What are correct metadata attributes when adding a zarr array

I have an image layer with prechunked data loaded with x,y,z dimension at 4,4,40nm voxel resolution. I want to add a zarr volume which is stored in z,y,x orientation with 40,8,8nm voxel resolution. I get the following source info after adding:

where the zarr array has the following .zattrs

{
    "_ARRAY_DIMENSIONS": ["z", "y", "x"],
    "offset": [
        0,
        0,
        0
    ],
    "resolution": [
        40,
        8,
        8
    ],
    "scale": 255
}

.zarray info

{
    "chunks": [
        15,
        304,
        304
    ],
    "compressor": {
        "id": "gzip",
        "level": 5
    },
    "dtype": "|u1",
    "fill_value": 0,
    "filters": null,
    "order": "C",
    "shape": [
        7063,
        67072,
        124416
    ],
    "zarr_format": 2
}

What is the correct way to do this? Perhaps I can add the resolution and bounds information directly to the metadata files.

[d-v-b]

Yes, we are making a constant effort to un-bake the magic dimension names from the spec 😃

[unidesigner]

Thanks @d-v-b for pointing out the discussion around ome/ngff. There seems to be a lot going on, and it seems very nice that a lot of the tools already or have the intention of supporting ngff. I think it would make sense to perhaps contribute some of the discussion/proposals here into possibly augmenting a proposal for the axes labeling in the ngff specification. And then go about adding support for OME/ngff to Neuroglancer, instead of another special-purpose zarr metadata spec that is not supported by a wider community.

A few points for discussion may be:

1. specification/support of structured data types
2. overall structure of the json. more like the unit proposal from @jbms , or @d-v-b proposal with separate scale, units fields with arrays.
3. proposal by @jbms about using '' and null
4. what format to use for units, e.g. using udunits2 to specify the unit strings
5. I saw that there is a proposal of adding a spec for sequences of spatial transforms. And @jbms made the point about possibly supporting affines for visualization purpose. I’d find it super nice and useful to have a way to specify an affine for a dataset for visualization purposes, i.e. the affine is applied when rendering the image data in Neuroglancer. I was wondering whether the planned transform specification could be the suitable place to store/look-up this affine. Or whether it would be more closely related to the translate field you have above. There is also the problem of arrays with non-spatial dimension on how to select the relevant axes to apply the affine too. I think there was some discussion about it in the ngff issue, but haven’t seen a conclusive solution yet.
6. how a translate field (in world units) would impact indexing operations e.g. in tensorstore. I haven’t checked in detail, but is the idea to support indexing operation in world space/units, v.s. index space of the array data itself?