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Frequently Asked Questions

Where can I find more documentation on the OHDSI ATLAS app?

ATLAS was developed and is maintained by the OHDSI community. Tutorials for the ATLAS tool can be found here and the Book of OHDSI can be found here. These resources contain a lot of useful information. You may find it particularly useful to read about Cohort Definition. If you need help, please reach out to our help desk at vadc@lists.uchicago.edu

What are harmonized variables?

Data harmonization is the process of unifying terminology across similar projects that use slightly different terms to describe the same variable. Harmonization helps to limit inconsistencies in data reporting and makes it easier to find and analyze data. For example, different studies use different names to describe the variable “date a participant enrolled”, like “index date”, “date of enrollment”, or “AnchorDate”. Data harmonization maps these different names to a single harmonized variable, “enrollment date”.

What will be included in the downloadable package at the end of a GWAS?

The package contains the following: Manhattan plot, QQ plot, metadata file containing all of your selections, your study's attrition table, and per-chromosome GWAS summary statistics.

How long will my GWAS take?

You may check the status of your analysis in the “GWAS Results” App. After you submit your analysis, it will be placed in the queue to run. While in the queue, the “GWAS Results” App will show the “Pending” status for your submitted analysis. Depending on the length of the queue, your analysis could take several minutes to several hours to start. After the analysis starts to run, the status will be changed to “In Progress”. Depending on your selection of cohort and variables, it could take a half an hour to three hours to finish. You may close your browser after you submit the analysis.

Why does the workspaces page give me an error?

Currently, workspaces are not available, and therefore you may see an error when attempting to log into this page. Generally, workspaces are secure data analysis environments in the cloud that can access data from one or more data resources. Workspaces may include Jupyter notebooks and JupyterLab, Python and RStudio. For more information about the Gen3 Workspace, you may refer to Gen3 Workspaces and Data Analysis in a Gen3 Data Commons.