Delete _README_

_README_

@@ -1,61 +0,0 @@
-This dataset is released in conjunction with the paper "VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution"
-
-The VoDaSuRe dataset consists of 32 volumetric scans of 16 samples acquired using laboratory CT (Lab-CT). It includes:
-- High-resolution (HR) volumes
-- Low-resolution (LR) volumes (unregistered)
-- Registered and intensity-matched low-resolution (REG) volumes
-
-For details on the features of VoDaSuRe, see the links below:
-1. Project page - https://augusthoeg.github.io/VoDaSuRe/
-2. ArXiv paper - https://arxiv.org/abs/2603.23153
-3. GitHub repository (frameworks, pipelines, data loaders) - https://github.com/AugustHoeg/VoxelSR
-
---------
-
-The dataset contains training and test splits stored under the "ome/train" and "ome/test" subfolders. 
-
-All data is stored in the OME-Zarr format, a hierarchical, chunked storage format that supports efficient and lazy loading.
-
-Each .zarr file has 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)
-
----------
-
-To download the data, click on a chosen file/folder, then click the download button in the top right corner.
-
-Alternatively, the data can be easily fetched through the command line, for example with:
-> wget https://archive.compute.dtu.dk/downloads/public/projects/VoDaSuRe
-
-The data should be decompressed before use:
-> cd VoDaSuRe && bash extract_files.sh
-
-Note: The full dataset requires approximately 500 GB of disk space.
-
----------
-
-If you use our dataset, please cite our work:
-
-@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/}
-}
-