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Are you, as a data steward, data scientist or researcher, constantly wasting time cleaning and structuring siloed data from different data sources and datasets? And does the way the data is stored and annotated hinder you in making biomedical discoveries?

Maybe you have just started looking into semantic models and knowledge graphs as a solution for these time and resource consuming efforts? This infographic shows the benefits of semantic models and knowledge graphs and how The Hyve, in collaboration with a top pharma company, solved their data integration challenge.

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Semantic Modeling

Having a semantic representation of data ensures that researchers and analysts as well as machines unambiguously understand the data, its context, and its 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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Knowledge Graphs

Having all data in a structured, queryable model stimulates internal and external collaboration and speeds up research and development processes. It also enables maximum (re-)use of all available data.

With knowledge graph capability, data integration is simplified because meaning has been standardized. Processes can be automated by reducing the need for reconciliation. Data teams get analytical flexibility and the ability to ask ‘what if’ questions of the data. Data stewards can manage data more efficiently as all data points as well as the relationships between data elements are captured.

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