Neuroshapes: Open SHACL schemas for FAIR neuroscience data
Sean Hill, Krembil Centre for Neuroinformatics & CAMH Andrew Davison, CNRS & Human Brain Project Anna-Kristin Kaufmann, EPFL & Blue Brain Project Huanxiang Lu, EPFL & Blue Brain Project
Tom Gillespie, UCSD & Neuroscience Information Framework
Genrich Ivaska, EPFL & Blue Brain Project
Oliver Schmid, EPFL & Human Brain Project
Jean-Denis Courcol, EPFL & Blue Brain Project
Samuel Kerrien, EPFL & Blue Brain Project
Jeff Muller, EPFL & Human Brain Project
Mohameth François Sy, EPFL & Blue Brain Project
Bogdan Roman, EPFL & Blue Brain Project
Pradeep Reddy Raamana, Rotman Research Institute
This Working Group aims to promote the use of standard semantic markups and linked data principles as ways to structure metadata and related data, to leverage the W3C RDF format’s developer-friendly JSON-LD serialization, and to promote use of the W3C SHACL (Shapes Constraint Language) recommendation as a rich metadata schema language which is formal and expressive; interoperable; machine-readable; and domain-agnostic. With SHACL, (meta)data quality can be enforced based on schemas and vocabularies (easily discoverable and searchable). SHACL also provides key interoperability capabilities and allows to incrementally build standard data models in terms of semantics and sophistication.
It also promotes the reuse of existing schemas and semantic markups (like schema.org) and existing ontologies and controlled vocabularies (including NIFSTD - NIF Standard Ontologies), and the use of the W3C PROV-O recommendation as a format to record (meta)data provenance.
This Working Group coordinates community efforts for the development of open, use case- driven and shared validatable data models (schemas, vocabularies) to enable the FAIR principles (Findable, Accessible, Interoperable and Reusable) for basic, computational and clinical neuroscience (meta)data. It uses provenance information to define the context from which scientific data was generated, including the type of data, its significance, quality and potential for integration and reuse. Data models have been developed thus far for electrophysiology, neuron morphology, brain atlases, and computational modelling. Future developments could include brain imaging, transcriptomic and clinical form data, as determined by the Working Group membership and community interests.
The following projects have adopted Neuroshapes: