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.

Abstract. 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. IntroductionIn a scathing 1990 review, the late historian of science John Farley complained that, "from its first twoissue volume in 1968, through its increase to three issues per year in 1982, until today, Journal of the History of Biology has provided an outlet for the self-perpetuating oligarchy of Darwin scholars" (Farley, 1990). "Is this healthy, I wonder?" Farley went on, "Has the profession now reached such a size that the members can afford to speak only to each other?". Farley enumerated a variety of themes and fields that, in his view, had been chronically underserved in the pages of JHB, including oceanography, ethology, botany, anatomy, physiology, biochemistry, bacteriology, and others. Worse, Farley seemed to suggest that JHB had nearly missed the social turn in the history of science, remaining fixated on "the history of biological concepts."It is worth considering the most charitable subtext of his assertions: that as the flagship periodical of the field, the contents of JHB are a window onto the diversity and the development of the history of biology. Indeed, early reviewers (e.g. Brown, 1968) hailed JHB as a signpost for the maturation of the history of biology as a distinct specialization within the history of science. The approaching quinquagenary of that first issue i...

Every project in digital and computational history of science starts with the collection of data. Depending on the research project, the subject of study, and other factors, data can comprise a variety of different types, including full texts, images, audio, video, and bibliographic metadata. Publications and project reports generally describe their results and the methods and algorithms employed, but few discuss the challenges of the initial data collection process or how it fits into the overall research data life cycle. This essay discusses a concrete research data life cycle and takes a look at the difficulties it involves. It also explores the strategies and challenges of data collection and the question of the comparability of datasets. Don't panic.-Douglas Adams, The Hitchhiker's Guide to the Galaxy E very project in the history of science relies on data. Collecting data by visiting archives, deciphering manuscripts, or describing artifacts was always essential in the daily work of the historian; digital methods are changing the amount of data that can be processed, and, more important, complex algorithms can be applied to analyze these data. In order for these methods to be applied, data has to be made available digitally. The focus of this essay is on how to work with data in this sense, although we are fully aware that digitizing sources is still one of the main challenges in the life cycle we will describe. 1 For a detailed discussion of this topic see, for example, Stéphane Nicolas, Thierry Paquet, and Laurent Heutte's essay "Digitizing Cultural Heritage Julia Damerow is a scientific software engineer at Arizona State University with a degree in computer science and a Ph.D. in computational history and philosophy of science. Her interests are the application of computation to the field of history and philosophy of science, software development in the field of digital humanities, and how these can be combined with digital humanities education. She is Head of Development and Cofounder of the Digital Innovation Group at

In the digital humanities, there is a constant need to turn images and PDF files into plain text to apply analyses such as topic modelling, named entity recognition, and other techniques. However, although there exist different solutions to extract text embedded in PDF files or run OCR on images, they typically require additional training (for example, scholars have to learn how to use the command line) or are difficult to automate without programming skills. The Giles Ecosystem is a distributed system based on Apache Kafka that allows users to upload documents for text and image extraction. The system components are implemented using Java and the Spring Framework and are available under an Open Source license on GitHub (https://github.com/diging/).

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