SciPy 2023

As scientists continue to embrace the Jupyter ecosystem for constructing computational narratives of their science through code, data, and rich text, they may encounter technical and community barriers to maintaining and sharing their science with new and existing audiences. We demonstrate the value of open-source science community building and getting there through reliance on the open-source Jupyter ecosystem, pre-packaged GitHub and BinderHub-based infrastructure, and documentation for creating, sharing, testing, and maintaining Pythia Cookbooks for their computational narratives.

Do you struggle to find and access useful data on THREDDS servers? Are you interested in machine learning but don’t know where to start?! Data scientists hate this one simple trick!

This poster demonstrates an end-to-end earth systems science machine learning workflow using Unidata tools within a Jupyter Notebook interface. This allows for rapid updating with near real time datasets and familiar interface for students, researchers, and practitioners alike.