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Abstract

Im Lab vertiefen die Studierenden ihre Kenntnisse und Fähigkeiten aus der Einführung in die DH. Einzeln oder in Gruppen werden Übungen gelöst und gleichzeitig eigene Vertiefungsprojekte (für Abschluss MA) diskutiert. Die Mitarbeitenden der DH stehen bei Fragen und Unklarheiten zur Verfügung.

Learning Outcomes

Die Studierenden:
…formulieren und präsentieren eigene Abschlussarbeiten
…lösen selbständig angewandte Probleme der DH
…bekommen über externe und interne Referierende Einblicke in laufende Projekte der DH sowie State-of-the-art Ansätze und Lösungen

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Abstract

Digital Humanities is field of computer-based research in the humanities and social sciences. The lecture series provides an overview over the most important developments and research priorities in the Digital Humanities at Swiss universities & cooperation partners abroad and is organised with the support of the initiative Digital Humanities Switzerland (DHCH). The interdisciplinary and inter-university event is aimed at all students, no prior expertise in Digital Humanities is necessary

The course will take place virtually and the individual lectures can be viewed online as videos. There are two consultation hours, in which students can exchange with the lecturer in an open discussion. Links to the videos and information for the consultation hours will be available on ILIAS by early February the latest.

Learning Outcomes

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Abstract

[Attention: The course begins on February 25, 2026] Texts are often more dynamic and problematic than they seem: what is the text of Anne Frank's diary, published only posthumously from several private notebooks and revised versions? What is the text of Dante's Commedia, among the more than 500 divergent copies? How to study and represent ancient texts preserved in fragmented documents, or the creative process of a modern author? Scholarly editions aim to answer these questions by producing critical texts and documenting the genetic process and/or textual transmission. Digital methods offer new approaches to this millennia-old discipline, contributing to data-driven workflows in line with open science.
The course will provide a theoretical and practical introduction to digital scholarly editing, including text encoding, automatic collation, web publishing, and editions in the web of data.
The course will be taught in English. We will primarily work with English texts, but we may also use texts in French, Italian, German, or Spanish depending on the students’ curricula.

Learning Outcomes

The participants
… gain an overview of the field of digital philology
… appreciate the diversity of editions, their theoretical positions and technical implementations
… learns to describe the main features of a digital scholarly edition
… are able to encode a text according to the TEI standard and to prepare a simple web publication
… are able to use automatic collation tools
… are able to define a basic workflow for the creation of a digital scholarly edition

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Algorithmen und Computer dominieren unseren Alltag und auch in den Geisteswissenschaften wird seit einigen Jahren durch Forschende Rechnerleistung genutzt, um im digitalen Raum "Experimente" mit digitalen oder digitalisierten Materialien durchzuführen. Ein Ausgangspunkt bleiben dabei Texte und Bilder, die in quantitativ grossen Mengen zwecks neuer Interpretationen ausgewertet werden. Im Unterschied zu naturwissenschaftlichen Beobachtungen ist die Auswertung dieser "Daten" weit weniger kanonisiert. Analysemodelle und -theorien (wie Stilometrie oder Distant Reading) werden ständig verworfen und neu propagiert. Darüber hinaus werden Verknüpfungen, Visualisierungen und Darstellungen möglich, die nach Auswertungen und neuen Narrativen verlangen.

Die Übung führt in die Digital Humanities ein und dient als «Hands-On» Veranstaltung, um den eigenen Umgang mit digitalen Materialien einzuüben und die jeweiligen Vorannahmen bei der Entwicklung von digitalen Tools, die medialen Umsetzungen und Auswertungen von Daten gesellschaftskritisch analysieren und kontextualisieren zu können. Gleichzeitig spielt die nachhaltige Aufbereitung von Daten und Algorithmen eine wichtige Rolle, die wiederholt angesprochen wird.

Informatische Vorkenntnisse sind keine gefordert, jedoch die Offenheit, mit Daten-, Text- und Bildbeständen zu spielen.

Learning Outcomes

Die Studierenden
- kennen die Diskussionen zur Definition und Einordnung der digital humanities,
- vermögen digitale Anwendungen hinsichtlich ihres epistemologischen/heuristischen/methodischen Potentials einzuordnen und zu kritisieren
- sind in der Lage ein eigenes digitales Projekt aufzusetzen

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Abstract

This seminar will introduce students to some of the key data analysis methodologies used in the humanities and social sciences (HSS), including descriptive statistics, multivariate analysis, network analysis, and the spatial projection of features. They will also learn about some classical machine learning approaches such as community detection and logistic regression. Using information from the Semantic Web in the form of Linked Open Data (LOD), particularly from DBpedia and Wikidata, as well as from German and French libraries and other topic-related resources, students will define research questions they wish to address regarding their chosen topic. They will then learn how to retrieve, clean and prepare data in order to apply analysis tools. They will gain new insights from the collected LOD information and discover how to answer research questions in the HSS domain and generate new knowledge from existing data.

Each student's individual seminar work will be documented in a GitHub repository comprising a project description, Jupyter notebooks detailing the analysis process, and web pages presenting the research process and results, including figures and comments. The aim is to present the insights discovered as a narrative and provide full documentation of the underlying process.
Participants will need to bring their own laptops to the seminar. While previous Python skills are not required, they are welcome and can be helpful.

Learning Outcomes

The seminar aims to provide a solid grounding in the basic data analysis methodologies used in the humanities and social sciences (HSS), and to explore the entire knowledge production cycle as it is practised in data science. Given the domain of interest, the produced knowledge will be descriptive rather than predictive, but will nonetheless be based on empirical evidence and statistical models.
Students will also learn the basic concepts of RDF and Semantic Web knowledge bases, and how to retrieve data using SPARQL queries from different connected triplestores. They will finally learn how to manage their own local RDF database for data storage and manipulation.
Further skills provided include using GitHub notebooks to document the entire research process and applying basic elements of data science storytelling from the perspective of HSS research.

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Abstract

“Things that Matter” addresses the tension between sources' materiality and digitization.
The recent advances in digital technology have created new modes of reproduction
and forms of consumption that have substantially reshaped the concepts of ‘object’ and of ‘collection’ at the heart of cultural institutions such as libraries and museums.

This preparatory course engages with critical questions arising from studying the past in the digital age. These issues include the changing nature of objects such as books and scientific instruments as source materials, the history and practice of collections and collecting digitization, and technological and intellectual challenges.

“Things that Matter” maps the possibilities and challenges the digital age poses for researchers. The ongoing process of digitization makes sources of the past available to a previously unknown extent, but what does this mean for researchers?

In the course, we will create a virtual exhibition based on objects from your University library/University collection. Furthermore, we will tackle questions of using OMEKA-S as a virtual means to present exhibits online.

Please be advised: The course requires participating online with international partners (in Groningen, Durham, Uppsala, and Tübingen).

--> The language of the course (at least within the hybrid parts) is going to be English.
--> The course can be taken independently from the summer school.

Learning Outcomes

After this course, the students will:
- Know how to prepare and critically conduct a virtual exhibition on a (digital) object.
- Be able to cooperate in an international context.
- Be familiar with the literature on challenges related to the digitisation of historical materials.

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Abstract

Zusammen mit Prof. Dr. Silke Schwandt, Universität Bielefeld

Künstliche Intelligenz und insbesondere maschinelle Lernverfahren ermöglichen umfangreiche historische Textkorpora für Forschung und Lehre verfügbar zu machen. Dazu müssen jedoch sinnvolle Workflowteile entwickelt und kombiniert werden.
Im Kurs diskutieren wir die verfügbaren Ansätze und testen sie an Datensätze aus Bielefeld, Bern und Lübeck aus.
Dabei werden sowohl die Grenzen als auch die Möglichkeiten der Verfahren getestet und erfahren.
Es werden keine Programmierkenntnisse vorausgesetzt, teilweise wird jedoch Python zum Prozessieren eingesetzt.

Vorbereitungstermine am 18.05 & XX.XX erfolgen hybrid/online
Blockdaten vom 08.06 - 10.06 an der Unitobler.

Learning Outcomes

Die Studierenden
- verstehen die Basics maschineller Lernverfahren
- können selber Ground Truth (Transkriptionen/Annotationen) herstellen
- können Workflow-Tools testen und einsetzen
- reflektieren den Einsatz der Tools auf grosse Korpora

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Data management plans (DMPs) are an indispensable tool for the organization of individual work in research projects, but also for project planning and management. They help researchers keep track of their data and systematically plan their research data management activities. The early creation and continuous development of a DMP is central to this process. This enables researchers to address specific challenges (e.g., handling large amounts of data, managing sensitive data, or archiving data) at an early stage. Participants learn about the benefits of DMPs at different stages of the project life cycle, develop practical skills using concrete examples, and create a first draft DMP for their own project.

Learning Outcomes

Participants will be able to
- discuss the benefits of a data management plan for project organization
- explain the requirements of research funding agencies (especially the SNSF) for data management plans
- develop a data management plan for their own project

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Abstract

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Learning Outcomes

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Abstract

Active research data management, the management of research data during an ongoing project, is an essential part of good scientific practice. Participants learn how to increase the efficiency of individual work processes and collaborations in larger teams by structuring data systematically. They will learn how to describe data with metadata and other forms of documentation in order to make the collection, preparation, and analysis of data transparent, thereby increasing the (re)usability of research data. In addition, concrete methods will be presented for ensuring the integrity and availability of data through a storage strategy, and for protecting data from loss and unauthorized access.

Learning Outcomes

Participants will be able to
- develop/optimize and document a file naming convention and folder structure
- choose appropriate file formats for reuse and archiving
- create their own data documentation
- explain the relevance of metadata and metadata standards and generate suitable metadata describing their dataset
- develop a reliable backup strategy using the resources available at UniBE and, if necessary, secure their data against unauthorized access using basic tools

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Publishing research data at the end of a project, as a supplement to a book or article publication, or as a stand-alone publication is essential for documenting research results transparently and making them available for reuse by other researchers. Therefore, it is required by research funding agencies such as the Swiss National Science Foundation. However, some data - e.g., personal data - cannot always be shared openly for legal and ethical reasons. In this module, participants learn to apply typical workflows for publishing data in accordance with current best practices. They familiarize themselves with standards in the area of persistent identifiers (PID) and learn to select and apply licenses for their own data publication. Finally, access restrictions depending on the sensitivity of the data will also be discussed. An additional focus will be on finding and evaluating data for participants’ own project.

Learning Outcomes

Participants will be able to
- Explain criteria for evaluating research data for publication and reuse.
- publish data sets in a repository according to typical workflows and, if necessary, apply access restrictions with regard to data protection requirements
- explain the relevance of persistent identifiers
- select and apply appropriate licenses for data and software upon publication

Abstract

The International Image Interoperability Framework (IIIF) is a set of open standards for the publication, manipulation and reuse of high-quality digital images across the web. These standards are widely adopted in libraries, archives, and collections and they guide best practices for image distribution. This submodule of the PPP course within the Digital Humanities Major Master program introduces participants to the basic concepts and workflows of IIIF, as well as ways to use it for their own projects. Through hands-on exercises and practical examples, participants will gain conceptual and technical insights into how IIIF supports the sustainable and interoperable use of digital images within the humanities. The submodule covers everything from image manipulation and annotation to publication via the UB IIIF server. While it is helpful to have some background in data management (e.g. the RDM I – III submodules within the DH program, offered by Open Science/UB), this is not a requirement.

Information on a bibliography will be communicated shortly before the workshop..

Learning Outcomes

Students are capable to
- describe the benefits of IIIF
- understand the core IIIF specifications
- work with IIIF-Manifests (create, manipulate, update)
- understand the concept of canvas
- of combining and manipulating images in Mirador/URL
- add annotation layers to images
- create a short presentation combining images from different sources
- use the UB playground to publish and manage images according to IIIF

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Das Seminar untersucht anhand von neusten Auseinandersetzungen die Transformation in der Anthropologie durch die digitale Wirklichkeit. Wir problematisieren die Unterscheidung und die innige Verflechtung zwischen Realität und Virtualität, Mensch und Maschine, materiellen Räumen und virtuellen Räumen und reflektieren wie Netzwerke, Beziehungen und Gemeinschaften «online» gestaltet werden.
Hauptthemen:
Wirklichkeit, Identität, Körper, Leben, Gesicht, Verstand, Herz,
Beziehung, Ritual, Spiritualität, Transzendenz, Kreativität.

Learning Outcomes

Die Studierenden können:
• Begriffe wie «virtual reality», «augumented reality”, “mixed reality”, “cyberspace” definieren und in anthropologischen Problematisierungen anwenden;
• neue Sichten über die Transformation der Kategorien wie „Identität“, „Körper“, „Relation“, „Gemeinschaft“ durch die digitale Revolution verstehen und vergleichen;
• die Neugestaltung der Gemeinschaften, Beziehungen und sozialen Netzwerken im virtuellen Raum analysieren und definieren, und in Beziehung mit dem „relationen Turn“ in der Philosophie und Theologie bringen;
• heutige Positionen in der „digitalen Theologie“ und „digitalen Anthropologie“ gegenüber „Embodiment“-Theologien reflektieren;
• eine eigene Fragestellung in „digitaler Anthropologie“ entwickeln und eine eigene Position in Auseinandersetzung mit den studierten Texten erarbeiten.

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Abstract

Deutungskontroversen gehören seit jeher zur Geschichtswissenschaft. Das Aufkommen von generativer Künstlicher Intelligenz (KI) stellt die fachwissenschaftlichen Debatten um die Auslegung der Vergangenheit vor zusätzliche Herausforderungen. Wie kann man erkennen, ob bestimmte Geschichtsvorstellungen auf tatsächlich existierenden Quellen basieren oder ob diese durch KI erstellt wurden? Wie gehen KI-Tools mit konflikthaften Interpretationen und Deutungskonkurrenzen um? Wo ist historisches Fachwissen noch gefragt? Der Kurs will sich diesen Fragen mit Studierenden für Studierende widmen und in 14-tägigen Blocknachmittagen gemeinsam Antworten finden. Während wir uns im ersten Teil des Semesters mit der Funktionsweise und den Merkmalen verschiedener KI-Tools auseinandersetzen, wollen wir im zweiten Teil des Semesters deren Arbeitsweise an ausgewählten, konflikthaften Interpretationen der west- und osteuropäischen Geschichte des 20. Jahrhunderts testen. Die Veranstaltung ist als Geschichtswerkstatt konzipiert, bei der die Studierenden aktiven Anteil an der Entwicklung der Übung haben sollen. In Gruppen werden wir anhand der kontroversen Forschungsdebatten eine disziplinäre Auseinandersetzung mit den Stärken und Schwächen der KI-Modelle für das universitäre Geschichtsstudium erarbeiten.
Die KI-Übung findet 3-stündig jeweils von 14:00-17:00 Uhr an folgenden Freitagen statt: 20.02., 06.03., 20.03., 27.03., 17.04., 01.05. sowie am 22.05.2026.

Learning Outcomes

Studierende lernen anhand konflikthafter Darstellungen der Vergangenheit den kritischen Umgang mit generativer Künstlicher Intelligenz und online verfügbaren Quellen und Geschichtsbildern. Sie lernen verschiedene KI-Modelle kennen, erhalten anhand praktischer Übungen einen fundierten Einblick in die Stärken und Schwächen der jeweiligen KI-Tools und erkennen hierbei die Relevanz, quellenbasiert stringent zu argumentieren.

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Abstract

Digitalisation, and the information age which it has fostered, have fundamentally altered the day-to-day practices of academic researchers. Almost without exception, every field has embraced modern methods from the broad new field known as data science. Historians may now analyse and synthesise swatches of historical texts in the blink of an eye; psychologists can examine the real-time behaviour of thousands of individuals simultaneously and extract elaborate patterns of their behaviour; economists may simulate and manipulate the dynamics of whole economic systems; climate scientists can incorporate real-time, granular data to predict climatic patterns in very specific regions of the world.
Despite the ubiquity of data science practices across these disciplines, the specific methods which are employed (and the reasons for which they are employed) can vary drastically, and methods which are employed widely in one field can be rather uncommon, or even ignored altogether, by another field. The goal of this lecture series is to facilitate the transdisciplinary use of data science methods and foster conversations across disciplines regarding the theory and practice of data science in the academy.
This lecture series is aimed at researchers from all academic disciplines, and lecturers are encouraged to make content jargon-free and accessible, using general and broad examples wherever possible. Each lecture should act as a jumping-off point on the relevant topic which can encourage attendees to explore topics of interest further.

Bachelor students are very welcome! However, the course can only be credited in the Master's program.

Learning Outcomes

Participants will understand the theoretical foundations and conceptual frameworks of data science in digitalised contexts.
Participants will gain familiarity with a variety of data science methods applicable across academic disciplines.
Participants will develop practical skills in data workflow management and implementing robust research practices.
Participants will critically evaluate the transdisciplinary application of data science methods and identify opportunities for cross-disciplinary collaboration.
Participants will be informed about cutting-edge advancements in machine learning and their implications for academic research.

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Bitte beachten: Wer an der Summer School teilnehmen möchte, muss sich nicht nur via KSL, sondern auch per Email bei der Programmkoordination (christa.schneider@unibe.ch). Eine Anmeldung ist erst gültig, wer eine Bestätigung von der Programmkoordination bekommen hat.

Kursbeschrieb und Programm folgen im FS 2026

Weitere Informationen folgen.

Learning Outcomes

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Abstract

The Summer School, within the framework of ENLIGHT (university network), brings together doctoral and MA students from the universities of Durham, Uppsala, Tübingen, and Bern for a joint reflection on the digital turn in research for early modern times.

This summer school will take place in Uppsala, Sweden.

If you are interested, please contact Christa Schneider (christa.schneider@unibe.ch) in parallel to book the course.

Ideally, (at least) parts of the "Things that matter" course should be attended.

Learning Outcomes

A small research project with doctoral students and students from the partner universities.