EyeInfo眼动信息数据
公开眼动视频、CSV和JSON数据，可用于眼位偏斜和注视行为分析，由Fabricio Narcizo提供，来自GitHub数据库，数据格式为多模态，适用于关键点标注任务，许可证为MIT。
基本信息
资源简介
公开眼动视频、CSV和JSON数据，可用于眼位偏斜和注视行为分析。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载，应该可以免费公开下载。
免登录有偿下载
Tips: 该数据集 Qianfanghub 可以协助提供有偿下载服务，注意，服务不针对数据相关产权，只是技术服务费。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/fabricionarcizo/eyeinfo.git
curl -L -o repo.zip https://github.com/fabricionarcizo/eyeinfo/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Usage
This project uses Data Version Control (DVC) to manage the dataset. The latest collected and processed eye-tracking data are available on the main branch of this GitHub repository.
Requirements
- An active Anaconda or Miniforge installation added to your
$PATH - (Recommended) Visual Studio Code installed
Installation
Create new environment called eyeinfo:
conda env create -f environment.yml
Activate the created environment:
conda activate eyeinfo
Download the Dataset
The EyeInfo Dataset’'s raw data (videos, text, CSV, and JSON files) are available as a DVC resource on Google Drive. The dataset contains approximately $285$ MB of files.
Use DVC to download the raw EyeInfo Dataset. From the root folder, execute the following command:
dvc pull
This command will open the web browser automatically, and you must to sign-in using your Google account to allow DVC downloading the dataset. You must give full permission to DVC access the Google Drive resources.
DVC will create the folders called 01_dataset with the raw data, 02_eye_feature with the eye features extracted from each collected video, and 03_metadata with the metadata of each processed video. The folder 01_dataset/0000 contains the $35$ videos of the right eye of the Participant #01. You can use these videos to understand how the dataset was created and organized.
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
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