if i may make one comment that we allow affines back as transformation. but optional and if included then translation and scale should not be used. for some of our use cases it would be really nice to be able to include the affine in the dataset itself rather than separately. we can wait till the next iteration of the spec, but if there are no objections that affines will also be axisIndices or dimension compliant, i don't see a reason why including it would be difficult.

let me first start with dimensions. at present it is time, channel, shape, shape, shape. while time can cover many aspects, we may want to include something like slice, channel, shape, shape, shape for 3d imaging, where we take a 3d tissue and slice it into thinner slices and then put them together as a single dataset.

this has a few additional requirements and this is where the affine part comes in. although a computed transform, it may be helpful to align these slices in 3D space. also we may have missing slices or uneven spacing. thus having affines be associated with each slice could be helpful.

in out practical use case, we have stains, not channels. so our dimensions would be:

slice, stain_index, shape, shape, shape, chunk

where chunks are partially overlapping acquisitions. chunks would be dropped if the images were prestitched.

in this use case, the affines could be associated with different slices and potentially chunks. for simplicity let's leave chunks aside.

then our transformations would be a list of affines for each slice [ aff1, aff2, ....] where the axisIndices would be [2,3,4] and the aff* would be [4x4] since axisIndices is of length 3.

thus there are a few requests here.

1. that we allow arbitrary dimensions
2. list of affines as transformations. since each slice could have been arbitrarily rotated and flipped (front to back) having a computed affine could really help place all the pieces together
3. additional metadata to situation the coordinate system better w.r.t some physical property of the sample by defining a global origin.

this figure may help visualize the situation: https://scalablebrainatlas.incf.org/human/BIGB13

cc: @thewtex