Semantic Modelling

Simplify your research data integration & management with a Semantic Model

Having a semantic representation of data ensures that both researchers and analysts, as well as machines, unambiguously understand the data, its context and the qualitative aspects. This may include experimental details (e.g type of assay, experimental factors), provenance (e.g. who produced the data, traceability), or other metadata (e.g usage conditions, qualified references to other sources).

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What we offer

Our Semantic Modelling service includes

Building a Semantic Model (RDF, OWL) across R&D (e.g. Project, Study, Assay, Dataset, Clinical Trial etc.) or integration of all types of heterogeneous biomedical data sources - PK/PD, Omics, ELNs, sample inventories, public datasets, CMC data.

Aligning metadata with enterprise data governance, i.e. the common vocabularies/ontologies/metadata guidelines in use in the enterprise

Data curation to increase interoperability and usability of data using open source industry-standard ontologies and vocabularies, and building knowledge graphs.

Challenges that can be solved with a Semantic Model

  • Data lives in specific vendor applications, hampering data integration, use of the data in new scenarios, and preventing a unified view of the data in the enterprise.

  • Having the data in application-specific structure is useful for use in the application, but might prevent use outside the application, which will especially be a problem when applications retire

  • Data is harmonized, not using the same vocabularies and shared standards

  • Legacy data becomes unusable because the meaning or context can not be retrieved anymore.

  • Manual inspection and processing is needed before it can be used before analysis and machine learning. This process needs to be repeated for every new use case of the data.

  • Traceability of research data through the development pipeline poses a particular problem because most data is currently stored in particular vendor solutions and departments.

  • Cross trial analytics is currently burdensome

  • Data across stages of R&D is unlinked and impossible to interpret

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