Debating Data Science

Abstract

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.

Data Science as an Interdiscipline: Historical Parallels from Information Science

“…Within information science, it has long been known that information organization and retrieval methods that once worked well will break down if not regularly revisited due to changes in how language is used across space and time (Shera 1970). Data scientists encounter such circularity as they attempt to standardize data within and across organizations, leading to the well-documented fact that a significant portion of recurring data science work involves data wrangling and cleaning (Beaton et al 2017;Kross & Guo 2019;Keller et al 2020).…”

Fundamentación teórica para la creación de un programa académico de ingeniería y ciencia de datos: una aplicación bibliométrica

“…Los pasos del procedimiento de esta nueva ciencia se resumen en tres [8]: 1) recolección de información, 2) definición de un conjunto de instrumentos que compiten por explicar los datos y, finalmente, 3) una evaluación de tales candidatos para elegir aquel que ofrezca mayor precisión en los pronósticos. Es así como el perfil del científico de datos se amplía, pues dentro de sus intereses se incorporan nuevos temas como la visualización de datos, el aprendizaje estadístico, la construcción de historias con datos y la preservación de los datos [34].…”