FAIR principles
The FAIR principles for research data are a set of guidelines aimed at improving the accessibility, interoperability, and reusability of research data. FAIR principles emphasize making research data 'Findable, Accessible, Interoperable, and Reusable.'
By adhering to these principles, researchers can ensure that their research data is easy to find and interpret, which promotes collaboration and knowledge sharing. The benefits of following FAIR principles include increased citation and reproducibility rates, improved data quality, and the potential to build upon existing research to achieve further breakthroughs. Overall, implementing FAIR principles can lead to more efficient and effective research and ultimately advance scientific knowledge.
- Strategy: You acquire a strategy for good data management in your project.
- Security: Avoid data loss.
- Visibility: Your work becomes more transparent and credible, and visibility will earn you recognition when used or cited, creating collaboration opportunities.
- Transparency: Helps colleagues and your future self understand the research project and data, and helps ensure the reliability and reproducibility of the research.
- Time-saving: You save time by finding research data that has already been generated and minimize the risk of wasting time and money duplicating work.
- Compliance: You comply with international standards and institutional policies.
- Derived research: You make it easy to reuse research data, for example, for new analyses and potential derivative projects.
- Community: You help society access publicly funded research data.
And thereby:
- Maximizing the potential from data.
- Maximizing the impact of research.
Findable
Accessible
Interoperable
You make your research data interoperable by:
- Preferring open, long-term viable file formats for your data and metadata.
- Including sufficient and standardised structural metadata in accordance with your research community’s standard controlled vocabulary or ontology.
- Including use of common standards, terminologies, vocabularies, ontologies and taxonomies for the data.