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SciPy 2025 tutorial plan #321

Description

@TomNicholas

As discussed yesterday - here's a meta-issue for discussing tutorial content for this year. This is the schedule we currently have, but we can and should make edits / replacements as necessary. Please feel free to make edits, create sub-issues, ask questions etc.

  • (10m) Introductions
  • (1hr) Understand xarray’s core data structures (Ian presenting, Ian preparing, support from Tom, Justus, Eni)
    • (30m) Lecture
      • (10m) Named arrays and coordinates (Variable)
      • (10m) Groups of arrays with coordinates (DataArray and Dataset)
      • (10m) Hierarchical trees of related groups (DataTree)
    • (20m) Exercises
    • (10m) Break
  • (1hr) Understand how to map typical xarray computations and workflows over hierarchical data (Justus presenting, Justus preparing, support from Alfonso + Tom)
  • (1hr) Hierarchical storage formats and their data models (Tom presenting, Tom preparing) - Page on storage formats #325
    • Could just rip this whole section from the cloud-optimized geospatial formats guide?
    • (30m) Lecture
      • (10m) HDF5 (including differences to DataTree)
      • (10m) Zarr (including differences to DataTree)
      • (10m) TIFF
    • (20m) Exercises
    • (10m) Break
  • (1hr) Intro to Zarr on the Cloud (Joe presenting, Negin preparing, support from Joe + Tom) - Zarr tutorial #323
    • (30m) Lecture
      • (10m) What is Zarr? (@rabernat said he already has a notebook for this somewhere?)
      • (10m) Object storage as a Zarr Store
      • (10m) Accessing open data as Zarr on the cloud
    • (20m) Exercises
  • Open a provided Zarr store on the cloud
    • We decided to use an existing non-DataTree example for expediency: ERA5, but...
    • I just noticed we already have a section for this, on CMIP6!!

We also need a few small (MBs or less) example datatree tutorial datasets to use for some of this content. Ideas are here, but summarized as:

All of these are intended to be smaller examples (<50MB) - they are done once they are in https://github.com/pydata/xarray-data.

Other miscellaneous tasks:

cc @eni-awowale @jhamman @ianhi @aladinor @chuckwondo @negin513 @keewis

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