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Professor: [http://people.epfl.ch/frederic.kaplan Frédéric Kaplan]
Professor: [http://people.epfl.ch/frederic.kaplan Frédéric Kaplan]


Assistants: [https://people.epfl.ch/vincent.buntinx Vincent Buntinx] and Lia Costiner
Assistants: Alexander Rusnak


Rooms: Wednesday (CMN1113) and Friday (CM1104)
Rooms: Wednesday (CM1110) and Thursday (BC03/BC04)


==Links==
==Links==
 
*[https://www.dropbox.com/scl/fi/lmievevb2b78lyxoqu130/FDH_Textbook.pdf?rlkey=c6dv6m97sf81l6dcz7nw777j0&dl=0 Textbook]
*[https://www.dropbox.com/scl/fo/w4dcnwdtfd4xq4p0bj3ub/AETpOIrGvsYisZPTbitU4xY?rlkey=heoqml8hsg4judym2yhzrzliy&dl=0 Slides]
*[https://timeatlas.eu Time Atlas (Public version)]
*[https://timeatlas-test.epfl.ch/app/ Time Atlas (Dev version)]
*[[Projects]]
* Sources (2024)
** [https://www.dropbox.com/scl/fi/s4zlaxrpka4w1h7mx6zvu/FDH_Morphographs.zip?rlkey=qp1omyyjj9dqfuko49rrd37tt&dl=0 Morphograph]
* Sources (2024)
** [https://www.dropbox.com/scl/fi/oekkk9ts0pnjyiqaezz7m/Toponomastica-Veneziana.pdf?rlkey=wfky7ht9ckxa1w1qo6c69bo98&dl=0 Tassini PDF], [https://www.dropbox.com/scl/fi/lfu8ugk25re333gca65g7/Toponomastica-Veneziana_with_pages_delim.txt?rlkey=fouht7678vs102918hn9fuxs1&dl=0 Tassini OCR]
** [https://www.dropbox.com/scl/fo/netqhm40dyw046withu8q/AJO_CPupJwuLw4Zvg1JF2jc?rlkey=ldswph81gb0n9xgi5qzz1b1i8&dl=0 Guido Commerciale PDF]
** [https://onlinebooks.library.upenn.edu/webbin/metabook?id=sanudodiary Sanudo PDF]
<!--
*[https://conference.timemachine.eu Time Machine Conference 2018]
*[https://cliowire.dhlab.epfl.ch Cliowire]
*[https://cliowire.dhlab.epfl.ch Cliowire]
*[https://moodle.epfl.ch/course/view.php?id=15281 Moodle]
*[https://www.getpostman.com API Software]
*[https://annuel2.framapad.org/p/fdh Framapad]
*[https://gallica.bnf.fr/accueil/?mode=desktop Gallica]
*[https://docs.opencv.org/3.1.0/db/d27/tutorial_py_table_of_contents_feature2d.html Feature matching tutorial]
*[[Feature matching code]]
*[https://www.mediawiki.org/wiki/Help:Formatting Wiki Syntax]
*[https://www.mediawiki.org/wiki/Help:Formatting Wiki Syntax]
*[https://www.getpostman.com API Software]
*[https://github.com/tootsuite/documentation/blob/master/Using-the-API/API.md API documentation]
*[https://github.com/tootsuite/documentation/blob/master/Using-the-API/API.md API documentation]
*[http://fdh.epfl.ch/peergrading/pg.php peer-grading]
-->


==Summary==
==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 for document processing (layout analysis, deep learning methods), knowledge modelling (semantic web, ontologies, graph databases) generative models and simulation (rule-based inference, deep learning based generation). 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 directly analysing and interpreting Cultural Datasets from ongoing large-scale research projects (Venice Time Machine, Swiss newspaper archives).
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 ==
==Plan ==
=== Introduction ===
=== 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 :


==== Week 1 : Structural tensions in Digital Humanities ====
* Pierre Vann, Julien Jordan / Architecture/Urbanism
20.09 (2h) 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. Structuring tensions 1: 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.
* 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


22.09 (2h) (a) Structuring tensions 2: Big Data Digital Humanities vs Small Data Digital Humanities. The 3 circles.  Exercise on relationship between elements in Digital Culture schema. (2h) Practical session: Introduction to MediaWiki. Objective: Learning the basic syntax of MediaWiki. Get a first experience of collaborative editing. Learning to write from a neutral point of view.  Creation of the articles by the student followed by peer-review by another student (enriching, completing references). Each student picks a DH person and DH concept, write a Wiki page for each (30 mn + 30 mn). Each student chooses another person and another concept among the ones already covered, enrich with complementary information and references (20 mn + 20 mn)
25.09 :
* DH historical figures: [[Roberto Busa]], H.G. Wells, [[Paul Otlet]], [[Emmanuel Le Roy Ladurie]], [[Aby Warburg]], Otto Bettmann, [[Tim Berners Lee]], Jimmy Wales, [[Elisée Reclus]], Albert Khan, [[Jules Maciet]].
* DH concepts and related :  [[Distant Reading]], Regulated Representations, Pattern, Culturomics, Ubiquitous Scholarship, Gamification, Thick Mapping, Design fiction, New Aesthetics, [[Skeuomorphism]], Digital Aura, [[Digital Heritage]], Attention Economy, [[Folksonomy]], Linguistic Capitalism, Open Access, Redocumentation, Open Hardware, Attention backbone, Opinion Mining, Topic Modelling, [[Gazetteer]], [[Uberisation]], Crowsifting, [[Copyleft]], Onboarding.


(25.09 2pm : Experiment with Digital Art History interface INN116)
Morning :  


==== Week 2 : Patrimonial capitalism and common goods  ====
* 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


27.09 (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.  Introduction to the TIme Machine FET Flagship and mutualised infrastructure approach. (1h) General presentation of the Time Machine pipeline at the Datasquare / ArtLab pavillon.
Afternoon


29.09 (4h) Forum ArtTech (Rolex Learning Center). Mininig Big Data of the Past. Patrimonial capitalism and businesses opportunities. Examples of FamilySearch, myHeritage, Corbis.
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 I : Pipelines ===
=== Part II : Media Pipelines ===


==== Week 3:  Documents pipeline ====
==== Week 4: Documents and Writing Systems ====


04.10 (2h) The Digitization Process and Pipelines. What is a document? What is a digital image? Exercise on Book Scanners typologies. Document digitisation as a problem of conversion of dimensions. Digitisation is logistic optimization. Alienation. Digitisation on demand. Fedorov's notion of optimal experiment.
01.10 :


06.10 (2h) Pipeline for Written documents.  Part I: Standards. Open Annotation Data Model. Shared Canvas. Part 2: Regulated Representations. DHCanvas. (2h) Presentation of the [[Projects]]. Presentation of the main databases used in the course and [[ClioWire]] platform.
(2h) Documents : Definition and Encoding. IIIF Format.  


==== Week 4: Artworks Pipeline ====
02.10 :
- (2h) Writing Systems


11.10 (2h) Pipeline for Artworks photographs. Image banks and phototarchives. Scanning techniques for photographs. Segmentation. Visual similarity vs visual connections.  
* (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.


13.10 (4h)  Introduction to deep learning approaches. Exercises with the Replica database and search engine. 5 mn presentation. Work on projects. Formation of the groups.
Vote on skills tutorial for Skills sessions


==== Week 5: Maps Pipeline ====
==== Week 5: Texts ====


18.10 (2h) What are cartographic documents. Exercice on ancient maps. History of cartography. Odometry. Triangulation. Coordinate systems. Metric systems. Projection. Cadaster. Aerial photography.
08.10 :


20.10 (4h) (a) Introduction to GIS. Points, Lines, Polygons. Coordinate Sytems. QGIS Hands On. Exercise on Venetian cadaster (Bastien).  (b) Oral presentation of project plan
(2h) Reading and Text encoding (2h)  


==== Week 6: 3D Pipeline ====
09.10 :


25.10 (2h) Pipeline for 3D spaces. Modelling vs Sampling : Part I : Modelling. Photogrammety. Demo Sketchup. Model-based Procedural methods. Architectural grammars. Class I and Class II elements. The question of realism.
(2h) Text Spaces and Text systems


27.10 (4h) Part II : Sampling.  Photogrammetric tutorial (Nils)
(2h) SKILL SESSION 1 (Vote for the tutorials) (Alex Rusnak)


=== Part II : Algorithms ===
==== Week 6: Paintings, Engravings and Photographs  ====


==== Week 7 : Deep Learning algorithms ====
15.10 :


01.11 (2h) A panorama of Deep learning methods. Successes. Fundamental principles. Neurons. Receptive Fields. Hierarchical representation / texture. Gradient descent. Credit Assignment Path. Most important architectures. Convolutional neural networks. Recurrent neural networks. Siamese Networks. Word2Vec. Generative Adversarial Networks. Style Transfer. Importance of Deep learning for Digital Humanities. Can Deep Learning networks and Big Data of the Past lead to new forms of Artificial Intelligence ?
(2h) Image reading. Image space and image systems


03.11 (2h)  Machine vision tutorial (Benoit, Sofia). Introduction to Anaconda, Jupyter, TensorFlow. Deep learning in practice. (2h) Work on bibliography
16.10 :


-- Deadline Bibliography and discussion of the state of the art (10%)
(2h) Example of the Replica Pipeline


==== Week 8 : Knowledge modelling ====


08.11 (2h) Knowledge modelling : Semantic web, ontologies, graph database, homologous points, disambiguation.
(2h) SKILLS SESSION 2 (Vote for the tutorials) (Alex Rusnak)


10.11 (4h) (a) Exercise in semantic modelling and inference (Maud) (b) Midterm presentation with project planning and prior art (10%)
==== Week Off  ====


15.11 (2h) Algorithms for Generative models and simulation : Rule-based inference. Discussion on new regimes of visibility.
22.10 :


17.11 (4h)  Project development
No course


-- Deadline for individual essay (30%)
23.10 :


=== Part III : Platform management ===
No Course


22.11 (2h) Data Management  : Computing infrastructure,  Data Management models, Sustainability. Apps. Management of uncertainty, incoherence and errors. Iconographic principle of precaution. Example of Wikipedia and Europeana.
==== Week 7: Maps ====


24.11 (4h) VENICE
29.10 :


29.11 (2h) User Management : Representation, Rights, Traceability, Vandalism, Motivation, Negotiation spaces. Right to be forgotten.
(2h) Are Maps different than images ? Alex Rusnak's Presentation on 2D/3D map encoding


01.12 (4h) Testing phase
30.10 :


-- Deadline peer-grading (5%)
Morning


06.12 (2h) Bot Management : Versioning. Open source repositories.
(2h) Remi Petitpierre presentation : Cartography at scale


08.12 (4h) Testing phase and report writing
Afternoon


13.12 (2h) Report writing
2h) Beatrice Vaienti presentation : Genealogies of Jerusalem's maps


-- Deadline for GitHub repository (10%)
==== Week 8: Design and Architecture  ====


-- Deadline Detailed description of the methods (10%) (>500 words)
05.11 :
(2h) 3D Models Systems and Encoding


-- Deadline Quantitive analysis of the performances (10%) (>300 words)
06.11 :


(2h) 3D Models Processing


15.12 (4h) Final project presentation (20%) and Discussion of the collective paper
(2h)Work on project : Definition of the MVP (Minimum Viable Product)


==References==
=== MidTerm Presentations ===


=== Key Figures ===
12.11 :
Identity map (Cardon)


Maps for Big Data Digital Humanities (Kaplan)
* You can select the presentation of this course : Music, Poetry, Fashion, Rituals, Web, Diagrams and Tables, Zeitgeist modelling (simulation of the everyday)


Semiotic Triangle (McCloud)
* Summary of the concept viewed so far. Publication of the study guide


Image similarity
13.11 Midterm presentations


Uncanny Valley (Mori)
{|class="wikitable"
! style="text-align:center;"| 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
|}


=== Databases ===
{|class="wikitable"
! style="text-align:center;"| 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
|}


(Page to be created indicating characteristics, quantity and copyright)
=== Part III : Knowledge modelling and processing ===


Le Temps Archives
==== Week 9 : Semantic modelling, Rule systems, simulations and parallel worlds  ====


Cini Photoarchive
19.11 :


Venice Time Machine documents
- (1h) Semantic modelling. RDF, Metaknowledge
- (1h) Universal Ontologies


Scans of Acedemic Book and journals about Venice
20.11 :


Linked Book
(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%)
 
{|class="wikitable"
! style="text-align:center;"| Time
! Project Name
|-
| 10:20 - 10:40
| Group 5
|-
| 10:40 - 11:00
| Group 1
|-
| 11:00 - 11:20
| Group 2
|-
| 11:20 - 11:40
| Group 3
|}
 
{|class="wikitable"
! style="text-align:center;"| Time
! Project Name
|-
| 13:15 - 13:35
| Group 4
|-
| 13:35 - 13:55
| Group 6
|-
| 13:55 - 14:15
| Group 7
|-
| 14:15 - 14:35
| Group 8
|}
 
== Various Resources ==
*[https://gallica.bnf.fr/accueil/?mode=desktop Gallica]
*[https://docs.opencv.org/3.1.0/db/d27/tutorial_py_table_of_contents_feature2d.html Feature matching tutorial]
*[[Feature matching code]]
*[https://www.mediawiki.org/wiki/Help:Formatting Wiki Syntax]


==Assessment and Notation grid ==
==Assessment and Notation grid ==


The final grade is based on 65% collective work and 35% individual work


a)  2 collective oral presentations
* (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.


* 1 midterm presenting the project planning and prior art (10%)
=== Quality of the project and code (20%) ===
* 1 final discussing the project result (20%)


b) Collective Written deliverables (Wiki writing)
* Quality of the realisation 10%
* Bibliography and discussion of the state of the art (10%) (>300 words)
* Code delivered on github  10%
* Detailed description of the methods (10%) (>500 words)
* Quantitive analysis of the performances (10%) (>300 words)


c) Collective Code deliverable
=== Exam on Course Content  (30%) ===
* Organisation of the GitHub repository (5%)


d) Individual essay (Word or Open Office)
* A series of questions on the course to ensure the core concepts are understood. [https://www.dropbox.com/scl/fi/9zrr1phua0jm1636ij5mn/FDH_2024_Exam.pdf?rlkey=ad4uj531ddolyklrk9qcntt8k&dl=0 Example from last year]
* Introduction/Motivation On the relevance of ClioWire in the Digital Humanities landscape and Beyond (15 %) (> 500 words)
* Discussion and Future Work  (15%) (> 500 words)
* Peergrading (5%)

Latest revision as of 08:50, 18 December 2025

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

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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