Making the data FAIR
Once datasets have been chosen for the project they will be made FAIR (Findable, Accessible, Interopeable, Reusable). There are two stages to this process:
- Design and implement a FAIRification process, which includes choosing standards to describe the data.
- Develop and implement a deployment plan for the FAIRified datasets, which will include choosing appropriate hosting platforms and tools.
The FAIRification process
- Define community standards to describe, identify and interlink key elements of the datasets.
- Identify metrics to measure the level of FAIRness of the datasets, pre and post FAIRification.
- Define the FAIRification process by implementing the standards and the metrics through Bring Your Own Data (BYOD) workshops. Workshops will target different stakeholder groups, employ data of different types and with different levels of FAIRness, and will involve using different tools.
- Provide methods for estimating return on the FAIRification investment.
|December 2019||FAIR cookbook infrastructure set up|
This work is being done by WP2 in the FAIRplus project (see How the project is organised).
Deployment plan and technical solutions
- Determine and technical test criteria for hosting solutions for IMI FAIR databases.
- Deliver and execute an iterative deployment plan using the identified hosting platforms, standards and tools.
- Apply and extend the FAIR tools stack.
- Validate the FAIRification progress and its success using metrics for intra and inter database interoperability.
- Deliver a FAIRification sustainability plan for data types and projects, with recommendations for future and running projects aimed at IMI and EFPIA participants.
|December 2019||First phase exemplar IMI projects FAIRified|
|December 2020||IMI FAIR metrics publication|
|December 2020||A report on IMI projects for data types and current technical solutions detailing phasing of implementation|
This work is being done by WP3 in the FAIRplus project (see How the project is organised).