DTU teachings: Introduction to Digital History

In the academic year 2018-2019, the DTU ‘Digital History & Hermeneutics’ had been teaching the course Introduction to Digital History in the Master ‘European History’ at the University of Luxembourg.












Course description

When researching collections of historical documents, multiple perspectives can be taken to the events described. What is written about the events, where and when did they take place, and who were the people involved? For a small number of documents, these questions can be answered by reading carefully. However, now that entire archives are available online, this is no longer the case. To research such vast collections, digital technology can be of aid.

This course introduced students to a variety of digital tools that can help historians discuss questions related to what, when, where, and who. Students will learn how to use such tools, and how to critically reflect on their possibilities and limitations. Since the output of many of these tools are in visual form online, students moreover learned how to integrate such visualizations in reports in digital form.


Week 1 – 18/09 – Introduction: Digital History & Hermeneutics
Tim van der Heijden

This introduction gives an overview of the course and presents a theoretical and methodological discussion on Digital History and Hermeneutics.

Week 2 – 25/09 – Publishing for the Web
Sytze Van Herck & Marleen de Kramer

This session discusses what content is suited for the web and demonstrates how you can publish your research on the web. We start the design of the pages where some of the course assignments will be published.

Week 3 – 02/10 – Digital Archives & Cultural heritage Databases
Eva Andersen, Marleen de Kramer & Christopher Morse

This session introduces students to digital archives and databases for cultural heritage through a series of three case studies. Topics covered include the evolving definition of archives, the selection process and digitisation of materials, and the usage and analysis of its contents.

Week 4 – 09/10 – Big Data & Machine Learning
Shohreh Haddadan & Antonio Fiscarelli

In this session we give an introduction to the basics of machine learning. We talk about different types of machine learning algorithms with respect to supervised/unsupervised learning. We show examples of different classification and clustering algorithms . We also discuss some terminologies of the field such as train/test, precision/recall, feature extraction, cross-fold validation and over-fitting. The second part of the session consists of a practical example: text mining of a conference papers dataset and topic modelling through unsupervised learning.

Week 5 – 16/10 – Distant & Close Reading
Eva Andersen & Shohreh Haddadan

This session does not only deal with the meaning behind the concepts of ‘distant reading’ and ‘close reading’ but also introduces different applications that are used in this approach. This includes for instance text mining and topic modelling, but also named entity recognition and sentiment analysis, approaches that are less known to historians. At the end of this lecture, you will have a basic idea of the opportunities, biases and pitfalls of distant reading along with some practical examples of distant reading in the field of digital humanities.

Week 6 – 23/10 – When? Timelines and Databases
Sytze Van Herck & Antonio Fiscarelli

In this session we briefly discuss the history of timeline visualisations. Since time can be recorded in varying ways, we identify useful date / time formats for historic events. Furthermore we introduce the Hillary Clinton email corpus and build a simple relational dataset.

Week 7 – 30/10 – When? Queries and Data Visualisation
Sytze Van Herck & Kaarel Sikk

Once you finished the dataset we can get started on the actual timelines. But first, we need to translate our research questions into a dataset query. During this session we learn how to build queries and extract the right data from our dataset. We introduce best practices in creating visualisations and experiment with Tableau.

Week 8 – 06/11 – What? (Digital) Textual Criticism
Christopher Morse

This session introduces students to textual criticism, primarily in the context of digital editions. Topics explored include how we define a text, the establishment of textual authority, and the debates concerning the creation of textual editions, both analog and digital. After an introduction to concepts and a discussion of historical examples, students create their own digital editions within Juxta, a text collation environment for the web.

Week 9 – 13/11 – What? Digital Hermeneutics and Voyant Tools
Thomas Durlacher

In this session, students are introduced to the basic concepts of digital hermeneutics and the web-based text analysis tool Voyant. After a theoretical introduction students are going to use Voyant to work on a number of practical exercises in which they explore a dataset on their own.

Week 10 – 20/11 – Where? Digital Historical Cartography (theory)
Sam Mersch & Jan Lotz

In this session students are introduced to cartography and its history. The reason for doing cartography and the subjectivity of maps will be the main focus before students will be introduced to historic cartography.

Week 11 – 27/11 – Where? Digital Historical Cartography (practice)
Sam Mersch, Kaarel Sikk & Jan Lotz

Students learn about QIS as an open-source GIS tool and its usage, while creating maps and visualising geographically relevant data.

Week 12 – 04/12 – Who? Social Network Analysis (theory)
Jakub Bronec & Antonio Fiscarelli

In this session students are introduced to Social Network Analysis, how to represent social networks using graphs and the concept of small world. How different centrality metrics (degree, betweenness, closeness) can be used to identify key actors in networks and how to choose the best metric for a specific purpose. How different type of networks (random network, small world, scale free) have different characteristics (clustering coefficient, characteristic path length) that can be computed, and how models can be used to recreate network similar to the ones found in real world. Finally, students learn how to rank “popular” nodes/actors in the network using ranking algorithms.

Week 13 – 11/12 – Who? Social Network Analysis (practice)
Jakub Bronec & Jan Lotz

In this session, we practice with extracting information from the sources and create a social network ourselves.

Week 14 – 18/12 – Wrap-up
Tim van der Heijden

Reflection on the course and explanation about the final assignment.