Making data Findable, Accessible, Interoperable and Reusable (FAIR) is now widely recognised as essential to advance open and reproducible research. Increasingly, this requires not only shared principles but also concrete digital implementations that enable interoperability across systems, disciplines, and infrastructures. However, it is very difficult to translate these principles into practical data management guidelines or operational digital solutions across disciplines. The goal of the session is to explore how best data management practices are developed, implemented, and adopted across disciplines, including through interoperable digital objects, persistent identifiers, and emerging data space concepts.

As part of this session, we invite submissions that:
1) Share good or bad experiences developing, implementing, and adopting data practices that align with both FAIR principles and the evolving needs of specific research communities.
2) Propose strategies for engaging researchers in adopting and refining best practices, with a focus on community-driven approaches to technical standards and infrastructures.
3) Explore the role of cultural change in enabling adoption of sustainable data practices.
4) Highlight efforts that harmonise data formats and workflows across disciplines while respecting domain-specific requirements, for example through interoperable data architectures or research data spaces.
5) Present technical or conceptual approaches that support the transition from data silos to interoperable, FAIR-aligned data ecosystems.

This session is aligned with the objectives of the Research Data Alliance (RDA) Earth, Space, and Environmental Sciences (ESES) Data Community of Practice and aims to foster cross-disciplinary dialogue, particularly among researchers in hydrology, seismology, and ocean sciences. However, we welcome contributions from all disciplines, especially where they provide insights or novel approaches to community engagement.
By learning from diverse experiences, this session seeks to advance collective understanding of how to build and sustain data practices that are both FAIR and fit for purpose, from community processes to concrete, interoperable digital implementations.