The FAIR Data Principles are a set of guidelines stating that scientific and research data should be Findable, Accessible, Interoperable, and Reusable by both humans and machines. They emphasise persistent identifiers, rich machine-readable metadata, standardised vocabularies, and clear usage licences to maximise the long-term value of data. FAIR is widely adopted in research-data management, open science, and knowledge-graph and ontology engineering.
Content
- Implementing FAIR relies on persistent identifiers (e.g. DOIs), structured machine-readable metadata, shared ontologies for interoperability, and explicit licences and provenance for reuse. The principles are pivotal in linked-data and semantic-web contexts, where ontologies and metadata schemas turn raw datasets into interconnected, queryable resources.