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 of climate modellers 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 re-use, data still misses context formed by producers, experiments, projects, devices, etc as well as metadata crosswalks.

Case study description

In this case study, selected ENES data collections will receive identifiers using the Kernel Information Types developed in the project as well as the DTR with its corresponding contents. The assignment of RAiDs to projects/experiments, provides (domain agnostic) users with an aggregated view on the entities (data, software, people involved, etc.) of the project. All this information will be made available to B2FIND15, the EUDAT discovery service, and thus discoverable via the interdisciplinary search portal. This metadata will be also supplied to OSGs via interfaces and represented as RD- and PIDGraph. In addition to the identifiers, the scientific metadata is also made available. An intensive working point here is the improvement of information that can enable meaningful crosswalks. The focus on improving the prerequisites for machine-aided analytics including semantic aspects is of high priority in this project, due to the commonly high data volumes and the high interdisciplinary requirements.

Expected impact

Climate researchers benefit from the new components through improved discoverability and reusability of data collections at all levels of granularity and the allocation of data to experiments and projects.