Rich human mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems. Unfortunately , existing mobility datasets-that are available to the research community-are restricted to location data captured through a single sensor (typically GPS) and have a low spatiotemporal granularity. They also lack ground-truth data regarding points of interest and the associated semantic labels (e.g., "home", "work", etc.). In this paper, we present Breadcrumbs, a rich mobility dataset collected from multiple sensors (incl. GPS, GSM, WiFi, Bluetooth) on the smartphones of 81 individuals. In addition to sensor data, Breadcrumbs contains ground-truth data regarding people points of interest (incl. semantic labels) as well as demographic attributes, contact records, calendar events, lifestyle information, and social relationship labels between the participants of the study. We describe the data collection methodology and present a preliminary quantitative analysis of the dataset. A sanitized version of the dataset as well as the source code will be made available to the research community.