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Welcome to the wiki of the course Foundation of Digital Humanities (DH-405).

Contact

Professor: Frédéric Kaplan

Assistants: Alexander Rusnak

Rooms: Wednesday (CM1110) and Thursday (BC03/BC04)

Links

Summary

This course gives an introduction to the fundamental concepts and methods of the Digital Humanities, both from a theoretical and applied point of view. The course introduces the Digital Humanities circle of processing and interpretation, from data acquisition to new understandings and services. The first part of the course presents the technical pipelines for digitising, analysing and modelling written documents (printed and handwritten), maps, photographs and 3d objects and environments. The second part of the course details the principles of the most important algorithms in particular deep learning approaches (for document analysis and image generation) and knowledge modelling (semantic web, ontologies, graph databases). The third part of the course focuses on platform management from the points of view of data, users and bots. Students will practise the skills they learn by engaging in a class-wide collective project.

Plan

Part I : Concepts and Fields

Week 1 : What are Digital Humanities?

10.09 :

(2h) Welcome and Introduction to the course

  • Getting know each others. Presentation of each student with a Photo illustrating their interest or history.
  • Introduction to the structure of the course and the FDH textbook.

11.09 :

What are Digital Humanities? What is their object of study?

  • Digital vs Analogue, Abstract vs Concrete, Information, Token, Data/Code equivalence
  • Maud Ehrmann, Impresso project and data acceleration regime (2h)

Week 2 : What are Digital Humanities (ctd.)

17.09 :

  • Humanities, Hermeneutics. Patterns

18.09 :

Morning :

  • Anatomy of a large-scale project : Venice Time Machine. European Time Machine.

Afternoon :

  • Manuel Ehrenfeld / Designing the Time Machine Atlas
  • Formation of the groups (ideally 2, max 3 students)

Week 3: Subfields Student Presentation

Groups of students will give a 10-minute presentation, using slides, followed by a 10-minute class discussion on the potential of Big Data in various fields of the Humanities. Presentations may include an overview of existing projects and/or projections of future research in these disciplines. (See graduation system below)

For each subfields, groups must

  • Define the specific object of study of the subfield,
  • Define the opportunities of a change of scale,
  • Illustrate with existing or prospective examples.

24.09 :

  • Pierre Vann, Julien Jordan / Architecture/Urbanism
  • Anaël Donini, Anastasia Meijer / Applied Sociology
  • Eliott Bell, Christophe Bitar / Big Data in History
  • Camille Dupre Tabti, Olivia Robles / Big Data and Musicology
  • Néhémie Frei, Niccholas Reiz Art History: Film Studies

25.09 :

Morning :

  • Camille Lannoye, Sophia Kovalenko / Art History
  • Yibo Yin, Jiajun Shen, Yifan Zhou / Big Data and History
  • Marguerite Novikov / Lingustics
  • Xiru Wang, Jingru Wnag / Big data and theology
  • Jérémy Hugentobler / Archeology

Afternoon

2.15 pm : Projects presentation by prof and TA. Examples of DH projects that can be selected during the course. It is also possible to invent new one. Each student has to select two and present their ideas the following week.

Part II : Media Pipelines

Week 4: Documents and Writing Systems

01.10 :

(2h) Documents : Definition and Encoding. IIIF Format.

02.10 :

- (2h) Writing Systems

  • (2h) Projects presentations. Each group present 2 projects. 5' per project with max 3 slides. At the end of the session, the goal is to select one really fitting the taste and skills of the students and the learning ambition of the course.

Vote on skills tutorial for Skills sessions

Week 5: Texts

08.10 :

(2h) Reading and Text encoding (2h)

09.10 :

(2h) Text Spaces and Text systems

(2h) SKILL SESSION 1 (Vote for the tutorials) (Alex Rusnak)

Week 6: Paintings, Engravings and Photographs

15.10 :

(2h) Image reading. Image space and image systems

16.10 :

(2h) Example of the Replica Pipeline


(2h) SKILLS SESSION 2 (Vote for the tutorials) (Alex Rusnak)

Week Off

22.10 :

No course

23.10 :

No Course

Week 7: Maps

29.10 :

(2h) Are Maps different than images ? Alex Rusnak's Presentation on 2D/3D map encoding

30.10 :

Morning

(2h) Remi Petitpierre presentation : Cartography at scale

Afternoon

2h) Beatrice Vaienti presentation : Genealogies of Jerusalem's maps

Week 8: Design and Architecture

05.11 :

(2h) 3D Models Systems and Encoding

06.11 :

(2h) 3D Models Processing

(2h)Work on project : Definition of the MVP (Minimum Viable Product)

MidTerm Presentations

12.11 :

  • You can select the presentation of this course : Music, Poetry, Fashion, Rituals, Web, Diagrams and Tables, Zeitgeist modelling (simulation of the everyday)
  • Summary of the concept viewed so far. Publication of the study guide

13.11 Midterm presentations

Time Project name
10:20-10:40 Group 7
10:40-11:00 Group 6
11:00-11:20 Group 5
11:20-11:40 Group 4
Time Project name
13:15-13:35 Group 3
13:35-13:55 Group 2
13:55-14:15 Group 1
14:15-14:35 Group 8

Part III : Knowledge modelling and processing

Week 9 : Semantic modelling, Rule systems, simulations and parallel worlds

19.11 :

- (1h) Semantic modelling. RDF, Metaknowledge - (1h) Universal Ontologies

20.11 :

(2h) Rule systems, simulations and parallel worlds

(2h) Preparation of the exam


Week 10 : In class Exam and Non conceptual knowledge systems and topological data science

26.11 :

(2h) In class exam

27.11 :

(2h) Non conceptual knowledge systems (2h) Work on Projects

Part IV : Platforms

Week 12 : Data, User and Agent Management

03.12 :

(2h) Data

04.12 :

(2h) User

(2h) Agents

Project work

10.12 Work on project

11.12 Work on project

Final Week : Project Presentation

17.12

-- Due: GitHub repository -- Due: Report writing

18.12

(4h) Final project presentation (20%)

Time Project Name
10:15 - 10:40 Group 1
10:40 - 11:05 Group 2
11:05 - 11:30 Group 3
11:30 - 11:55 Group 4
11:55 - 12:20 Group 5
Time Project Name
1:15 - 1:40 Group 6
1:40 - 2:05 Group 7
2:05 - 2:30 Group 8
2:30 - 2:55 Group 9
2:55 - 3:20 Group 10

Various Resources

Assessment and Notation grid

  • (Group work) 3 oral presentations (30%)
    • Presention of the potential of Big data for a subfield of Digital Humanities (10%)
    • 1 midterm presentation of the project (10%)
    • 1 final discussing the project result (10%)
  • (Group work) Written deliverables (Wiki writing) (20%)
  • (Group work) Quality of the project and code (20%)
  • (Individual work) Exam on Course Content (30%)

3 collective oral presentations (30%)

Subfield presentation (10%)

10' max presentation + 5' questions

Notation grid :

  • The presentation contains a description of the subfield in relation with Big Data (4)
  • + 0.5 The slides are clear and well presented
  • + 0.5 The oral presentation is dynamic and fluid
  • + 0.5 The applications are relevant
  • + 0.5 The students participate well to the collective discussion

Midterm presenting the project planning (10%)

10' max presentation + 5' questions

Notation grid :

  • The presentation contains a planning (4)
  • + 0.5 The slides are clear and well presented
  • + 0.5 The oral presentation is dynamic and fluid
  • + 0.5 The planning is realistic.
  • + 0.5 The students answer well to the questions

Final discussing the project result (10%)

10-15' for presentation and 5-10' for questions

Notation grid :

  • The presentation presents the results of the project (4)
  • + 0.5 The slides are clear and well presented
  • + 0.5 The oral presentation is dynamic and fluid
  • + 0.5 The results are well discussed
  • + 0.5 The students answer well to the questions

Written deliverables (Wiki writing) (20%)

  • Project plan and milestones (5%) (>300 words)
  • Motivation and description of the deliverables (5%) (>300 words)
  • Detailed description of the methods (5%) (>500 words)
  • Quality assessment and discussion of limitations (5%) (>300 words)

The indicated number of words is a minimal bound.

Quality of the project and code (20%)

  • Quality of the realisation 10%
  • Code delivered on github 10%

Exam on Course Content (30%)

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