---

Introduction

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.

Further resources

• Introducing the FAIR Principles for research software

Learning resources

• 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

References

1. 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. 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. 3. Fair principles. GO FAIR. Published online January 2022. https://www.go-fair.org/fair-principles/
4. 4. Action GODAN. GODAN Action Online Course on Open Data Management in Agriculture and Nutrition. Published online December 2019. doi:10.5281/zenodo.3588148

Edit this page