The FAIR Data Principles are guidelines that support the management and reuse of scholarly data. Introduced in 2016 by a paper published in Scientific Data, the principles have since been used to ensure that data is archived and preserved in digital repositories, upholding transparency and integrity in scientific publishing.
In this article, we explain exactly what the FAIR Data Principles are, why they matter, and how to make your data fair when posting a preprint.
What are the FAIR Data Principles?
Reliable data management is the cornerstone of scientific discovery and innovation, allowing important information to be accessible and easily shared with researchers and stakeholders around the world. The Fair Data Principles are guidelines that inform researchers and publishers about the best practices when it comes to handling and sharing data.
FAIR stands for:
- Findable: The ease with which your data can be found by humans and computers.
- Accessible: Others can access your data under clear conditions.
- Interoperable: Your data can be integrated with other data and is interoperable with other applications or workflows.
- Reuseable: Your data can be reused in a variety of contexts.
Even in cases where your data contains sensitive information and cannot be made publicly available, you can still apply the principles. FAIR data does not always translate to ‘open’ data.
Why FAIR matters
The FAIR Data Principles are a key component of a scholarly ecosystem that depends on knowledge being findable, accessible, adaptable, and reuseable to develop and advance human well-being and understanding. FAIR data practices and standards help sustain this global knowledge system, but it also helps you and your research in the long term.
Making your data FAIR benefits you by:
- Increasing the visibility and impact of your research.
- Supporting the transparency of your data.
- Increasing the chance of collaboration through data sharing and integration.
- Ensuring the long-term preservation and reusability of your data.
How to make your data FAIR
Making your data FAIR involves keeping in mind certain guidelines and actions to take before and during the data-sharing process. Here is a useful checklist.
Findable
- Does your dataset have a globally unique and persistent identifier, such as a Digital Object Identifier (DOI)?
- Are your data described by detailed, structured metadata?
- Do your metadata clearly include the identifier associated with your data?
- Is your dataset stored in a searchable repository or database?
Accessible
- Can your data and metadata be downloaded using a standardised communications protocol (such as HHTP) that nay standard computer can understand?
- Is the protocol universally accessible?
- Does the protocol allow for authentication and authorization, where necessary?
- Do your metadata remain accessible, even if your data are restricted?
Interoperable
- Are your data and metadata written in a standardised, machine-readable format?
- Are the terms used to describe your data and metadata themselves Findable, Accessible, Interoperable, and Reuseable?
- Do your data and metadata reference other data and metadata using reliable links?
Reuseable
- Is your dataset accompanied by enough descriptive information so others can understand, process, and reuse it?
- Is a clear, legally sound license applied to your dataset?
- Is a full history of the origin, methods, rationale, and modifications of your data and metadata described?
- Do your data and metadata follow established guidelines and formats created by a specific community or professional field?
Preprints and data sharing
Preprints allow researchers to share early versions of research articles before formal peer review, and coupling them with FAIR-compliant data deposition further strengthens scientific transparency and integrity.
Before posting a preprint, you are strongly recommended to upload your data and any supplementary files to a recognised data depository (such as one from Re3data), so long as there are no legal or confidentiality constraints in doing so.
These repositories use persistent identifiers to link your datasets directly back to your preprint, ensuring discoverability is maintained and visibility increased in the long term.
See our Instructions for Authors for more information on depositing your data and supplementary files.