IP4OS Toolbox: FAIR-R²L Rubric

About IP4OS Europe’s Research & Innovation potential is central to driving societal progress, environmental solutions and economic growth and advancing the green and digital transitions. To realise this potential, stakeholders in research and knowledge transfer must develop robust and effective Research Knowledge Valorisation Strategies that foster the circulation and re-use of research outputs while ensuring appropriate regulation through Intellectual Property rights. Addressing the European Research Area Policy Agendas (2022–2024 and 2025–2027), the Horizon Europe project IP4OS (2025–2026) provides practical guidance for an effective Valorisation approach that combines agile Intellectual Property management with the sharing of Findable, Accessible, Interoperable, and Re-usable (FAIR) research outputs (data, results, codes, etc.)—a concerted Intellectual Property–Open Science approach. Throughout the project, innovative guidance has been developed in the form of the IP4OS Toolbox, supporting European multi-professional teams (p. 1-4), researchers, Open Science Ambassadors, Knowledge & Technology Transfer Professionals, Librarians, Research Managers, Data Stewards et al., and institutions in achieving impact and value creation. About the FAIR-R²L Rubric The FAIR-R²L Rubric, part of the IP4OS Toolbox and developed by Miller International Knowledge, is an expansion of the FAIR principles (Go Fair), the FAIR-R concept (referring to AI-Readiness of data, Verhulst et al., 2025) and adds a further crucial dimension: datasets must also be Responsibly Licensed, ethically sound, in line with the PID strategy, sustainable and legally ready for reuse in AI and machine learning workflows. The FAIR-R²L Rubric is a checklist available as PDF in this publication and in a digital format here. Outcome Using the FAIR-R²L Rubric researchers, institutions, and policymakers ensure not only the technical openness of datasets (being Findable, Accessible, Interoperable, and Reusable), but also their responsible licensing for reuse. By evaluating real datasets, they can identify technical and legal gaps that constrain responsible reuse, thereby contributing to improved readiness for Open Science and AI-enabled research. _____________________________________________________________________________________________________________________________________________ Other materials from the IP4OS Toolbox IP4OS Toolbox: Knowledge Valorisation Rubric IP4OS Toolbox: Valorisation Consultancy Form IP4OS Toolbox: Concepts, Valorisation Pathway, and Resources for IP and OS IP4OS Toolbox: Guide on Multi-professional Teams and Consultations IP4OS Toolbox: Pilot Learning Lab