Tag openresearch

European Open Science Cloud “FAIR Metrics and Digital Objects Task Force” or the 2nd chapter of my participation with EOSC TFs

Anastasija Nikiforova 1 Comment

From 2021 to 2024, I had the privilege of being part of the “FAIR Metrics and Data Quality” Task Force, where we made strides in advancing the FAIR principles and improving data quality across the research community. Following the recent reorganization of these task forces, I am now excited to continue this work with the newly formed FAIR Metrics and Digital Objects Task Force, which, as we decided just yesterday, will be chaired by “the father” will be chaired by “the father” FAIR principles Mark Wilkinson and Elli Papadopoulou!

Our mission is to develop and implement metrics for the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) that enhance the utility and impact of digital objects in research. This initiative is crucial in advancing open science and ensuring that scientific data is more accessible, reusable, and beneficial for the global research community.

As a member of this task force, we will be collaborating with a diverse group of experts to:

  • Identify the limitations of the current FAIR assessment, which is mainly focused on the FAIRness of the repository, for evaluating the discoverability and reusability of data, which is insufficient for assessing the capability of data to be federated.
  • Watch and promote initiatives (such as GREI, Signposting, RO-Crates, etc) to facilitate the definition of common metadata schemas and their interoperability.
  • Identify issues on data privacy, considering data usage, data access and data licensing and specification for machine-actionable data usage policies (e.g. ODRL)
  • Analyse the impact of provenance, especially in the context of federated environments.
  • Identify synergies with the Data Spaces initiative.
  • Define FAIR metrics according to the objectives of the task force.
  • Engage with research clusters, empowering them to implement data quality practices tailored to their unique contexts by actionable recommendations, like DQ indicators to ensure data quality, addressing areas, for example, AI training and input data.

I am eager to contribute to this new chapter and look forward to the — hopefully — impactful changes we will bring to the scientific community.

Stay tuned for updates on our progress and initiatives, whereas the progress we made in the past years is documented here !