研究领域的可视化：系统科学计量学综述的方法论
Visualizing a Field of Research: A Methodology of Systematic Scientometric Reviews
摘要：
系统科学计量学评价，由科学计量学和视觉分析技术授权，提供了机会，以提高传统的系统评价的及时性，可访问性和再现性。虽然越来越容易获得的科学绘图工具使最终用户能够直观地了解研究领域的结构和动态，但目前实践中的一个共同瓶颈是建立一个学术出版物集，作为随后科学计量分析和可视化的投入。最终用户在准备过程中往往不得不面对一个两难境地：他们对一个知识领域了解得越多，就越容易找到相关数据来充分满足他们的需求;他们知道得越少，问题就越难解决。我们能做些什么来避免错过一些有价值的东西，但超出了我们最初的描述？在这篇文章中，我们介绍了一个灵活的和通用的方法，级联引用扩展，以提高构建一个书目数据集的系统评价的质量。此外，该方法简化了科学制图中的全球主义和地方主义的概念化，并将它们统一在一个一致和连续的光谱上。我们展示了一个应用程序的方法来研究基于文献的发现和比较五个数据集构建的基础上三个使用场景，即一个传统的基于关键字的搜索（一个数据集），扩展过程开始的突破性的文章的知识领域（两个数据集），和扩展过程开始与最近发表的评论文章由著名的专家在该领域（两个数据集）。每个数据集的独特覆盖范围通过网络可视化覆盖检查，并在广泛和综合的背景下参考其他数据集。
英文摘要：
Systematic scientometric reviews, empowered by scientometric and visual analytic techniques, offer opportunities to improve the timeliness, accessibility, and reproducibility of conventional systematic reviews. While increasingly accessible science mapping tools enable end users to visualize the structure and dynamics of a research field, a common bottleneck in the current practice is the construction of a collection of scholarly publications as the input of the subsequent scientometric analysis and visualization. End users often have to face a dilemma in the preparation process: the more they know about a knowledge domain, the easier it is for them to find the relevant data to meet their needs adequately; the little they know, the harder the problem is. What can we do to avoid missing something valuable but beyond our initial description? In this article, we introduce a flexible and generic methodology, cascading citation expansion, to increase the quality of constructing a bibliographic dataset for systematic reviews. Furthermore, the methodology simplifies the conceptualization of globalism and localism in science mapping and unifies them on a consistent and continuous spectrum. We demonstrate an application of the methodology to the research of literature-based discovery and compare five datasets constructed based on three use scenarios, namely a conventional keyword-based search (one dataset), an expansion process starting with a groundbreaking article of the knowledge domain (two datasets), and an expansion process starting with a recently published review article by a prominent expert in the domain (two datasets). The unique coverage of each of the datasets is inspected through network visualization overlays with reference to other datasets in a broad and integrated context.
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