Schwandt Silke, Wachter Christian
Digital History, Department of History, Bielefeld University, Bielefeld, Germany.
Front Big Data. 2024 May 10;7:1188620. doi: 10.3389/fdata.2024.1188620. eCollection 2024.
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 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."
可视化在数据驱动的研究中无处不在，既是知识生产的工具，也是知识传播的真正手段。尽管有人批评数字人文（DH）中可视化所谓的客观性，并反思它们如何在数据分析流程中既作为学术观点的体现，又作为不确定性的体现，但在DH讨论中，这些假设仍然明显缺乏深入的理论基础。我们的理解是，只有通过基本符号学原理和媒体模态视角等理论基础，才能全面评估可视化在学术解释创新中的用途和潜力。我们认为，可视化有能力在特定研究领域“有效地激发”现有的学术知识。这不仅仅意味着可视化描绘的数据集中的模式似乎与先前的研究不一致，从而激发更深入的研究。相辅相成的是，这里的“激发”包括可视化对其自身意义产生不确定性——然而，正是这种不确定性蕴含着获得更深刻见解的潜力。它引发了关于所描绘内容和未描绘内容的问题。事实证明，这是学术解释的宝贵资源，可以说，从这个意义上讲，大数据可视化尤其富有成效，因为由于其复杂性，研究人员没有可视化表示就无法解释数据。然而，我们认为“有效激发”也可能发生在大数据层面之下。我们认为这种潜力源于视觉媒体真正的符号学和语义属性，多模态研究，特别是在该领域的研究已经阐明了这一点：可视化对数据模式的整体概述与其语义模糊性并列，这为对该数据的深入解释和多种视角让路。我们用中世纪英国法律史的例子来阐明这种潜力。与法庭上各种人的法律职能和社会群体相关的数据可视化提供了惊人的见解，这些见解可以通过“有效激发”带来新知识。
