Mapping Research Topics at Multiple Levels of Detail

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Authors

Sara Lafia , Werner Kuhn, Kelly Caylor

Abstract

Institutional reviews typically rely on scientometrics, like the h-index and impact factors of their participants, to assess research productivity. Productivity is not the only review criterion however, and scientometrics can be difficult to generate and compare in multidisciplinary settings. “Distant reading” methods from the Digital Humanities can complement the current quantitative evaluation paradigm; these methods support qualitative narratives, comprehension, and discovery of knowledge by arranging vast bodies of text into graphs, maps, and trees. To test this idea, we apply distant reading methods to a multidisciplinary body of research authored by 240 researchers from the Earth Research Institute (ERI) at UC Santa Barbara over the past decade. We model cross-disciplinary topics of research publications and projects emerging at multiple levels of detail. From these, we design maps that reveal the latent thematic structure of multidisciplinary research. ERI’s researchers use and evaluate these maps of research topics in the context of an institutional review to “read” ERI’s body of research at a distance, i.e. at multiple levels of detail. We find that our approach strengthens the institutional review process by exposing thematic expertise, relationships between researchers, topical distributions and clusters of work, and the evolution of these aspects over time.

DOI

https://doi.org/10.31223/osf.io/523ex

Subjects

Geography, Physical Sciences and Mathematics, Social and Behavioral Sciences, Spatial Science

Keywords

data discovery, decision support, institutional review, knowledge representation, natural language processing, serendipity, spatialization, topic modeling

Dates

Published: 2020-07-12 19:30

Last Updated: 2020-08-31 19:15

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License

Academic Free License (AFL) 3.0

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