The capture, modelling and visualisation of uncertainty has become a hot topic in many areas of science, such as the digital humanities (DH). Fuelled by critical voices among the DH community, DH scholars are becoming more aware of the intrinsic advantages that incorporating the notion of uncertainty into their workflows may bring. Additionally, the increasing availability of ubiquitous, web-based technologies has given rise to many collaborative tools that aim to support DH scholars in performing remote work alongside distant peers from other parts of the world. In this context, this paper describes two user studies seeking to evaluate a taxonomy of textual uncertainty aimed at enabling remote collaborations on digital humanities (DH) research objects in a digital medium. Our study focuses on the task of free annotation of uncertainty in texts in two different scenarios, seeking to establish the requirements of the underlying data and uncertainty models that would be needed to implement a hypothetical collaborative annotation system (CAS) that uses information visualisation and visual analytics techniques to leverage the cognitive effort implied by these tasks. To identify user needs and other requirements, we held two user-driven design experiences with DH experts and lay users, focusing on the annotation of uncertainty in historical recipes and literary texts. The lessons learned from these experiments are gathered in a series of insights and observations on how these different user groups collaborated to adapt an uncertainty taxonomy to solve the proposed exercises. Furthermore, we extract a series of recommendations and future lines of work that we share with the community in an attempt to establish a common agenda of DH research that focuses on collaboration around the idea of uncertainty.

Different types of uncertainties occur in almost all datasets and are an inherent property of data across different academic disciplines, including digital humanities (DH). In this paper, we address, demonstrate and analyse spatio-temporal uncertainties in a non-standard German legacy dataset in a DH context. Although the data collection is primarily a linguistic resource, it contains a wealth of additional, comprehensive information, such as location and temporal detail. The addressed uncertainties have manifested because of a variety of reasons, and partly also because of decades of data transformation processes. We here propose our own taxonomy for capturing and classifying the various uncertainties, and show with numerous examples how the remedying but also re-introduction of uncertainties affects DH practices.

This article reports on the experience of co-designing an educational video game aimed at promoting good dietary habits in youngsters and fostering Sustainable Development Goals (SDGs), such as SDG 3 (Good Health and Well-Being), SDG 10 (Reduced Inequalities), and SDG 17 (Partnerships for the Goals). To ensure the quality of the results, we developed a methodology under a social innovation paradigm that enabled the co-creation of the game. The methodology was driven by a series of three workshops, during which we adopted several different gamification strategies to support a Participatory Design (PD) process with the stakeholders, a group of local pre-teen and teen girls at social risk (N = 22). Captured requirements materialized into intermediate prototype evaluations that motivated a progressive refinement of the game.

New methodological and co-creation approaches are needed in Digital Humanities (DH) for cross-disciplinary collaboration oriented to sustainability. More specifically—in the context of Environmental Humanities and regarding biocultural diversity—a key challenge is to set effective and engaging ways of rapid knowledge generation for public awareness and policy-making. According to the 2019 United Nations report on biodiversity, the world will lose one million species in the upcoming years. In parallel, it is acknowledged that the degradation of biological diversity also threatens cultural and linguistic diversity, as declared by UNESCO. In an attempt to contribute to knowledge safeguarding this diversity, a hybrid approach combining the tradition of participatory design with DH tools and with the collaboration of a variety of scholars and stakeholders, has resulted in the co-creation of a manifesto on “biodiversity in connection with linguistic and cultural diversity”. We outline the design thinking methodology applied for this experimental case study, and in parallel apply software-aided discourse analysis of the manifesto co-writing process—from a “sprint” collaborative writing format to an online publication, moving from a face-to-face context into digital collaboration channels. Results highlight the importance of facilitation, flexibility and adequate ways of visualization “in progress” (via analogue materials and DH tools) during the co-writing sprint process, as well as the need to set basic co-creation protocols in such transdisciplinary context, considering synchronous and asynchronous collaboration. We discuss the importance of advancing between DH and sustainability-driven experimental co-creation, connecting practices with opportunities to develop collaborative writing and knowledge generation in the margins of academic settings.

Extensive collections of data of linguistic, historical and socio-cultural importance are stored in libraries, museums and national archives with enormous potential to support research. However, a sizable portion of the data remains underutilised because of a lack of the required knowledge to model the data semantically and convert it into a format suitable for the semantic web. Although many institutions have produced digital versions of their collection, semantic enrichment, interlinking and exploration are still missing from digitised versions. In this paper, we present a model that provides structure and semantics to a non-standard linguistic and historical data collection on the example of the Bavarian dialects in Austria at the Austrian Academy of Sciences. We followed a semantic modelling approach that utilises the knowledge of domain experts and the corresponding schema produced during the data collection process. The model is used to enrich, interlink and publish the collection semantically. The dataset includes questionnaires and answers as well as supplementary information about the circumstances of the data collection (person, location, time, etc.). The semantic uplift is demonstrated by converting a subset of the collection to a Linked Open Data (LOD) format, where domain experts evaluated the model and the resulting dataset for its support of user queries.

In 1923, Walter Benjamin published translations of Baudelaire’s poetry with a prefatory essay the idea that translation is therefore not only the practice of addressing foreign readerships, but rather a process of authorship in which the original text is amplified with further significance. The authors use the term translation with a meaning that is not only linguistic but also visual. They analyze the coronavirus pandemic by translating scientific literacy through the techniques of natural language processing and data visualization. The Cartography of COVID-19 results from a visual translation that invites readers to explore the current pandemic from a different point of view that extends their perception.