Glossary Term

FAIR Data Principles

What are FAIR data principles?

A framework that makes research data Findable, Accessible, Interoperable, and Reusable, enabling both humans and machines to discover and use data with minimal barriers.

Published in 2016, the FAIR principles established four requirements for research data stewardship. Findable means data has persistent identifiers and rich metadata indexed in searchable resources. Accessible means data can be retrieved through standardized protocols, even if authentication is required. Interoperable means data uses shared vocabularies and formats that allow integration with other datasets. Reusable means data carries clear licensing, provenance tracking, and domain-relevant documentation.

FAIR doesn’t mean “open.” Data can be FAIR while still access-controlled. The point is that metadata is always discoverable and the rules for access are explicit. This distinction matters in regulated environments like pharmaceutical research, where proprietary data needs governance but also needs to be findable within an organization.

For institutions managing massive unstructured data environments, FAIR compliance starts with knowing what you have. That requires a data catalog capable of scanning billions of files across heterogeneous storage, extracting and enriching metadata, and making it searchable. Starfish Storage’s platform supports FAIR workflows by providing the metadata foundation: persistent file tracking, custom tagging, provenance history, and automated classification that transforms scattered research outputs into governed, discoverable assets. As funders like NIH, NSF, and the European Commission increasingly require FAIR-aligned data management plans, this infrastructure becomes a hard requirement.

Starfish Storage provides the metadata foundation for FAIR compliance: persistent file tracking, custom tagging, automated classification, and a searchable data catalog that makes unstructured research data findable and governed across any storage environment, without requiring researchers to change their workflows.