This short paper proposal considers how a recently launched web resource focused on Holocaust survivor memory can transform collected data through its metadata and digital methodologies into a more useful Digital Holocaust Memory website using digital humanities tools without compromising ethical and sensitive materials. As we struggle to define how our copiously developing collections can be expressed through informative and innovative humanist approaches rather than being perceived as a conventional collection of archived materials, we consider how we can use digital tools “see” the stories of the individuals in the collection beyond a simple, indexed networking by making well-informed metadata choices? While this website was established in one US Midwestern state, with the aim of centralizing narratives and imparting agency to Holocaust survivors and US service members whose units assisted in the liberation of Nazi camps, we believe it is essential to engage with European digital humanities counterparts to expand our multinational and multilingual efforts. A great deal of the collected data is either from European archives or focuses on content from our Holocaust survivors’ European home origins. We seek to identify the most significant patterns to configure our metadata before delving into deeper analysis for computational visualizations to enhance the narrative. By opening dialogue with other international digital humanities scholars who are not necessarily immersed in Holocaust-related content, we hope to gather observations and emerging concepts for our evolving project.

This paper investigates the tree motif as a visually recurrent and ideologically charged element in nineteenth-century Northern European landscape painting.

Taking the beech tree in Danish Golden Age art as a point of departure, the study situates arboreal imagery within broader processes of national identity formation. While motifs such as the Danish beech, the German oak, or Mediterranean cypress have long been discussed in symbolic terms, their large-scale visual distribution has not been systematically quantified.

To address this gap, we assemble a corpus of 4,727 nineteenth-century landscape paintings from Denmark, Norway, Sweden, Finland, Germany, and England and apply a prompt-based segmentation pipeline using the Segment Anything Model (Sam3).

With this, 63% of the corpus is identified as containing tree imagery, yielding approximately 27,000 detected instances. From these segmentations, we derive quantitative metrics capturing motif presence, spatial occupancy, compositional distribution, and low-level color and texture features.

The results demonstrate the feasibility of segmentation-based motif analysis for art history and reveal diachronic and regional patterns in tree representation.

The ongoing work explores fine-grained classification of tree species through CLIP-based model adaptation, enabling closer analysis of regional specificity and painter mobility. The study therefore contributes to a computationally grounded account of how ostensibly generic natural elements functioned as national symbols in nineteenth-century visual culture.

Serial administrative records – acquisition journals, loan registers, correspondence indices – have long occupied a marginal position in historical scholarship, treated as auxiliary sources rather than primary data. This paper argues that such documents constitute research data in their own right and proposes an event-centred modelling framework that makes them analytically accessible as structured relational data.

The empirical basis comprises approximately 1,800 pages of handwritten acquisition and loan journals from two Viennese herbaria, spanning 146 years. Rather than modelling entries as datasets about objects, the framework treats each journal entry as a historical event, capturing temporal and spatial references, participating actors, object-related information, and administrative context. Uncertainties are retained as explicit attributes; systematic omissions are interpreted as historically significant structural phenomena rather than mere data gaps.

The transferability of the modelling logic beyond serial journal material is tested on a structurally related yet functionally distinct administrative corpus: nine subsidy files documenting Heinrich Handel-Mazzetti's botanical expedition to China (1913–1919). Although differing in institutional context, these files share the same underlying administrative logic of recording transactions and decisions. Sequential linkage across 23 years reveals dynamics invisible when files are considered in isolation – approval timescales as indicators of institutional trust, non-linear temporality of expedition work, and the structural role of private funding.

The methodological contribution lies in repositioning routine administrative documents epistemically: not peripheral material, but independent infrastructure data. Event-centred relational modelling thus extends the methodological repertoire of Digital Humanities to a category of sources long treated as ancillary to historical inquiry.

This presentation evaluates the "servant village theory," a foundational model of early medieval Central European historiography formulated by Karol Modzelewski and Karol Buczek. The theory posits that 11th- and 12th-century states in Poland, Bohemia, and Hungary relied on a specialized administrative-economic system: specialized villages provided specific services (e.g., milling, smithing) to central courts and castles, facilitating state-building before the rise of a cash-based market economy.

Despite its long-standing influence, the theory remains problematic. Written records show a stark discrepancy: while hundreds of individuals were obligated to perform services, their residences rarely align with eponymous "service" village names. Traditional historiography explains this as a relic of an 11th-century system that dissolved by the 12th and 13th century. However, as written sources are insufficient to confirm this hypothesis, this study employs Geographic Information Systems (GIS) and Digital Humanities methodologies to test the theory’s spatial validity.

The paper examines the proximity and uniformity of service villages relative to administrative centers across two datasets: villages with documented 11th/12th-century services and those with service-derived names appearing before 1500.

Preliminary results challenge the traditional narrative. Spatial analysis reveals that part of villages with service names were established during 13th- and 14th-century colonization waves—long after the servant system’s supposed demise. These findings suggest that toponymy is an unreliable indicator of early medieval function and highlight a more dynamic socio-economic landscape than the servant village theory assumes.

In medieval Europe, stories circulated across linguistic boundaries, giving rise to multiple text versions and copies. Some of these stories, dealing for instance with king Arthur, Charlemagne or Alexander the Great, have been central in building a European literary tradition. During the process of their transmission, these stories evolved and adapted to different audiences, giving rise to many variations, blending inheritance with innovation. Many of them share common characters or places, giving rise to distinct cycles or storyverses. While philologists have long analyzed individual texts, a macro-level understanding of how these stories survived, transformed, or disappeared remains elusive. This gap underscores the need for large-scale, multilingual data collection to model textual transmission as a dynamic, multifaceted process. Our project addresses this challenge by building a corpus of medieval chivalric literature, structured around four core entities — story, text, witness, and document. The data is collected inside a relational database, using the Heurist software, to facilitate the collaboration among specialists across linguistic traditions. The data is aligned with major online resources for the different linguistic domains (e.g., Jonas, Arlima, Handschriftencensus) and integrates IIIF manifests, for the collection of full-text versions, using machine learning. The data pipeline transforms Heurist data into interoperable formats (TEI, JSON). Data is archived on Zenodo and will be made accessible following a Distributed Text Services API. By documenting our infrastructure and methodologies, we aim to support broader research in medieval studies and the digital humanities, fostering comparative analyses of textual transmission and its underlying variables.

Testimonies from Holocaust survivors are essential for Holocaust research, as they document events and perspectives that are not mentioned in official documents or reports by the perpetrators or are only mentioned in a distorted way. They also help to overcome the perception of passive victims and explore decision-making processes and agency. Additionally, they serve as a central component of remembrance culture and help keep memories alive for future generations. This is particularly important considering the much-discussed end of contemporary witness testimony.

Recently, in historical education and research, there has been a debate about the importance of emotion-based or emotion-historical approaches to texts in general as well as to memories of the Holocaust. Methods from computational linguistics and AI-supported transcription methods for the automated indexing of source material are established methods in digital history. But can these methods lead to false results and distortions?

A first test study on sentiment analysis of the Theresienstadt-diaries of Martha Glass classified them as positive, as many terms were interpreted as positive. This serves as the starting point for the analysis presented in this contribution. The central objectives are: 1) to answer the question of the extent to which the automated evaluation and analysis of biographical memories of the Holocaust can lead to false positive perceptions, 2) to develop a methodology to approach, examine, and understand possible misrepresentations, 3) to contribute to the discussion of the purposes for which and the ways in which AI can be used to answer historical questions in a source-critical way.