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FAIR data and software

Everyone working with research data will, at some point, come across the acronym of FAIR, which stands for Findable, Accessible, Interoperable and Reusable.

FAIR refers to 15 principles on how to manage research data.

The Leiden University’s Data Management regulations require you to make your data FAIR, and many funders do too. How it should be done in practice can vary from one discipline to another. Yet, there are a few steps that everyone can take to start making data FAIR.

The Centre for Digital Scholarship provides advice and support on how to make your data FAIR and organises workshops on demand, following the 3-point-FAIRification framework

FAIR means that your data is Findable, Accessible, Interoperable and Reusable for both humans and machines. Think of search engines that can find your data or tools that can combine, merge or mine your data.

Software can be made FAIR as well.

It is important to know that FAIR is not a synonym of open: you can have open data that is not FAIR, and FAIR data that is not openly shared.

Some crucial steps to take to start making your data FAIR:

  • Deposit your data in a trustworthy repository.
    Your data can be easily cited because it gets a persistent identifier (such as a DOI) and your data is better findable because repositories add rich metadata (subject terms, provenance information etc.).
    Tip: use our Research Data Services catalogue  to find trustworthy repositories.
  • Use common standards to encode and document your data.
    Others (including your future self) will better understand and trust your data and follow-up research is easier.
    Tip: Search these resources for standards used in your research domain: Research Data Alliance Metadata directory, FAIRsharing, BARTOC.
    Read the guidelines for creating a READ ME file from 4TU.ResearchData.
  • Choose interoperable and sustainable data formats
    Your data will be accessible for the long term and can be reused in combination with other datasets.
    Tip: this data horror story warns you against the risks of not using them. Luckily, lists of preferred formats are available, for instance at DANS and 4TUResearchData.
  • Add a clear license to your data.
    Others will understand what they can do with your data. Data without license cannot be reused.
    Tip: How do I license my research data?

Useful resources

What are funder requirements for FAIR data?

  • The European Commission has produced a set of Guidelines for FAIR data management.
  • ZonMW has defined key items that must be delivered at the end of a research project to monitor the ‘FAIRness’ of each dataset. 
  • NWO states that to make data open for the use by other researchers, research should become findable, accessible, interoperable and reusable (FAIR).

How FAIR are your data?

FAIR for different disciplines

More information on FAIR

You can find more details on how to make data FAIR on these pages from Force 11GO FAIR, and OpenAIRE.

Five recommendations for FAIR software.

The FAIR principles were originally published in 2016: Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016).

Leiden Silicon Valley of FAIR data.

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