Over the last few months, as the OpenAIRE Advance project phase has come to an end, we have used your specification and guidelines to apply to OpenAIRE's guidelines, which have been under constant development since 2010. This adaptation and its challenges were related to the institutional and thematic repositories, which, as you surely know, can contain not only datasets but also literature, software and other digital research outputs. We present this first evaluation results in our Community Call on June 2nd, 2021. The presentation is available at https://doi.org/10.5281/zenodo.4893736 . We will continue this work also in the OpenAIRE-NEXUS phase and would be delighted if this were also of interest to the WG.
Metrics and Certification Task Force of the EOSC FAIR Working Group
The Metrics and Certification Task Force of the EOSC FAIR Working Group recommends that the definition of metrics should be a continuous process, regularly tested and iterated to minimise these risks. Inclusiveness should be a key attribute, to recognise the diversity of practice across communities and the different stages of FAIR maturity. Existing work, in particular by the international FAIR Data Maturity Model Working Group of the Research Data Alliance (RDA) [RDA_FAIR_DMMWG], should be built upon and tailored to the EOSC context. This forum also provides an appropriate international community to iterate and maintain the metrics, ensuring collective, community governance.
The EOSC Task Force on FAIR Metrics and Data Quality is using the FDMM indicators as input. They will conduct a broad community survey on the 41 RDA FDMM indicators, to get a sense of the degree to which they are fit-for-purpose over a broad range of domains and stakeholders.
The European Commission 'Rolling Plan for ICT standardisation 2022' mentions the FDMM as one of the examples for the adoption of practices models and standards.
The project “Ecosystem Data Management: Analysis - Recommendation - FAIRification” (EcoDM) funded by the Germany Federal Ministry of Education and Research wants to translate some highly valued English documents into German to make them easier accessible for all kind of researchers that struggle with their English skills. That includes the "FAIR Data Maturity Model: specification and guidelines" and see that as one of the key documents we would like to translate.
The translation is online at: https://doi.org/10.5281/zenodo.5834115 (DOI: 10.5281/zenodo.5834115) It is called "Das FAIR Data Maturity Model. Spezifikation und Leitlinien"
FAIR Assessment of Research Data Objects
This paper presents practical solutions, namely metrics and tools, developed by the FAIRsFAIR project to pilot the FAIR assessment of research data objects in trustworthy data repositories. The metrics are mainly built on the indicators developed by the RDA FAIR Data Maturity Model Working Group. https://datascience.codata.org/articles/10.5334/dsj-2021-004/
Semantics4FAIR, an ontology-based approach for FAIR datasets (Nathalie Aussenac-Gilles, IRIT - CNRS) used the FDMM model to evaluate the FAIRness of Meteo France datasets at the beginning of the project.
Helmholtz (Markus Kubin) applied the FAIR Data Maturity Model, to research data from the natural sciences generated at a prototypical research instrument in a semi-automated way. In our poster contribution, we would like to discuss our approach and the lessons learned, which helped us to identify key activities to address community needs.
This is a two-year specific purpose contract to deliver DRI’s responsibilities in the WorldFAIR project, which is coordinated by the Committee on Data of the International Science Council (CODATA) and the Research Data Alliance (RDA). https://codata.org/initiatives/decadal-programme2/worldfair/
Undertake a range of activities including: Evaluate the DRI’s cultural heritage collections for FAIRness using, for example, the RDA FAIR data maturity model and F-UJI tool, and provide feedback to the RDA Maintenance Group on the application of the model
Draft recommendations for FAIR Photon and Neutron Data Management https://doi.org/10.5281/zenodo.4312825
Article on Assessing Research Repositories
Mathieu d'Aquin, Fabian Kirstein, Daniela Oliveira, Sonja Schimmler, Sebastian Urbanek; FAIREST: A Framework for Assessing Research Repositories. Data Intelligence 2022; doi: https://doi.org/10.1162/dint_a_00159
Article: An Integrated Quantitative FAIRness Assessment Grid for Semantic Resources and Ontologies
The main objective of this this work is to provide such a method to guide semantic stakeholders in making their semantic resources FAIR. We present an integrated quantitative assessment grid for semantic resources and propose candidate metadata properties–taken from the MOD ontology metadata model–to be used to make a semantic resource FAIR. Aligned and nourished with relevant FAIRness assessment state-of-the-art initiatives, our grid distributes 478 credits to the 15 FAIR principles in a manner which integrates existing generic approaches for digital objects (i.e., FDMM, SHARC) and approaches dedicated to semantic resources (i.e., 5-stars V, MIRO, FAIRsFAIR, Poveda et al.).
Article Finding Harmony in FAIRness
Peng, G. (2023), Finding harmony in FAIRness, Eos, 104, https://doi.org/10.1029/2023EO230216. Published on 6 June 2023.
Article Long-Term (Re-)Usability of FAIR Sensor Data through Contextualization
Matthias Bodenbenner, Jan Pennekamp, Benjamin Montavon, Klaus Wehrle, and Robert H. Schmitt. FAIR Sensor Ecosystem: Long-Term (Re-)Usability of FAIR Sensor Data through Contextualization. In Proceedings of the 21th IEEE International Conference on Industrial Informatics (INDIN ’23), 07 2023. https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-bodenbenner-fair-ecosystem.pdf
Article: Which FAIR are you?: A Detailed Comparison of Existing FAIR Metrics in the Context of Research Data Management
Mario Moser · Jonas Werheid · Tobias Hamann · Anas Abdelrazeq · [...] Sep 2023 · Proceedings of the Conference on Research Data Infrastructure. (PDF) Which FAIR are you?: A Detailed Comparison of Existing FAIR Metrics in the Context of Research Data Management (researchgate.net)
Article: Assessing the FAIRness of Deep Learning Models in Cardiovascular Disease Using Computed Tomography Images: Data and Code Perspective
Shiferaw KB, Zeleke A, Waltemath D. Assessing the FAIRness of Deep Learning Models in Cardiovascular Disease Using Computed Tomography Images: Data and Code Perspective. Stud Health Technol Inform. 2023 May 18;302:63-67. doi: 10.3233/SHTI230065. PMID: 37203610.