Improve OME-Zarr module

[tischi]

This should serve as meta-issue to collect thoughts about how the OME-Zarr module could be further improved. For items that we agree on we could then create dedicated issue and link to them in the below list

• Improve the figure, e.g. explaining the one-chunk <=> one-file concept (even though with sharding this not correct anymore...)
• Add activities teaching to viewing and downloading OME-Zarr at the BioImageArchive
• Use the NGFF Converter tool to create OME-Zarr
• View OME-Zarr in Java ecosystem
  • https://github.com/NEUBIAS/training-resources/issues/724
• View OME-Zarr in python ecosystem
  • https://github.com/NEUBIAS/training-resources/issues/725
• View OME-Zarr in the browser
  • TODO: Create issue
• Save array as OME-Zarr in Java ecosystem
  • TODO: Create issue
• Save array as OME-Zarr in Python ecosystem
  • https://github.com/NEUBIAS/training-resources/issues/723
• Add the OME-NGFF validator as an option to inspect S3 hosted OME-Zarr, e.g. using this URL: https://ome.github.io/ome-ngff-validator/?source=https://s3.embl.de/i2k-2020/platy-raw.ome.zarr
  • A nice feature is that this also finds the labels at https://s3.embl.de/i2k-2020/platy-raw.ome.zarr/labels/cells/
• Simplify activities that suggest multiple images to just use one image
• When using https://s3.embl.de/i2k-2020/platy-raw.ome.zarr also figure out how to visualise the segmentation at https://s3.embl.de/i2k-2020/platy-raw.ome.zarr/labels/cells/, e.g. in Neuroglancer and Fiji (for MoBIE I know how to do it now, this needs updated instructions).
• For activities that need specific tools such as minio and ome-zarr python, add links to installation instructions

Please let me know if

• you agree with this list
• you have further suggestions
• you would like to work on some of those points