B. R. Erick Peirson

Anomalous subgraph detection in publication networks: Leveraging truth

Analysis of social networks has the potential to provide insight into a wide range of applications. As datasets grow, a key challenge is the lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research on scientific innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework. The results suggest new ways for the study of innovation patterns in publication networks.

The diversity of experimental organisms in biomedical research may be influenced by biomedical funding

Contrary to concerns of some critics, we present evidence that biomedical research is not dominated by a small handful of model organisms. An exhaustive analysis of research literature suggests that the diversity of experimental organisms in biomedical research has increased substantially since 1975. There has been a longstanding worry that organism-centric funding policies can lead to biases in experimental organism choice, and thus negatively impact the direction of research and the interpretation of results. Critics have argued that a focus on model organisms has unduly constrained the diversity of experimental organisms. The availability of large electronic databases of scientific literature, combined with interest in quantitative methods among philosophers of science, presents new opportunities for data-driven investigations into organism choice in biomedical research. The diversity of organisms used in NIH-funded research may be considerably lower than in the broader biomedical sciences, and may be subject to greater constraints on organism choice.

Quantitative Perspectives on Fifty Years of the Journal of the History of Biology

Journal of the History of Biology provides a fifty-year long record for examining the evolution of the history of biology as a scholarly discipline. In this paper, we present a new dataset and preliminary quantitative analysis of the thematic content of JHB from the perspectives of geography, organisms, and thematic fields. The geographic diversity of authors whose work appears in JHB has increased steadily since 1968, but the geographic coverage of the content of JHB articles remains strongly lopsided toward the United States, United Kingdom, and western Europe and has diversified much less dramatically over time. The taxonomic diversity of organisms discussed in JHB increased steadily between 1968 and the late 1990s but declined in later years, mirroring broader patterns of diversification previously reported in the biomedical research literature. Finally, we used a combination of topic modeling and nonlinear dimensionality reduction techniques to develop a model of multi-article fields within JHB. We found evidence for directional changes in the representation of fields on multiple scales. The diversity of JHB with regard to the representation of thematic fields has increased overall, with most of that diversification occurring in recent years. Drawing on the dataset generated in the course of this analysis, as well as web services in the emerging digital history and philosophy of science ecosystem, we have developed an interactive web platform for exploring the content of JHB, and we provide a brief overview of the platform in this article. As a whole, the data and analyses presented here provide a starting-place for further critical reflection on the evolution of the history of biology over the past half-century.