README.md

---
license: cc-by-4.0
task_categories:
- visual-question-answering
- image-classification
language:
- en
tags:
- surgical
- laparoscopy
- foreign-objects
- staged-laboratory
- synthetic-physical
pretty_name: SD2 staged-laboratory foreign-object frames (heydonto)
size_categories:
- n<1K
configs:
- config_name: default
  data_files: sd2_annotations.parquet
---

# SD2 staged-laboratory foreign-object frames (heydonto)

**97 frames · 163 frame-level annotation rows · 8 staged laboratory takes · CC BY 4.0**

This dataset discloses and carries the **SD2 own-footage portion** of the training data of the ORena SAVE FOCUS challenge entry's FRAME-track component: 163 of that component's 58,086 pooled training rows. The other sources of that corpus are not part of this dataset.

## What is in it
- `frames/` — 97 JPEG frames (filename = first 16 hex of the frame's sha256 + take id + time in seconds), extracted from the platform encodes (mp4) of eight staged laboratory recordings (session `staged_session2`, recorded 2026-08-24/25; the AVI masters are retained privately and identified by sha256). The recordings are synthetic physical scenes (dataset family `sd2_synthetic_physical`): surgical props and butcher-meat tissue arranged on a locked setup. No patients are involved (program operator brief). Depiction: No individuals appear in the frames; the recording operator's thumb may appear occasionally when a QR code is shown (owner's statement, 2026-09-02, quoted verbatim below). The footage is the publisher's own, released under CC BY 4.0. Owner's statement, verbatim (2026-09-02 16:0xZ, owner's typed words, relayed by the program coordinator): "The recording does not show anyone, my thumb maybe sometimes when i show the QR. and does not show anyone else." (see CONSENT_ATTESTATION.json).
- `sd2_annotations.parquet` / `sd2_annotations.jsonl` — 163 rows in the challenge organizer's 14-column form (`id, video, procedure_type, question, answer, answer_format, track, generation, clinical_relevance, ood, timestamp_start, timestamp_end, primary_capability, secondary_capabilities`) plus provenance columns (`frame_file, frame_sha256, t_seconds, take, proposal_source, reviewer_signature_id, signer_class, adoption_event_id, provenance_row_id, license`).
- `CONSENT_ATTESTATION.json`, `LICENSE`, `MANIFEST.json` (sha256 and byte size of every file).

Answer formats: {"yes_no": 66, "state_label": 97}. Rows per take: {"D2-DRN01": 41, "D2-SPEC02": 15, "D2-ST01": 33, "D2-ST02": 18, "D2-ST03": 23, "D2-ST04": 12, "D2-ST06": 8, "D2-ST06R": 13}. Frames per take: {"D2-DRN01": 22, "D2-SPEC02": 10, "D2-ST01": 19, "D2-ST02": 11, "D2-ST03": 15, "D2-ST04": 7, "D2-ST06": 5, "D2-ST06R": 8}.

## Provenance
Labels are **machine state spans with human-verified labels** (`signer_class = machine_lineage_human_reviewed`; signer `trainer_study:machine-state-spans+desk-verified-labels`): the label origin is machine/analysis-derived (the trainer-study intake ledger's state spans, an analysis converged with the owner using a general-purpose VLM), and every label was verified by the annotation desk's frame reads before adoption — they are not human-authored annotations. Adoption event `ADOPT-2026-08-26-SD2ADOPTION2 (state/temporal + instrument/QC only)`. `timestamp_start/end` are floor(`t_seconds`) as HH:MM:SS; `t_seconds` is exact. The source recordings (AVI masters, ~3.3 GB) are not part of this dataset; they are retained privately as evidence and are identified in the program's records by sha256.

## Intended use
Research on foreign-object recognition and state description in laparoscopic-style staged scenes; this is the SD2 own-footage part of the ORena entry's FRAME-track training corpus. Frame-level questions only. Load the annotations from `sd2_annotations.parquet` (the README `configs` pins it as the default data file; the `.jsonl` mirror is value-identical but its all-empty `secondary_capabilities` lists infer as null-typed under generic JSON loaders).

## License and attribution
CC BY 4.0. Please attribute: *heydonto — SD2 staged-laboratory foreign-object frames (2026).*