Datasets:

anaisleila
/

computer-use-data-psai

license: mit
task_categories:
  - reinforcement-learning
  - robotics
  - video-classification
  - image-to-text
  - visual-question-answering
  - other
tags:
  - computer-use
  - gui-agents
  - multimodal
  - human-computer-interaction
  - screen-recording
  - vision-language-action
  - imitation-learning
  - agent-training
  - pretraining
  - fine-tuning
  - sft
  - supervised-fine-tuning
  - vision-language-model
  - action-prediction
pretty_name: Computer Use Dataset - Paradigm Shift AI
size_categories:
  - 1K<n<10K
language:
  - en

Computer Use Dataset - PSAI

A large-scale, multimodal dataset of human-computer interactions for training and evaluating AI agents.

🔗 Access Dataset: https://huggingface.co/datasets/anaisleila/computer-use-data-psai

📊 Dataset Overview

This dataset contains 3,167 completed tasks of human-computer interactions captured with video, screenshots, DOM snapshots, and detailed interaction events. Created by Paradigm Shift AI for advancing computer use AI agent research.

Key Statistics

Scale:

3,167 tasks with multimodal data
7.87 GB dataset parquet (includes embedded screenshots)
49.2 GB total (7.87 GB parquet + 16.9 GB videos + 24.4 GB DOM snapshots)
100% video coverage (all 3,167 tasks)

Task Distribution:

Browser Tasks: 2,220 (70.1%)
Computer Tasks: 947 (29.9%)
Difficulty: Easy (79.4%) | Medium (16.7%) | Hard (3.9%)
Platforms: Cross-platform (95.1%) | Windows (4.5%) | macOS (0.4%)

Data Coverage by Modality

Videos: 100% coverage (3,167/3,167 tasks) - 16.9 GB
All tasks have screen recordings in MP4 format.

Screenshots: 42.6% coverage (1,349/3,167 tasks)
14,740 images embedded directly in the parquet files (included in the 7.87 GB dataset size).

DOM Snapshots: 55.8% coverage (1,766/3,167 tasks) - 24.4 GB
HTML structure captures for web-based tasks.

Browser tasks: 77.5% have DOM snapshots
Computer tasks: 4.8% have DOM snapshots

Content Diversity

294 unique websites (browser tasks) - Amazon, Google, ArXiv, Apple, Booking, and more
173 unique applications (computer tasks) - MS Office Suite, File Explorer, Email clients, and more
31 subcategories spanning:
- Search & Research (928 | 29.3%)
- Shopping & E-commerce (490 | 15.5%)
- Social Media & Communication (210 | 6.6%)
- News & Media (149 | 4.7%)
- Document Editing (127 | 4.0%)
- Education & Learning (101 | 3.2%)
- Navigation & Maps (93 | 2.9%)
- Email Ops (71 | 2.2%)
- And 23 more categories...

🚀 Quick Start

Option 1: Load Dataset Only (7.87 GB)

Fast access to metadata and embedded screenshots:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("anaisleila/computer-use-data-psai")

# Access a task
task = dataset['train'][0]
print(f"Task: {task['task_name']}")
print(f"Category: {task['category']}")
print(f"Screenshots: {len(task['screenshots'])} images")

Option 2: Download Videos & DOMs On-Demand

Download specific files as needed:

from huggingface_hub import hf_hub_download

# Download a specific video
video_path = hf_hub_download(
    repo_id="anaisleila/computer-use-data-psai",
    filename=task['video_file'],  # e.g., "videos/{task_id}.mp4"
    repo_type="dataset"
)

# Download DOM snapshot
dom_path = hf_hub_download(
    repo_id="anaisleila/computer-use-data-psai",
    filename=task['dom_snaps_file'],  # e.g., "dom_snaps/{task_id}.zip"
    repo_type="dataset"
)

Option 3: Clone Full Dataset (49.2 GB)

Clone everything including videos and DOM files:

git lfs install
git clone https://huggingface.co/datasets/anaisleila/computer-use-data-psai

📚 What's in Each Task

Each task includes:

Metadata Fields

unique_data_id (string): Unique identifier for each recording
taskId (string): Task template ID (non-unique - same task done by different vendors)
task_name (string): Human-readable task description
category (string): BROWSER_TASK or COMPUTER_TASK
subCategory (list[string]): Specific categories (e.g., "Search & Research")
application_website (string): Application or website used
tags (list[string]): Descriptive tags
benchmark (string): Benchmark identifier
appType (string): SINGLE_APP or MULTI_APP
difficulty (string): EASY, MEDIUM, or HARD
os (string): CROSS_PLATFORM, WINDOWS, macOS, or LINUX
requires_login (string): Whether task requires authentication
completedAt (string): Timestamp (ISO 8601 format)

Multimodal Data

screenshots (list[images]): Screenshots at key moments - embedded and viewable
video_file (string): Path to screen recording (MP4) - download on demand
dom_snaps_file (string): Path to HTML DOM snapshot (ZIP) - download on demand
events (string): Keyboard/mouse interactions with timestamps (JSON)
reasoning_steps (list[string]): Step-by-step task completion reasoning
metadata (string): System info (OS, screen resolution, hardware) (JSON)

Note: Screenshots are embedded for instant browsing. Videos and DOM snapshots are stored separately to keep the dataset size manageable.

💡 Usage Examples

1. Browse and Explore Tasks

from datasets import load_dataset
import json

dataset = load_dataset("anaisleila/computer-use-data-psai")

# Browse tasks
for task in dataset['train'][:5]:
    print(f"Task: {task['task_name']}")
    print(f"  Category: {task['category']}")
    print(f"  Difficulty: {task['difficulty']}")
    
    # Parse metadata
    metadata = json.loads(task['metadata'])
    print(f"  System: {metadata.get('system')}")
    
    # Parse events
    if task['events']:
        events = json.loads(task['events'])
        print(f"  Events: {len(events)} interactions")

2. Filter by Criteria

# Filter by difficulty
hard_tasks = dataset['train'].filter(lambda x: x['difficulty'] == 'HARD')
print(f"Hard tasks: {len(hard_tasks)}")

# Filter by category
browser_tasks = dataset['train'].filter(lambda x: x['category'] == 'BROWSER_TASK')

# Complex filter
windows_hard = dataset['train'].filter(
    lambda x: x['difficulty'] == 'HARD' and x['os'] == 'WINDOWS'
)

3. Download Files for Specific Tasks

from huggingface_hub import hf_hub_download

# Find a task you're interested in
task = dataset['train'][0]

# Download video
video = hf_hub_download(
    repo_id="anaisleila/computer-use-data-psai",
    filename=task['video_file'],
    repo_type="dataset"
)

# Download DOM snapshot (if available)
if task['dom_snaps_file']:
    dom = hf_hub_download(
        repo_id="anaisleila/computer-use-data-psai",
        filename=task['dom_snaps_file'],
        repo_type="dataset"
    )

🎯 Use Cases

This dataset supports:

Training computer use AI agents (vision-language-action models)
Reinforcement learning for GUI interaction
Benchmark evaluation of computer use capabilities
Research in human-computer interaction patterns
Accessibility tools development
Software testing and quality assurance automation

📖 Dataset Details

Data Collection

Data was collected using a custom-built computer interaction capture tool that records:

Keyboard and mouse inputs with timestamps
Full screen video recordings
DOM snapshots for web-based tasks
Accessibility tree information
Detailed event streams

Human vendors performed tasks following specific instructions. All vendors signed disclosure agreements authorizing public release of the data.

Privacy & Consent

Data collected from consenting human vendors
Vendors signed disclosure agreements for public release
May contain some PII from vendor interactions
Users should be aware tasks may show personal information

Known Limitations

Some tasks may reference applications or websites that have changed since data collection
Not all tasks have screenshots or DOM snapshots (see coverage stats above for exact percentages)
Dataset contains 100 duplicate rows (3,267 total rows, 3,167 unique tasks)
- To deduplicate: dataset.to_pandas().drop_duplicates(subset=['unique_data_id'], keep='first')

📜 License

MIT License - see LICENSE for full details.

Copyright (c) 2025 Paradigm Shift AI
Anais Howland, Ashwin Thinnappan, Jameel Shahid Mohammed

🙏 Citation

If you use this dataset in your research, please cite:

@dataset{psai_computer_use_2025,
  title={Computer Use Data - Paradigm Shift AI},
  author={Anais Howland and Ashwin Thinnappan and Jameel Shahid Mohammed},
  organization={Paradigm Shift AI},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/anaisleila/computer-use-data-psai}
}

👥 Authors

Anais Howland, Ashwin Thinnappan, and Jameel Shahid Mohammed
Paradigm Shift AI

This dataset was created by the team at Paradigm Shift AI, including:

Data collection infrastructure and vendor coordination system
Custom screen recording and interaction capture tool
Dataset curation, validation, and quality assurance

📞 Contact & Contributions

This dataset is provided as-is for the research community.

For questions or issues:

Open a discussion on the HuggingFace dataset page
Contact: anaisaddad@gmail.com

📋 Changelog

v1.0 (2025): Initial public release with 3,167 tasks