Diving into the past: tools for recovering historic dive traces from film-based time depth recorders using data from Weddell seals by EmmaLi G. Tsai, Dylan W. Schwilk, Michael A. Castellini, Jennifer M. Burns

“…However, historic analog data from early TDRs contain empirical information on the behaviors of animals that can inform modern studies. …”
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Impact of doctor-shopping behavior on patient survival in lung cancer: findings from a 11-year cohort study using Korean claims data by Minwoo Hong, Il Yun, Jong Youn Moon

“…Abstract Background This population-based cohort study aimed to explore the impact of doctor-shopping behavior (DSB) on the survival of new patients with lung cancer in Korea to make policy suggestions for the efficient use of medical care for cancer patients. …”
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Classification Method For User Electricity Consumption Behavior Based on Power Big Data by Pengwei Guo, Shun Yu

“…The method which we proposed can classify the behavior of using electricity, then can support efficiency data for management and optimization of power system. …”
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Smart data in healthcare marketing: linking digital transformation to patient behavior and branding by Mohammad Farid Ashouri, Mehrdad Tarassoli

“…Data analysis was conducted using SPSS version 23 and SmartPLS version 3. …”
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Capturing continuous, long timescale behavioral changes in Drosophila melanogaster postural data. by Grace C McKenzie-Smith, Scott W Wolf, Julien F Ayroles, Joshua W Shaevitz

“…We identify stereotyped behaviors such as grooming, proboscis extension, and locomotion and use the resulting ethograms to explore how the flies' behavior varies across time of day and days in the experiment. …”
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Early Warning Method of Abnormal Electricity Consumption Behavior Based on Data Driven by WAN Wei, LIU Hongqi, SUN Hong-chang, ZHANG Feng, WANG Yang, SUN Wei-qing

“…In view of the huge economic losses caused to power companies by abnormal power consumption behaviors of users such as stealing and abusing electricity, based on data driven method, the daily load data of residents in the region is used to score the users′ energy consumption behavior quantitatively from the horizontal and vertical levels.Firstly, based on K-means and SVM (Support Vector Machine) classification model, the daily load data of individual residents data are compared with those of residents with similar electricity consumption behaviors to generate the user′s horizontal score.Secondly, the user load forecasting model is established by using LSTM (Long Short-Term Memory) model to realize the comparison with their own historical electricity consumption behavior and generate the user’s vertical score.Finally, according to the set weight, a comprehensive score is made. …”
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Data-driven analyses of behavioral strategies to eliminate cysticercosis in sub-Saharan Africa. by Laura A Skrip, Veronique Dermauw, Pierre Dorny, Rasmané Ganaba, Athanase Millogo, Zékiba Tarnagda, Hélène Carabin

“…<h4>Methodology/principal findings</h4>Province-specific data on human behaviors (i.e., latrine use and pork consumption) and serological prevalence of human and pig disease were used to inform a deterministic, compartmental model of the taeniosis/cysticercosis disease system. …”
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A method of IoT terminal’s network behavior monitoring based on network data by Ying LING, Yun QIU

“…The internet of things (IoT) is the network of physical devices,vehicles,home appliances and other items embedded with electronics,software,sensors,actuators and network connectivity which enable these objects to connect and exchange data.Every sensor node is a source of information.In the offered method,telecom network operators could analyze the network data and discover the abnormal situation of the IoT terminals,so as to ensure the normal use of the IoT terminals.A network behavior tag for the IoT terminals by analyzing the network data in the telecom network,and a real-time monitoring method for the IoT terminal network behavior were described.And take the wearable industry application for example,the analysis process of the internet of things network behavior was described.…”
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Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data by V. Fakoor

“…Moreover, the proposed estimator is used to estimate  the density function of a real data set.…”
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Student behavior patterns in vocational education big data based on clustering algorithm by Jing Jin

“…Understanding student behavior in vocational education is essential for tailoring interventions that enhance learning outcomes, and leveraging big data enables more nuanced, data-driven insights that support precision education strategies. …”
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Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data by Xiaowei Hu, Shi An, Jian Wang

“…This paper is based on taxi GPS trajectories data from Shenzhen to explore taxi driver’s operation behavior and passengers’ demand. …”
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SpiDa-MRI: behavioral and (f)MRI data of adults with fear of spiders by Mengfan Zhang, Alexander Karner, Kathrin Kostorz, Sophia Shea, David Steyrl, Filip Melinscak, Ronald Sladky, Cindy Sumaly Lor, Frank Scharnowski

“…To expand on these advancements, we present data on the heterogeneity of neural patterns in spider phobia combined with various psychological dimensions of spider phobia, using spider-relevant stimuli of various intensities. …”
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Extracting factors associated with vaccination from Twitter data and mapping to behavioral models by Md. Rafiul Biswas, Zubair Shah

“…Social media platform, particularly Twitter, is a rich data source that allows monitoring of public opinions and attitudes toward vaccines.Established behavioral models like the 5C psychological antecedents model and the Health Belief Model (HBM) provide a well-structured framework for analyzing shifts in vaccine-related behavior. …”
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Panel survey data on the investment and risk behavior of Neobroker users in GermanyUniversity of Trier by Jonas Freibauer, Marc Oliver Rieger, Silja Grawert

“…The data facilitate the exploration of collective investment and risk behavior across these groups, highlighting potential trends and commonalities. …”
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Behavioral risk factors and socioeconomic inequalities in ischemic heart disease mortality in the United States: A causal mediation analysis using record linkage data. by Yachen Zhu, Laura Llamosas-Falcón, William C Kerr, Jürgen Rehm, Charlotte Probst

“…Previous studies have reported inconsistent findings regarding the contributions of behavioral risk factors (BRFs) to socioeconomic inequalities in IHD mortality. …”
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Analysis and Prediction of Energy Consumption Data, Using Data Mining Software by KAZOLIS Dimitrios, FANTIDIS Jacob, FOTAKIS Christos Dionyshs, TRAMANTZAS Kostantinos, TSIANTOS Vassilios

“…This paper explores the ability of prediction in electricity consumption using data mining techniques and emphasizes the integration of weather conditions and previous consumption trends. …”
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Gender differences in the associations among adolescent problem behaviors: a secondary data analysis of the 2023 Korea Youth Risk Behavior Survey by Jaeyoung Lee, So Yeon Park

“…Methods This secondary analysis was conducted using cross-sectional data from the 19th Korea Youth Risk Behavior Survey, conducted in 2023 which included 52,880 middle and high school students in South Korea. …”
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Association between smartphone overdependence and sexual behavior in adolescents: a secondary data analysis of the 19th Youth Health Behavior Survey by Bo Ryeong Lee, Ji Hyeon Lee

“…This study aimed to examine the association between smartphone overdependence and sexual behavior among adolescents. Methods This study used data from the 19th Korea Youth Risk Behavior Web-Based Survey conducted in 2023. …”
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APPLICATION OF MACHINE LEARNING IN BEHAVIORAL MODIFICATION by Sudeep Varshney, Aditi Chandra, Pushpendra Kumar Rajput, Sunil Kumar, Gunjan Varshney

“…Machine Learning centers on applications that gain for a fact and further develop their dynamic or prescient exactness over the long run. Behavioral Modification is the use of basic learning techniques such as conditioning, biofeedback, assertiveness training, positive or negative reinforcement, aversion therapy to change unwanted individual or group behavior. …”
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The Impact of Daily Economic Behavior on Emotions: by Yuka Ozaki

“…The experience sampling method (ESM) refers to a set of research procedures involving repeated data collection several times a day for several consecutive days, enabling the researchers to gain insights into people’s behaviors, thoughts, and emotions as they occur in their everyday environment. …”
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