# EgoExOR: An Egocentric–Exocentric Operating Room Dataset for Comprehensive Understanding of Surgical Activities

许可证声明：宽松（Apache-2.0）；本站处理：只链接原出处。针对数据的声明允许商用和再分发。

- 能否商用：是 · 能否改编：是 · 能否再分发：是 · Mdexa 提供文件：否
- 许可证状态：宽松 (Apache-2.0)
- Mdexa 处理：只链接 — 许可证宽松：针对数据的声明允许商用和再分发。只链接原出处。
- 最近核实：2026-11-14
- 信息卡：https://mdexa.com/zh/datasets/egoexor/

## 关键事实

- 领域：临床医学, 医学影像
- 内容类型：标注语料
- 规模：— — 94 minutes (84,553 frames at 15 FPS)
- 语言：英文
- 模态：文本, 图像, 音频, 视频, 图谱
- 发布方：ardamamur
- 年份：—

## EgoExOR: An Egocentric–Exocentric Operating Room Dataset for Comprehensive Understanding of Surgical Activities 是否允许商业使用

- 商业使用：是。
- 许可状态：宽松。
- 条件：保留声明
结论仅记录来源所述，不构成法律意见。

## EgoExOR: An Egocentric–Exocentric Operating Room Dataset for Comprehensive Understanding of Surgical Activities 许可证证据

针对数据的声明允许商用和再分发

- **Hugging Face 卡片元数据** — Apache-2.0：在快照 s01（readme）中找到的声明 — "license: apache-2.0" — https://huggingface.co/datasets/ardamamur/EgoExOR (2026-11-12; 亲自打开)
- **README** — Apache-2.0：在快照 s01（readme）中找到的声明 — "Released under the **Apache 2.0 License**, permitting free academic and commercial use with attribution." — https://huggingface.co/datasets/ardamamur/EgoExOR (2026-11-12; 亲自打开)
- **数据集页面** — Apache-2.0：在快照 s02（page）中找到的声明 — "Released under the Apache 2.0 License , permitting free academic and commercial use with attribution." — https://huggingface.co/datasets/ardamamur/EgoExOR (2026-11-12; 亲自打开)

## 来源与血缘

—

## EgoExOR: An Egocentric–Exocentric Operating Room Dataset for Comprehensive Understanding of Surgical Activities 已知问题

- eye_gaze/coordinates 中的相机 ID 必须通过 metadata/sources 映射，不能用 take 级 sources 文件进行映射。

## 预检

未预检（数据未入库）。

## EgoExOR: An Egocentric–Exocentric Operating Room Dataset for Comprehensive Understanding of Surgical Activities 下载、接口与获取方式

Mdexa 站内不提供文件。原始出处：
- https://huggingface.co/datasets/ardamamur/EgoExOR

### 取用配方

- .gitattributes · 3 KB · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/.gitattributes
- README.md · 12 KB · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/README.md
- croissant.json · 7 KB · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/croissant.json
- metadata.yaml · 1 KB · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/metadata.yaml
- miss_1.h5 · 23.1 GB · sha256 0398a442dd05059bca8b032a5bf7105e315fa7373f4c72f38d27c400f69a757d · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/miss_1.h5
- miss_2.h5 · 10.9 GB · sha256 2a569dde7a0f93cc8eb92695691d33953e3e68a6c5d67053d0505051f42d890b · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/miss_2.h5
- miss_3.h5 · 19.2 GB · sha256 732c2665e0bc52bb430a8cd73323034af6f912b4fa25e503c3500a4c7d65a4df · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/miss_3.h5
- miss_4.h5 · 6.7 GB · sha256 ef9179ed13b1835ee51a4537ca327a7ec202aa6ab18ddc21d778a10d129459f8 · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/miss_4.h5
- splits.h5 · 3.1 MB · sha256 afffd85152f6177ee1e052d53c8374a4c7226bf94be923a4f1406062fd4f8a8b · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/splits.h5
- ultrasound_1.h5 · 1.7 GB · sha256 7ca909346e8bb9476682fbc1b9640fa88ec3919cc00c90c7b1eef0eb5fafd2b9 · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_1.h5
- ultrasound_2.h5 · 15.1 GB · sha256 6fc21e20a320200bf02af2a78f4fbfa3774f0e4acb3d279105c0bcf83493d6f0 · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_2.h5
- ultrasound_3.h5 · 25.4 GB · sha256 1c8e698ef69c60eb7725b563a1f1e8430aa787bb446ef0411879b21a8f4e577d · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_3.h5
- ultrasound_4.h5 · 38.2 GB · sha256 1149d69aa4e5e3c1c3007924c551fa2fddea5bdfa77d0684c2e78b1868d05b50 · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_4.h5
- ultrasound_5_14.h5 · 29.3 GB · sha256 1db79ef99299e2d54d7add56a13d516a6f2a81e45314d2d351ce9ddc89826f0e · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_5_14.h5
- ultrasound_5_58.h5 · 25.4 GB · sha256 b3dcb641b2e609a8ac0c3742e0eab39db96bb5f1bfdfa9026a6ebb4c251f5d47 · https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_5_58.h5

```sh
curl -L --create-dirs -o ".gitattributes" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/.gitattributes"
curl -L --create-dirs -o "README.md" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/README.md"
curl -L --create-dirs -o "croissant.json" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/croissant.json"
curl -L --create-dirs -o "metadata.yaml" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/metadata.yaml"
curl -L --create-dirs -o "miss_1.h5" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/miss_1.h5"
echo "0398a442dd05059bca8b032a5bf7105e315fa7373f4c72f38d27c400f69a757d  miss_1.h5" | sha256sum -c
curl -L --create-dirs -o "miss_2.h5" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/miss_2.h5"
echo "2a569dde7a0f93cc8eb92695691d33953e3e68a6c5d67053d0505051f42d890b  miss_2.h5" | sha256sum -c
curl -L --create-dirs -o "miss_3.h5" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/miss_3.h5"
echo "732c2665e0bc52bb430a8cd73323034af6f912b4fa25e503c3500a4c7d65a4df  miss_3.h5" | sha256sum -c
curl -L --create-dirs -o "miss_4.h5" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/miss_4.h5"
echo "ef9179ed13b1835ee51a4537ca327a7ec202aa6ab18ddc21d778a10d129459f8  miss_4.h5" | sha256sum -c
curl -L --create-dirs -o "splits.h5" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/splits.h5"
echo "afffd85152f6177ee1e052d53c8374a4c7226bf94be923a4f1406062fd4f8a8b  splits.h5" | sha256sum -c
curl -L --create-dirs -o "ultrasound_1.h5" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_1.h5"
echo "7ca909346e8bb9476682fbc1b9640fa88ec3919cc00c90c7b1eef0eb5fafd2b9  ultrasound_1.h5" | sha256sum -c
curl -L --create-dirs -o "ultrasound_2.h5" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_2.h5"
echo "6fc21e20a320200bf02af2a78f4fbfa3774f0e4acb3d279105c0bcf83493d6f0  ultrasound_2.h5" | sha256sum -c
curl -L --create-dirs -o "ultrasound_3.h5" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_3.h5"
echo "1c8e698ef69c60eb7725b563a1f1e8430aa787bb446ef0411879b21a8f4e577d  ultrasound_3.h5" | sha256sum -c
curl -L --create-dirs -o "ultrasound_4.h5" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_4.h5"
echo "1149d69aa4e5e3c1c3007924c551fa2fddea5bdfa77d0684c2e78b1868d05b50  ultrasound_4.h5" | sha256sum -c
curl -L --create-dirs -o "ultrasound_5_14.h5" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_5_14.h5"
echo "1db79ef99299e2d54d7add56a13d516a6f2a81e45314d2d351ce9ddc89826f0e  ultrasound_5_14.h5" | sha256sum -c
curl -L --create-dirs -o "ultrasound_5_58.h5" "https://huggingface.co/datasets/ardamamur/EgoExOR/resolve/dfa6db95b9b5fd67c2d30cb1b3402b07ccae3c8d/ultrasound_5_58.h5"
echo "b3dcb641b2e609a8ac0c3742e0eab39db96bb5f1bfdfa9026a6ebb4c251f5d47  ultrasound_5_58.h5" | sha256sum -c
```

## EgoExOR: An Egocentric–Exocentric Operating Room Dataset for Comprehensive Understanding of Surgical Activities 引用格式

原始资料没有给出推荐引用文本。

## 机器可读

- JSON: https://mdexa.com/datasets/egoexor.json
- 2026-11-14 经自动流水线收录并通过全部校验（门禁、逐字引用、判定引擎、两轮独立抽取）。

## 在本库继续查找

本条目是 109 条已处理并核实的记录之一（数据集、数据资源与开源模型）。查找其他条目：调用 `https://mcp.mdexa.com/v1/datasets?q=<关键词>`，或连接 MCP 服务 `https://mcp.mdexa.com/mcp`；每分钟 20 次以内无需密钥。每行返回不到 1 KB；空结果即"数据库未收录"。核对一份清单：一次批量核对（免费账号）即返回报告。说明：/agents.md。相似条目：CBLUE (https://mdexa.com/zh/datasets/cblue/)；PromptCBLUE (https://mdexa.com/zh/datasets/promptcblue/)；医学考试题库 JSON 数据集 (https://mdexa.com/zh/datasets/medical-exam-question-bank-json/)。