Skip to main content

INCF Working Group on NIDM (NeuroImaging Data Model)


David Keator, UC Irvine 
Camille Maumet, Inria, Univ Rennes, CNRS, Inserm

Not active


The NIDM Working Group is further developing the NeuroImaging Data Model (NIDM) originally conceived by the INCF Neuro Imaging DAta SHaring (NIDASH) Task Force and the BIRN Derived Data Working Group (DDWG).

NIDM describes an extension of the W3C PROV standard for the domain of human brain mapping and, in recent years, has been working closely with the Brain Imaging Data Structure (BIDS).

Currently this Working Group has two active subgroups focusing on:

  • NIDM-experiment: Interlinkage of existing BIDS datasets chaired by David Keator
  • BIDS-Prov: Representing neuroimaging provenance (follow up of NIDM-Worklows) chaired by Camille Maumet and Satrajit Ghosh

Previously this group developed NIDM-Results, a software-agnostic model to represent results of mass univariate studies (such as fMRI studies).

How we work

Regular calls. Work is coordinated on GitHub.


David Keator, UC Irvine
Camille Maumet, Inria, Univ Rennes, CNRS, Inserm
Alexander Bowring, Nuffield Department of Population Health, U Oxford
Boon Seng Liew, Hospital Sungai Buloh, Universiti Sains Malaysia
Dorota Jarecka, MIT
Jafri Malin Abdullah, Brain Behavior Cluster, Universiti Sains Malaysia
JB Poline, McGill
Junhua Li, U Essex
Karl Helmer, Harvard
Markus Butz-Ostendorf, Biomax
Michael Dayan, Campus Biotech Geneva
Nazek Queder, U California Irvine
Sanu Ann Abraham, MIT
Satrajit Ghosh, MIT
Sebastian Urchs, U Montreal
Thomas Nichols, Oxford U
Tibor Auer, U Surrey
Xinlin You, Hospital Sungai Buloh and Shah Alam
Yaroslav Halchenko, Dartmouth
Lavanya M K, Georg-August-Universität Göttingen


The goal of this Working Group is to foster collaboration among the standards developers working on NIDM, and to provide a common location where users of NIDM can find related information and resources.

  • NeuroImaging Data Model development: further develop the model to include harmonized terminology and provenance information for pipelines and datasets (expected)
  • Develop input interfaces to save information in standardized provenance model (eg. ReproSchema) (expected)
  • Developing ReproLake: NIDM metadata representation for a number of datasets and derived data will be made available in "ReproLake" hosted by NIF (expected)
Completed Deliverables