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