Main Page
Welcome to the wiki of the course Foundation of Digital Humanities (DH-405).
Contact
Professor: Frédéric Kaplan
assistants: ...
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. 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).
Plan
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. Differences between 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. Big Data Digital Humanities and related maps.
22.09 (4h) Introduction to MediaWiki. Creation of the articles by the student followed by peer-review and collective work. DH historical figures: Roberto Busa, H.G. Wells, Paul Otlet, Emmanuel Le Roy Ladurie, Any Warburg, 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, Right to be forgotten, 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. DH tools: Framapad, MediaWiki, Voyant, OpenRefine, QGIS, Jupyter
27.09 (2h) Introduction to the DH circle of processing and interpretation (acquisition, processing, analysis, visualisation, UX, interpretation). From data acquisition to new understandings. General presentation of the Time Machine pipeline. Digitisation campaign planning.
29.09 (4h) Introduction to Python and bot writing (Vincent).
Part I : Pipelines
04.10 (2h) Pipeline for Written documents (Printed and Handwritten). Scanning techniques for books and documents. Principles of Transcription. Transcription tools. Canvas concept. One canvas multiple images, one image multiple canvas. Short introduction to IIIF. Named Entities, Semantic modelling,Topic and Document modelling.
06.10 (4h) Presentation of DHCanvas (Orlin). Open Annotation Data Model. Shared Canvas. IIIF. Exercise : Transcription of Venetian document (Maud, Giovanni)
11.10 (2h) Pipeline for Maps. Vectorization. Alignment. Homologs Points.
13.10 (4h) QGIS Hands On. Exercise on Venetian cadaster (Bastien, Isabella)
18.10 (2h) Pipeline for Artworks photographs. Image banks and phototarchives. Scanning techniques for photographs. Segmentation. Features detection. Detail search.
20.10 (4h) Exercises with the Replica database and search engine (Benoit, Sofia)
25.10 (2h) Pipeline for 3D spaces. Photogrammety. Diachronic realignment. Multiscale indexation.
27.10 (4h) Photogrammetric tutorial (Nils)
Part II : Algorithms
01.11 (2h) Algorithms for Document processing : Document analysis and Deep learning methods
03.11 (4h) Machine vision tutorial (Benoit, Sofia). Introduction to Jupyter. Deep learning in practice.
08.11 (2h) Algorithms for Knowledge modelling : Semantic web, ontologies, graph database, homologous points, disambiguation.
10.11 (4h) Exercise in semantic modelling and inference (Maud)
15.11 (2h) Algorithms for Generative models and simulation : Rule-based inference, Deep learning based generation
17.11 (4h) Exercise in deep-learning based generation (Benoit, Sofia)
Part III : Platform management
22.11 (2h) Data Management : Computing infrastructure, Data Management models, Sustainability. Apps. Example of Wikipedia and Europeana.
24.11 (4h) Project development in teams
29.11 (2h) User Management : Representation, Rights, Traceability, Vandalism, Motivation, Negotiation spaces
01.12 (4h) Project development in teams
06.12 (2h) Bot Management : Versioning. Open source repositories.
08.12 (4h) Project development in teams
13.12 Exam
15.12 (4h)
References
Key Figures
Identity map (Cardon)
Maps for Big Data Digital Humanities (Kaplan)
Semiotic Triangle (McCloud)
Uncanny Valley
Databases
Le Temps Archives
Cini Phototech
Venice Time Machine documents
Scans of Acedemic Book and journals about Venice
Linked Book