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NARPS Open Pipelines #43

@bclenet

Description

@bclenet

Authors

Affiliations

Boris Clénet*1, Élodie Germani*1, Arshitha Basavaraj2, Remi Gau3, Yaroslav Halchenko4, Paul Taylor5, Camille Maumet1

  1. Univ Rennes, Inria, CNRS, Inserm, France
  2. Data Science and Sharing Team, NIMH, National Institutes of Health, Bethesda, MD, USA
  3. Origami lab, McGill University, Montréal, Québec, Canada
  4. Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
  5. 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:

  1. 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

  1. learn new skills:
  • Learn NiPype : joining the project was an opportunity to start using Nipype.
  1. advance pipeline reproductions

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}
}

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