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FAIR Principles

Definition
AI-generated

FAIR (Findable, Accessible, Interoperable, Reusable) principles are guidelines for stewarding digital research objects—including GWAS summary statistics, phenotypes, and code—so others can discover, access, integrate, and reuse them with minimal friction.

Topics
Epidemiology
Related
Genome-Wide Association Study (GWAS) GWAS Summary Statistics Preregistration

Why it matters in GWAS

Responsible sharing of summary statistics and individual-level data underpins meta-analysis, method development, and equity in who can reuse data; FAIR complements legal and ethical frameworks (consent, indigenous data sovereignty).

Example usage

"Downstream interpretation uses FAIR Principles to contextualize the main association signal."

Genome-Wide Association Study (GWAS) GWAS Summary Statistics Preregistration
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Wilkinson MD, et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci Data.
  • Uffelmann E, et al. (2021). Genome-wide association studies. Nat Rev Methods Primers. https://doi.org/10.1038/s43586-021-00056-9
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