FAIR principles in practice for health data

The FAIR principles have been developed to enable a better data management and stewardship in research by Wilkinson et. al. in 2016. They consist of a list of necessary criteria for making data Findable, Accessible, Interoperable and Reusable. However, the understanding of these principles and their concrete implementation can sometimes be abstract and difficult.
In this training we offer a detailed example of the implementation of FAIR principles, demonstrating how the different principles and criteria have been applied to the various components of the SPHN framework.
After the training you will be able to understand:
• why FAIR data does not necessarily mean “open data”;
• that there is not just one interpretation for each FAIR criterion, and they can be fulfilled in different ways;
• why FAIR is important in all stages of a project and not only for the data reuse;
• that data is not only either “FAIR” or “not FAIR”, but there are different levels in between.
Slides are available here: git.dcc.sib.sw...

[@metarasouli]

One of the most important aspects of data FAIRification is to consider security policies to prevent the misuse of datasets that each provider shares with other data analyzers to improve the quality of data availability in the literature. Therefore, how can we improve security issues for this case?

[@metarasouli]

Excellent lecture. Thanks for sharing this video.