Workbench - SciBite

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Workbench Harmonize complex data with a user-friendly tool that takes the effort out of tabular data curation.

When you’ve got a number of different people working in different labs using different terms and working in different ways, it can seem impossible to collate all this data into a practical, usable dataset. But with Workbench’s simple visual interface you can curate and annotate data and ontologies to finally bring order and harmony to your data.

Why choose Workbench?

Intuitive

Powerful and easy to use, Workbench is built to simplify the process of curating and standardizing data.

Automated

Reproduce annotations easily using VOCabs and public and privately available ontologies.

Time saving

Create and share rules that can significantly reduce the amount of time and effort spent curating new data.

Overview Recording, reusing and sharing data causes a number of challenges for scientists and researchers.

A number of these issues are exacerbated by complications with siloed information, different approaches and inconsistent terminology.

Workbench provides a solution in the form of a visual tool that simplifies the process of curating and editing term lists, personalized dictionaries, and semi-structured data sets to match your preferred terminology. The result is an easier journey to Findable, Accessible, Interoperable and Reusable data (FAIR).

Be more efficient with your experts

Cleaning datasets the old-fashioned way can be a time-consuming process that requires very specific expertise in the subject domain and the available standards. Workbench enables scientific data curators to carry out this work much more efficiently by streamlining the process and facilitating reuse and repetition of data and processes.

Annotate with ease

Workbench enables you to annotate data from selected columns with your chosen vocabulary or ontology, including VOCabs sourced from SciBite’s library of manually curated vocabularies enriched with 20M+ synonyms. It’s also quick and easy to upload your custom ontologies and use these for annotating your data.

Automate with simple annotation rules

Configure rules and fine-tune fuzzy-matching and variations in spelling and typographic errors with links to SciBite’s award-winning named entity recognition system, TERMite. This connects seamlessly into Workbench to support with the curation of data that’s riddled with internal codes or proprietary terms.

Build workflows and share data

Integrating Workbench functionality into your custom data curation is easy thanks to workflows a powerful REST API that provides programmatic access to the same core functions from the user interface.

In addition to this, you can also take advantage of built-in sharing functionalities that make collaborative curation projects easier than ever. As a data owner, you’re able to create a group where you can invite other colleagues to view or edit your annotations. And this annotated data can then be exported in Microsoft Excel for use with many third-party tools.

Do you need access to TERMite?

Workbench gives you an easy-to-use interface for annotating semi-structured datasets with ontologies and VOCabs. To do this effectively, Workbench needs to be able to access TERMite, which is SciBite’s named entity recognition application.

There are two ways to set this up. If you already license TERMite and have access to a server, you can configure Workbench to use that server. If you don’t currently have access to TERMite, you can run Workbench with an embedded TERMite server.

Take a closer look at Workbench

Get more details on how Workbench can support you and your team with automated data cleansing and standardization by implementing rules and ontologies with your existing data.

Workbench datasheet

Relevant resources, events and news

News Ontology mapping: Advancing data interoperability

Ontologies are crucial in unlocking information that helps fuel innovation. However, similar ontologies have emerged within the same domain, making it difficult for researchers to identify the right ontology to choose. Here we look at some of the complexities and strategies for handling ontology mappings.