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@@ -11,7 +11,6 @@ license: cc-by-4.0
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  > *VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution*
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  The dataset consists of **32 volumetric scans of 16 samples**, each acquired under varying imaging conditions, enabling research on generalization, robustness, and cross-domain learning in 3D super-resolution.
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  ---
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  ## 🔗 Resources
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  * **Project page**: https://augusthoeg.github.io/VoDaSuRe/
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  * **Paper (arXiv)**: https://arxiv.org/abs/2603.23153
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  * **Code & pipelines**: https://github.com/AugustHoeg/VoxelSR
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  ---
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  ## Dataset Structure
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  ```
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  Each split contains volumetric data stored in OME-Zarr format, a hierarchical and chunked format that enables efficient, lazy loading of large-scale volumetric data.
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  ---
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  ## Data Format (OME-Zarr)
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  * **HR**: High-resolution reference volumes
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  * **LR**: Low-resolution volumes (unregistered)
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  * **REG**: Registered and intensity-matched low-resolution volumes
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  ---
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  ## Dataset Size
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  * **Disk requirement after extraction**: ~500 GB
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  ⚠️ Ensure sufficient disk space before downloading.
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  ---
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  ## Download Instructions
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  git lfs install
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  git clone https://huggingface.co/datasets/AugustHoeg/VoDaSuRe
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  ```
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  ---
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  ## Data Usage
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  * `zarr`
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  * `ome-zarr-py`
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  * `dask`
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  ---
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  ## Intended Use
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  * Volumetric super-resolution (3D SR)
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  * Domain generalization and domain shift analysis
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  * Benchmarking learning-based SR methods under realistic acquisition scenarios
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  ---
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  ## Dataset Creation
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  The dataset was created using **laboratory CT (Lab-CT) imaging systems**, capturing paired high- and low-resolution volumetric scans under varying acquisition conditions.
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  Further details are available in the associated paper and project page.
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  ---
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  ## Citation
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  url={https://augusthoeg.github.io/VoDaSuRe/}
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  }
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  ```
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-
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  ---
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  ## Contact
 
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  > *VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution*
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  The dataset consists of **32 volumetric scans of 16 samples**, each acquired under varying imaging conditions, enabling research on generalization, robustness, and cross-domain learning in 3D super-resolution.
 
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  ---
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  ## 🔗 Resources
 
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  * **Project page**: https://augusthoeg.github.io/VoDaSuRe/
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  * **Paper (arXiv)**: https://arxiv.org/abs/2603.23153
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  * **Code & pipelines**: https://github.com/AugustHoeg/VoxelSR
 
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  ---
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  ## Dataset Structure
 
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  ```
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  Each split contains volumetric data stored in OME-Zarr format, a hierarchical and chunked format that enables efficient, lazy loading of large-scale volumetric data.
 
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  ---
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  ## Data Format (OME-Zarr)
 
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  * **HR**: High-resolution reference volumes
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  * **LR**: Low-resolution volumes (unregistered)
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  * **REG**: Registered and intensity-matched low-resolution volumes
 
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  ---
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  ## Dataset Size
 
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  * **Disk requirement after extraction**: ~500 GB
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  ⚠️ Ensure sufficient disk space before downloading.
 
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  ---
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  ## Download Instructions
 
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  git lfs install
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  git clone https://huggingface.co/datasets/AugustHoeg/VoDaSuRe
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  ```
 
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  ---
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  ## Data Usage
 
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  * `zarr`
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  * `ome-zarr-py`
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  * `dask`
 
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  ---
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  ## Intended Use
 
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  * Volumetric super-resolution (3D SR)
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  * Domain generalization and domain shift analysis
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  * Benchmarking learning-based SR methods under realistic acquisition scenarios
 
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  ---
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  ## Dataset Creation
 
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  The dataset was created using **laboratory CT (Lab-CT) imaging systems**, capturing paired high- and low-resolution volumetric scans under varying acquisition conditions.
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  Further details are available in the associated paper and project page.
 
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  ---
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  ## Citation
 
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  url={https://augusthoeg.github.io/VoDaSuRe/}
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  }
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  ```
 
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  ---
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  ## Contact