Collaborative Workspaces for Alzheimer’s Disease Research and Data-Sharing 

How do Research Teams Collaborate in AD Workbench Workspaces?

Research teams collaborate in AD Workbench Workspaces by sharing a private, access-controlled environment where members work together on the same files, tools, and data through R Studio, Jupyter, and virtual machines. 

Workspaces are private to their members, and user access can be granted and maintained by a Workspace Administrator. Once files and data are uploaded to your Workspace, team members can access them via R Studio, Jupyter, and virtual machines.

How are AD Workbench Workspaces Cloud-based and Customizable?

AD Workbench Workspaces are cloud-based, so they can be deployed in specific geographic regions to meet local data laws, and customizable, so storage, computing power, and add-ons can be configured to match each project's needs.

Cloud-based workspaces are located in regions to comply with geography-based data sharing policies and laws.

Each Workspace is configured based on file and database storage and computing requirements, virtual machine requirements, and any additional add-on services (such as the R Shiny server).

Every Workspace includes a broad selection of the most popular R and Python packages. Users can also upload their favorite tools and preprocessing pipelines to workspaces to analyze clinical, neuroimaging, and omics data made accessible via AD Workbench.

How Do I Get Started With an AD Workbench Workspace?

Once you create a Workspace and set up your environment, including which R and Python packages you want to use, you can invite members of your team, request datasets via the AD Discovery Portal, and get to work.

How Do Researchers use AD Workbench Workspaces?

Researchers use AD Workbench Workspaces for tasks like modeling disease heterogeneity, testing diagnostic algorithms, and preparing data for broader sharing across the ADRD research community. 

Some common use cases are:

1. Exploring Alzheimer's Disease Heterogeneity: Researchers investigate the heterogeneity of Alzheimer’s, particularly through factors like amyloid composition and differential protein expression across brain regions.
2. Testing Diagnostic and Predictive Models: Workspaces are created to develop models that may improve Alzheimer’s diagnostics, assess model performance, and create explainable AI models.
3. Studying Environmental and Demographic Factors: Projects focus on understanding Alzheimer’s prevalence in underrepresented populations and studying the influence of environmental conditions.
4. Omics Data Analysis: A common goal is to analyze omics data to understand disease mechanisms, identify biomarkers, and assess genetic contributions to Alzheimer’s progression.
5. Pilot Testing and Validation: Workspaces are used to test and validate newly generated datasets and tools in preparation for larger-scale studies.
6. Preparing Data for Sharing and Analysis: Many researchers create workspaces specifically to pre-process raw data, making it more suitable for downstream analysis and easier to share with the broader ADRD research community.

Create your AD Workbench Workspace today and start analyzing ADRD data with your research team in a secure, compliant environment.