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
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 Task Force
- BIDS PET imaging specification (expected)
- BIDS NIRS specification (expected)
- BIDS eye-tracking specification (expected)
- BIDS-matlab (expected)
- Enhancement of the BIDS-starter kit (expected)
- BIDSonym application the de-identification of neuroimaging data
- BIDS Specification (v1.4.1)
- BIDS validator (v1.5.1)
- Python tools for BIDS datasets
- BIDS training materials
NIDASH Task Force
- Developing ReproLake: NIDM metadata repository for a number of publicly-available datasets and derived data will be made available in "ReproLake" hosted by NIF with enhanced queries formed using high-level concept-based search. (expected 2021)
- Further develop the model using harmonized terminologies (eg. NIDM-terms) and integrated NIDM ontologies, provide additional tools for working with NIDM serializations (e.g. REST-style queries, linear models on selected data, export of subsets of data to BIDS) and provenance information for pipelines and datasets (eg. BIDS-PROV) (expected 2021)
- NI-DM IPython Notebooks
- NIDASH Ontology