Reading Texts and DataData Philology statt Data Science

In my article, I question the traditional separation between qualitative and quantitative text analysis and argue for an integrated approach, which I call data philology. I argue that reading texts and data are not fundamentally different in their complexity, especially when considering the role of diagrammatic transformations, algorithmization and machine processes. At the center of my argument is corpus linguistics, which enables large-scale text analysis through statistical methods and digital coding. These approaches transcend the conventional boundaries between >> close << and >> distant reading <<. I highlight how computers function as active participants in writing and reading practices and how digital as well as analog methods often involve diagrammatic operations embedded in complex interpretive processes. I argue that philology can benefit from the integration of data science methods by utilizing them for philological research interests. The aim is to avoid naive data positivism and to make statistical models accessible for interpretative purposes. My article calls for rethinking traditional perspectives and opening up to a data philology that critically integrates and utilizes the latest methods of data analysis for a wider variety of textual analyses. The title and abstract was automatically translated from German using DeepL (version 23.11).