The FAIR principles

The FAIR principles are a set of instructions formulated to maximize the use of data and other digital objects such as code and software. Their aim is to facilitate, encourage and guide researchers towards making their data easily findable and accessible. All the while ensuring that the data they make available is easily understood and well documented with the ultimate goal to make scientific data as reusable as possible.

To make it easier for researchers to make their data FAIR we have developed the FAIR cheatsheets as well as the Publishing and Sharing Data guide.

Potential benefits of making your data FAIR include:

• It may increase the impact of your research (e.g., reusing data for other purposes).
• It increases visibility and citations (e.g., through data citations).
• It improves reproducibility and reliability of your research (e.g., by publishing data besides a journal publication).
• It increases your opportunities for collaboration with researchers, societal partners, etc. (e.g., through increased visibility).
• New research questions may be answered, and other datasets may be strengthened (e.g., when your data is integrated into new datasets).

Findable

The first step in (re)using data is to find them. Metadata, and possibly data, should be easy to find for both humans and machines. In most cases this is easily achieved by placing your metadata in a repository.

Accessible

It should be possible for humans and machines to gain access to your data, under specific conditions or restrictions where appropriate. FAIR does not necessarily mean that data needs to be publicly available! If the data cannot be made openly accessible (e.g., due to privacy or commercial reasons), you can still make the metadata publicly available, which specify if and how the data can be accessed.

Interoperable

Both humans and machines ought to be able to integrate and operate your data without undue restrictions or hurdles. Storing your data in preferred formats and structuring it such that it can easily be explored and used by machines and humans is crucial to accomplishing interoperability.

Reusable

Research data should be easy to reuse by others. Generally, this requires good documentation and licensing of the data, so that others can understand the data and know what they may do with the data. When a researcher explores your data package, they ought to be able to understand which documents are present, what the different file formats are, how they can open them and they should be able to navigate across your files and folders with relative ease. Your variables must be explained as should your overall dataset logic, either in the information present within your dataset or in accompanying documentation that is stored alongside the data. A more thorough guide on making your data reusable is found in the “Publishing and Sharing Data” guide.

More information

• Read more in our guides “Metadata and documentation”, “Publishing and Sharing Data”, and “Handling personal data”
• Check out the FAIR Data Cheatsheets
• Learn more about FAIR data in the online training “Learn to write your Data Management Plan”
• More information about the FAIR principles can be found here and here
• Assess your awareness of the FAIR principles with the DANS FAIR-aware tool
• Assess the FAIRness of your data with the DANS Satisfyd tool