Delineating Knowledge Domains in the Scientific Literature Using Visual Information

TL;DR

This paper explores using scientific figures as markers of knowledge domains, demonstrating their effectiveness in classifying disciplines and understanding how visual communication reflects scientific ideas beyond traditional text and citation analysis.

Contribution

It introduces a method to encode and analyze scientific figures as visual signatures, revealing insights into disciplinary differences and idea propagation in scientific literature.

Findings

Figures can differentiate scientific communities as effectively as text and citations.

Visual signatures reveal discipline-specific visualization practices.

Figures propagate through literature, indicating a new pathway for idea flow.

Abstract

Figures are an important channel for scientific communication, used to express complex ideas, models and data in ways that words cannot. However, this visual information is mostly ignored in analyses of the scientific literature. In this paper, we demonstrate the utility of using scientific figures as markers of knowledge domains in science, which can be used for classification, recommender systems, and studies of scientific information exchange. We encode sets of images into a visual signature, then use distances between these signatures to understand how patterns of visual communication compare with patterns of jargon and citation structures. We find that figures can be as effective for differentiating communities of practice as text or citation patterns. We then consider where these metrics disagree to understand how different disciplines use visualization to express ideas. Finally, we further consider how specific figure types propagate through the literature, suggesting a new mechanism for understanding the flow of ideas apart from conventional channels of text and citations. Our ultimate aim is to better leverage these information-dense objects to improve scientific communication across disciplinary boundaries.