[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81644-en":3,"doc-seo-81644-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},81644,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","How Historians Use Visualization: A Corpus-Driven Mixed-Methods Study","Historical research increasingly adopts visualization, yet evidence on how historians actually use it remains limited. This corpus-driven mixed-methods study analyzes 14,021 images from 4,142 articles in history and digital humanities journals. A collaboratively designed, domain-informed visualization taxonomy and semi-automatic labeling pipeline identify 4,831 visualization instances and reveal patterns across venues, subfields, and time. Interviews with 11 historians, supported by Hi FigAtlas, uncover visualization roles and persistent adoption barriers.","Eurographics Conference on Visualization (EuroVis) 2026 N. Andrienko, M. Chen, and B. Wang  \n(Guest Editors)  \nCOMPUTER GRAPHICS forum Volume 45 (2026), Number 3  \narXiv :2605 .01456v2 [ cs .GR] 10 Jul 2026  \nHow Historians Use Visualization: A Corpus-Driven  \nMixed-Methods Study  \nX. Chen 1,2, Y. Zhang3,∗, W. Zheng4, C. Ma 1,2, and X. Yuan 1,2,∗  \n1National Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University, China  \n2PKU-WUHAN Institute for Artificial Intelligence, China  \n3Department of Computer Science, University of Oxford, UK  \n4 School of Data Science, Fudan University, China  \n∗corresponding authors  \nAbstract  \nVisualization in historical research is shifting from isolated attempts to systematic practices. However, data-driven evidence about how historians actually use visualization remains scarce. We present a corpus-driven, mixed-methods study that combines analysis of images from 4 , 142 research articles across history and digital humanities journals with a collaboratively developed visualization taxonomy and a semi-automatic labeling pipeline. We construct a corpus of 14 ,021 images, classify 4 , 831 visualization instances using a hierarchical, domain-informed taxonomy, and analyze patterns of visualization adoption across venues, history subfields, and time. To interpret these patterns, we conduct interviews with 11 historians and use Hi FigAtlas system as a boundary object to support joint inspection of the corpus. We identify distinct roles for visualizationsin historical research: primary-source, evidence-synthesis, communicative, confirmative, and exploratory. We further find that  \nwhile historians pursue diverse goals with figures, persistent epistemological and practical barriers, such as uncertainty, prove nance, justification burden, and publication constraints, impede the adoption of visualization. This work contributes a grounded account of visualization use in historical scholarship and points to opportunities to better support domain-specific needs.  \nCCS Concepts  \n• Human-centered computing → Visualization theory, concepts and paradigms; • Applied computing → Arts and humanities;  \n1. Introduction  \nWith the rise of digital humanities (DH) and quantitative history, historians are increasingly using data-driven approaches [Gra22, Dru11] . As the need to manage and interpret data grows, visualization is increasingly used in historical research for communication, analysis, and knowledge production. However, a systematic understanding of how historians use visualization is still lacking.  \nThe current understanding of visualization usage in historical scholarship faces two limitations. First, existing literature and design studies predominantly focus on digital humanities [PLP∗ 23, JFCS17, BEC∗ 18] . While valuable, they represent a technically oriented subset of the field, often overlooking the practices within“traditional” history journals where the majority of domain knowledge is produced. Furthermore, these studies rarely characterize the diversity of visualization styles and functions in research articles. Second, discussions on visualization usage in historical research remain conceptual [Dru11, LBT∗ 18] or fragmented [Ewa16], focusing on a few exemplary cases.  \nWithout a systematic corpus-driven analysis of visualization us-  \nage by historians, the landscape remains unclear. Consequently, it is difficult to answer the following questions at scale: Which visualization techniques are effectively adopted in historical research? How do visualization types distribute over time, publication venues, and history subfields? Why do historians use visualization, and what are their underlying motivations? Which visual forms are underused, misused, or largely ignored in historical practice? What are the potential barriers hindering their adoption?  \nTo address these gaps, we conduct a corpus-driven mixedmethods study of visualization in hist","cbCaiazqbxkz5XfZ","https://ap.wps.com/l/cbCaiazqbxkz5XfZ","pdf",5052287,1,12,"English","en",105,"# Introduction\n# Related Work\n## Visualization in Historical Scholarship\n## Visualization in Relation to Digital Humanities\n## Visualization Taxonomies","[{\"question\":\"What data and corpus does the study use to analyze historians’ visualization practices?\",\"answer\":\"The study builds a corpus of 14,021 images extracted from 4,142 research articles across history and digital humanities journals.\"},{\"question\":\"How are visualization instances identified and classified in the study?\",\"answer\":\"A collaboratively developed, domain-informed visualization taxonomy is used with a semi-automatic labeling pipeline to classify 4,831 visualization instances.\"},{\"question\":\"What methods are used to interpret patterns beyond quantitative evidence?\",\"answer\":\"The authors conduct interviews with 11 historians and use the Hi FigAtlas system as a boundary object to support joint inspection of the 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