Consortium Meeting 5.0

After 4 years of NFDI4Chem, we would like to collect examples where RDM is successfully brought into university education. Moreover, we would like to discuss challenges where NFDI4Chem can help or where other stakeholders should help to anchor RDM better in curricular teaching. We plan to write a perspective piece out of these discussions.

In NFDI4Chem, we have started to collect the Minimum Information on Chemical Information (MIChI) guidelines for several subdisciplines in Chemistry. Our repositories serve Metadata in several formats, from DataCite spiced with additional chemical information, to the extensive RADAR metadata, to SchemaOrg and Bioschemas. Ontologies are collected in the Terminology Service, and used in the services and parts of the Metadata.
For searching and finding research data, we currently rely on general purpose search engines, or the NFDI4Chem Search Service. The latter harvests metadata from the repositories in the NFDI4Chem federation. We even have first prototypes of Knowledge Graphs, which also import the metadata in the federation.
With these initial activities, we are now in the position to level-up, by integrating all of the above in the Semantic Data Hub. 
In this workshop, we will (briefly) recap the MIChIs, Ontologies, Metadata, Search Service and Knowledge graphs, and show examples where things do not work nicely together. The other examples you have seen before during the reports and the posters. We then propose the Semantic Data Hub, its existing and the new components. Finally, we ask you about what you would like to contribute, and how you would like to use it.
In NFDI4Chem we have made significant strides in advancing the accessibility and interoperability of chemical research data. Through the development of Minimum Information on Chemical Investigations (MIChI) guidelines, the improvement and implementation of metadata standards, and the integration of ontologies, we have laid the foundation for a comprehensive approach to managing and sharing chemical data across the NFDI federation and beyond. Our repositories currently support a wide range of metadata formats,  from DataCite spiced with additional chemical information, to the extensive RADAR metadata, to SchemaOrg and Bioschemas. Ontologies are managed and provided by the Terminology Service, and used in the services and parts of the Metadata.
The NFDI4Chem Search Service harvests and harmonizes metadata from the repositories in the NFDI4Chem federation. We even have first prototypes of Knowledge Graphs, which build on the harmonized metadata from the search service.
With these initial activities, we are now in the position to level-up, by integrating all of the above in the Semantic Data Hub (SDH). 
The SDH will provide a central point of access for harmonized, integrated, and semantically enriched chemical data, culminating in a chemistry knowledge graph. The SDH leverages machine-actionable data, enabling the seamless ingestion, querying, and cross-repository exploration of research data.
In this workshop, we will briefly review the components that led us here: MIChI profiles, ontology development, metadata standards, and the existing Search Service. We will also show examples where things do not work nicely together. We will then introduce the concept and approach of the Semantic Data Hub, including its core components—the Metadata Schema Service (MSS) and the Chemistry Knowledge Graph (KG). The focus will be on how the SDH integrates these elements into a unified framework that addresses semantic interoperability challenges. Finally, we ask you about what you would like to contribute, and how you would like to use it.
The workshop aims to provide a comprehensive understanding of the SDH’s goals and approach, laying the groundwork for future discussions and collaborations across the Task Areas but also the other NFDI consortia which want to reuse chemistry data. This workshop is particularly relevant for people involved in metadata development and creation (ELN, data repositories), ontology management, data integration, and data reuse scenarios