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| Laws Governing Outstanding ScientistsResearch Output: The Example of Lasker Award Winners |
| Ren Xiaoya1,2, Zhang Zhiqiang1,2, Chen Yunwei1,2 |
1.Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041
2.Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 |
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Abstract Important scientific breakthroughs are inseparable from the accumulation and inheritance of scientific knowledge and academic thoughts. Understanding the general laws of outstanding scientistsresearch output in their respective fields will not only help researchers grasp the main development trends and characteristics of relevant disciplines, but also help decision-makers understand the laws of scientific development in pertinent disciplines and improve management funding policies and mechanisms. In order to establish a theoretical model, this paper uses qualitative and quantitative cluster analysis as research methods. Using 319 scientists who won the Lasker Medical Research Awards in the field of biomedicine as representative samples, we obtained 31 095 articles, after data preprocessing. The research theme was extracted by LDA topic model, after which we conducted co-occurrence network analysis on the semantic level to find the general laws of outstanding scientists' research output within biomedicine. The article summarizes the laws in qualitative clustering of award-winning achievements, main research topics distribution, award-winning age trends, and publications distribution of outstanding scientists within the field of biomedical science.
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Received: 15 April 2019
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1 许合先. 诺贝尔自然科学奖领域知识与人才的传承规律及启示[J]. 科技管理研究, 2008, 28(6): 503-505.
2 叶鹰, 刘艳阳. 科技类诺贝尔奖定性聚类分析[J]. 科学学与科学技术管理, 2005, 26(2): 17-20.
3 李奕嬴, 朱军文. 经济学国际学术奖项的类别、相互关系及影响力高低的图谱分析[J]. 情报杂志, 2017, 36(5): 82-89.
4 MaC F, SuC, YuanJ P, et al. Papers written by Nobel Prize winners in physics before they won the prize: An analysis of their language and journal of publication[J]. Scientometrics, 2012, 93(3): 1151-1163.
5 JenkinJ. A unique partnership: William and Lawrence Bragg and the 1915 Nobel Prize in physics[J]. Minerva, 2011, 39(4): 373-392.
6 CharikerJ H, ZhangY H, PaniJ R, et al. Identification of successful mentoring communities using network-based analysis of mentor–mentee relationships across Nobel laureates[J]. Scientometrics, 2017, 111(3): 1733-1749.
7 张志强, 门伟莉. 诺贝尔物理学奖获得者中师承效应量化研究[J]. 情报学报, 2014, 33(9): 926-935.
8 KademaniB S, KalyaneV L, JangeS. Scientometric portrait of nobel laureate Dorothy Crowfoot Hodgkin[J]. Scientometrics, 1999, 45(2): 233-250.
9 ZhouZ W, XingR, LiuJ, et al. Landmark papers written by the Nobelists in physics from 1901 to 2012: A bibliometric analysis of their citations and journals[J]. Scientometrics, 2014, 100(2): 329-338.
10 李江, 姜明利, 李玥婷. 引文曲线的分析框架研究——以诺贝尔奖得主的引文曲线为例[J]. 中国图书馆学报, 2014, 40(2): 41-47.
11 EggheL, GunsR, RousseauR. Thoughts on uncitedness: Nobel laureates and Fields medalists as case studies[J]. Journal of the American Society for Information Science and Technology, 2011, 62(8): 1637-1644.
12 MazloumianA, EomY H, HelbingD, et al. How citation boosts promote scientific paradigm shifts and Nobel Prizes[J]. PLoS ONE, 2011, 6(5): e18975.
13 HuX J, RousseauR. Nobel Prize winners 2016: Igniting or sparking foundational publications?[J]. Scientometrics, 2017, 110(2): 1053-1063.
14 HuX J, RousseauR. Scientific influence is not always visible: The phenomenon of under-cited influential publications[J]. Journal of Informetrics, 2016, 10(4): 1079-1091.
15 唐川, 唐卷, 房俊民, 等. 图灵奖得主识别与预测研究——基于多文献计量指标和支持向量机[J]. 情报杂志, 2015, 34(2): 69-72, 78.
16 The Laker Awards拉斯克医学研究奖[EB/OL]. [2019-02-10]. http://www.laskerfoundation.org/awards-overview/.
17 AwardLaker[EB/OL]. [2019-04-10]. https://en.wikipedia.org/wiki/Lasker_Award.
18 班杜拉. 思想和行动的社会基础: 社会认知论[M]. 林颖, 等译. 上海: 华东师范大学出版社, 2001.
19 库尔特·勒温. 拓扑心理学原理[M]. 高觉敷, 译. 北京: 商务印书馆, 2003.
20 BleiD M, NgA Y, JordanM I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2012, 3: 993-1022.
21 王连喜, 曹树金. 学科交叉视角下的网络舆情研究主题比较分析——以国内图书情报学和新闻传播学为例[J]. 情报学报, 2017, 36(2): 159-169.
22 LiW. Random texts exhibit Zipf’s-law-like word frequency distribution[M]// Random Texts Exhibit Zipfs-Law-Like Word Frequency Distribution, 1992.
23 Derwent Data Analyzer[EB/OL]. [2019-03-25]. https://clarivate.com/products/derwent-data-analyzer/.
24 KNIME[EB/OL]. [2019-02-21]. https://www.knime.com/knime-software.
25 BastianM, HeymannS, JacomyM. Gephi: An open source software for exploring and manipulating networks[C]//Proceedings of the Third International AAAI Conference on Weblogs and Social Media. Palo Alto: AAAI Press, 2009, 8: 361-362.
26 SeglenP O. Why the impact factor of journals should not be used for evaluating research[J]. The BMJ, 1997, 314: 497.
27 中国科学院文献情报中心期刊分区表[EB/OL]. [2019-04-10]. http://www.fenqubiao.com/.
28 门伟莉, 张志强. 诺贝尔科学奖授奖时滞研究[J]. 情报学报, 2015, 34(4): 361-370. |
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