SciBite has created Workbench, a data harmonization tool that provides a simple visual interface for curating and annotating data to terminology and ontology standards.
Workbench allows you to align your data to SciBite’s extensive set of curated “VOCabs” or to existing public or private standards using SciBite’s TERMite Named Entity Recognition engine. Workbench is fast, easy to use, and can learn from previous curation work to accelerate data harmonization within your organization.
Recording data can pose organizations with numerous challenges. Individuals and groups may use idiomatic or historic nomenclature that limits data re-usability, and information can be siloed, restricting transparency and collaboration. Additionally, repetitively integrating data from multiple sources can be time-consuming and prone to errors.
Workbench is a data curation and harmonization tool powered by SciBite’s core semantic technologies. Powered by SciBite’s TERMite and VOCab technologies, Workbench supports organizations adopting a FAIR (Findable, Accessible, Interoperable, Reusable) approach to data management. A critical component to making data more FAIR is to enable data interoperability through aligning data to shared terminology and ontological standards.
With Workbench, you can also use annotation rules to map internal codes or proprietary terms to your selected ontology terms or vocabulary, eliminating the tedious and error-prone manual editing process. Additionally, Workbench enhances replicability by saving these annotation rules, which can be applied to future data annotation tasks.
Precision medicine is changing the way that we think about the treatment of disease, moving from broad-acting therapies to therapies tailored to the individual patient. This increasingly relies on real-world data (RWD), encompassing a diverse range of sources, spanning multi-omic molecular characterisation of the patient’s condition, clinical presentation, treatment, and broader medical histories.Read
Public ontologies are essential for applying FAIR principles to data but are not built for use in named entity recognition pipelines. At SciBite, we build on the public ontologies to create VOCabs optimized for NER. In this blog, discover how we create a SciBite VOCab from a Public Ontology.Read
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