  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, SD2, to enable flexible data partition and composition
to support various analysis requirements within a single system. SD2 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 SD2 and revise the
design and system implementation accordingly. The effectiveness of SD2 is
demonstrated via multiple usage scenarios with each aiming to answer a
specific, commonly raised question.
