A transversal research lab for Cultural Data Science

The Lab

Welcome to the Culture & Computation Lab! We are a transversal research unit hosted at the Department of Humanities of the University of Luxembourg. As such, we form part of the university’s focus area “Digital Transformation”.

Our main mission is to foster collaboration among scholars of the department, to promote research in Cultural Data Science as a comprehensive approach to the application of computational methods in the humanities, and to advance the analysis of (digital) culture from a scholarly point of view. In doing so, we are driven by a critical curiosity and open to everyone.

The lab serves four primary functions:

  • Thematic figurehead: It positions & promotes humanities research at the university in the national and international research landscape.
  • Project hub: It provides infrastructure, technical and development assistance for projects and researchers in the departmant.
  • Training platform: It offers training and teaching in Cultural Data Science and digital methods to scholars and students, and the scientific community beyond the university.
  • Anchoring point: It functions as a collaboration hub for incoming researchers, societal stakeholders, and industry partners in terms of project development and funding.

Thematic profile

The central starting point for the lab is a holistic analysis of the complex interrelations between culture and digitality. That is, in our work, we establish a close link between the development of technical solutions for the analysis of (digital) culture at scale and a thorough scholarly reflection on the impact that machine learning and digitality have on culture as a whole, including academic research practices. The Department of Humanities with its different scholarly disciplines (inter alia, culture theory, history, literature, linguistics, media studies, philosophy) offers the ideal academic environment for such an endaavor.

Focus areas

During its startup phase, the lab focuses on three thematic axes:

  • Critical Digitality Studies: We are evaluating the effects of digital practices and machine learning applications on human interaction, including the different forms of cultural and societal engeneering that emerge from them.
  • Culturally-grounded NLP: We are seeking to advance research in computational linguistics and NLP by grounding the statistical processing of language data in social practices and cultural data.
  • Data Science training platform: We are developing a Cultural Data Science training and teaching platform for reseachers and students at the university and beyond, in close cooperation with the C2DH.


As a transversal unit, the is sustained by all researchers and institutes of the humanities department. For organizational purposes, we have established a steering group that includes one representative of each institute, coordinated by a Head of Lab.

Head of Lab: Christoph Purschke

Lab personnel: Alistair Plum (Postdoc, Computational Linguistics), Catherine Tebaldi (Postdoc, Digital Anthropology)

Associate members: Ognyan Ognyanov Darinov (PhD, Literary Studies), Jacques Spedener (PhD, Computational Sociolinguistics), Johannes Pause (Research Scientist, Media Studies)

Steering group: Anne-Marie Millim (English Studies), Ingrid de Saint-Georges (Multilingualism), Martin Uhrmacher (History), Peter Gilles (Luxembourg Studies), Lars Wieneke (C2DH), Thomas Raleigh (Philosophy), Till Dembeck (German Studies), Tonia Raus (Romance Studies, Media and Arts)


As a transversal research unit and thematic figure head, our approach to Cultural Data Science is driven by a strong interest in interdisciplinary research. Therefore, we cooperate with various partners at the university level, most notably the Center for Contemporary and Digital History (C2DH), the Department of Computer Science (DCS), Melusina Press and the MediaCentre (FHSE). We also actively seek cooperation with societal stakeholders and industry partners to establish sustainable relations for innovative research in Cultural Data Science from a scholarly point of view.

Lab Notes

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