Our team has been conducting research related to mining information, building models, and interpreting data from the inaugural course offered by edX, 6.002 x: Circuits and Electronics, since the Fall of 2012. This involves a set of steps, undertaken in most data science studies, which entails positing a hypothesis, assembling data and features (aka properties, covariates, explanatory variables, decision variables), identifying response variables, building a statistical model then validating, inspecting and interpreting the model. In our domain, and others like it that require behavioral analyses of an online setting, a great majority of the effort (in our case approximately 70%) is spent assembling the data and formulating the features, while, rather ironically, the model building exercise takes relatively less time. As we advance to analyzing cross-course data, it has become apparent that our algorithms which deal with data assembly and feature engineering lack cross-course generality. This is not a fault of our software design. The lack of generality reflects the diverse, ad hoc data schemas we have adopted for each course. These schemas partially result because some of the courses are being offered for the first time and it is the first time behavioral data has been collected. As well, they arise from initial investigations taking a local perspective on each course rather than a global one extending across multiple courses. In this position paper, we advocate harmonizing and unifying disparate “raw” data formats by establishing an open-ended standard data description to be adopted by the entire education science MOOC oriented community.