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From data to the creation of meaning part I: unit of analysis as epistemological problem

Presenter 1
Justin Joque
University of Michigan

Aligning data and research infrastructure is, as we are often reminded in our work, an incredibly difficult process. While we often focus on research lifecycles, incentives, storage and transmission technologies, metadata and data sharing we tend to overlook the epistemological incongruencies of diverse research and data practices. All data creation processes, even if unknowingly, make assumptions about the world and what exists as a unique unit that can be analyzed. In attempting to make data meaningful to different audiences, especially across disciplines, we must pay attention to these epistemological assumptions. Failure to do so will inevitably frustrate our attempts to develop meaningful infrastructure for research data and even potentially undermine effective research through misunderstandings of data. Looking at spatial data as an example, this presentation will explore the issues of resolution and unit of analysis as an example of such disciplinary epistemological assumptions. Based on experiences working with spatial data across disciplines, this presentation will explore some of the misunderstandings that arise and suggest ways in which they are indicative of larger issues surrounding data collections, management and interpretation.

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