Addressing the challenge of predicting scientific impact and ranking researchers is a complex yet critical task, drawing significant attention from scholars across diverse fields. This effort plays a key role in improving research productivity, supporting decision-making processes, and advancing methodologies for scientific evaluation. Over time, various metrics such as citation counts, total publications, hybrid methods, the h index, and h-type indicators have been introduced to identify influential researchers. Despite these efforts, no single metric has been universally accepted as the best approach, as different metrics serve varying purposes and contexts. This study presents a novel index developed through comprehensive analysis of a dataset comprising 1060 Neuroscience researchers, including both awardees and non-awardees. The initial phase of the research involved evaluating specific metrics to determine their ability to place awardees among the top 100 researchers, leading to the identification of the five parameters most frequently associated with awardee inclusion. Advanced deep learning techniques were then applied to refine the selection, pinpointing the top five influential parameters and assessing the disjointness in their outputs. To further enhance the findings, seven statistical models were examined for their ability to combine the most disjoint parameter pair while retaining their individual strengths. Selecting the most disjoint pair ensures that the ranking process integrates diverse evaluation criteria rather than relying on redundant or highly correlated parameters. This approach captures a broader spectrum of researcher impact, reducing bias and increasing the robustness of the final ranking index. Among these models, the h2 upper and k indices exhibited the highest disjointness ratio at 0.97. Additionally, the Harmonic Mean approach demonstrated superior performance, achieving an average impact score of 0.76, and excelled at preserving the unique features of the selected parameter pair. Based on these results, a new index was formulated using the Harmonic Mean (HM) of the most disjoint pair. This index showed significantly improved performance compared to existing metrics, offering a robust solution for ranking researchers effectively.

H-Index The H-Index is a metric measuring the productivity of a scholar. It is measured based on the number of citations got by the person for his or her publications. The index was introduced in 2005.[1] Examples If a scholar has 15 papers, each of which has at least 15 citations, their h-index is 15.[2] Related pages References - ↑ Hirsch, J E (2005). "An index to quantify an individual's scientific research output". National Library of Medicine. Proc. Natl. Acad. Sci. USA. pp. 16569–16572. Retrieved June 17, 2025. - ↑ "Calculate Your Academic Footprint: Your H-Index". University of Waterloo. Retrieved June 17, 2025.

The h-index is an author-level metric that measures both the productivity and citation impact of the publications, initially used for an individual scientist The h-index is an author-level metric that measures both the productivity and citation impact of the publications, initially used for an individual scientist or scholar. The h-index correlates with success indicators such as winning the Nobel Prize, being accepted for research fellowships and holding positions at top universities. The index is based on the set of the scientist's most cited papers The h-index is an author-level metric that measures both the productivity and citation impact of the publications, initially used for an individual scientist or scholar. The h-index correlates with success indicators such as winning the Nobel Prize, being accepted for research fellowships and holding positions at top universities. The index is based on the set of the scientist's most cited papers and the number of citations that they have received in other publications. The index has more recently been applied to the productivity and impact of a scholarly journal as well as a group of scientists, such as a department or university or country. The index was suggested in 2005 by Jorge E. Hirsch, a physicist at UC San Diego, as a tool for determining theoretical physicists' relative quality and is sometimes called the Hirsch index or Hirsch number. Hirsch intended the h-index to address the main disadvantages of other bibliometric indicators. The total number of papers metric does not account for the quality of scientific publications. The total number of citations metric, on the other hand, can be heavily affected by participation in a single publication of major influence (for instance, methodological papers proposing successful new techniques, methods or approximations, which can generate a large number of citations). The index works best when comparing scholars working in the same field, since citation conventions differ widely among different fields. The h-index is intended to measure simultaneously the quality and quantity of scientific output. The Kendall's correlation of h-index with scientific awards in physics was found at 34 percent in 2010 and zero percent in 2019.

Research performance, crucial for advancing productivity, refers to the achievements scientists make in their research endeavours. As scientific work becomes increasingly complex, enhancing research performance is becoming more challenging. However, previous investigations suggest that there may be a certain correlation between scientists’ knowledge diversity and knowledge synergy characteristics, providing potential new avenues for improving research performance. Therefore, it is necessary to explore the impact of scientists’ knowledge diversity and knowledge synergy on research performance. First, this study quantifies scientists’ knowledge diversity and knowledge synergy based on the similarity between scientists’ research fields and the degree of knowledge transfer among scientists. Then a negative binomial regression model is constructed, by using scientists’ cumulative publication counts, cumulative citation counts, average citation counts per article, average citation counts per year and H-index as dependent variables, with knowledge diversity and knowledge synergy as independent variables. In addition, variables such as scientists’ career age were controlled for in the model. Finally, empirical analysis is conducted using the data set of scientific publications from the American Physical Society from 1979 to 2009. The results indicate, first, that there is an inverted U-shaped relationship between knowledge diversity and research performance; as scientists’ knowledge

Objectives: This study aimed to compare the impact of Gross Domestic Product (GDP) per capita, spending on Research and Development (R&D), number of universities, and Indexed Scientific Journals on total number of research documents (papers), citations per document and Hirsch index (H-index) in various science and social science subjects among Asian countries. Materials and Methods: In this study, 40 Asian countries were included. The information regarding Asian countries, their GDP per capita, spending on R&D, total number of universities and indexed scientific journals were collected. We rec

Traditional bibliometric indicators such as the h-index emphasize cumulative citation impact but provide limited insight into the efficiency and concentration of that impact across a researcher's publication portfolio. This study introduces the he-index as an interpretable indicator of the proportion of publications represented by the h-index core, relative to the researcher's total publication output. Using publicly accessible Scopus data from 54 researchers across engineering, medicine, and social sciences in the United States, China, and Australia, we examine the behavior of the he-index in relation to publication volume, career stage, and disciplinary context. As expected for a ratio-based indicator, the he-index exhibits a strong negative association with total publications (Spearman ρ = -0.77, <i>p</i> < 0.001), which may reflect structural sensitivity to publication volume. To improve cross-volume interpretability, we further propose a volume-normalized efficiency metric (<i>he</i> ^* = <i>h</i>/ĥ), where the expected h-index is estimated using a power-law scaling model (ĥ = 1.02<i>N</i> ^0.7, <i>R</i> ² ≈ 0.87). The normalized metric shows no significant dependence on publication volume (ρ = 0.064, <i>p</i> = 0.645) and exhibits weaker career-stage sensitivity, while discipline-level differences remain non-significant. External validation using field-weighted citation impact (FWCI) available for a subset of researchers provides additional support for the normalized metric, with <i>he</i> ^* positively associated with FWCI (ρ = 0.419, <i>p</i> = 0.003), whereas the raw he-index shows no significant association. Overall, the findings indicate that impact concentration and volume-normalized citation efficiency offer complementary perspectives to cumulative impact metrics, supporting more nuanced and multidimensional research evaluation.

Information in the human visual system is encoded in the activity of distributed populations of neurons, which in turn is reflected in functional magnetic resonance imaging (fMRI) data. Over the last fifteen years, activity patterns underlying a variety of perceptual features and objects have been decoded from the brains of participants in fMRI scans. Through a novel multi-study meta-analysis, we have analyzed and modeled relations between decoding strength in the visual ventral stream, and stimulus and methodological variables that differ across studies. We report findings that suggest: (i) several organizational principles of the ventral stream, including a gradient of pattern granulation and an increasing abstraction of neural representations as one proceeds anteriorly; (ii) how methodological choices affect decoding strength. The data also show that studies with stronger decoding performance tend to be reported in higher-impact journals, by authors with a higher h-index. As well as revealing principles of regional processing, our results and approach can help investigators select from the thousands of design and analysis options in an empirical manner, to optimize future studies of fMRI decoding.