2026-07-16 –, Johnson Great Room
JupyterLite takes the simplicity of the Jupyter notebook interface and hosts it entirely in the browser, eliminating the need to setup a JupyterHub, making the Jupyter notebook environment much more accessible to users. In this talk we'll explore how our team harnessed JupyterLite and web components to make the distance between browsing for data and coding against that data 10 seconds and a new tab.
We work at the Goddard Earth Sciences (GES) Data and Information Services Center (DISC), one of NASA's earth science data archives. Earth science data at NASA is freely available to anyone with an internet connection. NASA offers high quality, curated, validated datasets representing decades of measurements from a variety of remote sensing instruments and models.
Unfortunately, making data available is not the same as making data easy to use. For years, a major sticking point for our users has been transitioning from the in-browser experience of our search engines and visualization tools to compute environments on their own systems. Suddenly users are confronted with data files in weird binary formats with unpredictable metadata, which can be a challenge for a wide range of users, from scientists, students, policy professionals to highly experienced developers.
Jupyter is a great tool for making computational workflows more approachable, particularly for new and occasional programmers. Jupyter notebooks provide the perfect vehicle for combining documentation with runnable code examples. Unfortunately, not all users have easy access to their own Jupyter server..
Our team tackled this problem directly, starting with one of our simpler visualization tools, the Hydrology Time Series Service. This tool allows users to plot long time series from hydrology-focused, high temporal resolution data. For some users, the time series plot may be enough for their needs. But if it isn't, we offer a button to jump them directly into a JupyterLite notebook with their selected data loaded into python pandas and ready for further analysis. JupyterLite runs right in their browser, so there's no need for a server or any setup.
We think this solution is just about the most seamless jump from a pure GUI data exploration environment to a coding environment that we've seen. In this talk, we'll demo the integration and cover what the website is doing behind the scenes to make this jump happen, bringing the user's data along for the ride. Come join us to see how a little javascript can enable a whole lot of python.
I'm a principal software engineer at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). Our prime directive is to archive earth science data and make that data available to the public for free. Since joining the GES DISC, I've mainly focused on the services end of public data access, working on tools that allow users to do some initial data exploration and visualization without having to download, understand, and open raw data files. I'm happy to wax poetic about metadata, interoperability, and well designed colorbars.
