Update documentation to reflect Zarr as the new default data format (#8694)

This PR updates some documentation pages, to replace WKW with the new
Zarr3 default data format.

- [ ] Updated
[changelog](../blob/master/CHANGELOG.unreleased.md#unreleased)
- [x] Updated [documentation](../blob/master/docs) if applicable

Author: markbader

docs/data/image_stacks.md

@@ -6,7 +6,7 @@ WEBKNOSSOS works with a wide range of modern bio-imaging formats and image stack
 - [Multi layer file sequence](#multi-layer-image-file-sequence) containing multiple folders with image sequences that are interpreted as separate layers
 - [Single-file images](#single-file-images) (OME-TIFF, TIFF, PNG, czi, raw, etc)
 
-Image stacks need to be converted to [WKW](./wkw.md) for WEBKNOSSOS. This happens automatically when using the web upload on [webknossos.org](https://webknossos.org) or can be done manually (see below).
+Image stacks need to be converted to [Zarr](./zarr.md) or [WKW](./wkw.md) datasets for WEBKNOSSOS. This happens automatically when using the web upload on [webknossos.org](https://webknossos.org) or can be done manually (see below).
 
 ## Single-Layer Image File Sequence
 When uploading multiple image files, these files are sorted numerically, and each one is interpreted as single section/slice within a 3D dataset.
@@ -60,7 +60,7 @@ You can manually convert image stacks through:
 ### Conversion with CLI
 You can easily convert image stacks manually with the WEBKNOSSOS CLI.
 The CLI tool expects all image files in a single folder with numbered file names.
-After installing, you can convert image stacks to WKW datasets with the following command:
+After installing, you can convert image stacks to Zarr3 datasets with the following command:
 
 ```shell
 pip install webknossos
@@ -71,15 +71,15 @@ webknossos convert \
   data/source data/target
 ```
 
-This snippet converts an image stack that is located in directory called `data/source` into a WKW dataset which will be located at `data/target`.
+This snippet converts an image stack that is located in directory called `data/source` into a Zarr3 dataset which will be located at `data/target`.
 It will create a so called `color` layer containing your raw greyscale/color image.
 The supplied `--voxel-size` is specified in nanometers.
 
 Read the full documentation at [WEBKNOSSOS CLI](https://docs.webknossos.org/cli).
 
 ### Conversion with Python
 
-You can use the free [WEBKNOSSSO Python library](https://docs.webknossos.org/webknossos-py) to convert image stacks to WKW or integrate the conversion as part of an existing workflow. 
+You can use the free [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py) to convert image stacks to Zarr v3 or integrate the conversion as part of an existing workflow. 
 
 ```python
 import webknossos as wk

docs/data/index.md

@@ -10,8 +10,8 @@ WEBKNOSSOS natively supports loading and streaming data in the following formats
 - [N5](./n5.md)
 - [Image Stacks (through Conversion)](./image_stacks.md)
 
-The WEBKNOSSOS-wrap (WKW) container format is used for all internal voxel data representations - both for the raw (microscopy) image datasets and segmentations. Skeleton annotations are saved as NML files. 
+The Zarr3 format is used for all internal voxel data representations - both for the raw (microscopy) image datasets and segmentations. Skeleton annotations are saved as NML files. 
 
-Any dataset uploaded to webknossos.org will automatically be converted to WKW on upload - given its source file format is supported by WEBKNOSSOS. Alternatively, you can manually convert your datasets using the [WEBKNOSSOS CLI tool](https://docs.webknossos.org/cli) or use a custom script based on the [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/index.html).
+Any dataset uploaded to webknossos.org will automatically be converted to Zarr3 on upload - given its source file format is supported by WEBKNOSSOS. Alternatively, you can manually convert your datasets using the [WEBKNOSSOS CLI tool](https://docs.webknossos.org/cli) or use a custom script based on the [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/index.html).
 
 Read more about uploading and configuring datasets on the [datasets page](../datasets/settings.md).

docs/data/upload_ui.md

@@ -11,10 +11,10 @@ The easiest way to get started with working on your datasets is through the WEBK
 
 ![youtube-video](https://www.youtube.com/embed/ZvUJrv86w8w?start=17)
 
-Internally, WEBKNOSSOS uses the [WKW-format](./wkw.md) by default to display your data.
-If your data is already in WKW you can simply drag your folder (or zip archive of that folder) into the upload view.
+Internally, WEBKNOSSOS uses the [Zarr3](./zarr.md) format by default to display your data.
+If your data is already in a data format like [WKW](./wkw.md), [Zarr or Zarr3](./zarr.md) you can simply drag your folder (or zip archive of that folder) into the upload view.
 
-If your data is not in WKW, you can either:
+If your data is not in WKW or Zarr format, you can either:
 
 - upload the data in a supported file format and WEBKNOSSOS will automatically import or convert it ([webknossos.org](https://webknossos.org) only). 
 Depending on the size of the dataset, the conversion will take some time. 
@@ -34,4 +34,22 @@ In particular, the following file formats are supported for uploading (and conve
 - [Neuroglancer Precomputed datasets](./neuroglancer_precomputed.md)
 - [N5 datasets](./n5.md)
 
+We support a variety of data types for the uploaded data. To make sure that your data can be uploaded to WEBKNOSSOS take a look into this table of supported data types for color and segmentation layers:
+
+|   dtype    | Color Layers  |       Segmentation Layers   |
+|------------|------------|------------|
+|   uint8    |       ✓     |   ✓                       |
+|   uint16   |       ✓     |   ✓                       |
+|   uint24  rgb  |       ✓   |   does not apply        |
+|   uint32   |       ✓     |   ✓                       |
+|   uint64   |       ✗     |   (✓)  [(til 2⁵³−1)](https://github.com/scalableminds/webknossos/issues/6921)          |
+|   |  |  |
+|    int8    |       ✓     |        ✓                   |
+|    int16   |       ✓     |        ✓                   |
+|    int32   |       ✓     |        ✓                   |
+|    int64   |       ✗     |        ✓                   |
+|   |  |  |
+|    float   |       ✓     |        ✗                   |
+|    double  |       ✗     |        ✗                   |
+
 Once the data is uploaded (and potentially converted), you can further configure a dataset's [Settings](../datasets/settings.md) and double-check layer properties, fine tune access rights & permissions, or set default values for rendering.

docs/data/wkw.md

@@ -1,17 +1,11 @@
 # WKW
 
 [webknossos-wrap (WKW)](https://github.com/scalableminds/webknossos-wrap) is a format optimized for large datasets of 3D voxel imagery and supports compression, efficient cutouts, multi-channel, and several base datatypes.
-It works well for large datasets and is built with modern file systems in mind and drives the majority of WEBKNOSSOS datasets.
+It works well for large datasets and is built with modern file systems in mind and drives a lot of WEBKNOSSOS datasets.
 
-WKW is versatile in the image formats it can hold: Grayscale, Multi-Channel, Segmentation, RGB, as well as a range of data types (e.g., `uint8`,  `uint16`, `float32`).
+WKW is versatile in the image formats it can hold: Grayscale, Multi-Channel, Segmentation, RGB, as well as a range of data types (e.g., `uint8`, `uint16`, `float32`).
 Additionally, WKW supports compression for disk space efficiency.
 
-Each layer of a WKW dataset may contain one of the following:
-
-* Grayscale data (8 Bit, 16 Bit, Float), also referred to as `color` data
-* RGB data (24 Bit)
-* Segmentation data (8 Bit, 16 Bit, 32 Bit)
-
 ## Examples
 
 You can try the WKW support with the following datasets. Upload them to WEBKNOSSOS using the [web uploader](./upload_ui.md): 

docs/data/zarr.md

@@ -2,7 +2,7 @@
 
 WEBKNOSSOS works great with [OME-Zarr datasets](https://ngff.openmicroscopy.org/latest/index.html), sometimes called next-generation file format (NGFF).
 
-We strongly believe in this community-driven, cloud-native data format for n-dimensional datasets. Zarr is a first-class citizen in WEBKNOSSOS and will likely replace [WKW](./wkw.md) long term.
+We strongly believe in this community-driven, cloud-native data format for n-dimensional datasets. Therefore, Zarr is the new default data format in WEBKNOSSOS and replaced the previous [WKW](./wkw.md) format.
 
 Zarr datasets can both be uploaded to WEBKNOSSOS through the [web uploader](./upload_ui.md) or [streamed from a remote server or the cloud](./streaming.md). When streaming and using several layers, import the first Zarr group and then use the UI to add more URIs/groups.
 
@@ -121,23 +121,22 @@ webknossos convert \
   --layer-name em \
   --voxel-size 11.24,11.24,25 \
   --chunk-shape 64,64,64 \
-  --data-format zarr \
   --jobs 4 \
   input.tif output.zarr
 
 webknossos compress --jobs 4 output.zarr
 webknossos downsample --jobs 4 output.zarr
 ```
 
-This example will create an unsharded Zarr v2 dataset with a voxel size of (4,4,4) nm<sup>3</sup> and a chunk size of (64,64,64) voxel. 
+This example will create a sharded Zarr v3 dataset with a voxel size of (11.24, 11.24, 25) nm<sup>3</sup> and a chunk size of (64,64,64) voxel. 
 A maximum of 4 parallel jobs will be used to parallelize the conversion, compression and downsampling.
-Using the `--data-format zarr3` argument will produce sharded Zarr v3 datasets.
+Using the `--data-format zarr` argument will produce unsharded Zarr v2 datasets.
 
 Read the full documentation at [WEBKNOSSOS CLI](https://docs.webknossos.org/cli).
 
 ### Conversion with Python
 
-You can use the free [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py) to convert image stacks to Zarr or integrate the conversion as part of an existing workflow. 
+You can use the free [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py) to convert image stacks to Zarr3 or integrate the conversion as part of an existing workflow. 
 
 ```python
 import webknossos as wk
@@ -150,7 +149,6 @@ def main() -> None:
         voxel_size=(11, 11, 11),
         layer_category=wk.COLOR_CATEGORY,
         compress=True,
-        data_format=wk.Dataformat.Zarr
     )
 
     print(f"Saved {dataset.name} at {dataset.path}.")