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数据分析与知识发现  2024, Vol. 8 Issue (4): 88-98     https://doi.org/10.11925/infotech.2096-3467.2023.0149
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
融合创新性与影响力的论文代表作遴选方法研究*
刘佳程1,2,马廷灿1,2,3(),岳名亮1,2,3
1中国科学院武汉文献情报中心 武汉 430071
2中国科学院大学信息资源管理系 北京 100190
3科技大数据湖北省重点实验室 武汉 430071
Selecting Representative Papers Based on Innovation and Influence
Liu Jiacheng1,2,Ma Tingcan1,2,3(),Yue Mingliang1,2,3
1National Science Library(Wuhan), Chinese Academy of Sciences, Wuhan 430071, China
2Department of Information Resources Management, University of Chinese Academy of Sciences, Beijing 100190, China
3Hubei Key Laboratory of Big Data in Science and Technology, Wuhan 430071, China
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摘要 

【目的】 综合考虑论文的创新性和影响力,提出一种新的论文代表作遴选方法。【方法】 基于论文创新性和影响力测度指标,设计代表性指数,遴选代表性指数靠前的论文作为学者的论文代表作。以诺贝尔物理学奖获得者为例,遴选其论文代表作,将平均排名和准确率作为评价指标检验遴选方法的有效性和准确性。选取张涛院士和Hirsch J E教授进行案例分析,使用代表性指数遴选其论文代表作。【结果】 实证结果表明,与其他6种遴选方法对比,使用代表性指数遴选论文代表作在平均排名(2.838)和准确率(63.158%)两个指标上均排名第一。【局限】 使用的测度指标需要一定的引文积累,可能无法有效选出学者新近做出的重要工作。【结论】 所提论文代表作遴选方法具备可行性。

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刘佳程
马廷灿
岳名亮
关键词 论文代表作代表作遴选创新性影响力基础研究人才    
Abstract

[Objective] This study proposes a new method for selecting representative papers, considering their innovation and impact. [Methods] First, we designed a representative index based on each paper’s innovation and influence measurement. Then, we chose papers with higher representative indices as a scholar’s representative works. Taking Nobel Prize laureates in physics as an example, we selected their representative papers. We used the average ranking and precision as evaluation metrics to assess the effectiveness of the proposed model. We also analyzed Academician Zhang Tao and Professor Hirsch JE’s works using the new model and identified their representative papers. [Results] Compared with the other six methods, the proposed model ranks first in both average ranking (2.838) and precision (63.158%). [Limitations] Our metrics require citation accumulation and may not effectively identify a scholar’s recent contributions. [Conclusions] The proposed method is feasible for selecting representative papers.

Key wordsRepresentative Papers    Selection of Representative Works    Innovation    Influence    Basic Research Talents
收稿日期: 2023-03-02      出版日期: 2024-03-01
ZTFLH:  G350  
  G353  
基金资助:* 中国科学院战略研究与决策支持系统建设专项(GHJ-ZLZX-2021-22-2);中国科学院青年人才专项(青年创新促进会)(2019176)
通讯作者: 马廷灿,ORCID:0000-0001-5985-384X,E-mail:matc@whlib.ac.cn。   
引用本文:   
刘佳程, 马廷灿, 岳名亮. 融合创新性与影响力的论文代表作遴选方法研究*[J]. 数据分析与知识发现, 2024, 8(4): 88-98.
Liu Jiacheng, Ma Tingcan, Yue Mingliang. Selecting Representative Papers Based on Innovation and Influence. Data Analysis and Knowledge Discovery, 2024, 8(4): 88-98.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2023.0149      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2024/V8/I4/88
DOI 代表性指数 被引频次 CNCI NI 颠覆性指数 H指数 PageRank
10.1103/physrevlett.50.1153 59.668 1337 67.435 0.885 0.505 74 1.13×10-4
10.1103/physrevlett.61.2015 43.489 1643 85.572 0.508 0.007 106 1.29×10-4
10.1103/physrevlett.100.013904 18.311 448 29.193 0.627 0.026 43 2.46×10-5
10.1103/physrevlett.47.1840 17.110 586 30.946 0.553 0.026 65 5.77×10-5
10.1103/physrevlett.101.010504 13.880 600 39.098 0.355 -0.040 51 2.05×10-5
Table 1  论文指标计算结果示例(诺贝尔获奖者Haldane FDM部分论文计算结果)
评价指标 代表性指数 被引频次 CNCI NI 颠覆性指数 H指数 PageRank
平均排名 2.838 3.379 3.257 5.346 5.607 3.498 3.022
准确率 63.158% 59.211% 57.895% 15.789% 28.947% 60.526% 60.526%
Table 2  不同遴选方法的平均排名和准确率对比
论文序号 DOI 被引频次 创新性 影响力 代表性
1 10.1038/nchem.1095 2 953 0.618 69.113 42.689
2 10.1038/s41560-017-0078-8 822 0.252 43.588 10.976
3 10.1002/anie.200803233 558 0.376 12.209 4.595
4 10.1039/b804362k 283 0.509 6.169 3.139
5 10.1016/j.nanoen.2012.04.003 234 0.440 6.693 2.946
Table 3  张涛论文代表作得分情况
论文序号 DOI 被引频次 创新性 影响力 代表性
1 10.1073/pnas.0507655102 5 476 0.964 171.172 164.951
2 10.1103/physrevlett.83.1834 2 104 0.881 67.204 59.218
3 10.1103/physrevlett.54.1317 496 0.843 20.804 17.532
4 10.1103/physrevlett.56.2521 650 0.614 24.830 15.242
5 10.1103/physrevb.31.4403 824 0.329 33.577 11.043
Table 4  Hirsch论文代表作得分情况
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