A Multi-level Approach for Provenance Management And Exploration in Climate Workflows

Provenance and reproducibility are two key requirements for analytics workflows in Open Science contexts. Handling provenance at different levels of granularity and during the entire experiment lifecycle becomes key to properly and flexibly managing lineage information related to large-scale experiments as well as enabling reproducibility scenarios, which in turn foster re-usability, one of the FAIR guiding data principles. This contribution focuses on a multi-level approach applied to climate analytics experiments as a way to manage provenance information in a more structured and multifaceted way, and navigate and explore the provenance space across multiple dimensions, thus enabling the possibility to get coarse- or fine-grained information according to the actual requested level. Specifically, the yProv multi-level provenance service, a new core component within an Open Science-enabled research data lifecycle, is introduced by highlighting its design, main features and graph-based data model. Moreover, a climate models intercomparison data analysis use case is presented to showcase how to retrieve and visualize fine-grained provenance information, namely micro-provenance, compliant with the W3C PROV specifications. This work was partially funded by the EU InterTwin project (Grant Agreement 101058386), the EU Climateurope2 project (Grant Agreement 101056933)and partially under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 1031 of 17/06/2022 of Italian Ministry for University and Research funded by the European Union – NextGenerationEU (proj. nr. CN_00000013).