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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+# VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution
+
+## Dataset Summary
+
+**VoDaSuRe** is a large-scale dataset for volumetric super-resolution (VSR), designed to study **domain shift between laboratory CT (Lab-CT) acquisitions**. The dataset is released in conjunction with the CVPR 2026 paper:
+
+> *VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution*
+
+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.
+
+---
+
+## 🔗 Resources
+
+* **Project page**: https://augusthoeg.github.io/VoDaSuRe/
+* **Paper (arXiv)**: https://arxiv.org/abs/2603.23153
+* **Code & pipelines**: https://github.com/AugustHoeg/VoxelSR
+
+---
+
+## Dataset Structure
+
+The dataset is organized into **training and test splits**:
+
+```
+VoDaSuRe/
+└── ome/
+    ├── train/
+    └── test/
+```
+
+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.
+
+---
+
+## Data Format (OME-Zarr)
+
+Each sample is stored as a `.zarr` hierarchy with the following structure:
+
+```
+ome.zarr
+├── HR  (High-resolution volume)
+│   ├── 0  (full resolution)
+│   ├── 1  (2× downsampled)
+│   ├── 2  (4× downsampled)
+│   └── 3  (8× downsampled)
+│
+├── LR  (Unregistered low-resolution volume)
+│   ├── 0  (full resolution)
+│   ├── 1  (2× downsampled)
+│   ├── 2  (4× downsampled)
+│   └── 3  (8× downsampled)
+│
+└── REG (Registered + intensity-matched low-resolution volume)
+    ├── 0  (full resolution)
+    └── 1  (2× downsampled)
+```
+
+### Modalities
+
+* **HR**: High-resolution reference volumes
+* **LR**: Low-resolution volumes (unregistered)
+* **REG**: Registered and intensity-matched low-resolution volumes
+
+---
+
+## Dataset Size
+
+* **Total size**: ~489 GB (compressed)
+* **Disk requirement after extraction**: ~500 GB
+
+⚠️ Ensure sufficient disk space before downloading.
+
+---
+
+## Download Instructions
+
+You can download the dataset directly from the Hugging Face Hub:
+
+https://huggingface.co/datasets/AugustHoeg/VoDaSuRe
+
+### Python (recommended)
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="AugustHoeg/VoDaSuRe",
+    repo_type="dataset"
+)
+```
+
+### Git (with Git LFS)
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/AugustHoeg/VoDaSuRe
+```
+
+---
+
+## Data Usage
+
+The dataset is provided as compressed `.tar` archives containing `.zarr` folders.
+
+To extract:
+
+```bash
+cd VoDaSuRe && bash extract_files.sh
+```
+
+After extraction, the dataset can be accessed using libraries supporting OME-Zarr, such as:
+
+* `zarr`
+* `ome-zarr-py`
+* `dask`
+
+---
+
+## Intended Use
+
+VoDaSuRe is designed for:
+
+* Volumetric super-resolution (3D SR)
+* Domain generalization and domain shift analysis
+* Benchmarking learning-based SR methods under realistic acquisition scenarios
+
+---
+
+## Dataset Creation
+
+The dataset was created using **laboratory CT (Lab-CT) imaging systems**, capturing paired high- and low-resolution volumetric scans under varying acquisition conditions.
+
+Further details are available in the associated paper and project page.
+
+---
+
+## Citation
+
+If you use this dataset, please cite our paper:
+
+```bibtex
+@article{hoeg2026vodasure,
+  title={VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution},
+  author={August Leander Høeg and Sophia Wiinberg Bardenfleth and Hans Martin Kjer and Tim Bjørn Dyrby and Vedrana Andersen Dahl and Anders Dahl},
+  journal={Proceedings of the Computer Vision and Pattern Recognition Conference},
+  year={2026},
+  url={https://augusthoeg.github.io/VoDaSuRe/}
+}
+```
+
+---
+
+## Contact
+
+For questions or issues, please open an issue in the GitHub repository:
+
+https://github.com/AugustHoeg/VoxelSR