Données et méthodes des humanités numériques I

The discipline of digital humanities (DH) has emerged on the academic scene in recent decades. Mobilizing methods hitherto little used in the humanities and social sciences (HSS), it enables us to take a fresh look at issues arising in these fields. It can be divided into two main branches. The first concerns the way in which information is extracted, managed, manipulated and stored. This is one of the central aspects of the ICST master's degree, and is therefore reflected in several courses in the program (such as Introduction aux bases de données [STIC-B405], Édition numérique [STIC-B520], or Architecture des systèmes d’information [STIC-B415]).

The second branch of HN concerns the numerical methods by which information is analyzed, compared and critiqued, and is the focus of this course. Numerous numerical and quantitative techniques are now readily available, making it possible to study corpora of data from the SHS from original angles, posing new questions about them, or renewing the study of questions that have long been posed. The course provides an overview of these analysis methods, focusing on the characteristics of the data under study and using examples from the scientific literature.

This course is part of a set of twin courses (with Données et méthodes des humanités numériques II [STIC-B562]) given alternately every other academic year. Students can select either one or both of these courses (over two years), as they wish, the order in which the two courses follow each other being irrelevant.
Both courses cover similar content, but different types of data, methods and analyses.

In this course, the focus is on:
- epistemological questions around the definition and delimitation of digital humanities as a discipline;
- certain aspects of textual data analysis ;
- network analysis.

 

The aim of the course is to provide an overview of the data that is manipulated and the methods applied to it in the digital humanities. At the end of the course, students should be able to identify the nature of a data set, determine how a problem can be translated into an analytical task, and propose a list of applicable methods for carrying out this task, understanding their ins and outs. He/she must also be able to effectively manipulate such data and perform basic analysis.


 

The course consists of a series of lectures and practical sessions, alternating between theoretical presentations, practical examples from the scientific literature and moments of data manipulation using the statistical programming language R. The examples selected cover a wide range of disciplines (linguistics, archaeology, philosophy, communication, political science, etc.) and periods (contemporary, modern, medieval and ancient).

 

Course notes

• Université virtuelle

Contacts

Sébastien de Valeriola (sebastien.de.valeriola@ulb.be)

 

Campus

Solbosch

Method(s) of evaluation

• Project
• Oral presentation

Project

Oral presentation

Evaluation consists of a personal project and an oral exam.

The first involves the application of methods learned in the course to new data: the results of the analysis must be submitted in the form of a written project.

The second involves the analysis of two scientific articles in digital humanities, prepared in advance.
The scientific articles are selected by the student during the term (after validation by the teacher), according to his/her interests and profile. The analysis must reflect the points covered in the course, and include a presentation of the data used, the methods implemented and the results obtained by the authors of the articles.

With regard to the use of generative artificial intelligence tools, the terms and conditions of this course follow those set out for dissertations and TPMs in the Master "dissertation guide".

 

Mark calculation method (including weighting of intermediary marks)

The oral presentation and the project each count for half the grade (50%+50%).

Language(s) of evaluation

• french