FAIR

This FAIRsharing record describes: One of the grand challenges of data-intensive science is to facilitate knowledge discovery by assisting humans and machines in their discovery of, access to, integration and analysis of, task-appropriate scientific data and their associated algorithms and workflows. The term "FAIR" was launched at a Lorentz workshop in 2014, attended by a wide range of academic, corporate, and governmental stakeholders. The resulting draft FAIR Principles were initially made available for public comment via the websites of peer-initiatives such as, for example, Force11. Based on this feedback, the final Principles were published in 2016 (https://www.nature.com/articles/sdata201618). FAIR is a set of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable. These guidelines provide advice for those wishing to enhance the (re)usability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.