Abstract
AbstractModern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that data-centric practices in science are not monolithic because the opportunities and challenges presented by big data vary across scientific domains.
Highlights
Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced (Anderson 2008; Boyd and Crawford 2012; Harford 2014; MayerSchönberger and Cukier 2013; Kitchin 2014)
We argue that data-centric practices in science are not monolithic because the opportunities and challenges presented by big data vary across scientific domains
We extend our analysis to a feature that we claim is crucial for understanding data-centrism—the information architectures underlying databases—revealing that data-centric sciences can come in at least two forms, one of which avoids the use of sophisticated labeling systems and the potentially pernicious consequences that they bring
Summary
Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced (Anderson 2008; Boyd and Crawford 2012; Harford 2014; MayerSchönberger and Cukier 2013; Kitchin 2014). This is not unlike the cases Leonelli examines: biological researchers, including pharmaceutical companies, and public health organizations, are the primary stakeholders, with private companies like 23andMe becoming increasingly interested in the data These descriptions help demonstrate why both regional climate modeling and model organism research might embrace data-centrism’s focus on data as a scientific goal, rather than as just a means to develop theory. The data produced by regional climate modeling studies are distributed broadly and used for purposes beyond those imagined by their original producers Often, they are used for Version in Journal Editing Process: Please Do Not Cite or Quote more than theoretical insight, for example, by creating climate projections to support future planning. Like model organism research, regional climate modeling constitutes a data-centric area of research, we turn to analyzing the character of the data-centrism that regional climate modeling displays
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