FAIRsFAIR

Research data should “flow” unhindered and loss-free along the life cycle of research projects. “FAIR Data Principles” formulate principles that must be fulfilled when dealing with sustainably reusable research data:

According to the FAIR principles guiding the Open Data sharing in H2020 projects since 2016, data should be “Findable, Accessible, Interoperable, and Re-usable”.

The question therefore arises: How “FAIR” is research data in Europe today?

The FAIRsFAIR project supports the cultural change towards FAIR research data by examining data practices and data policies as well as developing trainings and education framework or establishing a certification for the FAIR Ecosystem. An overview of related publications is this poster.

The project particularly addresses the development and implementation of knowledge infrastructures, such as procedures and standards, in order to be able to better implement and certify the FAIR principles in the future. The strategies and practices developed are intended to contribute to the functioning infrastructure of the EOSC-hub and ultimately of the EOSC (European Open Science Cloud).

The project also aims to provide a platform for the use and implementation of the FAIR principles for research data providers and repositories.

How can this project help me?

As practical solutions and applications of FAIR research data are in the focus, various tools have already been developed to help the research community:

FAIR Aware:

FAIR-Aware is an online tool that allows researchers and data managers to assess how much they already know about FAIR requirements and what else is needed to make FAIR data.

FAIR Data Object Assessment Metrics:

So far 17 metrics have been established for the systematic evaluation of FAIR data objects. e.g. allocation of a globally unique identifier.

These can also serve as a checklist for researchers and repositories right now. The project welcomes feedbacks.

F-UJI Automated FAIR Data Assessment Tool:

Within the FAIRsFAIR project, a tool called F-UJI has been developed, which is a REST based service to pilot a programmatic evaluation of the FAIRness of research data sets.

The source code is available on GitHub, where also any feedback on improving can be added.