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This resource discusses the risks that cultural heritage faces at the moments and the needs that digital technologies must meet to prevent those risks. The course presents frameworks, guidelines and policies that surround heritage risk management and best practices.

This resource explores how a digital twin can represent, connect, and reason about entities of the real world through structured knowledge. It defines a semantically grounded approach to knowledge integration, management, and reasoning in the Cultural Heritage domain.

This resource is an introduction to building semantic and interoperable digital models for Reactive Heritage Digital Twins (RHDTs). It show how the respective ontology and CIDOC-CRM aligned modules enable interoperability across domains and support reasoning within the ARTEMIS Knowledge Base.

Incentives and advocacy of open science principles have spread for more than two decades in the social sciences and humanities. In this presentation, taking archaeology as a case study, the underlying principles of the “archeoViz” ecosystem will be presented and illustrated, to fuel a more general discussion about the advocacy of open science principles in the social sciences and humanities.

This resource provides an introduction to Linked Open Data and SPARQL. It explains how LOD is used to publish structured data on the web and basic concepts like RDF, ontologies and using URIs. Practical exercises using SPARQL queries and SPARQL endpoints in the SPARQL playground and the CLSCor Catalogue complete the course.

This online workshop is organised within the framework of the Computational Literary Studies Infrastructure (CLS INFRA) project. The event introduces the fundamentals of network analysis for humanities scholars, combining historical and literary case studies with hands-on practice. Participants will explore networks of literary characters, letter correspondence, and historical actors, and learn to visualise and interpret these structures using accessible tools like Gephi and EzLinaVis.

Born-digital culture refers to materials, environments and practices that originate in a digital form. In the 21st century these have rapidly expanded, with many of our contemporary cultural practices increasingly mediated in some way by digital media and technologies. In this course, designed by the Digital Humanities Research Hub at the School of Advanced Study, University of London, you will explore key concepts associated with digital culture and archives, ethical issues, and how to collect and analyse born-digital materials.

This free online course provides a practical introduction to working with digital texts and applying some of the tools available to digitise and interrogate them. It will help you to decide whether to use digital approaches to textual analysis and to select the correct tools for your research project.

Using the reader survey R package LitRiddle, this resource offers a gentle introduction to tabular data analysis in Rstudio. Following along the code examples, students will learn about Rstudio, Markdown files, and how to explore, analyse and transform quantitative data in R.

The emergence of ChatGPT and other generative AI tools presents both opportunities and challenges for academia. While these technologies offer powerful capabilities to support scholarship, their thoughtless adoption could undermine the very foundations of academic work. This talk from Dr. Mark Carrigan, presented as part of the DARIAH Friday Frontiers webinar series, introduces a framework for incorporating generative AI into academic practice in ways that enhance rather than replace human thought.

What connects analysing the creative process of a performance using 20,000 collected digital documents, reconstructing an artist's career from programme data, and preserving a touring show? Following a state-of-the-art review of research in performing arts and digital humanities (literature, history, and representation analysis), this Friday Frontiers webinar addresses current challenges, including data modelling, multimodal analysis, and artificial intelligence.

This webinar from Prof. Jennifer Edmond and Dr. Eleonora Lima at Trinity College Dublin discusses the Knowledge Technologies for Democracy (KT4D) project and its investigation into how democracy and civic participation can be better facilitated in the face of rapidly changing knowledge technologies, namely Artificial Intelligence (AI) and Big Data.

Scholarship in three dimensions can transcend the limitations of traditional two-dimensional representations of objects that exist in the physical world in three dimensions. This presentation showcases the scholarly potential of 3D digital scholarly editions, advocating for their adoption as a new tool in the DH toolkit for new formats for the dissemination and interrogation of knowledge.

This three-day training school organised by the CLS INFRA project focused on dynamic collections of literary texts manipulated programmatically. Learners will learn to find, evaluate, and select corpora using tools like CLSCor and DraCor, and gain skills in Python, Jupyter Notebooks, API querying, Linked Open Data, and Digital Literary Network Analysis. The training addresses reproducibility using Docker, promoting transparent, replicable research in Computational Literary Studies.

Explore this curriculum on Automatic Text Recognition (ATR) and learn how to efficiently extract full text from heritage material images. Perfectly tailored for researchers, librarians, and archivists, these resources not only enhance your archival research and preservation efforts but also unlock the potential for computational analysis of your sources.

This tutorial examines the evolution of Open Access by tracing its historical developments and unpacking the terminology and concepts that continue to shape the movement.

This lesson covers tokenization, part-of-speech tagging, and lemmatization, as well as automatic language detection, for non-English and multilingual text. You'll learn how to use the Python packages NLTK, spaCy, and Stanza to analyze a multilingual Russian and French text.

In this lesson, you'll learn computer vision and machine learning principles for object recognition, and how to apply these principles using Python to recognize and classify smiling faces in historical photographs.

In this lesson, you will learn how to download YouTube video comments and use the R programming language to analyze the dataset with Wordfish, an algorithm designed to identify opposing ideological perspectives within a corpus.

Increase the visibility of your ATR output while fostering Open Science.

Discover the subtleties of region and line segmentation and learn about the purpose of layout analysis for Automatic Text Recognition!

Dive into the fine-tuning of Automatic Text Recognition outputs!

This tutorial explores where and how to find, create, and collect images of textual material, a crucial initial step in any process using Automatic Text Recognition (ATR).

Humanities and social scientific data is fundamentally different in type to a great deal of data available in the sciences. This resource will help you to understand your data, and therefore how to handle it. This resource looks at humanities data and its reliability, as well as different types of data you may encounter.

This lesson demonstrates how to use nanDECK to design and publish your own deck of printed or digital playing cards, and use them to test a group's knowledge of historical events through a Timeline-like game mechanic. This lesson will also highlight best practices for handling digitized historical objects.