Assessing Researcher Interdisciplinarity: A Case Study of the University of Hawaii NASA Astrobiology Institute

TL;DR

This paper combines bibliometric techniques and machine learning to evaluate and visualize interdisciplinarity among researchers at the UHNAI, identifying collaboration opportunities and assessing research diversity.

Contribution

It introduces a novel method integrating bibliometrics with the sequential Information Bottleneck to measure and analyze interdisciplinarity in astrobiology research.

Findings

Most UHNAI researchers are engaged in interdisciplinary work

The method effectively clusters research abstracts by subject categories

Potential collaboration opportunities are identified through analysis

Abstract

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.