FAIR Data Principles
The FAIR Data Principles are guidelines for making digital assets findable, accessible, interoperable, and reusable. They are common in research, public-sector, life-sciences, and data-stewardship assessments.
Buyer question
“Can WWKG help us make data assets reusable by humans and machines without losing control over access?”
WWKG fit
| FAIR area | WWKG fit | Status |
|---|---|---|
| Findable | Workspaces, IRIs, catalog metadata, DCAT/DPROD direction, and queryable graph metadata help describe and discover assets. | Partial fit |
| Accessible | WWKG exposes query and graph APIs, but access remains governed by workspace membership and encryption. | Native fit |
| Interoperable | RDF, SPARQL, SHACL, PROV-style metadata, and shared vocabularies make data machine-readable and linkable. | Native fit |
| Reusable | Provenance, validation rules, version history, licenses, quality metadata, and data-product metadata support reuse decisions. | Partial fit |
What WWKG can say
WWKG helps FAIR assessments because metadata and data live in the same semantic environment. A buyer can describe a dataset, validate it, publish it, version it, and trace it without moving between unrelated catalog, quality, lineage, and storage systems.
WWKG can support FAIR evidence such as:
- Persistent IRIs and graph identifiers.
- Catalog records for workspaces, datasets, services, and distributions.
- Machine-queryable metadata through SPARQL.
- Validation rules and reports for quality.
- Provenance for origin and change history.
- Access metadata linked to real workspace controls.
Assessment boundary
FAIR is an outcome, not a product switch. A WWKG deployment still needs good metadata practice, stable vocabularies, publication policy, licensing, retention, and stewardship.
WWKG provides a strong semantic and governance substrate for FAIR data programs, but the buyer’s data-stewardship practice determines the final FAIR maturity.