Evaluation of h-index and its qualitative and quantitative variants in Neuroscience

Ameer, Madiha; Afzal, Muhammad Tanvır

doi:10.1007/s11192-019-03209-6

Cited by 20 publications

(9 citation statements)

References 34 publications

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“…There is no universally standardized approach for measuring a researcher’s potential [ 4 ]. Numerous quantitative research assessment parameters have been proposed to identify researchers who make innovative and impactful contributions to the scientific community [ 5 ]. Each technique proposed in the literature is based on its distinct criteria for determining the significance of a researcher’s work.…”

Section: Introductionmentioning

confidence: 99%

MRET: Modified Recursive Elimination Technique for ranking author assessment parameters

Mustafa,

Rauf,

Tanvir Afzal

2024

PLoS ONE

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In scientific research, assessing the impact and influence of authors is crucial for evaluating their scholarly contributions. Whereas in literature, multitudinous parameters have been developed to quantify the productivity and significance of researchers, including the publication count, citation count, well-known h index and its extensions and variations. However, with a plethora of available assessment metrics, it is vital to identify and prioritize the most effective metrics. To address the complexity of this task, we employ a powerful deep learning technique known as the Multi-Layer Perceptron (MLP) classifier for the classification and the ranking purposes. By leveraging the MLP’s capacity to discern patterns within datasets, we assign importance scores to each parameter using the proposed modified recursive elimination technique. Based on the importance scores, we ranked these parameters. Furthermore, in this study, we put forth a comprehensive statistical analysis of the top-ranked author assessment parameters, encompassing a vast array of 64 distinct metrics. This analysis gives us treasured insights in between these parameters, shedding light on the potential correlations and dependencies that may affect assessment outcomes. In the statistical analysis, we combined these parameters by using seven well-known statistical methods, such as arithmetic means, harmonic means, geometric means etc. After combining the parameters, we sorted the list of each pair of parameters and analyzed the top 10, 50, and 100 records. During this analysis, we counted the occurrence of the award winners. For experimental proposes, data collection was done from the field of Mathematics. This dataset consists of 525 individuals who are yet to receive their awards along with 525 individuals who have been recognized as potential award winners by certain well known and prestigious scientific societies belonging to the fields’ of mathematics in the last three decades. The results of this study revealed that, in ranking of the author assessment parameters, the normalized h index achieved the highest importance score as compared to the remaining sixty-three parameters. Furthermore, the statistical analysis results revealed that the Trigonometric Mean (TM) outperformed the other six statistical models. Moreover, based on the analysis of the parameters, specifically the M Quotient and FG index, it is evident that combining these parameters with any other parameter using various statistical models consistently produces excellent results in terms of the percentage score for returning awardees.

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Section: Introductionmentioning

confidence: 99%

MRET: Modified Recursive Elimination Technique for ranking author assessment parameters

Mustafa,

Rauf,

Tanvir Afzal

2024

PLoS ONE

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“…It is based on the scientist's most frequently cited papers, and the number of citations that they have received in other publications [3]. The h-index is used to assist in decisions related to hiring, tenure, and promotion for researchers in many subjects, including information science [4], neuroscience [5], and management [6]. In addition to the h-index, several related citation analysis methods were proposed, such as the g-index and e-index, which focuses more on more frequently cited articles [7].…”

Section: Introductionmentioning

confidence: 99%

VisualRPI: Visualizing Research Productivity and Impact

Hung

Lin

2022

Sustainability

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Research productivity and impact (RPI) is commonly measured through citation analysis, such as the h-index. Despite the popularity and objectivity of this type of method, it is still difficult to effectively compare a number of related researchers in terms of various citation-related statistics at the same time, such as average cites per year/paper, the number of papers/citations, h-index, etc. In this work, we develop a method that employs information visualization technology, and examine its applicability for the assessment of researchers’ RPI. Specifically, our prototype, a visualizing research productivity and impact (VisualRPI) system, is introduced, which is composed of clustering and visualization components. The clustering component hierarchically clusters similar research statistics into the same groups, and the visualization component is used to display the RPI in a clear manner. A case example using information for 85 information systems researchers is used to demonstrate the usefulness of VisualRPI. The results show that this method easily measures the RPI for various performance indicators, such as cites/paper and h-index.

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“…In order to overcome the above disadvantage of the h index its several variants, simultaneously retaining its advantages, have been widely proposed and discussed (for example, see: refs. [2][3][4][5][6][7][8][9][10][11][12][13]).…”

Section: Introductionmentioning

confidence: 99%

Comparative study of scaling parameters and research output of selected highly- and moderately-cited individual authors

Sangwal¹

2022

jcsi

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The real data of cumulative citations ln of selected nth paper of individual N papers published by some highly- and moderately-cited individual authors are analyzed to compare Hirsch and Hirsch-type indices h, h1, hf and hm, and citation radii R and Rf from consideration of: (1) the number An of coauthors of the paper, (2) the normalization of citations ln and cumulative fraction lnf of citation of the nth paper by mean and median citations of the citations ln of all Nc cited papers, and (3) the determination of effective rank neff of the lnf citations. Analysis of the ln(n), lnf(n) and lnf(neff) data was also carried out by using a Langmuir-type function l = l0[1-aKn/(1+Kn)], where l denotes the citations ln and lnf of all cited Nc papers arranged in the decreasing order, a is an effectiveness parameter, K is the so-called Langmuir constant, n denotes the rank n or neff of citations and l0 is the value of l when n or neff approaches zero. For a comparison of the publication output of different authors it was found that the hm index is more consistent than other indices, and it can be normalized to account for the publication career of different authors. However, Langmuir-type function is not adequate for comparison of the publication output of different authors because it describes the rank-order distribution patterns satisfactorily in terms of two parameters. To compare the publication output of different authors independent of their career length t, it is suggested to use scaling parameters h/t, hf/t and hm/t.

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Evaluation of h-index and its qualitative and quantitative variants in Neuroscience

Cited by 20 publications

References 34 publications

MRET: Modified Recursive Elimination Technique for ranking author assessment parameters

MRET: Modified Recursive Elimination Technique for ranking author assessment parameters

VisualRPI: Visualizing Research Productivity and Impact

Comparative study of scaling parameters and research output of selected highly- and moderately-cited individual authors

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