Students and scholars working at the intersections of history and science and technology studies (STS) have an unexpected opportunity when it comes to the growing profession of data science: the chance not only to document but also to actively shape a “new” scientific profession, one that seems intent to scale up swiftly and determined to claim considerable global influence. Of course, charting origins and tracing the early histories of scientific and technical professions is an enduring tradition within STS-informed studies. Examples include landmark works such as the 1960s research on the origins of psychology by Joseph Ben-David and Randall Collins or, to offer more recent examples, Nathan Ensmenger's work on the cultural politics of early computer experts and Katie Shilton's on Internet architecture engineering teams. In this sense, for those working at the intersection of history and STS to concern themselves with the origins of a scientific field, and with the early stages of a scientific profession, is hardly groundbreaking.
This roundtable, organized for the 2015 and 2016 meetings of the Society for Social Studies of Science (4S), attempted something new: to move beyond professional genealogies and traditions in order to try and critically apprehend the self-proclaimed “new” science of data, which has strong ties to what some might call “scientific entrepreneurship” and which, as part of its self-fashioning, claims to render obsolete many older, established research methods from other scientific fields and disciplines. In the following edited transcription of this roundtable discussion, Brian Beaton and his collaborators endeavor to historicize and trace data science as a social formation and political ideology by identifying some of the expert fields that the “new” data science has threatened to supplement or replace. In doing so, they lay the groundwork for a much larger and decidedly interventionist research agenda that probes recent data science initiatives, including their transnational implications, by drawing connections between practices of data science and the numerous critical bodies of literature on data and society that currently proliferate.
