Data from the Multi-Sensitivity Study includes sociodemographic factors, sensory processing sensitivity (a.k.a, highly sensitive personality), the Adult Sensory Profile, rejection sensitivity, mental health (distress, well-being, alcohol/cannabis use), discrimination, and social support (informational, emotional, and companionship). The data (n = 617, United States) was collected through an online cross-sectional self-report survey on Prolific. The Prolific representative sample option was used, stratifying based on sex, age, and ethnicity. The primary and processed data are available on OSF and have reuse potential for clinical and social psychology research, psychometrics research, methodological research on crowdsourcing for participant recruitment, and for use in statistics courses.
This dataset contains intensive longitudinal data on self-regulated learning processes and learning performance among German medical students preparing for the second written state examination in 2018. Data were collected online and include repeated questionnaires and working memory assessments, as well as objective indicators of learning behavior on a digital learning platform. In addition, the study involved an intervention, with two-thirds of students receiving planning prompts. The dataset includes pre-, post-, and daily ambulatory assessments as well as log data-based indicators of learning behavior from up to 365 participants. It is provided in non-proprietary formats with extensive documentation that facilitates reuse.
This article presents a comprehensive dataset on attitudes towards religious moderation among 4.197 secondary school students in the Bangka Belitung Islands Province, Indonesia. Data collection was conducted online via Google Forms between November and December 2022. The participants represent a homogeneous developmental group in late adolescence, with an age range generally between 15 and 19 years and a mean age of approximately 16.8 years. The gender distribution of the sample comprised 1.627 males and 2.570 females. This dataset measures four key dimensions of religious moderation (national commitment, tolerance, anti-violence, and accommodation of local culture). This paper presents a large-scale dataset on adolescent attitudes toward religious moderation, validated using the Rasch model to ensure high psychometric quality and reliability. This dataset represents a valuable resource for researchers in the fields of educational psychology, the sociology of religion, and policymakers. It can be used to identify potential risks of extremism, design evidence-based character education curricula, or serve as a baseline for longitudinal studies on religious harmony among the younger generation in multicultural societies.
We present data from a virtual Rod and Frame Test (RFT) used to screen for cybersickness and data from a gamified spatial orientation task (City) used as a reference for inducing cybersickness. Data include RFT and City performance, head tracking, self-reported cybersickness symptoms, and system logs. As cybersickness experiments are stressful, reusing this data reduces participant burden and supports ethical research. The dataset enables replication, further analyses (e.g., postural stability), and joint analyses. We further describe the open-source code for the Virtual Reality (VR) applications that make the logging rationale transparent. Moreover, this promotes efficient sampling, customizable scenarios, and methodological transparency.
Findings of educational science are too often defined by the contribution of student populations of the Global North. This imbalance is particularly notable in an understudied population of female first-year learners from a country that has only recently emerged from a strict patriarchal order. For these students, the freshman year is considerably challenging, as it requires countering persisting gender stereotypes. This dataset (https://doi.org/10.7910/DVN/KT8UTE) offers a snapshot of their functioning. It contains demographic information, self-reported memory functioning, anxiety, and self-efficacy. Data can enrich existing data sets, thereby contributing to the development of prediction models inclusive of human diversity, and, more broadly, to cross-cultural research.
