Abstract
The increasing specialization of science is motivating the fragmentation of traditional and well-established research areas into interdisciplinary communities of practice that focus on cooperation between experts to solve problems in a wide range of domains. This is the case of problem-driven visualization research (PDVR), in which groups of scholars use visualization techniques in different application domains such as the digital humanities, bioinformatics, sports science, or computer security. In this paper, we employ the findings obtained during the development of a novel visual text analytics tool we built in previous studies, GlassViz, to automatically detect interesting knowledge associations and groups of common interests between these communities of practice. Our proposed method relies on the statistical modeling of author-assigned keywords to make its findings, which are demonstrated in two use cases. The results show that it is possible to propose interactive, semisupervised visual approaches that aim at defragmenting a body of research using text-based, automatic literature analysis methods.
Highlights
The increasing specialization of science has motivated the surge of different novel interdisciplinary collaborations between research communities in a wide range of domains
In recent times, certain authors have started to introduce proposals to facilitate this desirable transfer of knowledge across communities [3], which is known in HCI and visualization research as methodology transfer (MT) [4]
Our contribution is inspired by other works in visualization design, visual analytics, information science, and text mining that we introduce
Summary
The increasing specialization of science has motivated the surge of different novel interdisciplinary collaborations between research communities in a wide range of domains. This is the case for problem-driven visualization research (PDVR) [1], a type of interdisciplinary practice that connects domain and visualization experts to solve non-trivial, specific domain problems in diverse areas such as biology, city planning, or sports science In this regard, it is usual that scholars involved in these kinds of collaborations gather in workshops and micro-conferences to discuss each area’s particularities, fragmenting visualization research into communities of practice. Despite the absolute utility value of these collections, they may be indicative of the creation of isolated communities within the visualization practice, a fact that could lead to an excess of redundant visualization solutions for generic, domain-agnostic tasks (establishing comparisons, creating summaries, or searching for specific elements) that are replicated across collaborations [2] This risk calls for novel approaches that allow a fluid exchange of ideas among practitioners from different knowledge domains to avoid wasting time and human resources that is potentially harming visualization research. GlassViz [5], we contributed a visual text analytics (VTA)
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