FAIR digital objects provide a conceptual and implementation framework to develop scalable cross-disciplinary capabilities, deal with the increasing data volumes and their inherent complexity, build tools that help to increase trust in data, create mechanisms to efficiently operate in the domain of scientific assertions, and promote data interoperability. Proposals should cover all of the following areas and activities:

• FAIR-by-Design digital objects creation: the development of tools that enable the creation of digital objects adhering to the FAIR (Findable, Accessible, Interoperable, Reusable) principles both from the “source” or as a result of an analysis.
• Automated standardisation and data quality assessment: development and provision of automated tools and procedures for standardisation and data quality assessment. This will ensure that data across different domains adhere to common standards, fostering greater compatibility and enhancing overall data quality.
• Operational data services specification: delivering technical specifications for operational data services that support the transformation of digital objects into FAIR entities. Such services may include the integration of AI-based tools capable of autonomously operating on data repositories, contributing to the automatic establishment of FAIR practices throughout the research ecosystem.
• Interoperability and training to promote the uptake of open standards: the action should make substantial contributions to the development, upkeep and widespread adoption of open standards for metadata, formats, vocabularies, semantics and APIs. Activities should foster compatibility among digital objects across different domains, facilitating seamless data exchange and integration. Training and dissemination activities will facilitate the uptake of these standards, fostering collaboration and compatibility.
• Collaboration and alignment with Common European Data Spaces: harmonisation of EOSC technical specifications with those of other Common European Data Spaces.
• Interoperability demonstration and content integration: demonstrating the tangible outcomes of applying FAIR tools, standards, and specifications will showcase the achieved interoperability and integration, strengthening the case for data sharing and reuse across disciplines and sectors.
• Cross-Sector data utilization: by enhancing content integration from data spaces, industry, and beyond, the reuse of science data in various sectors is to be encouraged. This will ease access to real-life data from other data spaces, fostering its utilization in research and expanding its impact.