@@ -129,7 +129,9 @@ datasets to OME-Zarr. For example, Zeiss .czi datasets [may contain][czi format
129129dimensions] dimensions such as H, I, and V to store different phases,
130130illumination directions, or views respectively. To say nothing of synthetic data
131131that may contain "artificial" dimensions such as principal components or axes of
132- other dimensionality reduction-techniques from many images.
132+ other dimensionality reduction-techniques from many images. They may also hamper
133+ the adoption of OME-Zarr as an acquisition-time format due to performance concerns
134+ when data must be manipulated to accommodate a strict dimension order.
133135
134136## Motivation
135137
@@ -164,9 +166,9 @@ in OME-Zarr:
164166
165167## Proposal
166168
167- This document proposes removing any restrictions on the number of dimensions
168- stored in OME-Zarr arrays. Additionally, it removes restrictions on the names
169- and types of included dimensions.
169+ This document proposes removing any restrictions on the number or order of
170+ dimensions stored in OME-Zarr arrays. Additionally, it removes restrictions on
171+ the names and types of included dimensions.
170172
171173To maximise compatibility with existing software, this proposal recommends that
172174images with 2-3 spatial dimensions SHOULD name them from the subset of "zyx"
@@ -267,6 +269,10 @@ arguable that Zarr chunking is in fact more important here — XYTCZ *could* be
267269a perfectly cromulent axis ordering for XY planes if the Zarr chunk size was
268270(1024, 1024, 1, 1, 1).
269271
272+ Moreover, imposing a fixed axis ordering can incur performance penalties at
273+ * write* time (where performance is often critical) if the data is not already
274+ in the expected order.
275+
270276Therefore, this proposal argues that any performance implications are better
271277addressed through good documentation and good defaults. Indeed, more flexible
272278dimension ordering could * improve* performance in some scenarios, such as
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