Climate Change

Challenge

ENES is a European research infrastructure that, in collaboration with partners around the world, supports climate modellers in their work. The community has always been characterised by a great openness regarding the use of their data in their own as well as other research domains. Providing researchers with services embedded in European and international infrastructures has therefore always been essential for their research. A huge data space exists in ENES, which cannot be found in the EOSC, neither on a fine-granular nor on a coarse-granular level. For some ENES data collections, DataCite DOIs are assigned. These usually refer to tens of thousands of data objects that need to be grouped into different levels of aggregation for which no PIDs are currently available. Additionally, especially crucial for the interdisciplinary reuse, data still misses context formed by producers, experiments, projects, devices, etc as well as metadata crosswalks.

Case study description

The Climate case study aims to demonstrate how the ENES community can leverage the integration of DTR, RAiD, PIDGraph, RDGraph, and MSCR within the data delivery workflow. This integration attempts to generate and supply data provenance information in conjunction with climate research data, thereby improving its discoverability and facilitating reuse. Using DTR, selected ENES data collections are given identifiers to define the relationships between data collections and data sets, and between data sets and data files. Using RAiDs, it should be possible to link climate datasets with corresponding projects and research activities, in order to credit initiatives that enable the dataset generation process. RAiDs will provide (domain agnostic) users with an aggregated view of the whole context (data, software, people involved, etc.). All this information will be made available to Open Science Graphs via interfaces and presented as RDGraph and PIDGraph. In addition to the identifiers, the scientific metadata will also be made available. An area of intensive work is the improvement of information that enables meaningful crosswalks using the MSCR. The focus on improving the prerequisites for machine-aided analysis, including semantic aspects, is a high priority in this project due to the generally high data volumes and the high interdisciplinary requirements.

Expected impact

Climate researchers gain advantages from the new components through enhanced discoverability and reusability of data collections, as well as better allocation of data to experiments and projects.