UC San Diego

Research in Science and Technology Studies (STS) and related fields has long taken up questions about how diagnostic entities and biomedical classifications are produced by social practices and come to change over time. Yet—and despite a growing interest in the role of bioinformatic data work in the turn to so-called “Big Biology”—little effort to date has sought to unpack how the design and development of data infrastructures has not only reconfigured work practices, but driven shifts in the conceptual landscape that work produces.

This dissertation deals with a set of actors sometimes called biobanks: organizations which collect and store human tissues and health data intended for research use. While the massive scale of biobank data collection sometimes creates the appearance of a perfectly “representative population,” however, these data are nothing of the sort: like all datasets, they index some people and not others, and make consequential decisions about how they are quantified, classified, and made visible as research subjects. Bridging theoretical sensibilities from critical data studies with concerns from the sociology of genetics, this project investigates the outcomes (and unintended consequences) of data-intensive efforts to bring order to the messy spectra of human health, behavior, and life-course. By attending to the problems latent in efforts to “scale up” biobank-driven research, I argue that the inbuilt structures of meaning in research databases come to inform what we learn about the world through them. Through mixed-method qualitative studies which examine the production and circulation of biobank data across contemporary research in microbiome science and biological psychiatry, I extend work on the social construction of human difference by showing the substantive influence data infrastructures now have over the ways we come to know human health, biology, and the genome itself.