SD^2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance

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Zhichun Guo, Jun Tao, Siming Chen, Nitesh V. Chawla, Chaoli Wang

 View presentation:2022-10-20T19:24:00ZGMT-0600Change your timezone on the schedule page
2022-10-20T19:24:00Z

Exemplar figure, described by caption below — SD^2 consists of three coordinated views: (a) scholar view, (b) publication view, and (c) hierarchical histogram view. The example shows a 2019 ACM Turing Award winner Yoshua Bengio�s papers with his co-authors Ian Goodfellow or Aaron Courville. In (c), the upper histogram shows papers authored by Goodfellow or Courville and the lower histogram shows papers authored by Bengio and Courville.

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Keywords

Scholarly performance, publication, citation, hierarchical histogram, visual analytics.

Abstract

Comprehensively evaluating and comparing researchers' academic performance is complicated due to the intrinsic complexity of scholarly data. Different scholarly evaluation tasks often require the publication and citation data to be investigated in various manners. In this paper, we present an interactive visualization framework, SD^2, to enable flexible data partition and composition to support various analysis requirements within a single system. SD^2 features the hierarchical histogram, a novel visual representation for flexibly slicing and dicing the data, allowing different aspects of scholarly performance to be studied and compared. We also leverage the state-of-the-art set visualization technique to select individual researchers or combine multiple scholars for comprehensive visual comparison. We conduct multiple rounds of expert evaluation to study the effectiveness and usability of SD^2 and revise the design and system implementation accordingly. The effectiveness of SD^2 is demonstrated via multiple usage scenarios with each aiming to answer a specific, commonly raised question.