The Behave dataset contains various scenes with human-object interactions, and is used to evaluate the proposed object-level 3D semantic mapping approach.

The Text-BEHAVE dataset is a large-scale dataset for 3D Human-Object Interaction, which integrates textual descriptions with the largest publicly available 3D HOI dataset.

The BEHAVE dataset is the largest dataset of human-object interactions in natural environments, with 3D human, object and contact annotation, to date. The dataset includes: 8 subjects interacting with 20 objects at 5 natural environments. In total 321 video sequences recorded with 4 Kinect RGB-D cameras. Each frame contains human and object masks and segmented point clouds. Every image is paired with 3D SMPL and object mesh registration in camera coordinate. Camera poses for every sequence. Textured scan reconstructions for the 20 objects.

Results of violence detection on the BEHAVE dataset.

Student Classroom Behavior

## Overview

Student Classroom Behavior is a dataset for object detection tasks - it contains Objects annotations for 1,001 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Human Behavior

## Overview

Human Behavior is a dataset for object detection tasks - it contains Human Walk Running Sleeping annotations for 558 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Web Camera People Behavior Dataset for computer vision tasks

Dataset includes 2,300+ individuals, contributing to a total of 53,800+ videos and 9,300+ images captured via webcams. It is designed to study social interactions and behaviors in various remote meetings, including video calls, video conferencing, and online meetings. By leveraging this dataset, developers and researchers can enhance their understanding of human behavior in digital communication settings, contributing to… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/web-camera-people-behavior.

This dataset is about book subjects. It has 2 rows and is filtered where the books is How to behave. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Autism Behavior

## Overview

Autism Behavior is a dataset for computer vision tasks - it contains Autism annotations for 653 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Behavior". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.

Insect Behavior

## Overview

Insect Behavior is a dataset for object detection tasks - it contains Bug annotations for 504 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Fabina Thasni TK

Released under MIT

Contents

Dataset Card for Content Behavior Corpus

The Content Behavior Corpus (CBC) dataset, consisting of content and the corresponding receiver behavior.

  Dataset Details

The progress of Large Language Models (LLMs) has largely been driven by the availability of large-scale unlabeled text data for unsupervised learning. This work focuses on modeling both content and the corresponding receiver behavior in the same space. Although existing datasets have trillions of content… See the full description on the dataset page: https://huggingface.co/datasets/behavior-in-the-wild/content-behavior-corpus.

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Behavioral data associated with the IBL paper: A standardized and reproducible method to measure decision-making in mice.This data set contains contains 3 million choices 101 mice across seven laboratories at six different research institutions in three countries obtained during a perceptual decision making task.When citing this data, please also cite the associated paper: https://doi.org/10.1101/2020.01.17.909838This data can also be accessed using DataJoint and web browser tools at data.internationalbrainlab.orgAdditionally, we provide a Binder hosted interactive Jupyter notebook showing how to access the data via the Open Neurophysiology Environment (ONE) interface in Python : https://mybinder.org/v2/gh/int-brain-lab/paper-behavior-binder/master?filepath=one_example.ipynbFor more information about the International Brain Laboratory please see our website: www.internationalbrainlab.comBeta Disclaimer. Please note that this is a beta version of the IBL dataset, which is still undergoing final quality checks. If you find any issues or inconsistencies in the data, please contact us at info+behavior@internationalbrainlab.org .

This dataset was created using LeRobot.

  Dataset Structure

meta/info.json: { "codebase_version": "v2.1", "robot_type": "R1Pro", "total_episodes": 10000, "total_frames": 119094660, "total_tasks": 50, "total_videos": 90000, "chunks_size": 10000, "fps": 30, "splits": { "train": "0:10000" }, "data_path": "data/task-{episode_chunk:04d}/episode_{episode_index:08d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/behavior-1k/2025-challenge-demos.

Dataset for modeling risky driver behaviors based on accelerometer (X,Y,Z axis in meters per second squared (m/s2)) and gyroscope (X,Y, Z axis in degrees per second (°/s) ) data. Sampling Rate: Average 2 samples (rows) per second Cars: Ford Fiesta 1.4, Ford Fiesta 1.25, Hyundai i20 Drivers: 3 different drivers with the ages of 27, 28 and 37 Driver Behaviors: Sudden Acceleration (Class Label: 1), Sudden Right Turn (Class Label: 2), Sudden Left Turn (Class Label: 3), Sudden Break (Class Label: 4) Best Window Size: 14 seconds Sensor: MPU6050 Device: Raspberry Pi 3 Model B Please See Summary Table for summary of the collected data.

This dataset contains 10,464 videos capturing mobile phone calling behavior in both indoor and outdoor environments. The data covers multiple scenes, multiple shooting angles and various video resolutions. The data can be used for tasks such as calling behavior detection, phone call recognition, human activity analysis, and related AI applications.

How does scale affect policy support? An exit poll of 1,660 voters and a national survey of over 3,000 respondents measure how support for housing varies between the city- and neighborhood-scale. While homeowners are sensitive to housing's proximity, renters typically do not express NIMBYism (“Not In My Back Yard”). However, in high-rent cities, renters exhibit NIMBYism on par with homeowners, despite supporting large increases in housing citywide. These scale-dependent preferences not only help explain the affordability crisis, but show how institutions can undersupply even widely supported public goods. When preferences are scale-dependent, the scale of decision making matters.

In many tasks, human behavior is far noisier than is optimal. Yet when asked to behave randomly, people are typically too predictable. We argue that these apparently contrasting observations have the same origin: the operation of a general-purpose local sampling algorithm for probabilistic inference. This account makes distinctive predictions regarding random sequence generation, not predicted by previous accounts—which suggests that randomness is produced by inhibition of habitual behavior, striving for unpredictability. We verify these predictions in two experiments: people show the same deviations from randomness when randomly generating from non-uniform or recently-learned distributions. In addition, our data show a novel signature behavior, that people’s sequences have too few changes of trajectory, which argues against the specific local sampling algorithms that have been proposed in past work with other tasks. Using computational modeling, we show that local sampling where direction is maintained across trials best explains our data, which suggests it may be used in other tasks too. While local sampling has previously explained why people are unpredictable in standard cognitive tasks, here it also explains why human random sequences are not unpredictable enough.