在斜率图表上检查文章特征的意想不到的异常数据模式:告别文献计量中的破裂条形图
Sher-Wei Lim1,2,3, Willy Chou4,3, Julie Chi Chow5,6
1Department of Neurosurgery, Chi-Mei Medical Center, Chiali, Tainan, Taiwan.
Medicine
|September 3, 2025
概括
图书识别可视化需要改进. 这项研究引入了斜率图来突出文章元数据中意想不到的异常数据模式 (UADPs),取代了信息较少的突破条图以获得更好的洞察力.
科学领域:
- 图书识别和科学识别
- 信息科学
- 数据可视化
背景情况:
- 传统的图书统计可视化工具 (如CiteSpace) 往往缺乏清晰度,
- 需要改进的方法来分析文章的元数据并确定重要的数据模式.
研究的目的:
- 建议和验证使用斜率图来可视化文献数据,强调意想不到的异常数据模式 (UADPs).
- 用更具信息性的斜率图来代替传统的破裂条形图.
主要方法:
- 来自"Heliyon"杂志的26555篇文章的元数据分析.
- 在斜率图中应用拉什模型来识别UADP.
- 使用性能分析,汇总报告和视觉验证模型.
主要成果:
- 中国在研究"Heliyon"的贡献方面处于领先地位,该研究通过UADP的斜率图表进行识别 (设备平均平方误差=5.28).
- 盟约大学 (尼日利亚) 也显示了UADP (装备平均平方误差=2.22).
- 关键字"性能"显示了一个典型的数据模式.
结论:
- 使用UADP的斜率图提供了比传统的破裂条形图更有价值的见解.
- 未来的文献分析应纳入UADP以更深入地了解文章的特点和研究趋势.
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