FAIR evaluation
Get a calibrated FAIR score for your paper — Findable, Accessible, Interoperable, Reusable. This is a parallel quality metric, independent of the DataRank citation score. See the FAIR showcase →
How the FAIR score works
FAIR measures how well a paper and its underlying data follow the FAIR principles — Findable, Accessible, Interoperable, Reusable. Each principle is scored 0–100, and the four combine into a single calibrated 0–100 score.
Findable
Rich, machine-readable metadata, a persistent DOI, and presence in repositories and indexes so the work and its data can be discovered.
Accessible
The paper and data are openly retrievable — Open Access, deposited files, and a clear protocol for how to obtain them.
Interoperable
Data uses standard formats and vocabularies and is linked through standard identifiers — dataset DOIs, accessions, and registered relations.
Reusable
A clear open license, provenance, versioning, and enough methodological detail for others to reproduce and reuse the work.
How it's scored. Each dimension blends objective signals we can verify — a DOI, Open Access status, deposited files, linked datasets and accessions, an open license, and version history — with an AI rubric that reads the paper's freely available full text (or its abstract when full text isn't available). When no AI model is configured the score falls back to the deterministic signals alone, so results stay reproducible.
Calibrated across papers. The overall score is standardized into a percentile over every paper we've evaluated, so you can see where a paper stands relative to the rest of the corpus.
Independent of DataRank. FAIR is a parallel quality metric. It is never folded into the citation-based DataRank score — it measures data stewardship, not citation impact.