On the one hand, some recent research projects, inspired by the widespread availability of sensor-provided smartphones, have built harvesting experiments to collect large quantities of data in urban areas. These efforts produced new real-world datasets, typically focusing on different technological aspects (GPS and Bluetooth mobility traces, WiFi indicators, ...) and, more recently, also on user-related data, from low-level accelerometer samples to higher-level social networking data. On the other hand, Mobile Crowd Sensing (MCS) blossomed with a few very recent projects, with the goal to efficiently coordinate user participation not only to collect passive monitoring data but also to allow active collaboration in participatory tasks. This paper presents the large- scale experience of the ParticipAct Living Lab, an ongoing experiment at the University of Bologna initiated 15 months ago, which involves about 170 students in MCS campaigns. In particular, the paper has two original goals: first, the comparison of the ParticipAct dataset against some primary datasets in the literature, such as the Nokia Mobile Data Challenge one; second, the robust evaluation and assessment of the original aspects of ParticipAct in itself, such as task assignment heuristics and consequent user acceptance of assigned MCS tasks. The reported results lead to an in-depth lessons learned discussion about socio- technical management aspects of MCS, valuable for the MCS community to design new MCS campaigns and to refine the whole MCS process to the purpose of better efficiency and scalability.