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Every project in digital and computational history of science starts with the collection of data. Depending on the research project, subject of study, and other factors, data can be comprised of a variety of different types such as full-texts, images, audio, video, or bibliographic metadata. Often publications and project reports describe a project’s results and the employed methods and algorithms, but few publications discuss the challenges of the initial data collection process or how it fits into the overall research data life cycle. In this paper, we discuss a concrete research data life cycle and take a look at the difficulties it involves. Furthermore, we explore the strategies and challenges of data collection, and the question of comparability of datasets. 

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.