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


Assistants: Alexander Rusnak, Tristan Karch, Tommy Bruzzese
Assistants: Alexander Rusnak


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


==Links==
==Links==
*[https://www.dropbox.com/scl/fi/j0xoe45dp0km4do41kk9c/FDH_Textbook.pdf?rlkey=mr5hdplu5ai7qts3tk0n4k5a0&dl=0 Textbook]
*[https://www.dropbox.com/scl/fi/lmievevb2b78lyxoqu130/FDH_Textbook.pdf?rlkey=c6dv6m97sf81l6dcz7nw777j0&dl=0 Textbook]
*[https://www.dropbox.com/scl/fo/tu5waw0623hcp4537lx6u/AKx-eznaH6BRddo1goaF7OE?rlkey=jiewdfpk5ysyv92m1817sk5qc&st=01697apo&dl=0 Slides]
*[https://www.dropbox.com/scl/fo/w4dcnwdtfd4xq4p0bj3ub/AETpOIrGvsYisZPTbitU4xY?rlkey=heoqml8hsg4judym2yhzrzliy&dl=0 Slides]
*[https://annuel2.framapad.org/p/fdh Framapad]
*[https://timeatlas.eu Time Atlas (Public version)]
*[https://pov-dev.up.railway.app/ Application Parcels of Venice (Dev version)]
*[https://timeatlas-test.epfl.ch/app/ Time Atlas (Dev version)]
*[https://pov.up.railway.app/ Application Parcels of Venice (Stable version)]
*[[Projects]]
*[[Projects]]
* Sources
* 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/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://www.dropbox.com/scl/fo/netqhm40dyw046withu8q/AJO_CPupJwuLw4Zvg1JF2jc?rlkey=ldswph81gb0n9xgi5qzz1b1i8&dl=0 Guido Commerciale PDF]
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==Plan ==
==Plan ==
=== Part I : Concepts ===
=== Part I : Concepts and Fields ===


==== Week 1 : What are Digital Humanities? ====
==== 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 :
11.09 :


(2h) Welcome and Introduction to the course
What are Digital Humanities? What is their object of study?
* FDH-0 (1h)  Introduction to the course and Digital Humanities, structure of the course. Introduction to Framapad and Slido with a simple exercise. Principle of collective note talking and use in the course. State of the Digital Humanities at EPFL.  
* 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 :


12.09 :
*  Anatomy of a large-scale project : Venice Time Machine. European Time Machine.  


(4h) What are Digital Humanities? What is their object of study?
Afternoon :  
* 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.
* 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.
* FDH-1-3 (2h) Big Data of the Past. Data acceleration regime. Inferred Patterns. Redocumentation. Fictional Spaces.


==== Week 2 :  Patrimonial Capitalism and Commons  ====
* Manuel Ehrenfeld / Designing the Time Machine Atlas


18.09 : 
* Formation of the groups (ideally 2, max 3 students)


* 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.
==== Week 3:  Subfields Student Presentation ====


* 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.
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)


19.09 :
For each subfields, groups must


* FDH 1-6 Anatomy of a large-scale project (1h) Venice Time Machine. European Time Machine.
* Define the specific object of study of the subfield,
* Venice Datasets
* Define the opportunities of a change of scale,
* Illustrate with existing or prospective examples.


=== Part II : Pipelines ===
24.09 :


==== Week 3: Digitisation  ====
* 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 :
25.09 :


* FDH 1-7 Venice Data presentation (Paul Guhennec)
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


26.09 :
Afternoon


* (2h) FDH 2-1 Introduction to the Digitization Process. Document digitization as a problem of conversion of dimensions. Digitization is logistic optimization. Alienation. Digitization on demand. FDH 2-2 Document Structure. General presentation of the pipeline. Content and Structure. Circulation. Standards. Open Annotation Data Model. Shared Canvas. IIIF.
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.


* (2h) Project presentation by prof and TA.
=== Part II : Media Pipelines ===


==== Week 4: Writing Systems and Text Encoding ====
==== Week 4: Documents and Writing Systems  ====


2.10 :
01.10 :


(2h) FDH 2-3 : Writing Systems
(2h) Documents : Definition and Encoding. IIIF Format.


3.10 :
02.10 :
   
   
- (2h) FDH 2-4 : Text Encoding
- (2h) Writing Systems


* (2h)  [[Projects]] presentations. 5' per project with max 3 slides. Write to Tristan before the course. You can find a group using the [https://annuel2.framapad.org/p/fdh framapad].
* (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.


==== Week 5: Text Processing and Understanding ====
Vote on skills tutorial for Skills sessions


9.10 :
==== Week 5: Texts ====


(2h) FDH 2-5 Text Processing : Diachronic and synchronic analysis. n-grams, TF-IDF, Topic Modeling, Word Space Models and Word embeddings (2h)
08.10 :


10.10 :
(2h) Reading and Text encoding (2h)


(2h) FDH 2-6 Text Understanding : Close, surface, distant and machine reading, Information extraction, Named Entities, Resources, Large-Scale Projects (2h)  Work on Project (2h).
09.10 :


==== Week 6:  Images  ====
(2h) Text Spaces and Text systems


16.10 :
(2h) SKILL SESSION 1 (Vote for the tutorials) (Alex Rusnak)


(2h) FDH 2-7 : Image systems.
==== Week 6: Paintings, Engravings and Photographs  ====


17.10 :
15.10 :


(2h) FDH 2-8 : Image processing  (2h) Work on project.  
(2h) Image reading. Image space and image systems


(FDH 2-9 : Image understanding not done this year)
16.10 :


(2h) Example of the Replica Pipeline
(2h) SKILLS SESSION 2 (Vote for the tutorials) (Alex Rusnak)


==== Week Off  ====
==== Week Off  ====


23.10 :
22.10 :


No course
No course


24.10 :
23.10 :


No Course
No Course


==== Week 7: Maps ====
==== Week 7: Maps ====
29.10 :
(2h) Are Maps different than images ? Alex Rusnak's Presentation on 2D/3D map encoding


30.10 :
30.10 :


(2h) FDH-2-10 Map systems
Morning


31.10 :
(2h) Remi Petitpierre presentation : Cartography at scale


(2h) FDH-2-11 Map processing (Remi Petitpierre and Beatrice Vaienti) presentations
Afternoon
(2h) Work on project


==== Week 8: 3D Models ====
2h) Beatrice Vaienti presentation : Genealogies of Jerusalem's maps
 
==== Week 8: Design and Architecture ====
 
05.11 :
(2h) 3D Models Systems and Encoding


06.11 :
06.11 :
(2h) FDH-2-12: 3D Models Systems and Encoding
07.11 :


(2h) FDH-2-13: 3D Models Processing : Alex Rusnak Thesis
(2h) 3D Models Processing  
  (2h)Work on project
 
(2h)Work on project : Definition of the MVP (Minimum Viable Product)


=== MidTerm Presentations ===
=== MidTerm Presentations ===


13.11 :
12.11 :


Work on Project
* You can select the presentation of this course : Music, Poetry, Fashion, Rituals, Web, Diagrams and Tables, Zeitgeist modelling (simulation of the everyday)


14.11 Midterm presentations
* Summary of the concept viewed so far. Publication of the study guide
 
13.11 Midterm presentations


{|class="wikitable"
{|class="wikitable"
Line 158: Line 197:
|-
|-
|10:20-10:40
|10:20-10:40
| Group 9
| Group 7
|-
|-
|10:40-11:00
|10:40-11:00
| Group 8
| Group 6
|-
|-
|11:00-11:20
|11:00-11:20
| Group 7
| Group 5
|-
|-
|11:20-11:40
|11:20-11:40
| Group 6
| Group 4
|-
|11:40-12:00
| Group 5
|}
|}


Line 178: Line 214:
|-
|-
|13:15-13:35
|13:15-13:35
|  Group 2
|  Group 3
|-
|-
|13:35-13:55
|13:35-13:55
|  Group 3
|  Group 2
|-
|-
|13:55-14:15
|13:55-14:15
|  Group 4
|  Group 1
|-
|-
|14:15-14:35
|14:15-14:35
|  Group 1
|  Group 8
|}
|}


Line 193: Line 229:


==== Week 9 : Semantic modelling, Rule systems, simulations and parallel worlds  ====
==== Week 9 : Semantic modelling, Rule systems, simulations and parallel worlds  ====
19.11 :
- (1h) Semantic modelling. RDF, Metaknowledge
- (1h) Universal Ontologies


20.11 :
20.11 :


- (1h) FDH-3-0 Summary of the concept viewed so far and introduction to part 3
(2h) Rule systems, simulations and parallel worlds


- (1h) FDH-3-1 Semantic modelling. RDF, Metaknowledge
(2h) Preparation of the exam


21.11 :


(2h) FDH 3-2 Universal Ontologies
==== Week 10 : In class Exam and Non conceptual knowledge systems and topological data science  ====


(2h) FDH 3-3 Rule systems, simulations and parallel worlds
26.11 :


 
(2h) In class exam
==== Week 10 : Non conceptual knowledge systems and topological data science ====


27.11 :
27.11 :


(2h) FDH 3-4 Non conceptual knowledge systems  
(2h) Non conceptual knowledge systems
 
28.11 :
 
(2h) FDH 3-5 Topological data science / Publication of Study Guide
(2h) Work on Projects
(2h) Work on Projects


=== Part IV : Platforms ===
=== Part IV : Platforms ===


==== Week 12 : Data, User and Bot Management  ====
==== Week 12 : Data, User and Agent Management  ====


4.12 :
03.12 :


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


5.12 :
04.12 :


(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) User


(2h) Bot Management : Three case studies on bot management : Twitter, Wikipedia, Google.
(2h) Agents


==== Course Exam and Project work  ====
==== Project work  ====


11.12 In class exam
10.12 Work on project


12.12 Work on project
11.12 Work on project


==== Final Week : Project Presentation ====
==== Final Week : Project Presentation ====
17.12
-- Due: GitHub repository
-- Due: Report writing


18.12
18.12


-- Due: GitHub repository (10%)
(4h) Final project presentation  (20%)


-- Due: Report writing (40%)
{|class="wikitable"
! style="text-align:center;"| 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
|}


19.12
{|class="wikitable"
! style="text-align:center;"| 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
|}


(4h) Final project presentation  (20%)
== Various Resources ==
 
== Resources ==
*[https://gallica.bnf.fr/accueil/?mode=desktop Gallica]
*[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]
*[https://docs.opencv.org/3.1.0/db/d27/tutorial_py_table_of_contents_feature2d.html Feature matching tutorial]
*[[Feature matching code]]
*[[Feature matching code]]
*[https://www.mediawiki.org/wiki/Help:Formatting Wiki Syntax]
*[https://www.mediawiki.org/wiki/Help:Formatting Wiki Syntax]
*[https://annuel2.framapad.org/p/r.664b6addb1d73a5242943f306814e898 Introduction to Rhino and Grasshopper]


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




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


=== 2 collective oral presentations (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%) ====
==== Midterm presenting the project planning  (10%) ====
Line 279: Line 364:
* + 0.5 The students answer well to the questions
* + 0.5 The students answer well to the questions


==== Final discussing the project result (20%) ====
==== Final discussing the project result (10%) ====
10-15' for presentation and 5-10' for questions
10-15' for presentation and 5-10' for questions


Line 296: Line 381:
* Quality assessment and discussion of limitations  (5%) (>300 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.
The indicated number of words is a minimal bound.  
 
=== Production  (30%) ===


* Quality of the realisation 20%
=== Quality of the project and code (20%) ===
* Code deliverable on github  10%


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


=== Exam on Course Content  (20%) ===
=== Exam on Course Content  (30%) ===


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

Latest revision as of 09:34, 13 November 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
10:15 - 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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