Assessing Researcher Interdisciplinarity: A Case Study of the University of Hawaii NASA Astrobiology Institute
In this study, we combine bibliometric techniques with a machine learning algorithm, the sequential Information Bottleneck, to assess the interdisciplinarity of research produced by the University of Hawaii NASA Astrobiology Institute (UHNAI). In particular, we cluster abstract data to evaluate Thom...
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| creator | Gowanlock, Michael G Gazan, Rich |
| description | In this study, we combine bibliometric techniques with a machine learning
algorithm, the sequential Information Bottleneck, to assess the
interdisciplinarity of research produced by the University of Hawaii NASA
Astrobiology Institute (UHNAI). In particular, we cluster abstract data to
evaluate Thomson Reuters Web of Knowledge subject categories as descriptive
labels for astrobiology documents, assess individual researcher
interdisciplinarity, and determine where collaboration opportunities might
occur. We find that the majority of the UHNAI team is engaged in
interdisciplinary research, and suggest that our method could be applied to
additional NASA Astrobiology Institute teams in particular, or other
interdisciplinary research teams more broadly, to identify and facilitate
collaboration opportunities. |
| doi_str_mv | 10.48550/arxiv.1204.5563 |
| format | Article |
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algorithm, the sequential Information Bottleneck, to assess the
interdisciplinarity of research produced by the University of Hawaii NASA
Astrobiology Institute (UHNAI). In particular, we cluster abstract data to
evaluate Thomson Reuters Web of Knowledge subject categories as descriptive
labels for astrobiology documents, assess individual researcher
interdisciplinarity, and determine where collaboration opportunities might
occur. We find that the majority of the UHNAI team is engaged in
interdisciplinary research, and suggest that our method could be applied to
additional NASA Astrobiology Institute teams in particular, or other
interdisciplinary research teams more broadly, to identify and facilitate
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algorithm, the sequential Information Bottleneck, to assess the
interdisciplinarity of research produced by the University of Hawaii NASA
Astrobiology Institute (UHNAI). In particular, we cluster abstract data to
evaluate Thomson Reuters Web of Knowledge subject categories as descriptive
labels for astrobiology documents, assess individual researcher
interdisciplinarity, and determine where collaboration opportunities might
occur. We find that the majority of the UHNAI team is engaged in
interdisciplinary research, and suggest that our method could be applied to
additional NASA Astrobiology Institute teams in particular, or other
interdisciplinary research teams more broadly, to identify and facilitate
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algorithm, the sequential Information Bottleneck, to assess the
interdisciplinarity of research produced by the University of Hawaii NASA
Astrobiology Institute (UHNAI). In particular, we cluster abstract data to
evaluate Thomson Reuters Web of Knowledge subject categories as descriptive
labels for astrobiology documents, assess individual researcher
interdisciplinarity, and determine where collaboration opportunities might
occur. We find that the majority of the UHNAI team is engaged in
interdisciplinary research, and suggest that our method could be applied to
additional NASA Astrobiology Institute teams in particular, or other
interdisciplinary research teams more broadly, to identify and facilitate
collaboration opportunities.</abstract><doi>10.48550/arxiv.1204.5563</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Digital Libraries Physics - Physics and Society |
| title | Assessing Researcher Interdisciplinarity: A Case Study of the University of Hawaii NASA Astrobiology Institute |
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