D4.5 Use case dissemination package

The IMI FAIRplus project aims to make datasets used or generated in other IMI projects more FAIR (Findable, Accessible, Interoperable, Reusable). The IMI project datasets to be FAIRified were carefully selected, and from this selection six were chosen to be highlighted as use cases, as presented in this report, i.e., data from eTOX, RESOLUTE, APPROACH, COMBINE, EBiSC, and CARE. Four of these use cases are completed at time of writing this report, meeting the threshold of three use cases as defined in the project description. The final two descriptions are still in draft and will be completed in the final months of the FAIRplus project to increase the value of the dissemination package.

The selected use cases not only demonstrate the value of data FAIRification, but also produce lessons learned about the possible benefits and pitfalls when FAIRifying a dataset, as well as important considerations before starting FAIRification. The use cases will thus help inform researchers about the experiences of others in their FAIRification journey. 

The most important lessons learned from the use cases can be summarised as follows:

1. FAIR-by-design is highly preferred over FAIRification after initial data generation. 

2. Other research or researchers will benefit from your FAIRification journey.

3. Systematically planned FAIRification works better than bottom-up FAIRification.

4. FAIRification takes time and needs expertise.

5. An agreement about data use should be put in place prior to the start of the research consortium (if sensitive data is involved). 

6. Education about what FAIR can do and what it cannot do is key for FAIR sustainability and broad adoption. 

7. It is essential to define your FAIRification goals before you start.