Chapter 2: FAIR Principles

2.1 Understanding FAIR

The FAIR principles, published in 2016 in Scientific Data, provide guidelines for making research data Findable, Accessible, Interoperable, and Reusable. Rather than a standard or specification, FAIR is a set of guiding principles that emphasize machine-actionability—the capacity of computational systems to find, access, and process data with minimal human intervention.

FAIR emerged from the recognition that research data's long-term value depends on more than just availability. Data must be discoverable, obtainable under clear conditions, combinable with other data, and structured for reuse. These principles apply to data itself, to metadata, and to the infrastructure enabling discovery and access.

2.2 Findable

The first step toward data reuse is discovery. Data and metadata must be easy for both humans and machines to find:

Implementing Findability

Requirement	Implementation	Examples
Persistent Identifiers	Assign DOIs or similar PIDs	DataCite DOI, ORCID for researchers
Rich Metadata	Use community standards	Dublin Core, DataCite Metadata Schema
Indexing	Register in searchable catalogs	re3data, Google Dataset Search
Keywords	Use controlled vocabularies	MeSH, SNOMED CT, ontologies

2.3 Accessible

Once data is found, users need to know how to access it. Accessible means data can be retrieved, not necessarily that it must be open to everyone:

Access Considerations

• Standard Protocols: Use HTTP/HTTPS for data access; FTP and proprietary protocols reduce accessibility
• Authentication: Implement standard authentication (OAuth, OIDC) when access restrictions are needed
• Access Levels: Define clear access tiers (open, registered, controlled, closed)
• Metadata Persistence: Keep metadata available even if data is removed for privacy reasons

2.4 Interoperable

Data often needs to be integrated with other data or used with diverse applications and workflows:

Interoperability Standards

Domain	Standards	Purpose
Clinical	HL7 FHIR, OMOP CDM, CDISC	Clinical data exchange and analysis
Genomics	GA4GH standards, VCF, FASTQ	Genomic data formats and exchange
Imaging	DICOM, NIfTI, OME-TIFF	Medical and microscopy imaging
Ontologies	OWL, SNOMED CT, MeSH, HPO	Semantic annotation and linking

2.5 Reusable

The ultimate goal of FAIR is data reuse. Data should be well-described so it can be replicated or combined with new research:

Reusability Requirements

• Clear Licensing: Use standard licenses (CC0, CC-BY, Data Use Agreements) and make license machine-readable
• Provenance: Document how data was collected, processed, and transformed
• Context: Provide sufficient information to understand what data means
• Community Standards: Follow domain minimum information standards (MIAME, STROBE, etc.)

2.6 FAIR Assessment

Several tools and frameworks exist to assess FAIR compliance:

Tool	Approach	Use Case
FAIR Evaluator	Automated checks via API	Technical compliance testing
FAIRshake	Community assessments	Repository evaluation
FAIR Maturity Indicators	RDA working group standards	Formal assessment framework
FAIR Self-Assessment	Questionnaire-based	Planning and gap analysis