This dataset contains information about 3,900 customers and their purchase behavior in an e-commerce setting. The data spans various customer demographics, purchase preferences, and transactional details. It is designed to help analyze customer behavior, shopping patterns, and marketing effectiveness

jin-ying-so-cute/ecommerce-user-behavior-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Running Behavior

## Overview

Running Behavior is a dataset for object detection tasks - it contains Peoples annotations for 569 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 package contains datasets derived from experimental data from two studies. Both studies employed a mixed-methods approach with university participants using an industrial VR application for training in electrical maintenance tasks. The first dataset corresponds to a study that used an experimental design with 60 participants divided into two groups: the interactive VR group (labeled as 'VR') and the passive monitor viewing group (labeled as 'Monitor'). This data was used to perform various analytical methods to examine learning outcomes and self-efficacy. The second dataset comes from a study that increased the number of participants in the VR group by 27, bringing the total to 57 participants. This study used a quantitative research design and the data was used to implement a Structural Equation Modelling (SEM) approach. This analysis was conducted to investigate the different factors affecting learning in VR. The experimental design and data management plan received approval from the Tilburg University ethics committee (REDC # 20201035).

In this paper, a video-based behavioural recognition dataset for beef cattle is constructed. The dataset covers five behaviours of beef cattle: standing, lying, drinking, feeding, and ruminating. Six beef cows in a captive barn were selected and monitored for 168 hours. Different light conditions and nighttime data were considered. The dataset was collected by 1 surveillance video camera. The data collection process required deploying cameras, memory, routers and laptops. Data annotation was automated using the YOLOv8 target detection model and the ByteTrack multi-target tracking algorithm to annotate each beef cow's coordinates and identity codes. The FFmpeg tool cut out individual beef cow video clips and manually annotated them with behavioural labels. The dataset includes 500 video clips, 2000 image recognition samples, over 4000 target tracking samples, and over 10G of frame sequence images. 4974 video data of different behavioural types are labelled, totalling about 14 hours. Based on this, a TimeSformer multi-behaviour recognition model for beef cattle based on video understanding is proposed as a baseline evaluation model. The experimental results show that the model can effectively learn the corresponding category labels from the behavioural category data of the dataset, with an average recognition accuracy of 90.33% on the test set. In addition, a data enhancement and oversampling strategy was adopted to solve the data imbalance problem and reduce the risk of model overfitting. The dataset provides a data basis for studying beef cattle behaviour recognition. It is of great significance for the intelligent perception of beef cattle health status and improvement of farming efficiency.

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.

The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). The passenger behaviors include passenger normal behavior, passenger abnormal behavior (passenger carsick behavior, passenger sleepy behavior, passenger lost items behavior). In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as in-vehicle passenger monitoring and safety AI systems.

1) Data Introduction • The Extrovert vs. Introvert Behavior Data is a tabular 2,900 psychological and behavioral dataset of individual social behaviors (time alone, frequency of going out, number of friends, social media activities, etc.) and personality types (outward/inward).

2) Data Utilization (1) Extrovert vs. Introvert Behavior Data has characteristics that: • Each row includes time spent alone in a day, stage fright, frequency of social gatherings, frequency of going out, post-socialization fatigue, number of friends, frequency of social media posts, and target variable, personality type (Extrovert/Introvert). • Data has some missing values, but the outward and introverted classes are distributed in a balanced way, making them suitable for personality prediction and behavioral analysis. (2) Extrovert vs. Introvert Behavior Data can be used to: • Personality Type Predictive Model Development: Using social behavioral characteristics and personality labels, we can build an outward/introverted personality predictive model based on machine learning. • Social Behavior Patterns and Psychological Analysis: It can be used for research in various fields such as psychology, sociology, and marketing by analyzing the correlation between various variables such as time alone, the number of friends, and social media activities.

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.

Context

Driver behavior is one of the most important aspects in the design, development, and application of Advanced Driving Assistance Systems (ADAS) and Intelligent Transportation Systems (ITS), which can be affected by many factors. If you are able to measure the driving style of your staff, there is a lot of actions you can take in order to improve fleet safety, global road safety as well as fuel efficiency and emissions.

Content

• 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: 1.Sudden Acceleration (Class Label: 1) 2.Sudden Right Turn (Class Label: 2) 3.Sudden Left Turn (Class Label: 3) 4.Sudden Break (Class Label: 4)
• Best Window Size: 14 seconds
• Sensor: MPU6050
• Device: Raspberry Pi 3 Model B

Acknowledgements

Yuksel, Asim; Atmaca, Şerafettin (2020), “Driving Behavior Dataset”, Mendeley Data, V2, doi: 10.17632/jj3tw8kj6h.2

Data set is available in below link- Click here

Aggressive Behavior Dataset

## Overview

Aggressive Behavior Dataset is a dataset for object detection tasks - it contains People Bad Behaviors annotations for 529 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).

🎙️ Behavior-SD

Official repository for our NAACL 2025 paper:Behavior-SD: Behaviorally Aware Spoken Dialogue Generation with Large Language ModelsSehun Lee*, Kang-wook Kim*, Gunhee Kim (* Equal contribution)

🏆 SAC Award Winner in Speech Processing and Spoken Language Understanding

  🔗 Links

Project Page Code

  📖 Overview

We explores how to generate natural, behaviorally-rich full-duplex spoken dialogues using large language models (LLMs). We introduce:… See the full description on the dataset page: https://huggingface.co/datasets/yhytoto12/behavior-sd.

abhayesian/quirky-behavior-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

We present the first multi-year mobile sensing datasets. Our multi-year data collection studies span four years (10 weeks each year, from 2018 to 2021). The four datasets contain data collected from 705 person-years (497 unique participants) with diverse racial, ability, and immigrant backgrounds. Each year, participants would install a mobile app on their phones and wear a fitness tracker. The app and wearable device passively track multiple sensor streams in the background 24×7, including location, phone usage, calls, Bluetooth, physical activity, and sleep behavior. In addition, participants completed weekly short surveys and two comprehensive surveys on health behaviors and symptoms, social well-being, emotional states, mental health, and other metrics. Our dataset analysis indicates that our datasets capture a wide range of daily human routines, and reveal insights between daily behaviors and important well-being metrics (e.g., depression status). We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.

Behavior

## Overview

Behavior is a dataset for object detection tasks - it contains Monitor Discuss Operate Paper annotations for 326 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).

Variation in individual cognition affects how animals learn about and communicate information to others. We provide evidence that differences in how individual honey bees learn influences the collective foraging dynamics of a colony. By creating colonies of distinct learning phenotypes, we evaluated how bees make foraging choices in the field. Colonies containing individuals that learn to ignore unimportant information preferred familiar food locations; however, colonies of individuals that are unable to ignore familiar information visit novel and familiar feeders equally. Colonies with a 50/50 mix of these phenotypes prefer familiar food locations because individuals who learn the familiar location recruit nestmates by dancing more intensely. Our results reveal that cognitive variation among individuals nonlinearly shapes collective behavioral outcomes.

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 advancements in technology and software designed for remote collaboration. - Get the data

Metadata for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F5d15deaf6757f20132a06e256ce14618%2FFrame%201%20(9).png?generation=1743156643952762&alt=media" alt="">

Dataset boasts an impressive >97% accuracy in action recognition (including actions such as sitting, typing, and gesturing) and ≥97% precision in action labeling, making it a highly reliable resource for studying human behavior in webcam settings.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Researchers can utilize this dataset to explore the impacts of web cameras on social and professional interactions, as well as to study the security features and audio quality associated with video streams. The dataset is particularly valuable for examining the nuances of remote working and the challenges faced during video conferences, including issues related to video quality and camera usage.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

The NYC Youth Risk Behavior Survey (YRBS) is conducted through an ongoing collaboration between the New York City Department of Health and Mental Hygiene (DOHMH), the Department of Education (DOE), and the National Centers for Disease Control and Prevention (CDC). The New York City's YRBS is part of the CDC's National Youth Risk Behavior Surveillance System (YRBSS). The survey's primary purpose is to monitor priority health risk behaviors that contribute to the leading causes of mortality, morbidity, and social problems among youth in New York City. For more information see EpiQuery, https://a816-health.nyc.gov/hdi/epiquery/visualizations?PageType=ps&PopulationSource=YRBS

Recent research on free-range chickens shows that individual behavioral differences may link to range use. However, most of these studies explored individual behavioral differences only at one time point or during a short time window, assessed differences when animals were out of their social group and home environment (barn and range), and in specific tests or situations. Therefore, it is yet unclear how different behaviors relate to range use and how consistent these behaviors are at the individual level. To fill this gap, we here aimed to describe the behavioral budget of slow-growing male broiler chickens (S757N) when in their social group and home environment during the whole rearing period (from the second week of life to the twelfth week, before slaughter), and to relate observed behavioral differences to range use. For this, we followed a sample of individuals in two flocks (n = 60 focal chickens out of 200 chickens per flock), over two seasons, during three periods: before range access (from 14 to 25 days old), during early range access (first weeks of range access, from 37 to 53 days old), and during late range access (last weeks of range access, from 63 to 87 days old). By the end of each period, individual tests of exploration and social motivation were also performed, measuring exploration/activity and sociability propensities. Our results show that foraging (i.e., pecking and scratching at the ground) was the only behavior that correlated to range use for all three rearing periods, independent of the season. Foraging was also the only behavior that showed within-individual consistency from an early age and across the three rearing periods. Foraging may, therefore, serve as a useful behavioral predictor of range use in free-range broiler chickens. Our study increases the knowledge of how behaviors develop and relate to each other in a domesticated and intensely selected species, and improves our understanding of the biology of free-range broiler chickens. These findings can, ultimately, serve as a foundation to increase range use and improve chicken welfare.

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