Domain specific/interesting end goal use cases

Nobody has claimed this yet.

While initial development work will focus more heavily on synthetic data/toy examples of some relatively low-level artists, it is a good idea to keep in mind a wide range of applications, as that is the actual end goal that makes this work actually useful.

In particular the following, in no particular order:

- Oceanography/Geospatial data
- large datasets, subsampling data
- transforms into map coordinates
- integrations with cartopy, etc.
- Astronomy data
- It is a NASA funded grant, after all
- large datasets
- stress test units
- spatial-type data
- integration with data sources used in that domain
- Biological data
- Of particular interest to CZI grant
- Microscopy data/images
- Spectroscopy data
- It is my own area of expertise, I have several kinds of plots that serve a variety of levels of difficulty
- Specialized domain-specific data format
- Composing multiple artists
- particularly hard units support (spectroscopists can never agree what units to use, and like to say that length and energy units are interoperable)
- easy "quick" plots from a self describing data format
- multidimensional data, slicing into, etc.
- interactivity, stress testing the level of hooks provided to modify the plot
- Sports analytics data
- relatively unique visualizations
- see https://hockeyviz.com for many examples of a wide variety of plot types (made with matplotlib)
- potential interest for live updating

These are just a few of the domains for which this dataset-centric approach may be useful, feel free to add more.

The issue names no files, tests, or entry points; begin by reviewing the project's current scope against the listed oceanography, astronomy, biological, spectroscopy, and sports-analytics use cases. Done means turning selected use cases into concrete, prioritized requirements or follow-up implementation issues.

Written by the indexing model from the issue text.