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FAIR

Learn about the FAIR principles and how you can apply them to your data.

Background

One major concern nowadays is sustainability. This applies not only to our physical but also to the digital world (which again impacts our physical world). Sustainability in the digital world means expanding the life-cycle of data. A vast number of data is produced but never reused. All this data does, then, is feeding the data garbage dump. Put differently, not reusing data simply is producing digital disposables. To make data more sustainable, good data management is key.

The FAIR principles

In 2016, Wilkinson et al. published the FAIR guiding principles. These principles were designed to support good data management and to acknowledge the significance of machines in data-rich environments. Today, the FAIR principles are well known in the Open Science Community and an important point of reference.

Findable - The first step to be able to reuse data is finding it. Rich Metadata and a unique and permanent ID for your data help humans as well as computers to easily find your data.

Accessible - Once data is found, the user needs to know how to access it. A standardized communication protocol states the access procedure, also including authentication and authorization procedures when necessary.

Interoperable - Data should be integratable with other data and also interoperable with applications or workflows for analysis, storage, and processing.

Reusable - Data and metadata have to be sufficiently well-described to allow proper integration and citation.

What benefits do I have by making my data FAIR?

Applying the FAIR principles to your data benefits you by increasing your …

  • data quality
  • accountability
  • reputation in the science community
  • time efficacy
  • visibility & transparency

amongst others.

Furthermore, it helps you to get more and valuable feedback from the community and find collaborators as well as new research more easily. Another important point to mention is that some science funders and publishers require data management and stewardship plans for publicly funded experiments. The FAIR principles can guide you toward that.

How can I make my data FAIR?

The FAIR community gives details for practical application of the four principles:

ImageFAIR

For a more detailed description and links to resources on how to make your data FAIR, have a look at Go FAIR or Openaire.how-to-make-your-data-fair.

What to remember:

  • By making your data FAIR you expand the life-cycle of your data and contribute to sustainability in the digital world, which brings benefits for your own research as well as the whole science community.

  • FAIR does not equal open. Following the FAIR principles does not necessarily mean that you have to share your data with everyone. Your data can meet the FAIR principles but still be shared under certain restrictions.

  • The intention of the FAIR principles is not to have everybody apply all the principles to all their data perfectly right away. Implementing just a few of the recommended steps helps you and the science community to get one step further toward open science. The important thing is simply to start somewhere to get used to practicing the FAIR principles on your data regularly. Step by step.

  • The FAIR principles are guidelines, not a standard.