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Description
Authors
Affiliations
Boris Clénet*1, Élodie Germani*1, Arshitha Basavaraj2, Remi Gau3, Yaroslav Halchenko4, Paul Taylor5, Camille Maumet1
- Univ Rennes, Inria, CNRS, Inserm, France
- Data Science and Sharing Team, NIMH, National Institutes of Health, Bethesda, MD, USA
- Origami lab, McGill University, Montréal, Québec, Canada
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
- Scientific and Statistical Computing Core, NIMH, National Institutes of Health, Bethesda, MD, USA
Contacts
Boris Clénet [email protected]
Élodie Germani [email protected]
Arshitha Basavaraj [email protected]
Remi Gau [email protected]
Yaroslav Halchenko [email protected]
Paul Taylor [email protected]
Camille Maumet [email protected]
Summary
Introduction
Different analytical choices can lead to variations in the results, a phenomenon that was illustrated in neuroimaging by the NARPS project (Botvinik-Nezer et al., 2020). In NARPS, 70 teans were tasks to analyze the same dataset to answer 9 yes/no research questions. Each team share their final results as well as a textual description (COBIDAS-compliant Nichols et al., 2017) of their analysis.
The goal of NARPS Open Pipelines is to create a codebase reproducing the 70 pipelines of the NARPS project and share this as an open resource for the community.
Results
The OHBM Brainhack 2023 gave the oppurtunity to:
- make the repository more welcoming to new contributions:
-
Proof-read and test the contribution process : everyone helped in finding and fixing inconsitencies in the documentation and in the processes related to contributing. PR#66, PR#65, PR#63, PR#64, PR#52, PR#50
-
Create GitHub Actions workflows for enabling continuous integration, i.e.: testing existing pipelines everytime there are changes on them. PR#47
-
Develop a new GitHub Actions workflow to detect typos in code comments and documentations at each commit. PR#48
- learn new skills:
- Learn NiPype : joining the project was an opportunity to start using Nipype.
- advance pipeline reproductions
-
Better understand AFNI pipelines : thanks to Paul Taylor, the project will benefit from a deeper understanding of AFNI pipelines, with afni_proc examples of the AFNI team's pipeline in NARPS (Paul A Taylor et al., 2023 and associated repository).
-
Start new pipeline reproductions. PR#62, Issue#61, PR#59, Issue#57, Issue#60, PR#55, Issue#51, Issue#49
In the end a total of:
- 6 pull requests were merged, 4 opened ;
- 4 issues were closed, 6 opened.
References (Bibtex)
@article{botvinik2020,
author = "Botvinik-Nezer, R. et al.",
title = "Variability in the analysis of a single neuroimaging dataset by many teams",
journal = "Nature",
year = 2020
}
@article{taylor2023
author = "Paul A Taylor et al.",
title = "Highlight Results, Don't Hide Them: Enhance interpretation, reduce biases and improve reproducibility",
journal = "NeuroImage",
year = 2023
}
@article{nichols2017best,
title={Best practices in data analysis and sharing in neuroimaging using MRI},
author={Nichols, Thomas E and Das, Samir and Eickhoff, Simon B and Evans, Alan C and Glatard, Tristan and Hanke, Michael and Kriegeskorte, Nikolaus and Milham, Michael P and Poldrack, Russell A and Poline, Jean-Baptiste and others},
journal={Nature neuroscience},
volume={20},
number={3},
pages={299--303},
year={2017},
publisher={Nature Publishing Group US New York}
}