Alzheimer's Disease Workbench
We can accelerate progress towards new and better treatments and diagnostic tools for AD and related dementias by connecting researchers with the data they need. To get there, a coalition of 10 organizations collaborated to build the AD Workbench (to read more about the pilot phase, click here), a data platform infrastructure that allows researchers to:
Data Sharing Principles
The AD Workbench (ADWB) is guided by three main principles: increasing data sharing, easing data access, and developing new tools and analytics for researchers to use and share.
- By increasing the interoperability of participating data platforms;
- By adding new data platforms and datasets as quickly as possible;
- By offering incentives to academic and industry researchers who choose to share resources and data.
- To search available data;
- To manage the datasets they find;
- To navigate data use agreements (DUAs);
- To request access to new datasets;
- To work from a library of existing analytics tools and scripts;
- To see the usage and quality of available datasets on a clear, transparently presented dashboard.
- By providing a place for researchers to use and share their own code and save their work;
- By allowing users to allocate workloads to virtual machines, providing them with elasticity in computing power;
- By sponsoring hackathons and other organized challenges that expand the community of users.
Different types of researchers and members of the AD and science communities will interact with the ADWB in different ways, which include users
A user with a specific question can utilize a set of common analytical tools that are available for quick data exploration. ADWB workspaces also include a suite of R shiny apps to assist users with further data analysis.
A user who plans to analyze existing datasets using their own code can also utilize the available analytical tools for quick data exploration. The R code used to load the data and generate plots is available for export and further analysis, and users can upload their own code or develop their own mini-apps that can be shared more broadly with others in the community. Jupyter notebooks will soon be embedded in the workspaces. For more computing power or specific software, users can configure available virtual machines.