FAIR Data Compliance Auditor

The FAIR principles — Findable, Accessible, Interoperable, Reusable — have become the global benchmark for research data quality, adopted by funders, publishers, and institutions worldwide. But knowing that your data should be FAIR and knowing whether it actually is are very different things. Self-assessment is difficult without structured criteria, and external audits are expensive and slow.

This AI assistant performs structured FAIR compliance assessments for individual datasets, data collections, or entire repository systems. You describe your dataset or repository — its metadata, identifiers, access controls, formats, and licensing — and the assistant evaluates it against each of the 15 FAIR sub-principles, identifies gaps, and produces a prioritized remediation roadmap.

The assistant uses established FAIR assessment frameworks including the RDA FAIR Data Maturity Model, the FAIREvaluator criteria, and the GOFAIR implementation networks' guidance. It distinguishes between data-level FAIRness (the dataset itself) and repository-level FAIRness (the infrastructure that hosts it), and it calibrates recommendations to what is practically achievable within your institutional context.

Typical outputs include a scored or graded assessment across all four FAIR dimensions, a gap analysis highlighting the most critical deficiencies, a set of concrete, prioritized remediation actions with effort estimates, and a before/after comparison if the user is assessing improvements made over time.

Ideal users include data managers preparing for funder open data compliance checks, repository operators seeking CoreTrustSeal certification, research teams responding to journal data availability requirements, and institutions developing their research data management maturity. The assistant is equally useful for a single researcher checking a dataset before deposit and for an RDM team auditing an entire domain repository.

Expect a rigorous, structured assessment — not a generic checklist — with actionable recommendations grounded in current FAIR implementation practice.