Harmonise technology through scientific ontologies adhering to public standards, whilst curating vocabularies
Learn moreThe challenges of harmonising data to be Findable, Accessible, Interoperable and Reusable
Learn moreTransform previously unusable text to data in a richly annotated, machine-readable and standardise data formats
Learn moreSemantic search, visualise results, integrate into your existing platforms and automate your workflows
Learn moreSemantic enrichment technology that facilitates the production of knowledge graphs
Learn moreCombine deep learning and semantic algorithms to build powerful models that can exploit life science data and accelerate its use in R&D
Learn moreAt SciBite, we're passionate about helping our customers get more from their data. We love what we do and we think you'll love working with us, here's why...
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Within the life sciences, evidence-based decision-making is imperative; wrong decisions can have dire consequences. As such, it is vital that systems that support the generation and validation of hypotheses provide direct links, or provenance, to the data that was used to generate them. But how can one implement such a workflow?
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In our final installment of this series, we demonstrate how to extract a relevant subset of patients from the simulated data using two approaches – one using SciBite tools and one without.
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In the fourth and final blog in this series Scibite’s Head of Ontologies, Jane Lomax, shares her top 10 simple rules to start and progress your FAIR data journey.
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