FAIR data

Findable, Accessible, Interoperable and Reusable research data

Under construction

We will provide three FAIR packages from FAIR enough to FAIRy innovative. The FAIR enough package concerns the mimimal requirements for FAIR data that research teams could apply with minimum assistance from data stewards. This package is basically table 3 from the (Dutch) FAIR report . The FAIRy innovative package concerns a multidisciplinary approach to make data more FAIR at the source following the FAIRication workflow and involves involves developing a semantic model, see for example here .


Knowledge clip: FAIR data principles. https://www.youtube.com/watch?v=2uZxFu9SFi8

Wilkinson et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientic Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18 .

Jacobsen et al. (2020). A Generic Workflow for the Data FAIRification Process. Data Intelligence, 2:1-2, 56-65. https://doi.org/10.1162/dint_a_00028 .

Kanis et al. (2020). FAIR: Geen woorden maar data. University of Amsterdam / Amsterdam University of Applied Sciences. Online resource. https://uvaauas.figshare.com/projects/FAIR_Geen_woorden_maar_data/83840 .

De Personal Health Train in de zorg. Verhalen uit de praktijk (juli 2020). Retrieved from https://pht.health-ri.nl/sites/healthtrain/files/2020-07/PHT%20in%20de%20zorgpraktijk.pdf .

Deist et al. (2020). Distributed learning on 20 000+ lung cancer patients – The Personal Health Train. Radiotherapy and Oncology, 144, 189-200. https://doi.org/10.1016/j.radonc.2019.11.019 .

Published by  Urban Vitality 22 December 2020