Main Page

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

Contact

Professor: Frédéric Kaplan

Assistants: Alexander Rusnak, Tristan Karch, Tommy Bruzzese

Rooms: Wednesday (CM1110) and Thursday (BC03)

Links

• Textbook
• Slides
• Framapad
• Application Parcels of Venice (Dev version)
• Application Parcels of Venice (Stable version)
• Projects
• Sources
  • Tassini PDF, Tassini OCR
  • Guido Commerciale PDF
  • Sanudo PDF

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 :

(4h) What are Digital Humanities? What is their object of study?

• Digital Humanities, Digital Studies, Humanities Computing and Studies about Digital Culture. Digital Humanism vs. Digital Humanities. Why digital methods tend to dissolve traditional disciplinary frontiers. A focus on practice. Translation issues.
• Digital Humanities as a field : Big Data Digital Humanities vs Small Data Digital Humanities. Big Data of the Past
• Maud Ehrmann, Impresso project and data acceleration regime.

Week 2 : Patrimonial Capitalism and Commons

17.09 :

• FDH 1-4 Patrimonial Capitalism (1h) Introduction to the DH circle linking the digitisation of sources, their processing, their analysis, visualisation and the creation of societal value (insight, culture) leading ultimately to the digitisation of new sources. Presentation of some sustainable DH circles (genealogy, image banks). Patrimonial capitalism and the risk of monopolistic companies. Parallelism with the race for sequencing the Human Genome.

• FDH 1-5 The Commons (1h) What are the commons ? What is the public domains ? History and evolution. Copyright overreaching. Frontal collision. Governing with the commons.

18.09 :

• FDH 1-6 Anatomy of a large-scale project : Venice Time Machine. European Time Machine.
• Manuel Ehrenfeld / Designing the Time Machine Atlas

Week 3: Subfields

24.09 :

• Digital History
• Digital Art History
• Digital Archeology
• Digital Musicology
• Digital Philology

25.09 :

• Digital Literature studies
• Digital Philosophy
• Digital Anthropology
• Digital Theology
• Digital Social Sciences

• Projects presentation by prof and TA.

Part II : Pipelines

Week 4: Writing Systems and Text Encoding

01.10 :

(2h) Writing Systems

02.10 :

- (2h) Text Encoding /

• (2h) Projects presentations. 5' per project with max 3 slides.

Week 5: Language Models

08.10 :

(2h) Text Processing : Diachronic and synchronic analysis. n-grams, TF-IDF, Topic Modeling, Word Space Models and Word embeddings (2h)

09.10 :

(2h) Text Understanding : Close, surface, distant and machine reading, Information extraction, Named Entities, Resources, Large-Scale Projects /

SKILL:

• Working with pretrained models from huggingface or github
• Data pipelining
• LLM named entity recognition
• MLLM OCR

Week 6: Images

15.10 :

(2h) Image systems.

16.10 :

(2h) Isabella di Lenardo / Application to Art History

(2h) SKILLS

• LLM for non-standard output formats (i.e. Json etc) and regression prediction etc.
• Embedding models and similarity search
• Finetuning vs adapters
• Pytorch from scratch (i.e. Training an MLP) + extensions

Week Off

22.10 :

No course

23.10 :

No Course

Week 7: Maps

29.10 :

(2h) Map systems

30.10 :

(2h) Map processing (Remi Petitpierre and Beatrice Vaienti) presentations (2h) Work on project

Week 8: 3D Models

05.11 :

(2h) 3D Models Systems and Encoding

06.11 :

(2h) Alex Rusnak Thesis / 3D Models Processing

(2h)Work on project

MidTerm Presentations

12.11 :

No course

13.11 Midterm presentations

Time	Project name
10:20-10:40	Group 8
10:40-11:00	Group 9
11:00-11:20	Group 7
11:20-11:40	Group 6
11:40-12:00	Group 3

Time	Project name
13:15-13:35	Group 2
13:35-13:55	Group 5
13:55-14:15	Group 4
14:15-14:35	Group 1
14:35-14:55	Group 10

Part III : Knowledge modelling and processing

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

19.11 :

- (1h) FDH-3-0 Summary of the concept viewed so far and introduction to part 3

- (1h) FDH-3-1 Semantic modelling. RDF, Metaknowledge

20.11 :

(2h) FDH 3-2 Universal Ontologies

(2h) FDH 3-3 Rule systems, simulations and parallel worlds

Week 10 : Non conceptual knowledge systems and topological data science

26.11 :

(2h) FDH 3-4 Non conceptual knowledge systems

27.11 :

(2h) Work on Projects / Publication of Study Guide (2h) Work on Projects

Part IV : Platforms

Week 12 : Data, User and Bot Management

03.12 :

(2h) Study Guide Discussion

04.12 :

(2h) Work on Project

(2h) Work on Project

Course Exam and Project work

10.12 In class exam

11.12 Work on project

Final Week : Project Presentation

19.12

-- Due: GitHub repository (10%)

-- Due: Report writing (40%)

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

Resources

• Gallica
• Feature matching tutorial
• Feature matching code
• Wiki Syntax
• Introduction to Rhino and Grasshopper

Assessment and Notation grid

• (Group work) 2 oral presentations (30%)
  • 1 midterm presentation of the project (10%)
  • 1 final discussing the project result (20%)
• (Group work) Written deliverables (Wiki writing) (20%)
• (Group work) Quality of the project (30%)
• (Individual work) Exam on Course Content (20%)

2 collective oral presentations (30%)

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 (20%)

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. Detailed description can in particular be extended if needed.

Production (30%)

• Quality of the realisation 20%
• Code deliverable on github 10%

Exam on Course Content (20%)

• A series of questions on the course to ensure the core concepts are understood.