Advancing Data Management: A Novel Digital Ecosystem for FAIR Time Series Data Management in Earth System Sciences

Robust infrastructures for managing and accessing high volume data are an essential foundation for unraveling complex spatiotemporal processes in the earth system sciences. Addressing multifaceted research questions demands data from diverse sources; however, isolated solutions hinder effective collaboration and knowledge advancement.

We present a novel digital ecosystem for FAIR time series data management, deeply rooted in contemporary software engineering and developed at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. Designed to flexibly address discipline-specific requirements and workflows, the system emphasizes user-centric accessibility, ensuring the reliability, efficiency, and sustainability of time series data across different domains and scales.

Our time series ecosystem includes a user-centric web-based frontend for (real-time) data flow and metadata management, a versatile data integration layer, a robust time series database, efficient object storage, near real-time quality control, and comprehensive data visualization capabilities. Supporting modern and classical data transfer protocols, the system ensures compliance with OGC standards for data access, facilitating efficient progress in the data lifecycle through high-performance computing. This fully integrated and containerized solution enables swift deployment and seamless integration with existing services.

Illustrating the practical application of the system, we showcase its success in managing Cosmic Ray Neutron Sensing data from the TERENO project. This success story underscores the system's effectiveness in addressing challenges associated with time series data management in earth system sciences, fostering more efficient research and facilitating informed decision-making processes.

This contribution aligns seamlessly with the session's focus on connecting RDIs. We aim to promote transferable approaches, use existing standards, and facilitate collaborations transcending barriers among RDI providers, developers, and researchers. By presenting our experiences and best practices, this presentation invites engagement and discussions to collectively address the challenges in bringing research data infrastructures together.