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Mapping research topics at multiple levels of detail

The institutional review of interdisciplinary bodies of research lacks methods to systematically produce higher-level abstractions. Abstraction methods, like the “distant reading” of corpora, are increasingly important for knowledge discovery in the sciences and humanities. We demonstrate how abstra...

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Detalles Bibliográficos
Autores principales: Lafia, Sara, Kuhn, Werner, Caylor, Kelly, Hemphill, Libby
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961178/
https://www.ncbi.nlm.nih.gov/pubmed/33748794
http://dx.doi.org/10.1016/j.patter.2021.100210
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author Lafia, Sara
Kuhn, Werner
Caylor, Kelly
Hemphill, Libby
author_facet Lafia, Sara
Kuhn, Werner
Caylor, Kelly
Hemphill, Libby
author_sort Lafia, Sara
collection PubMed
description The institutional review of interdisciplinary bodies of research lacks methods to systematically produce higher-level abstractions. Abstraction methods, like the “distant reading” of corpora, are increasingly important for knowledge discovery in the sciences and humanities. We demonstrate how abstraction methods complement the metrics on which research reviews currently rely. We model cross-disciplinary topics of research publications and projects emerging at multiple levels of detail in the context of an institutional review of the Earth Research Institute (ERI) at the University of California at Santa Barbara. From these, we design science maps that reveal the latent thematic structure of ERI's interdisciplinary research and enable reviewers to “read” a body of research at multiple levels of detail. We find that our approach provides decision support and reveals trends that strengthen the institutional review process by exposing regions of thematic expertise, distributions and clusters of work, and the evolution of these aspects.
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spelling pubmed-79611782021-03-19 Mapping research topics at multiple levels of detail Lafia, Sara Kuhn, Werner Caylor, Kelly Hemphill, Libby Patterns (N Y) Article The institutional review of interdisciplinary bodies of research lacks methods to systematically produce higher-level abstractions. Abstraction methods, like the “distant reading” of corpora, are increasingly important for knowledge discovery in the sciences and humanities. We demonstrate how abstraction methods complement the metrics on which research reviews currently rely. We model cross-disciplinary topics of research publications and projects emerging at multiple levels of detail in the context of an institutional review of the Earth Research Institute (ERI) at the University of California at Santa Barbara. From these, we design science maps that reveal the latent thematic structure of ERI's interdisciplinary research and enable reviewers to “read” a body of research at multiple levels of detail. We find that our approach provides decision support and reveals trends that strengthen the institutional review process by exposing regions of thematic expertise, distributions and clusters of work, and the evolution of these aspects. Elsevier 2021-02-15 /pmc/articles/PMC7961178/ /pubmed/33748794 http://dx.doi.org/10.1016/j.patter.2021.100210 Text en © 2021 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Lafia, Sara
Kuhn, Werner
Caylor, Kelly
Hemphill, Libby
Mapping research topics at multiple levels of detail
title Mapping research topics at multiple levels of detail
title_full Mapping research topics at multiple levels of detail
title_fullStr Mapping research topics at multiple levels of detail
title_full_unstemmed Mapping research topics at multiple levels of detail
title_short Mapping research topics at multiple levels of detail
title_sort mapping research topics at multiple levels of detail
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961178/
https://www.ncbi.nlm.nih.gov/pubmed/33748794
http://dx.doi.org/10.1016/j.patter.2021.100210
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