Open-source information is everywhere—potential data sources continuously surround us. Yet, as the volume of available sources grows, the ability to effectively use and understand their contents becomes a struggle. One way to address this need to balance depth of understanding with breadth of collection is to quantify the open-source data into a dataset or database. Synthesizing diverse open sources systematically can create robust and reliable data. In turn, those data can make it easier to process large quantities of information and facilitate analyses of entirely new sets of questions. This chapter presents an instructional account of one method of quantifying open-source data, based on the Berkeley Protocols on Digital Open-Source Investigations and experience from the CURIA lab. It argues that a process that prioritizes transparency, triangulation, metadata, and sustainability ensures a robust and reliable result. It demonstrates this method in the creation of a spatiotemporal dataset of cultural violence in the Syrian Conflict from 2014 to 2017 and discusses some of the applications for the resulting quantitative data.