Mapping research topics at multiple levels of detail

Abstract

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.

Keywords: data discovery; decision support; institutional review; knowledge representation; science mapping; spatialization; topic modeling.

Figure 1

Distribution of research documents per year for analysis period

Figure 2

Distribution of research documents by word count

Figure 3

Coherence scores for NMF and LDA topic models with 2–100 topics

Figure 4

ERI research documents clustered by 9 topics with t-SNE (left) and UMAP (right); each color corresponds to the document's main topic, labeled with three term descriptors

Figure 5

Coarse (9 topic) map of research documents (2009–2019)

Figure 6

Detailed (36 topic) map of research documents (2009–2019)

Figure 7

Search panel of the interactive research map dashboard