Pub Date : 2021-10-01DOI: 10.1109/vis4dh53644.2021.00003
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Pub Date : 2021-10-01DOI: 10.1109/VIS4DH53644.2021.00006
Anessa Petteruti, Cindy Nguyen, D. Laidlaw
We present the results of a comparative analysis of various layout and design displays for historical drawings. This involved developing a new visualization environment, known as the ”Virtual Rosetta” for a collection of early twentieth century Vietnamese drawings known as Technique du Peuple Annamite (Mechanics and Crafts of the People of Annam) compiled by French colonial administrator Henri J. Oger (1872-1929). In this paper, we discuss similar work that has been pursued by researchers in digital humanities, specifically working in virtual displays of artwork, the design of the Virtual Rosetta, including text readability, wall and text contrast, and organization of drawings, as well as the associated results. We used an analysis technique known as hierarchical clustering utilizing sentence embeddings, a technique in natural language processing in which sentences are mapped to numerical vectors, to organize a subset of the drawings so that drawings with similar text descriptions are located near each other. Performing this on an entire dataset would allow humanities researchers to most effectively and efficiently visualize their findings. We report on user experience and efficacy for a number of virtual environment layouts for displaying drawings and visual information for research analysis in virtual reality.
我们提出了一种比较分析的结果，各种布局和设计展示的历史图纸。这涉及到开发一个新的可视化环境，被称为“虚拟罗塞塔”，用于20世纪早期越南绘画的集合，称为Technique du Peuple Annamite(安南人民的机械和工艺)，由法国殖民统治者Henri J. Oger(1872-1929)编译。在本文中，我们讨论了数字人文学科研究人员所从事的类似工作，特别是艺术品的虚拟展示，虚拟罗塞塔的设计，包括文本可读性，墙壁和文本对比，以及图纸的组织，以及相关的结果。我们使用了一种被称为分层聚类的分析技术，利用句子嵌入(一种自然语言处理技术，其中句子被映射到数字向量)来组织绘图的子集，以便具有相似文本描述的绘图彼此靠近。在整个数据集上执行此操作将使人文学科研究人员能够最有效地将他们的发现可视化。我们报告了一些虚拟环境布局的用户体验和效果，用于显示虚拟现实中的绘图和视觉信息。
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Pub Date : 2021-10-01DOI: 10.1109/VIS4DH53644.2021.00005
Arianna Ciula, Miguel Vieira, Ginestra Ferraro, Tiffany Ong, S. Perovic, Rosa Mucignat, Niccolò Valmori, Brecht Deseure, E. Mannucci
This paper uses the collaborative project Radical Translations [1] as case study to examine some of the theoretical perspectives informing the adoption and critique of data visualization in the digital humanities with applied examples in context. It showcases how data visualization is used within a King’s Digital Lab project lifecycle to facilitate collaborative data exploration within the project interdisciplinary team – to support data curation and cleaning and/or to guide the design process – as well as data analysis by users external to the team. Theoretical issues around bridging the gap between approaches adopted for small and/or large-scale datasets are addressed from functional perspectives with reference to evolving data modelling and software development lifecycle approaches and workflows. While anchored to the specific context of the project under examination, some of the identified trade-offs have epistemological value beyond the specific case study iterations and its design solutions.
本文以合作项目Radical Translations[1]为案例研究，通过语境中的应用实例，研究了数字人文学科中数据可视化的采用和批评的一些理论观点。它展示了如何在King 's Digital Lab项目生命周期中使用数据可视化来促进项目跨学科团队内的协作数据探索，以支持数据管理和清理和/或指导设计过程，以及团队外部用户的数据分析。围绕弥合小型和/或大型数据集所采用的方法之间的差距的理论问题，从功能角度出发，参考不断发展的数据建模和软件开发生命周期方法和工作流。虽然锚定在审查项目的特定背景下，但一些确定的权衡具有超越特定案例研究迭代及其设计解决方案的认识论价值。
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Pub Date : 2021-10-01DOI: 10.1109/vis4dh53644.2021.00001
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Pub Date : 2021-10-01DOI: 10.1109/VIS4DH53644.2021.00007
Valerie Müller, Christian Sieg, L. Linsen
Topic modeling is a state-of-the-art technique for analyzing text corpora. It uses a statistical model, most commonly Latent Dirichlet Allocation (LDA), to discover abstract topics that occur in the document collection. However, the LDA-based topic modeling procedure is based on a randomly selected initial configuration as well as a number of parameter values than need to be chosen. This induces uncertainties on the topic modeling results, and visualization methods should convey these uncertainties during the analysis process. We propose a visual uncertainty-aware topic modeling analysis. We capture the uncertainty by computing topic modeling ensembles and propose measures for estimating topic modeling uncertainty from the ensemble. Then, we propose to enhance state-of-the-art topic modeling visualization methods to convey the uncertainty in the topic modeling process. We visualize the entire ensemble of topic modeling results at different levels for topic and document analysis. We apply our visualization methods to a text corpus to document the impact of uncertainty on the analysis.
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