: @cnolan\r\n\r\n### Brainhack Global 2023 Event\r\n\r\nBrainhack Australasia\r\n\r\n### Project Description\r\n\r\nWe consistently use massive data sets across neuroscience and psychology. The routine gathering of big data requires that we are well equipped with tools that allow us to conduct appropriate multivariate statistics.\r\n\r\nMultivariate statistical analysis typically follows a two stage procedure, an omnibus test of the global null hypothesis followed by post-hoc tests of specific effects. It is not well known that under certain circumstances this leads to a drastically inflated rate of type 1 error. It is even less well known that this procedure can also lead to an even lessor known type IV error (incorrect interpretation of a correctly rejected hypothesis)!\r\n\r\nIt is possible to avoid these dragons by using an alternative procedure where all inferences are derived from simultaneous confidence intervals (SCIs) on contrasts of interest. This approach provides interval inferences on effect sizes and it also ensures that the familywise type 1 error rate associated with directional inferences (the inferences usually derived from tests of null hypotheses) is controlled at alpha. One piece of software (PSY) can produce SCIs appropriate for planned analyses (where contrasts are defined independently of the data) and for more flexible analyses where contrasts are defined on a post hoc basis. However, this software is only available for use on windows and cannot be scripted into reproducible workflows.\r\n\r\nOur goal is to build an R package that implements the functions of PSY, and to make this method of statistical inference available to the masses! \r\n\r\n### Link to project repository/sources\r\n\r\nTBC\r\n\r\n### Goals for Brainhack Global\r\n\r\n**Milestones**\r\n\r\n1. Implement analyses that can be done in PSY in R [intermediate]\r\n2. Convert R implementation into generalisable functions [intermediate/advanced]\r\n3. Outline the math involved in computing critical values for STP tests under the null hypothesis [advanced]\r\n4. Interpret the PASCAL code that underpins the computation of critical values in PSY [advanced]\r\n5. Investigate the R world for a comprehensive list of packages that share some functionality with PSY [beginner]\r\n6. Test R functions and implement own analysis [beginner]\r\n7. Interpret what you think our R code does, to help us write more readable code [beginner]\r\n8. Document the PSY software functions and write documentation for the new R functions [beginner]\r\n9. Create project management board (e.g. trello) to keep us on track! [beginner]\r\n\r\n### Good first issues\r\n\r\n1. Implement analyses that can be done in PSY in R\r\n\r\n2. Document existing PSY software functions \r\n\r\n3. Investigate the R world for a comprehensive list of packages that share some functionality with PSY\r\n\r\n4. Create project management board (e.g. trello) to keep us on track!\r\n\r\n5. Create github repo for the project \r\n\r\n### Communication channels\r\n\r\nhttps://mattermost.brainhack.org/brainhack/channels/psy2r\r\n\r\n### Skills\r\n\r\n- R: all levels\r\n- stats/math: all levels\r\n- github: all levels\r\n- comp sci: intermediate/advanced\r\n\r\n### Onboarding documentation\r\n\r\n_No response_\r\n\r\n### What will participants learn?\r\n\r\nYou'll learn more about statistical analysis of big datasets, collaborative coding using R and github, and hopefully a bit about package development and coding for other users.\r\n\r\n### Data to use\r\n\r\n_No response_\r\n\r\n### Number of collaborators\r\n\r\nmore\r\n\r\n### Credit to collaborators\r\n\r\nProject contributors will be listed on the github repository's README.md\r\n\r\n\r\n### Image\r\n\r\n![DALL\u00b7E 2023-11-19 14 53 27 - draw me the greek character 'psi' with a capital R beside it in Quentin Blake style watercolour](https://github.com/brainhackorg/global2023/assets/7220723/224e6486-1a5f-4c96-a4a5-579bef546944)\r\n\r\n\r\n### Type\r\n\r\ncoding_methods, data_management, documentation, method_development\r\n\r\n### Development status\r\n\r\n0_concept_no_content\r\n\r\n### Topic\r\n\r\nreproducible_scientific_methods, statistical_modelling\r\n\r\n### Tools\r\n\r\nother\r\n\r\n### Programming language\r\n\r\nC++, documentation, `R`, other\r\n\r\n### Modalities\r\n\r\nbehavioral, DWI, ECOG, EEG, eye_tracking, fMRI, MEG, MRI, PET\r\n\r\n### Git skills\r\n\r\n1_commit_push\r\n\r\n### Anything else?\r\n\r\n_No response_\r\n\r\n### Things to do after the project is submitted and ready to review.\r\n\r\n- [X] Add a comment below the main post of your issue saying: `Hi @brainhackorg/project-monitors my project is ready!`\r\n- [X] Twitter-sized summary of your project pitch.", + "project_description": "\r\n\r\nWe consistently use massive data sets across neuroscience and psychology. The routine gathering of big data requires that we are well equipped with tools that allow us to conduct appropriate multivariate statistics.\r\n\r\nMultivariate statistical analysis typically follows a two stage procedure, an omnibus test of the global null hypothesis followed by post-hoc tests of specific effects. It is not well known that under certain circumstances this leads to a drastically inflated rate of type 1 error. It is even less well known that this procedure can also lead to an even lessor known type IV error (incorrect interpretation of a correctly rejected hypothesis)!\r\n\r\nIt is possible to avoid these dragons by using an alternative procedure where all inferences are derived from simultaneous confidence intervals (SCIs) on contrasts of interest. This approach provides interval inferences on effect sizes and it also ensures that the familywise type 1 error rate associated with directional inferences (the inferences usually derived from tests of null hypotheses) is controlled at alpha. One piece of software (PSY) can produce SCIs appropriate for planned analyses (where contrasts are defined independently of the data) and for more flexible analyses where contrasts are defined on a post hoc basis. However, this software is only available for use on windows and cannot be scripted into reproducible workflows.\r\n\r\nOur goal is to build an R package that implements the functions of PSY, and to make this method of statistical inference available to the masses! \r\n\r\n" } \ No newline at end of file diff --git a/content/project/project_61.md b/content/project/project_61.md new file mode 100644 index 0000000..b17ff28 --- /dev/null +++ b/content/project/project_61.md @@ -0,0 +1,145 @@ +{ + "Title": "The evolution of Nipype into Pydra", + "link_to_issue": "https://github.com/brainhackorg/global2023/issues/61", + "issue_number": 61, + "labels": [ + { + "name": "git_skills:1_commit_push", + "description": "", + "color": "5B6C2C" + }, + { + "name": "git_skills:2_branches_PRs", + "description": "", + "color": "5B6C2C" + }, + { + "name": "modality:DWI", + "description": "", + "color": "20CF02" + }, + { + "name": "modality:fMRI", + "description": "", + "color": "20CF02" + }, + { + "name": "modality:MRI", + "description": "", + "color": "20CF02" + }, + { + "name": "programming:documentation", + "description": "", + "color": "347C53" + }, + { + "name": "programming:Python", + "description": "", + "color": "347C53" + }, + { + "name": "project_development_status:2_releases_existing", + "description": null, + "color": "ededed" + }, + { + "name": "project_tools_skills:familiar", + "description": null, + "color": "ededed" + }, + { + "name": "project_type:documentation", + "description": null, + "color": "ededed" + }, + { + "name": "topic:reproducible_scientific_methods", + "description": null, + "color": "ededed" + }, + { + "name": "project_type:pipeline_development", + "description": null, + "color": "ededed" + }, + { + "name": "project_type:method_development", + "description": null, + "color": "ededed" + }, + { + "name": "status:web_ready", + "description": "", + "color": "0E8A16" + }, + { + "name": "git_skills:3_continuous_integration", + "description": null, + "color": "ededed" + }, + { + "name": "project_tools_skills:comfortable", + "description": null, + "color": "ededed" + }, + { + "name": "tools:AFNI", + "description": null, + "color": "ededed" + }, + { + "name": "tools:ANTs", + "description": null, + "color": "ededed" + }, + { + "name": "tools:DIPY", + "description": null, + "color": "ededed" + }, + { + "name": "tools:FieldTrip", + "description": null, + "color": "ededed" + }, + { + "name": "tools:fMRIPrep", + "description": null, + "color": "ededed" + }, + { + "name": "tools:Freesurfer", + "description": null, + "color": "ededed" + }, + { + "name": "tools:FSL", + "description": null, + "color": "ededed" + }, + { + "name": "tools:MNE", + "description": null, + "color": "ededed" + }, + { + "name": "tools:MRtrix", + "description": null, + "color": "ededed" + }, + { + "name": "tools:Nipype", + "description": null, + "color": "ededed" + }, + { + "name": "tools:SPM", + "description": null, + "color": "ededed" + } + ], + "content": "### Title\r\n\r\nThe evolution of Nipype into Pydra\r\n\r\n### Leaders\r\n\r\nTom Close (GH+MM: @tclose) and Arkiev D'Souza (GH: @arkiev MM: adsouza)\r\n\r\n### Collaborators\r\n\r\nDorota Jarecka (GH: @djarecka MM: dorota), Chris Markiewicz (GH: @effigies, MM: markiewicz), Yibei Chen (GH: @yibeichan MM: yibeichen), Ghislain Vaillant (GH+MM: @ghisvail) and Satra Ghosh (GH+MM: @satra)\r\n\r\n### Brainhack Global 2023 Event\r\n\r\nBrainhack Australasia\r\n\r\n### Project Description\r\n\r\n[Nipype](https://nipype.readthedocs.io/) is a Python library that provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow. It forms the basis of widely-used pipelines such as [C-PAC](https://fcp-indi.github.io/) and [fMRIPrep](https://fmriprep.org/).\r\n\r\nDespite Nipype's success and longevity, some limitations of its design have become apparent over time. In particular,\r\n\r\n- the complexity of designing new task interfaces\r\n- inability to run workflow nodes in separate software containers\r\n- inability to split/join workflow nodes over lists generated at execution time\r\n- difficulty following the workflow construction syntax due to the separation of nodes and connections\r\n\r\nTherefore, at the 2018 OHBM BrainHack in Singapore, a number of Nipype's core developers sat down to start planning a rewritten 2nd generation, which eventually turned into [Pydra](https://pydra.readthedocs.io/en/latest/).\r\n\r\nIn the intervening years, Pydra has matured into a fully functioning alternative to Nipype that is almost ready for production. However, it is missing the large library of tool interfaces that have been developed for Nipype over the years. Therefore, the [Nipype2Pydra](https://github.com/nipype/nipype2pydra) conversion tool has been developed to semi-automatically convert existing Nipype interfaces into Pydra syntax. Separate repositories/packages have been created for each toolkit implemented in Nipype, containing YAML specifications to guide the conversion process (e.g. [pydra-freesurfer](https://github.com/nipype/pydra-freesurfer)).\r\n\r\nIn this hackathon, we aim to work through the semi-converted interfaces and complete the conversion process by editing corresponding the YAML specs. Starting off with some of the most popular toolkits, we will hopefully be able to build up enough of a library of interfaces to allow popular Nipype workflows to be ported across to Pydra.\r\n\r\nPlease read the [contribution guide](https://github.com/nipype/pydra/blob/master/CONTRIBUTING.md) for tips on getting started and our policies on acknowledging contributions.\r\n\r\n### Link to project repository/sources\r\n\r\n- https://github.com/nipype/pydra\r\n- https://github.com/nipype/nipype2pydra\r\n\r\n### Goals for Brainhack Global\r\n\r\n- Convert as many interfaces from Nipype to Pydra as possible\r\n- Complete the Nipype2Pydra workflow converter\r\n\r\n### Good first issues\r\n\r\nTODO\r\n\r\n\r\n### Communication channels\r\n\r\nhttps://mattermost.brainhack.org/brainhack/channels/nipype\r\n\r\n### Skills\r\n\r\nRequired: Some Python\r\nNice to have: experience working with neuroimaging toolkits (e.g. FSL, ANTs) but not essential\r\nRecommended: Reasonably comfortable with git\r\n\r\n### Onboarding documentation\r\n\r\nhttps://github.com/nipype/pydra/blob/master/CONTRIBUTING.md\r\n\r\n### What will participants learn?\r\n\r\n* How to design Pydra interfaces\r\n* Gain familiarity with neuroimaging toolkits\r\n\r\n### Data to use\r\n\r\nThis project is not focused on any specific dataset. We will be typically using dummy datasets and sample datasets from [OpenNeuro](https://openneuro.org/), e.g. [ds000114](https://openneuro.org/datasets/ds000114).\r\n\r\n### Number of collaborators\r\n\r\nmore\r\n\r\n### Credit to collaborators\r\n\r\nProject collaborators are listed in the projects' Zenodo reference\r\n\r\n### Image\r\n\r\nLeave this text if you don't have an image yet.\r\n\r\n### Type\r\n\r\ndocumentation, method_development, pipeline_development\r\n\r\n### Development status\r\n\r\n2_releases_existing\r\n\r\n### Topic\r\n\r\nreproducible_scientific_methods\r\n\r\n### Tools\r\n\r\nAFNI, ANTs, DIPY, FieldTrip, fMRIPrep, Freesurfer, FSL, MNE, MRtrix, Nipype, SPM\r\n\r\n### Programming language\r\n\r\nPython\r\n\r\n### Modalities\r\n\r\nDWI, fMRI, MRI\r\n\r\n### Git skills\r\n\r\n1_commit_push, 2_branches_PRs, 3_continuous_integration\r\n\r\n### Anything else?\r\n\r\n_No response_\r\n\r\n### Things to do after the project is submitted and ready to review.\r\n\r\n- [ ] Add a comment below the main post of your issue saying: `Hi @brainhackorg/project-monitors my project is ready!`\r\n- [ ] Twitter-sized summary of your project pitch.", + "project_url": "https://github.com/nipype/pydra-", + "project_description": "\r\n\r\n[Nipype](https://nipype.readthedocs.io/) is a Python library that provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow. It forms the basis of widely-used pipelines such as [C-PAC](https://fcp-indi.github.io/) and [fMRIPrep](https://fmriprep.org/).\r\n\r\nDespite Nipype's success and longevity, some limitations of its design have become apparent over time. In particular,\r\n\r\n- the complexity of designing new task interfaces\r\n- inability to run workflow nodes in separate software containers\r\n- inability to split/join workflow nodes over lists generated at execution time\r\n- difficulty following the workflow construction syntax due to the separation of nodes and connections\r\n\r\nTherefore, at the 2018 OHBM BrainHack in Singapore, a number of Nipype's core developers sat down to start planning a rewritten 2nd generation, which eventually turned into [Pydra](https://pydra.readthedocs.io/en/latest/).\r\n\r\nIn the intervening years, Pydra has matured into a fully functioning alternative to Nipype that is almost ready for production. However, it is missing the large library of tool interfaces that have been developed for Nipype over the years. Therefore, the [Nipype2Pydra](https://github.com/nipype/nipype2pydra) conversion tool has been developed to semi-automatically convert existing Nipype interfaces into Pydra syntax. Separate repositories/packages have been created for each toolkit implemented in Nipype, containing YAML specifications to guide the conversion process (e.g. [pydra-freesurfer](https://github.com/nipype/pydra-freesurfer)).\r\n\r\nIn this hackathon, we aim to work through the semi-converted interfaces and complete the conversion process by editing corresponding the YAML specs. Starting off with some of the most popular toolkits, we will hopefully be able to build up enough of a library of interfaces to allow popular Nipype workflows to be ported across to Pydra.\r\n\r\nPlease read the [contribution guide](https://github.com/nipype/pydra/blob/master/CONTRIBUTING.md) for tips on getting started and our policies on acknowledging contributions.\r\n\r\n" +} \ No newline at end of file