FAIR principles in practice for health data was developed in the context of the Swiss Personalized Health Network (SPHN) initiative and is part of a series of trainings centered around the SPHN Interoperability Framework developed by the SPHN Data Coordination Center (DCC). The framework aims at facilitating collaborative research by providing a decentralized infrastructure sustained by a strong semantic layer (SPHN Dataset) and graph technology, based on RDF, for the exchange and storage of 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.

Prerequisites:

None

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.

Resources:

All resources are available on the training's GitLab space