Data Cleaners for Pristine Datasets: Visibility and Invisibility of Data Processors in Social Science

Abstract

This article investigates the work of processors who curate and “clean” the data sets that researchers submit to data archives for archiving and further dissemination. Based on ethnographic fieldwork conducted at the data processing unit of a major US social science data archive, I investigate how these data processors work, under which status, and how they contribute to data sharing. This article presents two main results. First, it contributes to the study of invisible technicians in science by showing that the same procedures can make technical work invisible outside and visible inside the archive, to allow peer review and quality control. Second, this article contributes to the social study of scientific data sharing, by showing that the organization of data processing directly stems from the conception that the archive promotes of a valid data set—that is, a data set that must look “pristine” at the end of its processing. After critically interrogating this notion of pristineness, I show how it perpetuates a misleading conception of data as “raw” instead of acknowledging the important contribution of data processors to data sharing and social science.