A Graph Data Model-based Micro-Provenance Approach for Multi-level Provenance Exploration in End-to-End Climate Workflows

Open Science is a vital part in the current and future research agenda worldwide. In order to meet Open Science goals, it is of paramount importance to fully support the research process, which includes also properly addressing provenance and reproducibility of scientific experiments. Indeed, 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. To this end, this work introduces the micro-provenance concept, and it provides an in-depth description of its design, implementation and exploitation in the context of a multi-model climate analytics workflow.