FAIR - Findable, Accessible, Interoperable, Reusable - is a set of guiding principles published in 2016 with the fundamental purpose that both humans and machines can understand digital scholarly objects. In addition, FAIR ensures robust research data management and enhanced reusability and discovery of digital assets.
The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. For instance, the Findable principle defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component).
“FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.”
Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
The four principles
The first step in (re)using data is to find them. Metadata and data should be easy to discover for both humans and computers. Machine-readable metadata are essential for the automatic discovery of datasets and services, so this is an essential component of the FAIRification process. If you deposit research data in our eData repository, the metadata created is automatically shared and harvested to aid discoverability. It is always good practice to include a data access statement in publications that rely on the data.
Once the user finds the required data, they need to know how they can be accessed, possibly including authentication and authorisation. It is recommended that access to data is made as seamless as possible by making it openly available under a permissive reuse licence (e.g. CC BY) unless there are good reasons for restricting access.
Reuse of research data may require it to be integrated with other data. In addition, the data may need to interoperate with applications or workflows for analysis, storage, and processing. Interoperability is facilitated by using open file formats, standardised metadata and any discipline specific schemas, vocabularies or ontologies where they exist.
The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described to be replicated and/or combined in different settings. A README file that describes the data may be helpful, as is sharing any code used to generate or analyse the data. Metadata should include links to other research outputs which give context to the data, or have been published using the data.
At the beginning of your research project, you have to decide:
- How to collect and document your data
- What data formats will you use for storage
- File and data naming conventions
- How to deal with data preservation and sharing
- How to licence the data for reuse
Record those decisions in a Data Management Plan so you can undertake the relevant tasks and actions further down the line during your research project.
Several tools have been developed to assist researchers in improving their knowledge and awareness of FAIR, such as the following:
- FAIR-aware: Test your knowledge of the FAIR principles before uploading your data to a repository with this online tool provided by FAIRsFAIR
- Go-FAIR principles: Further information on FAIR principles with detailed guidance.
You can learn more about FAIR in the relevant section of the University of Birmingham Canvas Open Researcher course (Self-enroll)
If you have queries, please contact email@example.com