Use Cases

Discover how SciBite’s powerful solutions are supporting scientists and researchers.

Use Cases Overview

Bioassay Registration Departmental Search Drug Safety Electronic Laboratory Notebooks
Enterprise Fair Data LLMs & GenAI Knowledge Graphs Target Validation + Drug Repositioning
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SciBite / Solutions / Datasets

Access datasets from millions of accredited sources, all in one place

Accessing, analyzing and managing the huge datasets associated with life science can be overwhelming, especially when there are additional complexities thrown into the mix. Data from multiple sources often lacks standardized formatting, terminology and structure which can hinder integration and make it difficult to harmonize.

SciBite enables your team to combine their internal knowledge with our domain-specific curated and enriched data to answer R&D questions with even greater precision than ever before. Using our ontology management and named-entity extraction (NER) tools, SciBite can FAIRify data and prepare it for machine consumption.
Spotted owlet family (Athene brama) at nest
Custom datasets curated by domain experts

Our team is made up of experts from all specialisms in Life Science and beyond. From highly skilled biochemists to ontologists, geneticists and informaticians, we’ve got the combined knowledge and expertise to help you solve data integration challenges and deliver applied analytics to augment your work.

Avoid multiple database subscriptions and speak to our team to help find you relevant and valuable datasets to enrich your ontologies, create bespoke vocabularies and support your project outcomes.
Data packages that add value to your research

Only focusing on a select few sources could mean missing out on huge amounts of data for your use case. With SciBite, you can get access to public, third party and proprietary data sources, plus Elsevier full text articles, all expertly curated and peer reviewed. Plus, if you purchase an Elsevier product such as Science Direct or Embase datasets, you won’t need to import it into search as it’s seamlessly integrated. You’ll be able to examine the relationship between entities from day one.

With access to so much more, you’ll have all the tools you need to give you a competitive edge in R&D.
Enabling accelerated discovery with accurate analysis tools:

At Elsevier, our datasets are harnessing AI and predictive modeling to solve the challenges faced by R&D companies. Highly cited scientific literature is extracted by our team of domain experts to ensure datasets are accurate, relevant and machine readable by a process of indexing, clarifying and enriching.

This means you can integrate reliable, actionable scientific data into custom applications to help your use cases. SciBite equips you with everything you need to collate, sort and analyze data and integrate it into your computational ecosystems.
Turn validated data into scientific insights
Semantic search

Ensure you’re not missing vital information in your search queries with semantic search. By autosuggesting terms as you type, all relevant data is found regardless of which synonym you use.

 Business Intelligence dashboards

Designed to support informed decision making, our analytic dashboards can reveal key insights, benchmarks, metrics and trends across a wide range of scientific disciplines.

 Knowledge graph creation

With CENtree, SciBite’s ontology management tool and TERMite, our named entity recognition engine, you can create knowledge graphs which explore the relationships across an entire domain and interconnectivity between complex networks.

 Rising star and KOL analyses

Identify industry influencers based on their research output, citation analysis and media presence to give your company the edge in fast moving fields.

 Train algorithms and neural networks

High-quality, curated data is vital to train algorithms and neural networks. SciBite can help with all aspects of curation, from standardization to automated annotation.

 Develop predictive models

SciBite helps to develop these through pattern and relationship recognition and can even evaluate their performance.

 Perform protein-ligand binding QSAR

Our datasets can help you to perform QSAR by providing complete data on biological activities and binding affinities.

 Automate tasks and workflows

Our APIs allow seamless integration of datasets into existing workflows, automating the extraction from multiple sources, saving you time and resources.
Explore a range of resources relating to SciBite Search

From blogs and articles to use cases and downloads, these handy resources offer valuable insights into our products and our industry.
https://scibite.com/knowledge-hub/resources/organoid-based-predictive-toxicology-model/ thumbnail image Resource Develop an organoid-based predictive toxicology model [Case study]

Discover how Elsevier helped a top pharma develop an organoid-based predictive toxicology model using comprehensive, custom datasets.

 Datasets

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https://scibite.com/knowledge-hub/resources/datasets-for-drug-discovery-use-case/ thumbnail image Resource Datasets for drug discovery [Use Case]

Discover trusted datasets for drug discovery to enhance virtual screening, target identification, biomarker prioritization, and treatment assessments.

 Datasets

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https://scibite.com/knowledge-hub/resources/datasets-for-post-market-surveillance/ thumbnail image Resource Datasets for post-market surveillance [Use Case]

Comprehensive datasets for post-market surveillance enable high-precision adverse event screening, drug safety signal detection, and device monitoring.

 Datasets

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https://scibite.com/knowledge-hub/resources/datasets-for-pre-clinical-and-clinical-rd/ thumbnail image Resource Datasets for pre-clinical and clinical R&D [Use Case]

Comprehensive datasets for pre-clinical and clinical R&D support predictive toxicology, QSAR models, and clinical outcome modeling.

 Datasets

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We work with world-leading organizations Knowledge Center Information Manager, top 5 pharmaceutical company

“We’ve benefited immensely from the use of SciBite tools. It’s really been a driver of in our ability to make data FAIR and to use a FAIR data enviornment.”
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