论文标题
SD2：切片和划分学术数据，用于交互式评估学业表现
SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance
论文作者
论文摘要
由于学术数据的内在复杂性，全面评估和比较研究人员的学业成绩是复杂的。不同的学术评估任务通常需要以各种方式研究出版和引文数据。在本文中，我们提出了一个交互式可视化框架SD2，以启用灵活的数据分区和组成，以支持单个系统中的各种分析要求。 SD2具有分层直方图，这是一种灵活切片和划分数据的新型视觉表示，可以研究和比较学术表现的不同方面。我们还利用最先进的可视化技术来选择个别研究人员或结合多个学者进行全面的视觉比较。我们进行了多轮专家评估，以研究SD2的有效性和可用性，并相应地修改设计和系统实施。 SD2的有效性是通过多种用法方案来证明的，每个方案旨在回答一个特定的，通常提出的问题。
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.
