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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(izing) science. 

The logic of domains has become a key organizing principle for contemporary computing projects and in broader science policy. The logic parses collectives of expertise into ‘domains’ that are to be studied or engaged in order to inform computational advancements and/or interventions on the domains themselves. The concept of a domain is set against a proposition that there is a more general, domain independent or agnostic technique that can serve to intermediate the domains. This article contrasts instances of this discourse, organizing and techne, drawing from cases in artificial intelligence, software engineering, and science policy to illustrate three ongoing figurations of the logic as i) experimental research, ii) formalization in method and software tools, and iii) a de facto organizing principle for science policy and technology development.