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Comparison of researchers’ impact indices

机译:研究人员影响指数的比较

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Researchers contribute to the frontiers of knowledge by establishing facts and reaching new conclusions through systematic investigations, and by subsequently publishing the outcomes of their research findings in the form of research papers. These research publications are indicative of researchers' scientific impact. Different bibliometric indices have been proposed to measure the impact or productivity of a researcher. These indices include publication count, citation count, number of coauthors, h-index, etc. The h-index, since its inception, has been ranked as the foremost impact indicator by many studies. However, as a consequence of the various short comings identified in h-index, some variants of h-index have been proposed. For instance, one dimension which requires significant attention is determining the ability of exceptional performers in a particular research area. In our study, we have compared effectiveness of h-index and some of its recent variants in identifying the exceptional performers of a field. We have also found correlation of h-index with recently proposed indices. A high correlation indicates same effect of these indices as of h-index and low correlation means these indices make non-redundant contribution while ranking potential researchers of a field of study. So far, effectiveness of these indices has not been explored/validated on real data sets of same field. We have considered these variants/modifications of h-index along with h-index and tested on comprehensive data set for the field of Computer Science. The Award winners’ data set is considered as the benchmark for the evaluation of these indices for individual researchers. Results show that there is a low correlation of these indices with h-index, and in identifying exceptional performers of a field these indices perform better than h-index.
机译:研究人员通过制定事实和通过系统调查来达到新的结论,以及随后以研究论文的形式出版其研究结果的结果来促进知识前沿。这些研究出版物表明研究人员的科学影响。已经提出了不同的尺寸指数来测量研究人员的影响或生产力。这些指数包括出版物计数,引文计数,共同奉献,H-Index等的数量,自成立以来的H-Index被许多研究被排名为最重要的影响指标。然而,由于H折射中鉴定的各种短暂转化,已经提出了H-Index的一些变体。例如,一种需要重大关注的一个维度是确定特定研究区域中具有特殊表现者的能力。在我们的研究中,我们在识别领域的特殊表演者时比较了H-Index的有效性和最近的一些变体。我们还发现H-Index与最近提出的指标的相关性。高相关表示H型指数的这些指数的效果相同,低相关意味着这些索引在排名研究领域的潜在研究人员进行非冗余贡献。到目前为止,这些指数的有效性尚未在相同领域的实际数据集上探索/验证。我们已经考虑了H-Index的这些变体/修改以及H-Index并测试了计算机科学领域的综合数据集。奖获奖者的数据集被认为是为个人研究人员评估这些指数的基准。结果表明,这些指数与H级指数的相关性低,并且识别这些索引比H-Index更好的字段的卓越表演者。

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