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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.

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.

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.

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.

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 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).

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

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

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

A digital gazetteer records information associated with specific places. This lesson teaches you how to create a gazetteer from a historical text, using the Linked Places Delimited (LP-TSV) format.

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.

This presentation demonstrates how specific XR technologies have been developed and used at different outdoor cultural heritage sites in Iceland and reflects on how technologies can be adapted to specific circumstances.

Get ready to improve the quality of your scanned images before moving to the processing phase of your ATR project.

In this presentation we learn about how 3D scanning of a sculpture museum dedicated to a single Icelandic artist has been used to engage schoolchildren under the umbrella "art for everyone". It also explores other projects with making digital twins for cultural heritage purposes and the role of the private sector in this endeavor.

\"What gets into your dataset and what doesn't?\" For database projects in the humanities and social sciences, having a concrete idea of your project scope can be very important. This resource covers scoping methods for Database projects to help narrow down and accurately size the database you are working with in your research.

In this presentation we learn about how a computer game company collaborated with a national museum to produce a computer game about the Icelandic Viking past with a focus on women. The game, and collaboration, centers around a single key object in the museum holding. The presentation also discusses plans to develop a virtual museum within the game, to display other objects from the museum for gamers to engage with.

Word embeddings allow you to analyze the usage of different terms in a corpus of texts by capturing information about their contextual usage. Through a primarily theoretical lens, this lesson will teach you how to prepare a corpus and train a word embedding model. You will explore how word vectors work, how to interpret them, and how to answer humanities research questions using them.

In this podcast, produced by virtualculture.ch, sociologist Jane Haller, Digitales Schaudepot president, is conversing with Sorin Marti, a data steward in the Research Infrastructure Support Entity (RISE) at the University of Basel to discuss aspects of data management for public consumption.

What does "curating data stories" mean from a technical and academic perspective? This podcast from the curiositas5.0 project features a discussion between Jane Haller and Joana Meier about experimenting with digital exhibitions.

In this podcast, produced by virturalculture.ch, Jane Haller, a sociologist, digital project manager, and president of the Digitales Schaudepot, is in conversation with Esaù Dozio, a curator at the Antikenmuseum Basel. Within their chat, they discuss the process of selecting items for special exhibitions, and the mistakes and challenges that can arise.

This Friday Frontiers presentation provides a rich insight to the design and development of the University College London's Sloane Lab knowledge base, the modelling choices, and priorities in relation to semantics and vocabularies and the range of challenges addressed in the process of aggregation in terms of data disparity, integration facility, conflicting information and inconsistency, uncertainty and data absence.

The data you generate in humanities and social science projects may well need longer term storage beyond the scope of your own research project. Medium to long term data storage is vital for allowing other scholars to examine and test your data and models, and ensuring open access to your data is an increasingly prominent issue. This resource will guide you through a thoughtful discussion of Data Management and Storage.