An Integrated Environmental and Perceptual Dataset for Predicting Comfort in Smart Campuses During the Fall Semester

Indoor environmental comfort plays a central role in occupants' well-being, learning effectiveness, and productivity, especially in educational buildings characterized by high occupancy variability and diverse activities. This paper presents a real-world dataset collected at the Cesena Campus of the University of Bologna, aimed at supporting occupant-centric comfort analysis and prediction in classrooms and laboratories. The dataset integrates continuous environmental measurements, such as temperature, humidity, noise, air pressure, and CO2 concentration, with subjective comfort feedback gathered from students during regular lectures. Data were collected using permanently installed ceiling sensors and additional control sensors placed near occupants, enabling both longitudinal monitoring and validation analyses. Furthermore, the dataset includes both repeated comfort perception reports and a one-time comfort definition phase capturing individual relevance weights for different comfort dimensions. By combining objective and subjective data in realistic academic settings, the dataset provides a valuable resource for developing, benchmarking, and validating data-driven models for smart campus applications, indoor comfort prediction, and human-centered building analytics.