  Scientific results are communicated visually in the literature through
diagrams, visualizations, and photographs. These information-dense objects have
been largely ignored in bibliometrics and scientometrics studies when compared
to citations and text. In this paper, we use techniques from computer vision
and machine learning to classify more than 8 million figures from PubMed into 5
figure types and study the resulting patterns of visual information as they
relate to impact. We find that the distribution of figures and figure types in
the literature has remained relatively constant over time, but can vary widely
across field and topic. Remarkably, we find a significant correlation between
scientific impact and the use of visual information, where higher impact papers
tend to include more diagrams, and to a lesser extent more plots and
photographs. To explore these results and other ways of extracting this visual
information, we have built a visual browser to illustrate the concept and
explore design alternatives for supporting viziometric analysis and organizing
visual information. We use these results to articulate a new research agenda --
viziometrics -- to study the organization and presentation of visual
information in the scientific literature.
