Research Data Management
- They are not a technical specification!
- A minimal set of community-agreed guiding principles and practices to ensure that research data is:
- 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
LIBER (Association of European Research Libraries) FACTSHEET: Implementing FAIR Data Principles: The Role of Libraries (2017)
GO FAIR is a bottom-up, stakeholder-driven and self-governed initiative that aims to implement the FAIR data principles, making data Findable, Accessible, Interoperable and Reusable (FAIR). They provide FAIR data resources to assist researchers.
FAIR self-assessment tool created by the Australian Research Data Commons. The 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
FAIRsharing.org is a curated educational resource on data and metadata standards
FAIR-Aware tool includes 10 simple questions with practical tips to improve data FAIRness before deposit
LIBER’s Practical Guide to FAIR Principles in Research Libraries includes guidelines for libraries or librarians for making data FAIR
Wilkinson, Mark D., Dumontier, Michel, Aalbersberg, IJsbrand Jan, Appleton, Gabrielle, Axton, Myles, Baak, Arie, . . . Mons, Barend. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. doi: 10.10
Wilkinson MD, Verborgh R, Bonino da Silva Santos LO, Clark T, Swertz MA, Kelpin FDL, Gray AJG, Schultes EA, van Mulligen EM, Ciccarese P, Kuzniar A, Gavai A, Thompson M, Kaliyaperumal R, Bolleman JT, Dumontier M. 2017. Interoperability and FAIRness through a novel combination of Web technologies. PeerJ Computer Science 3:e110 https://doi.org/10.7717/peerj-cs.110
- Last Updated: Jan 13, 2023 12:30 PM
- URL: https://libguides.library.nuigalway.ie/researchdatamanagement
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