桑基网络 (SankeyNetwork):一个清晰简洁的可视化工具,用于图书统计数据
Sher-Wei Lim1,2,3, Willy Chou3,4, Lifan Chen5
1Department of Neurosurgery, Chi-Mei Medical Center, Chiali, Tainan, Taiwan.
MethodsX
|June 23, 2025
概括
本研究引入了桑基图和斜率图,以改进文献计量分析,增强作者合作和研究主题的可视化. 这些方法为研究模式和关键贡献者提供了更清晰的见解.
科学领域:
- 圣经计量学和科学计量学
- 信息科学 信息科学 信息科学
- 研究评估研究评估
背景情况:
- 传统的图书识别可视化,如同词网络,在识别关键贡献者和主题方面存在局限性.
- 现有的方法往往在表示研究景观的视觉复杂性和清晰度方面扎.
研究的目的:
- 提出一个新的框架,将桑基图与作者合作和共同词的出现整合起来.
- 通过克服传统方法的局限性来增强文献计量可视化.
- 更有效地识别关键贡献者和新兴研究主题.
主要方法:
- 来自METHODSX杂志 (2020-2024) 的 2252 篇文章的分析.
- 将桑基图和斜率图与图库数据集成在一起.
- 使用简要的绩效表和跟进领先的集群算法 (FLCA).
主要成果:
- 美国,国际共生协会 (印度) 和翔X汉被确定为突出的实体/作者.
- "模型"成为最常见的关键词.
- 2020年Wondimagegn Mengist的一篇文章获得了最高的引用数量 (370).
- 斜率图显示了四年来四个核心元素的上升趋势.
结论:
- 拟议的框架结合了性能表,桑基图和斜率图,提供了更清晰的洞察力和更少的视觉复杂性.
- 这些方法被推用于未来的文献计量分析,以检测研究热点和不断变化的模式.
- 桑基图有效地增强了作者合作的传统文献识别可视化方法.
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