Science is becoming increasingly data-driven, interconnected, and transdisciplinary. To ensure that data can be effectively discovered, reused, and linked – not only by humans but also by machines – we need concepts that provide reliability, consistency, and scalability across domains. FAIR Digital Objects (FDOs) address this need by providing a framework to uniquely identify, semantically describe, and connect data, software, models, and other digital resources at scale, making them actionable in complex research environments.
Applications range from improved findability and citability of scientific outputs to the integration of data spaces and fully automated, machine-actionable workflows that open new possibilities for analysis and modelling. In this way, FDOs are emerging as a key enabler of interoperable, open and reproducible science, and sustainable research infrastructures.
This session invites contributions that address both conceptual perspectives and practical implementations of FDOs in the geosciences and beyond, including:

- Conceptual foundations, roadmaps, and recent developments

- Integration into domain-specific research infrastructures and transdisciplinary data spaces and workflows (e.g., demonstrators, pilots)

- Strategies for achieving technical and semantic interoperability

- Machine-actionable workflows and automation enabled by FDOs

- Community adoption and implementation challenges

The session is aimed at researchers, data experts, and infrastructure developers who are interested in shaping the next steps toward a more connected, FAIR, and machine-assisted scientific ecosystem.