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

Contact

Professor: Frédéric Kaplan

Assistant: Raphaël Barman

Rooms: Wednesday (CM1113) and Thursday (BC03)

Links

• Moodle
• Framapad
• Projects

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

Week 1 : What are Digital Humanities?

16.09 (2h) Welcome and Introduction to the course

• FDH-0 (1h) Introduction to the course and Digital Humanities, structure of the course. Introduction to Framapad with a simple exercise. Principle of collective note talking and use in the course. State of the Digital Humanities at EPFL, in Switzerland and in Europe. Video recording link.

17.09 (4h) What are Digital Humanities? What is their object of study?

• FDH-1-1 (1h) What Are Digital Humanities : 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. Video recording link.
• FDH-1-2 (1h) Digital Humanities as a field : Big Data Digital Humanities vs Small Data Digital Humanities. The 3 circles. Exercise on relationship between elements in Digital Culture schema. Video recording link.
• FDH-1-3 (2h) Big Data of the Past. Data acceleration regime. Inferred Patterns. Redocumentation. Fictional Spaces Video recording link.

Week 2 : Patrimonial Capitalism and Commons

23.09 :

• 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. Video recording link.
• 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 Video recording link.

24.09

• FDH 1-6 Anatomy of a large-scale project (1h) Venice Time Machine. European Time Machine. Video recording link (pre-recorded). Video recording link (live)
• Past projects presentation. Video recording link.
• FDH 1-7 Projects.See also Projects page Video recording link (pre-recorded). Video recording link (live).

Part II : Pipelines

Week 3: Digitisation

30.09

• FDH 2-1 Introduction to the Digitization Process. The Story of Google books. Document digitization as a problem of conversion of dimensions. Digitization is logistic optimization. Alienation. Digitization on demand. Video recording link

01.10

• (2h) FDH 2-2 Document Structure. General presentation of the pipeline. Content and Structure. Circulation. Standards. Open Annotation Data Model. Shared Canvas.IIIF. Synchronic patterns and diachronic homology. Video recording link

• (2h) Projects presentations. 5' per project with max 3 slides. Fill out the group table before the course. You can find a group using the framapad. Video recording link

Week 4: Writing Systems and Text Encoding

07.10 (2h) FDH 2-3 : Writing Systems Video recording link

08.10 - (2h) FDH 2-4 : Text Encoding Video recording link - (2h) Work and support on projects

Week 5: Text Processing and Understanding

14.10 (2h) FDH 2-5 Text Processing : Diachronic and synchronic analysis. n-grams, TF-IDF, Topic Modeling, Word Space Models and Word embeddings (2h) Video recording link.

15.10 (2h) FDH 2-6 Text Understanding : Close, surface, distant and machine reading, Information extraction, Named Entities, Resources, Large-Scale Projects (2h) Video recording link.. Work on Project (2h).

Week 6: Images

21.10 (2h) FDH 2-7 : Image systems. Video recording link

22.10 (2h) FDH 2-8 : Image processing Video recording link Time machine search engine (2h) Work on project

(FDH 2-9 : Image understanding not done this year)

Week 7: Maps

26.10 (2h) FDH-2-10 Map systems Video recording link

27.10 (2h) FDH-2-11 Map processing (2h) Work on project

Week 8: 3D and 4D

03.11 (2h) Pipeline for 3D spaces. Modelling vs Sampling : Model-based Procedural methods. Architectural grammars. Class I and Class II elements. The question of realism.

04.10 (2h) 4D and mirror world (2h) Work on project

Part III : Knowledge modelling and processing

Week 9 : Knowledge modelling

11.11 (2h) The beauty of Knowledge modelling. Tables. Databases. Semantic web, Ontologies, URI, RDF, CIDOC-CRM, How to code event, places and influence. Metaknowledge. The Hypergraph

12.11 Midterm presentation (10%)

Time	First student name	Second student name	Third student name	Project name
10:15-10:30
10:30-10:45			-
10:45-11:00			-
11:15-11:30			-
11:30-11:45			-

Week 10 : Deep learning representation and Generative process

18.11 (2h) Deep learning representation

19.11 (2h) Generative processes

-- Project plan and milestones deliverable on the Wikipage of each project (10%)

Part IV : Platforms

Week 11 : Work on Project

25.11 (2h) Work on project

26.11 (4h) Work on project

Week 12 : Data, User and Bot Management

02.12 (2h) Data Management  : FAIR principle, Creative Commons, Data Management models, Sustainability, Right to Forgotten. Management of uncertainty, incoherence and errors. Iconographic principle of precaution

03.12 (2h) User Management : Part I: Persona. Part II: Motivation and onboarding dynamics. Three case studies: Twitter. Quora. Wikipedia. Part III: "Wisdom" of the crowds. Collectivism vs Liberalism. Open source as a form of liberalism for engineering. The ambiguous of fork. Part IV: The "power" of the crowds. Mechanical Turk. Crowdflower. Crowdfunding (2h) Bot Management : Three case studies on bot management : Twitter, Wikipedia, Google.

Week 13 : Work on projects

09.12 (2h) Work on project

10.12 (4h) work on project

-- Deadline for GitHub repository (10%)

-- Deadline for Report writing (40%)

Week 14 : Exam

16.12 (2h) Final project presentation (20%)

17.12 (2h) ----

Resources

• Gallica
• Feature matching tutorial
• Feature matching code
• Wiki Syntax

Assessment and Notation grid

• 2 oral presentations (30%)
  • 1 midterm presentation of the project (10%)
  • 1 final discussing the project result (20%)
• Written deliverables (Wiki writing) (40%)
• Quality of the project (30%)

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) (40%)

• Projet plan and milestones (10%) (>300 words)
• Historical introduction to the source(s) (5%) (>200 words)
• Detailed description of the methods (10%) (>500 words)
• Quality assessment (10%) (>300 words)
• Motivation and description of the website (5%) (>200 words)

Production (30%)

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