-
Ontology Management
Harmonise technology through scientific ontologies adhering to public standards, whilst curating vocabularies
Learn more -
Enterprise FAIR Data
The challenges of harmonising data to be Findable, Accessible, Interoperable and Reusable
Learn more -
Text Analytics + Semantic Enrichment
Transform previously unusable text to data in a richly annotated, machine-readable and standardise data formats
Learn more -
Semantic Search
Semantic search, visualise results, integrate into your existing platforms and automate your workflows
Learn more -
Knowledge Graphs
Semantic enrichment technology that facilitates the production of knowledge graphs
Learn more -
Artificial Intelligence
Combine deep learning and semantic algorithms to build powerful models that can exploit life science data and accelerate its use in R&D
Learn more
Why choose SciBite?
At 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...
-
Built by scientists for scientists
Our suite of semantic solutions is the culmination of the perfect storm of tens of years of experience
-
Unique deep learning approach
Transforming previously unusable but scientifically relevant textual content into machine-readable clean data
-
Putting customers at the heart
Our philosophy is to listen, engage & work together with our customers to make ground-breaking achievements
-
Supporting the top 20 pharma
Our customers are utilising higher quality data, integrating more data much faster with greater accuracy
Our customers
News and opinion
-
What is Retrieval Augmented Generation and why is the data you feed it so important?
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?
Read -
Unlocking important RWE from patient data (Part 3) – Can we find all the relevant patients?
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.
Read -
FAIR data – Ten simple rules to FAIRify your data
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.
Read
© Copyright © 2023 Elsevier Ltd., its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
