Big data meets open political science: an empirical assessment of transparency standards 2008–2019

Abstract

Over the last decade, the field of political science has been exposed to two concomitant developments: a surge of Big Data (BD) and a growing demand for transparency. To date, however, we do not know the extent to which these two developments are compatible with one another. The purpose of this article is to assess, empirically, the extent to which BD political science (broadly defined) adheres to established norms of transparency in the discipline. To address this question, we develop an original dataset of 1555 articles drawn from the Web of Science database covering the period 2008–2019. In doing so, we also provide an assessment of the current level of transparency in empirical political science and quantitative political science in general. We find that articles using Big Data are significantly less likely than other, more traditional works of political science, to share replication files. Our study also illustrates some of the promises and challenges associated with extracting data from Web of Science and similar databases.