DTU-D4H

D4H logo

 

The mass digitization 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, visualization, and the 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.

The Deep Data Science of Digital History (D4H) is a new Doctoral Training Unit (DTU) at the University of Luxembourg that focuses on the multiple challenges intersecting history and data science. Its main aims and ambitions are:

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. The D4H will focus on three thematic and methodological pillars:

deep data and knowledge

deep analytics and learning

deep visualisation and interpretation

  • 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 close reading with machine-based methods of distant reading (also known as scalable reading);
  • To develop a shared understanding of the human/machine nexus in collecting, curating, managing, analyzing, 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 logo dots

The concept of deep data and knowledge addresses the challenges of creating digital datasets which, in the field of history, are often characterized by their heterogeneity of data and their unstable or fluid nature in terms of volume and integrity. Doctoral students are being trained in the analysis of characteristics, formats, histories, and infrastructures of historical data alongside 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 analyzing large historical datasets. Deep visualization and interpretation enter epistemological discussions about how visualization techniques and dynamic interfaces transform historical imagination and interpretation. Based on recent trends in explainable artificial intelligence, information visualization, 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.

Partner Institutions

DTU D4H Board

The DTU D4H Board plays a vital role as an administrative body, defining the strategic foundations for training and research activities, handling organisational issues etc.
The board represent all institutions involved and all career
stages (supervisors, post-docs and PhD candidates).

International
Partners

Luxembourg
Partners

  • LuxProvide S.A. is a company providing high performance data analytics and Al solutions on an international scale as Luxembourg’s high-performance computing (HPC) centre. Joint workshops will address the basics of HPC and how to use the supercomputer for research.
  • The Bibliotheque nationale de Luxembourg (BnL) is providing us with access to Luxembourg’s heritage collections. The BnL is involved in a large-scale ongoing digitisation project and is continuously adapting its practices. Workshops are planned on copyright issues, data modelling and application development.
  • The Archives nationales de Luxembourg is the biggest holder of governmental and state records in Luxembourg, systematically conserving and inventorying. It has extensive expertise in corpus identification and metadata description technologies.
  • The Centre National de l’Audiovisuel (CNA) is committed to sharing its expertise in data management and digitisation of audiovisual cultural heritage collections and is an established partner for public outreach activities.

Research
Partners

  • The Time Machine Organisation, Vienna, links more than 600 research institutions worldwide to build a large-scale simulator mapping 5,000 years of European history, transforming an unprecedented volume of archives and large collections from museums into a digital information system.
  • Professor Johanna Drucker of the University of California has conducted groundbreaking research on visualisation, digital hermeneutics and interpretation. She will support PhD and postdoc projects in visualising and interpreting multiple chronologies and timescales.
  • Software Heritage (INRIA) is the largest publicly available open archive of software source code, with more than 11 billion unique source files collected from a variety of platforms, from over 100 million projects.
  • The School of Natural & Built Environment (SNBE) at Queen’s University Belfast, will be our main scientific partner for the geospatial aspects in deep mapping and cultural heritage visualisation, historical GIS surveying, digital analysis and landscape characterisation.
  • The German Archaeological Institute (DAI) in Berlin is involved in building a large-scale digital collection of research materials from excavations and developing a modular database system for the documentation of field research projects, supporting projects with open and FAIR research data.