“Deep Data Science of Digital History” (D4H) is a new Doctoral Training Unit funded through the FNR’s PRIDE programme, that will launch in autumn 2022.

The mass digitisation of historical sources and the exponential growth native digital online sources have catapulted the discipline of history from an “age of scarcity” to an “age of abundance”. Making sense of the “big data of the past” requires new approaches to data management, data mining, visualisation and interpretation of data. In the future, the study of massive migration flows, climatic changes, or public opinion formation on social media platforms will both necessitate a critical digital literacy by historians and humanist approach to data analytics.

“Deep Data Science of Digital History” (D4H) is a new Doctoral Training Unit funded through the FNR’s PRIDE programme, that will launch in autumn 2022. This interdisciplinary DTU focuses on multiple challenges at the intersection between the disciplines of history and data science and builds on the epistemological and methodological learnings of the interdisciplinary DTU “Digital History and Hermeneutics” (see https://dhh.uni.lu). Its main aims and ambitions are:

  • To bridge research in humanities and sciences by creating an interdisciplinary “trading zone” building on the concept of “digital hermeneutics”;
  • To train a new generation of digitally literate PhD students to deal with “big data of the past” in a critical and competent way, combining the epistemic tradition of close reading with machine-based methods of distant reading (“scalable reading”);
  • To develop a shared understanding of the human/machine nexus in collecting, curating, managing, analysing, interpreting, and visualizing historical data;
  • To problematize the multi-layered temporalities of datasets and experiment with new forms and formats of historical models and simulations in a longue-durée / deep time perspective.

D4H involves the University of Luxembourg’s Centre for Contemporary and Digital History (C²DH), the Faculty of Science, Technology and Medicine (FSTM), the Institute of History (IHIST) of the Faculty of Humanities, Education and Social Sciences (FHSE), the Luxembourg Institute of Science and Technology (LIST) and the Luxembourg Institute of Socio-Economic Research (LISER). The DTU will include a total of 18 PhD positions. It will be led by Prof. Andreas Fickers, Director of the Luxembourg Centre for Contemporary and Digital History (C²DH).

The new Doctoral Training Unit proposes to deepen the interdisciplinary collaboration between digital history and computer science by exploring the concepts of deep history and deep data science. D4H will focus on three thematic and methodological pillars:

  1. deep data and knowledge
  2. deep analytics and learning
  3. deep visualisation and interpretation

The concept of “Deep data and knowledge” addresses the challenges of creating digital datasets which, in the field of history, are often characterised by their heterogeneity of data and their unstable or fluid nature in terms of volume and integrity. Doctoral students will be trained in the analysis of characteristics, formats, histories, and infrastructures of historical data and train our PhD students in historical data criticism and traceable data management. Deep analytics and learning engage with state-of-the-art approaches in machine learning technologies and the use of artificial intelligence for analysing large historical datasets. Deep visualisation and interpretation enter epistemological discussions about how visualisation techniques and dynamic interfaces transform historical imagination and interpretation. Based on recent trends in explainable artificial intelligence, information visualisation, and human-computer interaction, the aim is to promote critical debates about how historical arguments can be turned into “graphic arguments”, and how new techniques of representing big historical datasets can be turned into explorative modes for the temporal and spatial sampling of historical information.

For detailed information about the conceptual framework and the training program of the Doctoral Training Unit see here.

For a list of supervisors involved in the DTU see C²DH supervisors, FSTM supervisors, IHIST–FHSE supervisors, LIST supervisor, LISER supervisors.

You want to apply for a PhD position within the DTU? Please click here. (Link will be available soon)