From Networks to From Networks to Narratives – Visualization of historical research data
25. November 2025
Data visualization is mainly based on statistical representation and is a common method for quantitative research approaches. Now visualization is an emerging tool, also in the humanities, but according to Drucker (2011), the visual expression of the interpretative, multimodal and cultural-historical dimension of historical data is still a desideratum in humanities research, because no visual language to represent social and cultural phenomena and their complex interplay has been established yet. Visualization is a powerful method to present research results and acts as an epistemic tool by triggering cognition, recognizing patterns and relations (Card et al., 1999; Munzner, 2014) and enabling new ideas (Freyberg 2021, 77ff./Stjernfelt 2007, 7). There is a need for a humanities approach to the graphical expression of historical data with more nuanced forms to show ambiguity, uncertainty and complexity (Drucker 2011, 2; 9). This in particular applies to historical research, where the analysis of huge amounts of historical records is challenging, since historical data often lacks metadata descriptions and standardized semantic contextualization. Also, researchers do not always have the technical skills to handle this messy data. But there are AI-based possibilities and software, like Gephi, which can facilitate data workflows and enable the visualization of research data. The visual representations of historical data are so far mainly restricted to timelines, networks and maps, using historical network analysis as main method (Menzel et al. 2020). But current information visualization examples already offer narrative and explorative approaches on the data, providing for interactive and dynamic interfaces. Also, visualizations are integrated in storytelling, building “visual narratives” (Segel & Heer 2010, 1140) for instance in data journalism.
In this evening event these new approaches on data visualization of historical data were discussed with three researchers‘ inputs.
References
Card, Stuart K.; Mackinlay, Jock D. & Shneiderman, Ben (1999): Information Visualization. In: Readings in Information Visualization. Using Vision to Think. San Francisco: Morgan Kaufmann, S. 1-34.
Drucker, Johanna (2011): Humanities Approaches to Graphical Display. In: Digital Humanities Quarterly, 005.1 2011. (http://www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html).
Freyberg, Linda (2021): Ikonizität der Information: Die Erkenntnisfunktion struktureller und gestalteter Bildlichkeit in der digitalen Wissensorganisation. Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft, 484. Berlin: Humboldt Universität zu Berlin. (https://doi.org/10.18452/23813).
Menzel, Sina; Bludau, Mark-Jan; Leitner, Elena; Dörk, Marian; Moreno-Schneider, Julián; Petras, Vivien & Rehm, Georg (2020): Graph technologies for the analysis of historical social networks using heterogeneous data sources. In: Andrews, Tara; Diehr, Franziska; Efer, Thomas; Kuczera, Andreas & Van Zundert, Joris (Hrsg.): Graph technologies in the humanities, Proceedings 2020. (https://ceur-ws.org/Vol-3110/paper7.pdf).
Munzner, Tamara (2014): Visualization analysis and design, 1. Aufl. New York: CRC press. (https://doi.org/10.1201/b17511).
Segel, Edward & Jeffrey Heer (2010): Narrative visualization: Telling stories with data. In: IEEE transactions on visualization and computer graphics 16 (6), S. 1139-1148. (DOI: 10.1109/TVCG.2010.179)
Stjernfelt, Frederik (2007): Diagrammatology: An Investigation on the Borderlines of
Phenomenology, Ontology, and Semiotics. Dordrecht: Springer.
Windhager, Florian, et al. (2024): The Knowledge Graph as a Data Sculpture: Visualising Arts and Humanities Data with Maps, Graphs, and Sets Over Time. In: Geographical Research in the Digital Humanities, Bielefeld, S. 113-134. (https://doi.org/10.1515/9783839469187-007).
Folien Vorträge
Tomas Vancisin, PhD (University of Edinburgh, Scotland):Visualizing Historical University Records
Tomas discussed his work with historical university records, showing how data visualization can offer new ways of studying these collections. He highlighted both the possibilities and limitations of visualization, emphasizing the importance of ethical and transparent research practices and the critical role of data provenance—both in this work and beyond.
Dr. Florian Windhager (University for Continuing Education Krems, Austria): A Banquet for Visualizations: Many Forms, One Table
The canon of historical data visualization has its holy triad: the timeline, the network, the map – while other forms wait in the wings. Florian suggestes a modest heresy by fusing them into a synoptic modeling scheme, PolyCube (Windhager et al., 2024), open to narrative and exploratory modes of experience. As a facilitator for interfaith dialogue, this framework seeks to represent cultural and historical data without enforcing one single orthodoxy of form.
Prof. Mathieu Jacomy (Assistant Professor, Aalborg University, Denmark & Co-creator of Gephi):Irreductionist visualization: mediating nuance from network to semantic maps
Mathieu will discussed his recent work on visualizing large unstructured datasets, transitioning from co-word networks to semantic maps based on embedding models. Recent AI techniques allow operationalizing the mediation of nuanced patterns such as ambiguity, polyvalence, or contradiction. Is this Johanna Drucker’s dream of “non-representational” approaches, or a nightmare? Drawing on Donna Haraway and Bruno Latour, Mathieu articulates these techniques as “irreductionist”, i.e. reductionist in a reversible way, to examine their potential for more nuanced computational hermeneutics.