Introduction to Behaviour Data

What is/are Behaviour Data?

Behaviour Data - Driving performance and behaviour data were collected continuously using multiple measurement devices for vehicle speed, lane position, electrocardiogram, and gaze pattern. ^[1] By carefully designing the analysis pipeline and cross-validating the results, we offer more insights on the interpretation of CCA applied to brain-behaviour data. ^[2] The acquired skills and the results of the processes on tourists' behaviour data were then used by the students to create a communication product such as a marketing campaign, a website, a blog or an Instagram site, an advertising video on the touristic offer of their usual residences, typical Italian towns in the South of Italy. ^[3] Behaviour data were processed extracting the duration, frequency and variability of behaviours. ^[4] 0 to demographic, dietary, sexual health and behaviour data, recent medication use and STI diagnosis. ^[5] A wide array of data sources was used for suicide research, with most articles (n=45) using social media and web-based behaviour data. ^[6] This study combined Global Positioning System (GPS) tracking data of 12 urban-nesting lesser black-backed gulls, Larus fuscus, with habitat and behaviour data over three breeding seasons (2016–2018). ^[7] Sociodemographic and behaviour data were also collected. ^[8] Background In the absence of detailed information about the population size and behaviour data of men-who-have-sex-with-men (MSM), the estimation of prevalence rates of sexually transmitted infections (STIs) and the design of public health interventions become difficult. ^[9]

Sexual Behaviour Data

Patient’s socio-demographic and sexual behaviour data were obtained using a standard interviewer-administered questionnaire. ^[1] Sexual behaviour data were analyzed using descriptive methods. ^[2] We analysed associations between the stage of syphilis (primary vs secondary) and sexual behaviour data collected by computer-assisted self-interview (CASI). ^[3] We parameterised the model using sexual behaviour data and Swedish spatial and demographic data. ^[4] Our WGS results, when viewed in conjunction with the extremely-detailed sexual behaviour data, support the notion that gonorrhoea diagnoses in this network were likely the result of within-network transmission. ^[5] Most sexual behaviour data is egocentric in nature. ^[6] We parameterised the model using sexual behaviour data and Swedish spatial and demographic data. ^[7]

User Behaviour Data

In China, the government has been widely using body temperature, geolocation and user behaviour data to monitor the spread of the infection and commitment to quarantine, and scientists in the USA are processing global scale data to find vaccines for the virus. ^[1] The collected user behaviour data are made public to support further research. ^[2] A large number of library user behaviour data generated in real time in the era of big data artificial intelligence requires more efficient and scientific analysis technology to help libraries improve the level and quality of personalized services, while the increasingly popular campus Internet of Things system needs to be more Active network security precautions, proactively detect unreliable abnormal behavior of the network and feedback users to improve security awareness. ^[3] User behaviour data is essential for modern companies, as it allows them to measure the impact of decisions they make and to gain new insights. ^[4]

Driving Behaviour Data

A new approach is proposed that collects individualised driving behaviour data from smartphone GPS module, combined with geographical network information and dynamic traffic conditions, to identify driving risk factors and evaluate driving behaviours in various contexts. ^[1] Furthermore, this research presents a comparative analysis of select driving simulator studies and an NDS considering only driving behaviour data as a means to consistently compare the findings of both methodologies. ^[2] The driving behaviour data have been collected from driving simulator which contain driver’s actions mainly steering, accelerator and foot brake operations. ^[3] As a result of the rapid development of data collection technologies and machine learning techniques in recent years, extraction and recognition of drivers’ unique driving style from actual driving behaviour data become possible. ^[4]

Driver Behaviour Data

The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. ^[1] Controlled experiments were conducted using an instrumented vehicle to collect instantaneous driver behaviour data in the presence of on-road static obstacles. ^[2] The data collected by GPS receivers was appropriately analysed to establish a driver behaviour dataset. ^[3] The algorithm is compared in simulation against a charge-depleting/charge-sustaining (CDCS) strategy and dynamic programming (DP) using real world driver behaviour data, and it is demonstrated that the algorithm achieves 90% of the fuel savings obtained using DP with a 3000-fold reduction in computational time. ^[4]

Consumer Behaviour Data

A novel clustering algorithm, that has shown high scalability in previous applications, is applied to analyse and segment an online consumer behaviour dataset. ^[1] Consumer behaviour data on issues related to SDGs, research on employee’s work motivation and the goals of investors, both institutional and individual, are investigated. ^[2] There are also vast private administrative data holdings – including utility data, and consumer behaviour data. ^[3] The data used uses primary data in the form of data on characteristics of farmer households who are members of Rimbo Jaya Gapoktan, inputs used, production, farmer work activities and rice cultivation that produce aromatic rice, aromatic rice receipts and expenditure and marketing and consumer behaviour data. ^[4]

Health Behaviour Data

Socio-demographic, oral health status, and oral health behaviour data were obtained using a questionnaire, and OHL was determined by a validated Malay version of the 14 items Health Literacy in Dentistry Scale (HeLD-14). ^[1] These included selfreported data on level of caring responsibilities (0;1-19;2049;50+ hours per week), whether or not they left employment due to their caring role, mental health using the short Mental Health Inventory (MHI-5) and health behaviour data on smoking status, physical activity and dietary habits. ^[2] In the home setting, consent and baseline oral health behaviour data will be collected. ^[3]

Human Behaviour Data

Smartphones can be considered the cheapest and well-penetrated devices for collecting everyday human behaviour data. ^[1] However, when training an ML algorithm, obtaining large sets of micro-level human behaviour data is often problematic. ^[2] Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. ^[3]

Travel Behaviour Data

In this paper, we examine the use of generative machine learning approach for analyzing multiple discrete-continuous (MDC) travel behaviour data. ^[1] This is the first such an attempt in Poland, and is based on the travel behaviour data of 26,000 Poles. ^[2] The study, using Swedish national travel behaviour data, in line with international research, shows that a minority produces the majority of all passenger mileage by car. ^[3]

Vehicle Behaviour Data

This paper presents a detailed methodological framework for collecting microscopic driver and vehicle behaviour data over a long road segment with an application to the entire stretch of a freeway. ^[1] Firstly, collect vehicle behaviour data by naturalistic driving method; secondly, extract fragments in the data containing traffic conflict information using the automatic segmentation technology; thirdly, cluster these fragments by kernel density analysis to find the location of the high-density point of traffic conflict (O tc); finally, calculate the stopping sight distance (SSD) that vehicles need to stop completely from O tc, measure the distance Lꞌ between O tc and the work area, and compare SSD, Lꞌ with the buffer section length L, then the safety of the buffer section of maintenance work zone can be assessed. ^[2]

Learning Behaviour Data

It is found that the applied focus of learning analytics is more oriented towards monitoring/analysis and prediction/intervention, and the commonly used types of student-analytics data are learning behaviour data and learning level data. ^[1] Aiming at the problem of computer network courses with more teaching content and rapid knowledge update, the paper builds a computer network adaptive learning system by collecting learning behaviour data in the education process and using the learning analysis technology of big data. ^[2]

Task Behaviour Data

Generalized linear mixed models clustered at the individual level were used to examine on-task behaviour data. ^[1] Linear mixed models were used to investigate the intervention effects on cognitive functions and sedentary patterns; generalised linear mixed models were used to analyse on-task behaviour data. ^[2]

Smoking Behaviour Data

Many nationally representative surveys collect youth smoking behaviour data from, or in the presence of, the youth’s parent or caregiver. ^[1] METHODS Smoking behaviour data were collected at diagnosis and then every month for 6 months from 133 newly diagnosed people with lung cancer who had recently quit smoking or continued to smoke at diagnosis. ^[2]

Pedestrian Behaviour Data

Quantitative analysis of pedestrian behaviour data and user experience data were performed in order to investigate the impact of the technological differences between the two VR techniques. ^[1] Collecting pedestrian behaviour data is vital to understand pedestrian behaviour. ^[2]

behaviour data collected

The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. ^[1] Add-to-cart recommendations are then generated from the resulting graph based on user-behaviour data collected from our digital touch points (app, web) and transactional data from purchases made online, or in one of our IKEA stores. ^[2] We analysed associations between the stage of syphilis (primary vs secondary) and sexual behaviour data collected by computer-assisted self-interview (CASI). ^[3]