Researchers collected data on the movements of students living in apartment-style housing to create accurate occupancy profiles. By tracking when students entered and exited their apartments, they created detailed schedules of when people were likely to be present. This data can help improve predictions of energy use in buildings and understand how students behave in their living spaces. The study found differences in occupancy patterns between weekdays and weekends, with distinct clusters showing varying levels of occupancy. These datasets can be useful for building energy simulations and for researchers studying occupant behavior in student housing.