FAIR is a set of guiding principles for scientific data management: chemistry datasets become far more valuable to researchers and machines when they are Findable, Accessible, Interoperable and Reusable. Each dataset node here is linked to a metadata node, an ontology node and a repository node; as each pillar score rises, links brighten and new interoperability edges appear between datasets that share standardized vocabularies.
FAIR score = 0.25*(F + A + I + R)
Interop links(t) ≈ N * (I/100)² / 2
Findability index = F * (1 + 0.3 * has_persistent_id)
- Findable / Accessible / Interoperable / Reusable sliders — set each FAIR pillar's completeness for the whole dataset collection.
- Dataset count — changes how many dataset cube nodes populate the graph.
- Reset — returns all pillars to a neutral 50% baseline.
Repositories like PubChemQC and NOMAD apply FAIR scoring to chemistry datasets so that computational and experimental results can be discovered and reused across labs without manual reformatting.