Recent advances and upcoming developments in Earth System Science (ESS) are facing the challenge of having to i) efficiently handle close-to exascale data amounts stemming from sophisticated modeling and observational systems and ii) providing methods to make the information content readily accessible and usable by both scientists and downstream communities, e.g. decision makers at the policy level.
A sensible orchestration of these two aspects requires the application of seamless workflows enabling the capture of required provenance information to - in an extreme case - rerun large-simulations and analysis routines to provide trust in model fidelity, data integrity and decision-making processes. Here, reproducibility, or even replicability, dedication to Open Science and FAIR data principles are key. Further, this enables communities of practice to establish best practices in applying future-proof workflows among a critical mass of users, thereby facilitating adoption.
To explore the current landscape of developments and ideas to enable such workflows, this session will explore
- current approaches, concepts and developments in the area of reproducible workflows in ESS and downstream communities
- questions on requirements for reproducibility and replicability including provenance tracking, not only when using emerging ML/AI approaches, but also simple to complex working processes
- technological and methodological components required for data reusability and future-proof research workflows like the concept of FAIR Digital Objects, (meta)data standards, linked-data approaches, virtual research environments and Open Science principles
- use cases and best practices (or failures) to guide future developments
We welcome contributions covering all geoscientific disciplines showcasing aspects from science, infrastructure providers and downstream communities alike!