Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).
Data increasingly shapes outcomes across digital, biological, and social systems. Yet designing data ecosystems remains difficult because the mechanisms by which data produces effects are poorly understood. This article introduces data ecology, a framework for analyzing how data affects ecosystems through dataflows, the movements of data across agents. We define a potential-effect function that characterizes the system-level impact of data allocations, separating the instrumental value of data from value judgments about tasks and avoiding reliance on utility or behavioral assumptions. The model reveals structural properties of data ecosystems, including unavoidable spillovers, path dependence in data accumulation, and the emergence of data hubs. These properties explain why common approaches to governing data often fall short and provide a foundation for designing interventions that more effectively shape data ecosystems.
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