We have just launched a community forum on vector borne diseases! Join the discussion and connect with other researchers and practitioners in the field.
Learn about forum
FAIR data principlesLink to FAIR data principles
Findable. Accessible. Interoperable. Reusable.
The FAIR data principles help make data easier to discover, access, combine, and reuse. In practice, they ensure data can be found reliably, interpreted correctly, connected with other sources, and used again by others.
Using FAIR improves research integrity, gets more value from research investment, and promotes responsible sharing and reuse. It also helps meet funder expectations, increases the visibility of your work, and makes collaboration easier.
The FAIR principles were first published in a 2016 Scientific Data article. A detailed explanation is available from the GO FAIR initiative.
Findable
- F1. (Meta)data have a globally unique, persistent identifier
- F2. Data are described using rich metadata (see R1)
- 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 remain 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 multiple 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
Putting FAIR into practice is not one-size-fits-all. It is often a gradual, context-specific process shaped by data type, research domain, and available resources.
A responsible sharing strategy is central. That might mean using a trusted repository, data archive, or centre that assigns persistent identifiers such as DOIs, supports open standards like OAI-PMH for metadata harvesting, and provides clear licensing to guide reuse.
FAIR also depends on well-structured data and strong supporting materials. Provide rich metadata and accessible documentation, use community-endorsed schemas and vocabularies where appropriate, and align with disciplinary standards and best practices.
To review your approach and spot opportunities to improve FAIRness, try the FAIR-Aware self-assessment tool developed by DANS, the Dutch national centre of expertise and repository for research data.
Watch this short video on the FAIR data principles.