There are four interrelated datasets in this collection: In Study 1 we investigated the effects of musical characteristics (i.e., presence of lyrics and loudness) in the context of simulated urban driving. We studied the potentially distracting effects of processing lyrics through exposing young drivers to the same piece of music with/without lyrics and at different sound intensities (60 dBA [soft] and 75 dBA [loud]) using a counterbalanced, within-subjects design. Six simulator conditions were included that comprised low-intensity music with/without lyrics, high-intensity music with/without lyrics, plus two controls – ambient in-car noise and spoken lyrics. Between-subjects variables of driving style (defensive vs. assertive) and sex (women vs. men) were explored. The SPSS data file contains demographic data (i.e. sex, age, ethnicity), anthropometric data (i.e. height, weight, body mass index [BMI]), years of driving experience, estimated annual mileage, International Personality Item Pool (IPEP) items, Multidimensional Driving Style Inventory items, Simulator Sickness Questionnaire items, NASA Task Load Index (NASA-TLX) items, Affect Grid responses (affective valence and arousal), Rating Scale Mental Effort (RSME) responses, word-search scores ('filler' task), HRV indices, simulator data (e.g. total time, mean speed), simulator trigger ratings (for five on-road triggers), standardised scores for variables and studentised residuals. In Study 2, we investigated the interactive effects of driving task load and music tempo on cognition, affect, cardiac response and safety-relevant behaviour during simulated driving. A counterbalanced, within-subjects design was used. The SPSS data file contains the demographic data (i.e. sex, age, age group [1 = young adult, 2 = middle-aged adult], personality [1 = introvert, 2 = extrovert]) for each of the 46 participants (presented with one participant per row). Behavioural measures relating to the driving simulation are included. These include the elapsed time (mins) for each trial. Also, mean speed (mph), brake pedal use (i.e. 0 = no pressure applied, 1 = maximal braking), accelerator pedal use (i.e., 0 = no pressure applied, 1 = maximal acceleration) and risk ratings (on a scale from 1 [safe driving] to 4 [reckless driving]). Note that these performance-related measures appear 12 times in total; that is for each simulator trigger (i.e. a pedestrian who walked at 5 km/h across a zebra crossing, a garbage truck that moved slowly in the left-hand lane and prompted an overtaking manoeuvre, traffic lights that changed to red, a slow vehicle on a stretch of road on which overtaking was prohibited and a vehicle that cut across unexpectedly at a four-way intersection) across all three high-load (urban) conditions. Additionally, the scores across all conditions for the measures of the NASA Task Load Index (NASA-TLX), Affect Grid (affective valence and affective arousal), Rating Scale Mental Effort (RSME) and word-search ('filler' task) are included. The psychophysiological measures of heart rate variability (HRV) and mean heart rate (HR) are also included. For HRV and HR, specifically, we present mean HR, minimum HR, maximum HR, standard deviation of normal RR intervals (SDNN), HR standard deviation and root mean square of successive differences (RMSSD). z-scores (i.e. standardised scores) for each variable are also included. Note that each participant was exposed to six experimental conditions (high load/fast tempo, high load/slow tempo, high load/no music, low load/fast music, low load/slow music and low load/no music). Accordingly, the measures that pertain to each trial (i.e. NASA-TLX, RSME, Affect Grid, word-search task, HRV indices, risk ratings, mean speed, brake pedal use and accelerator pedal use) appear six times in the data file. In Study 3, we investigated the effect of participant-selected (PSel) and researcher-selected (RSel) music on urban driving behaviour in young men. A counterbalanced, within-subjects design was used with four simulated driving conditions: PSel fast-tempo music, PSel slow-tempo music, RSel music and an urban traffic-noise control. The between-subjects variable of personality (introverts vs. extroverts) was explored. As evident in the SPSS data file, a range of psychological measures was administered to assess subjective mental workload, core affect and personality traits. To measure perceived mental workload, we employed the Rating Scale Mental Effort (RSME) and a multidimensional, six-item instrument, the NASA Task Load Index (NASA-TLX). To measure core affect after each trial, we administered the Affect Grid; a single-item instrument used to assess affect along the dimensions of pleasure–displeasure (relating to affective valence) and arousal–sleepiness (relating to affective arousal). To measure personality, we used the Big Five Inventory, which is a relatively brief, self-report inventory designed to measure the Big Five personality dimensions (i.e., extroversion, agreeableness, conscientiousness, neuroticism and openness). Three types of behavioural data were acquired from the urban driving simulation: (a) a risk-rating (on the scale from 1 [safe driving] to 4 [reckless driving]) derived from video data (without any audible sound) and pertaining to driving performance in the entire trial. Three members of the research team conducted the ratings and inter-rater reliabilities were computed; (b) course completion time (min); (c) mean speed (mph), and (d) accelerator and brake pedal positions (i.e., 0 = no pressure applied, 1 = maximum braking). In Study 4, we conducted an inductive content analysis to assess driving experiences under a variety of music conditions. This Excel data file includes each participant’s responses to the 11 qualitative questions that were asked in the survey after a set of experimental trials. The 11 questions were: (1) Are you able to describe and differences among the six simulator trials that you completed over the last 90 minutes?; (2) How did each of the trials make you feel emotionally, in general, while you drove in the simulator (try to be specific)?; (3) Prior to this study, had you ever used music to influence your emotional state while driving in an urban environment and, if so, how exactly?; (4) Has listening to music during an urban driving simulation changed your perception of the experience in any way and, if so, how?; (5) Would listening to music during real urban driving make you likely to drive more safely in the future?; (6) What aspects of your emotions of behaviour during real urban driving is music likely to change?; (7) Would listening to a talk radio station or podcast during real urban driving make you likely to drive more safely in the future?; (8) What aspects of your emotions or behaviour during real urban driving would a talk radio station or podcase be likely to change?; (9) What sort of music would help you to drive more safely in a real urban environment (try to also give specific artists/albums/tracks)?; (10) Which trial did you think was most conducive to safe urban driving in the simulator and why?; and (11) Are there any other comments you would like to make in relation to the experimental protocol you have just completed?The emotional states, attitudes and awareness of drivers is increasingly being acknowledged as the principal cause of serious traffic accidents; the largest cause of fatality among young adults with a compound cost to their families and communities. Drivers have listened to music as stimulant and distraction since the early days of car audio in the 1930s, and more widely since the 1960s when the technology became far more widely available. It is telling that, whilst there is currently much research effort directed at understanding a range of in-car distractions such as mobile phones and sat-nav devices, there has been a relative absence of targeted research examining the effects of the emotional states brought about through music on driving behaviour and performance. Laboratory-based tests have found that music can interfere with and shape mental processing. Significantly, the effects in question are very much dependent on the task being performed, and the person performing it. In this project, we aim to investigate the effects of emotional states induced through music on driving behaviour and performance across a range of scenarios. Some of these effects may be positive – for instance, stimulating an otherwise tired driver – whilst others may be negative, such as causing a distraction from the road ahead. The research will address a number of questions: Under what driving conditions do musically-induced emotions influence performance? Which kinds of music are likely to impact upon the safety of drivers, passengers, and other road users? Does music improve alertness or driving performance in any way? Are such effects similar for the young and the old, and for both males and females? Brunel University London has formed a partnership with Coventry University's National Transport Design Centre (NTDC), insurers Direct Line PLC, and the Manchester Science and Industry Museum (organisers of the 'Turn It Up: The Power of Music' exhibition). The programme entails an innovative sequence of studies aiming to explore the effects of emotions and attitudes on drivers. The studies will take place in a state-of-the-art driving simulator based around a modified road car. The sophisticated battery of measures includes motion sensors, an eye-movement capture system, brain monitors, expert interviews, and driving performance assessments focussing on accuracy, decision-making, and the timely response to critical and unexpected events. The team consists of Dr Costas Karageorghis alongside the TRL's Professor Nick Reed. For two decades, Karageorghis has led internationally-renowned research into the effects of music in various physical activity contexts from healthcare to gymnasia-based exercise. Having developed an experimental and theoretical paradigm to account for music's emotional and motivational power, Karageorghis and his fellow scientists aim to apply this expertise in a highly prevalent social space in which considerations of risk are paramount, that of the public road. In both contexts, music is used as a form of distraction, a means to improve emotions, and a stimulus. Both Direct Line PLC and the Manchester Museum of Science and Industry are providing dissemination opportunities for the project, which will engage the driving public with the new findings. This novel programme of research has been meticulously designed to address fundamental questions connected to driving psychology with the aim of aiding policy makers, while feeding in to safety guidelines, product development and driver education. With music playing such a ubiquitous role in the experience of car ownership, especially among the young, formal and revealing investigation of the way that music alters driving behaviour is timely. The ultimate aim is to prime public awareness and stimulate debate, contribute towards improvements in road safety, and thus play an indirect role in preventing accidents and their associated casualties.

In Study 1, we collected demographic data, psychometric data, heart rate variability data and performance-related driving simulator data. In Study 2, we collected demographic data, psychometric data, heart rate variability data and performance-related driving simulator data. In Study 3, we collected demographic data, psychometric data, heart rate variability data, eye-tracking data (number of blinks) and performance-related driving simulator data. In Study 4, we collected demographic data and qualitative data using a handwritten survey.