FAIR data principles

Findable. Accessible. Interoperable. Reusable.

The FAIR data principles are designed to improve the Findability, Accessibility, Interoperability, and Reusability of data. These principles help ensure that data can be reliably located, understood, integrated with other data, and reused by others.

Adopting the FAIR principles contributes to higher research integrity, maximises the value of research investments, and supports responsible data sharing and reuse. Making data FAIR supports compliance with funder requirements, enhances the visibility of your research, and facilitates collaboration.

The FAIR principles were originally published in a 2016 article in Scientific Data. The principles are described in detailed by the GO FAIR initiative.

Findable

F1. (Meta)data are assigned a globally unique and persistent identifier

F2. Data are described with rich metadata (defined by R1 below)

F3. Metadata clearly and explicitly include the identifier of the data they describe

F4. (Meta)data are registered or indexed in a searchable resource

Accessible

A1. (Meta)data are retrievable by their identifier using a standardised communications protocol

A1.1 The protocol is open, free, and universally implementable

A1.2 The protocol allows for an authentication and authorisation procedure, where necessary

A2. Metadata are accessible, even when the data are no longer available

Interoperable

I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation

I2. (Meta)data use vocabularies that follow FAIR principles

I3. (Meta)data include qualified references to other (meta)data

Reusable

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

R1.3. (Meta)data meet domain-relevant community standards

FAIR in Practice

Implementing the FAIR principles isn’t a one-size-fits-all process. Making data FAIR can be a gradual and most often context-dependent effort, shaped by the type of data, the research domain, and the available resources.

A key part of FAIR implementation is choosing a responsible data sharing strategy. This may include ensuring you use a trustworthy digital repository/data archive or centre, which assigns persistent identifiers such as DOIs, supports open, standardised protocols like OAI-PMH for metadata harvesting and provides clear licensing information to guide reuse.

FAIRness also relies on the quality and structure of the data and accompanying metadata and documentation. Therefore you should ensure you supply rich and clear metadata, and accessible documentation, use community-endorsed coding schemas and vocabularies, where applicable, and align with disciplinary standards and best practices.

If you want to evaluate your data sharing approach and identify opportunities to make your data more FAIR, or simply explore the FAIR principles in more depth, try the FAIR-Aware self-assessment tool developed by DANS, the Dutch national centre of expertise and repository for research data.