Verily Platform
Verily Workbench Platform
Verily Workbench is a secure research environment for governing and analyzing global multimodal biomedical data. Workbench aims to transform biomedical research by making the world’s most scientifically valuable data discoverable, accessible, and analyzable. This platform provides researchers with access to complementary global multimodal datasets for deeper clinical and molecular characterization of Parkinson’s disease and related disorders. By connecting scientific communities in a secure environment, Workbench enables co-analysis across datasets, strengthens the health data ecosystem and supports shared discovery to accelerate precision health approaches and bring vital therapies to those who need them.
Verily Workbench AMP PDRD Resources
For a more in-depth guide to Verily Workbench and its features, please access the link above.
This document provides a short introduction on how to set up your account, an overview of the AMP PDRD resources that are available in Workbench, and a simple end-to-end demonstration of how to use the Workbench to access AMP PDRD data and perform a simple analysis on it.
For AMP PDRD users who have created workspaces and analyses in Terra, and would like to migrate their work into Verily Workbench and use it there, we have created a migration script to make the process easier. As Terra and Verily Workbench do not have identical functionality, not all contents of a Terra Workspace can be migrated, but most major components can be. This guide provides step-by-step instructions on how to install and use the migration script, and a simple example to follow.
This guide provides a collaborative introduction to accessing and working with the AMP PDRD, GP2, and ASAP CRN datasets within Verily Workbench. While each program offers its own “Getting Started” workspace, with sample notebooks and dataset‑specific instructions, this companion document brings the pieces together. It outlines the key steps, access pathways, and resources you’ll need to navigate these datasets collectively, supporting a more streamlined and integrated exploration across programs.