Speakers
Description
Publishing data in a FAIR [1] way is already part of good scientific practice. While institutional policy as well as funding and publishing guidelines support this, scientist, technicians, and data stewards struggle to realize it when handling their research data. The reason is that the FAIR principles are high level principles and guidelines rather than concrete implementations. This is one of the key missions of HMC: support the Helmholtz community in making their data FAIR in an easy and comparable way. Developing a sustainable strategy for this requires a detailed understanding of practices, strengths, and deficiencies with respect to applying each of the FAIR principles. Here, tools that assess data FAIRness in comparison to a set of specific implementations in a quantitative fashion can help. When handling a dataset, such measures can aid the understanding of how FAIR a dataset actually is, as well as how to improve its FAIRness.
In this Blitzlicht-Talk, HMC Hub Matter and Hub Information will jointly present insights, benefits, and pitfalls from applying and further developing such metrics. For this we used the F-UJI tool [2,3], a python-based development by the FAIRsFAIR project, in two complementary projects.
In a “top-down” approach, we evaluate data repositories based on the data contained. The analyzed results are then used towards informing infrastructural development towards improving data FAIRness.
In a second, “bottom-up” approach, data publications from individual research centers or specific fields are evaluated with F-UJI. The results are gathered and visualized in an interactive pilot dashboard. This helps to identify and quantify the usage of repositories by Helmholtz‘s research communities as well as to better support the development of relevant infrastructure for FAIR data practices.
We discuss our experience from these automatic FAIR assessment approaches and compare them to complementary insights from a manual FAIR assessment of a particular data pipeline [4] using the FAIR Data Maturity Model [5]. We discuss future plans for metric development and the potential use of such metrics in user-sided tooling.
