diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index 7056dd0..067f317 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -10,7 +10,7 @@ jobs:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.9"
+          python-version: "3.10"
       - uses: abatilo/actions-poetry@v2
       - run: poetry install
       - run: poetry run pre-commit run --all-files
@@ -22,7 +22,8 @@ jobs:
         python-version:
           - "3.9"
           - "3.10"
-          - "3.11"
+          - "3.10"
+          - "3.12"
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index db3e79d..2c2a769 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -5,18 +5,16 @@ default_stages: [commit, push]
 default_language_version:
   python: python3
 repos:
-  - repo: https://github.com/psf/black
-    rev: 22.12.0
-    hooks:
-    - id: black
-      language_version: python3.9
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.4.0
     hooks:
     - id: check-yaml
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
+  - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: 'v0.0.245'
+    rev: v0.1.8
     hooks:
+      # Run the linter.
       - id: ruff
-        args: [--fix, --exit-non-zero-on-fix]
\ No newline at end of file
+        args: [ --fix ]
+      # Run the formatter.
+      - id: ruff-format
\ No newline at end of file
diff --git a/README.md b/README.md
index 7b01b8f..f103641 100644
--- a/README.md
+++ b/README.md
@@ -14,9 +14,6 @@ supported metadata models:
 `omero` and `bioformats2raw` are not currently supported, but contributions adding
 support for those models would be welcome.
 
-Support for container-level validation (i.e., checking that multiscale or coordinate 
-transformation metadata is compatible with a hierarchical array / group layout) is being added incrementally. 
-
 ## installation
 
 ```bash
diff --git a/docs/api/v04/axis.md b/docs/api/v04/axis.md
new file mode 100644
index 0000000..5f1cec1
--- /dev/null
+++ b/docs/api/v04/axis.md
@@ -0,0 +1 @@
+::: pydantic_ome_ngff.v04.axis
\ No newline at end of file
diff --git a/docs/api/v04/label.md b/docs/api/v04/label.md
new file mode 100644
index 0000000..fa0d46d
--- /dev/null
+++ b/docs/api/v04/label.md
@@ -0,0 +1 @@
+::: pydantic_ome_ngff.v04.label
\ No newline at end of file
diff --git a/docs/api/v04/multiscale.md b/docs/api/v04/multiscale.md
new file mode 100644
index 0000000..f8bf791
--- /dev/null
+++ b/docs/api/v04/multiscale.md
@@ -0,0 +1 @@
+::: pydantic_ome_ngff.v04.multiscale
\ No newline at end of file
diff --git a/docs/api/v04/plate.md b/docs/api/v04/plate.md
new file mode 100644
index 0000000..add6722
--- /dev/null
+++ b/docs/api/v04/plate.md
@@ -0,0 +1 @@
+::: pydantic_ome_ngff.v04.plate
\ No newline at end of file
diff --git a/docs/api/v04/transform.md b/docs/api/v04/transform.md
new file mode 100644
index 0000000..5aa4b1e
--- /dev/null
+++ b/docs/api/v04/transform.md
@@ -0,0 +1 @@
+::: pydantic_ome_ngff.v04.transform
\ No newline at end of file
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000..80dbce8
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,487 @@
+# pydantic-ome-ngff
+
+[Pydantic](https://docs.pydantic.dev/latest/) models for [OME-NGFF](https://ngff.openmicroscopy.org/)
+
+# About
+
+This library aims to model the metadata objects and Zarr hierarchies described in the [OME-NGFF](https://ngff.openmicroscopy.org/) specifications, with an emphasis on the core multiscale metadata. 
+
+You can use this library to:
+
+- Read existing OME-NGFF data
+- Create your own OME-NGFF data  
+
+See the [reading](#reading-a-multiscale-group) and [writing](#creating-a-multiscale-group) examples for basic usage. 
+
+The base Pydantic models for Zarr groups and arrays used in this library are defined in [`pydantic-zarr`](https://janelia-cellmap.github.io/pydantic-zarr/)
+
+## Limitations
+
+### Supported versions
+
+Version 0.4 of OME-NGFF has pretty extensive support, although my focus has been on getting the `Multiscales` metadata right; I don't use `well` or `plate` metadata, so it's highly likely that I have missed something there. I have not put a lot of effort into supporting `0.5-dev`, as it's not clear when that version will be released, or even what will be in it, but contributions to rectify this are welcome. If you find something that I didn't implement correctly, please [open an issue](https://github.com/JaneliaSciComp/pydantic-ome-ngff/issues).
+
+### Array data
+
+This library only models the *structure* of a Zarr hierarchy, i.e. the layout of Zarr groups and arrays, and their metadata; it provides no functionality for efficiently reading or writing data from Zarr arrays. Use [`zarr-python`](https://github.com/zarr-developers/zarr-python) or [`tensorstore`](https://google.github.io/tensorstore/) for getting data in and out of Zarr arrays.
+
+# Examples
+
+## Reading a Multiscale group
+
+This example demonstrates how to use the [`Group`](./api/v04/multiscale.md#Group) class defined in [`pydantic_ome_ngff.v04.multiscale`](./api/v04/multiscale.md) to model a multiscale group from cloud storage.
+
+```python
+from pydantic_ome_ngff.v04.multiscale import Group
+import zarr
+# example data served over http
+url = "https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr"
+
+# open the Zarr group
+zgroup = zarr.open(url)
+
+# group_model is a `GroupSpec`, i.e. a Pydantic model of a Zarr group
+group_model = Group.from_zarr(zgroup)
+
+# it has an `attributes` attribute, which in turn has a `multiscales` attribute 
+# which models the OME-NGFF multiscales metadata
+multi_meta = group_model.attributes.multiscales
+print(multi_meta)
+"""
+(
+    MultiscaleMetadata(
+        version='0.4',
+        name=None,
+        type=None,
+        metadata=None,
+        datasets=(
+            Dataset(
+                path='0',
+                coordinateTransformations=(
+                    VectorScale(
+                        type='scale',
+                        scale=(
+                            1.0,
+                            0.5002025531914894,
+                            0.3603981534640209,
+                            0.3603981534640209,
+                        ),
+                    ),
+                ),
+            ),
+            Dataset(
+                path='1',
+                coordinateTransformations=(
+                    VectorScale(
+                        type='scale',
+                        scale=(
+                            1.0,
+                            0.5002025531914894,
+                            0.7207963069280418,
+                            0.7207963069280418,
+                        ),
+                    ),
+                ),
+            ),
+            Dataset(
+                path='2',
+                coordinateTransformations=(
+                    VectorScale(
+                        type='scale',
+                        scale=(
+                            1.0,
+                            0.5002025531914894,
+                            1.4415926138560835,
+                            1.4415926138560835,
+                        ),
+                    ),
+                ),
+            ),
+        ),
+        axes=(
+            Axis(name='c', type='channel', unit=None),
+            Axis(name='z', type='space', unit='micrometer'),
+            Axis(name='y', type='space', unit='micrometer'),
+            Axis(name='x', type='space', unit='micrometer'),
+        ),
+        coordinateTransformations=None,
+    ),
+)
+"""
+
+# to get the Zarr arrays referenced by the multiscale metadata, we access them by name from the Zarr group.
+arrays = [zgroup[d.path] for d in multi_meta[0].datasets]
+print(arrays)
+"""
+[<zarr.core.Array '/0' (2, 236, 275, 271) uint16>, <zarr.core.Array '/1' (2, 236, 137, 135) uint16>, <zarr.core.Array '/2' (2, 236, 68, 67) uint16>]
+"""
+```
+
+## Creating a multiscale group from arrays
+
+`pydantic-ome-ngff` provides a direct way to create multiscale metadata from a collection of arrays accompanied by spatial metadata. Note that the data in these arrays will not be accessed -- the arrays are used to create models of Zarr arrays, and so their `shape` and `dtype` attributes are necessary, and other attributes (like `chunks`, if present), can be used to template the resulting Zarr arrays.
+
+The basic workflow is as follows:
+
+ 1. Use in-memory numpy or dask arrays and spatial metadata to instantiate a model of the OME-NGFF multiscale Zarr group we want to create. This model contains attributes and models of Zarr arrays, but no array data (which keeps the model lightweight). 
+ 2. Serialize the model to a storage backend, which will create the Zarr groups and arrays defined by the model, along with their metadata. 
+3. Write array data to the newly created Zarr arrays, using a method that suits your application. 
+
+```python
+from pydantic_ome_ngff.v04.multiscale import Group
+from pydantic_ome_ngff.v04.axis import Axis
+import numpy as np
+import zarr
+
+# define the axes
+axes = [
+    Axis(name='t', unit='second', type='time'),
+    Axis(name='z', unit='nanometer', type='space'),
+    Axis(name='y', unit='nanometer', type='space'),
+    Axis(name='x', unit='nanometer', type='space')
+]
+
+ndim = len(axes)
+
+# the chunk size we want to use for our Zarr arrays
+store_chunks = (1, 2, 2, 2)
+
+# simulate a multiscale pyramid
+shapes = (10,) * ndim, (5,) * ndim
+arrays = [np.zeros(s) for s in shapes]
+
+# arrays will be named s0, s1, etc
+paths = [f's{idx}' for idx in range(len(shapes))]
+
+# downsampling by 2 in each axis
+scales = [
+    [1.0, 2.0, 2.0, 2.0],
+    [2.0, 4.0, 4.0, 4.0],
+]
+
+# s0 is at the origin; s1 is at the offset induced by downsampling
+translations = [
+    [0.0, 0.0, 0.0, 0.0],
+    [0.5, 1.0, 1.0, 1.0]
+]
+
+# this is now a complete model of the Zarr group
+group_model = Group.from_arrays(
+    axes=axes,
+    paths=paths,
+    arrays=arrays,
+    scales=scales,
+    translations=translations,
+    chunks=store_chunks,
+    compressor=None)
+
+print(group_model.model_dump())
+"""
+{
+    'zarr_version': 2,
+    'attributes': {
+        'multiscales': (
+            {
+                'version': '0.4',
+                'name': None,
+                'type': None,
+                'metadata': None,
+                'datasets': (
+                    {
+                        'path': 's0',
+                        'coordinateTransformations': (
+                            {'type': 'scale', 'scale': (1.0, 2.0, 2.0, 2.0)},
+                            {
+                                'type': 'translation',
+                                'translation': (0.0, 0.0, 0.0, 0.0),
+                            },
+                        ),
+                    },
+                    {
+                        'path': 's1',
+                        'coordinateTransformations': (
+                            {'type': 'scale', 'scale': (2.0, 4.0, 4.0, 4.0)},
+                            {
+                                'type': 'translation',
+                                'translation': (0.5, 1.0, 1.0, 1.0),
+                            },
+                        ),
+                    },
+                ),
+                'axes': (
+                    {'name': 't', 'type': 'time', 'unit': 'second'},
+                    {'name': 'z', 'type': 'space', 'unit': 'nanometer'},
+                    {'name': 'y', 'type': 'space', 'unit': 'nanometer'},
+                    {'name': 'x', 'type': 'space', 'unit': 'nanometer'},
+                ),
+                'coordinateTransformations': None,
+            },
+        )
+    },
+    'members': {
+        's0': {
+            'zarr_version': 2,
+            'attributes': {},
+            'shape': (10, 10, 10, 10),
+            'chunks': (1, 2, 2, 2),
+            'dtype': '<f8',
+            'fill_value': 0,
+            'order': 'C',
+            'filters': None,
+            'dimension_separator': '/',
+            'compressor': None,
+        },
+        's1': {
+            'zarr_version': 2,
+            'attributes': {},
+            'shape': (5, 5, 5, 5),
+            'chunks': (1, 2, 2, 2),
+            'dtype': '<f8',
+            'fill_value': 0,
+            'order': 'C',
+            'filters': None,
+            'dimension_separator': '/',
+            'compressor': None,
+        },
+    },
+}
+"""
+
+# to do something useful with this model, we have to serialize it to storage
+# we make an in-memory zarr store for demo purposes
+# with real data, you would use `zarr.storage.DirectoryStore` or `zarr.storage.FSStore`
+store = zarr.MemoryStore()
+stored_group = group_model.to_zarr(store, path='foo')
+
+# check that the expected arrays are present
+print(stored_group.tree())
+"""
+foo
+ ├── s0 (10, 10, 10, 10) float64
+ └── s1 (5, 5, 5, 5) float64
+"""
+
+# NOTE:
+# no data has been written to these arrays, you must do that separately.
+# e.g., stored_group[s0] = arrays[0]
+
+# check that the expected attributes are present
+print(stored_group.attrs.asdict())
+"""
+{
+    'multiscales': (
+        {
+            'version': '0.4',
+            'name': None,
+            'type': None,
+            'metadata': None,
+            'datasets': (
+                {
+                    'path': 's0',
+                    'coordinateTransformations': (
+                        {'type': 'scale', 'scale': (1.0, 2.0, 2.0, 2.0)},
+                        {'type': 'translation', 'translation': (0.0, 0.0, 0.0, 0.0)},
+                    ),
+                },
+                {
+                    'path': 's1',
+                    'coordinateTransformations': (
+                        {'type': 'scale', 'scale': (2.0, 4.0, 4.0, 4.0)},
+                        {'type': 'translation', 'translation': (0.5, 1.0, 1.0, 1.0)},
+                    ),
+                },
+            ),
+            'axes': (
+                {'name': 't', 'type': 'time', 'unit': 'second'},
+                {'name': 'z', 'type': 'space', 'unit': 'nanometer'},
+                {'name': 'y', 'type': 'space', 'unit': 'nanometer'},
+                {'name': 'x', 'type': 'space', 'unit': 'nanometer'},
+            ),
+            'coordinateTransformations': None,
+        },
+    )
+}
+"""
+```
+
+## Creating a multiscale group directly
+
+It's also possible to create a multiscale group without using the `from_arrays` method demonstrated in the previous example, but it's a bit more involved.
+
+```python
+from pydantic_zarr.v2 import ArraySpec
+from pydantic_ome_ngff.v04.multiscale import Group, MultiscaleMetadata, create_dataset, GroupAttrs
+from pydantic_ome_ngff.v04.axis import Axis
+import numpy as np
+import zarr
+
+# define the axes
+axes = [
+    Axis(name='t', unit='second', type='time'),
+    Axis(name='z', unit='nanometer', type='space'),
+    Axis(name='y', unit='nanometer', type='space'),
+    Axis(name='x', unit='nanometer', type='space')
+]
+
+ndim = len(axes)
+
+# the chunk size we want to use for our Zarr arrays
+store_chunks = (1, 2, 2, 2)
+
+# simulate a multiscale pyramid
+shapes = (10,) * ndim, (5,) * ndim
+dtypes = ('uint8', 'uint8')
+
+# arrays will be named s0, s1, etc
+paths = [f's{idx}' for idx in range(len(shapes))]
+
+# downsampling by 2 in each axis
+scales = [
+    [1.0, 2.0, 2.0, 2.0],
+    [2.0, 4.0, 4.0, 4.0],
+]
+
+# s0 is at the origin; s1 is at the offset induced by downsampling
+translations = [
+    [0.0, 0.0, 0.0, 0.0],
+    [0.5, 1.0, 1.0, 1.0]
+]
+
+datasets = tuple(create_dataset(p, s, t) for p, s, t in zip(paths, scales, translations))
+
+multiscales = MultiscaleMetadata(
+    datasets=datasets,
+    axes=axes)
+
+attributes = GroupAttrs(multiscales=(multiscales,))
+members = {p: ArraySpec(shape=s, dtype=d, chunks=store_chunks) for p,s,d in zip(paths, shapes, dtypes)}
+
+group = Group(attributes=attributes, members = members)
+
+print(group.model_dump())
+"""
+{
+    'zarr_version': 2,
+    'attributes': {
+        'multiscales': (
+            {
+                'version': '0.4',
+                'name': None,
+                'type': None,
+                'metadata': None,
+                'datasets': (
+                    {
+                        'path': 's0',
+                        'coordinateTransformations': (
+                            {'type': 'scale', 'scale': (1.0, 2.0, 2.0, 2.0)},
+                            {
+                                'type': 'translation',
+                                'translation': (0.0, 0.0, 0.0, 0.0),
+                            },
+                        ),
+                    },
+                    {
+                        'path': 's1',
+                        'coordinateTransformations': (
+                            {'type': 'scale', 'scale': (2.0, 4.0, 4.0, 4.0)},
+                            {
+                                'type': 'translation',
+                                'translation': (0.5, 1.0, 1.0, 1.0),
+                            },
+                        ),
+                    },
+                ),
+                'axes': (
+                    {'name': 't', 'type': 'time', 'unit': 'second'},
+                    {'name': 'z', 'type': 'space', 'unit': 'nanometer'},
+                    {'name': 'y', 'type': 'space', 'unit': 'nanometer'},
+                    {'name': 'x', 'type': 'space', 'unit': 'nanometer'},
+                ),
+                'coordinateTransformations': None,
+            },
+        )
+    },
+    'members': {
+        's0': {
+            'zarr_version': 2,
+            'attributes': {},
+            'shape': (10, 10, 10, 10),
+            'chunks': (1, 2, 2, 2),
+            'dtype': '|u1',
+            'fill_value': 0,
+            'order': 'C',
+            'filters': None,
+            'dimension_separator': '/',
+            'compressor': None,
+        },
+        's1': {
+            'zarr_version': 2,
+            'attributes': {},
+            'shape': (5, 5, 5, 5),
+            'chunks': (1, 2, 2, 2),
+            'dtype': '|u1',
+            'fill_value': 0,
+            'order': 'C',
+            'filters': None,
+            'dimension_separator': '/',
+            'compressor': None,
+        },
+    },
+}
+"""
+```
+
+## Data validation
+
+This library attempts to detect invalid OME-NGFF containers and provide useful error messages when something is broken. The following examples illustrate a few ways in which OME-NGFF metadata can be broken, and what the error messages look like.
+
+```python
+from pydantic import ValidationError
+from pydantic_zarr.v2 import ArraySpec
+from pydantic_ome_ngff.v04.multiscale import Group
+from pydantic_ome_ngff.v04.axis import Axis
+import numpy as np
+
+arrays = np.zeros((10,10)), np.zeros((5,5))
+scales = ((1,1), (2,2))
+translations = ((0,0), (0.5, 0.5))
+paths = ('s0','s1')
+axes = (
+    Axis(name='y', unit='nanometer', type='space'),
+    Axis(name='x', unit='nanometer', type='space')
+)
+
+# create a valid multiscale group
+group_model = Group.from_arrays(arrays, paths=paths, axes=axes, scales=scales, translations=translations)
+
+# convert that group to a dictionary, so we can break it
+group_model_missing_array = group_model.model_dump()
+
+# remove one of the arrays. this invalidates the multiscale metadata
+group_model_missing_array['members'].pop('s0')
+
+try:
+    Group(**group_model_missing_array)
+except ValidationError as e:
+    print(e)
+    """
+    1 validation error for Group
+      Value error, Dataset s0 was specified in multiscale metadata, but no array with that name was found in the hierarchy. All arrays referenced in multiscale metadata must be contained in the group. [type=value_error, input_value={'zarr_version': 2, 'attr...: 'zstd', 'level': 3}}}}, input_type=dict]
+        For further information visit https://errors.pydantic.dev/2.6/v/value_error
+    """
+
+group_model_wrong_array = group_model.model_dump()
+
+# insert an array with incorrect dimensionality
+group_model_wrong_array['members']['s0'] = ArraySpec.from_array(np.arange(10)).model_dump()
+
+try:
+    Group(**group_model_wrong_array)
+except ValidationError as e:
+    print(e)
+    """
+    1 validation error for Group
+      Value error, Transform type='scale' scale=(1, 1) has dimensionality 2, which does not match the dimensionality of the array found in this group at s0 (1). Transform dimensionality must match array dimensionality. [type=value_error, input_value={'zarr_version': 2, 'attr...: 'zstd', 'level': 3}}}}, input_type=dict]
+        For further information visit https://errors.pydantic.dev/2.6/v/value_error
+    """
+```
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000..3b3dd04
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,65 @@
+site_name: pydantic-ome-ngff
+site_url: https://janeliascicomp.github.io/pydantic-ome-ngff/
+site_author: Davis Bennett
+site_description: >-
+  Pydantic models for the OME-NGFF file format.
+
+# Repository
+repo_name: janelia-cellmap/pydantic-ome-ngff
+repo_url: https://github.com/janeliascicomp/pydantic-ome-ngff
+
+# Copyright
+copyright: Copyright &copy; 2016 - 2023 HHMI / Janelia
+
+theme:
+  features:
+    - navigation.expand
+  name: material
+  palette: 
+  # Palette toggle for light mode
+  - scheme: default
+    toggle:
+      icon: material/brightness-7 
+      name: Switch to dark mode
+
+  # Palette toggle for dark mode
+  - scheme: slate
+    toggle:
+      icon: material/brightness-4
+      name: Switch to light mode
+
+nav:
+    - About: index.md
+    - API:
+      - v04:
+        - api/v04/multiscale.md
+        - api/v04/axis.md
+        - api/v04/transform.md
+        - api/v04/label.md
+        - api/v04/plate.md
+
+plugins:
+- mkdocstrings:
+    handlers:
+        python:
+          options:
+            docstring_style: numpy
+            members_order: source
+            separate_signature: true
+            filters: ["!^_"]
+            docstring_options:
+              ignore_init_summary: true
+            merge_init_into_class: true
+
+markdown_extensions:
+  - pymdownx.highlight:
+      anchor_linenums: true
+      line_spans: __span
+      pygments_lang_class: true
+  - pymdownx.inlinehilite
+  - pymdownx.snippets
+  - pymdownx.superfences
+  - toc:
+      baselevel: 2
+      toc_depth: 4
+      permalink: "#"
diff --git a/poetry.lock b/poetry.lock
index b11fe59..bba67bb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,138 +1,365 @@
-# This file is automatically @generated by Poetry 1.4.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
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+s3 = ["s3fs"]
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+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+tqdm = ["tqdm"]
+
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+
+[[package]]
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+description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
+optional = false
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 [[package]]
 name = "identify"
-version = "2.5.26"
+version = "2.5.35"
 description = "File identification library for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -276,21 +671,38 @@ license = ["ukkonen"]
 
 [[package]]
 name = "idna"
-version = "3.4"
+version = "3.6"
 description = "Internationalized Domain Names in Applications (IDNA)"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
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-    {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
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+    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
+]
+
+[[package]]
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+version = "7.1.0"
+description = "Read metadata from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
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 ]
 
+[package.dependencies]
+zipp = ">=0.5"
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+perf = ["ipython"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -300,32 +712,44 @@ files = [
 
 [[package]]
 name = "isort"
-version = "5.12.0"
+version = "5.13.2"
 description = "A Python utility / library to sort Python imports."
-category = "dev"
 optional = false
 python-versions = ">=3.8.0"
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@@ -340,53 +764,415 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
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 [package.dependencies]
@@ -397,14 +1183,13 @@ typing-extensions = ">=4.1.0"
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
 install-types = ["pip"]
-python2 = ["typed-ast (>=1.4.0,<2)"]
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 reports = ["lxml"]
 
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 version = "1.0.0"
 description = "Type system extensions for programs checked with the mypy type checker."
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 optional = false
 python-versions = ">=3.5"
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 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
 files = [
@@ -427,44 +1211,147 @@ files = [
 [package.dependencies]
 setuptools = "*"
 
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-
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-dotenv = ["python-dotenv (>=0.10.4)"]
-email = ["email-validator (>=1.0.3)"]
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+windows-terminal = ["colorama (>=0.4.6)"]
+
+[[package]]
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+description = "Extension pack for Python Markdown."
+optional = false
+python-versions = ">=3.8"
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+[package.dependencies]
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+pyyaml = "*"
+
+[package.extras]
+extra = ["pygments (>=2.12)"]
 
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-version = "7.4.0"
+version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
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 [package.dependencies]
@@ -570,7 +1562,6 @@ testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "no
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 version = "4.1.0"
 description = "Pytest plugin for measuring coverage."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -585,11 +1576,40 @@ pytest = ">=4.6"
 [package.extras]
 testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
 
+[[package]]
+name = "pytest-examples"
+version = "0.0.9"
+description = "Pytest plugin for testing examples in docstrings and markdown files."
+optional = false
+python-versions = ">=3.7"
+files = [
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+
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+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
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 description = "YAML parser and emitter for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
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+[[package]]
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+description = "A custom YAML tag for referencing environment variables in YAML files. "
+optional = false
+python-versions = ">=3.6"
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 [[package]]
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 python-versions = ">=3.8"
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 optional = false
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 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
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+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8397a3817d7dcdd14bb266283cd1d6fc7264a48c186b986f32e86d86d35fbac5"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e0381b4ce23ff92f8170080c97678040fc5b08da85e9e292292aba67fdac6c34"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:23d32a2594cb5d565d358a92e151315d1b2268bc10f4610d098f96b147370136"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ddb2a5c08a4eaaba605340fdee8fc08e406c56617566d9643ad8bf6852778fc7"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:26a1dc6285e03f3cc9e839a2da83bcbf31dcb0d004c72d0730e755b33466c30e"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:18580f672e44ce1238b82f7fb87d727c4a131f3a9d33a5e0e82b793362bf18b4"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:29e0f83f37610f173eb7e7b5562dd71467993495e568e708d99e9d1944f561ec"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:1f23e4fe1e8794f74b6027d7cf19dc25f8b63af1483d91d595d4a07eca1fb26c"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:db8e58b9d79200c76956cefd14d5c90af54416ff5353c5bfd7cbe58818e26ef0"},
+    {file = "yarl-1.9.4-cp39-cp39-win32.whl", hash = "sha256:c7224cab95645c7ab53791022ae77a4509472613e839dab722a72abe5a684575"},
+    {file = "yarl-1.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:824d6c50492add5da9374875ce72db7a0733b29c2394890aef23d533106e2b15"},
+    {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
+[[package]]
+name = "zarr"
+version = "2.17.1"
+description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "zarr-2.17.1-py3-none-any.whl", hash = "sha256:e25df2741a6e92645f3890f30f3136d5b57a0f8f831094b024bbcab5f2797bc7"},
+    {file = "zarr-2.17.1.tar.gz", hash = "sha256:564b3aa072122546fe69a0fa21736f466b20fad41754334b62619f088ce46261"},
+]
+
+[package.dependencies]
+asciitree = "*"
+fasteners = {version = "*", markers = "sys_platform != \"emscripten\""}
+numcodecs = ">=0.10.0"
+numpy = ">=1.21.1"
+
+[package.extras]
+docs = ["numcodecs[msgpack]", "numpydoc", "pydata-sphinx-theme", "sphinx", "sphinx-automodapi", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
+jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
+
+[[package]]
+name = "zipp"
+version = "3.18.1"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
+    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9"
-content-hash = "8f9c860337789e81ded8ae4a32435c60b9be73f28993b839a0c8bf304b4af637"
+content-hash = "2675a477b55c6cd7ada8aeae2a51d11d86d2758644d83321fa248d3f3a2f163c"
diff --git a/pyproject.toml b/pyproject.toml
index fa9b46b..b241c32 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,8 +9,8 @@ packages = [{include = "pydantic_ome_ngff", from="src"}]
 
 [tool.poetry.dependencies]
 python = "^3.9"
-pydantic = "^1.10.4"
-
+pydantic-zarr = "0.7.0"
+rich = "^13.6.0"
 
 [tool.poetry.group.dev.dependencies]
 pre-commit = "^3.0.4"
@@ -20,6 +20,13 @@ pytest = "^7.2.1"
 mypy = "^1.0.1"
 requests = "^2.28.2"
 pytest-cov = "^4.0.0"
+fsspec = {version = "^2024.2.0", extras=["http"]}
+
+
+[tool.poetry.group.docs.dependencies]
+mkdocs-material = "^9.5.5"
+mkdocstrings = {extras = ["python"], version = "^0.24.0"}
+pytest-examples = "^0.0.9"
 
 [build-system]
 requires = ["poetry-core"]
@@ -44,4 +51,32 @@ disallow_untyped_defs = true
 init_forbid_extra = true
 init_typed = true
 warn_required_dynamic_aliases = true
-warn_untyped_fields = true
\ No newline at end of file
+warn_untyped_fields = true
+
+
+[tool.coverage.run]
+source = ['src']
+branch = true
+context = '${CONTEXT}'
+
+[tool.coverage.report]
+precision = 2
+exclude_lines = [
+    'pragma: no cover',
+    'raise NotImplementedError',
+    'if TYPE_CHECKING:',
+    'if typing.TYPE_CHECKING:',
+    '@overload',
+    '@typing.overload',
+    '\(Protocol\):$',
+    'typing.assert_never',
+    'assert_never',
+]
+
+[tool.coverage.paths]
+source = [
+    'src/'
+]
+
+[tool.coverage.html]
+directory = "htmlcov"
diff --git a/src/pydantic_ome_ngff/__about__.py b/src/pydantic_ome_ngff/__about__.py
new file mode 100644
index 0000000..9068a16
--- /dev/null
+++ b/src/pydantic_ome_ngff/__about__.py
@@ -0,0 +1,4 @@
+# SPDX-FileCopyrightText: 2023-present Davis Vann Bennett <davis.v.bennett@gmail.com>
+#
+# SPDX-License-Identifier: MIT
+__version__ = "0.3.0"
diff --git a/src/pydantic_ome_ngff/base.py b/src/pydantic_ome_ngff/base.py
index bc4ac4a..acb4010 100644
--- a/src/pydantic_ome_ngff/base.py
+++ b/src/pydantic_ome_ngff/base.py
@@ -1,14 +1,11 @@
-from pydantic import BaseModel, Extra
+from pydantic import BaseModel
 
 
-class StrictBase(BaseModel):
+class StrictBase(BaseModel, extra="forbid"):
     """
     A pydantic basemodel that refuses extra fields.
     """
 
-    class Config:
-        extra = Extra.forbid
-
 
 class VersionedBase(BaseModel):
     """
@@ -22,5 +19,3 @@ class StrictVersionedBase(VersionedBase, StrictBase):
     """
     An internally versioned pydantic basemodel that refuses extra fields.
     """
-
-    ...
diff --git a/src/pydantic_ome_ngff/latest/__init__.py b/src/pydantic_ome_ngff/latest/__init__.py
index aa98389..1fe6284 100644
--- a/src/pydantic_ome_ngff/latest/__init__.py
+++ b/src/pydantic_ome_ngff/latest/__init__.py
@@ -1,6 +1,6 @@
-from pydantic_ome_ngff.latest.axes import Axis  # noqa
-from pydantic_ome_ngff.latest.multiscales import Multiscale  # noqa
-from pydantic_ome_ngff.latest.imageLabel import ImageLabel  # noqa
-from pydantic_ome_ngff.latest.well import Well  # noqa
-from pydantic_ome_ngff.latest.plate import Plate  # noqa
+from pydantic_ome_ngff.latest.axis import Axis  # noqa
+from pydantic_ome_ngff.latest.multiscale import MultiscaleMetadata  # noqa
+from pydantic_ome_ngff.latest.label import ImageLabel  # noqa
+from pydantic_ome_ngff.latest.well import WellMeta  # noqa
+from pydantic_ome_ngff.latest.plate import PlateMeta  # noqa
 from pydantic_ome_ngff.latest.base import version  # noqa
diff --git a/src/pydantic_ome_ngff/latest/axes.py b/src/pydantic_ome_ngff/latest/axes.py
deleted file mode 100644
index 15d4a3c..0000000
--- a/src/pydantic_ome_ngff/latest/axes.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from __future__ import annotations
-from pydantic_ome_ngff.latest.base import version
-import pydantic_ome_ngff.v04.axes as axv04
-
-
-class Axis(axv04.Axis):
-    """
-    Axis metadata.
-    See https://ngff.openmicroscopy.org/latest/#axes-md
-    """
-
-    _version = version
diff --git a/src/pydantic_ome_ngff/latest/axis.py b/src/pydantic_ome_ngff/latest/axis.py
new file mode 100644
index 0000000..f44723a
--- /dev/null
+++ b/src/pydantic_ome_ngff/latest/axis.py
@@ -0,0 +1,12 @@
+import pydantic_ome_ngff.v04.axis as axisV04
+
+AxisType = axisV04.AxisType
+
+
+class Axis(axisV04.Axis):
+    """
+    Axis metadata.
+    See https://ngff.openmicroscopy.org/latest/#axes-md
+
+    Note: this metadata is unchanged from version 0.4 of the spec.
+    """
diff --git a/src/pydantic_ome_ngff/latest/base.py b/src/pydantic_ome_ngff/latest/base.py
index 17d27e7..7998d64 100644
--- a/src/pydantic_ome_ngff/latest/base.py
+++ b/src/pydantic_ome_ngff/latest/base.py
@@ -1 +1,7 @@
-version = "0.5-dev"
+from __future__ import annotations
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Literal
+
+version: Literal["0.5-dev"] = "0.5-dev"
diff --git a/src/pydantic_ome_ngff/latest/coordinateTransformations.py b/src/pydantic_ome_ngff/latest/coordinateTransformations.py
deleted file mode 100644
index ee0203a..0000000
--- a/src/pydantic_ome_ngff/latest/coordinateTransformations.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from __future__ import annotations
-import textwrap
-from typing import Union
-import pydantic_ome_ngff.v04.coordinateTransformations as ctx
-
-
-class IdentityTransform(ctx.IdentityTransform):
-    """
-    An identity transform with no parameters.
-    See https://ngff.openmicroscopy.org/latest/#trafo-md
-    """
-
-
-class PathTransform(ctx.PathTransform):
-    """ "
-    A coordinateTransform with at "path" field.
-    See https://ngff.openmicroscopy.org/latest/#trafo-md
-    """
-
-
-class VectorTranslationTransform(ctx.VectorTranslationTransform):
-    """
-    A translation transform with a `translate` field that is a vector.
-    See https://ngff.openmicroscopy.org/latest/#trafo-md
-    """
-
-
-class VectorScaleTransform(ctx.VectorScaleTransform):
-    """
-    A scale transform with a `scale` field that is a vector.
-    See https://ngff.openmicroscopy.org/latest/#trafo-md
-    """
-
-
-def get_transform_ndim(
-    transform: Union[VectorScaleTransform, VectorTranslationTransform],
-) -> int:
-    """
-    Get the dimensionality of a vector transform (scale or translation).
-    """
-    if transform.type == "scale" and hasattr(transform, "scale"):
-        return len(transform.scale)
-    elif transform.type == "translation" and hasattr(transform, "translation"):
-        return len(transform.translation)
-    else:
-        msg = (
-            f"Transform must be either VectorScaleTransform or "
-            f"VectorTranslationTransform. Got {type(transform)} instead."
-        )
-
-        raise ValueError(textwrap.fill(msg))
-
-
-ScaleTransform = Union[VectorScaleTransform, PathTransform]
-TranslationTransform = Union[VectorTranslationTransform, PathTransform]
-CoordinateTransform = Union[ScaleTransform, TranslationTransform, IdentityTransform]
diff --git a/src/pydantic_ome_ngff/latest/imageLabel.py b/src/pydantic_ome_ngff/latest/label.py
similarity index 72%
rename from src/pydantic_ome_ngff/latest/imageLabel.py
rename to src/pydantic_ome_ngff/latest/label.py
index c1f78d3..0f8212f 100644
--- a/src/pydantic_ome_ngff/latest/imageLabel.py
+++ b/src/pydantic_ome_ngff/latest/label.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
-from typing import Optional
-from pydantic_ome_ngff.v04.imageLabel import ImageLabel as ImageLabelV04
+
 from pydantic_ome_ngff.latest.base import version
+from pydantic_ome_ngff.v04.label import ImageLabel as ImageLabelV04
 
 
 class ImageLabel(ImageLabelV04):
@@ -13,4 +13,4 @@ class ImageLabel(ImageLabelV04):
     # we need to put the version here as a private class attribute because the version
     # is not required by the spec...
     _version = version
-    version: Optional[str] = version
+    version: str | None = version
diff --git a/src/pydantic_ome_ngff/latest/multiscale.py b/src/pydantic_ome_ngff/latest/multiscale.py
new file mode 100644
index 0000000..109c031
--- /dev/null
+++ b/src/pydantic_ome_ngff/latest/multiscale.py
@@ -0,0 +1,56 @@
+from __future__ import annotations
+from typing import Literal
+from pydantic_ome_ngff.latest.base import version
+import pydantic_ome_ngff.v04.multiscale as msv04
+
+
+class Dataset(msv04.Dataset):
+    """
+    A single entry in the multiscales.datasets list.
+    See https://ngff.openmicroscopy.org/latest/#multiscale-md
+
+    Note that this class is unchanged from the v04 counterpart.
+
+    Attributes:
+    ----------
+    path: str
+        The path to the Zarr array that stores the image described by this metadata.
+        This path should be relative to the group that contains this metadata.
+    coordinateTransformations: ctx.ScaleTransform | ctx.TranslationTransform
+        The coordinate transformations for this image.
+    """
+
+    ...
+
+
+class MultiscaleMetadata(msv04.MultiscaleMetadata):
+    """
+    Multiscale image metadata.
+    See https://ngff.openmicroscopy.org/latest/#multiscale-md
+    """
+
+    _version = version
+    version: Literal["0.5-dev"] = version
+
+
+class GroupAttrs(msv04.GroupAttrs):
+    """
+    Attributes of a multiscale group.
+    See https://ngff.openmicroscopy.org/latest/#multiscale-md
+    """
+
+
+class Group(msv04.Group):
+    """
+    A model of a Zarr group that implements OME-NGFF Multiscales metadata, version 0.5-dev.
+
+    Attributes
+    ----------
+
+    attributes: MultiscaleAttrs
+        The attributes of this Zarr group, which should contain valid `MultiscaleAttrs`. Extra
+        values are allowed.
+    members Dict[Str, ArraySpec | GroupSpec]:
+        The members of this Zarr group. Should be instances of `pydantic_zarr.GroupSpec` or `pydantic_zarr.ArraySpec`.
+
+    """
diff --git a/src/pydantic_ome_ngff/latest/multiscales.py b/src/pydantic_ome_ngff/latest/multiscales.py
deleted file mode 100644
index d287218..0000000
--- a/src/pydantic_ome_ngff/latest/multiscales.py
+++ /dev/null
@@ -1,223 +0,0 @@
-from __future__ import annotations
-from collections import Counter
-import textwrap
-from typing import Any, Dict, List, Optional, Tuple, Union, cast
-import warnings
-
-from pydantic import BaseModel, conlist, root_validator, validator
-from pydantic_ome_ngff.base import VersionedBase
-from pydantic_ome_ngff.latest.base import version
-from pydantic_ome_ngff.latest import coordinateTransformations as ctx
-from pydantic_ome_ngff.tree import Array, Attrs, Group
-from pydantic_ome_ngff.utils import duplicates
-from pydantic_ome_ngff.v04.axes import AxisType
-from pydantic_ome_ngff.latest.axes import Axis
-
-
-class MultiscaleDataset(BaseModel):
-    path: str
-    coordinateTransformations: conlist(
-        Union[ctx.ScaleTransform, ctx.TranslationTransform], min_items=1, max_items=2
-    )
-
-    @validator("coordinateTransformations")
-    def check_transforms_dimensionality(
-        cls,
-        transforms: List[
-            Union[ctx.VectorScaleTransform, ctx.VectorTranslationTransform]
-        ],
-    ) -> List[Union[ctx.VectorScaleTransform, ctx.VectorTranslationTransform]]:
-        ndims = []
-        for tx in transforms:
-            # this repeated conditional logic around transforms is so awful.
-            if type(tx) in (ctx.VectorScaleTransform, ctx.VectorTranslationTransform):
-                ndims.append(ctx.get_transform_ndim(tx))
-        if len(set(ndims)) > 1:
-            msg = (
-                "Elements of coordinateTransformations must have the same "
-                f"dimensionality. Got elements with dimensionality = {ndims}."
-            )
-            raise ValueError(textwrap.fill(msg))
-        return transforms
-
-    @validator("coordinateTransformations")
-    def check_transforms_types(
-        cls, transforms: List[ctx.CoordinateTransform]
-    ) -> List[ctx.CoordinateTransform]:
-        if (tform := transforms[0].type) != "scale":
-            msg = (
-                "The first element of coordinateTransformations must be a scale "
-                f"transform. Got {tform} instead."
-            )
-            raise ValueError(textwrap.fill(msg))
-        if len(transforms) == 2:
-            if (tform := transforms[1].type) != "translation":
-                msg = (
-                    "The second element of coordinateTransformations must be a "
-                    f"translation transform. Got {tform} instead."
-                )
-                raise ValueError(textwrap.fill(msg))
-        return transforms
-
-
-class Multiscale(VersionedBase):
-    """
-    Multiscale image metadata.
-    See https://ngff.openmicroscopy.org/latest/#multiscale-md
-    """
-
-    # we need to put the version here as a private class attribute because the version
-    # is not required by the spec...
-    _version = version
-
-    version: str = version
-    # SPEC: why is this nullable instead of reserving the empty string
-    # SPEC: untyped!
-    name: Optional[Any]
-    # SPEC: not clear what this field is for, given the existence of .metadata
-    # SPEC: untyped!
-    type: Optional[Any]
-    # SPEC: should default to empty dict instead of None
-    metadata: Optional[Dict[str, Any]] = None
-    datasets: List[MultiscaleDataset]
-    # SPEC: should not exist at top level and instead
-    # live in dataset metadata or in .datasets
-    axes: conlist(Axis, min_items=2, max_items=5)
-    # SPEC: should not live here, and if it is here,
-    # it should default to an empty list instead of being nullable
-    coordinateTransformations: Optional[
-        List[Union[ctx.ScaleTransform, ctx.TranslationTransform]]
-    ]
-
-    @validator("name")
-    def check_name(cls, name: Optional[str]) -> Optional[str]:
-        if name is None:
-            msg = (
-                f"The name field was set to `None`. Version {cls._version} of the "
-                "OME-NGFF spec states that the `name` field of a Multiscales object "
-                "should not be None."
-            )
-            warnings.warn(textwrap.fill(msg))
-        return name
-
-    @validator("axes")
-    def check_axes(cls, axes: List[Axis]) -> List[Axis]:
-        name_dupes = duplicates(a.name for a in axes)
-        if len(name_dupes) > 0:
-            msg = (
-                f"Axis names must be unique. Axis names {tuple(name_dupes.keys())} "
-                "are repeated."
-            )
-            raise ValueError(textwrap.fill(msg))
-        axis_types = tuple(ax.type for ax in axes)
-        type_census = Counter(axis_types)
-        num_spaces = type_census["space"]
-        if num_spaces < 2 or num_spaces > 3:
-            msg = (
-                f"Invalid number of space axes: {num_spaces}. Only 2 or 3 space "
-                "axes are allowed."
-            )
-            raise ValueError(textwrap.fill(msg))
-
-        elif not all(a == "space" for a in axis_types[-num_spaces:]):
-            msg = f"Space axes must come last. Got axes with order: {axis_types}"
-            raise ValueError(textwrap.fill(msg))
-
-        if (num_times := type_census["time"]) > 1:
-            msg = (
-                f"Invalid number of time axes: {num_times}. "
-                "Only 1 time axis is allowed."
-            )
-            raise ValueError(textwrap.fill(msg))
-
-        if (num_channels := type_census["channel"]) > 1:
-            msg = (
-                f"Invalid number of channel axes: {num_channels}. "
-                "Only 1 channel axis is allowed."
-            )
-            raise ValueError(textwrap.fill(msg))
-
-        custom_axes = set(axis_types) - set(AxisType._member_names_)
-        if len(custom_axes) > 1:
-            msg = (
-                f"Invalid number of custom axes: {custom_axes}. "
-                "Only 1 custom axis is allowed."
-            )
-            raise ValueError(textwrap.fill(msg))
-        return axes
-
-
-class MultiscaleAttrs(Attrs):
-    """
-    Attributes of a multiscale group.
-    See https://ngff.openmicroscopy.org/latest/#multiscale-md
-    """
-
-    multiscales: List[Multiscale]
-
-
-class MultiscaleGroup(Group):
-    attrs: MultiscaleAttrs
-
-    @root_validator
-    def check_arrays_exist(cls, values: Dict[str, Any]) -> Dict[str, Any]:
-        children: List[Union[Array, Group]] = values["children"]
-        child_arrays = []
-        child_groups = []
-
-        for child in children:
-            if isinstance(child, Group):
-                child_groups.append(child)
-            else:
-                child_arrays.append(child)
-        child_array_names = [a.name for a in child_arrays]
-        multiscales: List[Multiscale] = values["attrs"].multiscales
-        for multiscale in multiscales:
-            for dataset in multiscale.datasets:
-                if (dpath := dataset.path) not in child_array_names:
-                    msg = (
-                        f"Dataset {dpath} was specified in multiscale metadata, "
-                        "but no array with that name was found in the children of "
-                        "that group. All arrays in multiscale metadata must be "
-                        "children of the group."
-                    )
-                    raise ValueError(textwrap.fill(msg))
-        return values
-
-    @root_validator
-    def check_array_ndim(cls, values: Dict[str, Any]) -> Dict[str, Any]:
-        array_children: Tuple[Array, ...] = tuple(
-            filter(lambda v: hasattr(v, "shape"), values["children"])
-        )
-        multiscales: List[Multiscale] = values["attrs"].multiscales
-
-        ndims = [len(a.shape) for a in array_children]
-        if len(set(ndims)) > 1:
-            msg = (
-                "All arrays must have the same dimensionality. "
-                f"Got arrays with dimensionality {ndims}."
-            )
-            raise ValueError(textwrap.fill(msg))
-
-        # check that each transform has compatible rank
-        for multiscale in multiscales:
-            tforms: List[ctx.CoordinateTransform] = []
-            if multiscale.coordinateTransformations is not None:
-                tforms.extend(multiscale.coordinateTransformations)
-            for dataset in multiscale.datasets:
-                tforms.extend(dataset.coordinateTransformations)
-            for tform in tforms:
-                if hasattr(tform, "scale") or hasattr(tform, "translation"):
-                    tform = cast(
-                        Union[ctx.VectorScaleTransform, ctx.VectorTranslationTransform],
-                        tform,
-                    )
-                    if (tform_dims := ctx.get_transform_ndim(tform)) not in set(ndims):
-                        msg = (
-                            f"Transform {tform} has dimensionality {tform_dims} which "
-                            "does not match the dimensionality of the arrays in this "
-                            "group ({ndims}). Transform dimensionality must match "
-                            "array dimensionality."
-                        )
-                        raise ValueError(textwrap.fill(msg))
-        return values
diff --git a/src/pydantic_ome_ngff/latest/plate.py b/src/pydantic_ome_ngff/latest/plate.py
index 4e7a54b..d6d85f5 100644
--- a/src/pydantic_ome_ngff/latest/plate.py
+++ b/src/pydantic_ome_ngff/latest/plate.py
@@ -1,18 +1,14 @@
 from __future__ import annotations
-from typing import Optional
-
 
 from pydantic_ome_ngff.latest.base import version
-from pydantic_ome_ngff.v04.plate import Plate as PlateV04
+from pydantic_ome_ngff.v04.plate import PlateMetadata as PlateMetaV04
 
 
-class Plate(PlateV04):
+class PlateMeta(PlateMetaV04):
     """
     Plate metadata.
     See https://ngff.openmicroscopy.org/latest/#plate-md
     """
 
-    # we need to put the version here as a private class attribute because the version
-    # is not required by the spec...
     _version = version
-    version: Optional[str] = version
+    version: str | None = version
diff --git a/src/pydantic_ome_ngff/latest/transform.py b/src/pydantic_ome_ngff/latest/transform.py
new file mode 100644
index 0000000..ec0de32
--- /dev/null
+++ b/src/pydantic_ome_ngff/latest/transform.py
@@ -0,0 +1,57 @@
+from __future__ import annotations
+from typing import Union
+import pydantic_ome_ngff.v04.transform as tx
+
+
+class Identity(tx.Identity):
+    """
+    An identity transform with no parameters.
+    See https://ngff.openmicroscopy.org/latest/#trafo-md
+    """
+
+    ...
+
+
+class PathScale(tx.PathScale):
+    """ "
+    A coordinateTransform with at "path" field.
+    See https://ngff.openmicroscopy.org/latest/#trafo-md
+    """
+
+    ...
+
+
+class PathTranslation(tx.PathTranslation):
+    """ "
+    A coordinateTransform with at "path" field.
+    See https://ngff.openmicroscopy.org/latest/#trafo-md
+    """
+
+    ...
+
+
+class VectorTranslation(tx.VectorTranslation):
+    """
+    A translation transform with a `translate` field that is a vector.
+    See https://ngff.openmicroscopy.org/latest/#trafo-md
+    """
+
+    ...
+
+
+class VectorScale(tx.VectorScale):
+    """
+    A scale transform with a `scale` field that is a vector.
+    See https://ngff.openmicroscopy.org/latest/#trafo-md
+    """
+
+    ...
+
+
+Scale = Union[VectorScale, PathScale]
+Translation = Union[VectorTranslation, PathTranslation]
+Transform = Union[Scale, Translation, Identity]
+
+scale_translation = tx.scale_translation
+ensure_dimensionality = tx.ensure_dimensionality
+ndim = tx.ndim
diff --git a/src/pydantic_ome_ngff/latest/well.py b/src/pydantic_ome_ngff/latest/well.py
index 350d63b..6d81597 100644
--- a/src/pydantic_ome_ngff/latest/well.py
+++ b/src/pydantic_ome_ngff/latest/well.py
@@ -1,10 +1,9 @@
 from __future__ import annotations
-from typing import Optional
 from pydantic_ome_ngff.latest.base import version
-from pydantic_ome_ngff.v04.well import Well as WellV04
+from pydantic_ome_ngff.v04.well import WellMetadata as WellMetaV04
 
 
-class Well(WellV04):
+class WellMeta(WellMetaV04):
     """
     Well metadata.
     See https://ngff.openmicroscopy.org/latest/#well-md
@@ -13,4 +12,4 @@ class Well(WellV04):
     # we need to put the version here as a private class attribute because the version
     # is not required by the spec...
     _version = version
-    version: Optional[str] = version
+    version: str | None = version
diff --git a/src/pydantic_ome_ngff/tree.py b/src/pydantic_ome_ngff/tree.py
deleted file mode 100644
index b14b93f..0000000
--- a/src/pydantic_ome_ngff/tree.py
+++ /dev/null
@@ -1,79 +0,0 @@
-from __future__ import annotations
-from typing import (
-    Protocol,
-    Tuple,
-    Dict,
-    Any,
-    Iterable,
-    Literal,
-    List,
-    Union,
-    runtime_checkable,
-)
-import textwrap
-from pydantic import BaseModel, Field
-from pydantic_ome_ngff.base import StrictBase
-
-NodeType = Literal["array", "group"]
-
-
-class Attrs(BaseModel):
-    ...
-
-
-class Node(StrictBase):
-    node_type: NodeType
-    name: str
-    attrs: Attrs = Attrs()
-
-
-class Array(Node):
-    node_type: NodeType = Field("array", const=True)
-    shape: Tuple[int, ...]
-    dtype: str
-
-
-class Group(Node):
-    node_type: NodeType = Field("group", const=True)
-    children: List[Union[Group, Array]]
-
-
-class NodeLike(Protocol):
-    basename: str
-    attrs: Dict[str, Any]
-
-
-@runtime_checkable
-class ArrayLike(NodeLike, Protocol):
-    shape: Tuple[int, ...]
-    dtype: Any
-
-
-@runtime_checkable
-class GroupLike(NodeLike, Protocol):
-    def values(self) -> Iterable[Union[GroupLike, ArrayLike]]:
-        """
-        Iterable of the children of this group
-        """
-        ...
-
-
-def build_tree(element: Union[GroupLike, ArrayLike]) -> Union[Group, Array]:
-    """
-    Recursively parse an array-like or group-like into an Array or Group.
-    """
-    result: Union[Group, Array]
-    name = element.basename
-    attrs = Attrs(**element.attrs)
-
-    if isinstance(element, ArrayLike):
-        result = Array(
-            shape=element.shape, name=name, dtype=str(element.dtype), attrs=attrs
-        )
-    elif isinstance(element, GroupLike):
-        children = list(map(build_tree, element.values()))
-        result = Group(name=name, attrs=attrs, children=children)
-    else:
-        msg = f"Object of type {type(element)} cannot be processed."
-        raise ValueError(textwrap.fill(msg))
-    return result
diff --git a/src/pydantic_ome_ngff/utils.py b/src/pydantic_ome_ngff/utils.py
index b1b71bc..310385a 100644
--- a/src/pydantic_ome_ngff/utils.py
+++ b/src/pydantic_ome_ngff/utils.py
@@ -1,5 +1,11 @@
+from __future__ import annotations
+from typing import TYPE_CHECKING, runtime_checkable
 from collections import Counter
-from typing import Dict, Iterable, Hashable
+from typing import Protocol
+import numpy as np
+
+if TYPE_CHECKING:
+    from typing import Dict, Iterable, Hashable, Tuple, Any
 
 
 def duplicates(values: Iterable[Hashable]) -> Dict[Hashable, int]:
@@ -10,3 +16,24 @@ def duplicates(values: Iterable[Hashable]) -> Dict[Hashable, int]:
     """
     counts = Counter(values)
     return {k: v for k, v in counts.items() if v > 1}
+
+
+@runtime_checkable
+class ArrayLike(Protocol):
+    shape: Tuple[int, ...]
+    dtype: np.dtype[Any]
+
+
+@runtime_checkable
+class ChunkedArrayLike(ArrayLike, Protocol):
+    chunks: Tuple[int, ...]
+
+
+def listify_numpy(data: Any) -> Any:
+    """
+    If the input is a numpy array, turn it into a list and return it.
+    Otherwise return the input unchanged.
+    """
+    if isinstance(data, np.ndarray):
+        return data.tolist()
+    return data
diff --git a/src/pydantic_ome_ngff/v04/__init__.py b/src/pydantic_ome_ngff/v04/__init__.py
index e69208d..c702016 100644
--- a/src/pydantic_ome_ngff/v04/__init__.py
+++ b/src/pydantic_ome_ngff/v04/__init__.py
@@ -1,7 +1,17 @@
-from pydantic_ome_ngff.v04.axes import Axis  # noqa
-from pydantic_ome_ngff.v04.coordinateTransformations import CoordinateTransform  # noqa
-from pydantic_ome_ngff.v04.multiscales import Multiscale  # noqa
-from pydantic_ome_ngff.v04.imageLabel import ImageLabel  # noqa
-from pydantic_ome_ngff.v04.well import Well  # noqa
-from pydantic_ome_ngff.v04.plate import Plate  # noqa
-from pydantic_ome_ngff.v04.base import version  # noqa
+from pydantic_ome_ngff.v04.axis import Axis
+from pydantic_ome_ngff.v04.transform import Transform
+from pydantic_ome_ngff.v04.multiscale import MultiscaleMetadata
+from pydantic_ome_ngff.v04.label import ImageLabel
+from pydantic_ome_ngff.v04.well import WellMetadata
+from pydantic_ome_ngff.v04.plate import PlateMetadata
+from pydantic_ome_ngff.v04.base import version
+
+__all__ = [
+    "Axis",
+    "Transform",
+    "MultiscaleMetadata",
+    "ImageLabel",
+    "WellMetadata",
+    "PlateMetadata",
+    "version",
+]
diff --git a/src/pydantic_ome_ngff/v04/axes.py b/src/pydantic_ome_ngff/v04/axes.py
deleted file mode 100644
index fb87f9f..0000000
--- a/src/pydantic_ome_ngff/v04/axes.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from __future__ import annotations
-from pydantic_ome_ngff.base import StrictVersionedBase
-from pydantic_ome_ngff.v04.base import version
-import warnings
-from enum import Enum
-from typing import Any, Dict, Optional
-from pydantic import validator
-import textwrap
-
-
-class AxisType(str, Enum):
-    space = "space"
-    time = "time"
-    channel = "channel"
-
-
-class SpaceUnit(str, Enum):
-    angstrom = "angstrom"
-    attometer = "attometer"
-    centimeter = "centimeter"
-    decimeter = "decimeter"
-    exameter = "exameter"
-    femtometer = "femtometer"
-    foot = "foot"
-    gigameter = "gigameter"
-    hectometer = "hectometer"
-    inch = "inch"
-    kilometer = "kilometer"
-    megameter = "megameter"
-    meter = "meter"
-    micrometer = "micrometer"
-    mile = "mile"
-    millimeter = "millimeter"
-    nanometer = "nanometer"
-    parsec = "parsec"
-    petameter = "petameter"
-    picometer = "picometer"
-    terameter = "terameter"
-    yard = "yard"
-    yoctometer = "yoctometer"
-    yottameter = "yottameter"
-    zeptometer = "zeptometer"
-    zettameter = "zettameter"
-
-
-class TimeUnit(str, Enum):
-    attosecond = "attosecond"
-    centisecond = "centisecond"
-    day = "day"
-    decisecond = "decisecond"
-    exasecond = "exasecond"
-    femtosecond = "femtosecond"
-    gigasecond = "gigasecond"
-    hectosecond = "hectosecond"
-    hour = "hour"
-    kilosecond = "kilosecond"
-    megasecond = "megasecond"
-    microsecond = "microsecond"
-    millisecond = "millisecond"
-    minute = "minute"
-    nanosecond = "nanosecond"
-    petasecond = "petasecond"
-    picosecond = "picosecond"
-    second = "second"
-    terasecond = "terasecond"
-    yoctosecond = "yoctosecond"
-    yottasecond = "yottasecond"
-    zeptosecond = "zeptosecond"
-    zettasecond = "zettasecond"
-
-
-class Axis(StrictVersionedBase):
-    """
-    Axis metadata.
-    See https://ngff.openmicroscopy.org/0.4/#axes-md
-    """
-
-    _version = version
-    # SPEC: this should almost certainly be a string, but the spec doesn't specify the type: https://github.com/ome/ngff/blob/ee4d5dab677636a28f1f65c248a751e279a0d1fe/0.4/index.bs#L243
-    name: Any
-    type: Optional[str]
-    unit: Optional[str]
-
-    @validator("unit")
-    def check_unit(cls, unit: str, values: Dict[str, AxisType]) -> str:
-        typ = values["type"]
-        if typ == AxisType.space:
-            if unit not in SpaceUnit.__members__:
-                msg = (
-                    f'Unit "{unit}" is not recognized as a standard unit for an '
-                    f'axis with type "{typ}".'
-                )
-                warnings.warn(textwrap.fill(msg))
-
-        elif typ == AxisType.time:
-            if unit not in TimeUnit.__members__:
-                msg = (
-                    f'Unit "{unit}" is not recognized as a standard unit for an '
-                    f'axis with type "{typ}".'
-                )
-                warnings.warn(textwrap.fill(msg))
-        elif typ == AxisType.channel:
-            pass
-        elif typ is None:
-            msg = (
-                f"Null axis type. Version {cls._version} of the OME-NGFF spec "
-                "states that the `type` field of an axis should be a string."
-            )
-            warnings.warn(textwrap.fill(msg))
-        else:
-            msg = (
-                f'Unknown axis type "{typ}". Version {cls._version} of the '
-                "OME-NGFF spec states that the `type` field of an axis should be "
-                f"one of {AxisType._member_names_}."
-            )
-            warnings.warn(textwrap.fill(msg))
-        if unit is None:
-            msg = (
-                f"Null unit. Version {cls._version} of the OME-NGFF spec states "
-                "that the `unit` field of an axis should be a string."
-            )
-            warnings.warn(textwrap.fill(msg))
-        return unit
diff --git a/src/pydantic_ome_ngff/v04/axis.py b/src/pydantic_ome_ngff/v04/axis.py
new file mode 100644
index 0000000..f74bc44
--- /dev/null
+++ b/src/pydantic_ome_ngff/v04/axis.py
@@ -0,0 +1,145 @@
+from __future__ import annotations
+from enum import Enum
+from typing import Optional
+import warnings
+
+
+from pydantic_ome_ngff.base import StrictVersionedBase
+from pydantic_ome_ngff.v04.base import version
+
+
+class AxisType(str, Enum):
+    """
+    String enum representing the three axis types (`space`, `time`, `channel`) defined in the specification.
+    """
+
+    space = "space"
+    time = "time"
+    channel = "channel"
+
+
+class SpaceUnit(str, Enum):
+    angstrom = "angstrom"
+    attometer = "attometer"
+    centimeter = "centimeter"
+    decimeter = "decimeter"
+    exameter = "exameter"
+    femtometer = "femtometer"
+    foot = "foot"
+    gigameter = "gigameter"
+    hectometer = "hectometer"
+    inch = "inch"
+    kilometer = "kilometer"
+    megameter = "megameter"
+    meter = "meter"
+    micrometer = "micrometer"
+    mile = "mile"
+    millimeter = "millimeter"
+    nanometer = "nanometer"
+    parsec = "parsec"
+    petameter = "petameter"
+    picometer = "picometer"
+    terameter = "terameter"
+    yard = "yard"
+    yoctometer = "yoctometer"
+    yottameter = "yottameter"
+    zeptometer = "zeptometer"
+    zettameter = "zettameter"
+
+
+class TimeUnit(str, Enum):
+    attosecond = "attosecond"
+    centisecond = "centisecond"
+    day = "day"
+    decisecond = "decisecond"
+    exasecond = "exasecond"
+    femtosecond = "femtosecond"
+    gigasecond = "gigasecond"
+    hectosecond = "hectosecond"
+    hour = "hour"
+    kilosecond = "kilosecond"
+    megasecond = "megasecond"
+    microsecond = "microsecond"
+    millisecond = "millisecond"
+    minute = "minute"
+    nanosecond = "nanosecond"
+    petasecond = "petasecond"
+    picosecond = "picosecond"
+    second = "second"
+    terasecond = "terasecond"
+    yoctosecond = "yoctosecond"
+    yottasecond = "yottasecond"
+    zeptosecond = "zeptosecond"
+    zettasecond = "zettasecond"
+
+
+def check_type_unit(model: Axis) -> Axis:
+    """
+    Check that the `unit` attribute of an `Axis` object is valid.
+    This function emits warnings when the `unit` attribute of of an `Axis` object
+    is spec-compliant but contravenes a "SHOULD" statement in the spec.
+    """
+
+    typ = model.type
+    unit = model.unit
+
+    if typ == AxisType.space:
+        if unit not in [e.value for e in SpaceUnit]:
+            msg = (
+                f"Unit '{unit}' is not recognized as a standard unit "
+                f"for an axis with type '{typ}'."
+            )
+            warnings.warn(msg, stacklevel=1)
+    elif typ == AxisType.time:
+        if unit not in [e.value for e in TimeUnit]:
+            msg = (
+                f"Unit '{unit}' is not recognized as a standard unit "
+                f"for an axis with type '{typ}'."
+            )
+            warnings.warn(msg, stacklevel=1)
+    elif typ == AxisType.channel:
+        pass
+    elif typ is None:
+        msg = (
+            f"The `type` field of this axis was set to `None`. Version {model._version} of the OME-NGFF spec states "
+            "that the 'type' field of an axis should be set to a string."
+        )
+        warnings.warn(msg, stacklevel=1)
+    else:
+        msg = (
+            f"Unknown axis type '{typ}'. Version {model._version} of the OME-NGFF "
+            " spec states that the 'type' field of an axis should be one of "
+            f"{AxisType._member_names_}."
+        )
+        warnings.warn(msg, stacklevel=1)
+
+    if unit is None:
+        msg = (
+            f"The `unit` field of this axis was set to `None`. Version {model._version} of the OME-NGFF spec states "
+            "that the `unit` field of an axis should be set to a string."
+        )
+        warnings.warn(msg, stacklevel=1)
+    return model
+
+
+class Axis(StrictVersionedBase):
+    """
+    Axis metadata.
+
+    See [https://ngff.openmicroscopy.org/0.4/#axes-md](https://ngff.openmicroscopy.org/0.4/#axes-md) for the specification of this data structure.
+
+    Attributes
+    ----------
+
+    name: str
+        The name for this axis.
+    type: str | None
+        The type for this axis, e.g. "space".
+    unit: str | None
+        The unit of measure associated with the interval defined by this axis.
+    """
+
+    _version = version
+    name: str
+    type: Optional[str] = None
+    unit: Optional[str] = None
diff --git a/src/pydantic_ome_ngff/v04/coordinateTransformations.py b/src/pydantic_ome_ngff/v04/coordinateTransformations.py
deleted file mode 100644
index bfca241..0000000
--- a/src/pydantic_ome_ngff/v04/coordinateTransformations.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from __future__ import annotations
-import textwrap
-from typing import List, Literal, Union
-from pydantic import BaseModel
-
-
-class IdentityTransform(BaseModel):
-    """
-    An identity transform with no parameters.
-    See https://ngff.openmicroscopy.org/0.4/#trafo-md
-    """
-
-    # SPEC why does this exist, as opposed to translation by 0 or scale by 1?
-    type: str = "identity"
-
-
-class PathTransform(BaseModel):
-    """
-    A coordinateTransform with a `path` field.
-    See https://ngff.openmicroscopy.org/0.4/#trafo-md
-    """
-
-    # SPEC: the existence of this type is a massive sinkhole in the spec
-    # translate and scale are both so simple that nobody should be using a path
-    # argument to refer to some remote resource representing a translation
-    # or a scale transform
-    type: Union[Literal["scale"], Literal["translation"]]
-    path: str
-
-
-class VectorTranslationTransform(BaseModel):
-    """
-    A translation transform with a `translate` field that is a vector.
-    See https://ngff.openmicroscopy.org/0.4/#trafo-md
-    """
-
-    type: Literal["translation"] = "translation"
-    translation: List[
-        float
-    ]  # SPEC: redundant field name -- we already know it's translation
-
-
-class VectorScaleTransform(BaseModel):
-    """
-    A scale transform with a `scale` field that is a vector.
-    See https://ngff.openmicroscopy.org/0.4/#trafo-md
-    """
-
-    type: Literal["scale"] = "scale"
-    scale: List[float]  # SPEC: redundant field name -- we already know it's scale
-
-
-def get_transform_ndim(
-    transform: Union[VectorScaleTransform, VectorTranslationTransform],
-) -> int:
-    """
-    Get the dimensionality of a vector transform (scale or translation).
-    """
-    if transform.type == "scale" and hasattr(transform, "scale"):
-        return len(transform.scale)
-    elif transform.type == "translation" and hasattr(transform, "translation"):
-        return len(transform.translation)
-    else:
-        msg = (
-            f"Transform must be either VectorScaleTransform or "
-            f"VectorTranslationTransform. Got {type(transform)} instead."
-        )
-        raise ValueError(textwrap.fill(msg))
-
-
-ScaleTransform = Union[VectorScaleTransform, PathTransform]
-TranslationTransform = Union[VectorTranslationTransform, PathTransform]
-CoordinateTransform = Union[ScaleTransform, TranslationTransform, IdentityTransform]
diff --git a/src/pydantic_ome_ngff/v04/imageLabel.py b/src/pydantic_ome_ngff/v04/imageLabel.py
deleted file mode 100644
index b8627ac..0000000
--- a/src/pydantic_ome_ngff/v04/imageLabel.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from __future__ import annotations
-import warnings
-from typing import List, Optional, Tuple
-import textwrap
-from pydantic import BaseModel, Field, validator
-from pydantic_ome_ngff.base import VersionedBase
-from pydantic_ome_ngff.utils import duplicates
-from pydantic_ome_ngff.v04.base import version
-
-
-class Color(BaseModel):
-    label_value: int = Field(..., alias="label-value")
-    rgba: Optional[Tuple[int, int, int, int]]
-
-
-class Source(BaseModel):
-    image: Optional[str] = "../../"
-
-
-class Properties(BaseModel):
-    label_value: int = Field(..., alias="label-value")
-
-
-class ImageLabel(VersionedBase):
-    """
-    image-label metadata.
-    See https://ngff.openmicroscopy.org/0.4/#label-md
-    """
-
-    # we need to put the version here as a private class attribute because the version
-    # field is not required by the spec...
-    _version = version
-
-    # SPEC: version is either unset or a string?
-    version: Optional[str] = version
-    colors: Optional[List[Color]]
-    properties: Optional[List[Properties]]
-    source: Optional[Source]
-
-    @validator("version")
-    def check_version(cls, ver: str) -> str:
-        if ver is None:
-            msg = f"""
-            The field "version" is "None". Version {cls._version} of
-            the OME-NGFF spec states that "version" must either be unset or the string
-            "{cls._version}"
-            """
-            warnings.warn(textwrap.fill(msg))
-        return ver
-
-    @validator("colors")
-    def check_colors(cls, colors: Optional[List[Color]]) -> Optional[List[Color]]:
-        if colors is None:
-            msg = f"""
-            The field "colors" is "None". Version {cls._version} of
-            the OME-NGFF spec states that "colors" should be a list of label
-            descriptors.
-            """
-            warnings.warn(textwrap.fill(msg))
-        else:
-            dupes = duplicates(x.label_value for x in colors)
-            if len(dupes) > 1:
-                msg = f"""
-                    Duplicated label-value: {tuple(dupes.keys())}.
-                    label-values must be unique across elements of `colors`
-                    """
-                raise ValueError(textwrap.fill(msg))
-
-        return colors
diff --git a/src/pydantic_ome_ngff/v04/label.py b/src/pydantic_ome_ngff/v04/label.py
new file mode 100644
index 0000000..94913a6
--- /dev/null
+++ b/src/pydantic_ome_ngff/v04/label.py
@@ -0,0 +1,126 @@
+from __future__ import annotations
+import warnings
+from typing import Literal, Optional, Tuple, Annotated, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import List
+
+from pydantic import AfterValidator, BaseModel, Field, model_validator
+from pydantic_ome_ngff.base import VersionedBase
+from pydantic_ome_ngff.utils import duplicates
+from pydantic_ome_ngff.v04.base import version as NGFF_VERSION
+
+import pydantic_ome_ngff.v04.multiscale as multiscale
+
+ConInt = Annotated[int, Field(strict=True, ge=0, le=255)]
+RGBA = Tuple[ConInt, ConInt, ConInt, ConInt]
+
+
+class Color(BaseModel):
+    """
+    A label value and RGBA as defined in https://ngff.openmicroscopy.org/0.4/#label-md
+    """
+
+    label_value: int = Field(..., serialization_alias="label-value")
+    rgba: Optional[RGBA]
+
+
+class Source(BaseModel):
+    # todo: add validation that this path resolves to something
+    image: Optional[str] = "../../"
+
+
+class Property(BaseModel):
+    label_value: int = Field(..., serialization_alias="label-value")
+
+
+def parse_colors(colors: List[Color] | None) -> List[Color] | None:
+    if colors is None:
+        msg = (
+            f"The field `colors` is `None`. Version {NGFF_VERSION} of"
+            "the OME-NGFF spec states that `colors` should be a list of label descriptors."
+        )
+        warnings.warn(msg, stacklevel=1)
+    else:
+        dupes = duplicates(x.label_value for x in colors)
+        if len(dupes) > 0:
+            msg = (
+                f"Duplicated label-value: {tuple(dupes.keys())}."
+                "label-values must be unique across elements of `colors`."
+            )
+            raise ValueError(msg)
+
+    return colors
+
+
+def parse_version(version: Literal["0.4"] | None) -> Literal["0.4"] | None:
+    if version is None:
+        _ = (
+            f"The `version` attribute is `None`. Version {NGFF_VERSION} of "
+            f"the OME-NGFF spec states that `version` should either be unset or the string {NGFF_VERSION}"
+        )
+        # This goes against a recommendation from the spec, but emitting a warning is annoying.
+        # leaving this here as a placeholder.
+    return version
+
+
+def parse_imagelabel(model: ImageLabel):
+    """
+    check that label_values are consistent across properties and colors
+    """
+    if model.colors is not None and model.properties is not None:
+        prop_label_value = [prop.label_value for prop in model.properties]
+        color_label_value = [color.label_value for color in model.colors]
+
+        prop_label_value_set = set(prop_label_value)
+        color_label_value_set = set(color_label_value)
+        if color_label_value_set != prop_label_value_set:
+            msg = (
+                "Inconsistent `label_value` attributes in `colors` and `properties`."
+                f"The `properties` attributes have `label_values` {prop_label_value}, "
+                f"The `colors` attributes have `label_values` {color_label_value}, "
+            )
+            raise ValueError(msg)
+    return model
+
+
+class ImageLabel(VersionedBase):
+    """
+    image-label metadata.
+    See https://ngff.openmicroscopy.org/0.4/#label-md
+    """
+
+    _version: Literal["0.4"] = NGFF_VERSION
+
+    version: Annotated[
+        Literal["0.4"] | None, AfterValidator(parse_version)
+    ] = NGFF_VERSION
+    colors: Annotated[Optional[tuple[Color, ...]], AfterValidator(parse_colors)] = None
+    properties: Optional[tuple[Property, ...]] = None
+    source: Optional[Source] = None
+
+    @model_validator(mode="after")
+    def parse_model(self):
+        return parse_imagelabel(self)
+
+
+class GroupAttrs(multiscale.GroupAttrs):
+    """
+    Attributes for a Zarr group that contains `image-label` metadata.
+    Inherits from `v04.multiscales.MultiscaleAttrs`.
+
+    See https://ngff.openmicroscopy.org/0.4/#label-md
+
+    Attributes
+    ----------
+    image_label: `ImageLabel`
+        Image label metadata.
+    multiscales: tuple[v04.multiscales.Multiscales]
+        Multiscale image metadata.
+    """
+
+    image_label: Annotated[ImageLabel, Field(..., serialization_alias="image-label")]
+
+
+class Group(multiscale.Group):
+    attributes: GroupAttrs
diff --git a/src/pydantic_ome_ngff/v04/multiscale.py b/src/pydantic_ome_ngff/v04/multiscale.py
new file mode 100644
index 0000000..642a2e9
--- /dev/null
+++ b/src/pydantic_ome_ngff/v04/multiscale.py
@@ -0,0 +1,492 @@
+from __future__ import annotations
+from typing import TYPE_CHECKING
+from collections import Counter
+from typing_extensions import Literal
+import numpy as np
+
+if TYPE_CHECKING:
+    from typing_extensions import Self
+    from numcodecs.abc import Codec
+
+from typing import Annotated, Any, Dict, Sequence, Union, cast
+from pydantic import AfterValidator, BaseModel, Field, model_validator
+from pydantic_zarr.v2 import GroupSpec, ArraySpec
+from pydantic_ome_ngff.utils import (
+    ArrayLike,
+    ChunkedArrayLike,
+    duplicates,
+)
+from pydantic_ome_ngff.base import StrictBase, StrictVersionedBase
+from pydantic_ome_ngff.v04.base import version
+from pydantic_ome_ngff.v04.axis import Axis, AxisType
+import pydantic_ome_ngff.v04.transform as tx
+from numcodecs import Zstd
+import zarr
+from zarr.errors import ArrayNotFoundError, ContainsGroupError
+from zarr.util import guess_chunks
+
+VALID_NDIM = (2, 3, 4, 5)
+NUM_TX_MAX = 2
+
+
+def ensure_scale_translation(
+    transforms: Sequence[Union[tx.VectorScale, tx.VectorTranslation]],
+) -> Sequence[Union[tx.VectorScale, tx.VectorTranslation]]:
+    """
+    Ensures that the first element is a scale transformation, the second element,
+    if present, is a translation transform, and that there are only 1 or 2 transforms.
+    """
+
+    if len(transforms) == 0 or len(transforms) > 2:
+        msg = f"Invalid number of transforms: got {len(transforms)}, expected 1 or 2"
+        raise ValueError(msg)
+
+    maybe_scale = transforms[0]
+    if maybe_scale.type != "scale":
+        msg = (
+            "The first element of `coordinateTransformations` must be a scale "
+            f"transform. Got {maybe_scale} instead."
+        )
+        raise ValueError(msg)
+    if len(transforms) == NUM_TX_MAX:
+        maybe_trans = transforms[1]
+        if (maybe_trans.type) != "translation":
+            msg = (
+                "The second element of `coordinateTransformations` must be a "
+                f"translation transform. Got {maybe_trans} instead."
+            )
+            raise ValueError(msg)
+
+    return transforms
+
+
+def ensure_transforms_length(
+    transforms: Sequence[tx.VectorScale | tx.VectorTranslation],
+) -> Sequence[tx.VectorScale | tx.VectorTranslation]:
+    if (num_tx := len(transforms)) not in (1, 2):
+        msg = f"Invalid number of transforms: got {num_tx}, expected 1 or 2"
+        raise ValueError(msg)
+    return transforms
+
+
+class Dataset(StrictBase):
+    """
+    A single entry in the `multiscales.datasets` list.
+
+    See [https://ngff.openmicroscopy.org/0.4/#multiscale-md](https://ngff.openmicroscopy.org/0.4/#multiscale-md) for the specification of this data structure.
+
+    Attributes
+    ----------
+    path: str
+        The path to the Zarr array that stores the image described by this metadata. This path should be relative to the group that contains this metadata.
+    coordinateTransformations: ctx.ScaleTransform | ctx.TranslationTransform
+        The coordinate transformations for this image.
+    """
+
+    path: str
+    coordinateTransformations: Annotated[
+        tuple[tx.Scale] | tuple[tx.Scale, tx.Translation],
+        AfterValidator(ensure_scale_translation),
+        AfterValidator(tx.ensure_dimensionality),
+    ]
+
+
+# consider making this a classmethod of `Dataset`
+def create_dataset(
+    path: str, scale: Sequence[int | float], translation: Sequence[int | float]
+) -> Dataset:
+    """
+    Create a `Dataset` from a path, a scale, and a translation. This metadata models a Zarr array that partially comprises a multiscale group.
+
+    Parameters
+    ----------
+
+    path: str
+        The path, relative to the multiscale group, of the Zarr array.
+    scale: Sequence[int | float]:
+        The scale parameter for data stored in the Zarr array. This should define the spacing between elements of the coordinate grid of the data.
+    translate: Sequence[int | float]:
+        The translation parameter for data stored in the Zarr array. This should define the origin of the coordinate grid of the data.
+
+    Returns
+    -------
+
+    `Dataset`
+    """
+    return Dataset(
+        path=path,
+        coordinateTransformations=(
+            tx.scale_translation(scale=scale, translation=translation)
+        ),
+    )
+
+
+def ensure_axis_length(axes: Sequence[Axis]) -> Sequence[Axis]:
+    """
+    Ensures that there are between 2 and 5 axes (inclusive)
+    """
+    if (len_axes := len(axes)) not in VALID_NDIM:
+        msg = f"Incorrect number of axes provided ({len_axes}). Only 2, 3, 4, or 5 axes are allowed."
+        raise ValueError(msg)
+    return axes
+
+
+def ensure_axis_names(axes: Sequence[Axis]) -> Sequence[Axis]:
+    """
+    Ensures that the names of the axes are unique.
+    """
+    name_dupes = duplicates(a.name for a in axes)
+    if len(name_dupes) > 0:
+        msg = f"Axis names must be unique. Axis names {tuple(name_dupes.keys())} are repeated."
+        raise ValueError(msg)
+    return axes
+
+
+def ensure_axis_types(axes: Sequence[Axis]) -> Sequence[Axis]:
+    """
+    Ensures that the following conditions are true:
+
+    - there are only 2 or 3 axes with type `space`
+    - the axes with type `space` are last in the list of axes
+    - there is only 1 axis with type `time`
+    - there is only 1 axis with type `channel`
+    - there is only 1 axis with a type that is not `space`, `time`, or `channel`
+    """
+    axis_types = [ax.type for ax in axes]
+    type_census = Counter(axis_types)
+    num_spaces = type_census["space"]
+    if num_spaces < 2 or num_spaces > 3:
+        msg = f"Invalid number of space axes: {num_spaces}. Only 2 or 3 space axes are allowed."
+        raise ValueError(msg)
+
+    if not all(a == "space" for a in axis_types[-num_spaces:]):
+        msg = f"Space axes must come last. Got axes with order: {axis_types}."
+        raise ValueError(msg)
+
+    if (num_times := type_census["time"]) > 1:
+        msg = f"Invalid number of time axes: {num_times}. Only 1 time axis is allowed."
+        raise ValueError(msg)
+
+    if (num_channels := type_census["channel"]) > 1:
+        msg = f"Invalid number of channel axes: {num_channels}. Only 1 channel axis is allowed."
+        raise ValueError(msg)
+
+    custom_axes = set(axis_types) - set(AxisType._member_names_)
+    if (num_custom := len(custom_axes)) > 1:
+        msg = f"Invalid number of custom axes: {num_custom}. Only 1 custom axis is allowed."
+        raise ValueError(msg)
+    return axes
+
+
+class MultiscaleMetadata(StrictVersionedBase):
+    """
+    Multiscale image metadata.
+
+    See [https://ngff.openmicroscopy.org/0.4/#multiscale-md](https://ngff.openmicroscopy.org/0.4/#multiscale-md) for the specification of this data structure.
+
+    Attributes
+    ----------
+
+    name: Any
+        The name for this multiscale image. Optional. Defaults to `None`.
+    type: Any
+        The type of the multiscale image. Optional. Defaults to `None`.
+    metadata: Dict[str, Any] | None
+        Metadata for this multiscale image. Optional. Defaults to `None`.
+    datasets: tuple[Dataset, ...]
+        A collection of descriptions of arrays that collectively comprise this multiscale image.
+    axes: tuple[Axis, ...]
+        A tuple of `Axis` objects that define the semantics for the different axes of the multiscale image.
+    coordinateTransformations: tuple[tx.Scale] | tuple[tx.Scale, tx.Translation] | None. Defaults to `None`.
+        Coordinate transformations that express a scaling and translation shared by all elements of
+        `datasets`.
+    """
+
+    _version = version
+    version: Any = version
+    name: Any = None
+    type: Any = None
+    metadata: Dict[str, Any] | None = None
+    datasets: Annotated[tuple[Dataset, ...], Field(..., min_length=1)]
+    axes: Annotated[
+        tuple[Axis, ...],
+        AfterValidator(ensure_axis_length),
+        AfterValidator(ensure_axis_names),
+        AfterValidator(ensure_axis_types),
+    ]
+    coordinateTransformations: tuple[tx.Scale] | tuple[
+        tx.Scale, tx.Translation
+    ] | None = None
+
+
+class GroupAttrs(BaseModel):
+    """
+    A model of the required attributes of a Zarr group that implements OME-NGFF Multiscales metadata.
+
+    See [https://ngff.openmicroscopy.org/0.4/#multiscale-md](https://ngff.openmicroscopy.org/0.4/#multiscale-md) for the specification of this data structure.
+
+    Attributes
+    ----------
+    multiscales: tuple[MultiscaleMetadata]
+        A list of `MultiscaleMetadata`. Each element of `multiscales` specifies a multiscale image.
+    """
+
+    multiscales: Annotated[tuple[MultiscaleMetadata, ...], Field(..., min_length=1)]
+
+
+class Group(GroupSpec[GroupAttrs, Union[ArraySpec, GroupSpec]]):
+    """
+    A model of a Zarr group that implements OME-NGFF Multiscales metadata.
+
+    See [https://ngff.openmicroscopy.org/0.4/#multiscale-md](https://ngff.openmicroscopy.org/0.4/#multiscale-md) for the specification of this data structure.
+
+    Attributes
+    ----------
+
+    attributes: GroupAttrs
+        The attributes of this Zarr group, which should contain valid `GroupAttrs`.
+    members: Dict[Str, ArraySpec | GroupSpec]:
+        The members of this Zarr group. Should be instances of `pydantic_zarr.GroupSpec` or `pydantic_zarr.ArraySpec`.
+
+    """
+
+    @classmethod
+    def from_zarr(cls, node: zarr.Group) -> Group:
+        """
+        Create an instance of `Group` from a `node`, a `zarr.Group`. This method discovers Zarr arrays in the hierarchy rooted at `node` by inspecting the OME-NGFF
+        multiscales metadata.
+
+        Parameters
+        ---------
+        node: zarr.Group
+            A Zarr group that has valid OME-NGFF multiscale metadata.
+
+        Returns
+        -------
+        Group
+            A model of the Zarr group.
+        """
+        # on unlistable storage backends, the members of this group will be {}
+        guess = GroupSpec.from_zarr(node)
+
+        try:
+            multi_meta_maybe = guess.attributes["multiscales"]
+        except KeyError as e:
+            msg = (
+                "Failed to find mandatory `multiscales` key in the attributes of the zarr group at ",
+                f"{node.store.path}/{node.path}",
+            )
+            raise ValueError(msg) from e
+
+        multi_meta = GroupAttrs(multiscales=multi_meta_maybe)
+        members_tree_flat = {}
+        for multiscale in multi_meta.multiscales:
+            for dataset in multiscale.datasets:
+                array_path = "/".join([node.path, dataset.path])
+                try:
+                    array = zarr.open_array(store=node.store, path=array_path, mode="r")
+                    array_spec = ArraySpec.from_zarr(array)
+                except ArrayNotFoundError:
+                    msg = (
+                        f"Expected to find an array at {array_path}, ",
+                        "but no array was found there.",
+                    )
+                    raise ArrayNotFoundError(msg)
+                except ContainsGroupError:
+                    msg = (
+                        f"Expected to find an array at {array_path}, ",
+                        "but a group was found there instead.",
+                    )
+                    raise ContainsGroupError(msg)
+                members_tree_flat["/" + dataset.path] = array_spec
+        members_normalized = GroupSpec.from_flat(members_tree_flat)
+
+        guess_inferred_members = guess.model_copy(
+            update={"members": {**guess.members, **members_normalized.members}}
+        )
+        result = cls(**guess_inferred_members.model_dump())
+        return result
+
+    @classmethod
+    def from_arrays(
+        cls,
+        arrays: Sequence[ArrayLike | ChunkedArrayLike],
+        *,
+        paths: Sequence[str],
+        axes: Sequence[Axis],
+        scales: Sequence[tuple[int | float]],
+        translations: Sequence[tuple[int | float]],
+        name: str | None = None,
+        type: str | None = None,
+        metadata: Dict[str, Any] | None = None,
+        chunks: tuple[int, ...] | tuple[tuple[int, ...]] | Literal["auto"] = "auto",
+        compressor: Codec = Zstd(3),
+        fill_value: Any = 0,
+        order: Literal["C", "F", "auto"] = "auto",
+    ) -> Self:
+        """
+        Create a `Group` from a sequence of multiscale arrays and spatial metadata.
+
+        The arrays are used as templates for corresponding `ArraySpec` instances, which model the Zarr arrays that would be created if the `Group` was stored.
+
+        Parameters
+        ----------
+        paths: Sequence[str]
+            The paths to the arrays.
+        axes: Sequence[Axis]
+            `Axis` objects describing the dimensions of the arrays.
+        arrays: Sequence[ArrayLike] | Sequence[ChunkedArrayLike]
+            A sequence of array-like objects that collectively represent the same image
+            at multiple levels of detail. The attributes of these arrays are used to create `ArraySpec` objects
+            that model Zarr arrays stored in the Zarr group.
+        scales: Sequence[Sequence[int | float]]
+            A scale value for each axis of the array, for each array in `arrays`.
+        translations: Sequence[Sequence[int | float]]
+            A translation value for each axis the array, for each array in `arrays`.
+        name: str | None, default = None
+            A name for the multiscale collection. Optional.
+        type: str | None, default = None
+            A description of the type of multiscale image represented by this group. Optional.
+        metadata: Dict[str, Any] | None, default = None
+            Arbitrary metadata associated with this multiscale collection. Optional.
+        chunks: tuple[int] | tuple[tuple[int, ...]] | Literal["auto"], default = "auto"
+            The chunks for the arrays in this multiscale group.
+            If the string "auto" is provided, each array will have chunks set to the zarr-python default value, which depends on the shape and dtype of the array.
+            If a single sequence of ints is provided, then this defines the chunks for all arrays.
+            If a sequence of sequences of ints is provided, then this defines the chunks for each array.
+        fill_value: Any, default = 0
+            The fill value for the Zarr arrays.
+        compressor: `Codec`
+            The compressor to use for the arrays. Default is `numcodecs.ZStd`.
+        order: "auto" | "C" | "F"
+            The memory layout used for chunks of Zarr arrays. The default is "auto", which will infer the order from the input arrays, and fall back to "C" if that inference fails.
+        """
+
+        chunks_normalized = normalize_chunks(
+            chunks,
+            shapes=[s.shape for s in arrays],
+            typesizes=[s.dtype.itemsize for s in arrays],
+        )
+
+        members_flat = {
+            "/" + key.lstrip("/"): ArraySpec.from_array(
+                arr,
+                cnks,
+                attributes={},
+                compressor=compressor,
+                filters=None,
+                fill_value=fill_value,
+                order=order,
+            )
+            for key, arr, cnks in zip(paths, arrays, chunks_normalized)
+        }
+
+        multimeta = MultiscaleMetadata(
+            name=name,
+            type=type,
+            metadata=metadata,
+            axes=axes,
+            datasets=[
+                create_dataset(path=path, scale=scale, translation=translation)
+                for path, scale, translation in zip(paths, scales, translations)
+            ],
+            coordinateTransformations=None,
+        )
+        return cls(
+            members=GroupSpec.from_flat(members_flat).members,
+            attributes=GroupAttrs(multiscales=[multimeta]),
+        )
+
+    @model_validator(mode="after")
+    def check_arrays_exist(self) -> "Group":
+        """
+        Check that the arrays referenced in the `multiscales` metadata are actually contained in this group.
+        """
+        attrs = self.attributes
+        flattened = self.to_flat()
+
+        for multiscale in attrs.multiscales:
+            for dataset in multiscale.datasets:
+                dpath = "/" + dataset.path.lstrip("/")
+                if dpath in flattened:
+                    if not isinstance(flattened[dpath], ArraySpec):
+                        msg = (
+                            f"The node at {dpath} should be an array, "
+                            f"found {type(flattened[dpath])} instead"
+                        )
+                        raise ValueError(msg)
+                else:
+                    msg = (
+                        f"Dataset {dataset.path} was specified in multiscale metadata, but no "
+                        "array with that name was found in the hierarchy. "
+                        "All arrays referenced in multiscale metadata must be contained in the group."
+                    )
+                    raise ValueError(msg)
+        return self
+
+    @model_validator(mode="after")
+    def check_array_ndim(self) -> "Group":
+        """
+        Check that all the arrays referenced by the `multiscales` metadata have dimensionality consistent with the
+        `coordinateTransformations` metadata.
+        """
+        multimeta = self.attributes.multiscales
+
+        flat_self = self.to_flat()
+
+        # check that each transform has compatible rank
+        for multiscale in multimeta:
+            for dataset in multiscale.datasets:
+                arr: ArraySpec = flat_self["/" + dataset.path.lstrip("/")]
+                arr_ndim = len(arr.shape)
+                tforms = dataset.coordinateTransformations
+
+                if multiscale.coordinateTransformations is not None:
+                    tforms += multiscale.coordinateTransformations
+
+                for tform in tforms:
+                    if hasattr(tform, "scale") or hasattr(tform, "translation"):
+                        tform = cast(
+                            Union[tx.VectorScale, tx.VectorTranslation],
+                            tform,
+                        )
+                        if (tform_ndim := tx.ndim(tform)) != arr_ndim:
+                            msg = (
+                                f"Transform {tform} has dimensionality {tform_ndim}, "
+                                "which does not match the dimensionality of the array "
+                                f"found in this group at {dataset.path} ({arr_ndim}). "
+                                "Transform dimensionality must match array dimensionality."
+                            )
+
+                            raise ValueError(msg)
+
+        return self
+
+
+def normalize_chunks(
+    chunks: Any, shapes: tuple[tuple[int, ...]], typesizes: tuple[tuple[int]]
+) -> tuple[tuple[int, ...]]:
+    """
+    If chunks is "auto", then use zarr default chunking based on the largest array for all the arrays.
+    If chunks is a sequence of ints, then use those chunks for all arrays.
+    If chunks is a sequence of sequences of ints, then use those chunks for each array.
+    """
+    if chunks == "auto":
+        # sort shapes by descending size
+        params_sorted_descending = sorted(
+            zip(shapes, typesizes), key=lambda v: np.prod(v[0]), reverse=True
+        )
+        return (guess_chunks(*params_sorted_descending[0]),) * len(shapes)
+    if isinstance(chunks, Sequence):
+        if all(isinstance(element, int) for element in chunks):
+            return (chunks,) * len(shapes)
+        if all(isinstance(element, Sequence) for element in chunks):
+            if all(map(lambda v: all(isinstance(k, int) for k in v), chunks)):
+                return tuple(map(tuple, chunks))
+            else:
+                raise ValueError(
+                    f"Expected a sequence of sequences of ints. Got {chunks} instead."
+                )
+    raise TypeError(
+        f'Input must be a sequence or the string "auto". Got {type(chunks)}'
+    )
diff --git a/src/pydantic_ome_ngff/v04/multiscales.py b/src/pydantic_ome_ngff/v04/multiscales.py
deleted file mode 100644
index e18cda1..0000000
--- a/src/pydantic_ome_ngff/v04/multiscales.py
+++ /dev/null
@@ -1,227 +0,0 @@
-from __future__ import annotations
-from collections import Counter
-import textwrap
-import warnings
-from typing import Any, Dict, List, Optional, Tuple, Union, cast
-
-from pydantic import BaseModel, conlist, root_validator, validator
-
-from pydantic_ome_ngff.utils import duplicates
-from pydantic_ome_ngff.base import VersionedBase
-from pydantic_ome_ngff.tree import Group, Attrs, Array
-from pydantic_ome_ngff.v04.base import version
-from pydantic_ome_ngff.v04.axes import Axis, AxisType
-import pydantic_ome_ngff.v04.coordinateTransformations as ctx
-
-
-class MultiscaleDataset(BaseModel):
-    path: str
-    coordinateTransformations: conlist(
-        Union[ctx.ScaleTransform, ctx.TranslationTransform], min_items=1, max_items=2
-    )
-
-    @validator("coordinateTransformations")
-    def check_transforms_dimensionality(
-        cls,
-        transforms: List[
-            Union[ctx.VectorScaleTransform, ctx.VectorTranslationTransform]
-        ],
-    ) -> List[Union[ctx.VectorScaleTransform, ctx.VectorTranslationTransform]]:
-        ndims = []
-        for tx in transforms:
-            # this repeated conditional logic around transforms is so awful.
-            if type(tx) in (ctx.VectorScaleTransform, ctx.VectorTranslationTransform):
-                ndims.append(ctx.get_transform_ndim(tx))
-        if len(set(ndims)) > 1:
-            msg = (
-                "Elements of coordinateTransformations must have the same "
-                f"dimensionality. Got elements with dimensionality = {ndims}."
-            )
-            raise ValueError(textwrap.fill(msg))
-        return transforms
-
-    @validator("coordinateTransformations")
-    def check_transforms_types(
-        cls,
-        transforms: List[
-            Union[ctx.VectorScaleTransform, ctx.VectorTranslationTransform]
-        ],
-    ) -> List[Union[ctx.VectorScaleTransform, ctx.VectorTranslationTransform]]:
-        if (tform := transforms[0].type) != "scale":
-            msg = (
-                "The first element of coordinateTransformations must be a scale "
-                f"transform. Got {tform} instead."
-            )
-            raise ValueError(textwrap.fill(msg))
-        if len(transforms) == 2:
-            if (tform := transforms[1].type) != "translation":
-                msg = (
-                    "The second element of coordinateTransformations must be a "
-                    f"translation transform. Got {tform} instead."
-                )
-                raise ValueError(textwrap.fill(msg))
-
-        return transforms
-
-
-class Multiscale(VersionedBase):
-    """
-    Multiscale image metadata.
-    See https://ngff.openmicroscopy.org/0.4/#multiscale-md
-    """
-
-    # we need to put the version here as a private class attribute because the version
-    # is not required by the spec...
-    _version = version
-    # SPEC: why is this optional? why is it untyped?
-    version: Optional[Any] = version
-    # SPEC: why is this nullable instead of reserving the empty string
-    # SPEC: untyped!
-    name: Optional[Any]
-    # SPEC: not clear what this field is for, given the existence of .metadata
-    # SPEC: untyped!
-    type: Optional[Any]
-    # SPEC: should default to empty dict instead of None
-    metadata: Optional[Dict[str, Any]] = None
-    datasets: List[MultiscaleDataset]
-    # SPEC: should not exist at top level and instead
-    # live in dataset metadata or in .datasets
-    axes: conlist(Axis, min_items=2, max_items=5)
-    # SPEC: should not live here, and if it is here,
-    # it should default to an empty list instead of being nullable
-    coordinateTransformations: Optional[
-        List[Union[ctx.ScaleTransform, ctx.TranslationTransform]]
-    ]
-
-    @validator("name")
-    def check_name(cls, name: str) -> str:
-        if name is None:
-            msg = (
-                f"The name field was set to `None`. Version {cls._version} of the "
-                "OME-NGFF spec states that the `name` field of a Multiscales object "
-                "should not be None."
-            )
-            warnings.warn(textwrap.fill(msg))
-        return name
-
-    @validator("axes")
-    def check_axes(cls, axes: List[Axis]) -> List[Axis]:
-        name_dupes = duplicates(a.name for a in axes)
-        if len(name_dupes) > 0:
-            msg = (
-                f"Axis names must be unique. Axis names {tuple(name_dupes.keys())} "
-                "are repeated."
-            )
-            raise ValueError(textwrap.fill(msg))
-        axis_types = [ax.type for ax in axes]
-        type_census = Counter(axis_types)
-        num_spaces = type_census["space"]
-        if num_spaces < 2 or num_spaces > 3:
-            msg = (
-                f"Invalid number of space axes: {num_spaces}. Only 2 or 3 space "
-                "axes are allowed."
-            )
-            raise ValueError(textwrap.fill(msg))
-
-        elif not all(a == "space" for a in axis_types[-num_spaces:]):
-            msg = f"Space axes must come last. Got axes with order: {axis_types}"
-            raise ValueError(textwrap.fill(msg))
-
-        if (num_times := type_census["time"]) > 1:
-            msg = (
-                f"Invalid number of time axes: {num_times}. "
-                "Only 1 time axis is allowed."
-            )
-            raise ValueError(textwrap.fill(msg))
-
-        if (num_channels := type_census["channel"]) > 1:
-            msg = (
-                f"Invalid number of channel axes: {num_channels}. "
-                "Only 1 channel axis is allowed."
-            )
-            raise ValueError(textwrap.fill(msg))
-
-        custom_axes = set(axis_types) - set(AxisType._member_names_)
-        if len(custom_axes) > 1:
-            msg = (
-                f"Invalid number of custom axes: {custom_axes}. "
-                "Only 1 custom axis is allowed."
-            )
-            raise ValueError(textwrap.fill(msg))
-        return axes
-
-
-class MultiscaleAttrs(Attrs):
-    """
-    Attributes of a multiscale group.
-    See https://ngff.openmicroscopy.org/0.4/#multiscale-md
-    """
-
-    multiscales: List[Multiscale]
-
-
-class MultiscaleGroup(Group):
-    attrs: MultiscaleAttrs
-
-    @root_validator
-    def check_arrays_exist(cls, values: Dict[str, Any]) -> Dict[str, Any]:
-        children: List[Union[Array, Group]] = values["children"]
-        child_arrays = []
-        child_groups = []
-
-        for child in children:
-            if isinstance(child, Group):
-                child_groups.append(child)
-            else:
-                child_arrays.append(child)
-        child_array_names = [a.name for a in child_arrays]
-        multiscales: List[Multiscale] = values["attrs"].multiscales
-        for multiscale in multiscales:
-            for dataset in multiscale.datasets:
-                if (dpath := dataset.path) not in child_array_names:
-                    msg = (
-                        f"Dataset {dpath} was specified in multiscale metadata, "
-                        "but no array with that name was found in the children of "
-                        "that group. All arrays in multiscale metadata must be "
-                        "children of the group."
-                    )
-                    raise ValueError(textwrap.fill(msg))
-        return values
-
-    @root_validator
-    def check_array_ndim(cls, values: Dict[str, Any]) -> Dict[str, Any]:
-        array_children: Tuple[Array, ...] = tuple(
-            filter(lambda v: hasattr(v, "shape"), values["children"])
-        )
-        multiscales: List[Multiscale] = values["attrs"].multiscales
-
-        ndims = tuple(len(a.shape) for a in array_children)
-        if len(set(ndims)) > 1:
-            msg = (
-                "All arrays must have the same dimensionality. "
-                f"Got arrays with dimensionality {ndims}."
-            )
-            raise ValueError(textwrap.fill(msg))
-
-        # check that each transform has compatible rank
-        for multiscale in multiscales:
-            tforms = []
-            if multiscale.coordinateTransformations is not None:
-                tforms.extend(multiscale.coordinateTransformations)
-            for dataset in multiscale.datasets:
-                tforms.extend(dataset.coordinateTransformations)
-            for tform in tforms:
-                if hasattr(tform, "scale") or hasattr(tform, "translation"):
-                    tform = cast(
-                        Union[ctx.VectorScaleTransform, ctx.VectorTranslationTransform],
-                        tform,
-                    )
-                    if (tform_dims := ctx.get_transform_ndim(tform)) not in set(ndims):
-                        msg = (
-                            f"Transform {tform} has dimensionality {tform_dims} which "
-                            "does not match the dimensionality of the arrays in this "
-                            "group ({ndims}). Transform dimensionality must match "
-                            "array dimensionality."
-                        )
-                        raise ValueError(textwrap.fill(msg))
-        return values
diff --git a/src/pydantic_ome_ngff/v04/plate.py b/src/pydantic_ome_ngff/v04/plate.py
index 3f0fe48..01aaee6 100644
--- a/src/pydantic_ome_ngff/v04/plate.py
+++ b/src/pydantic_ome_ngff/v04/plate.py
@@ -1,15 +1,24 @@
 from __future__ import annotations
-from typing import List, Optional
 
-from pydantic import BaseModel, PositiveInt, NonNegativeInt
-from pydantic_ome_ngff.base import VersionedBase
+from pydantic import (
+    BaseModel,
+    NonNegativeInt,
+    PositiveInt,
+    ValidationError,
+    field_validator,
+)
+from pydantic_zarr.v2 import ArraySpec, GroupSpec
 
+from pydantic_ome_ngff.base import VersionedBase
 from pydantic_ome_ngff.v04.base import version
+from typing import Union
+
+import pydantic_ome_ngff.v04.well as well
 
 
 class Acquisition(BaseModel):
-    id: NonNegativeInt
-    name: Optional[str]
+    id: PositiveInt
+    name: str | None = None
     maximumfieldcount: PositiveInt
 
 
@@ -17,26 +26,43 @@ class Entry(BaseModel):
     name: str
 
 
-class Well(BaseModel):
+class WellMetadata(BaseModel):
     # must be {rowName}/{columnName}
     path: str
     rowIndex: NonNegativeInt
     columnIndex: NonNegativeInt
 
 
-class Plate(VersionedBase):
+class PlateMetadata(VersionedBase):
     """
     Plate metadata
     see https://ngff.openmicroscopy.org/0.4/#plate-md
     """
 
-    # we need to put the version here as a private class attribute because the version
-    # is not required by the spec...
+    # the version here as a private class attribute because the version is not required by the spec
     _version = version
-    version: Optional[str] = version
-    name: Optional[str]
-    acquisitions: List[Acquisition]
-    columns: List[Entry]
-    rows: List[Entry]
+    version: str | None = version
+    name: str | None = None
+    acquisitions: tuple[Acquisition, ...]
+    columns: tuple[Entry, ...]
+    rows: tuple[Entry, ...]
     field_count: PositiveInt
-    wells: List[Well]
+    wells: tuple[WellMetadata, ...]
+
+
+class GroupAttrs(BaseModel):
+    plate: PlateMetadata
+
+
+class Group(GroupSpec[GroupAttrs, Union[well.Group, GroupSpec, ArraySpec]]):
+    @field_validator("members", mode="after")
+    @classmethod
+    def contains_well_group(
+        cls, members: Union[Group, GroupSpec, ArraySpec]
+    ) -> Union[Group, GroupSpec, ArraySpec]:
+        """
+        Check that .members contains a WellGroup
+        """
+        if not any(map(lambda v: isinstance(v, well.Group), members.values())):
+            raise ValidationError
+        return members
diff --git a/src/pydantic_ome_ngff/v04/transform.py b/src/pydantic_ome_ngff/v04/transform.py
new file mode 100644
index 0000000..3217d64
--- /dev/null
+++ b/src/pydantic_ome_ngff/v04/transform.py
@@ -0,0 +1,179 @@
+from __future__ import annotations
+
+from typing import Annotated, Literal, Sequence, Union
+
+from pydantic import BeforeValidator
+
+from pydantic_ome_ngff.base import StrictBase
+
+from pydantic_ome_ngff.utils import ArrayLike, listify_numpy
+
+
+class Identity(StrictBase):
+    """
+    An identity transform. It has no parameters other than `type`, and no valid use according to the spec.
+
+    See [https://ngff.openmicroscopy.org/0.4/#trafo-md](https://ngff.openmicroscopy.org/0.4/#trafo-md) for the specification of this data structure.
+
+    Attributes
+    ----------
+
+    type: Literal["identity"]
+        The string "identity".
+
+    """
+
+    type: Literal["identity"] = "identity"
+
+
+class PathTranslation(StrictBase):
+    """
+    A translation transformation with a `path` field. The spec states that `path` should resolve to "binary data".
+
+    See [https://ngff.openmicroscopy.org/0.4/#trafo-md](https://ngff.openmicroscopy.org/0.4/#trafo-md) for the specification of this data structure.
+
+    Attributes
+    ----------
+
+    type: Literal["translation"]
+        The string "translation".
+    path: str
+        A string reference to something that can be interpreted as defining a translation transformation.
+    """
+
+    type: Literal["translation"] = "translation"
+    path: str
+
+
+class PathScale(StrictBase):
+    """
+    A scaling transformation with a `path` field. The spec states that `path` should resolve to "binary data".
+
+    See [https://ngff.openmicroscopy.org/0.4/#trafo-md](https://ngff.openmicroscopy.org/0.4/#trafo-md) for the specification of this data structure.
+
+    Attributes
+    ----------
+    type: Literal["scale"]
+        The string "scale".
+    path: str
+        A string reference to something that can be interpreted as defining a scaling transformation.
+    """
+
+    type: Literal["scale"] = "scale"
+    path: str
+
+
+class VectorTranslation(StrictBase):
+    """
+    A translation transformation defined by a sequence of numbers.
+
+    See [https://ngff.openmicroscopy.org/0.4/#trafo-md](https://ngff.openmicroscopy.org/0.4/#trafo-md) for the specification of this data structure.
+
+    Attributes
+    ----------
+    type: Literal["translation"]
+        The string "translation".
+    translation: tuple[float | int]
+        A sequence of numbers that define an N-dimensional translation transformation.
+    """
+
+    type: Literal["translation"] = "translation"
+    translation: Annotated[tuple[float | int, ...], BeforeValidator(listify_numpy)]
+
+
+class VectorScale(StrictBase):
+    """
+    A scaling transformation defined by a sequence of numbers.
+
+    See [https://ngff.openmicroscopy.org/0.4/#trafo-md](https://ngff.openmicroscopy.org/0.4/#trafo-md) for the specification of this data structure.
+
+    Attributes
+    ----------
+    type: Literal["scale"]
+        The string "scale".
+    scale: tuple[float | int]
+        A sequence of numbers that define an  N-dimensional scaling transformation.
+    """
+
+    type: Literal["scale"] = "scale"
+    scale: Annotated[tuple[float | int, ...], BeforeValidator(listify_numpy)]
+
+
+def ndim(
+    transform: VectorScale | VectorTranslation,
+) -> int:
+    """
+    Get the dimensionality of a `VectorScale` or `VectorTranslation`.
+    """
+    if hasattr(transform, "scale"):
+        return len(transform.scale)
+
+    else:
+        return len(transform.translation)
+
+
+def scale_translation(
+    scale: Sequence[float], translation: Sequence[float]
+) -> tuple[Scale, Translation]:
+    """
+    Create a `VectorScale` and a `VectorTranslation` from a scale and a translation
+    parameter.
+    """
+
+    len_scale = len(scale)
+    len_translation = len(translation)
+
+    if len_scale < 1:
+        msg = "Not enough values in scale. Got 0, expected at least 1."
+        raise ValueError(msg)
+
+    if len_translation < 1:
+        msg = "Not enough values in translation. Got 0, expected at least 1."
+        raise ValueError(msg)
+
+    vec_scale = VectorScale(scale=scale)
+
+    if len(translation) != len_scale:
+        msg = (
+            f"Length of `scale` and `translation` do not match. `scale` has length = {len_scale}"
+            f"but `translation` has length = {len_translation}"
+        )
+        raise ValueError(msg)
+
+    vec_trans = VectorTranslation(translation=translation)
+
+    return (vec_scale, vec_trans)
+
+
+Scale = Union[VectorScale, PathScale]
+Translation = Union[VectorTranslation, PathTranslation]
+Transform = Union[Scale, Translation]
+
+
+def ensure_dimensionality(
+    transforms: Sequence[VectorScale | VectorTranslation],
+) -> Sequence[VectorScale | VectorTranslation]:
+    """
+    Ensures that the elements in the input sequence define transformations with identical dimensionality.
+    """
+    ndims = tuple(ndim(tx) for tx in transforms)
+    ndims_set = set(ndims)
+    if len(ndims_set) > 1:
+        msg = (
+            "The transforms have inconsistent dimensionality. "
+            f"Got transforms with dimensionality = {ndims}."
+        )
+        raise ValueError(msg)
+    return transforms
+
+
+def array_transform_consistency(
+    array: ArrayLike, transforms: Sequence[VectorScale, VectorTranslation]
+) -> bool:
+    """
+    Check if an array is consistent, in terms of dimensionality, with a collection of transforms.
+    """
+    # check that the transforms are themselves consistent
+    transforms_checked = ensure_dimensionality(transforms=transforms)
+    # we only need to compare the array to the first transform
+    return len(array.shape) == ndim(transforms_checked[0])
diff --git a/src/pydantic_ome_ngff/v04/well.py b/src/pydantic_ome_ngff/v04/well.py
index 9c602f2..26bfd89 100644
--- a/src/pydantic_ome_ngff/v04/well.py
+++ b/src/pydantic_ome_ngff/v04/well.py
@@ -1,25 +1,43 @@
 from __future__ import annotations
-from typing import List, Optional
+from typing import Union
 
-from pydantic import BaseModel
-from pydantic_ome_ngff.base import VersionedBase
+from pydantic import BaseModel, ValidationError, field_validator
+from pydantic_zarr.v2 import ArraySpec, GroupSpec
 
+from pydantic_ome_ngff.base import VersionedBase
 from pydantic_ome_ngff.v04.base import version
+import pydantic_ome_ngff.v04.multiscale as multiscale
 
 
 class Image(BaseModel):
     path: str
-    acquisition: Optional[int]
+    acquisition: int | None
 
 
-class Well(VersionedBase):
+class WellMetadata(VersionedBase):
     """
     Well metadata.
     See https://ngff.openmicroscopy.org/0.4/#well-md
     """
 
-    # we need to put the version here as a private class attribute because the version
-    # is not required by the spec...
     _version = version
-    version: Optional[str] = version
-    images: List[Image]
+    version: str | None = version
+    images: tuple[Image, ...]
+
+
+class GroupAttrs(BaseModel):
+    well: WellMetadata
+
+
+class Group(GroupSpec[GroupAttrs, Union[multiscale.Group, GroupSpec, ArraySpec]]):
+    @field_validator("members", mode="after")
+    @classmethod
+    def contains_multiscale_group(
+        cls, members: Union[Group, GroupSpec, ArraySpec]
+    ) -> Union[Group, GroupSpec, ArraySpec]:
+        """
+        Check that .members contains a MultiscaleGroup
+        """
+        if not any(map(lambda v: isinstance(v, multiscale.Group), members.values())):
+            raise ValidationError
+        return members
diff --git a/tests/conftest.py b/tests/conftest.py
index d39d3a5..c5e2e12 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,11 +1,9 @@
-from typing import Tuple, Any, Optional, Union, List, Dict
-from pathlib import Path
-import json
+from typing import Any
 
 import requests
 
 
-def fetch_schemas(version: str, schema_name: str) -> Tuple[Any, Any]:
+def fetch_schemas(version: str, schema_name: str) -> tuple[Any, Any]:
     """
     Get the relaxed and strict schemas for a given version of the spec.
     """
@@ -16,25 +14,3 @@ def fetch_schemas(version: str, schema_name: str) -> Tuple[Any, Any]:
         f"https://ngff.openmicroscopy.org/{version}/schemas/{schema_name}.schema"
     ).json()
     return base_schema, strict_schema
-
-
-FIXTURE_DIR = Path(__file__).resolve().parent / "fixtures"
-
-Jso = Optional[Union[List["Jso"], Dict[str, "Jso"], int, float, bool, str]]
-
-
-class JsonLoader:
-    def __init__(self, root: Union[str, Path]) -> None:
-        self.root = FIXTURE_DIR.joinpath(root)
-
-    def _path(self, path: Union[str, Path]) -> Path:
-        p = self.root.joinpath(path)
-        if not p.name.lower().endswith(".json"):
-            p = p.with_name(p.name + ".json")
-        return p
-
-    def load_str(self, path: Union[str, Path]) -> str:
-        return self._path(path).read_text()
-
-    def load_obj(self, path: Union[str, Path]) -> dict[str, Jso]:
-        return json.loads(self.load_str(path))
diff --git a/tests/latest/__init__.py b/tests/latest/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/latest/test_multiscales.py b/tests/latest/test_multiscales.py
new file mode 100644
index 0000000..2d9ad04
--- /dev/null
+++ b/tests/latest/test_multiscales.py
@@ -0,0 +1,403 @@
+from __future__ import annotations
+from typing import List, Optional, Tuple
+from pydantic import ValidationError
+import pytest
+import jsonschema as jsc
+from pydantic_zarr.v2 import ArraySpec
+
+from pydantic_ome_ngff.latest.axis import Axis
+from pydantic_ome_ngff.latest.multiscale import (
+    MultiscaleMetadata,
+    GroupAttrs,
+    Dataset,
+    Group,
+)
+
+from pydantic_ome_ngff.latest.transform import (
+    Transform,
+    VectorScale,
+    VectorTranslation,
+)
+from tests.conftest import fetch_schemas
+
+
+@pytest.fixture
+def multi_meta() -> MultiscaleMetadata:
+    axes = [
+        Axis(name="z", type="space", unit="meter"),
+        Axis(name="x", type="space", unit="meter"),
+        Axis(name="y", type="space", unit="meter"),
+    ]
+    rank = len(axes)
+    num_datasets = 3
+    datasets = [
+        Dataset(
+            path=f"path{idx}",
+            coordinateTransformations=(
+                VectorScale(
+                    scale=[
+                        1,
+                    ]
+                    * rank
+                ),
+                VectorTranslation(
+                    translation=[
+                        0,
+                    ]
+                    * rank
+                ),
+            ),
+        )
+        for idx in range(num_datasets)
+    ]
+
+    multi = MultiscaleMetadata(
+        name="foo",
+        axes=axes,
+        datasets=datasets,
+        coordinateTransformations=(
+            VectorScale(
+                scale=[
+                    1,
+                ]
+                * rank
+            ),
+        ),
+    )
+    return multi
+
+
+def test_multiscale(multi_meta: MultiscaleMetadata) -> None:
+    _, strict_schema = fetch_schemas("latest", schema_name="image")
+    jsc.validate({"multiscales": [multi_meta.model_dump(mode="json")]}, strict_schema)
+
+
+def test_multiscale_unique_axis_names() -> None:
+    axes = [
+        Axis(name="y", type="space", unit="meter"),
+        Axis(name="x", type="space", unit="meter"),
+    ]
+
+    # this should be fine
+
+    datasets = [
+        Dataset(
+            path="path",
+            coordinateTransformations=(
+                VectorScale(scale=[1, 1, 1]),
+                VectorTranslation(translation=[0, 0, 0]),
+            ),
+        )
+    ]
+
+    MultiscaleMetadata(
+        name="foo",
+        axes=axes,
+        datasets=datasets,
+        coordinateTransformations=(VectorScale(scale=[1, 1, 1]),),
+    )
+
+    # make axis names collide
+    axes = [
+        Axis(name="x", type="space", unit="meter"),
+        Axis(name="x", type="space", unit="meter"),
+    ]
+    datasets = [
+        Dataset(
+            path="path",
+            coordinateTransformations=(
+                VectorScale(scale=[1, 1, 1]),
+                VectorTranslation(translation=[0, 0, 0]),
+            ),
+        )
+    ]
+
+    with pytest.raises(ValidationError, match="Axis names must be unique."):
+        MultiscaleMetadata(
+            name="foo",
+            axes=axes,
+            datasets=datasets,
+            coordinateTransformations=(VectorScale(scale=[1, 1, 1]),),
+        )
+
+
+@pytest.mark.parametrize(
+    "axis_types",
+    [
+        ("space", "space", "channel"),
+        ("space", "channel", "space", "channel"),
+    ],
+)
+def test_multiscale_space_axes_last(axis_types: List[Optional[str]]) -> None:
+    units_map = {"space": "meter", "time": "second"}
+    axes: list[Axis] = []
+    for idx, t in enumerate(axis_types):
+        if t is None or t == "channel":
+            ax = Axis(name=str(idx), type=t, unit=units_map.get(t, None))
+        else:
+            ax = Axis(name=str(idx), type=t, unit=units_map.get(t, None))
+        axes.append(ax)
+
+    rank = len(axes)
+    datasets = [
+        Dataset(
+            path="path",
+            coordinateTransformations=(
+                VectorScale(
+                    scale=[
+                        1,
+                    ]
+                    * rank
+                ),
+                VectorTranslation(
+                    translation=[
+                        0,
+                    ]
+                    * rank
+                ),
+            ),
+        )
+    ]
+    # TODO: make some axis-specifc exceptions
+    with pytest.raises(ValidationError, match="Space axes must come last."):
+        MultiscaleMetadata(
+            name="foo",
+            axes=axes,
+            datasets=datasets,
+            coordinateTransformations=(
+                VectorScale(
+                    scale=[
+                        1,
+                    ]
+                    * rank
+                ),
+            ),
+        )
+
+
+@pytest.mark.parametrize("num_axes", [0, 1, 6, 7])
+def test_multiscale_axis_length(num_axes: int) -> None:
+    rank = num_axes
+    axes = [Axis(name=str(idx), type="space", unit="meter") for idx in range(num_axes)]
+    datasets = [
+        Dataset(
+            path="path",
+            coordinateTransformations=(
+                VectorScale(
+                    scale=[
+                        1,
+                    ]
+                    * rank
+                ),
+                VectorTranslation(
+                    translation=[
+                        0,
+                    ]
+                    * rank
+                ),
+            ),
+        )
+    ]
+    with pytest.raises(ValidationError, match="Incorrect number of axes provided"):
+        MultiscaleMetadata(
+            name="foo",
+            axes=axes,
+            datasets=datasets,
+            coordinateTransformations=(
+                VectorScale(
+                    scale=[
+                        1,
+                    ]
+                    * rank
+                ),
+            ),
+        )
+
+
+def test_coordinate_transforms_invalid_ndims() -> None:
+    tforms = [
+        VectorScale(scale=(1, 1)),
+        VectorTranslation(translation=(1, 1, 1)),
+    ]
+    with pytest.raises(
+        ValidationError,
+        match="The transforms have inconsistent dimensionality.",  # noqa
+    ):
+        Dataset(path="foo", coordinateTransformations=tforms)
+
+
+@pytest.mark.parametrize(
+    "transforms",
+    [
+        [
+            VectorScale(scale=(1, 1, 1)),
+            VectorTranslation(translation=(1, 1, 1)),
+            VectorTranslation(translation=(1, 1, 1)),
+        ],
+        [
+            VectorScale(scale=(1, 1, 1)),
+        ]
+        * 5,
+    ],
+)
+def test_coordinate_transforms_invalid_length(
+    transforms: List[Transform],
+) -> None:
+    with pytest.raises(
+        ValidationError, match=f"after validation, not {len(transforms)}"
+    ):
+        Dataset(path="foo", coordinateTransformations=transforms)
+
+
+@pytest.mark.parametrize(
+    "transforms",
+    [
+        [
+            VectorTranslation(translation=(1, 1, 1)),
+        ]
+        * 2,
+        [
+            VectorTranslation(translation=(1, 1, 1)),
+            VectorScale(scale=(1, 1, 1)),
+        ],
+    ],
+)
+def test_coordinate_transforms_invalid_first_element(
+    transforms: Tuple[Transform, Transform],
+) -> None:
+    with pytest.raises(
+        ValidationError,
+        match="Input should be a valid dictionary or instance of VectorScale",
+    ):
+        Dataset(path="foo", coordinateTransformations=transforms)
+
+
+@pytest.mark.parametrize(
+    "transforms",
+    (
+        [
+            VectorScale(scale=(1, 1, 1)),
+            VectorScale(scale=(1, 1, 1)),
+        ],
+    ),
+)
+def test_coordinate_transforms_invalid_second_element(
+    transforms: Tuple[Transform, Transform],
+) -> None:
+    with pytest.raises(
+        ValidationError,
+        match="Input should be a valid dictionary or instance of VectorTranslation",
+    ):
+        Dataset(path="foo", coordinateTransformations=transforms)
+
+
+def test_multiscale_group_datasets_exist(
+    multi_meta: MultiscaleMetadata,
+) -> None:
+    meta_ndim = len(multi_meta.axes)
+    group_attrs = GroupAttrs(multiscales=[multi_meta])
+    good_items = {
+        d.path: ArraySpec(
+            shape=(1,) * meta_ndim, dtype="uint8", chunks=(1,) * meta_ndim
+        )
+        for d in multi_meta.datasets
+    }
+    Group(attributes=group_attrs, members=good_items)
+
+    bad_items = {
+        d.path + "x": ArraySpec(
+            shape=(1,) * meta_ndim, dtype="uint8", chunks=(1,) * meta_ndim
+        )
+        for d in multi_meta.datasets
+    }
+
+    with pytest.raises(
+        ValidationError,
+        match="array with that name was found in the hierarchy.",
+    ):
+        Group(attributes=group_attrs, members=bad_items)
+
+
+def test_multiscale_group_datasets_rank(multi_meta: MultiscaleMetadata) -> None:
+    meta_ndim = len(multi_meta.axes)
+    group_attrs = GroupAttrs(multiscales=[multi_meta])
+    good_items = {
+        d.path: ArraySpec(
+            shape=(1,) * meta_ndim, dtype="uint8", chunks=(1,) * meta_ndim
+        )
+        for d in multi_meta.datasets
+    }
+    Group(attributes=group_attrs, members=good_items)
+
+    match = "Transform dimensionality must match array dimensionality."
+    # arrays with varying rank
+    bad_items = {
+        d.path: ArraySpec(
+            shape=(1,) * (idx + 1),
+            dtype="uint8",
+            chunks=(1,) * (idx + 1),
+        )
+        for idx, d in enumerate(multi_meta.datasets)
+    }
+
+    with pytest.raises(ValidationError, match=match):
+        Group(attributes=group_attrs, members=bad_items)
+
+    with pytest.raises(ValidationError, match=match):
+        # arrays with rank that doesn't match the transform
+        bad_items = {
+            d.path: ArraySpec(
+                shape=(1,),
+                dtype="uint8",
+                chunks=(1,),
+            )
+            for d in multi_meta.datasets
+        }
+        Group(attributes=group_attrs, members=bad_items)
+
+
+@pytest.fixture
+def default_multiscale() -> MultiscaleMetadata:
+    axes = [
+        Axis(name="c", type="channel", unit=None),
+        Axis(name="z", type="space", unit="meter"),
+        Axis(name="x", type="space", unit="meter"),
+        Axis(name="y", type="space", unit="meter"),
+    ]
+    rank = len(axes)
+    num_datasets = 3
+    datasets = [
+        Dataset(
+            path=f"path{idx}",
+            coordinateTransformations=(
+                VectorScale(
+                    scale=[
+                        1,
+                    ]
+                    * rank
+                ),
+                VectorTranslation(
+                    translation=[
+                        0,
+                    ]
+                    * rank
+                ),
+            ),
+        )
+        for idx in range(num_datasets)
+    ]
+
+    multi = MultiscaleMetadata(
+        name="foo",
+        axes=axes,
+        datasets=datasets,
+        coordinateTransformations=(
+            VectorScale(
+                scale=[
+                    1,
+                ]
+                * rank
+            ),
+        ),
+    )
+    return multi
diff --git a/tests/test_docs.py b/tests/test_docs.py
new file mode 100644
index 0000000..9429392
--- /dev/null
+++ b/tests/test_docs.py
@@ -0,0 +1,7 @@
+import pytest
+from pytest_examples import find_examples, CodeExample, EvalExample
+
+
+@pytest.mark.parametrize("example", find_examples("docs"), ids=str)
+def test_docstrings(example: CodeExample, eval_example: EvalExample):
+    eval_example.run_print_check(example)
diff --git a/tests/test_latest.py b/tests/test_latest.py
deleted file mode 100644
index 3745696..0000000
--- a/tests/test_latest.py
+++ /dev/null
@@ -1,369 +0,0 @@
-from __future__ import annotations
-from typing import Optional, Tuple, List, Type
-import jsonschema as jsc
-import pytest
-from pydantic import ValidationError, BaseModel
-from pydantic_ome_ngff.tree import Array
-from pydantic_ome_ngff.latest.multiscales import (
-    Multiscale,
-    MultiscaleAttrs,
-    MultiscaleDataset,
-    MultiscaleGroup,
-)
-from pydantic_ome_ngff.latest.well import Well
-from pydantic_ome_ngff.latest.plate import Plate
-from pydantic_ome_ngff.latest.imageLabel import ImageLabel
-from pydantic_ome_ngff.latest.coordinateTransformations import (
-    CoordinateTransform,
-    VectorScaleTransform,
-    VectorTranslationTransform,
-)
-from pydantic_ome_ngff.latest.axes import Axis
-from .conftest import fetch_schemas, JsonLoader
-
-loader = JsonLoader("latest")
-
-
-@pytest.fixture
-def default_multiscale():
-    axes = [
-        Axis(name="z", type="space", unit="meter"),
-        Axis(name="x", type="space", unit="meter"),
-        Axis(name="y", type="space", unit="meter"),
-    ]
-    rank = len(axes)
-    num_datasets = 3
-    datasets = [
-        MultiscaleDataset(
-            path=f"path{idx}",
-            coordinateTransformations=[
-                VectorScaleTransform(
-                    scale=[
-                        1,
-                    ]
-                    * rank
-                ),
-                VectorTranslationTransform(
-                    translation=[
-                        0,
-                    ]
-                    * rank
-                ),
-            ],
-        )
-        for idx in range(num_datasets)
-    ]
-
-    multi = Multiscale(
-        name="foo",
-        axes=axes,
-        datasets=datasets,
-        coordinateTransformations=[
-            VectorScaleTransform(
-                scale=[
-                    1,
-                ]
-                * rank
-            ),
-        ],
-    )
-    return multi
-
-
-def test_multiscale(default_multiscale):
-    base_schema, strict_schema = fetch_schemas("latest", schema_name="image")
-    jsc.validate({"multiscales": [default_multiscale.dict()]}, strict_schema)
-
-
-def test_multiscale_unique_axis_names():
-
-    axes = [
-        Axis(name="y", type="space", unit="meter"),
-        Axis(name="x", type="space", unit="meter"),
-    ]
-
-    datasets = [
-        MultiscaleDataset(
-            path="path",
-            coordinateTransformations=[
-                VectorScaleTransform(scale=[1, 1, 1]),
-                VectorTranslationTransform(translation=[0, 0, 0]),
-            ],
-        )
-    ]
-
-    Multiscale(
-        name="foo",
-        axes=axes,
-        datasets=datasets,
-        coordinateTransformations=[
-            VectorScaleTransform(scale=[1, 1, 1]),
-        ],
-    )
-
-    # make axis names collide
-    axes = [
-        Axis(name="x", type="space", unit="meter"),
-        Axis(name="x", type="space", unit="meter"),
-    ]
-    datasets = [
-        MultiscaleDataset(
-            path="path",
-            coordinateTransformations=[
-                VectorScaleTransform(scale=[1, 1, 1]),
-                VectorTranslationTransform(translation=[0, 0, 0]),
-            ],
-        )
-    ]
-
-    with pytest.raises(ValidationError, match="Axis names must be unique."):
-        Multiscale(
-            name="foo",
-            axes=axes,
-            datasets=datasets,
-            coordinateTransformations=[
-                VectorScaleTransform(scale=[1, 1, 1]),
-            ],
-        )
-
-
-@pytest.mark.parametrize(
-    "axis_types",
-    (
-        ("space", "space", "channel"),
-        ("space", "channel", "space", "channel"),
-        (None, "space", "space", None),
-        ("time", "time"),
-    ),
-)
-def test_multiscale_semantic_axis_order(axis_types: List[Optional[str]]):
-    units_map = {"space": "meter", "time": "second"}
-    axes: list[Axis] = []
-    for idx, t in enumerate(axis_types):
-        if t is None or t == "channel":
-            with pytest.warns(UserWarning, match="Null"):
-                ax = Axis(name=str(idx), type=t, unit=units_map.get(t, None))
-        else:
-            ax = Axis(name=str(idx), type=t, unit=units_map.get(t, None))
-        axes.append(ax)
-
-    rank = len(axes)
-    datasets = [
-        MultiscaleDataset(
-            path="path",
-            coordinateTransformations=[
-                VectorScaleTransform(
-                    scale=[
-                        1,
-                    ]
-                    * rank
-                ),
-                VectorTranslationTransform(
-                    translation=[
-                        0,
-                    ]
-                    * rank
-                ),
-            ],
-        )
-    ]
-    # TODO: make some axis-specifc exceptions
-    with pytest.raises(ValidationError):
-        Multiscale(
-            name="foo",
-            axes=axes,
-            datasets=datasets,
-            coordinateTransformations=[
-                VectorScaleTransform(
-                    scale=[
-                        1,
-                    ]
-                    * rank
-                ),
-            ],
-        )
-
-
-@pytest.mark.parametrize("num_axes", (0, 1, 6, 7))
-def test_multiscale_axis_length(num_axes: int):
-    rank = num_axes
-    axes = [Axis(name=str(idx), type="space", unit="meter") for idx in range(num_axes)]
-    datasets = [
-        MultiscaleDataset(
-            path="path",
-            coordinateTransformations=[
-                VectorScaleTransform(
-                    scale=[
-                        1,
-                    ]
-                    * rank
-                ),
-                VectorTranslationTransform(
-                    translation=[
-                        0,
-                    ]
-                    * rank
-                ),
-            ],
-        )
-    ]
-    with pytest.raises(ValidationError, match="type=value_error.list"):
-        Multiscale(
-            name="foo",
-            axes=axes,
-            datasets=datasets,
-            coordinateTransformations=[
-                VectorScaleTransform(
-                    scale=[
-                        1,
-                    ]
-                    * rank
-                ),
-            ],
-        )
-
-
-def test_coordinate_transforms_invalid_ndims():
-    tforms = [
-        VectorScaleTransform(scale=(1, 1)),
-        VectorTranslationTransform(translation=(1, 1, 1)),
-    ]
-    with pytest.raises(ValidationError, match="Got elements with dimensionality"):
-        MultiscaleDataset(path="foo", coordinateTransformations=tforms)
-
-
-@pytest.mark.parametrize(
-    "transforms",
-    (
-        [
-            VectorTranslationTransform(translation=(1, 1, 1)),
-        ]
-        * 3,
-        [
-            VectorScaleTransform(scale=(1, 1, 1)),
-        ]
-        * 5,
-        [
-            VectorScaleTransform(scale=(1, 1, 1)),
-            VectorTranslationTransform(translation=(1, 1, 1)),
-            VectorTranslationTransform(translation=(1, 1, 1)),
-        ],
-    ),
-)
-def test_coordinate_transforms_invalid_lenght(
-    transforms: Tuple[CoordinateTransform, CoordinateTransform]
-):
-    with pytest.raises(ValidationError, match="ensure this value has at most 2 items"):
-        MultiscaleDataset(path="foo", coordinateTransformations=transforms)
-
-
-@pytest.mark.parametrize(
-    "transforms",
-    (
-        [
-            VectorTranslationTransform(translation=(1, 1, 1)),
-        ]
-        * 2,
-        [
-            VectorScaleTransform(scale=(1, 1, 1)),
-        ]
-        * 2,
-        [
-            VectorTranslationTransform(translation=(1, 1, 1)),
-            VectorScaleTransform(scale=(1, 1, 1)),
-        ],
-    ),
-)
-def test_coordinate_transforms_invalid_elements(
-    transforms: Tuple[CoordinateTransform, CoordinateTransform]
-):
-    with pytest.raises(
-        ValidationError, match="element of coordinateTransformations must be a"
-    ):
-        MultiscaleDataset(path="foo", coordinateTransformations=transforms)
-
-
-def test_multiscale_group_datasets_exist(default_multiscale: Multiscale):
-    good_children = [
-        Array(name=d.path, shape=(1,) * len(default_multiscale.datasets), dtype="")
-        for d in default_multiscale.datasets
-    ]
-    MultiscaleGroup(
-        name="",
-        attrs={"multiscales": [default_multiscale.dict()]},
-        children=good_children,
-    )
-
-    with pytest.raises(
-        ValidationError,
-        match="must be children of the group.",
-    ):
-        bad_children = [
-            Array(name=d.path + "bla", shape=(1, 1, 1, 1), dtype="")
-            for d in default_multiscale.datasets
-        ]
-        MultiscaleGroup(
-            name="",
-            attrs={"multiscales": [default_multiscale.dict()]},
-            children=bad_children,
-        )
-
-
-def test_multiscale_group_datasets_rank(default_multiscale: Multiscale):
-    good_children = [
-        Array(name=d.path, shape=(1,) * len(default_multiscale.datasets), dtype="")
-        for d in default_multiscale.datasets
-    ]
-    MultiscaleGroup(
-        name="",
-        attrs={"multiscales": [default_multiscale.dict()]},
-        children=good_children,
-    )
-
-    with pytest.raises(
-        ValidationError, match="All arrays must have the same dimensionality."
-    ):
-        # arrays with varying rank
-        bad_children = [
-            Array(name=d.path, shape=(1,) * (idx + 1), dtype="")
-            for idx, d in enumerate(default_multiscale.datasets)
-        ]
-        MultiscaleGroup(
-            name="",
-            attrs={"multiscales": [default_multiscale.dict()]},
-            children=bad_children,
-        )
-
-    with pytest.raises(ValidationError, match="Transform dimensionality"):
-        # arrays with rank that doesn't match the transform
-        bad_children = [
-            Array(name=d.path, shape=(1,), dtype="")
-            for d in default_multiscale.datasets
-        ]
-        MultiscaleGroup(
-            name="",
-            attrs={"multiscales": [default_multiscale.dict()]},
-            children=bad_children,
-        )
-
-
-@pytest.mark.parametrize(
-    ("fname", "key", "Class"),
-    [
-        ("image-label", "image-label", ImageLabel),
-        ("plate0", "plate", Plate),
-        ("plate1", "plate", Plate),
-        ("well0", "well", Well),
-        ("well1", "well", Well),
-        (
-            "multiscales",
-            None,
-            MultiscaleAttrs,
-        ),
-    ],
-)
-def test_examples(fname: str, key: str, Class: Type[BaseModel]):
-    obj = loader.load_obj(fname)
-    if key is not None:
-        obj = obj[key]
-    Class.parse_obj(obj)
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 0000000..8ae66fd
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,29 @@
+from __future__ import annotations
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from typing import Any, List
+
+from pydantic_ome_ngff.utils import duplicates, listify_numpy
+import pytest
+
+
+@pytest.mark.parametrize(
+    "data", [[0], [0, 1, 1, 1, 2], ["a", "a", "b", "b", "c", "c", "d"]]
+)
+def test_duplicates(data: List[Any]) -> None:
+    dupes = duplicates(data)
+    for key, value in dupes.items():
+        assert data.count(key) == value
+        assert value > 1
+
+
+@pytest.mark.parametrize("data", [np.arange(100), "100", np.dtype("int"), (0, 1, 2, 3)])
+def test_listify_numpy(data) -> None:
+    observed = listify_numpy(data)
+    if isinstance(data, np.ndarray):
+        assert observed == data.tolist()
+    else:
+        assert observed == data
diff --git a/tests/test_v04.py b/tests/test_v04.py
deleted file mode 100644
index 067c5c8..0000000
--- a/tests/test_v04.py
+++ /dev/null
@@ -1,372 +0,0 @@
-from __future__ import annotations
-from typing import Optional, Tuple, List, Type
-import jsonschema as jsc
-import pytest
-from pydantic import ValidationError, BaseModel
-from pydantic_ome_ngff.tree import Array
-from pydantic_ome_ngff.v04.imageLabel import ImageLabel
-from pydantic_ome_ngff.v04.plate import Plate
-from pydantic_ome_ngff.v04.well import Well
-from .conftest import fetch_schemas, JsonLoader
-from pydantic_ome_ngff.v04.multiscales import (
-    Multiscale,
-    MultiscaleAttrs,
-    MultiscaleDataset,
-    MultiscaleGroup,
-)
-from pydantic_ome_ngff.v04.coordinateTransformations import (
-    CoordinateTransform,
-    VectorScaleTransform,
-    VectorTranslationTransform,
-)
-from pydantic_ome_ngff.v04.axes import Axis
-
-loader = JsonLoader("v04")
-
-
-@pytest.fixture
-def default_multiscale():
-    axes = [
-        Axis(name="z", type="space", unit="meter"),
-        Axis(name="x", type="space", unit="meter"),
-        Axis(name="y", type="space", unit="meter"),
-    ]
-    rank = len(axes)
-    num_datasets = 3
-    datasets = [
-        MultiscaleDataset(
-            path=f"path{idx}",
-            coordinateTransformations=[
-                VectorScaleTransform(
-                    scale=[
-                        1,
-                    ]
-                    * rank
-                ),
-                VectorTranslationTransform(
-                    translation=[
-                        0,
-                    ]
-                    * rank
-                ),
-            ],
-        )
-        for idx in range(num_datasets)
-    ]
-
-    multi = Multiscale(
-        name="foo",
-        axes=axes,
-        datasets=datasets,
-        coordinateTransformations=[
-            VectorScaleTransform(
-                scale=[
-                    1,
-                ]
-                * rank
-            ),
-        ],
-    )
-    return multi
-
-
-def test_multiscale(default_multiscale):
-    base_schema, strict_schema = fetch_schemas("0.4", schema_name="image")
-    # breakpoint()
-    jsc.validate({"multiscales": [default_multiscale.dict()]}, strict_schema)
-
-
-def test_multiscale_unique_axis_names():
-
-    axes = [
-        Axis(name="y", type="space", unit="meter"),
-        Axis(name="x", type="space", unit="meter"),
-    ]
-
-    # this should be fine
-
-    datasets = [
-        MultiscaleDataset(
-            path="path",
-            coordinateTransformations=[
-                VectorScaleTransform(scale=[1, 1, 1]),
-                VectorTranslationTransform(translation=[0, 0, 0]),
-            ],
-        )
-    ]
-
-    Multiscale(
-        name="foo",
-        axes=axes,
-        datasets=datasets,
-        coordinateTransformations=[
-            VectorScaleTransform(scale=[1, 1, 1]),
-        ],
-    )
-
-    # make axis names collide
-    axes = [
-        Axis(name="x", type="space", unit="meter"),
-        Axis(name="x", type="space", unit="meter"),
-    ]
-    datasets = [
-        MultiscaleDataset(
-            path="path",
-            coordinateTransformations=[
-                VectorScaleTransform(scale=[1, 1, 1]),
-                VectorTranslationTransform(translation=[0, 0, 0]),
-            ],
-        )
-    ]
-
-    with pytest.raises(ValidationError, match="Axis names must be unique."):
-        Multiscale(
-            name="foo",
-            axes=axes,
-            datasets=datasets,
-            coordinateTransformations=[
-                VectorScaleTransform(scale=[1, 1, 1]),
-            ],
-        )
-
-
-@pytest.mark.parametrize(
-    "axis_types",
-    (
-        ("space", "space", "channel"),
-        ("space", "channel", "space", "channel"),
-        (None, "space", "space", None),
-        ("time", "time"),
-    ),
-)
-def test_multiscale_semantic_axis_order(axis_types: List[Optional[str]]):
-    units_map = {"space": "meter", "time": "second"}
-    axes: list[Axis] = []
-    for idx, t in enumerate(axis_types):
-        if t is None or t == "channel":
-            with pytest.warns(UserWarning, match="Null"):
-                ax = Axis(name=str(idx), type=t, unit=units_map.get(t, None))
-        else:
-            ax = Axis(name=str(idx), type=t, unit=units_map.get(t, None))
-        axes.append(ax)
-
-    rank = len(axes)
-    datasets = [
-        MultiscaleDataset(
-            path="path",
-            coordinateTransformations=[
-                VectorScaleTransform(
-                    scale=[
-                        1,
-                    ]
-                    * rank
-                ),
-                VectorTranslationTransform(
-                    translation=[
-                        0,
-                    ]
-                    * rank
-                ),
-            ],
-        )
-    ]
-    # TODO: make some axis-specifc exceptions
-    with pytest.raises(ValidationError):
-        Multiscale(
-            name="foo",
-            axes=axes,
-            datasets=datasets,
-            coordinateTransformations=[
-                VectorScaleTransform(
-                    scale=[
-                        1,
-                    ]
-                    * rank
-                ),
-            ],
-        )
-
-
-@pytest.mark.parametrize("num_axes", (0, 1, 6, 7))
-def test_multiscale_axis_length(num_axes: int):
-    rank = num_axes
-    axes = [Axis(name=str(idx), type="space", unit="meter") for idx in range(num_axes)]
-    datasets = [
-        MultiscaleDataset(
-            path="path",
-            coordinateTransformations=[
-                VectorScaleTransform(
-                    scale=[
-                        1,
-                    ]
-                    * rank
-                ),
-                VectorTranslationTransform(
-                    translation=[
-                        0,
-                    ]
-                    * rank
-                ),
-            ],
-        )
-    ]
-    with pytest.raises(ValidationError, match="type=value_error.list"):
-        Multiscale(
-            name="foo",
-            axes=axes,
-            datasets=datasets,
-            coordinateTransformations=[
-                VectorScaleTransform(
-                    scale=[
-                        1,
-                    ]
-                    * rank
-                ),
-            ],
-        )
-
-
-def test_coordinate_transforms_invalid_ndims():
-    tforms = [
-        VectorScaleTransform(scale=(1, 1)),
-        VectorTranslationTransform(translation=(1, 1, 1)),
-    ]
-    with pytest.raises(ValidationError, match="Got elements with dimensionality"):
-        MultiscaleDataset(path="foo", coordinateTransformations=tforms)
-
-
-@pytest.mark.parametrize(
-    "transforms",
-    (
-        [
-            VectorTranslationTransform(translation=(1, 1, 1)),
-        ]
-        * 3,
-        [
-            VectorScaleTransform(scale=(1, 1, 1)),
-        ]
-        * 5,
-        [
-            VectorScaleTransform(scale=(1, 1, 1)),
-            VectorTranslationTransform(translation=(1, 1, 1)),
-            VectorTranslationTransform(translation=(1, 1, 1)),
-        ],
-    ),
-)
-def test_coordinate_transforms_invalid_lenght(
-    transforms: Tuple[CoordinateTransform, CoordinateTransform]
-):
-    with pytest.raises(ValidationError, match="ensure this value has at most 2 items"):
-        MultiscaleDataset(path="foo", coordinateTransformations=transforms)
-
-
-@pytest.mark.parametrize(
-    "transforms",
-    (
-        [
-            VectorTranslationTransform(translation=(1, 1, 1)),
-        ]
-        * 2,
-        [
-            VectorScaleTransform(scale=(1, 1, 1)),
-        ]
-        * 2,
-        [
-            VectorTranslationTransform(translation=(1, 1, 1)),
-            VectorScaleTransform(scale=(1, 1, 1)),
-        ],
-    ),
-)
-def test_coordinate_transforms_invalid_elements(
-    transforms: Tuple[CoordinateTransform, CoordinateTransform]
-):
-    with pytest.raises(
-        ValidationError, match="element of coordinateTransformations must be a"
-    ):
-        MultiscaleDataset(path="foo", coordinateTransformations=transforms)
-
-
-def test_multiscale_group_datasets_exist(default_multiscale: Multiscale):
-    good_children = [
-        Array(name=d.path, shape=[1] * len(default_multiscale.datasets), dtype="")
-        for d in default_multiscale.datasets
-    ]
-    MultiscaleGroup(
-        name="",
-        attrs={"multiscales": [default_multiscale.dict()]},
-        children=good_children,
-    )
-
-    with pytest.raises(
-        ValidationError,
-        match="must be children of the group",
-    ):
-        bad_children = [
-            Array(name=d.path + "bla", shape=(1, 1, 1, 1), dtype="")
-            for d in default_multiscale.datasets
-        ]
-        MultiscaleGroup(
-            name="",
-            attrs={"multiscales": [default_multiscale.dict()]},
-            children=bad_children,
-        )
-
-
-def test_multiscale_group_datasets_rank(default_multiscale: Multiscale):
-    good_children = [
-        Array(name=d.path, shape=(1,) * len(default_multiscale.datasets), dtype="")
-        for d in default_multiscale.datasets
-    ]
-    MultiscaleGroup(
-        name="",
-        attrs={"multiscales": [default_multiscale.dict()]},
-        children=good_children,
-    )
-
-    with pytest.raises(
-        ValidationError, match="All arrays must have the same dimensionality."
-    ):
-        # arrays with varying rank
-        bad_children = [
-            Array(name=d.path, shape=(1,) * (idx + 1), dtype="")
-            for idx, d in enumerate(default_multiscale.datasets)
-        ]
-        MultiscaleGroup(
-            name="",
-            attrs={"multiscales": [default_multiscale.dict()]},
-            children=bad_children,
-        )
-
-    with pytest.raises(ValidationError, match="Transform dimensionality"):
-        # arrays with rank that doesn't match the transform
-        bad_children = [
-            Array(name=d.path, shape=(1,), dtype="")
-            for d in default_multiscale.datasets
-        ]
-        MultiscaleGroup(
-            name="",
-            attrs={"multiscales": [default_multiscale.dict()]},
-            children=bad_children,
-        )
-
-
-@pytest.mark.parametrize(
-    ("fname", "key", "Class"),
-    [
-        ("image-label", "image-label", ImageLabel),
-        ("plate0", "plate", Plate),
-        ("plate1", "plate", Plate),
-        ("well0", "well", Well),
-        ("well1", "well", Well),
-        (
-            "multiscales",
-            None,
-            MultiscaleAttrs,
-        ),
-    ],
-)
-def test_examples(fname: str, key: str, Class: Type[BaseModel]):
-    obj = loader.load_obj(fname)
-    if key is not None:
-        obj = obj[key]
-    Class.parse_obj(obj)
diff --git a/tests/v04/__init__.py b/tests/v04/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/v04/test_label.py b/tests/v04/test_label.py
new file mode 100644
index 0000000..66c4434
--- /dev/null
+++ b/tests/v04/test_label.py
@@ -0,0 +1,55 @@
+from __future__ import annotations
+from typing import List, Literal
+import pytest
+from pydantic import ValidationError
+from pydantic_ome_ngff.v04 import version as NGFF_VERSION
+
+from pydantic_ome_ngff.v04.label import Color, ImageLabel, Property
+
+
+@pytest.mark.parametrize("version", (None, "0.4"))
+def test_imagelabel(version: Literal["0.4"] | None):
+    color = Color(label_value=1, rgba=[0, 0, 0, 0])
+    model = ImageLabel(colors=[color], version=version)
+    dumped = model.model_dump()
+
+    assert dumped["colors"] == (color.model_dump(),)
+    # check that if the version is None, then we didn't write it out
+    if version == "0.4":
+        assert dumped["version"] == NGFF_VERSION
+
+
+def test_properties_colors_match():
+    color = Color(label_value=0, rgba=(0, 0, 0, 0))
+    prop = Property(label_value=1)
+
+    with pytest.raises(
+        ValidationError,
+        match="Inconsistent `label_value` attributes in `colors` and `properties`.",
+    ):
+        ImageLabel(colors=[color], properties=[prop])
+
+
+@pytest.mark.parametrize("version", ("0.5", "0.3"))
+def test_imagelabel_version(version: str) -> None:
+    with pytest.raises(ValidationError, match="Input should be '0.4'"):
+        _ = ImageLabel(version=version)
+
+
+@pytest.mark.parametrize(
+    "colors",
+    (
+        None,
+        [
+            Color(label_value=10, rgba=[0, 0, 0, 0]),
+            Color(label_value=10, rgba=[0, 0, 0, 0]),
+        ],
+    ),
+)
+def test_imagelabel_colors(colors: List[Color] | None):
+    if colors is None:
+        with pytest.warns(UserWarning):
+            ImageLabel(colors=colors)
+    else:
+        with pytest.raises(ValidationError, match="Duplicated label-value"):
+            ImageLabel(colors=colors)
diff --git a/tests/v04/test_multiscales.py b/tests/v04/test_multiscales.py
new file mode 100644
index 0000000..cec131a
--- /dev/null
+++ b/tests/v04/test_multiscales.py
@@ -0,0 +1,477 @@
+from __future__ import annotations
+from typing import Dict, Literal, Tuple
+from pydantic import ValidationError
+import pytest
+import jsonschema as jsc
+from zarr.util import guess_chunks
+from pydantic_zarr.v2 import ArraySpec
+from pydantic_ome_ngff.v04.multiscale import (
+    MultiscaleMetadata,
+    GroupAttrs,
+    Dataset,
+    Group,
+)
+
+from pydantic_ome_ngff.v04.transform import (
+    Transform,
+    VectorScale,
+    VectorTranslation,
+)
+from pydantic_ome_ngff.v04.axis import Axis
+from tests.conftest import fetch_schemas
+import numpy as np
+from itertools import accumulate
+import operator
+
+
+@pytest.fixture
+def default_multiscale() -> MultiscaleMetadata:
+    axes = [
+        Axis(name="c", type="channel", unit=None),
+        Axis(name="z", type="space", unit="meter"),
+        Axis(name="x", type="space", unit="meter"),
+        Axis(name="y", type="space", unit="meter"),
+    ]
+    rank = len(axes)
+    num_datasets = 3
+    datasets = [
+        Dataset(
+            path=f"path{idx}",
+            coordinateTransformations=(
+                VectorScale(
+                    scale=[
+                        1,
+                    ]
+                    * rank
+                ),
+                VectorTranslation(
+                    translation=[
+                        0,
+                    ]
+                    * rank
+                ),
+            ),
+        )
+        for idx in range(num_datasets)
+    ]
+
+    multi = MultiscaleMetadata(
+        name="foo",
+        axes=axes,
+        datasets=datasets,
+        coordinateTransformations=(
+            VectorScale(
+                scale=[
+                    1,
+                ]
+                * rank
+            ),
+        ),
+    )
+    return multi
+
+
+def test_multiscale(default_multiscale: MultiscaleMetadata) -> None:
+    base_schema, strict_schema = fetch_schemas("0.4", schema_name="image")
+    jsc.validate(
+        {"multiscales": [default_multiscale.model_dump(mode="json")]}, strict_schema
+    )
+
+
+def test_multiscale_unique_axis_names() -> None:
+    axes = [
+        Axis(name="y", type="space", unit="meter"),
+        Axis(name="x", type="space", unit="meter"),
+    ]
+
+    # this should be fine
+
+    datasets = [
+        Dataset(
+            path="path",
+            coordinateTransformations=(
+                VectorScale(scale=[1, 1, 1]),
+                VectorTranslation(translation=[0, 0, 0]),
+            ),
+        )
+    ]
+
+    MultiscaleMetadata(
+        name="foo",
+        axes=axes,
+        datasets=datasets,
+        coordinateTransformations=(VectorScale(scale=[1, 1, 1]),),
+    )
+
+    # make axis names collide
+    axes = [
+        Axis(name="x", type="space", unit="meter"),
+        Axis(name="x", type="space", unit="meter"),
+    ]
+    datasets = [
+        Dataset(
+            path="path",
+            coordinateTransformations=(
+                VectorScale(scale=[1, 1]),
+                VectorTranslation(translation=[0, 0]),
+            ),
+        )
+    ]
+
+    with pytest.raises(ValidationError, match="Axis names must be unique."):
+        MultiscaleMetadata(
+            name="foo",
+            axes=axes,
+            datasets=datasets,
+            coordinateTransformations=(VectorScale(scale=[1, 1]),),
+        )
+
+
+@pytest.mark.parametrize(
+    "axis_types",
+    [
+        ("space", "space", "channel"),
+        ("space", "channel", "space", "channel"),
+    ],
+)
+def test_multiscale_space_axes_last(axis_types: list[str | None]) -> None:
+    units_map = {"space": "meter", "time": "second"}
+    axes = [
+        Axis(name=str(idx), type=t, unit=units_map.get(t, None))
+        for idx, t in enumerate(axis_types)
+    ]
+    rank = len(axes)
+    datasets = [
+        Dataset(
+            path="path",
+            coordinateTransformations=(
+                VectorScale(
+                    scale=[
+                        1,
+                    ]
+                    * rank
+                ),
+                VectorTranslation(
+                    translation=[
+                        0,
+                    ]
+                    * rank
+                ),
+            ),
+        )
+    ]
+    # TODO: make some axis-specifc exceptions
+    with pytest.raises(ValidationError, match="Space axes must come last."):
+        MultiscaleMetadata(
+            name="foo",
+            axes=axes,
+            datasets=datasets,
+            coordinateTransformations=(
+                VectorScale(
+                    scale=[
+                        1,
+                    ]
+                    * rank
+                ),
+            ),
+        )
+
+
+@pytest.mark.parametrize("num_axes", [0, 1, 6, 7])
+def test_multiscale_axis_length(num_axes: int) -> None:
+    rank = num_axes
+    axes = [Axis(name=str(idx), type="space", unit="meter") for idx in range(num_axes)]
+    datasets = [
+        Dataset(
+            path="path",
+            coordinateTransformations=(
+                VectorScale(
+                    scale=[
+                        1,
+                    ]
+                    * rank
+                ),
+                VectorTranslation(
+                    translation=[
+                        0,
+                    ]
+                    * rank
+                ),
+            ),
+        )
+    ]
+    with pytest.raises(ValidationError, match="Incorrect number of axes provided"):
+        MultiscaleMetadata(
+            name="foo",
+            axes=axes,
+            datasets=datasets,
+            coordinateTransformations=(
+                VectorScale(
+                    scale=[
+                        1,
+                    ]
+                    * rank
+                ),
+            ),
+        )
+
+
+@pytest.mark.parametrize(
+    "scale, translation", [((1, 1), (1, 1, 1)), ((1, 1, 1), (1, 1))]
+)
+def test_transform_invalid_ndims(
+    scale: tuple[int, ...], translation: tuple[int, ...]
+) -> None:
+    tforms = [
+        VectorScale(scale=scale),
+        VectorTranslation(translation=translation),
+    ]
+    with pytest.raises(
+        ValidationError,
+        match="The transforms have inconsistent dimensionality.",
+    ):
+        Dataset(path="foo", coordinateTransformations=tforms)
+
+
+@pytest.mark.parametrize(
+    "transforms",
+    [
+        [
+            VectorScale(scale=(1, 1, 1)),
+            VectorTranslation(translation=(1, 1, 1)),
+            VectorTranslation(translation=(1, 1, 1)),
+        ],
+        [
+            VectorScale(scale=(1, 1, 1)),
+        ]
+        * 5,
+    ],
+)
+def test_transform_invalid_length(
+    transforms: list[Transform],
+) -> None:
+    with pytest.raises(
+        ValidationError, match=f"after validation, not {len(transforms)}"
+    ):
+        Dataset(path="foo", coordinateTransformations=transforms)
+
+
+@pytest.mark.parametrize(
+    "transforms",
+    [
+        [
+            VectorTranslation(translation=(1, 1, 1)),
+        ]
+        * 2,
+        [
+            VectorTranslation(translation=(1, 1, 1)),
+            VectorScale(scale=(1, 1, 1)),
+        ],
+    ],
+)
+def test_transform_invalid_first_element(
+    transforms: Tuple[Transform, Transform],
+) -> None:
+    with pytest.raises(
+        ValidationError,
+        match="Input should be a valid dictionary or instance of VectorScale",
+    ):
+        Dataset(path="foo", coordinateTransformations=transforms)
+
+
+@pytest.mark.parametrize(
+    "transforms",
+    (
+        [
+            VectorScale(scale=(1, 1, 1)),
+            VectorScale(scale=(1, 1, 1)),
+        ],
+    ),
+)
+def test_transform_invalid_second_element(
+    transforms: Tuple[VectorScale, VectorScale],
+) -> None:
+    with pytest.raises(
+        ValidationError,
+        match="Input should be a valid dictionary or instance of VectorTranslation",
+    ):
+        Dataset(path="foo", coordinateTransformations=transforms)
+
+
+def test_multiscale_group_datasets_exist(
+    default_multiscale: MultiscaleMetadata,
+) -> None:
+    group_attrs = GroupAttrs(multiscales=[default_multiscale])
+    good_items = {
+        d.path: ArraySpec(
+            shape=(1, 1, 1, 1),
+            dtype="uint8",
+            chunks=(1, 1, 1, 1),
+        )
+        for d in default_multiscale.datasets
+    }
+    Group(attributes=group_attrs, members=good_items)
+
+    bad_items = {
+        d.path + "x": ArraySpec(
+            shape=(1, 1, 1, 1),
+            dtype="uint8",
+            chunks=(1, 1, 1, 1),
+        )
+        for d in default_multiscale.datasets
+    }
+
+    with pytest.raises(
+        ValidationError,
+        match="array with that name was found in the hierarchy",
+    ):
+        bad_items = {
+            d.path + "x": ArraySpec(
+                shape=(1, 1, 1, 1),
+                dtype="uint8",
+                chunks=(1, 1, 1, 1),
+            )
+            for d in default_multiscale.datasets
+        }
+        Group(attributes=group_attrs, members=bad_items)
+
+
+def test_multiscale_group_datasets_rank(default_multiscale: MultiscaleMetadata) -> None:
+    group_attrs = GroupAttrs(multiscales=[default_multiscale])
+    good_items = {
+        d.path: ArraySpec(
+            shape=(1, 1, 1, 1),
+            dtype="uint8",
+            chunks=(1, 1, 1, 1),
+        )
+        for d in default_multiscale.datasets
+    }
+    Group(attributes=group_attrs, members=good_items)
+
+    # arrays with varying rank
+    bad_items = {
+        d.path: ArraySpec(
+            shape=(1,) * (idx + 1),
+            dtype="uint8",
+            chunks=(1,) * (idx + 1),
+        )
+        for idx, d in enumerate(default_multiscale.datasets)
+    }
+    match = "Transform dimensionality must match array dimensionality."
+    with pytest.raises(ValidationError, match=match):
+        # arrays with varying rank
+        bad_items = {
+            d.path: ArraySpec(
+                shape=(1,) * (idx + 1),
+                dtype="uint8",
+                chunks=(1,) * (idx + 1),
+            )
+            for idx, d in enumerate(default_multiscale.datasets)
+        }
+        Group(attributes=group_attrs, members=bad_items)
+
+    # arrays with rank that doesn't match the transform
+    bad_items = {
+        d.path: ArraySpec(shape=(1,), dtype="uint8", chunks=(1,))
+        for d in default_multiscale.datasets
+    }
+    with pytest.raises(ValidationError, match=match):
+        # arrays with rank that doesn't match the transform
+        bad_items = {
+            d.path: ArraySpec(shape=(1,), dtype="uint8", chunks=(1,))
+            for d in default_multiscale.datasets
+        }
+        Group(attributes=group_attrs, members=bad_items)
+
+
+@pytest.mark.parametrize("name", [None, "foo"])
+@pytest.mark.parametrize("type", [None, "foo"])
+@pytest.mark.parametrize("path_pattern", ["{0}", "s{0}", "foo/{0}"])
+@pytest.mark.parametrize("metadata", [None, {"foo": 10}])
+@pytest.mark.parametrize("ndim", [2, 3, 4, 5])
+@pytest.mark.parametrize("chunks", ["auto", "tuple", "tuple-of-tuple"])
+@pytest.mark.parametrize("order", ["auto", "C", "F"])
+def test_from_arrays(
+    name: str | None,
+    type: str | None,
+    path_pattern: str,
+    metadata: Dict[str, int] | None,
+    ndim: int,
+    chunks: Literal["auto", "tuple", "tuple-of-tuple"],
+    order: Literal["auto", "C", "F"],
+) -> None:
+    arrays = [np.arange(x**ndim).reshape((x,) * ndim) for x in [3, 2, 1]]
+    paths = [path_pattern.format(idx) for idx in range(len(arrays))]
+    scales = [(2**idx,) * ndim for idx in range(len(arrays))]
+    translations = [
+        (t,) * ndim
+        for t in accumulate(
+            [(2 ** (idx - 1)) for idx in range(len(arrays))], operator.add
+        )
+    ]
+
+    all_axes = [
+        Axis(
+            name="x",
+            type="space",
+        ),
+        Axis(name="y", type="space"),
+        Axis(name="z", type="space"),
+        Axis(name="t", type="time"),
+        Axis(name="c", type="barf"),
+    ]
+    # spatial axes have to come last
+    if ndim in (2, 3):
+        axes = all_axes[:ndim]
+    else:
+        axes = [*all_axes[4:], *all_axes[:3]]
+
+    if chunks == "auto":
+        chunks_arg = chunks
+        chunks_expected = (
+            guess_chunks(arrays[0].shape, arrays[0].dtype.itemsize),
+        ) * len(arrays)
+    elif chunks == "tuple":
+        chunks_arg = (2,) * ndim
+        chunks_expected = (chunks_arg,) * len(arrays)
+    elif chunks == "tuple-of-tuple":
+        chunks_arg = tuple((idx,) * ndim for idx in range(1, len(arrays) + 1))
+        chunks_expected = chunks_arg
+
+    if order == "auto":
+        order_expected = "C"
+    else:
+        order_expected = order
+
+    group = Group.from_arrays(
+        paths=paths,
+        axes=axes,
+        arrays=arrays,
+        scales=scales,
+        translations=translations,
+        name=name,
+        type=type,
+        metadata=metadata,
+        chunks=chunks_arg,
+        order=order,
+    )
+
+    group_flat = group.to_flat()
+
+    assert group.attributes.multiscales[0].name == name
+    assert group.attributes.multiscales[0].type == type
+    assert group.attributes.multiscales[0].metadata == metadata
+    assert group.attributes.multiscales[0].coordinateTransformations is None
+    assert group.attributes.multiscales[0].axes == tuple(axes)
+    for idx, array in enumerate(arrays):
+        array_model: ArraySpec = group_flat["/" + paths[idx]]
+        assert array_model.order == order_expected
+        assert array.shape == array_model.shape
+        assert array.dtype == array_model.dtype
+        assert chunks_expected[idx] == array_model.chunks
+        assert group.attributes.multiscales[0].datasets[
+            idx
+        ].coordinateTransformations == (
+            VectorScale(scale=scales[idx]),
+            VectorTranslation(translation=translations[idx]),
+        )
diff --git a/tests/v04/test_transform.py b/tests/v04/test_transform.py
new file mode 100644
index 0000000..3dfe537
--- /dev/null
+++ b/tests/v04/test_transform.py
@@ -0,0 +1,65 @@
+from __future__ import annotations
+from typing import Sequence, Tuple
+import pytest
+from pydantic_ome_ngff.v04.transform import (
+    VectorScale,
+    VectorTranslation,
+    ensure_dimensionality,
+    ndim,
+    scale_translation,
+)
+
+
+@pytest.mark.parametrize(
+    "scale, translation",
+    (
+        [(0,), (0,)],
+        [(1, 1), (0, 0)],
+        [(1,), (0, 0)],
+        [(), (10,)],
+        [(10,), ()],
+    ),
+)
+def test_scale_translation(
+    scale: Tuple[int, ...], translation: Tuple[int, ...]
+) -> None:
+    if len(scale) == len(translation):
+        result = scale_translation(scale=scale, translation=translation)
+        assert isinstance(result[0], VectorScale)
+        assert isinstance(result[1], VectorTranslation)
+        assert result[0].scale == scale
+        assert result[1].translation == translation
+    else:
+        if len(scale) == 0:
+            match = "Not enough values in scale. Got 0, expected at least 1."
+        elif len(translation) == 0:
+            match = "Not enough values in translation. Got 0, expected at least 1."
+        else:
+            match = (
+                f"Length of `scale` and `translation` do not match. `scale` has length = {len(scale)}"
+                f"but `translation` has length = {len(translation)}"
+            )
+        with pytest.raises(ValueError, match=match):
+            scale_translation(scale, translation)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        (VectorScale(scale=(10, 10)), VectorTranslation(translation=(1,))),
+        (VectorScale(scale=(10,)), VectorTranslation(translation=(1, 1))),
+    ],
+)
+def test_ensure_dimensionality(data: Sequence[VectorScale | VectorTranslation]) -> None:
+    with pytest.raises(
+        ValueError, match="The transforms have inconsistent dimensionality."
+    ):
+        ensure_dimensionality(data)
+
+
+def test_ndim() -> None:
+    tx = VectorScale(scale=(1, 1, 1))
+    assert ndim(tx) == 3
+
+    tx = VectorTranslation(translation=(1, 1, 1))
+    assert ndim(tx) == 3