Mirror Mirror: reflections of FAIR Data evolution across a national portfolio of projects

Dr Richard Ferrers1, Mr  Keith Russell1

1Australian Research Data Commons, Melbourne, Australia

Introduction.

ARDC in late 2019 invested in a programme of 42 Data and Services Discovery projects  focusing on discovering elements required to create Transformative Data collections (32 projects) and the Institutional Role in a Data Commons (10 projects). One aspect in these projects was discovering how the data involved in the projects could be made more FAIR (Findable, Accessible, Interoperable and Reusable).

Method.

Projects were asked to self-assess the FAIRness of the data at the project start, project end and expected state two years after the projects. The results were included in the final reports and have now been collated. This poster provides a summary of the findings from those survey responses. FAIR was assessed through 14 questions. For each question a scale rated the level of FAIRness.

Results.

Key findings include: (1) while FAIR maturity varied substantially across projects, discipline did not appear a substantial indicator of FAIR maturity, and (2) across all projects, types of FAIRness in practice were much more evenly rated than expected.

The poster will provide graphs indicating the spread of FAIRness by project and FAIR category, and how through the projects their FAIRness improved. Some projects had mature FAIRness at the beginning and added minor improvements, while others started with low FAIR maturity and added to their FAIRness substantially – as big improvers.

Conclusion.

The poster presents a national programme of data projects through the lens of FAIR, across a range of disciplines and project types.


Biography:

Richard is a Research Data Specialist at The Australian Research Data Commons, where he works in the Engagements team with Victorian and Tasmanian Universites, and Trusted Data Community.

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