Implementation & Adoption Stories

This FAIR Implementation Story outlines the specific aims and actions of The Ersilia Open Source Initiative in relation to their participation in one or both support actions.

Participants from the Medical Informatics Laboratory at the University Medicine Greifswald and the Berlin Institute of Health in Germany sought guidance for using FAIR tools to undertake a FAIR assessment and improve the FAIRness of their Core Dataset, used within the German Medical Informatics Initiative.

Participants set out to explore implementations that allow the setting up of a data repository for Spanish research institutions that complies with the FAIR principles. During the opencall period, the team managed to install a Dataverse instance in the staging environment and check the FAIR Signposting and RO-Crate functionalities.

Software plays a crucial role in academic research, not only as a tool for data analysis but also as a research outcome or result, or even the object of research itself. FAIR (Findable, Accessible, Interoperable, Reusable) research software can increase the transparency, reproducibility, and reusability of research. For this to happen, software needs to be well-described (by metadata), inspectable, documented and appropriately structured so that it can be executed, replicated, built upon, combined, reinterpreted, reimplemented, and/or used in different settings.

In Luxembourg, LNDS provides services that support value creation through reuse of data made available by public sector organisations and research institutions. The initial plan the LNDS team had when enrolling in the program was to learn from the best practices of others on how support entities can grow national-level maturity in the adoption of FAIR data management and stewardship. LNDS is in the process of shaping its service portfolio, and in line with this as a second objective, they wanted to have an action plan that aligns with their service development roadmap and can be disseminated to the relevant national stakeholders.

The team of participants from the German National Library of Medicine (ZB MED - Information Centre for Life Sciences) applied FAIR Signposting and RO-Crate to the team’s web pages as a proof-of-concept for later developments. The team web pages now support FAIR Signposting and RO-Crate for projects and theses, with the next step being implementation for software.

The team of participants from LIBIS at KU Leuven sought to implement signposting and RO-Crate integration in Dataverse (import and export). Since signposting was implemented in Dataverse before the start of the support action, focus shifted to implementing available features within the Dataverse instance.

Armed with previous experience of RO-Crate, the participant sought to implement RO-Crate support for their own fork of Dataverse and in the standard, stock Dataverse 6.0 codebase based on this fork. This was successfully achieved and submitted to the Dataverse GitHub. 

A team from the British Oceanographic Data Centre tested different methods and tools (10 simple rules, recommendations for FAIR semantics, O’FAIRe, FOOPS!, and F-UJI) in the task of assessing the FAIRness of their controlled vocabulary.

A researcher from the Life Cycle Assessment (LCA) domain assessed the FAIRness of one of their datasets using F-UJI and an ontology using FOOPS! and FAIRsFAIR Semantic Recommendations, as part of broader ongoing efforts to understand the level of FAIRness in the LCA domain and gain insights to define good practice workflows for data sharing.

A team from FIZ Karlsruhe - Leibniz Institute for Information Infrastructure used F-UJI to assess the FAIRness of representative datasets from the RADAR4Chem repository, which helped them identify some improvements and inspired them to include a FAIRness check functionality in their repository.

A team from the Medical University of Gdansk (MUG) Main Library increased the FAIRness and machine readability of their repository datasets with the help of the F-UJI tool.