  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.
