2.1 Relevance of research data management: FAIR an… 2.1.1 FAIR principles
FAIR principles
The FAIR principles play a pivotal role in Research Data Management, emphasising the broad and diverse utilisation of research data while striving to minimise redundant research efforts. Consequently, research data should remain accessible without undue restrictions for an extended duration. This applies to the use of research data collected by the researchers themselves, but also to research data that researchers make available to each other.
The FAIR principles were developed in 2014 in a workshop at the Lorentz Center in the Netherlands and published for the first time in March 2016 in the journal Scientific Data [1].
Data has to adhere to certain properties to be Findable, Accessible, Interoperable and Reusable, for example machine-readability, being deposited in a trusted repository etc. The FAIR principles have been significantly refined recently [2]; in addition, tools have been developed to evaluate your data according to FAIR [3]. Ultimately, the entire research data management is geared towards fulfilling these concrete verge requirements of the FAIR principles
[1] Wilkinson, M.D., Dumontier, M., Aalbersberg, Ij.J., Appleton, G., Axton, M., Baak, A., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 3, https://www.doi.org/160018.
[2] Carroll, S.R., Garba, I., Figueroa-Rodríguez, O.L., Holbrook, J., Lovett, R., Materechera, S., et al. (2020). The CARE Principles for Indigenous Data Governance. Data Science Journal, 19, 43. https://doi.org/10.5334/dsj-2020-043
[3] Lang, K., Assmann, C., Neute, N., Gerlach, R. & Rex, J. (2023). FAIR Assessment Tools Overview.