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.

中文翻译：

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澄清事实：数据对环境倡导的意外限制。


是什么使得一个数据集对于公民倡导非常强大，而另一个数据集却表现不佳？根据一个关于环境健康的公民科学项目，我认为数据集的质量（它们的拓扑结构）未被充分认识，它塑造了它们可以维持或颠覆的社会、文化和政治可能性。数据拓扑是数据集的形式特征，它将数据收集者的意图与可以执行和不能执行的计算类型联系起来。这配置了数字论证的提出方式，以及这些论证维持或颠覆的社会技术想象。公民科学项目的数据拓扑使得任何关于共同接触污染物或单一健康影响的简单概念都被瓦解了。这些数据似乎讲述了一个大规模的非典型故事，每个人因不同的暴露而遭受不同的痛苦。由于缺乏集中趋势，或者不同亚群体不成比例地携带一些趋势，公民科学家在监管环境中使用数据变得更加困难，而不是更容易，在监管环境中，占主导地位的社会技术想象在流行病学和毒理学术语上设想了差异。