According to the FAIR Guiding Principles for Scientific Data Management, first published in Nature in 2016, research data collected through public funds be accessible to all for a longer period of time. Furthermore, not only people should be able to access and understand the data but also machines. This means that machines should be able to read the data and analyze it with no or limited human involvement (which is one of the key prerequisites for machine learning). This type of artificial intelligence revolves around utilizing the extensive processing capacities of computers to handle or assist with processing data, and in that way meet the increased needs for automation in data analyses of extensive and complicated research data. Since the publication of the FAIR principles, the European Union and numerous international funders and universities have expressed support for the principles and taken them into account in their policy-making.
According to the FAIR principles, "research data should be as open as possible, as closed as necessary". This assumes a degree of flexibility where sensitive data can be placed in restricted access. In this way, research data can be published in restricted or controlled access but still be considered FAIR.
What are the FAIR principles?
The FAIR guiding principles are comprised of 15 principles that describe how scientific data should be organized so it can be more easily accessed, understood, exchanged and reused. These principles are divided into four main components which state that research data should be: Findable, Accessible, Interoperable and Reusable.
