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Datasets:
bridenmj
/
porcine-wound-forecasting-processed

Tags:
computer-vision
longitudinal-imaging
medical-imaging
wound-healing
porcine
image-generation
Libraries:
WebDataset
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Illegal slicing argument for scalar dataspace
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 83, in _generate_tables
                  pa_table = _recursive_load_arrays(h5, self.info.features, start, end)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 269, in _recursive_load_arrays
                  arr = _recursive_load_arrays(dset, features[path], start, end)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 271, in _recursive_load_arrays
                  arr = _load_array(dset, path, start, end)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 238, in _load_array
                  arr = dset[start:end]
                        ~~~~^^^^^^^^^^^
                File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
                File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
                File "/usr/local/lib/python3.14/site-packages/h5py/_hl/dataset.py", line 931, in __getitem__
                  selection = sel2.select_read(fspace, args)
                File "/usr/local/lib/python3.14/site-packages/h5py/_hl/selections2.py", line 101, in select_read
                  return ScalarReadSelection(fspace, args)
                File "/usr/local/lib/python3.14/site-packages/h5py/_hl/selections2.py", line 86, in __init__
                  raise ValueError("Illegal slicing argument for scalar dataspace")
              ValueError: Illegal slicing argument for scalar dataspace

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Porcine Wound Forecasting Processed Data

This repository contains the processed data artifacts used by the Wound Forecasting project for longitudinal porcine wound-image generation and extrapolation.

The repository includes image-space data and the model-ready representations used by the DyneODE and River experiments.

Contents 

.
├── manifest.json
├── images_256x256/
│   └── 256x256.zip
├── dyneode_inversions/
│   ├── inversions-00000.tar
│   └── manifest.jsonl
└── river_h5/
    ├── davinci_train.h5
    └── davinci_val.h5

Processed images

images_256x256/256x256.zip contains the processed longitudinal wound images used by the project.

The images are 256 × 256 pixels. Some historical experiment paths used the directory name 512x512; that name does not describe the released image dimensions.

DyneODE inversions

dyneode_inversions/inversions-00000.tar contains 1,650 serialized PyTorch latent tensors used by the DyneODE experiments.

dyneode_inversions/manifest.jsonl maps each source-relative latent path to its member name within the TAR archive.

The wound-domain StyleGAN generator required to decode these representations is released separately:

The final DyneODE checkpoint is available at:

River HDF5 data

river_h5/davinci_train.h5 and river_h5/davinci_val.h5 contain the model-ready HDF5 data used by the final River workflow.

The final River-only model weights and configuration are released at:

River uses the external VQ-MUSE autoencoder. Its upstream weights are not duplicated in this dataset repository:

Project code

Source code, configurations, evaluation utilities, and loading documentation are available at:

Source dataset

These artifacts were derived from the public longitudinal porcine wound-healing dataset:

Citation

If you use these processed data or derived representations, please cite the original dataset publication:

@article{zlobina2025high,
  title={A high-resolution temporal transcriptomic and imaging dataset of porcine wound healing},
  author={Zlobina, Ksenia and Yang, Hsin-ya and Kesapragada, Manasa and Lu, Fan and Gallegos, Anthony and Villa-Martinez, Guillermo and Alhamo, Moyasar A and Zhu, Kan and Recendez, Cynthia and Collins, Craig and others},
  journal={Scientific Data},
  volume={12},
  number={1},
  pages={1635},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

Please also cite the associated Wound Forecasting paper when its publication information becomes available.

Data organization

The source images are organized longitudinally by pig, wound, day, and within-day burst. Consult manifest.json for the released dataset organization and dyneode_inversions/manifest.jsonl for TAR member lookup.

Intended use

The dataset is intended for research on longitudinal visual forecasting, generative modeling, representation learning, and wound healing progression.

Release status

This repository remains private while public-release permissions and licensing are finalized.
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