Cultural heritage images are among the primary media for communicating and preserving the cultural values of a society. The images represent concrete and abstract content and symbolise the social, economic, political, and cultural values of the society. However, an enormous amount of such values embedded in the images is left unexploited partly due to the absence of methodological and technical solutions to capture, represent, and exploit the latent information. With the emergence of new technologies and availability of cultural heritage images in digital formats, the methodology followed to semantically enrich and utilise such resources become a vital factor in supporting users need. This paper presents a methodology proposed to unearth the cultural information communicated via cultural digital images by applying Artificial Intelligence (AI) technologies (such as Computer Vision (CV) and semantic web technologies). To this end, the paper presents a methodology that enables efficient analysis and enrichment of a large collection of cultural images covering all the major phases and tasks. The proposed method is applied and tested using a case study on cultural image collections from the Europeana platform. The paper further presents the analysis of the case study, the challenges, the lessons learned, and promising future research areas on the topic.

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.

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.

The exploreAT! project aims to give insights into the richness of the German language in the Austrian area through a rich and unique collection of dialect words of the Bavarian dialects recorded during the former Austrian-Hungariann Monarchy period and beyond. Originally collected by means of questionnaires, words were noted in handwriting on individual paper slips, covering topics from nature and food to religious festivities, etc. Once digitized, the full database contains around 3.5 million single data entries with an estimated 200,000 headwords, which requires substantial effort if the analysts want to access specific information from the data set. It should also be noted that the data presents a high heterogeneity in terms of its nature and origin (from questionnaires, collectors, scientists, spoken language, hand written notes, etc.), which calls for the creation of a homogeneous database containing all of the available information. In this paper we present a tool aimed to improve the comprehension of that massive amount of data through visualization means, thus trying to help in the reach of meaningful conclusions and the acquisition of valuable insights in easy and fast ways. With it, analysts can discover cultural issues and access them through means of language and visualization. This is possible thanks to a multidimensional approach to data analysis based on the use of maps, projections and other visualization artifacts. To reach our goal, a team of experts with different backgrounds worked together trying to close the gap between the Humanities and Computer Sciences fields through the creation of our prototype and its multiple iterations.