Fair Data

FAIR Data Principles — Findable, Accessible, Interoperable, Reusable

Overview

Guidelines for making scientific data FAIR: Findable, Accessible, Interoperable, and Reusable.

Findable

• Assign globally unique persistent identifiers (DOIs) to datasets
• Rich metadata describing the dataset (title, authors, description, keywords, dates)
• Metadata registered in searchable resources (DataCite, re3data, FAIRsharing)
• Data indexed in domain-specific repositories

Accessible

• Data retrievable by identifier using standardized protocol (HTTP, FTP)
• Metadata accessible even if data is restricted
• Authentication/authorization where necessary, clearly documented
• Long-term preservation plan (minimum 10 years for funded research)

Interoperable

• Use formal, shared vocabularies (ontologies: GO, ChEBI, EFO, MeSH)
• Standard file formats (CSV, JSON, HDF5, NetCDF — not proprietary)
• Include references to related datasets and publications
• Machine-readable metadata (JSON-LD, Dublin Core, schema.org)

Reusable

• Clear data usage license (CC-BY, CC0 recommended for scientific data)
• Detailed provenance (how data was collected, processed, quality controlled)
• Meet community standards (MIAME for microarrays, MINSEQE for sequencing)
• Version control for datasets that evolve

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