diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
new file mode 100644
index 0000000..2e0872d
--- /dev/null
+++ b/.github/copilot-instructions.md
@@ -0,0 +1,125 @@
+# Copilot Instructions for multiscale-spatial-image
+
+## Project Overview
+
+This library generates multiscale, chunked, multi-dimensional spatial image data
+structures serializable to OME-NGFF format. It's built on Xarray DataTree with
+spatial-image Dataset nodes, creating image pyramids for visualization and
+analysis.
+
+## Core Architecture
+
+### Key Components
+
+- **MultiscaleSpatialImage**: Xarray DataTree accessor
+  (`@register_datatree_accessor("msi")`) providing NGFF-compatible multiscale
+  operations
+- **to_multiscale()**: Main entry point for creating pyramids with configurable
+  downsampling methods
+- **Methods enum**: Defines downsampling algorithms (XARRAY*COARSEN, ITK*_,
+  DASK*IMAGE*_)
+- **Operations module**: Provides dataset operations that skip non-dimensional
+  nodes
+
+### Data Structure Pattern
+
+```python
+# DataTree structure: /scale0, /scale1, /scale2, etc.
+# Each scale contains Dataset with same variable name as input
+multiscale = to_multiscale(image, [2, 4])  # Creates 3 scales total
+multiscale['scale0'].ds  # Original resolution
+multiscale['scale1'].ds  # 2x downsampled
+multiscale['scale2'].ds  # 8x downsampled (2*4)
+```
+
+### Zarr Integration Patterns
+
+- **Always use `dimension_separator='/'`** for NGFF compliance
+- Support both Zarr v2 (DirectoryStore) and v3 (LocalStore) with fallback
+  pattern in `_data.py`
+- NGFF metadata automatically added in `to_zarr()` method with coordinate
+  transformations
+
+## Development Workflows
+
+### Environment Management (Pixi-based)
+
+```bash
+pixi install -a                    # Install all environments
+pixi run -e test test              # Run unit tests
+pixi run test-notebooks            # Test example notebooks
+pixi shell                         # Activate development shell
+```
+
+### Testing Patterns
+
+- **Baseline comparison testing**: Use `verify_against_baseline()` for image
+  output validation
+- **Test data**: IPFS-hosted via pooch, SHA256-verified downloads
+- **Notebook testing**: nbmake integration tests all examples
+- **Multiple backends**: Tests run against ITK, dask-image, and xarray methods
+
+### Adding Test Data
+
+```python
+# Temporary add this line to generate new baseline:
+store_new_image(dataset_name, baseline_name, multiscale)
+# Remove after running tests, then update data.tar.gz and SHA256 in _data.py
+```
+
+## Critical Patterns
+
+### Dimension Handling
+
+- **skip_non_dimension_nodes decorator**: Essential for operations on DataTree
+  root nodes without dimensions
+- **Default chunking**: Uses 64 for 3D (with 'z'), 256 for 2D, 1 for 't'
+  dimension
+- **Coordinate transforms**: Scale and translation automatically computed from
+  coordinate spacing
+
+### Multi-Backend Support
+
+```python
+# Pattern for optional dependencies
+try:
+    from zarr.storage import DirectoryStore  # Zarr v2
+except ImportError:
+    from zarr.storage import LocalStore     # Zarr v3
+```
+
+### Downsampling Method Dispatch
+
+- Each method in `to_multiscale/` subdirectory follows `_downsample_{method}`
+  naming
+- Methods handle chunking alignment via `_align_chunks()` helper
+- Scale factors can be uniform int or per-dimension dict:
+  `[2, {'x': 2, 'y': 4}]`
+
+## Integration Points
+
+### External Dependencies
+
+- **spatial-image**: Base SpatialImage input type
+- **xarray-datatree**: Core DataTree functionality
+- **OME-NGFF**: Metadata standard compliance via `multiscales` attribute
+- **Optional**: ITK (medical imaging), dask-image (distributed), pyimagej
+
+### File Patterns
+
+- `multiscale_spatial_image.py`: Core accessor class with to_zarr() and
+  operations
+- `to_multiscale/`: Downsampling method implementations
+- `operations/`: Dataset operations with dimension-aware decorators
+- `examples/`: Jupyter notebooks demonstrating usage patterns
+
+## Key Conventions
+
+- Use `promote_attrs=True` when converting DataArrays to Datasets to preserve
+  metadata
+- Coordinate names follow spatial conventions: 't' (time), 'c' (channel),
+  'x'/'y'/'z' (space)
+- Error handling validates scale factors against current image dimensions before
+  processing
+- NGFF axes metadata includes type classification (time/channel/space) and
+  optional units
diff --git a/.github/workflows/notebook-test.yml b/.github/workflows/notebook-test.yml
index e106312..8d209c6 100644
--- a/.github/workflows/notebook-test.yml
+++ b/.github/workflows/notebook-test.yml
@@ -4,7 +4,7 @@ on: [push, pull_request]
 
 jobs:
   run:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     name: Test notebooks with nbmake
     steps:
       - uses: actions/checkout@v4
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 7f5e67e..e3d2539 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -8,8 +8,8 @@ jobs:
     strategy:
       max-parallel: 5
       matrix:
-        os: [ubuntu-22.04, windows-2022, macos-12]
-        python-version: ["3.10", "3.11", "3.12"]
+        os: [ubuntu-24.04, windows-2022, macos-13]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - uses: actions/checkout@v4
@@ -20,7 +20,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install -e ".[test]"
+          python -m pip install -e ".[test,itk,dask-image]"
       - name: Test with pytest
         run: |
           pytest --junitxml=junit/test-results.xml
diff --git a/README.md b/README.md
index bea4bdd..2c53e3b 100644
--- a/README.md
+++ b/README.md
@@ -153,14 +153,6 @@ method and `assign_coords`, equivalent to `xr.Dataset` `assign_coords` method.
 Store as an Open Microscopy Environment-Next Generation File Format ([OME-NGFF])
 / [netCDF] [Zarr] store.
 
-It is highly recommended to use `dimension_separator='/'` in the construction of
-the Zarr stores.
-
-```python
-store = zarr.storage.DirectoryStore('multiscale.zarr', dimension_separator='/')
-multiscale.to_zarr(store)
-```
-
 **Note**: The API is under development, and it may change until 1.0.0 is
 released. We mean it :-).
 
diff --git a/examples/ConvertPyImageJDataset.ipynb b/examples/ConvertPyImageJDataset.ipynb
index b39441d..b74105b 100644
--- a/examples/ConvertPyImageJDataset.ipynb
+++ b/examples/ConvertPyImageJDataset.ipynb
@@ -9,7 +9,9 @@
    "source": [
     "import sys, os\n",
     "!conda install --yes --prefix {sys.prefix} -c conda-forge openjdk=8\n",
-    "os.environ['JAVA_HOME'] = os.sep.join(sys.executable.split(os.sep)[:-2] + ['jre'])\n",
+    "# In case of being already installed through pixi it should not be set to this path.\n",
+    "if 'JAVA_HOME' not in os.environ:\n",
+    "    os.environ['JAVA_HOME'] = os.sep.join(sys.executable.split(os.sep)[:-2] + ['jre'])\n",
     "!{sys.executable} -m pip install multiscale-spatial-image matplotlib zarr pyimagej"
    ]
   },
diff --git a/multiscale_spatial_image/multiscale_spatial_image.py b/multiscale_spatial_image/multiscale_spatial_image.py
index f87ea8f..84b70b9 100644
--- a/multiscale_spatial_image/multiscale_spatial_image.py
+++ b/multiscale_spatial_image/multiscale_spatial_image.py
@@ -4,13 +4,19 @@
 import numpy as np
 from collections.abc import MutableMapping, Hashable
 from pathlib import Path
-from zarr.storage import BaseStore
+import zarr.storage
 from multiscale_spatial_image.operations import (
     transpose,
     reindex_data_arrays,
     assign_coords,
 )
 
+# Zarr Python 3
+if hasattr(zarr.storage, "StoreLike"):
+    StoreLike = zarr.storage.StoreLike
+else:
+    StoreLike = Union[MutableMapping, str, Path, zarr.storage.BaseStore]
+
 
 @register_datatree_accessor("msi")
 class MultiscaleSpatialImage:
@@ -33,7 +39,7 @@ def __init__(self, xarray_obj: DataTree):
 
     def to_zarr(
         self,
-        store: Union[MutableMapping, str, Path, BaseStore],
+        store: StoreLike,
         mode: str = "w",
         encoding=None,
         **kwargs,
@@ -43,7 +49,7 @@ def to_zarr(
 
         Metadata is added according the OME-NGFF standard.
 
-        store : MutableMapping, str or Path, or zarr.storage.BaseStore
+        store : StoreLike
             Store or path to directory in file system
         mode : {{"w", "w-", "a", "r+", None}, default: "w"
             Persistence mode: “w” means create (overwrite if exists); “w-” means create (fail if exists);
@@ -125,6 +131,9 @@ def to_zarr(
         ngff_metadata = {"multiscales": multiscales, "multiscaleSpatialImageVersion": 1}
         self._dt.ds = self._dt.ds.assign_attrs(**ngff_metadata)
 
+        # Ensure zarr v2 format for NGFF v0.4
+        if "zarr_format" not in kwargs:
+            kwargs["zarr_format"] = 2
         self._dt.to_zarr(store, mode=mode, **kwargs)
 
     def transpose(self, *dims: Hashable) -> DataTree:
diff --git a/multiscale_spatial_image/operations/operations.py b/multiscale_spatial_image/operations/operations.py
index 6560993..51e02a4 100644
--- a/multiscale_spatial_image/operations/operations.py
+++ b/multiscale_spatial_image/operations/operations.py
@@ -15,4 +15,22 @@ def transpose(ds: Dataset, *args: Any, **kwargs: Any) -> Dataset:
 
 @skip_non_dimension_nodes
 def reindex_data_arrays(ds: Dataset, *args: Any, **kwargs: Any) -> Dataset:
-    return ds["image"].reindex(*args, **kwargs).to_dataset()
+    # Extract the first argument as indexers, and pass the rest as keyword arguments
+    if args:
+        indexers = args[0]
+        # Map positional arguments to their parameter names
+        reindex_kwargs = {}
+        if len(args) > 1:
+            reindex_kwargs["method"] = args[1]
+        if len(args) > 2:
+            reindex_kwargs["tolerance"] = args[2]
+        if len(args) > 3:
+            reindex_kwargs["copy"] = args[3]
+        if len(args) > 4:
+            reindex_kwargs["fill_value"] = args[4]
+        # Add any additional keyword arguments
+        reindex_kwargs.update(kwargs)
+        return ds["image"].reindex(indexers, **reindex_kwargs).to_dataset()
+    else:
+        # Fall back to original behavior if no arguments
+        return ds["image"].reindex(**kwargs).to_dataset()
diff --git a/pixi.lock b/pixi.lock
index 0850030..fc8b702 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,4 +1,4 @@
-version: 5
+version: 6
 environments:
   data:
     channels:
@@ -7,143 +7,146 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hf71b8c6_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hf71b8c6_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.7.14-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.7.14-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py310h8deb56e_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.3.2-pyhd8ed1ab_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.2.0-h4852527_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.2-pyhd8ed1ab_0.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.44-h1423503_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.1-hecca717_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha39cb0e_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda
       osx-64:
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/brotli-python-1.1.0-py310h53e7c6a_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/brotli-python-1.1.0-py310h6954a95_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-hfdf4475_7.conda
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.0.0-pyh9f0ad1d_0.tar.bz2
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.7-pyhd8ed1ab_0.conda
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-  sha256: 0e2a6ced111fd99b66b76ec797804ab798ec190a88a2779060f7a8787c343ee0
-  md5: 00ec9f2a5e21bbbd22ffbbc12b3df286
+  size: 281565
+  timestamp: 1731585108039
+- conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-ha9f60a1_7.conda
+  sha256: 15cc8e2162d0a33ffeb3f7b7c7883fd830c54a4b1be6a4b8c7ee1f4fef0088fb
+  md5: e03f2c245a5ee6055752465519363b1c
   depends:
-  - __osx >=10.13
   - krb5 >=1.21.3,<1.22.0a0
-  - libcxx >=17
   - libsodium >=1.0.20,<1.0.21.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 290634
-  timestamp: 1728364170966
-- kind: conda
-  name: zipp
-  version: 3.19.2
-  build: pyhd8ed1ab_0
-  subdir: noarch
-  noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda
-  sha256: e3e9c8501f581bfdc4700b83ea283395e237ec6b9b5cbfbedb556e1da6f4fdc9
-  md5: 49808e59df5535116f6878b2a820d6f4
+  size: 2527503
+  timestamp: 1731585151036
+- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhd8ed1ab_0.conda
+  sha256: 7560d21e1b021fd40b65bfb72f67945a3fcb83d78ad7ccf37b8b3165ec3b68ad
+  md5: df5e78d904988eb55042c0c97446079f
   depends:
-  - python >=3.8
+  - python >=3.9
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/zipp?source=conda-forge-mapping
-  size: 20917
-  timestamp: 1718013395428
-- kind: conda
-  name: zstandard
-  version: 0.23.0
-  build: py310h2665a74_1
-  build_number: 1
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py310h2665a74_1.conda
-  sha256: a90d06cbfa50fc9b3c37bd092d559452475f22425bacf28f04ecac2e8b1c389c
-  md5: 81b300570a423c9c9521b79f8f2ed1ba
+  - pkg:pypi/zipp?source=hash-mapping
+  size: 22963
+  timestamp: 1749421737203
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda
+  sha256: f9b76c2f8a0f96e656843553272e547170182f5b8aba1a6bcba28f7611d87c23
+  md5: f9254b5b0193982416b91edcb4b2676f
   depends:
-  - __osx >=11.0
+  - __glibc >=2.17,<3.0.a0
   - cffi >=1.11
+  - libgcc >=13
   - python >=3.10,<3.11.0a0
-  - python >=3.10,<3.11.0a0 *_cpython
   - python_abi 3.10.* *_cp310
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 320810
-  timestamp: 1725305704555
-- kind: conda
-  name: zstandard
-  version: 0.23.0
-  build: py310h41d873f_1
-  build_number: 1
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py310h41d873f_1.conda
-  sha256: 449fd094d91e509421ddbe7b707c58191473355f29373c0f3d603875b8d2b801
-  md5: cbf02a084007c683a22172094d31eac6
+  size: 722119
+  timestamp: 1745869786772
+- conda: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py310hbb8c376_2.conda
+  sha256: fad4ae15d30c13d0d51748139064fc0bb59359719881e7a370ca8652c4917828
+  md5: 5b75d4015ead2df6bf15bc372edfa139
   depends:
   - __osx >=10.13
   - cffi >=1.11
   - python >=3.10,<3.11.0a0
   - python_abi 3.10.* *_cp310
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 400765
-  timestamp: 1725305605347
-- kind: conda
-  name: zstandard
-  version: 0.23.0
-  build: py310ha39cb0e_1
-  build_number: 1
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha39cb0e_1.conda
-  sha256: fcd784735205d6c5f19dcb339f92d2eede9bc42a01ec2c384381ee1b6089d4f6
-  md5: f49de34fb99934bf49ab330b5caffd64
+  size: 681744
+  timestamp: 1745869885563
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py310h078409c_2.conda
+  sha256: 6fdb3e71c6af5fe9c2469befb724a80d8c874078df1fa9738d84cf857d84d4b1
+  md5: a617ab3d9042eef702d8d163c50e9b5e
   depends:
-  - __glibc >=2.17,<3.0.a0
+  - __osx >=11.0
   - cffi >=1.11
-  - libgcc >=13
   - python >=3.10,<3.11.0a0
+  - python >=3.10,<3.11.0a0 *_cpython
   - python_abi 3.10.* *_cp310
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 408309
-  timestamp: 1725305719512
-- kind: conda
-  name: zstandard
-  version: 0.23.0
-  build: py310he5e10e1_1
-  build_number: 1
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py310he5e10e1_1.conda
-  sha256: 4e8aff4d0d42024e9f70783e51666186a681384d59fdd03fafda4b28f1fd540e
-  md5: 2a879227ccc1a10a2caddf12607ffaeb
+  size: 522323
+  timestamp: 1745870245475
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py310ha8f682b_2.conda
+  sha256: 76bf75ef83e952ef4974e0e6656a7a90b4c4c1c22cea984cb9fc29aca05e5999
+  md5: fdc36a989175bb166109e400c106defa
   depends:
   - cffi >=1.11
   - python >=3.10,<3.11.0a0
@@ -9192,78 +7524,9 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 311278
-  timestamp: 1725306039901
-- kind: conda
-  name: zstd
-  version: 1.5.6
-  build: h0ea2cb4_0
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.6-h0ea2cb4_0.conda
-  sha256: 768e30dc513568491818fb068ee867c57c514b553915536da09e5d10b4ebf3c3
-  md5: 9a17230f95733c04dc40a2b1e5491d74
-  depends:
-  - libzlib >=1.2.13,<2.0.0a0
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 349143
-  timestamp: 1714723445995
-- kind: conda
-  name: zstd
-  version: 1.5.6
-  build: h915ae27_0
-  subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.6-h915ae27_0.conda
-  sha256: efa04a98cb149643fa54c4dad5a0179e36a5fbc88427ea0eec88ceed87fd0f96
-  md5: 4cb2cd56f039b129bb0e491c1164167e
-  depends:
-  - __osx >=10.9
-  - libzlib >=1.2.13,<2.0.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 498900
-  timestamp: 1714723303098
-- kind: conda
-  name: zstd
-  version: 1.5.6
-  build: ha6fb4c9_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
-  sha256: c558b9cc01d9c1444031bd1ce4b9cff86f9085765f17627a6cd85fc623c8a02b
-  md5: 4d056880988120e29d75bfff282e0f45
-  depends:
-  - libgcc-ng >=12
-  - libstdcxx-ng >=12
-  - libzlib >=1.2.13,<2.0.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 554846
-  timestamp: 1714722996770
-- kind: conda
-  name: zstd
-  version: 1.5.6
-  build: hb46c0d2_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
-  sha256: 2d4fd1ff7ee79cd954ca8e81abf11d9d49954dd1fef80f27289e2402ae9c2e09
-  md5: d96942c06c3e84bfcc5efb038724a7fd
-  depends:
-  - __osx >=11.0
-  - libzlib >=1.2.13,<2.0.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 405089
-  timestamp: 1714723101397
+  size: 435740
+  timestamp: 1745870314659
diff --git a/pyproject.toml b/pyproject.toml
index 03b7e06..4390493 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,6 @@ description = "Generate a multiscale, chunked, multi-dimensional spatial image d
 authors = [{name = "Matt McCormick", email = "matt@mmmccormick.com"}]
 readme = "README.md"
 license.file = "LICENSE"
-home-page = "https://github.com/spatial-image/multiscale-spatial-image"
 classifiers = [
    "License :: OSI Approved :: Apache Software License",
     "Programming Language :: Python",
@@ -18,6 +17,7 @@ classifiers = [
     'Programming Language :: Python :: 3.10',
     'Programming Language :: Python :: 3.11',
     'Programming Language :: Python :: 3.12',
+    'Programming Language :: Python :: 3.13',
    ]
 keywords = [
     "itk",
@@ -30,13 +30,14 @@ keywords = [
 ]
 dynamic = ["version"]
 
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.10"
 dependencies = [
     "numpy",
     "dask",
     "python-dateutil",
-    "spatial_image>=0.2.1",
-    "xarray>=2024.10.0",
+    "spatial_image >=1.2.2",
+    "xarray >=2025.1.2",
+    "xarray-dataclass >=3.0.0",
     "zarr",
 ]
 
@@ -47,20 +48,17 @@ Issues = "https://github.com/spatial-image/multiscale-spatial-image"
 
 [project.optional-dependencies]
 test = [
-    "itk-filtering>=5.3.0",
-    "dask_image",
     "jsonschema",
     "pooch",
     "pytest",
     "pytest-mypy",
     "fsspec",
-    "ipfsspec",
     "urllib3",
     "nbmake",
 ]
 
 itk = [
-    "itk-filtering>=5.3.0",
+    "itk-filtering >=5.3.0",
 ]
 dask-image = [
     "dask-image",
@@ -68,7 +66,11 @@ dask-image = [
 imagej = [
   "pyimagej",
 ]
-notebooks = ["matplotlib>=3.9.2,<4", "ome-types>=0.5.1.post1,<0.6", "tqdm>=4.66.4,<5"]
+notebooks = [
+  "matplotlib >=3.9.2,<4",
+  "ome-types >=0.5.1.post1,<0.6",
+  "tqdm >=4.66.4,<5"
+]
 
 [tool.black]
 line-length = 88
@@ -103,9 +105,10 @@ openjdk = "8.*"
 maven = ">=3.9.8,<3.10"
 jupyterlab = ">=4.2.4,<4.3"
 python = "3.10.*"
+jpype1 = ">=1.4.0,<1.5.0"
 
 [tool.pixi.feature.notebooks.tasks]
-init-imagej = { cmd = "python3 -c \"import imagej; ij = imagej.init('2.15.0'); print(ij.getVersion())\"", description = "Initialize the python imagej installation" }
+init-imagej = { cmd = "python -c \"import imagej; ij = imagej.init('2.15.0'); print(ij.getVersion())\"", description = "Initialize the python imagej installation" }
 test-notebooks = { cmd = "pytest --nbmake --nbmake-timeout=3000 examples/ConvertImageioImageResource.ipynb examples/ConvertITKImage.ipynb examples/ConvertPyImageJDataset.ipynb examples/ConvertTiffFile.ipynb examples/HelloMultiscaleSpatialImageWorld.ipynb", depends-on = ["init-imagej"], description = "Test the notebooks" }
 dev-notebooks = { cmd = "jupyter lab examples", description = "Start Jupyter Lab" }
 
diff --git a/test/_data.py b/test/_data.py
index ff69a81..9247c4c 100644
--- a/test/_data.py
+++ b/test/_data.py
@@ -2,7 +2,6 @@
 
 import pytest
 import pooch
-from zarr.storage import DirectoryStore
 import xarray as xr
 
 test_data_ipfs_cid = "bafybeiaskr5fxg6rbcwlxl6ibzqhubdleacenrpbnymc6oblwoi7ceqzta"
@@ -29,23 +28,41 @@ def input_images():
     test_data.fetch("data.tar.gz", processor=untar)
     result = {}
 
-    store = DirectoryStore(
-        test_data_dir / "input" / "cthead1.zarr", dimension_separator="/"
-    )
+    store_path = test_data_dir / "input" / "cthead1.zarr"
+    try:
+        from zarr.storage import DirectoryStore
+
+        store = DirectoryStore(store_path, dimension_separator="/")
+    except ImportError:
+        from zarr.storage import LocalStore
+
+        store = LocalStore(store_path)
     image_ds = xr.open_zarr(store)
     image_da = image_ds.cthead1
     result["cthead1"] = image_da
 
-    store = DirectoryStore(
-        test_data_dir / "input" / "small_head.zarr", dimension_separator="/"
-    )
+    store_path = test_data_dir / "input" / "small_head.zarr"
+    try:
+        from zarr.storage import DirectoryStore
+
+        store = DirectoryStore(store_path, dimension_separator="/")
+    except ImportError:
+        from zarr.storage import LocalStore
+
+        store = LocalStore(store_path)
     image_ds = xr.open_zarr(store)
     image_da = image_ds.small_head
     result["small_head"] = image_da
 
-    store = DirectoryStore(
-        test_data_dir / "input" / "2th_cthead1.zarr",
-    )
+    store_path = test_data_dir / "input" / "2th_cthead1.zarr"
+    try:
+        from zarr.storage import DirectoryStore
+
+        store = DirectoryStore(store_path, dimension_separator="/")
+    except ImportError:
+        from zarr.storage import LocalStore
+
+        store = LocalStore(store_path)
     image_ds = xr.open_zarr(store)
     image_da = image_ds["2th_cthead1"]
     result["2th_cthead1"] = image_da
@@ -54,10 +71,15 @@ def input_images():
 
 
 def verify_against_baseline(dataset_name, baseline_name, multiscale):
-    store = DirectoryStore(
-        test_data_dir / f"baseline/{dataset_name}/{baseline_name}",
-        dimension_separator="/",
-    )
+    store_path = test_data_dir / f"baseline/{dataset_name}/{baseline_name}"
+    try:
+        from zarr.storage import DirectoryStore
+
+        store = DirectoryStore(store_path, dimension_separator="/")
+    except ImportError:
+        from zarr.storage import LocalStore
+
+        store = LocalStore(store_path)
     dt = xr.open_datatree(store, engine="zarr", mode="r")
     xr.testing.assert_equal(dt.ds, multiscale.ds)
     for scale in multiscale.children:
@@ -68,8 +90,15 @@ def store_new_image(dataset_name, baseline_name, multiscale_image):
     """Helper method for writing output results to disk
     for later upload as test baseline"""
     path = test_data_dir / f"baseline/{dataset_name}/{baseline_name}"
-    store = DirectoryStore(
-        str(path),
-        dimension_separator="/",
-    )
+    try:
+        from zarr.storage import DirectoryStore
+
+        store = DirectoryStore(
+            str(path),
+            dimension_separator="/",
+        )
+    except ImportError:
+        from zarr.storage import LocalStore
+
+        store = LocalStore(str(path))
     multiscale_image.to_zarr(store, mode="w")
diff --git a/test/test_ngff_validation.py b/test/test_ngff_validation.py
index 0bc3e4c..6d9733a 100644
--- a/test/test_ngff_validation.py
+++ b/test/test_ngff_validation.py
@@ -1,6 +1,7 @@
 import json
 from typing import Dict
 import urllib3
+import asyncio
 
 from referencing import Registry, Resource
 from jsonschema import Draft202012Validator
@@ -29,12 +30,102 @@ def load_schema(version: str = "0.4", strict: bool = False) -> Dict:
 
 
 def check_valid_ngff(multiscale: DataTree):
-    store = zarr.storage.MemoryStore(dimension_separator="/")
+    store = zarr.storage.MemoryStore()
     assert isinstance(multiscale.msi, MultiscaleSpatialImage)
     multiscale.msi.to_zarr(store, compute=True)
-    zarr.convenience.consolidate_metadata(store)
-    metadata = json.loads(store.get(".zmetadata"))["metadata"]
-    ngff = metadata[".zattrs"]
+
+    # Handle consolidate_metadata for both zarr v2 and v3
+    try:
+        # Zarr v3
+        zarr.consolidate_metadata(store)
+    except AttributeError:
+        # Zarr v2
+        zarr.convenience.consolidate_metadata(store)
+
+    # Get NGFF metadata
+    ngff = None
+
+    # Method 1: Try to access via zarr group attrs (works for both v2 and v3)
+    try:
+        group = zarr.open_group(store=store, mode="r")
+        ngff = dict(group.attrs)
+    except Exception:
+        pass
+
+    # Method 2: Try zarr v2 dict-like store access
+    if ngff is None and hasattr(store, "__getitem__"):
+        try:
+            # Try consolidated metadata
+            if ".zmetadata" in store:
+                metadata = json.loads(store[".zmetadata"])["metadata"]
+                ngff = metadata[".zattrs"]
+            # Try direct attributes
+            elif ".zattrs" in store:
+                ngff = json.loads(store[".zattrs"])
+        except Exception:
+            pass
+
+    # Method 3: Try zarr v3 async store API
+    if ngff is None and hasattr(store, "get"):
+        try:
+
+            async def get_attrs_v3():
+                # Get default buffer prototype for v3
+                if hasattr(zarr, "buffer"):
+                    prototype = zarr.buffer.default_buffer_prototype()
+                else:
+                    # Fallback for older versions
+                    prototype = None
+
+                # Try zarr.json first (v3 format)
+                try:
+                    if prototype is not None:
+                        zarr_json_bytes = await store.get(
+                            "zarr.json", prototype=prototype
+                        )
+                    else:
+                        zarr_json_bytes = await store.get("zarr.json")
+
+                    if zarr_json_bytes:
+                        if hasattr(zarr_json_bytes, "to_bytes"):
+                            data = zarr_json_bytes.to_bytes()
+                        else:
+                            data = zarr_json_bytes
+                        zarr_json = json.loads(data)
+                        return zarr_json.get("attributes", {})
+                except Exception:
+                    pass
+
+                # Try .zattrs (legacy/compatibility)
+                try:
+                    if prototype is not None:
+                        attrs_bytes = await store.get(".zattrs", prototype=prototype)
+                    else:
+                        attrs_bytes = await store.get(".zattrs")
+
+                    if attrs_bytes:
+                        if hasattr(attrs_bytes, "to_bytes"):
+                            data = attrs_bytes.to_bytes()
+                        else:
+                            data = attrs_bytes
+                        return json.loads(data)
+                except Exception:
+                    pass
+
+                return None
+
+            # Run async function
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            try:
+                ngff = loop.run_until_complete(get_attrs_v3())
+            finally:
+                loop.close()
+        except Exception:
+            pass
+
+    if ngff is None:
+        raise ValueError("Could not find NGFF metadata in store")
 
     image_schema = load_schema(version="0.4", strict=False)
     # strict_image_schema = load_schema(version="0.4", strict=True)