How personality traits, sports anxiety, and general imagery could influence the physiological response measured by SCL to imagined situations in sports?

The data were collected to understand how individual differences in personality (e.g. neuroticism), general imagery, and situational sports anxiety are linked to arousal measuring with skin conductance level (SCL) in situational imagery (as scripted for sport-related scenes). Thirty persons participated in the study, aged between 14 and 42 years, with sports experience ranging between 2 and 20 years, representing different sports disciplines and levels: international level, national level, regional and local level. In the study three questionnaires were used: the Imagination in Sport Questionnaire (ISQ), the Sport Anxiety Scale (SAS), and the Big Five Inventory-Short (BFI-S). The research was based on SCL data, collected at a frequency of 40 Hz and expressed in units of microsiemens (μS). Subjects listened to a pre-recorded script and were then asked to imagine the scene they listened to for around one minute. The study used scripts for 6 sport-related scenes: "A slow start", "Fitness activity", "Start in the high-level championship", "Successful competition", "Training session", and "Your 'home' venue". All collected data were properly inspected and preprocessed. The predictive regression model, based on artificial intelligence methods was constructed by relying on the theoretical premises.
The dataset contains 181 files. The results of psychological tests (three questionnaires) for 30 participants are given in the file "Psychological_tests.csv". The first column of this file contains the ID of the participant. The consecutive columns contain values coming from questionnaires: general imagery, somatic anxiety, worry, concentration disruption, and neuroticism. The time-dependent values of SCL for all participants and six sport-related scenes are provided in files with names created by the combination of the participant ID and the title of the scenario (for example "SCL-person_1-A_slow_start.csv"). The first five seconds of each time series of SCL were used to calculate the reference average value of the SCL. The rest part of the time-dependent SCL ranging from 5.025 [s] to the end of the time series was used, after preprocessing stage, to create the machine learning model.