The FAIR Principles
According to the FAIR Guiding Principles for Scientific Data Management, researchers are encouraged to make their data Findable, Accessible, Interoperable, and Reusable.
The FAIR Principles are not rigid rules or a technical standard, but rather a framework supported by the European Commission to help researchers manage, share, and preserve data so that it can be effectively used by both humans and machines. Applying these principles ensures that data are easier to locate, understand, exchange, and reuse, while also supporting long-term preservation.
Importantly, FAIR does not require data to be openly available to everyone.
- Data can be FAIR but not open, for instance, when access is restricted for privacy, ethical, or legal reasons. However, metadata and conditions for reuse need to be clearly described.
- Conversely, open data may not be FAIR if they lack sufficient documentation, metadata, or licensing information to enable proper understanding and reuse.
Findable
Data should be easy to discover and identify by people and machines. Each dataset should have a persistent, unique identifier (such as a DOI) and be described by metadata that can be located through disciplinary, institutional, or global portals. Even if the data themselves are restricted, the metadata should remain available and searchable.
Accessible
Data and metadata should be retrievable using standardized protocols. A clear and transparent license or access policy must describe the terms of use. FAIR does not mean that all data must be open. Sensitive or confidential data can remain closed, provided that access conditions are documented and discoverable.
Interoperable
Data should be stored and described in formats and vocabularies that are widely recognized within the research community. Metadata should indicate relationships between datasets and use consistent identifiers so that data can be integrated, exchanged, and reused between disciplines, institutions, and countries.
Reusable
To enable long-term reuse, data should include comprehensive documentation, provenance information, and clear licensing. They should retain their full informational value, not just a subset used for publication. Using community standards and detailed metadata ensures that others can interpret and reuse the data appropriately.
FAIR in practice
Researchers should consider FAIR principles from the beginning of their project, ensuring that:
- Datasets are assigned persistent identifiers (e.g., DOIs)
- Metadata is openly available even if data access is limited
- Open, interoperable formats and recognized vocabularies are used
- Licenses clearly state who can use the data and under what conditions
- Provenance and contextual information are recorded to support future reuse
The CARE Principles
The CARE Principles, created by the Global Indigenous Data Alliance (GIDA) in 2018–2019, are a set of people- and purpose-oriented principles consisting of Collective Benefit, Authority to Control, Responsibility, and Ethics.
Collective Benefit
Research should be about supporting the goals of the people involved. Digital tools and databases should be built to serve the public and let communities take the lead in how their information is managed.
Authority to Control
Cultural knowledge should not come at the cost of community authority. It is necessary to obtain informed consent before collecting personal data. People should keep the rights over data that represents them, as well as retain the right to access, control, and withdraw data at any point.
Responsibility
For research to be ethical, it must be reciprocal. This means moving away from “one-off” projects and instead focusing on lasting partnerships that share resources and help communities reach their own goals.
Ethics
Every project must match the rules and customs of the people involved while actively preventing any harm, with a clear eye on the long-term impact of the work. It is essential to treat all involved parties with respect, ensuring that their personal privacy and cultural traditions are never compromised.
Help and further information
Checklist: How FAIR are your data?
DATICE – The Icelandic Research Data Service
GIDA – Global Indigenous Data Alliance
GOFAIR – The Global Open FAIR guides for people and organisations on solutions for making data Findable, Accessible, Interoperable, Reusable for people and machines
OpenAIRE – Open Access Infrastructure for Research in Europe
SSH Open Marketplace & FAIR Practices Training Series 2026 Session 1: FAIR, CARE & Open Science Principles
Wilkinson, Dumontier, Aalbersberg et al. 2016. “The FAIR Guiding Principles for scientific data management and stewardship”
Guidelines on FAIR Data Management in Horizon 2020.