Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data “prospecting” and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science. Prospecting aims to render the data, knowledge, expertise, and practices of worldly domains available and tractable to data science method and epistemology. Prospecting precedes data synthesis, analysis, (...) or visualization, and is constituted by the upstream work of discovering disordered or inaccessible data resources, thereafter to be ordered and rendered available for computation. Through this work, data science positions itself in the middle of all things—capable of engaging this, that, or any domain—and thus prospecting is a key driver of data science’s ongoing formation as a universal science. (shrink)

Science and technology studies and the emerging field of data science share surprising elective affinities. At the growing intersections of these fields, there will be many opportunities and not a few thorny difficulties for STS scholars. First, I discuss how both fields frame the rollout of data science as a simultaneously social and technical endeavor, even if in distinct ways and for diverging purposes. Second, I discuss the logic of domains in contemporary computer, information, and data science circles. While STS (...) is often agnostic about the borders between the sciences or with industry and state—occasionally taking those boundaries as an object of study—data science takes those boundaries as its target to overcome. These two elective affinities present analytic and practical challenges for STS but also opportunities for engagement. Overall, in addition to these typifications, I urge STS scholars to strategically position themselves to investigate and contribute to the breadth of transformations that seek to touch virtually every science and newly bind spheres of academy, industry, and state. (shrink)

The article focuses on stories and storytelling practices as explanatory resources in standardization processes. It draws upon an ethnographic study of the development of a technical standard for data sharing in an ecological research community, where participants struggle to articulate the difficulties encountered in implementing the standard. Building from C. Wright Mills’ classic distinction between private troubles and public issues, the authors follow the development of a story as it comes to assist in transforming individual troubles in standard implementation into (...) an institutional issue for the ecological scientific community. The authors present the “hands-on” social science collaboration in this study as an example of a mechanism for supporting institutionalization of issues. Finally, the authors argue that narratives can serve as effective organizing principles within institutional settings, thereby providing an approach to understand the practical, substantive difficulties that occur in work with data in the sciences. (shrink)

It is the purpose of this special issue to acknowledge the shifting definitions and uses of the conceptual and empirical in the field of Science and Technology Studies, and to explore the constructive potential of this condition. In this introductory essay we point to four formulations in STS for the relation between the conceptual and the empirical which do not figure them as binaries or opposites: the empirical as a path to the conceptual, the conceptual as practical and empirical, the (...) empirical as an instantiation of the conceptual, and a conceptual minimalism. We then point to some inspirations in contemporary thought for engaging creatively with the conceptual and empirical, and conclude by summarizing the contributions to this issue. (shrink)