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FAIR principles in research data management
FAIR principles ensure that research outputs are findable, accessible, interoperable and reusable. In practice, they enable higher visibility and reuse potential for data.
Updated: 2 May, 2020
A research data management plan must consider who will be able to reuse the data, under what conditions, and how. If circumstances change during a project, you can review the data management plan and update it accordingly.
A key concept (introduced [1] in 2016) to improve your data sharing practices is that data should be FAIR [2]:
- Findable: your data should include metadata and a persistent identifier, to make it discoverable.
- Accessible: data and metadata should be retrievable through a free and open communications protocol. Metadata should always be available, even if data is not.
- Interoperable: metadata should use controlled vocabularies [3], be machine-readable and include references to other metadata. Data should use open formats whenever possible.
- Reusable: metadata should conform to standards for greatest reusability. It should be clear to humans and machines alike. Data should also come with a clear and accessible licence to regulate reuse.
Accessibility does not imply that data should always be open, as access might be constrained due to legitimate concerns around privacy, national security, or commercial interests. In such cases, you should explain why the data is not open and what the conditions for access are.
Responsibilities for making data FAIR are shared [4] between researchers and infrastructure providers (e.g. online repositories [5]).
What are the benefits of FAIR data?
Making data FAIR benefits all the stakeholders in the research data landscape [6]. You will be able to increase the visibility of your research and the number of citations. In addition, you will make the research reproducible and aligned with international standards. Researchers and universities can leverage FAIR data to build new partnerships and answer new research questions. The impact of research data can be maximised when data is FAIR. This follows from its being easily and clearly accessible, both technically and legally.
EUDAT developed a useful checklist to help you [7] understand whether your research data management plan complies with the FAIR principles, and how you can improve it.
Further reading
Footnotes
- [1] The FAIR Guiding Principles for scientific data management and stewardship (DOI) http://dx.doi.org/10.1038/sdata.2016.18
- [2] Guiding principles for findable, accessible, interoperable and re-usable data https://www.force11.org/fairprinciples
- [3] Vocabularies and research data https://www.ands.org.au/__data/assets/pdf_file/0003/690870/Vocabularies-and-Research-Data.pdf
- [4] Explanation of the FAIR data principles http://www.snf.ch/SiteCollectionDocuments/FAIR_principles_translation_SNSF_logo.pdf
- [5] UKDS - The 'FAIR' principles for scientific data management https://www.ukdataservice.ac.uk/news-and-events/newsitem/?id=4615
- [6] The FAIR data principles https://www.ands.org.au/working-with-data/fairdata
- [7] How FAIR are your data? (DOI) http://doi.org/10.5281/zenodo.1065991
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