What makes one dataset powerful for civic advocacy, and another fall flat? Drawing from a citizen science project on environmental health, I argue that there is an underacknowledged quality of datasets—their topology—that shapes the social, cultural, and political possibilities they can sustain or subvert. Data topologies are formal qualities of a dataset that connect data collectors’ intentions with the types of calculations that can and cannot be performed. This configures how numerical arguments are made, and the sociotechnical imaginaries those arguments sustain or subvert. The citizen science project’s data topology made any easy notion of shared exposure to pollutants, or singular health effects, unravel. The data appeared to tell a story of atypicality at scale, where each person suffers differently from different exposure. Lacking a central tendency, or pockets of tendency disproportionately carried by different subgroups, it became it harder, not easier, for citizen scientists to use data in regulatory contexts, where dominant sociotechnical imaginaries conceive of difference in epidemiological and toxicological terms.
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