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.

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.

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)

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

Data from the 2013-2014 New York Expanded Behavioral Risk Factor Surveillance System (eBRFSS) Survey and the 2016, 2018, 2021 Behavioral Risk Factor Surveillance System were used to generate percentages of adult (18+) NYS residents for various health indicators for a range of geographies.

The share contains the behavioral data in MatLab format, as well as a demographic file with age and IQ information. The data are organized per column as follows:

% subject number % session number % conditions
% checktime % choice side % choice correct % factual outcome (depending on the condition) % alternative outcome (depending on the condition) % reaction time (millisecond)

Cognitive behavioral therapy is an effective treatment for improving mental health problems among university students. However, intervention components have different effects on mental health problems. This paper is a meta-analysis of the data concerning the relationship between cognitive behavioral variables and mental health status among university students. A total of 4 electronic databases were reviewed, and 1,227 articles met the initial selection criteria. Reviewers applied standardized coding schemes to extract the correlational relationship between cognitive behavioral variables and mental health status. A total of 54 articles were included in the meta-analysis. Correlations were found for three cognitive behavioral variables (attention, thought, and behavior) across nine mental health domains (negative affect, positive affect, happiness, social function, stress response, psychological symptom, quality of life, well-being, and general health). Across each cognitive behavioral process and all mental health domains, the estimated mean correlation is modest (.29 - .41), and the correlation depended on the domain of mental health.

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.

Behavioral data supporting the manuscript "Lightening the Mind: Comparing Audiovisual Stimulation and Meditation for Mood and Cognition Enhancement".This description will be updated upon peer-review with a full detail of all variable names.

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.

Behavioral data of mice performing a decision-making task, associated with 2020 publication of the IBL.

The code that created this dataset can be seen in https://github.com/nitzanfarhi/SecurityPatchDetection and can be reproduced by running: console python data_collection\create_dataset.py --all -o data_collection\data Notice that this dataset doesn't include the commits' generated data as it is very big. This can be generated by running only : console python data_collection\create_dataset.py --commits -data_collection\data

A repository name is symbolised by

License This dataset is publicly available for researchers. If you are using our dataset, you should cite our related research paper which outlines the details of the dataset and its underlying principles:

@article{farhi2023detecting, title={Detecting Security Patches via Behavioral Data in Code Repositories}, author={Farhi, Nitzan and Koenigstein, Noam and Shavitt, Yuval}, journal={arXiv preprint arXiv:2302.02112}, year={2023} } As well as mentioning gharchive.org, if you use their data as well.

This dataset includes data on adult's diet, physical activity, and weight status from Behavioral Risk Factor Surveillance System. This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding.

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

This dataset stems from the project ‘Beprepared’: (https://be-prepared-consortium.nl/) which aims to provide in-depth analyses of mixed-method behavioural science data collected throughout the unprecedented COVID-19 pandemic and inform preparedness strategies for future outbreaks. In approaching the research from a behavioural and social science perspective, researchers focus on four main themes:

· Prevention behaviour, psychosocial and contextual determinants, and (communication) interventions

· Resilience and engagement of citizens, communities and organisations

· Research methodology and preparedness

· Effective and integrated policy advice

This resource links to the theme ‘research methodology’ and provides an overview of datasets that have been used internationally to study the behavioral effects of the Covid-19 pandemic. These datasources can be used to study how people behave in a variety of settings during the Covid pandemic and so to inform policy-makers, but also to study the effects of behavioral interventions. It includes datasources that for example study mobility behavior at a regional or national level, physical distancing in public, health adherence behaviors (like handwashing, mask wearing), social contacts on- and offline, purchasing behaviors (shopping) etc.

The resource consists of two datasets:

1. A dataset (in .xlsx and .csv format) of the search strategy used to come to the list of datasources called “search strategy”

2. A dataset (in .xslx and .csv format) of the results of the search, called “search results”

3. A dataset (in .xslx and .csv format) of a step where duplicate studies are identified

4. A dataset (in .xslx and .csv format) where for 131 studies the data quality was assessed

Data for publication:Dopaminergic action prediction errors serve as a value-free teaching signalAnimals’ choice behavior is characterized by two main tendencies: taking actions that led to rewards and repeating past actions. Theory suggests these strategies may be reinforced by different types of dopaminergic teaching signals: reward prediction error to reinforce value-based associations and movement-based action prediction errors to reinforce value-free repetitive associations. Here we use an auditory-discrimination task in mice to show that movement-related dopamine activity in the tail of the striatum encodes the hypothesized action prediction error signal. Causal manipulations reveal that this prediction error serves as a value-free teaching signal that supports learning by reinforcing repeated associations. Computational modelling and experiments demonstrate that action prediction errors alone cannot support reward-guided learning but when paired with the reward prediction error circuitry they serve to consolidate stable sound-action associations in a value-free manner. Together we show that there are two types of dopaminergic prediction errors that work in tandem to support learning, each reinforcing different types of association in different striatal areas.These datasets generate main Figures 1, 4, and supplementary panels

In today’s rapidly evolving digital landscape, understanding consumer behavior has never been more crucial for businesses seeking to thrive. Our Consumer Behavior Data database serves as an essential tool, offering a wealth of comprehensive insights into the current trends and preferences of online consumers across the United States. This robust database is meticulously designed to provide a detailed and nuanced view of consumer activities, preferences, and attitudes, making it an invaluable asset for marketers, researchers, and business strategists.

Extensive Coverage of Consumer Data Our database is packed with thousands of indexes that cover a broad spectrum of consumer-related information. This extensive coverage ensures that users can delve deeply into various facets of consumer behavior, gaining a holistic understanding of what drives online purchasing decisions and how consumers interact with products and brands. The database includes:

Product Consumption: Detailed records of what products consumers are buying, how frequently they purchase these items, and the spending patterns associated with these products. This data allows businesses to identify popular products, emerging trends, and seasonal variations in consumer purchasing behavior. Lifestyle Preferences: Insights into the lifestyles of consumers, including their hobbies, interests, and activities. Understanding lifestyle preferences helps businesses tailor their marketing strategies to resonate with the values and passions of their target audiences. For example, a company selling fitness equipment can use this data to identify consumers who prioritize health and wellness.

Product Ownership: Information on the types of products that consumers already own. This data is crucial for businesses looking to introduce complementary products or upgrades. For instance, a tech company could use product ownership data to target consumers who already own older versions of their gadgets, offering them incentives to upgrade to the latest models.

Attitudes and Beliefs: Insights into consumer attitudes, opinions, and beliefs about various products, brands, and market trends. This qualitative data is vital for understanding the emotional and psychological drivers behind consumer behavior. It helps businesses craft compelling narratives and brand messages that align with the values and beliefs of their target audience.

This dataset contains annotation segments of peoples' smiles and laughs as well as their intensities, expressed by interlocutors in conversational contexts.They also contain annotation segments of the interlocutors' roles (speaker, listener or none) during their conversations.Please refer to [1] and its annexed file for a more detailed description of the annotations. The audiovisual content of the datasets annotated here can be accessed as follows:Cardiff Conversational Database (CCDB) [2]The IFADV Corpus (IFADV) [3] The Nonverbal Dyadic Conversation on Moral Emotions (NDC-ME) [4]: Contact kevin [dot] elhaddad [at] umons [dot] ac [dot] beThe CBA Toolkit contains modules for processing interaction data and to implement interaction related systems and be accessedPlease refer to the Examples section of the CBA-toolkit's repository README for obtaining the annotation files grouped as interlocutor pairs.[1] El Haddad, Kevin, Sandeep Nallan Chakravarthula, and James Kennedy. ""Smile and Laugh Dynamics in Naturalistic Dyadic Interactions: Intensity Levels, Sequences and Roles."" 2019 International Conference on Multimodal Interaction. 2019. [2] A.J. Aubrey, D. Marshall, P.L. Rosin, J. Vandeventer, D.W. Cunningham, C. Wallraven, ”Cardiff Conversation Database (CCDb): A Database of Natural Dyadic Conversations”, V & L Net Workshop on Language for Vision, CVPR 2013. [3] Van Son, R. J. J. H., et al. ""The IFADV corpus: A free dialog video corpus."" (2008). [4] Heron, Louise, et al. ""A dyadic conversation dataset on moral emotions."" 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). IEEE, 2018.

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This is a mysql export of a database used by the arachnolingua knowledgebase of spider behavior. This database is used by the arachadmin and owlbuilder tools, found in my workspace (pmidford) on github. The development of this database and accompanying tools is blogged here: http://arachnolingua.wordpress.com/.

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).