FAIR principles

In 2016, an article was published in Scientific Data "The FAIR Guiding Principles for scientific data management and stewardship", who presented the FAIR principles or guidelines for improving the discoverability, accessibility, interoperability and re-use of digital resources. Since then, these principles have gained international recognition and have been widely applied in different scientific disciplines, creating a sustainable open science ecosystem where data is seen as a freely accessible resource.
FAIR principles are guidelines for what research data should look like.
  • Available at (English: findable): data and its metadata are easily found by other researchers and systems
  • Available at (English: accessible): the data are accessible and the conditions for access are clearly stated
  • Interoperable (English: interoperable): data are compatible with other systems and datasets
  • Reusable (English: reusable): data that have been prepared in such a way that they can be reused in the future
FAIR principles emphasise machine readability (i.e. the ability of computing systems to find, access, interact with and reuse data without or with minimal human intervention). Due to the increasing volume, complexity and speed of data creation, researchers and research support staff increasingly rely on computational support for data processing. Ensuring FAIR principles is therefore becoming increasingly important as research data needs to be properly managed.

FAIR principles

In 2016, an article was published in Scientific Data "The FAIR Guiding Principles for scientific data management and stewardship", who presented the FAIR principles or guidelines for improving the discoverability, accessibility, interoperability and re-use of digital resources. Since then, these principles have gained international recognition and have been widely applied in different scientific disciplines, creating a sustainable open science ecosystem where data is seen as a freely accessible resource.
FAIR principles are guidelines for what research data should look like.
  • Available at (English: findable): data and its metadata are easily found by other researchers and systems
  • Available at (English: accessible): the data are accessible and the conditions for access are clearly stated
  • Interoperable (English: interoperable): data are compatible with other systems and datasets
  • Reusable (English: reusable): data that have been prepared in such a way that they can be reused in the future
FAIR principles emphasise machine readability (i.e. the ability of computing systems to find, access, interact with and reuse data without or with minimal human intervention). Due to the increasing volume, complexity and speed of data creation, researchers and research support staff increasingly rely on computational support for data processing. Ensuring FAIR principles is therefore becoming increasingly important as research data needs to be properly managed.