About Us
DTU-D4H
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.
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
The D4H is made up of the University of Luxembourg’s
Centre for Contemporary and Digital History (C²DH),
the Faculty of Science, Technology and Medicine (FSTM),
the Faculty of Humanities, Education and Social Sciences (FHSE),
namely its Institute for History (IHIST).
They are joined by
the Luxembourg Institute of Science and Technology (LIST)
and
the Luxembourg Institute of Socio-Economic Research (LISER).
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).
- Andreas Fickers – D4H director
- Joris Hulstijn – D4H Post-Doc
- Richard Albrecht – PhDs representative
- Florentina Armaselu – C²DH
- Andrea Binsfeld / Martin Uhrmacher – IHIST
- Christoph Schommer – FSTM
- Cedric Pruski – LIST
- Christina Gathman – LISER
International
Partners
- The UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent Artificial Intelligence at the University of Bath, which aims at developing world-leading AI via an explicitly interdisciplinary approach.
- The Data Science Centre at the University of Amsterdam facilitates data-driven research. Similar to D4H, its focus is on providing training in AI for other fields via an interdisciplinary doctoral-level approach.
- The History Lab at Columbia University (USA) is aggregating large corpora of US and international documents using machine learning and natural language processing techniques. It offers exchange, advanced training and a valuable link to the American digital humanities community.
- The Ghent Centre for Digital Humanities is renowned in the Benelux region for its focus in the areas of geo-spatial humanities, semantic web technologies and deep mapping. It provides training in data standards, tools and linked data.
- The Digital Humanities research group at the Fondazione Bruno Kessler in Trento, Italy is leading in the field of textual data processing and the use of AI in historical investigation in multilingual digital archives.
- DARIAH European Research Infrastructure Consortium, Paris will facilitate workshops with a focus on network-building and professionalisation for future leading researchers in the field of digital humanities.
- The German Historical Institute, Paris will support the D4H with its expertise in science communication and open access. It runs the blogging infrastructure hypotheses.org.
- The Digital Humanities Lab at the EPFL has considerable expertise in the customisation and application of computational tools and software for big data analysis and visualisation and initiated the Time Machine flagship project.
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.