OmniRAS-PR/
  used-in-paper/          # Primary phase benchmark: contexts and microclips
  yt-chole-shared-videos/ # Phase clips from shared YT-Chole footage
YT-Chole-Triplets/        # Tool–verb–target clips
annotations-splits-provenance-001.tar
indexes/                 # Train/validation clip indexes for the preview
FILES.tsv                # Extracted-file inventory
ARCHIVES.json            # Archive inventory
SHA256SUMS               # Download checksums

Extract the desired media archives and the annotation archive into the same directory. The annotation archive contains source annotations, label mappings, and portable train/validation split CSVs. Use the split CSVs to select the released benchmark clips; source annotations also cover the original collection.

After extraction, primary and shared-video phase files are under OmniRAS-PR/private/ and OmniRAS-PR/public/, respectively.

Inference code, configurations, and class mappings are available in the OmniRAS model repository.

License

The OmniRAS-owned materials in this dataset release, including original annotations, benchmark splits, clip selections, indexes, and documentation, are licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This applies to both OmniRAS-PR and YT-Chole Triplets, and to released video material to the extent that the OmniRAS rights holders can license it. Sharing and adaptation, including for noncommercial research, are permitted with attribution. Commercial use requires separate permission from the rights holders.

Third-party source footage, including the SurgeNet Surgical YouTube footage, retains its original licenses and terms. The CC BY-NC 4.0 license covers the OmniRAS contributions and does not grant rights held by the source-video owners or waive applicable privacy and data-protection requirements.

Please cite OmniRAS

If you use anything from this repository or release—including datasets, annotations, splits, code, model weights, or checkpoints—please cite the OmniRAS paper:

Leonardo Borgioli, Neil Getty, et al. OmniRAS: Standardizing Foundation Model Training and Evaluation in Robot-Assisted Surgery. arXiv:2608.31048, 2026. Paper.

@misc{borgioli2026omniras,
  title = {{OmniRAS}: Standardizing Foundation Model Training and Evaluation in Robot-Assisted Surgery},
  author = {Leonardo Borgioli and Neil Getty and Wenli Xiu and Jessica Cassiani and Alvaro Ducas and Carlos Agustin Orda and Hira Waris and Fangfang Xia and Rick Stevens and Pier Cristoforo Giulianotti and Milos Zefran},
  year = {2026},
  eprint = {2608.31048},
  archivePrefix = {arXiv},
  primaryClass = {eess.IV},
  doi = {10.48550/arXiv.2608.31048},
  url = {https://arxiv.org/abs/2608.31048}
}

Shared source footage: please also cite SurgeNet

YT-Chole Triplets and the YT-Chole shared-video subset of OmniRAS-PR (OmniRAS-PR/yt-chole-shared-videos/) use robotic-cholecystectomy footage from the Surgical YouTube collection released with the SurgeNet paper by Jaspers et al. These two tasks share source-video batches; their clip boundaries, labels, and training/validation assignments differ. The larger OmniRAS-PR subset, labeled used-in-paper/, is a separate source collection.

SurgeNet provides the underlying public source footage. The custom phase and tool–verb–target annotations, task-specific clips, and benchmark splits in this release are part of the OmniRAS work. If you use YT-Chole Triplets or the shared-video phase subset, please cite SurgeNet as well as OmniRAS.

Tim J. M. Jaspers et al. Scaling up self-supervised learning for improved surgical foundation models. Medical Image Analysis, 108:103873, 2026. Paper · Official repository and citation.

@article{JASPERS2026103873,
  title = {Scaling up self-supervised learning for improved surgical foundation models},
  author = {Tim J.M. Jaspers and Ronald L.P.D. de Jong and Yiping Li and Carolus H.J. Kusters and Franciscus H.A. Bakker and Romy C. van Jaarsveld and Gino M. Kuiper and Richard van Hillegersberg and Jelle P. Ruurda and Willem M. Brinkman and Josien P.W. Pluim and Peter H.N. de With and Marcel Breeuwer and Yasmina Al Khalil and Fons van der Sommen},
  journal = {Medical Image Analysis},
  volume = {108},
  pages = {103873},
  year = {2026},
  doi = {10.1016/j.media.2025.103873},
  url = {https://www.sciencedirect.com/science/article/pii/S1361841525004190}
}

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Paper for BorgioliSITL/OmniRAS

OmniRAS: Standardizing Foundation Model Training and Evaluation in Robot-Assisted Surgery

Paper • 2608.31048 • Published Sep 9