INCF Working Group on Reproducibility and Best Practices in Human Brain Imaging
Jean-Baptiste Poline, McGill
Krzysztof Gorgolewski, Stanford University
David Kennedy, University of Massachusetts
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This Working Group formed as the result of a merger of several INCF Working Groups working in the areas of neuroimaging and reproducibility. The group has several separate projects that all have reproducibility in neuroimaging as an overarching theme, specifically focusing on data sharing, data management, and data description. The working group is composed of 3 task forces: Brain Imaging Informatics (NIDASH), Brain Imaging Data Structure (BIDS), Neuroimaging Data Model (NIDM).
On GitHub (NIDASH, BIDS, NIDM) and on the BIDS mailing list.
Jean-Baptiste Poline, McGill University
Krzysztof Gorgolewski, Stanford University
David Kennedy, University of Massachusetts Medical School
Kirstie Whitaker, Alan Turing Institute
Ross Blair, Stanford Center for Reproducible Neuroscience
Stefan Appelhoff, Max Planck Institute for Human Development
Taylor Salo, Florida International University
Camille Maumet, Inria
Manjari Narayan, Stanford University
Constellates, github user
Vanessa Sochat, Stanford Research Computing
Tibor Auer, University of Surrey
Johanna Bayer, The University of Melbourne
The Working Group aims to collect, compile, synthesize, and distribute information from task forces working on separate projects but with reproducibility in neuroimaging as an overarching theme. Current projects center on neuroimaging data description and sharing.
- BIDS PET imaging specification (expected)
- BIDS NIRS specification (expected)
- BIDS eye-tracking specification (expected)
- BIDS-matlab (expected)
- Enhancement of the BIDS-starter kit (expected)
- NI-DM IPython Notebooks (expected)
- NIDASH Ontology (expected)
- Developing ReproLake: NIDM metadata repository for a number of publicly-available datasets and derived data (expected )
- Develop the NIDM model further: harmonized terminologies, additional tools, provenance for pipelines (expected)
- BIDS training materials (2018)