Moreover, we plan to utilize tesseract’s equ language data to detect mathematical equations in documents. We take the use of mathematical formulas as a signal of affiliation with the mathematical side of the discourse on theoretical biology. This way, each document is assigned a gradual mathematization score. To model the mathematization of theoretical biology, we then analyze the mathematization scores per year and the scores’ correlations with topics. The score can in turn be used for visual classification in the visualization by choosing different symbols for documents in the scatter plot based on their score.