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Comprehensively evaluating and comparing researchers' academic performance iscomplicated due to the intrinsic complexity of scholarly data. Differentscholarly evaluation tasks often require the publication and citation data tobe investigated in various manners. In this paper, we present an interactivevisualization framework, SD2, to enable flexible data partition and compositionto support various analysis requirements within a single system. SD2 featuresthe hierarchical histogram, a novel visual representation for flexibly slicingand dicing the data, allowing different aspects of scholarly performance to bestudied and compared. We also leverage the state-of-the-art set visualizationtechnique to select individual researchers or combine multiple scholars forcomprehensive visual comparison. We conduct multiple rounds of expertevaluation to study the effectiveness and usability of SD2 and revise thedesign and system implementation accordingly. The effectiveness of SD2 isdemonstrated via multiple usage scenarios with each aiming to answer aspecific, commonly raised question.

SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance