How to make data FAIR

Plan data management with sharing and re-use in mind

Include data sharing in your project planning and data management plan from the outset.

• Plan data management
• Guide: How to set up a DMP in DMPonline

Use sustainable and interoperable file formats

Ensure data accessibility by using open, widely used and machine-readable file formats that can be opened with standard or open software.

The Swedish National Data Service (SND) provides a list (link below) of criteria for file formats suitable for accessibility and long-term preservation, as well as a list of suggested formats for text, spreadsheets, audio, video, etc.

• SND: Choosing a file format

Think about what information will be needed to understand and re-use the data

Provide rich documentation of the data (metadata) at project, dataset and variable level to make it interpretable and reusable by others (both people and computers). When available, follow data management standards and guidelines established in your field of research, and use standard vocabularies and ontologies.

• Read about metadata on our page Collect, organise, and store data.

Publish data

Publish data in a trusted data repository that supports many of the FAIR principles. The Swedish National Data Service (SND), of which SLU is a member, has created a research data catalogue that supports many of the FAIR principles, including providing data with a persistent identifier (e.g. a DOI).

• Read more about publishing in SND in our guide: How to publish data via SND.
• For more ways to publish data, see Share and publish data.

For legal or ethical reasons, it may not always be possible to publish data openly. In this case, the FAIR way is to still publish a description of the dataset (i.e. metadata) so that it can be discovered, but with restricted access.

The following checklists can help you assess whether or not you are on the right track towards FAIR data, and also help you learn more about how to apply the principles:

• How FAIR are your data? – a short checklist to help you “think FAIR” when it comes to data.
• FAIR assessment tool – by filling out a form you can get an idea of the FAIRness of the data in your project and what steps could be taken to increase FAIRness.
• FAIR Aware – this tool can help you to better understand the FAIR Principles and how making data FAIR can increase the potential value and impact of research data

Did you find this guide useful? Is there anything missing, or is there another aspect of data management you would like guidance on? Please let us know!

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