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Research Data Management: FAIR Principles

FAIR principles

  • They are not a technical specification!
  • A minimal set of community-agreed guiding principles and practices to ensure that research data is Findable, Accessible, Interoperable, Reusable
  • Initially developed by Dutch Tech Centre for the Life Sciences in 2014
  • Reviewed and refined through multi-stakeholder practitioner groups, including Force11 and the Research Data Alliance
  • Published in Nature Scientific Data, 2016 Wilkinson, Mark D., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. doi: 10.1038/sdata.2016.18
  • In 2016 the G20 leaders issued a statement endorsing the application of FAIR principles to research at the 2016 G20 Hangzhou summit
  • A 2017 paper by advocates of FAIR data reported that awareness of the FAIR concept was increasing among various researchers and institutes, but also, understanding of the concept was becoming confused as different people apply their own differing perspectives to it
  • In June 2018 the European Commission published "Turning FAIR data into reality: interim report from the European Commission Expert Group on FAIR data", Zenodo, doi:10.5281/ZENODO.1285272


•Principles being adopted by publishers, funders, institutions

What is FAIR data?

FAIR Data Management Plan (DMP)

Data Stewardship Wizard from the GoFAIR organisation provides a smart questionnaire to guide you through the creation of a data stewardship plan. It includes hints multimedia content, external resources and help.

A FAIR Data Management Plan (DMP) has to ensure that data are:
Findable i.e. discoverable with metadata, identifiable and locatable by means of a standard identification mechanism;
Accessiblei.e. always available and obtainable;
Interoperable i.e. both syntactically parseable and semantically understandable, allowing data exchange and reuse between researchers, institutions, organisations or countries
Reusable i.e. sufficiently described and shared with the least restrictive licences, allowing the widest reuse possible and the least cumbersome integration with other data sources.

Resources to help with FAIR

The ANDS-Nectar-RDS (Australian National Data Service)  FAIR data self-assessment tool enables you to assess the 'FAIRness' of a dataset and determine how to enhance its FAIRness (where applicable).

FAIRdat is a tool developed by DANS (Data Archiving and Networked Services) for rating datasets on a scale of 1 to 5 for how well they comply with the FAIR principles (Findable, Accessible, Interoperable, and Reusable). It uses Survey Monkey and is still being at the pilot stage. The tool runs a series of questions (usually only maximum of 5 per principle) which follow routing options to display the star rating scored per principle. At the end of the assessment, the tool will display the star score of each principle and will also calculate and display the overall ‘R’ FAIRness score.

Guidelines from the Western Australian Governement about how to create machine readable data is a curated educational resource on data and metadata standards