Micro-Synchrophasor Measurement Units (μPMUs) offer a compelling stream of data for quantifying, characterizing, and analyzing electrical grid conditions. They can measure local current and voltage phasors twice per cycle, resulting in 120 Hertz data. Furthermore, they are time-synchronized between locations, permitting time-sensitive analysis, such as phase angle comparisons between locations. This paper utilizes μPMU data from three locations within the Lawrence Berkeley National Laboratory (LBNL) campus to detect, characterize, and analyze grid events. The Berkeley Tree DataBase (BTrDB) is employed as a centered data repository, which permits rapid data sifting and event isolation by representing raw data statistically. The Twenty-six voltage sags detected on the LBNL campus across three μPMU locations are presented here, and aggregated into a event library which is open source and available to the research community. An analysis of these short-lived events is conducted by the Ward and K-Means clustering method, and the utilization for proactive control application is discussed.