Table 1.

The 6 modules comprising the All of Us Tx3 Series.

Modules	Main content	Pre-lab
Module 1 Introduction to the Workbench and the Series	The All of Us Research Program and its data	Familiarize yourself with the User Support Hub and its “Getting Started” materials
	The All of Us User Support Hub
	The All of Us Researcher Workbench
	Featured Workspaces on the Researcher Workbench
	What you can expect from this series
Module 2 Your first analysis of the All of Us dataset	Visualizing and statistically comparing All of Us data: (A) Comparing normal distributions. (B) Example python code to plot histogram. (C) Example python code to perform a T-test.	Familiarize yourself with the Cohort Builder
	Steps to create a project and data selection on the Researcher Workbench: (A) Create/copy a workspace. (B) Select a cohort and data to compare. (C) View selected data in the Jupyter notebook.
	Plot histograms and assess height differences using python codes: (A) Identify the correct data in DataFrames. (B) Plot and save histograms. (C) Compare distributions with a T-test. (D) Celebrate your first AoU data analysis.
Module 3 Creating a dataset: Workspaces, Phenotypes, and Cohorts	Brief Review: (A) Workbench Components. (B) Create or Duplicate a Workspace.	Creating an All of Us dataset by setting your phenotype correctly
	Introduction to phenotypes: (A) Terminology in phenotype study. (B) Tutorial workspace for phenotype selection.
	Create an All of Us dataset: (A) Cohort Builder. (B) Concept Sets. (C) Create a Dataset. (D) Tutorial workspace examples.
	Jupyter Notebook Introduction: (A) Background on Jupyter Notebook. (B) Access the All of Us data through Jupyter Notebook.
Module 4 Using Jupyter Notebooks and Code Snippets	Brief Review: (A) Define your phenotype. (B) Create cohorts, concept sets, and datasets.	Create a test educational workspace and duplicate a workspace
	Jupyter Notebooks on the Researcher Workbench: (A) Exporting a Dataset. (B) Computing environments. (C) File storage options.	Create an Analysis Environment by exporting a workspace dataset
	Getting started with Jupyter Notebooks: (A) Introduction to the Jupyter Notebook. (B) Introduction to code snippets. (C) Using code snippets to save and retrieve data. (D) Backing up your Jupyter Notebook. (E) Other helpful tips.	Jupyter Notebook Features and Code Snippets
		Using code snippets to interact with Workspace Bucket
		Back Up Notebooks—Save HTML or HTML Snapshots
Module 5 Data Quality, Wrangling + Statistical Analysis and Plotting	Getting to the Support Hub While Logged In	Review the Featured Workspace: Data Wrangling
	Getting Started Resources
	Data Wrangling Examples
	Further Data Checking and Cleaning
	Statistical analysis resources
Module 6 Genomic Analysis: GWAS and PheWAS	Brief review of previous modules	Tutorial workspace—How to work with All of Us Genomic Data
	Significance of the All of Us genomic data: (A) Inclusive genomics improves everyone’s health. (B) Genomics data available.	Demo—Polygenic Risk Score Genetic Ancestry Calibration
	Background on a Genome-Wide Association Study (GWAS): (A) The missing diversity in human genetic studies. (B) What is a GWAS? (C) Simplest Regression Model of Association.	Demo—Siloed Analysis of All of Us and UK Biobank Genomic data
	Steps to a GWAS project on the All of Us Researcher Workbench: (A) An Introduction to GWAS using Hail.	Demo—PheWas smoking
		Phenotype—Type 2 diabetes