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
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 Humanities, Digital Studies, Humanities Computing and Studies about Digital Culture (1h). 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 (1h) : Big Data Digital Humanities vs Small Data Digital Humanities. Big Data of the Past
- Maud Ehrmann, Impresso project and data acceleration regime (2h)
Week 2 : Big Data of the Past, Patrimonial Capitalism and Commons
17.09 :
- 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.
- 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 :
Morning :
- Anatomy of a large-scale project : Venice Time Machine. European Time Machine.
Afternoon :
- Manuel Ehrenfeld / Designing the Time Machine Atlas
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)
24.09 :
- Big Data and History
- Big Data and Art History
- Big Data and Archeology
- Big Data and Musicology
- Big Data and Philology
25.09 :
Morning :
- Big Data and Literature studies
- Big Data and Philosophy
- Big Data and Anthropology
- Big Data and Theology
- Big Data and Social Sciences
Afternoon
- 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 : Pipelines
Week 4: Writing Systems and Text Encoding
01.10 :
(2h) Writing Systems
02.10 :
- (2h) Text Encoding /
- (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 student and the learning ambition of the course.
Week 5: Language Models
08.10 :
(2h) Text Systems and Language Models (2h)
09.10 :
(2h) Text Understanding : Close, surface, distant and machine reading, Information extraction, Named Entities, Resources, Large-Scale Projects /
(2h) SKILL SESSION 1 (Alex Rusnak)
Week 6: Images
15.10 :
(2h) Image systems.
16.10 :
(2h) Image systems
(2h) SKILLS SESSION 2 (Alex Rusnak)
Week Off
22.10 :
No course
23.10 :
No Course
Week 7: Maps
29.10 :
(2h) Map systems
30.10 :
Morning
(2h) Remi Petitpierre presentation : Cartography at scale
Afternoon
2h) Beatrice Vaienti presentation : Genealogies of Jerusalem's maps
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 :
Summary of the concept viewed so far.
Publication of the study guide
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) 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 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 Work on project
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 |
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%)
Midterm presenting the project planning (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%)
- A series of questions on the course to ensure the core concepts are understood.