Introduction to Digital Humanities
Autumn Semester 2026 · Tuesdays 10:15–12:00 · Unitobler, Lerchenweg 36, Room F-112
Introduction to Digital Humanities provides the conceptual half of the programme: what data is, how it is modelled, published and analysed, and what that means for humanities research. The hands-on half — command line, Git, SQLite, APIs, TEI, OpenRefine, notebooks — happens in the DH Lab, which runs in parallel on the same Tuesdays. The two courses share a weekly rhythm: what you meet as a concept in the morning, you build in the Lab.
What this course is about
Algorithms and computers dominate our everyday lives, and for several years now researchers in the humanities have also been using computational power to conduct “experiments” in the digital realm with digital or digitized materials. A starting point remains texts and images, which are analyzed in large quantitative volumes in order to enable new interpretations. In contrast to observations in the natural sciences, the evaluation of these “data” is far less standardized. Analytical models and theories (such as stylometry or distant reading) are continually being discarded and newly promoted. Moreover, new forms of linking, visualization, and representation become possible, which in turn call for interpretation and the development of new narratives.
This course provides an introduction to the Digital Humanities and is designed as a hands-on class to help participants develop their own approaches to working with digital materials. It also aims to foster a critical awareness of the underlying assumptions in the development of digital tools, as well as the media-specific forms of data processing and analysis, placing them in a broader societal context. At the same time, the sustainable preparation and handling of data and algorithms plays an important role and will be addressed repeatedly throughout the course.
No prior programming or technical knowledge is required; however, a willingness to engage with and experiment using datasets, texts, and image collections is expected.
How the semester is structured
The course follows the chain from source to published, analysed data end to end:
- What is DH? Competing definitions of the field, and why the disagreement is productive.
- What is data? Forms of data in the humanities, the difference between data and information, and metadata as the layer that makes data findable.
- Data modelling. Conceptual modelling, relational and graph structures, domain standards (Dublin Core, METS), and what modelling decisions hide or reveal.
- Linked Open Data. RDF, ontologies, and connecting your own data to Wikidata.
- Data publication. The web as an interface: HTML, CSS, XML, GitHub Pages — and the scholarly digital edition as a genre.
- Data analysis. Networks, text corpora and geovisualisation, each applied to material you produced yourself earlier in the semester.
- Algorithms, machine learning and LLMs. How statistical models work, where bias enters, and what “research agents” can and cannot do.
- Critique and sustainability. Data feminism, intersectional perspectives, and the FAIR and CARE principles.
A thread runs through the semester: the bibliography you start building in week 2 becomes your data model in week 4, your CSV in week 5, your network in week 8, and your text corpus in week 9. By the end you will have carried one small dataset through a complete digital research workflow — which is exactly what the assignment asks you to document.
Learning outcomes
By the end of the course you will be able to:
- Situate Digital Humanities within the humanities and explain why the field is contested
- Distinguish data, information and metadata, and describe humanities material in standard metadata vocabularies
- Build a conceptual data model for a humanities research question and justify its choices
- Read and produce Linked Open Data, and connect a dataset to authority files such as Wikidata
- Publish a small dataset and its documentation on the open web
- Apply network analysis, text analysis and geovisualisation to your own data, and judge what those methods can and cannot show
- Explain in non-mathematical terms how machine learning and large language models work, and identify where bias is introduced
- Assess digital research projects against the FAIR and CARE principles
Who the course is for
The course is compulsory for students in the MA Major and MA Minor Digital Humanities at the University of Bern.
It is also open to students from other programmes, in particular History, Near Eastern Studies and German Studies, as well as to anyone in the humanities who wants a grounded introduction to digital methods. No programming experience is expected, and none is required to pass.
If you are enrolled in another programme, please check with your own study coordinator how the credits are recognised.
Organisation
| Semester | Autumn Semester 2026 (HS 2026) |
| Time | Tuesdays, 10:15–12:00 |
| First session | 15 September 2026 |
| Last session | 15 December 2026 |
| Room | Unitobler, Lerchenweg 36, F-112 |
| Language | English (discussion in German is welcome) |
| Companion course | DH Lab |
| Course materials | ILIAS course space |
| Assessment | Project website + about page, see Assignment |
Bibliographic details for every reading are on the Syllabus page, with a link to the open-access version wherever one exists. Scans of texts that are not openly available are provided in ILIAS and are accessible to enrolled students only. If a scan is missing, please open an issue or tell us in class.
Tools you will sign up for
You do not need to install anything unusual, but you will need accounts for a small number of services. Create them in the week they are announced, not the night before:
- Zotero — reference management (from week 1)
- Join the DH UniBe Zotero group — a shared library for the department. Membership is by request: ask to be added and we will approve you.
- GitHub — version control and web publishing (from week 2)
- The DH Lab introduces further tools; see the Lab website.
Weekly programme
The Intro and the Lab run on the same fourteen Tuesdays. This table shows both, so you can see how the concept and the practice line up each week. Follow the links for the full session pages.
| # | Date | Introduction to DH | DH Lab |
|---|---|---|---|
| 1 | 15 Sep | Welcome · What is DH: a bird’s-eye view Course team | Programme information, introductions, individual consultations Course team |
| 2 | 22 Sep | What is DH? · What is data? Hodel | Get to know your computer: CLI, paths, storage Demleitner |
| 3 | 29 Sep | What is data? Forms and metadata Hodel | Useful stuff: Markdown, Obsidian, GitHub, VSCodium Hodel |
| 4 | 06 Oct | Data structures Beretta | Replacing Excel with SQLite Beretta |
| 5 | 13 Oct | Data modelling: Linked Open Data Beretta | From LOD to RiC-O, LRM and CIDOC-CRM Hart |
| 6 | 20 Oct | Data publication: the web as interface Hodel | Introduction to APIs Hodel |
| 7 | 27 Oct | Data modelling: editions Spadini | Scholarly editing as data publication (TEI) Spadini |
| 8 | 03 Nov | Data analysis: networks Hodel | Introduction to Omeka Hodel |
| 9 | 10 Nov | Data analysis: text corpora Hodel | Introduction to Colab / Noto Hodel |
| 10 | 17 Nov | Data analysis: geovisualisation Hodel | Data cleaning: OpenRefine and reconciliation Demleitner |
| 11 | 24 Nov | From algorithms to machine learning Hodel | Text recognition: eScriptorium Hodel |
| 12 | 01 Dec | LLMs: stochastic parrots and research agents Hodel | GenAI in action: multimodal LLMs Prada Ziegler |
| 13 | 08 Dec | Data feminism Hodel | Workflow presentations Course team |
| 14 | 15 Dec | FAIR, CARE and digital sustainability Course team | Final-year presentations (Major / Minor) Course team |
Hodel Tobias Hodel Spadini Elena Spadini Beretta Francesco Beretta Hart Stephen Hart Demleitner Adrian Demleitner Prada Ziegler Ismail Prada Ziegler Course team all three convenors
Assessment
The course is graded. It is assessed through a project website: a small, publicly hosted site built around a bibliography on a topic of your choice, with an About page documenting your workflow. The full brief, the criteria and the deadlines are on the Assignment page.
Contact and imprint
University of Bern
Walter Benjamin Kolleg / Digital Humanities
Muesmattstrasse 45
3012 Bern