Frameworks & Standards
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 Legra help us make data assets reusable by humans and machines without losing control over access?"
Legra fit
| FAIR area | Legra fit | Status |
|---|---|---|
| Findable | Workspaces, IRIs, catalog metadata, DCAT/DPROD descriptions, and queryable graph metadata help describe and discover assets. | Partial fit |
| Accessible | Legra 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 Legra can say
Describe each dataset with its purpose, owner, access conditions, quality requirements, and provenance. Use queries and version history to review those descriptions as the dataset changes.
Legra 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 Legra deployment still needs good metadata practice, stable vocabularies, publication policy, licensing, retention, and stewardship.
Legra supports metadata, access, and provenance workflows for FAIR data programs, but the buyer's data-stewardship practice determines the final FAIR maturity.