This technical report describes a dataset which contains diachronic data about the everyday life of one hundred fifty-eight university students over a period of four weeks, and also additional synchronic data about profile, e.g., demographics, routines, personality. The diachronic data are collected from sixteen sensors, both hardware and software, associated to around 100+ thousand self-reported annotations. The dataset has been collected based on an ontological representation of the situational context and following various reference standards, e.g., HETUS and the Big Five. The data collection is motivated by the rise of so-called people-centric sensing paradigm, wherein sensors embedded in mobile phones and other wireless devices are used to collect large quantities of continuous data pertaining to the behavior of individuals and social networks. These datasets offer unique opportunities to investigate the diversity and daily routines of university students in a multi-layered perspective.
Use cases
Below is a list of studies that are based on this dataset:
- Giunchiglia, F., Bignotti, E., & Zeni, M. (2017, March). Personal context modelling and annotation. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 117-122). IEEE. Link
- Giunchiglia, F., Zeni, M., Bignotti, E., & Zhang, W. (2018, March). Assessing annotation consistency in the wild. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 561-566). IEEE. Chicago. Link
- Giunchiglia, F., Zeni, M., Gobbi, E., Bignotti, E., & Bison, I. (2018). Mobile social media usage and academic performance. Computers in Human Behavior, 82, 177-185. Link
- Bontempelli A., Teso S., Giunchiglia F., Passerini A. (2020). Learning in the Wild with Incremental Skeptical Gaussian Processes. IJCAI-20. 2886-2892. Link
- Osman, N., Chenu-Abente, R., Shen, Q., Sierra, C., & Giunchiglia, F. (2021). Empowering Users in Online Open Communities. SN Computer Science, 2(4), 1-19. Chicago. Link
- Zhang, W., Shen, Q., Teso, S., Lepri, B., Passerini, A., Bison, I. and Giunchiglia, F., (2021). Putting human behavior predictability in context. EPJ Data Science, 10(1), p.42. Link