ABSTRACT In our current moment, there is considerable interest in networks, in how people and things are connected. This essay outlines one approach that brings together insights from actor-network theory, social network analysis, and digital history to interpret past scientific activity. Multispecies network analysis (MNA) is a means of understanding the historical interactions among scientists, institutions, and preferred experimental animals. A reexamination of studies of sexual behavior funded by the Committee for Research in Problems of Sex between the 1920s and (...) the 1940s demonstrates the applicability of MNA to clarifying the relations that sustained this area of psychology. The measures of weighted degree and betweenness can highlight which nodes (whether organisms or institutions) were particularly “central” to this network. Rats featured as the animals most widely studied during this period, but the analysis also reveals distinct institutional and disciplinary cultures where different species were favored as either surrogates for humans or representatives of more general biological groups. (shrink)

The gradual crowding out of singleton and small team science by large team endeavors is challenging key features of research culture. It is therefore important for the future of scientific practice to reflect upon the individual scientist’s ethical responsibilities within teams. To facilitate this reflection we show labor force trends in the US revealing a skewed growth in academic ranks and increased levels of competition for promotion within the system; we analyze teaming trends across disciplines and national borders demonstrating why (...) it is becoming difficult to distribute credit and to avoid conflicts of interest; and we use more than a century of Nobel prize data to show how science is outgrowing its old institutions of singleton awards. Of particular concern within the large team environment is the weakening of the mentor–mentee relation, which undermines the cultivation of virtue ethics across scientific generations. These trends and emerging organizational complexities call for a universal set of behavioral norms that transcend team heterogeneity and hierarchy. To this end, our expository analysis provides a survey of ethical issues in team settings to inform science ethics education and science policy. (shrink)

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. (shrink)

Computational social science, as an emerging interdisciplinary discipline, is a field ushered in by long-term development of traditional social science. It is committed to supplying data thinking, resources, and analytics to study human social behavior and social operation laws to accurately grasp and judge the developing path of the discipline, which is of great significance to promote the innovation and development of social sciences. This study is to conduct a systematic quantitative analysis from a bibliometric perspective, aiming to provide a (...) reference for scholars to explore the paths and changing rules in the field. We use the relevant literature in Web of Science as the dataset. After eliminating journal calls and irrelevant literature, R language and SciMAT tools are used to visualize and analyze the number of articles, keyword clustering, keyword cooccurrence network, and theme evolution, so as to summarize and sort out the paths of computational social science research. The study found that the annual volume of publications has been gradually increasing and will probably remain active in the next few years with high productivity. Subject themes in different periods are diversified, and the evolutionary relationship is found complex as well. Besides, as a cross discipline, scientific knowledge from different fields cross collides and couples with each other in the big data environment, changing the traditional concept of computational social science and forming a new development path. Recently, the emergence of “big data+” has promoted the rise of new subject areas, making the development of new disciplines a reality. (shrink)

Many researchers insist that computational methods will transform the historical profession, while an equally large number reject these claims as unwarranted hype. This study attempts to place the debate in historical and social context. The essay is divided into three parts. The first part offers a brief review of computational history. It asks whether the “computational turn” bears any resemblance to quantitative history and how it fits within the digital humanities. The second part describes the authors’ recent attempts to apply (...) computational methods to an existing project in the history of science using a standardized workflow. It demonstrates that each step of the workflow adds another layer of subjectivity. The third part reflects on what computational methods mean for the historical profession. It systematically reviews the positive aspects of computational history (open access, interdisciplinary collaboration, and new perspectives) as well as the negative aspects (inequality, fragility, and the threat of automation) and offers prescriptions based on the authors’ experiences. (shrink)

Digital technologies have transformed both the historical record and the historical profession. This Focus section examines how computational methods have influenced, and will influence, the history of science. The essays discuss the new types of questions and narratives that computational methods enable and the need for better data management in the history and philosophy of science (HPS) community. They showcase various methodological approaches, including textual and network analyses, and they place the computational turn in historiographical and societal context. Rather than (...) surrendering to either technophilia or technophobia, the essays articulate both the benefits and the drawbacks of computational HPS. They agree that the future of the field depends on the successful integration of technological developments, social practices, and infrastructural support and that historians of science must learn to embrace collaboration both within and beyond disciplinary boundaries. (shrink)