Egocentric Video Dataset

Car Accident Video Dataset contains 5,000 annotated video clips capturing 3 seconds before, during, and after traffic accidents, providing structured data for car accident detection and prediction tasks. This video dataset supports training models for traffic detection, road accident analysis, and crash recognition, offering consistent MP4 footage for developing reliable accident detection systems.

Scene Scanning Video Dataset includes 10,000 high-resolution (1920×1080, 30 FPS) MP4 videos capturing people looking around and scanning their environment in controlled settings. This video dataset contains annotated metadata labels—such as environment type, camera motion, participant count, gender, duration, and resolution—providing structured training data for computer vision, face detection, face recognition, and object detection.

Fall Detection Dataset contains 10,000 high-resolution videos of staged human fall events recorded across indoor and outdoor settings using static and moving cameras. This camera dataset supports fall detection computer vision research by providing well-structured metadata, realistic fall scenarios, and consistent 1080p video quality for training and evaluating detection systems.

Weapon Detection Dataset contains 10,000 annotated images of staged individuals with visible weapons, collected from public CCTV footage and internet sources for training weapon detection models. This surveillance dataset includes bounding box annotations and rich metadata, supporting accurate weapons detection, small object detection, and real-time security systems across CCTV and camera-based monitoring scenarios.

This fight detection dataset contains 1,000 high-quality videos of simulated physical altercations recorded in controlled environments, captured from static and moving surveillance camera views at up to 1920×1080 resolution and 30 FPS. Designed for violence detection, action recognition, and public safety systems, this surveillance dataset includes rich metadata annotations enabling accurate camera fight analysis and training violence detection models.

Lerobot SO-101 Manipulations Dataset is a compact, high-quality set of 20 teleoperated robotic arm recordings (~30 FPS) captured on the calibrated robot studio SO-101 arm using multi-view cameras (front, wrist, top) in the Lerobot framework. Designed for imitation learning and model training on the Lerobot platform, it demonstrates pick-and-place and basic manipulation tasks with structured LeRobot v2.1 annotations. 

This dataset is intended as a limited reference set and contains 20 episodes; however, full custom data collection in Lerobot format can be provided on request.

Materials Platform for Data Science Dataset (Project Pauling Files) is a highly curated inorganic materials dataset with a 30-year track record, based on about half a million peer-reviewed scientific publications and powering several successful commercial products from the world's leading publishers. It integrates materials data from 405,100 publications, linking 139,005 phase diagrams, 409,771 crystalline nanostructures, and 1,075,676 physical property sets into 189,682 materials phases within a large-scale materials graph of ~5 million nodes and ~150 million edges, making it a comprehensive materials platform for data science and a cornerstone among modern material science datasets and materials project database resources.

This droid dataset comprises 1,000 hours of multimodal recordings of cleaning, laundry folding, and dishwashing activities, combining head-mounted video with detailed robotic data streams from seven 9-axis IMU units positioned on the chest, upper arms, forearms, and wrists. The robot dataset captures complete robotic data through 3-axis accelerometer (linear acceleration), 3-axis gyroscope (angular velocity), and 3-axis magnetometer (magnetic field orientation) sensors, using onboard sensor-fusion to generate orientation quaternions rather than raw motion signals, enabling precise motion tracking and real-world robotic learning.

This Serbian road traffic dataset contains 5,000 high-resolution (≥1080p) videos and annotated frame images with JSON bounding box annotations for vehicle detection and classification tasks. Collected from bridge vantage points, it provides detailed data on traffic flows, vehicle types (cars and minivans), and traffic density, supporting AI development for traffic prediction, road safety analysis, and computer vision model training.

SWE-Bench Coding Tasks Dataset  is an extended programming languages dataset that builds on the original SWE-Bench benchmark with broader language coverage, golden/test patches, and real-world coding tasks like bug fixing, code completion, and automated code review. It supports coding agents, language models, and developer tools with verified benchmark scores and multi-language test sets.