FAIR Data Principles
The FAIR data principles are a concise and measurable set of principles that may act as a guideline for those wishing to enhance the reusability of their data holdings. FAIR stands for Findable, Accessible, Interoperable and Reusable 1. The FAIR data principles aims at 1,2:
- Improving the infrastructure supporting the reuse of scholarly data.
- Enhancing the ability of machines to automatically find and use data.
- Supporting the reuse of data by individuals, which “is essential to the advancement of research and clinical practice”.
The principles 1,3 are reproduced below:
To be Findable
- (Meta)data are assigned a globally unique and persistent identifier.
- Data are described with rich metadata.
- Metadata clearly and explicitly include the identifier of the data it describes.
- (Meta)data are registered or indexed in a searchable resource (e.g. data repository).
To be Accessible
- (Meta)data are retrievable by their identifier using a standardized communications protocol (e.g. http(s)).
- The protocol is open, free, and universally implementable.
- The protocol allows for an authentication and authorization procedure, where necessary.
- Metadata are accessible, even when the data are no longer available.
To be Interoperable
Data interoperability is the ability of a dataset to work with other datasets or systems without special effort on the part of the user 4.
- (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation (e.g. controlled vocabularies/ontologies/thesauri, a good data model).
- (Meta)data use vocabularies that follow the FAIR principles (e.g. using FAIR Data Point).
- (Meta)data include qualified references to other (meta)data (e.g. specifying if one dataset builds on another one, properly citing all datasets).
To be Reusable
- Meta(data) are richly described with a plurality of accurate and relevant attributes (i.e. metadata that richly describes the context under which the data was generated such as the experimental protocols, the species used).
- (Meta)data are released with a clear and accessible data usage license.
- (Meta)data are associated with detailed provenance.
- FAIR Cookbook
- FAIR in (biological) practice
- FAIR sharing and access
- D7.4 How to be FAIR with your data. A teaching and training handbook for higher education institutions
How to make data FAIR?
- PARTHENOS Guidelines to FAIRify data management and make data reusable
- Preparing data for sharing: The FAIR Principles
- Top 10 FAIR Data & Software Things
How to assess the FAIRness of your dataset(s)?
- How FAIR are your data?
- Self-Assessment Tool to Improve the FAIRness of Your Dataset (SATIFYD)
- The FAIR Data Maturity Model: An Approach to Harmonise FAIR Assessments
- 1. Wilkinson MD, Dumontier M, Aalbersberg IJJ, et al. The Fair Guiding Principles for Scientific Data Management and Stewardship. Scientific Data. 2016;3(1). doi:10.1038/sdata.2016.18
- 2. Lowenberg D. COVID Tracking Project Data Now Available in Dryad. Dryad news. Published online August 2021. https://blog.datadryad.org/2021/08/04/
- 3. Fair principles. GO FAIR. Published online January 2022. https://www.go-fair.org/fair-principles/
- 4. Action GODAN. GODAN Action Online Course on Open Data Management in Agriculture and Nutrition. Published online December 2019. doi:10.5281/zenodo.3588148