Who was “Central” in the History of Chinese Buddhism? : A Social Network Approach

Hidden in the Buddhist biographical literature on eminent monks is a large amount of information about who knew whom. It is especially rich for the time between 300 and 1000 CE, when the four major collections of “Biographies of Eminent Monks” (gaoseng zhuan) allow us to date and locate the relationships of individuals to a degree unimaginable for the religious history of Europe or India in that period. Using open data from the Gaoseng Zhuan projects conducted between 2007 and 2012 at Dharma Drum Mountain, Taiwan, this article applies centrality measures to identify key players in the currently available data. The dataset connects actors with places and other actors; often connections can be dated. The version of the large, undirected network used here contains ca. 6,500 actors and ca. 13,000 links. The largest component contains ca. 5,500 actors connected by ca. 10,000 links. Comparing the set of key players based on Degree Centrality with those indicated by Betweenness Centrality, a meaningful constellation appears. Degree based centrality yields a list of translators and important patrons. Translation teams constitute cliques that contribute to the high degree value of their leader. Imperial patrons interface with monastic leaders as well as with the secular domain, moreover, records of such interactions are privileged in the sources. Betweenness Centrality, on the other hand, yields famous Chan masters of the late Tang and early Song Dynasty. This reflects both the rising importance of the lineage paradigm in Chinese Buddhist historiography as well as the seminal position of these figures between earlier and later forms of Chinese Buddhis

From Entity Description to Semantic Analysis: The Case of Theodor Fontane’s Notebooks

Within the last few decades, TEI has become a major instrument for philologists in the digital age, particularly since a set of mechanisms has recently been incorporated which facilitates the encoding of genetic editions. Editors use the XML syntax while aiming to preserve the quantity and quality of old books and manuscripts and publish many more of them online, mostly under free licenses. Scholars all over the world are now able to use huge datasets for further research. There are now many digital editions available, but only a few tools to analyze them. This article explores how web technologies (XML and related technologies as well as JavaScript) can be used to enrich the forthcoming edition of Theodor Fontane’s notebooks with data-driven visualizations of named entities and how at the same time applications can be built on these visualizations which are reusable for other edition projects in the TEI world. Because of the density and historical scope of references to named entities and the variety of entity types, Fontane’s notebooks lend themselves to advanced methods of semantic analysis

On the Use of Historical Social Network Analysis in the Study of Chinese Buddhism: The Case of Dao’an, Huiyuan, and Kumārajīva

This paper is part of a larger research project that attempts to apply historical social network analysis to the study of Chinese Buddhist history. The underlying research questions are whether social network analysis (SNA) metrics can be gainfully applied to Buddhist history, and whether network visualizations can enable us to better understand historical constellations and discover new patterns. Fundamental to this effort is a dataset of Buddhist biographies and lineage data that has been growing steadily over the past thirteen years: the Historical Social Network of Chinese Buddhism. The current dataset records interactions between more than 17,500 actors in Chinese Buddhist history. It is openly available and, in principle, all visualizations and metrics below are reproducible. This paper focuses on a characteristic formation at the beginning of the main network component, a “triangle” formed by the communities of Dao’an 道安 (314–385 CE), Huiyuan 慧遠 (334–416), and Kumārajīva (ca. 344–413). The first section interprets this joint formation as a factor in the establishment of Mahāyāna Buddhism in China. The second section explores how social network analysis can be used to identify hitherto neglected, but still important, actors in Buddhist history

Close and Distant Reading Visualizations for the Comparative Analysis of Digital Humanities Data

Traditionally, humanities scholars carrying out research on a specific or on multiple literary work(s) are interested in the analysis of related texts or text passages. But the digital age has opened possibilities for scholars to enhance their traditional workflows. Enabled by digitization projects, humanities scholars can nowadays reach a large number of digitized texts through web portals such as Google Books or Internet Archive. Digital editions exist also for ancient texts; notable examples are PHI Latin Texts and the Perseus Digital Library. This shift from reading a single book “on paper” to the possibility of browsing many digital texts is one of the origins and principal pillars of the digital humanities domain, which helps developing solutions to handle vast amounts of cultural heritage data – text being the main data type. In contrast to the traditional methods, the digital humanities allow to pose new research questions on cultural heritage datasets. Some of these questions can be answered with existent algorithms and tools provided by the computer science domain, but for other humanities questions scholars need to formulate new methods in collaboration with computer scientists. Developed in the late 1980s, the digital humanities primarily focused on designing standards to represent cultural heritage data such as the Text Encoding Initiative (TEI) for texts, and to aggregate, digitize and deliver data. In the last years, visualization techniques have gained more and more importance when it comes to analyzing data. For example, Saito introduced her 2010 digital humanities conference paper with: “In recent years, people have tended to be overwhelmed by a vast amount of information in various contexts. Therefore, arguments about ’Information Visualization’ as a method to make information easy to comprehend are more than understandable.” A major impulse for this trend was given by Franco Moretti. In 2005, he published the book “Graphs, Maps, Trees”, in which he proposes so-called distant reading approaches for textual data that steer the traditional way of approaching literature towards a completely new direction. Instead of reading texts in the traditional way – so-called close reading –, he invites to count, to graph and to map them. In other words, to visualize them. This dissertation presents novel close and distant reading visualization techniques for hitherto unsolved problems. Appropriate visualization techniques have been applied to support basic tasks, e.g., visualizing geospatial metadata to analyze the geographical distribution of cultural heritage data items or using tag clouds to illustrate textual statistics of a historical corpus. In contrast, this dissertation focuses on developing information visualization and visual analytics methods that support investigating research questions that require the comparative analysis of various digital humanities datasets. We first take a look at the state-of-the-art of existing close and distant reading visualizations that have been developed to support humanities scholars working with literary texts. We thereby provide a taxonomy of visualization methods applied to show various aspects of the underlying digital humanities data. We point out open challenges and we present our visualizations designed to support humanities scholars in comparatively analyzing historical datasets. In short, we present (1) GeoTemCo for the comparative visualization of geospatial-temporal data, (2) the two tag cloud designs TagPies and TagSpheres that comparatively visualize faceted textual summaries, (3) TextReuseGrid and TextReuseBrowser to explore re-used text passages among the texts of a corpus, (4) TRAViz for the visualization of textual variation between multiple text editions, and (5) the visual analytics system MusikerProfiling to detect similar musicians to a given musician of interest. Finally, we summarize our and the collaboration experiences of other visualization researchers to emphasize the ingredients required for a successful project in the digital humanities, and we take a look at future challenges in that research field