Making data FAIR
The FAIR Data Principles
The FAIR data principles are guiding principles on how to make data Findable, Accessible, Interoperable and Reusable.
Why use the FAIR principles?
The FAIR principles are a useful framework for thinking about sharing data in a way that will enable maximum use and reuse.
The first users and re-users of data generated will be yourself and your colleagues. If your research data are findable (the exact version you are needing) and accessible, and you can use them across different tools and platforms for re-use, this can benefit your ability to gain maximum potential of use. Similarly, once your research is complete, other researchers in different institutions, different regions, and even different disciplines can build upon your work and credit your efforts. Not only can sharing data in this way be valuable for science, but it can also benefit you and your career.
Sharing research data can lead to:
- Citations of your published data resources
- More citations of your related published research articles
- Greater discoverability and enhanced visibility
- Credit for your work, helping you to gain recognition
- New opportunities for collaboration
- Improved transparency, veracity, robustness and reproducibility of your results
- Increased potential for both human-driven and machine-driven research data activities
- Reach a broader audience
Many funders are now requiring you to state, as part of proposals, how you will meet expectations around open science and sometimes specifically how you will meet the FAIR principles. If you understand what these mean and what is required, you will increase your chance of a successful proposal.
Plans to enable FAIR data should be incorporated into a data management plan and actioned throughout your research. Meeting FAIR principles will be easier if part of the plan is to archive data with a recognised long-term data repository. For example, any data resource deposited into a NERC Environmental Data Centre would go a long way to meeting all the FAIR requirements.
Findable
To be properly findable (and discoverable), your data will need describing in a web-accessible record in a well-managed catalogue and have a unique identifier assigned to it, e.g., a Digital Object Identifier (DOI). Data should be clearly described using discipline specific words and rich keywords and descriptions where you can, so that your record can be found via web-searching, text and data mining. Always cite the data as a reference (using the DOI), include a data access statement in any related publications and link the data to your other output(s).
Accessible
To be accessible, links will need to permanently work (dereferencing a DOI guarantees this), ensure there are no constraints to accessing your record and if there are constraints of accessing the actual data these should be minimal and clear. Your data must be properly maintained (use a trusted repository, such as a NERC Environmental Data Centre, who guarantee long-term data services). When data are sensitive, there should be clarity and transparency around the conditions governing access and reuse.
Interoperable
For data to be interoperable, use your research community's accepted languages, formats and vocabularies in the data and metadata. Share data in non-proprietary formats which can be opened and read using commonly used, ideally free/low cost, software. This may require reformatting data into other easily accessible formats. For example, using comma separated value (.csv) files and not MS Excel files. Use a trusted repository, such as a NERC Environmental Data Centre, and ensure that indexing used by the repository follows FAIR data principles.
Reusable
To be reusable, data should use discipline-specific vocabularies and metadata with appropriate metadata standards to provide contextual information. You should provide separate supporting information so that a future user can easily understand what you did, using standard discipline-specific descriptions and keywords.
FAIR4RS
The FAIR principles for research software (FAIR4RS) build on the FAIR principles seeking to maximise the research value of digital assets beyond data including models, code, software etc. The principles are:
Findable: Software, and its associated metadata, is easy for both humans and machines to find.
Accessible: Software, and its metadata, is retrievable via standardised protocols.
Interoperable: Software interoperates with other software by exchanging data and/or metadata, and/or through interaction via application programming interfaces (APIs), described through standards.
Reusable: Software is both usable (can be executed) and reusable (can be understood, modified, built upon, or incorporated into other software).
References for further information
Barker, M., Chue Hong, N.P., Katz, D.S. et al. Introducing the FAIR Principles for research software. Sci Data 9, 622 (2022). https://doi.org/10.1038/s41597-022-01710-x
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018 doi: 10.1038/sdata.2016.18 (2016).