FAIR Principles
In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reusability of digital assets.
FAIR Principles
The FAIR Principles are a set of international guidelines for research data management and stewardship designed to enhance the reusability of scholarly data. FAIR stands for Findable, Accessible, Interoperable, and Reusable.
First formally published in Scientific Data in 2016, these principles provide a concise and measurable framework for researchers, institutions, publishers, and funding agencies to improve data infrastructure and support the verification, validation, reproducibility, and reuse of scientific data.
F – Findable
Data and metadata must be easy to discover for both humans and machines.
F1. (Meta)data are assigned a globally unique and persistent identifier (e.g., DOI, ORCID)
F2. Data are described with rich metadata
F3. Metadata clearly and explicitly include the identifier of the data it describes
F4. (Meta)data are registered or indexed in a searchable resource (e.g., data repository, catalog)
1. Policy Statement
A – Accessible
Once found, users must be able to access the data through clear, standardized procedures.
A1. (Meta)data are retrievable by their identifier using a standardized communications protocol
A1.1 The protocol is open, free, and universally implementable (e.g., HTTP/HTTPS)
A1.2 The protocol allows for authentication and authorization procedures, where necessary
A2. Metadata remain accessible even when the data are no longer available
I – Interoperable
Data must be structured and described using shared standards so they can be integrated with other datasets and workflows.
I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation
I2. (Meta)data use vocabularies that follow FAIR principles (standardized terminologies, ontologies)
I3. (Meta)data include qualified references to other (meta)data
R – Reusable
Data should be well-described with clear usage conditions so they can be reliably reused in different contexts.
R1. (Meta)data are richly described with a plurality of accurate and relevant attributes
R1.1 (Meta)data are released with a clear and accessible data usage license
R1.2 (Meta)data are associated with detailed provenance (origin, methods, versioning)
R1.3 (Meta)data meet domain-relevant community standards
Key Characteristics
Machine-actionable: FAIR emphasizes enabling machines to automatically find, access, and use data, in addition to supporting human reuse.
Not a certification: FAIR is a set of guiding goals, not a formal certification or mandatory checklist.
Applies to metadata and data: Both the data itself and its accompanying metadata must follow FAIR principles.
Supports Open Science: FAIR is a core pillar of open science and responsible research practices.
Why FAIR Matters
Improves reproducibility and transparency in research
Enables data integration across disciplines and institutions
Maximizes research investment by allowing data reuse
Supports compliance with funder and publisher data-sharing policies
Facilitates machine-learning and automated analysis workflows