Speaker
Description
Quantitatively understanding a research field is important for funding allocation, teaching and curriculum development, research organisation, and scientific communication. One way to achieve this understanding is through bibliometric analysis, which involves using statistical methods to examine patterns in scientific publication metadata and citation networks. We present two case studies that use bibliometric analysis. First, we obtain a comprehensive overview of mathematical oncology as a research field. Our analysis shows that since the 1960s, mathematical oncology has become more globally connected, and more interactive with external disciplines, with larger research teams and evolving research topics \citep{pugh2025bibliometric}. Moreover, our results quantitatively demonstrate that mathematical oncology benefits both mathematics and the life sciences. Second, we investigate the cross-disciplinary exchange in biological agent-based modelling. Our findings suggest limited cross-talk between disciplines, with citations largely self-contained. We discuss the potential advantages and disadvantages of research fields being isolated versus those that are more interconnected.
Bibliography
@article{pugh2025bibliometric,
title={A bibliometric study on mathematical oncology: interdisciplinarity, internationality, collaboration and trending topics},
author={Pugh, Kira and Gyllingberg, Linn{\'e}a and Stratiev, Stanislav and Hamis, Sara},
journal={Bulletin of Mathematical Biology},
volume={87},
number={12},
pages={174},
year={2025},
publisher={Springer}
}
