Research Data Management (RDM)

Semantic Modelling

A semantic model is a formal specification of entities (such as genes, proteins, and assays) and relations between them, in a given domain. In contrast to other types of data models, it specifies the meaning, or semantics, of domain-specific concepts.

Having a semantic model will enable you to standardize and harmonize structured data so that it is comprehensible for both humans and machines. It is an essential component to make your data more FAIR and it facilitates data integration, within and across R&D data domains.

Knowledge Graphs

A knowledge graph typically connects entities from multiple domains, with formal semantics provided by a semantic model. It serves as a flexible framework for data integration, harmonization, analytics, and sharing of data from multiple sources.

A knowledge graph contains data from multiple sources organized as a graph that can be queried and navigated in a uniform manner.

The Hyve designs and builds knowledge graphs that may cover distinct biomedical domains, typically from the R&D pharma departments such as: small and large molecule research, omics, clinical, manufacturing etc..

FAIR by Design solutions

Design your own scalable research data management solution. The Hyve provides software engineering services to build or customize open source tools and enable integrations with existing data sources, in order to support knowledge creation and collaboration.

We created a platform - Fairspace - that can be used by data stewards to design data models, and by researchers to annotate and publish their existing research data as digital FAIR data objects. We also have the capabilities and knowledge of using other open source data management tools such as Gen3, iRODs and COLID.