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Also known as: FAIR data principles
Guiding principles to make data Findable, Accessible, Interoperable, and Reusable for humans and machines.
DESCRIPTION
The FAIR Guiding Principles define practices that ensure datasets are discoverable, accessible under clear conditions, interoperable via standards, and reusable with rich metadata and licenses. Originating in scholarly data stewardship, FAIR promotes persistent identifiers, machine-readable metadata, and community standards to maximize scientific and organizational data value. FAIR complements open data and responsible data governance frameworks.
ALTERNATIVE DEFINITIONS
GO FAIR
The FAIR guiding principles describe how data should be Findable, Accessible, Interoperable, and Reusable.
Concise statement of the four FAIR dimensions.
Nature Scientific Data
We present the FAIR Guiding Principles, emphasizing machine-actionability to support the discovery, access, integration and analysis of scholarly data.
Highlights machine-actionability as a core emphasis of FAIR.
European Commission
FAIR stands for Findable, Accessible, Interoperable and Reusable: principles that aim to make research data more useful and usable.
Policy-oriented framing for research data in the EU.
RELATED TERMS
Persistent Identifier Metadata Data Discovery Platform DCAT Schema.org Open Dataset Provenance Digital Object Identifier ORCID Data Stewardship
COMPETING TERMS
CARE Principles Open Data Principles
KNOWLEDGE AREAS
Data Management Data Governance Metadata Management Data Privacy & Compliance
TARGET AUDIENCE
Data Steward Chief Data Officer Data Governance Manager Data Management Consultant Data Scientist
EXTERNAL SOURCES
- Wikipedia - FAIR data
- GO FAIR - Canonical FAIR principles description
- Nature Scientific Data - Original FAIR principles paper
- FORCE11 - Community articulation of FAIR
TOP QUESTIONS
What are the FAIR Principles?
FAIR stands for Findable, Accessible, Interoperable, and Reusable, guiding how data should be managed to maximize usability by humans and machines.
Category: Definition
Why are the FAIR Principles important?
They improve data discoverability, interoperability, and reuse, accelerating research, compliance, and organizational value creation.
Category: Benefits
How do I make data Findable under FAIR?
Assign persistent identifiers, provide rich metadata, and register datasets in searchable catalogs.
Category: HowTo
What does Accessible mean in FAIR?
Access should be clearly documented, authenticated if necessary, and provided through standardized protocols with metadata available even when data are restricted.
Category: Clarification
How is Interoperability achieved?
Use shared vocabularies, ontologies, and standards like DCAT, schema.org, and machine-readable formats.
Category: HowTo
What ensures Reusability?
Rich, accurate metadata, provenance, licensing, and adherence to community standards enable confident reuse.
Category: Requirements
Are FAIR and Open Data the same?
No. Data can be FAIR without being open; FAIR emphasizes usability under clear conditions, not unrestricted access.
Category: Misunderstanding
What is a common mistake implementing FAIR?
Skimping on metadata richness and persistent identifiers, which undermines findability and reuse.
Category: Mistakes
Who is responsible for FAIR in organizations?
Data stewards and governance leaders coordinate FAIR practices, supported by architects and domain experts.
Category: Roles