Yep. I worked in scientific applications, and when developing some new data cleaning and processing pipelines for our hydrology data, Jupyter was phenomenal.

It was easy to use as a presentation, with figures and plots embedded. With controls enabled, you could demonstrate what varying certain parameters would do and pitch proposed cleaning profiles.

I was rather easily able to send a directory and it's notebooks/data sources to colleagues in the water sciences team so they could validate my results on their own (they were luckily also familiar with Python and Jupyter), and caught some minor bugs in the pipeline.

This was all much more collaborative and concise, and I feel Jupyter played a huge part in it.

Once it was done, it and a "final draft" pdf were added to the Docs in the repo and the pipeline was written out into a full application of it's own.