2015/03/11 by Michael Ginda, Andrea Scharnhorst, Ginda, Michael +4
Computer Science · Decision Sciences · Physics and Astronomy · #Complex Systems and Decision Making #Data Visualization and Analytics #Digital Libraries (cs.DL) #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #cs.DL #physics.soc-ph #scientometrics and bibliometrics research
paper · pdf · doi:10.48550/arxiv.1503.03287
data and large scale maps http://cns.iu.edu/2015-ModSci.html, Ginda, Michael, Andrea Scharnhorst, and Katy Börner. "Modelling Science". In Theories of Informetrics: A Festschrift in Honor of Blaise Cronin, edited by Sugimoto, Cassidy. Munich: De Gruyter Saur
arxiv created 2015/03/11 · openalex publication_date 2015/03/11 · arxiv updated 2015/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Scientific research is a major driving force in a knowledge based economy. Income, health and wellbeing depend on scientific progress. The better we understand the inner workings of the scientific enterprise, the better we can prompt, manage, steer, and utilize scientific progress. Diverse indicators and approaches exist to evaluate and monitor research activities, from calculating the reputation of a researcher, institution, or country to analyzing and visualizing global brain circulation. However, there are very few predictive models of science that are used by key decision makers in academia, industry, or government interested to improve the quality and impact of scholarly efforts. We present a novel 'bibliographic bibliometric' analysis which we apply to a large collection of books relevant for the modelling of science. We explain the data collection together with the results of the data analyses and visualizations. In the final section we discuss how the analysis of books that describe different modelling approaches can inform the design of new models of science.