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科学家等级-引文分布的统计规律。

Statistical regularities in the rank-citation profile of scientists.

机构信息

IMT Lucca Institute for Advanced Studies, 55100 Lucca, Italy.

出版信息

Sci Rep. 2011;1:181. doi: 10.1038/srep00181. Epub 2011 Dec 5.

Abstract

Recent science of science research shows that scientific impact measures for journals and individual articles have quantifiable regularities across both time and discipline. However, little is known about the scientific impact distribution at the scale of an individual scientist. We analyze the aggregate production and impact using the rank-citation profile c(i)(r) of 200 distinguished professors and 100 assistant professors. For the entire range of paper rank r, we fit each c(i)(r) to a common distribution function. Since two scientists with equivalent Hirsch h-index can have significantly different c(i)(r) profiles, our results demonstrate the utility of the β(i) scaling parameter in conjunction with h(i) for quantifying individual publication impact. We show that the total number of citations C(i) tallied from a scientist's N(i) papers scales as [Formula: see text]. Such statistical regularities in the input-output patterns of scientists can be used as benchmarks for theoretical models of career progress.

摘要

最近的科学计量学研究表明,期刊和单篇文章的科学影响力衡量指标在时间和学科上都具有可量化的规律性。然而,对于个体科学家的科学影响力分布,我们知之甚少。我们使用 200 名杰出教授和 100 名助理教授的排名-引文分布 c(i)(r)来分析总体的产出和影响力。对于整个论文排名 r 范围,我们将每个 c(i)(r)拟合到一个共同的分布函数中。由于具有相同 Hirsch h-index 的两位科学家可能具有明显不同的 c(i)(r)分布,因此我们的结果证明了β(i)标度参数与 h(i)结合用于量化个体发表影响的有效性。我们表明,从科学家的 N(i)篇论文中汇总的总引文数 C(i)呈[公式:见正文]的规模。科学家的输入-输出模式中的这种统计规律可以用作职业发展理论模型的基准。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbc0/3240955/721421914c07/srep00181-f1.jpg

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