DSW - Data Science Workbench

Flexibility and full reproducibility in managed servers

A logo with a blue interconnected hexagonal network inside a rounded outline and the letters 'DSW' below.

The Sycamore DSW provides centrally managed access to compute resources and content, with user actions audited and changes versioned for traceability. It supports both GxP and non-GxP workflows with segregation of work and content and can be used to enable a wide range of analysis functions, including working with real-world, imaging, genomics, and biomarker data.

We provides the tools for developing, validating, and deploying packaged runtimes that can be used for programming and analysis. Administrators can deliver any number of packaged runtimes that are tailored to a specific need, while analysts can select a runtime for their project and developers can create and deploy tools and Apps using a specific runtime. This ensures reproducibility in the system and maintains traceability between the runtime, the data, and the results.

Unique Benefits:

  • Scalable & extensible computing & storage with programming from browsers or Windows/Linux

  • Flexible for languages (R, Python, Julia) & dev tools (Jupyter, RStudio, SAS Studio, and others)

  • Customizable app launcher UI, and readiness to integrate with a variety of data sources

  • Analysis and Data Science-specific workflows with model, tool, & analysis operations monitoring

  • 100% reproducibility of analyses over time using automated containerization

Diagram of Sycamore DSW architecture showing data science app launcher, managed compute nodes, container registry, admin console, and support for tools like IDEs, version control, HPC, databases, and storage.

Add-on Modules

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