Assessing researcher interdisciplinarity: a case study of the University of Hawaii NASA Astrobiology Institute
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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.
KeywordsAstrobiology Bibliometrics Information bottleneck method Interdisciplinary science Machine learning Text mining
We thank David Schanzenbach for devising scripts, and Mahdi Belcaid and the anonymous reviewers for insightful comments. This material is based upon work supported by the National Aeronautics and Space Administration through the NASA Astrobiology Institute under Cooperative Agreement No. NNA08DA77A issued through the Office of Space Science.
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