Although they originate from the life sciences, the principles can be – and have been - applied within other research disciplines, including the humanities and the social sciences. Since their publication, the European Union as well as individual funders and universities have declared their support for and approval of the FAIR principles. This spans from the creation of data management tools and infrastructures to defining policies for data handling. Some implementations stick closely to the original definitions, while others are inspired by the spirit of the FAIR principles.
A fundamental prerequisite for understanding FAIR is to know that both humans and machines are intended as digesters of data. This will lead to an ecosystem that is fast to respond to change and automatically adapts to new findings or changes. That is the reason for focusing on standards for the data, identification mechanisms, availability of data etc. Secondly, the FAIR principles apply to both data and its metadata – i.e. records about data sets. That is why the term “(meta)data” is stated in the principles. Thirdly, the principles are not only about open data. You can work in a FAIR manner with data that is not intended for public availability.
The FAIR principles do not represent a quality standard that you can use to evaluate tools, data, policies etc. This would soon make the principles out-of-date and inapplicable across research disciplines. The implementation of FAIR can be a gradual and systematic adaptation of new work routines or a huge leap where you replace one type of infrastructure with another. The implementation of the principles should be adapted to each research area, meaning that each community will make the principles work in their respective contexts.
Go to the webpage for A FAIRy tale for more information about the FAIR principles.
Based on 'A FAIRy tale' CC-BY-SA 4.0 ‘DK Fair på tværs’.
