Cultural Science Meets Cultural Data Analytics

Abstract For developing Cultural Science as a research field and practice it is worthwhile reconsidering the ways to approach the study of large corpora of digital content and data. In this context, Digital Humanities (DH) has been a success story in the academic world. However, we argue that it is better to consider DH as a transitory phenomenon that needs to be developed into more specific research fields, while at the same time it could benefit from being extended towards an even more multidisciplinary science. To achieve this, it is vital to first transcend the artificial division of cultural inquiry into the qualitative analysis of idiographic phenomena and the quantification of nomothetic phenomena. It is furthermore important to surpass the dichotomy of specific versus general as research objects; for example replacing this with the notion of the semiosphere as a research object, defined as the ‘smallest’ functioning element of culture by Juri Lotman. In this perspective, the singular cultural unit is always conditioned by the whole of the semiosphere, while the whole can be always changed by the singular, both in line with classic hermeneutic inquiry and recent notions of complexity science. Further, the label of ‘humanities’ in DH is at the same time both too large and too restrictive. We instead argue for a study of meaning-making practices in human society, but without confining ourselves to traditional humanities scholarship, but rather, learning from new developments in systems biology, evolutionary economics, complexity science and many more. We think that this new transdisciplinary field of study can help define the scope of the Cultural Science Journal . Indeed, it has already found practical application in a variety of ‘post-DH’ collaborations in ‘Cultural Data Analytics’, often with the aim to explore the dynamics of meaning-making practices by computational means and by looking at a spectrum of materials (textual, sonic, visual, multimodal, etc.) both regarding the longue durée and in real-time applications, if not anticipating the future.