How EBRAINS organizes metadata in the KG

Brain research data are heterogeneous and acquired with complex scientific protocols. Describing data across different domains is challenging. To make metadata (and thereby the underlying data) findable, accessible, interoperable and reproducible (FAIR), EBRAINS provides professional curation support, which ensures high quality metadata and the use of a "common language" (e.g. ontologies, controlled vocabularies, and terminologies).

The EBRAINS Knowledge Graph supports this endeavor by providing the toolset for the curation workflows with an "open world" approach, making heavy use of standards of the semantic web (JSON-LD):

The "open world" approach

Metadata in the Knowledge Graph are searchable and help describe and make the data discoverable. The open world approach means that existing data models can be extended at any point in time with new properties and connections as opposed to conventional, fixed-schema data structures.

Iterative, collaborative development of metadata annotation conventions

In close interaction with the research community, the curation support service and ontology engineering teams define the basic metadata standards. Once defined, the tools provided support its users to comply with the conventions.

In a highly dynamic field like neuroscience, metadata standards as well as routines for data curation will evolve over time: New fields are added, new connections built, values and ontologies introduced and/or deprecated, etc.

The EBRAINS Knowledge Graph provides tools and mechanisms, which incorporate standards and conventions while supporting the extension and adaptation of metadata for future needs.