This dataset contains the raw behavioral data of the spatial attention task. The zip-file containts .csv files for each participant and experimental session. Each file shows parameters (columns) for each trial (rows), including direction of the cue ('cue'), location of the target stimulus ('location'), orientation of the target stimulus ('orientation').

edit 29-11-2023: data of participant 30 was missing and has now been added.

Behavioral Data.

BEHAVIORAL DATA

Behavioral data for the manuscript: "Pharmacological elevation of catecholamine levels improves perceptual decisions, but not metacognitive insight"The file contains trial-wise information about, among others:presented stimulus (0: CW or 1: CCW)behavioral response (0: CW or 1: CCW)confidence rating (0: low confidence or 1: high confidence)RTDrugBaseline pupil size (and bin)

This dataset contains the output of HCAI’s Supply and Demand Model for California’s Behavioral Health Workforce. It includes the estimated supply and demand for providers in Full-Time Equivalent (FTE) for each role or role group included in the model for the years 2022-2023. These metrics can be compared by role, region, county, and year. For in-depth details on our modeling methodology, please see our online comprehensive methodology documentation at https://hcai.ca.gov/wp-content/uploads/2025/05/Public-Modeling-Methodology-v.1.1_5-2025.pdf.

During Task-counting, participants counted the number of perceived alternations in rotation direction and reported the total count after each run (of 10 minutes). The data is cointained in 'behav_counting_upload.mat'.During Task-pressing, participants pressed whenever they perceived an alternations in rotation direction and the number of button presses was summed. The data is cointained in 'behav_pressing_upload.mat'Each matrix is of size 28x3, where 28 is the number of subjects and 3 the number of pharma conditions (1 = placebo, 2 = atomoxetine, 3 = donepezil). The data is averaged across 2 runs per session. See Materials and Methods of the paper for more details.

Synthetic AI Developer Productivity Dataset — Behavioral + Cognitive Simulation

A synthetic data generation resource for modeling behavioral and cognitive dynamics in developers.

  📘 About This Dataset

This dataset simulates productivity data from AI-assisted software developers. It blends behavioral signals, physiological inputs, and productivity metrics to explore the nuanced relationships between deep work, distractions, caffeine, AI usage, and cognitive strain.… See the full description on the dataset page: https://huggingface.co/datasets/syncora/developer-productivity-simulated-behavioral-data.

This dataset provides a comprehensive collection of consumer behavior data that can be used for various market research and statistical analyses. It includes information on purchasing patterns, demographics, product preferences, customer satisfaction, and more, making it ideal for market segmentation, predictive modeling, and understanding customer decision-making processes.

The dataset is designed to help researchers, data scientists, and marketers gain insights into consumer purchasing behavior across a wide range of categories. By analyzing this dataset, users can identify key trends, segment customers, and make data-driven decisions to improve product offerings, marketing strategies, and customer engagement.

Key Features: Customer Demographics: Understand age, income, gender, and education level for better segmentation and targeted marketing. Purchase Behavior: Includes purchase amount, frequency, category, and channel preferences to assess spending patterns. Customer Loyalty: Features like brand loyalty, engagement with ads, and loyalty program membership provide insights into long-term customer retention. Product Feedback: Customer ratings and satisfaction levels allow for analysis of product quality and customer sentiment. Decision-Making: Time spent on product research, time to decision, and purchase intent reflect how customers make purchasing decisions. Influences on Purchase: Factors such as social media influence, discount sensitivity, and return rates are included to analyze how external factors affect purchasing behavior.

Columns Overview: Customer_ID: Unique identifier for each customer. Age: Customer's age (integer). Gender: Customer's gender (categorical: Male, Female, Non-binary, Other). Income_Level: Customer's income level (categorical: Low, Middle, High). Marital_Status: Customer's marital status (categorical: Single, Married, Divorced, Widowed). Education_Level: Highest level of education completed (categorical: High School, Bachelor's, Master's, Doctorate). Occupation: Customer's occupation (categorical: Various job titles). Location: Customer's location (city, region, or country). Purchase_Category: Category of purchased products (e.g., Electronics, Clothing, Groceries). Purchase_Amount: Amount spent during the purchase (decimal). Frequency_of_Purchase: Number of purchases made per month (integer). Purchase_Channel: The purchase method (categorical: Online, In-Store, Mixed). Brand_Loyalty: Loyalty to brands (1-5 scale). Product_Rating: Rating given by the customer to a purchased product (1-5 scale). Time_Spent_on_Product_Research: Time spent researching a product (integer, hours or minutes). Social_Media_Influence: Influence of social media on purchasing decision (categorical: High, Medium, Low, None). Discount_Sensitivity: Sensitivity to discounts (categorical: Very Sensitive, Somewhat Sensitive, Not Sensitive). Return_Rate: Percentage of products returned (decimal). Customer_Satisfaction: Overall satisfaction with the purchase (1-10 scale). Engagement_with_Ads: Engagement level with advertisements (categorical: High, Medium, Low, None). Device_Used_for_Shopping: Device used for shopping (categorical: Smartphone, Desktop, Tablet). Payment_Method: Method of payment used for the purchase (categorical: Credit Card, Debit Card, PayPal, Cash, Other). Time_of_Purchase: Timestamp of when the purchase was made (date/time). Discount_Used: Whether the customer used a discount (Boolean: True/False). Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (Boolean: True/False). Purchase_Intent: The intent behind the purchase (categorical: Impulsive, Planned, Need-based, Wants-based). Shipping_Preference: Shipping preference (categorical: Standard, Express, No Preference). Payment_Frequency: Frequency of payment (categorical: One-time, Subscription, Installments). Time_to_Decision: Time taken from consideration to actual purchase (in days).

Use Cases: Market Segmentation: Segment customers based on demographics, preferences, and behavior. Predictive Analytics: Use data to predict customer spending habits, loyalty, and product preferences. Customer Profiling: Build detailed profiles of different consumer segments based on purchase behavior, social media influence, and decision-making patterns. Retail and E-commerce Insights: Analyze purchase channels, payment methods, and shipping preferences to optimize marketing and sales strategies.

Target Audience: Data scientists and analysts looking for consumer behavior data. Marketers interested in improving customer segmentation and targeting. Researchers are exploring factors influencing consumer decisions and preferences. Companies aiming to improve customer experience and increase sales through data-driven decisions.

This dataset is available in CSV format for easy integration into data analysis tools and platforms such as Python, R, and Excel.

Behavioral Data Analytics with AI Market Outlook

According to our latest research, the global Behavioral Data Analytics with AI market size reached USD 8.3 billion in 2024, and it is poised to expand at an impressive CAGR of 18.7% from 2025 to 2033. By the end of 2033, the market is projected to achieve a valuation of USD 41.2 billion. This robust growth trajectory is propelled by the increasing adoption of AI-driven analytics in diverse sectors, with organizations seeking to leverage behavioral data for enhanced decision-making, risk mitigation, and customer engagement.

One of the primary drivers fueling the growth of the Behavioral Data Analytics with AI market is the exponential rise in digital interactions across industries. As businesses transition towards omnichannel engagement, vast amounts of behavioral data are generated through online transactions, social media activities, and IoT-enabled devices. The integration of AI-powered analytics platforms enables organizations to extract actionable insights from this data, leading to improved customer personalization, predictive marketing, and operational efficiency. Furthermore, the proliferation of advanced machine learning algorithms has significantly improved the accuracy and speed of behavioral data processing, making it feasible for both large enterprises and SMEs to adopt these solutions seamlessly.

Another key growth factor is the escalating focus on fraud detection and risk management in sectors such as BFSI, healthcare, and retail. With cyber threats and fraudulent activities becoming increasingly sophisticated, organizations are investing in AI-driven behavioral analytics tools that can identify anomalous patterns in real time. These solutions not only enhance security protocols but also minimize financial losses and reputational damage. Additionally, regulatory mandates around data privacy and compliance are compelling organizations to deploy advanced analytics systems that ensure transparency and accountability while handling sensitive behavioral data.

The market is also witnessing significant momentum due to the rising demand for workforce analytics and customer experience management. Enterprises are leveraging behavioral data analytics with AI to optimize talent acquisition, employee engagement, and retention strategies. By analyzing employee behavior and sentiment, organizations can foster a more productive work environment and address issues proactively. On the customer front, AI-powered analytics facilitate hyper-personalized experiences, driving customer loyalty and lifetime value. This dual application in workforce and customer domains is expected to further accelerate the adoption of behavioral data analytics solutions globally.

From a regional standpoint, North America continues to dominate the Behavioral Data Analytics with AI market in 2024, accounting for approximately 38% of the global market share. This leadership is attributed to the high concentration of technology-driven enterprises, early adoption of AI technologies, and a mature digital infrastructure. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitization, increasing investments in AI research, and a burgeoning e-commerce sector. Europe also holds a significant share, driven by stringent data protection regulations and the widespread adoption of AI solutions in financial services and healthcare. The Middle East & Africa and Latin America are gradually catching up, with governments and businesses recognizing the strategic value of behavioral data analytics for economic growth and innovation.

Component Analysis

The Behavioral Data Analytics with AI market by component is segmented into Software, Services, and Platforms. Software solutions constitute the largest share of the market, driven by the increasing demand for advanced analytics tools capable of processing large volumes of behavioral data. These software offerings are designed to integrate seamlessly with existing enterprise systems, enabling real-time data ingestion, cleansing

During a survey carried out among decision-makers in charge of customer engagement/retention strategy from ** countries worldwide, ** percent of respondents stated that they collected customer channel engagement data.

Find health behavior data in Massachusetts. This dataset provides selected data from the Behavioral Risk Factor Surveillance System (BRFSS), an annual telephone survey that collects data on emerging public health issues, health conditions, risk factors and behaviors.

1984-2023. Centers for Disease Control and Prevention (CDC). BRFSS Survey Data. The BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss).

This dataset belongs to the Collection:ter Wal, Marije; Linde Domingo, Juan; Lifanov, Julia; Roux, Frederic; Kolibius, Luca; Gollwitzer, Stephanie; et al. (2020): Data for: Theta rhythmicity governs the timing of behavioural and hippocampal responses in humans specifically during memory-dependent tasks. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5192567This dataset contains the following data:1) behavioral data from 95 participants performing visual tasks (experiments 1-4)2) behavioral data from 226 participants performing memory tasks (experiments 5-13; including 10 iEEG patients)For more details about the data please see the Collection.

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

The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based system of health surveys that collects information on health risk behaviors, preventive health practices, and health care access primarily related to chronic disease and injury. For many states, the BRFSS is the only available source of timely, accurate data on health-related behaviors.

Socially transmitted behaviors are widespread across the animal kingdom, yet comprehensive datasets documenting their distribution and ecological significance remain scarce. Knowledge of animal behavioral traditions could be essential for understanding many species’ responses to anthropogenic disturbances and further enhancing conservation efforts. Here, we introduce the first open-access database that synthesizes data on animal cultural behaviors and traditions. The Animal Culture Database (ACDB) contains descriptions of 128 behaviors including forms of vocal communication, migration, predator defense, foraging practices, habitat alteration, play, mating displays, and other social behaviors for a sample of 61 species. In addition to offering an open-access resource for researchers, educators, and conservationists, the ACDB represents a critical step toward recognizing the role of social learning in animal populations. The ACDB can be accessed at: https://datadiversitylab.github.io/ACDB/.The database includes a total of four tables. The species table contains taxonomic data for each species included in the database as well as species-specific information on social structure (species.csv). The groups table contains data pertaining to each group of animals, including location, size, and where it falls in a species’ social structure when applicable (groups.csv). One record in the species table can correspond to multiple records in the groups table. The behaviors table contains descriptions for each cultural behavior recorded, including information on social transmission and potential effects from human activity (behaviors.csv). One record in the groups table can correspond to multiple records in the behaviors table. The sources table includes details on the relevant primary references for a given entry in the dataset (sources.csv). Note that the species, groups, and behaviors table have fields specifying sources for particular variables (e.g. the primary social unit of a species, the size of a group, or the transmission mode of a behavior) linking them to the sources table. The database is also stored as ACDB_v01.sql.ACDB_sqlite_build.R is primarily for illustrative purposes. It contains the code used to build the database from the csv files. If you would like to access the data, you can look at the individual tables as the CSV files or download the current database as ACDB_v01.sql. The example_queries.R script can also be used to load the SQLite database in R and run example queries on the tables.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

The following dashboard shows statewide Behavioral Health Help Line (BHHL) utilization data and some demographic data about BHHL callers. This data is collected by the Massachusetts Behavioral Health Partnership (MBHP), the vendor that operates the BHHL, and maintained by the Department of Mental Health (DMH).

This dataset contains real-time behavioral and physiological data collected from college students to monitor their engagement, attention, emotional states, and overall well-being in a classroom setting. The data is gathered using a combination of IoT sensors and wearable technology that capture various metrics such as student attendance, facial expressions, posture, movement, heart rate, skin temperature, and breathing rate. Classroom conditions like noise levels and lighting are also included to provide a comprehensive view of the learning environment.

Each entry in the dataset represents a unique combination of data points for a student at a specific timestamp over a period of 30 days. This includes:

Behavioral metrics: Attendance, facial expressions (e.g., happy, sad, neutral), posture (e.g., upright, slouched), and interaction levels. Physiological data: Heart rate, skin temperature, and breathing rate. Environmental factors: Classroom noise levels and lighting. Engagement analysis: The dataset includes labels such as attention, engagement levels, and inactivity, which are classified based on the collected data.

During a survey carried out among decision-makers in charge of customer engagement/retention strategy from 20 countries worldwide, ** percent of respondents stated that they thought it was important or critical to collect customer channel engagement data; ************* named real-time experience in this context.

Welcome to the Confidence Database!

The Confidence Database provides a place where data on subjective ratings of any kind can be found in one place using a common set of formatting.

The database is shared under CC0 (“No Rights Reserved”) license. However, if you use any data from the database, please cite the paper describing the database: Rahnev et al. (2020). The Confidence Database. Nature Human Behaviour, 4(3):317-325. In addition, consider citing the individual papers from where the data came from, which can be find in the readme files.

Contributors: Dobromir Rahnev Kobe Desender Xue, Kai Date created: 2019-05-28 06:52 PM | Last Updated: 2023-07-13 09:38 PM

Category: Project

Description: Database of confidence studies

License: CC0 1.0 Universal