This paper explores the concept of "co-design" in digital heritage based on a proposed project that aims to develop a digital public engagement tool for a heritage site in Africa namely the Brandberg National Monument Area (BNMA) in Namibia. The heritage material within the BNMA has, over the years, been researched and digitised within an open repository hosted by the University of Cologne in Germany. The project intends to use the pre-existing digital content, which currently serves an academic research intent, to develop a tool that can be used by the general public to engage with the rich existing collection. To this extent, the project seeks to use stakeholders of the BNMA in designing a solution in the form of a digital tool. The project proposes a different approach to co-design which includes the combination of Design Science Research with Participatory Action Research.

The here presented Virtual Campus project developed a 3D geodata modelling toolchain, and an open source software based technology stack for creating a Digital geoTwin of the University of Cologne (UoC). We also develop and present a reactjs based web application, a flutter and Unity based mobile app with AR features, as well as an UnrealEngine based VR application to access, browse and visualize the geoTwin data and models in the UoC CAVE 3D environment. The 3D models of the Universities Buildings will include indoor models based on 2D floorplans, which will facilitate indoor positioning in the mobile application of the project through geometry matching and simultaneous localization and mapping (SLAM) technology. For distribution and streaming the 3D building models, we utilze the OGC Standard 3D Tiles, for which we present a tool to georeference the glTF 3D models and assets accurately on the globe.

Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or "fuzzy" data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always prominently represent uncertainty. In this article, we present a mathematical and logical model of uncertainty as a range of values which can be used in network visualizations. We review some of the principles for visualizing uncertainty of different kinds, visual variables that can be used for representing uncertainty, and how these variables have been used to represent different data types in visualizations drawn from a range of non-humanities fields like climate science and bioinformatics. We then provide examples of two diagrams: one in which the variables displaying degrees of uncertainty are integrated/pinto the graph and one in which glyphs are added to represent data certainty and uncertainty. Finally, we discuss how probabilistic data and what-if scenarios could be used to expand the representation of uncertainty in humanities network visualizations.

Chapter 4, Modelling as media transformation, dwells on the tangible physical forms of models as material and mediated media products expressed and shared in human communication. The forms models take are discussed in terms of configurations of media modalities. This intermedia studies approach, whereby modelling is studied as a media transformation process, complements the semiotic perspective of chapter 3 by revisiting some of the previous examples and integrating them with a variety of heterogeneous models, from archaeology and theatre studies to numerical mathematics, and media transformation processes, including formalisations undertaken in DH research.

This paper discusses how project management tools and methods can help students to organize projects they run as part of their digital humanities (DH) studies.This topic was discussed and analyzed at a MA seminar at the Department for Digital Humanities at the University of Cologne in 2021 called Management of Software Projects, taught by Øyvind Eide.The goal was to further develop Agile management methods for student projects, taking into account the specifics of such projects.Agile methods are applied mainly for productoriented projects, be they free software or not, and commercial or not.The extent to which similar methods can be used for student projects was examined in the seminar.To analyze how students manage their student projects, a questionnaire was developed, and different groups of BA students were asked about their project management tools and methods.The study aimed to evaluate the knowledge of students regarding project management and to improve their project management skills by evaluating common problems and missing knowledge, rather than to address research questions in digital pedagogy.The results of the surveys were evaluated by MA students.Based on the results, the MA students created a collection of helpful information for the BA students in order to give them tips for the management of their first DH projects.The collection contained suggestions for project communication, organization, and tools as well as suggestions for further selfstudy.

Chapter 3, Modelling as semiotic process, refers to model-making as theorised within a semiotic framework. This is complementary to but also in contrast with the common theorisation of the practice of modelling in DH informed by the techno-sciences and computer science in particular. Modelling is framed as a process of signification (semiotic process or meaning making). This semiotic framework allows us to see modelling primarily as a strategy to make sense (signification) via practical thinking (creating and manipulating models). It enables to stress the dynamic nature of models and modelling, and to reinstate in renewed terms the understanding of modelling as an open process of signification enacting a triadic cooperation (among object, representamen and interpreter). Referring to Charles Sanders Peirce’s classification of hypoicons, we reflect on some DH examples of modelling in the form of images, diagrams and metaphors, claiming that a semiotic understanding of modelling could ultimately allow us to surpass the rigid duality object vs. model, as well as sign vs. context.

Chapter 1, Towards a new language for modelling, proposes a selection of lexical ramifications and a semantic excursus on the terms model/modelling. Some etymological reflections on these terms and selected occurrences in the Western history of thought are mapped out. The focus of the chapter is on the history and the polysemy characterising the terms model and modelling with the aim to offer some reflections on their current use, and to foreground the pragmatic elements implied by the concept of model in modelling practices and by the use of language (metalanguage). The underlying assumption is that by analysing this metalanguage, we can acquire a deeper understanding of the practices of modelling and the related processes of conceptualisation, representation, visualisation and communication. In addition, the concept of “pragmatic modelling” (further discussed in chapter 2), is introduced and contextualised.

Chapter 5, Modelling text – a case study, presents a case study examining examples of activities of modelling around the concepts of text and textuality. It qualifies as an anthology, a gallery, an empirical study, and an experiment on finding a different mode of argumentation to “change the launch pad” into future discussions around modelling. In this chapter, models are exposed primarily as specific and situated visual representations we experience when studying and modelling texts. They are presented following a What You See is What You Get approach. The argument takes a different form of expression from the other chapters by discussing models and their visualisations with the presentation of topical quotes extracted from the literature alongside their iconic counterparts, either in their original version or as interpreted visually by the authors and the designers. This effort is in itself an example of modelling as a translation process in action. The chapter spans various disciplines and illustrates different modes of making implicit and explicit models, covering a broad range from theoretical descriptions to concrete applications in the realm of text technologies and knowledge representation. The overall selection for examples aims to offer a “graphical” argument of how different models represent conceptualisations of and perspectives on texts in different ways, illustrating key concepts discussed in the previous chapters, and encouraging the readers to engage with the topic further.

Chapter 2, Modelling and metaphoric reasoning, discusses the act of modelling, especially, its representative and descriptive functions and how it operates within a context which includes a metaphorical language. Metaphors adapt to and at the same time transform this language. The concept of “pragmatic modelling” is discussed further and is connected to how metaphorical language operates in Digital Humanities (DH) as well as other (mainly interdisciplinary) modelling contexts. Furthermore, the chapter exemplifies how metaphors themselves are models of knowledge, as they define the schemes within which specific concepts operate and knowledge is established and expressed. In particular, in a DH context, the use of metaphors can have practical outcomes in how affordances influence data processing, storage, and design, and in how data are presented and interfaces are built. In this chapter, we propose to consider modelling as a creative and usually highly pragmatic process of thinking and reasoning in which metaphors assume a central role and where meaning is negotiated through the creation and manipulation of external representations combined with an imaginative use of formal and informal languages.

This volume presents an exploration of Digital Humanities (DH), a field focused on the reciprocal transformation of digital technologies and humanities scholarship. Central to DH research is the practice of modelling, which involves translating intricate knowledge systems into computational models. This book addresses a fundamental query: How can an effective language be developed to conceptualize and guide modelling in DH? Modelling, with its historical roots, carries multifaceted meanings influenced by various disciplinary contexts. Modelling Between Digital and Humanities innovatively connects DH with the historical tradition of model-based thinking in the humanities, cultural studies, and the sciences. It endeavors to reshape interpretative frameworks by contextualizing DH's modelling practices within a broader conceptual landscape. Through an exploration of digital, visual and data models, the book asserts that DH holds the potential to be a cornerstone of a novel cultural literacy paradigm. By probing the interplay between technology and thought, the book ultimately positions DH as a catalyst for transformative cultural insights.

This article presents a number of visual experiments with a special focus on processes of media transformation and modelling. In the selected examples, the development and use of computer systems are used to create different visual artefacts. These experiments can only be performed with the use of models in modelling processes. This chapter shows how important modelling and the use and development of software is for the analytical reflections and thus for the gaining of new knowledge. We also show how these points tend towards a more general pattern: visual experiments in the digital humanities cannot be performed without the use of models.

Inspired by explanations of machine learning concepts in children’s books, we developed an approach to introduce supervised, unsupervised, and reinforcement learning using a block-based programming language in combination with the benefits of educational robotics. Instead of using blocks as high-end APIs to access AI cloud services or to reproduce the machine learning algorithms, we use them as a means to put the student “in the algorithm’s shoes.” We adapt the training of neural networks, Q-learning, and k-means algorithms to a design and format suitable for children and equip the students with hands-on tools for playful experimentation. The children learn about direct supervision by modifying the weights in the neural networks and immediately observing the effects on the simulated robot. Following the ideas of constructionism, they experience how the algorithms and underlying machine learning concepts work in practice. We conducted and evaluated this approach with students in primary, middle, and high school. All the age groups perceived the topics to be very easy to moderately hard to grasp. Younger students experienced direct supervision as challenging, whereas they found Q-learning and k-means algorithms much more accessible. Most high-school students could cope with all the topics without particular difficulties.