The field of learning analytics requires that educational data is available in order for researchers to effectively answer questions. Although the amount of data being collected in educational settings has increased exponentially, the sharing of these data has been less successful. One success story has been DataShop hosted at the LearnLab at Carnegie Mellon University. DataShop has become world’s largest open data repository of transactional educational data collected from online learning courses, intelligent tutors, educational games, and simulations. The data is fine-grained, with student actions recorded roughly every 10 seconds, and it is longitudinal, spanning semester or yearlong courses. As of July 2013, over 430 datasets are stored including over 100 million student actions which equates to over 250,000 student hours of data. Most student actions are “coded” meaning they are not only graded as correct or incorrect, but are categorized in terms of the hypothesized competencies or knowledge components needed to perform that action. DataShop allows researchers to import data in order to use the provided analysis tools, and to export data from the repository to perform additional analysis. Researchers have analyzed these data to better understand student cognitive and affective states and the results have been used to redesign instruction and demonstrably improve student learning. In this workshop we will talk about how DataShop has implemented a useful form of data sharing and how DataShop tools can be used in learning analytics and educational data mining.