Almost a decade ago, the FAIR data guiding principles were introduced to the broader research community. These principles proposed a framework to increase the reusability of data in and across domains during and after the completion of e.g. research projects. In subdomains of the Earth System Sciences (ESS), like atmospheric sciences or partly geosciences, data reuse across institutions and geographical borders was already well-established, supported by community-specific and cross-domain standards like netCDF-CF, geospatial standards (e.g.OGC). Further, authoritative data producers such as CMIPs were already using Persistent Identifiers and corresponding handle systems for data published in their repositories – so it was often thought and communicated this data is “FAIR by design”.
However, fully implementing FAIR principles, particularly machine-actionability—the core idea behind FAIR—has proven challenging. Despite progress in awareness, standard-compliant data sharing, and the automation of data provenance, the ESS community continues to struggle to reach a community-wide consensus on the design, adoption, interpretation and implementation of the FAIR principles.
In this session, we invite contributions from all fields in Earth System Sciences that provide insights, case studies, and innovative approaches to advancing the adoption of the FAIR data principles. We aim to foster a collaborative dialogue on the progress our community has made, the challenges that lie ahead, and the strategies needed to achieve widespread acceptance and implementation of these principles, ultimately enhancing the future of data management and reuse.
We invite contributions focusing on, but not necessarily limited to,
- Challenges and solutions in interpreting and implementing the FAIR principles in different sub-domains of the ESS
- FAIR onboarding strategies for research communities
- Case studies of successful FAIR data implementation (or partial implementation) in ESS at infrastructure and research project level
- Methods and approaches to gauge the impact of FAIR data implementation in ESS
- Considerations on how AI might help to implement FAIR
- Future direction for FAIR data in ESS