FAIR Data

What is FAIR? 

FAIR stands for Findable, Accessible, Interoperable, and Reusable. 

These principles (https://www.go-fair.org/fair-principles/) aim to enhance the value of research data by ensuring it can be discovered, understood, and reused by others, including machines. 

Findable 

Goal: Data should be easy to find for both humans and computers. 

Key Actions: 

• Register datasets in a trusted repository (e.g., UK Data Service, Zenodo, Pure) that can assign a persistent identifier (PID) (e.g., DOI). 

• Provide rich metadata (descriptive, structured, and machine-readable). 

• Use standardised naming conventions and keywords. 

Accessible 

Goal: Data and metadata should be retrievable using standard protocols. 

Key Actions: 

• Store data in repositories.

• Ensure metadata remains accessible even if the data is restricted or removed. 

• Provide clear licensing and access conditions (e.g., Creative Commons). 

Interoperable 

Goal: Data should integrate with other data and tools. 

Key Actions: 

• Use standard vocabularies, ontologies, and, where possible, open-source formats (e.g., CSV, JSON, RDF). 

• Include machine-readable metadata using community standards (e.g., Dublin Core, DataCite). 

• Link to related datasets and publications using PIDs. 

Reusable 

Goal: Data should be well-described so it can be replicated and reused. 

Key Actions: 

• Provide detailed documentation (e.g., README files, codebooks). 

• Apply clear usage licences (e.g., CC-BY). 

• Include provenance information (who created the data, when and how). 

• Align with disciplinary standards and best practices.