This document provides the set of rules, processes, and templates that help communities to create, in a transparent and reusable manner, the conceptual components of the FAIR assistance/assessment framework. It establishes a systematic process through which community representatives can articulate how each FAIR Principle should be interpreted within their disciplinary and digital object context, and how those interpretations should be operationalised in assessment. The creation of a FAIR benchmark requires not only detailed knowledge of a community’s (meta)data structures, but also a transparent and accountable interpretation of how the FAIR Principles are defined, implemented, and tested. While communities typically possess strong expertise in their own data structures, the formalisation of FAIR requirements in a consistent and reusable manner can be challenging. By capturing narrative requirements in a standardised format, the framework enables clarity in how FAIR claims are defined, measured, and evaluated. It promotes transparency in FAIR assessment behaviour, facilitates reuse and comparability of metrics across communities, and strengthens accountability in the interpretation of FAIR principles. The resulting document serves as a foundation for creating the conceptual components of an Assessment Interoperability Framework (e.g., benchmarks and associated metrics), supporting consistent and transparent FAIR assessment and assistance services.
This deliverable presents an overview of EOSC-related activities and projects that could be taken into account for the design and implementation of Competence Centres (CCs), positioned as key instruments to support data-intensive, FAIR-compliant, and interdisciplinary research within the European Open Science Cloud (EOSC). It synthesises existing practices, conceptual frameworks, and policy recommendations drawn from ongoing and past projects. CCs are understood by most of the research communities as decentralised, composable structures that may consolidate community expertise, support training and guidance for data sharing and reuse or provide embedded services across diverse research contexts. The deliverable outlines the different types of contributions of the domain-specific clusters. Each science cluster intends to align its CC strategies on either thematic priorities, governance approaches, training assets etc, and reflect on how they then could align within the OSCARS CC design and definition proposed in the framework of OSCARS WP1 (Bodera Sempere et al., 2024). The result of this landscaping highlights existing or in development principles, acknowledges heterogeneous implementations, foster cross-community learning and will lay the groundwork for a future inter-OSCARs project and inter-community paper on all kind of Competence Centres that can act in the framework of EOSC (discipline specific or thematic, local, regional, national…). The document identifies key interdisciplinary challenges such as multimodal data integration and large-scale metadata analysis emphasising the need for cultural change, capacity building, and embedded support mechanisms close to research practice, Challenges identified demand robust infrastructures, sustained collaboration, and the realisation of the “FAIR web of data,” a central EOSC ambition. The OSCARS CC model builds upon these insights to propose a federated and scalable ecosystem of competence. The models offer a practical roadmap to foster uptake, interoperability, and sustainability of Open Science across European research communities.
