META: scipy.datasets tracker

[AnirudhDagar]

Maintaining below a list of TODO tasks and follow-ups compile after the recently added submodule scipy.datasets.

• Add a public download_all utility method to fetch and cache all the datasets at once. (PR ENH: Add download_all utility method & script  #17163)
• Add a clear_cache function which automatically cleans scipy.datasets cache directory based on the users' platform. This will help avoid the tedious manual way for a user to empty cache if required. (PR ENH: Add clear_cache utility for scipy.datasets #17478)
• When running the complete SciPy test suite offline, we should intelligently skip tests given there is no network connection. Also when pooch is not installed, all dataset tests can be skipped. Maybe they also need an XSLOW marker. BLD: Allow disabling network requiring tests #17965
• Move other dataset files (eg. scipy.stats has its own test datasets like nist within the repo) to their respective new repos (like https://github.com/scipy/dataset-ascent) and utilize the functionality from scipy.datasets and pooch to reduce the wheel size by moving these datasets to scipy.datasets. Since the data files are only used in testing, it will NOT introduce any new dependency (like pooch) on the stats module.
• As mentioned in the comment about the restrictions and a packager needing to adhere to Debian rules, add a separate script or package (Eg: pip install scipy-datasets) to download everything at once before SciPy is built or tested. (Handled in PR ENH: Add download_all utility method & script  #17163)
• Remove filterwarnings for pooch import after new cerifi release. certifi had issues with py3.11 which are now fixed in master. Pending release of Fix deprecation warning on Python 3.11 certifi/python-certifi#199. (PR MAINT: remove certifi py3.11 warning filter #17149)
• Add more datasets to scipy.datasets submodule:
  • Iris Dataset
  • Manua Loa CO2 Dataset (https://gml.noaa.gov/ccgg/trends/data.html)
  • Discuss on the mailing list about other datasets that could be useful in SciPy apart from the ones that will be moved (See 4th point).

I'll start working on the tasks above, feel free to pick up one of the tasks if it interests you (comment below before you start working).

cc @rgommers

[tupui]

Thank you for all the work here @AnirudhDagar.

About more dataset: in general, it would be good to think about datasets in cooperation with Scikit-learn and Scikit-image (non exhaustive list at all). Since these are outside of SciPy, it would make sense to have a wide community effort.