Frontiers in Big Data (May 2024)
Visualization as irritation: producing knowledge about medieval courts through uncertainty
Abstract
Visualizations are ubiquitous in data-driven research, serving as both tools for knowledge production and genuine means of knowledge communication. Despite criticisms targeting the alleged objectivity of visualizations in the digital humanities (DH) and reflections on how they may serve as representations of both scholarly perspective and uncertainty within the data analysis pipeline, there remains a notable scarcity of in-depth theoretical grounding for these assumptions in DH discussions. It is our understanding that only through theoretical foundations such as basic semiotic principles and perspectives on media modality one can fully assess the use and potential of visualizations for innovation in scholarly interpretation. We argue that visualizations have the capacity to “productively irritate” existing scholarly knowledge in a given research field. This does not just mean that visualizations depict patterns in datasets that seem not in line with prior research and thus stimulate deeper examination. Complementarily, “irritation” here consists of visualizations producing uncertainty about their own meaning—yet it is precisely this uncertainty in which the potential for greater insight lies. It stimulates questions about what is depicted and what is not. This turns out to be a valuable resource for scholarly interpretation, and one could argue that visualizing big data is particularly prolific in this sense, because due to their complexity researchers cannot interpret the data without visual representations. However, we argue that “productive irritation” can also happen below the level of big data. We see this potential rooted in the genuinely semiotic and semantic properties of visual media, which studies in multimodality and specifically in the field of Bildlinguistik have carved out: a visualization's holistic overview of data patterns is juxtaposed to its semantic vagueness, which gives way to deep interpretations and multiple perspectives on that data. We elucidate this potential using examples from medieval English legal history. Visualizations of data relating to legal functions and social constellations of various people in court offer surprising insights that can lead to new knowledge through “productive irritation.”
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