Upcoming programming and scientific computing workshops, available for free through the UM Libraries:
Jupyter Notebooks is an Interactive Development Environment (IDE) runs as an application within the Researcher Workbench (RWB). There are important differences between this environment and a local Python IDE, which is what many people use when they write scripts.
Differences:
Jupyter Notebooks in the RWB runs on a virtual machine in the All of Us cloud environment
Running Jupyter Notebooks in the RWB uses computational credits / costs
Jupyter Notebooks has two storage options in the RWB: Persistent Desk and Workspace Bucket
You cannot export individual-level datasets out of the RWB
Similarities:
You can load any Python packages (see this list of pre-installed modules in the RWB)
The Jupyter Notebooks interface looks very similar to other Python notebooks
You can incorporate both Python code and formatted (Markdown) text within the notebook file (.ipynb)
Good to know: The All of Us team has curated a catalog of "code snippets" that contain pre-written code to execute functions that are commonly used by researchers. Read more about code snippets here, and browse the catalog of code snippets here.
The Jupyter environment can be found on the right-hand side of the workspace window.
Unlike RStudio, the virtual machine running your Jupyter environment can be customized to fit your needs. Cloud computing allows you to use much more computing power than you otherwise might have access to; however, please be aware that the cost of running the machine will scale with the computing power.
Once you have chosen the settings for your virtual machine and created the notebook, you will be able to start your analysis.
The Jupyter Notebooks application within the Research Workbench (RWB) has two storage options: the persistent disk and the workspace bucket.
The persistent disk is your default storage. It is attached to the cloud environment and is affiliated with the user who initiates the notebook. Only you, the user, can view the files in the persistent disk.
You also have a workspace bucket provided as Google cloud storage. In contrast to the persistent disk, the files you place in the workspace bucket are sharable with other RWB collaborators.
You can write your notebooks to the persistent disk using traditional Python functions. Then you can copy your files from the persistent disk to the workspace bucket using gsutil (Google Cloud).
See this page from the All of Us user support hub for additional information.
Jupyter Notebooks (and Jupyter Labs) are IDEs that are widely used in Python programming and data science applications. They allow the user to combine blocks of executable code with Markdown, allowing you to integrate formatted text, links, images, and even videos in between the blocks of code. This is especially useful in teaching applications and when sharing analyses with collaborators and stakeholders who are not coders.
Read more about Jupyter Notebooks and their use in the Researcher Workbench below:
Featured Workspaces are demonstration notebooks where the All of Us team has written guides and tutorials on how to do common functions within the Researcher Workbench, such as data wrangling, working with survey, genomic, or wearable data, explaining best practices for data science, and more.
Workspaces in the Demonstration Projects section of Featured Workspaces will show you end-to-end analysis performed using All of Us data. These projects demonstrate the quality, utility, and diversity of the All of Us data by replicating findings in previously published studies.
The UM Libraries is home to several certified Software Carpentry instructors. The Carpentries is a nonprofit volunteer group that teaches basic coding and data science skills to researchers. We regularly host events for the UM community and would be happy to talk to you about what we do. Check the calendar on the left for information about upcoming events through the Libraries, or reach out to carpentries@olemiss.edu for more information.
