. 2023 Jul 10;160(3):223–251. doi: 10.1007/s00418-023-02209-1

Table 3.

Comparison of access methods to data stored in the IDR

	Download (via Aspera)	IDR API access (via Ice)	OME-Zarr access (via S3)
Load image subregion, e.g., single chunk or tile	No, only per file	Yes	Yes
Lazy loading	No	No	Yes. Use Dask collections: da.from_zarr (endpoint_url)
Easily analyze in parallel	No, depends on file format which may require a translation library	Difficult due to the transfer protocol used (zeroc-ice)	Yes. Use Dask schedulers: dask.delayed (analyze) (t, c, z)

Download (via Aspera)

IDR API access (via Ice)

OME-Zarr access (via S3)

Load image subregion, e.g., single chunk or tile

No, only per file

Yes

Lazy loading

Yes. Use Dask collections:

da.from_zarr (endpoint_url)

Easily analyze in parallel

No, depends on file format which may require a translation library

Difficult due to the transfer protocol used (zeroc-ice)

Yes. Use Dask schedulers:

dask.delayed (analyze) (t, c, z)

OME-Zarr provides the fastest and most flexible access when accessing less than an entire dataset. A notebook documenting the OME-Zarr access is available at https://github.com/ome/omero-guide-python/blob/master/notebooks/idr0044_zarr_segmentation_parallel.ipynb