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8 | 8 | [](https://pre-commit.com/) |
9 | 9 | [](https://www.apache.org/licenses/LICENSE-2.0) |
10 | 10 | [](https://docs.python.org) |
11 | | - |
| 11 | +[](https://earthmover-community.slack.com/archives/C08EXCE8ZQX) |
12 | 12 | [](https://github.com/zarr-developers/VirtualiZarr/releases) |
13 | 13 | [](https://pypistats.org/packages/virtualizarr) |
14 | 14 | [](https://anaconda.org/conda-forge/virtualizarr) |
16 | | -[](https://earthmover-community.slack.com/archives/C08EXCE8ZQX) |
17 | 16 |
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18 | | -## Cloud-Optimize your Scientific Data as Virtual Zarr stores, using xarray syntax. |
| 17 | + |
| 18 | +## Cloud-Optimize your Scientific Data as a Virtual Zarr Datacube, using Xarray syntax. |
19 | 19 |
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20 | 20 | The best way to distribute large scientific datasets is via the Cloud, in [Cloud-Optimized formats](https://guide.cloudnativegeo.org/) [^1]. But often this data is stuck in archival pre-Cloud file formats such as netCDF. |
21 | 21 |
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22 | | -**VirtualiZarr[^2] makes it easy to create "Virtual" Zarr stores, allowing performant access to archival data as if it were in the Cloud-Optimized [Zarr format](https://zarr.dev/), _without duplicating any data_.** |
| 22 | +**VirtualiZarr[^2] makes it easy to create "Virtual" Zarr datacubes, allowing performant access to archival data as if it were in the Cloud-Optimized [Zarr format](https://zarr.dev/), _without duplicating any data_.** |
23 | 23 |
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24 | 24 | Please see the [documentation](https://virtualizarr.readthedocs.io/en/stable/index.html). |
25 | 25 |
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26 | 26 | ### Features |
27 | 27 |
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28 | | -* Create virtual references pointing to bytes inside a archival file with [`open_virtual_dataset`](https://virtualizarr.readthedocs.io/en/latest/usage.html#opening-files-as-virtual-datasets), |
29 | | -* Supports a [range of archival file formats](https://virtualizarr.readthedocs.io/en/latest/faq.html#how-do-virtualizarr-and-kerchunk-compare), including netCDF4 and HDF5, |
30 | | -* [Combine data from multiple files](https://virtualizarr.readthedocs.io/en/latest/usage.html#combining-virtual-datasets) into one larger store using [xarray's combining functions](https://docs.xarray.dev/en/stable/user-guide/combining.html), such as [`xarray.concat`](https://docs.xarray.dev/en/stable/generated/xarray.concat.html), |
| 28 | +* Create virtual references pointing to bytes inside an archival file with [`open_virtual_dataset`](https://virtualizarr.readthedocs.io/en/latest/usage.html#opening-files-as-virtual-datasets). |
| 29 | +* Supports a [range of archival file formats](https://virtualizarr.readthedocs.io/en/latest/faq.html#how-do-virtualizarr-and-kerchunk-compare), including netCDF4 and HDF5, and has a pluggable system for supporting new formats. |
| 30 | +* [Combine data from multiple files](https://virtualizarr.readthedocs.io/en/latest/usage.html#combining-virtual-datasets) into one larger datacube using [xarray's combining functions](https://docs.xarray.dev/en/stable/user-guide/combining.html), such as [`xarray.concat`](https://docs.xarray.dev/en/stable/generated/xarray.concat.html). |
31 | 31 | * Commit the virtual references to storage either using the [Kerchunk references](https://fsspec.github.io/kerchunk/spec.html) specification or the [Icechunk](https://icechunk.io/) transactional storage engine. |
32 | | -* Users access the virtual dataset using [`xarray.open_dataset`](https://docs.xarray.dev/en/stable/generated/xarray.open_dataset.html#xarray.open_dataset). |
| 32 | +* Users access the virtual datacube simply as a single zarr-compatible store using [`xarray.open_zarr`](https://docs.xarray.dev/en/stable/generated/xarray.open_zarr.html). |
33 | 33 |
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34 | 34 | ### Inspired by Kerchunk |
35 | 35 |
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