Neuroshapes

Modern scientific data management requires comprehensive support for the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Neuroshapes is a general approach, or design pattern, for supporting FAIR principles for diverse neuroscience data with the following benefits:

• Neuroshapes ensures that the key scientific and technical activities and agents of the data generation process are expressed in a validatable provenance-based data model.

Neuroshapes captures the contextual information necessary to:

• Interpret the scientific meaning of the data.
• Infer the resulting data types.
• Evaluate trust and quality.
• Ensure attribution of all contributors.
• Support data reuse, integration, interoperability and longevity.

The main goal is to provide design patterns, best practices as well as tools to promote:

• The use of standard semantic markups and linked data principles as ways to structure metadata and related data.
• The 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.
• The reuse of existing schemas and semantic markups ( schema.org , W3C PROV-O ) and existing ontologies and controlled vocabularies (including NIFSTD - Neuroscience Information Framework Standard Ontologies).
• The use of the W3C PROV-O recommendation as a format to record (meta)data provenance.