Department of Computer Science, Capital University of Science & Technology, Islamabad, Pakistan.
PLoS One. 2020 May 29;15(5):e0233765. doi: 10.1371/journal.pone.0233765. eCollection 2020.
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.
研究人员通过系统的调查来确定事实并得出新的结论，从而为知识的前沿做出贡献，并随后以研究论文的形式发表他们的研究结果。这些研究出版物表明了研究人员的科学影响力。已经提出了不同的文献计量学指标来衡量研究人员的影响力或生产力。这些指标包括出版物数量、引用数量、合著者数量、h 指数等。自 h 指数提出以来，它一直被许多研究评为最重要的影响力指标。然而，由于 h 指数存在各种缺陷，已经提出了一些 h 指数的变体。例如，需要特别关注的一个维度是确定特定研究领域中杰出表现者的能力。在我们的研究中，我们比较了 h 指数及其一些变体在识别特定领域的杰出表现者方面的有效性。我们还发现了 h 指数与最近提出的指标之间的相关性。高相关性表明这些指标与 h 指数具有相同的效果，而低相关性意味着这些指标在对研究领域的潜在研究人员进行排名时做出了非冗余的贡献。到目前为止，这些指标的有效性还没有在相同领域的真实数据集上进行探索/验证。我们已经考虑了这些 h 指数的变体/修改，并在计算机科学领域的综合数据集上进行了测试。奖项获得者的数据集被视为评估这些指标对单个研究人员的基准。结果表明，这些指标与 h 指数的相关性较低，在识别特定领域的杰出表现者方面，这些指标的表现优于 h 指数。
