html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/3564#issuecomment-1439022617,https://api.github.com/repos/pydata/xarray/issues/3564,1439022617,IC_kwDOAMm_X85Vxb4Z,1839645,2023-02-21T20:01:04Z,2023-02-21T20:01:04Z,MEMBER,"Oops I think the url just changed

https://chrisholdgraf.com/blog/2019/2019-10-22-xarray-neuro/","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-565605904,https://api.github.com/repos/pydata/xarray/issues/3564,565605904,MDEyOklzc3VlQ29tbWVudDU2NTYwNTkwNA==,1839645,2019-12-13T20:57:01Z,2019-12-13T20:58:09Z,MEMBER,"> For larger datasets, rather than storing them in github, a good approach is to create an archive on zenodo.org from which the data can be pulled.

Another note from MNE - we have a ""datasets"" sub-module that knows how to pull a few datasets from various online repositories (and in different structures). These store in a local folder (by default, `~/mne_data` I believe) and then they get fast-loaded after the first download. Many of the datasets are then stored in online repositories like OSF (https://osf.io/rxvq7/).

For datasets that aren't gigantic it's a pretty nice system. https://mne.tools/stable/overview/datasets_index.html?highlight=datasets","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165
https://github.com/pydata/xarray/issues/3564#issuecomment-557633282,https://api.github.com/repos/pydata/xarray/issues/3564,557633282,MDEyOklzc3VlQ29tbWVudDU1NzYzMzI4Mg==,1839645,2019-11-22T18:04:50Z,2019-11-22T18:04:50Z,MEMBER,"In case it's helpful for inspiration, we took a similar approach with the MNE-Python package (neuro electrophysiology package):

https://mne.tools/stable/index.html

Maybe there are at least 3 levels in there, actually:

* **Examples** - short vignettes that highlight one very specific piece of functionality, key-words for the example should be `ctrl-f`able in the title
* **Tutorials** - in-depth guides through a common part of workflow that xarray wishes to enable, with more explanation and detail
* **Domain use-cases** - examples of how xarray can facilitate use-cases in particular fields. Probably cover at a high-level many of the steps that multiple tutorials cover in-depth. More for ""inspiration and buy-in"" than in-depth learning.

Does that make sense?","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,527323165