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Calculation of characteristic index (c-index). Location Total # Cites Year Row # = Σ wh Position wc = 1/p Σ wc
Source publication
Background: Many author indices exist to gauge academic productivity. Several of these indices are calculated based upon an author's scholarly publication record, but the measurement methodology to calculate each index varies considerably, and the precise function being used, as well as the end result, is often complex and difficult to assess. Meth...
Contexts in source publication
Context 1
... for first or last authorship wc is 1/1 = 1, for second or second-to-last authorship wc is 1/2 = 0.5, for third or third-to-last authorship wc is 1/3 = 0.33, and so on. In Table 2, the top cited papers of author J, Table 1, are given as an example, since the h-index is relatively low, so that the number of rows needed for illustration will be relatively short. Shown for each paper, columns from left to right, are the author's location in the author list, the total number of authors on the paper, the number of citations attributed to the paper, its year of publication, the row number (which is also the sum of contributions to the h-index), the author's position in the author list according to the weighting paradigm described above (for example if listed at location 6 out of 7 authors, position is 2, if listed at location 7 of 9 authors, position is 3), the weight according to the characteristic index wc, and the sum of weighted contributions according to the characteristic index. ...Context 2
... described in the Introduction, the h-index is defined as the row after which the value in the citation column is less than the row number. For the h-index calculation, author weight wh is unity for all papers regardless of author position, and thus the sum of h weighted contributions (Σ wh, Table 2) increases by 1 in every row. The h-index is therefore the row after which the value in the citation column becomes less than Σ wh. ...Context 3
... value is 28, an integer, noted by an asterisk. Similarly, the c-index is defined as the row after which the value in the citation column becomes less than the sum of c-index weighted contributions (column Σ wc, Table 2). Note that the sum of c contributions increases fractionally, except when the author is listed as first or last author, in which case the weighted contribution equals 1, the same as for the h-index. ...Context 4
... Table 2, when the contribution of the author to each paper is small (i.e., author's position is toward the middle of the author list), the sum of weighted contributions (Σ wc) will increase more slowly than the h index, and thus the crossing with the citations column will occur further down in the list (c-index is smaller than h-index). However, if the author location for all published papers were to be either first or last in the list of authors, then wc would equal 1 in each row, and the sum of contributions column would be the same as for the h-index (Σ wc = Σ wh). ...Context 5
... weighed c' contribution is wc' = 1/y. An example is shown in Table 3 (author J, as in Table 2). For the paper with the most cites, in row 1, the author is 6th of 7 total authors. ...Context 6
... c-index values (Table 4) are less than h-index values (Table 1) for all authors. Other information is shown including the average position of the author in the list of authors for all publications (Av Pos), average number of authors per publication (Av Au/ Table 2. The average position, authors per paper, and cites per paper were calculated for Author J using 33 papers, and so on for the other authors in the table. ...Context 7
... h-index ranks authors by determining h publications with h or more citations. However, the characteristic indices c-and c'-modify the calculation by altering the weighting of author contribution for each paper (Tables 2 and 3). The c-index calculation considers author position in weighting author contribution. ...Similar publications
This paper examines the publications of Universities in NIRF. The study has considered
the publications of top 10 Universities in Scopus, Web of Science (WoS) & Indian Citation
Index (ICI), patent publications and citations. Further, the Citation Rate (CR) for the publications of the Universities have also been found using simple calculation method...
Citations
... First, various online tools such as Google Scholar and Scopus were launched (both in 2004), enabling data on scientific production and impact to be gathered for quantitative evaluation (Assimakis and Adam, 2010). Second, bibliometric scholars had developed indicators to measure academic performance (Ciaccio et al., 2019). Since the 2000s, research assessment frameworks have used bibliometric measurements of productivity either as a complementary tool for peer review or as a primary means of evaluation (Assimakis and Adam, 2010). ...
Purpose
The purpose of this article is to theoretically outline and empirically test two contribution-based indicators: (1) the scholars' annual contribution-based measurement and (2) the annual contribution modified h-index, computing six criteria: total number of papers, computed SCImago Journal Rank values, total number of authors, total number of citations of a scholar’s work, number of years since paper publication and number of annual paper citations.
Design/methodology/approach
Despite widespread scholarly agreement about the relevance of research production in evaluation and recruitment processes, the proposed mechanisms for gauging publication output are still rather elementary, consequently obscuring each individual scholar’s contributions. This study utilised the Technique for Order of Preference by Similarity to Ideal Solution method, and the authors built two indicators to value author's contribution.
Findings
To test both indicators, this study focussed on the most productive scholars in communication during a specific time period (2017–2020), ranking their annual research contribution and testing it against standard productivity measures (i.e. number of papers and h-index).
Originality/value
This article contributes to current scientometric studies by addressing some of the limitations of aggregate-level measurements of research production, providing a much-needed understanding of scholarly productivity based on scholars' actual contribution to research.
... The author characteristic index (c-index) was calculated for the top nine most productive authors. This assigns weighting for an author's position in the authorship of each publication, where the first author and last author of a publication are given more weight [19]. ...
Background and Objectives: Single-Event Multi-Level Surgery (SEMLS) is a complex surgical programme in which soft tissue contractures and bony torsional deformities at the ankle, knee and hip, in both lower limbs are surgically corrected during a single operative session, requiring one hospital admission and one period of rehabilitation. The aim of SEMLS is to improve gait and function in ambulant children with cerebral palsy. Utilisation of the SEMLS concept can reduce the number of surgical events, hospital inpatient stays and reduce rehabilitation requirements to a single intensive episode. Three-dimensional gait analysis is a pre-requisite to plan intervention at multiple anatomic levels to correct fixed deformities and to improve gait and function. Materials and Methods: This study was a bibliometric analysis of SEMLS in cerebral palsy using the Clarivate Web of Science Core Collection database from 1900 to 29 May 2023. Results: A total of 84 studies met the inclusion criteria. The most highly cited article was “Correction of severe crouch gait in patients with spastic diplegia with use of multilevel orthopaedic surgery” by Rodda et al. (2006) with 141 citations. The most productive institutions by number of articles were the Royal Children’s Hospital Melbourne (Australia), Murdoch Children’s Research Institute (Australia) and University of Melbourne (Australia). The most productive author by number of citations was HK Graham (Australia). Conclusions: The literature base for SEMLS consists largely of retrospective cohort studies. The aforementioned three institutes in Melbourne, Australia, which frequently collaborate together, have contributed the greatest number of studies in this field.
... There are several methods to measure an author's contribution. We applied the weighted contribution impact technique, which was used in some previous studies (e.g., Ciaccio et al., 2019;Shapiro et al., 1994). This method considers the first and last authors as the major contributors, the second and next-to-last authors as the second-largest contributors to the research, and so on. ...
Scientific collaboration in almost every discipline is mainly driven by the need of sharing knowledge, expertise, and pooled resources. Science is becoming more complex which has encouraged scientists to involve more in collaborative research projects in order to better address the challenges. As a highly interdisciplinary field with a rapidly evolving scientific landscape, artificial intelligence calls for researchers with special profiles covering a diverse set of skills and expertise. Understanding gender aspects of scientific collaboration is of paramount importance, especially in a field such as artificial intelligence that has been attracting large investments. Using social network analysis, natural language processing, and machine learning and focusing on artificial intelligence publications for the period from 2000 to 2019, in this work, we comprehensively investigated the effects of several driving factors on acquiring key positions in scientific collaboration networks through a gender lens. It was found that, regardless of gender, scientific performance in terms of quantity and impact plays a crucial part in possessing the “social researcher” role in the network. However, subtle differences were observed between female and male researchers in acquiring the “local influencer” role.
... [9] However, it makes a difference which h-index will be used for the scholar evaluation as the h-index depends on the source by which publications and citations will be counted. [10] There are several databases from which h-index can be retrieved for an individual scholar and they include Google Scholar (GS), Scopus, and Web of Science (WoS). GS, unlike the Scopus and WoS, is freely available and researchers can make their own GS profiles easily. ...
Measuring scientific impact has long become a fact of academic life. Better scholarly output is related to higher chances of being promoted and winning a research grant. There are numerous ways to measure scholarly impact, such as through the number of publications and citation analysis. The most widely used databases for assessing these
metrics are Google Scholar (GS), Scopus, and Web of Science (WoS). The goal of the present paper is to provide an in-depth analysis of GS profiles and to compare GS metrics with different metric indices from Scopus and WoS. An additional goal is to do a qualitative analysis of profiles that were identified as outliers through the visual inspection of various metric indices ratios. The sample for this study consisted of 100 researchers from the University of Sarajevo with highest number of citations according to their GS profiles. The results of this study indicated a high correlation between different metric indices.
Outlier analysis revealed several errors in GS profiles, some of which are attributable to GS algorithms. An in-depth analysis of outliers provided important data for identifying limitations of all metrics currently used in researcher’s evaluation. We conclude the article
with several suggestions on how to improve the evaluation of individual scholar’s research output.
... There are several methods to measure an author's contribution. We applied the weighted contribution impact technique, which was used in some previous studies (e.g., Ciaccio et al. 2019;Shapiro et al. 1994). This method considers the first and last authors as the major contributors, second and next to last authors as the second-largest contributors to the research, and so on. ...
Scientific collaboration in almost every discipline is mainly driven by the need of sharing knowledge, expertise, and pooled resources. Science is becoming more complex which has encouraged scientists to involve more in collaborative research projects in order to better address the challenges. As a highly interdisciplinary field with a rapidly evolving scientific landscape, artificial intelligence calls for researchers with special profiles covering a diverse set of skills and expertise. Understanding gender aspects of scientific collaboration is of paramount importance, especially in a field such as artificial intelligence that has been attracting large investments. Using social network analysis, natural language processing, and machine learning and focusing on artificial intelligence publications for the period from 2000 to 2019, in this work, we comprehensively investigated the effects of several driving factors on acquiring key positions in scientific collaboration networks through a gender lens. It was found that, regardless of gender, scientific performance in terms of quantity and impact plays a crucial in possessing the "social researcher" in the network. However, subtle differences were observed between female and male researchers in acquiring the "local influencer" role.
... A recuperação de publicações originais é facilitada por bancos de dados, como Web of Science, PubMed, PLoS, Scopus, ResearchGate ou Google Scholar, porém pode haver variações entre eles e exclusão de capítulos de livros, relatórios técnicos e artigos de conferências (Anker, Hadzibegovic, Lena & Haverkamp, 2019;Bertoli-Barsotti & Lando, 2017). Alguns dos indicadores bibliométricos mais aplicados mundialmente pela comunidade científica são: SCImago Journal Rank, JCR Impact Factor, índice-h e suas derivações, incluindo índice-h´, índice-e, índice-h5, índice-hc, fator carbono_h, índice-p, índice-Ab, índice-Pr, índice-Bh, índice-v, dentre outros (Ciaccio et al., 2019;Carpenter et al., 2014;Bornmann, Marx, Gasparyan & Kitas, 2012). ...
Indicadores bibliométricos são úteis para avaliar o impacto da produtividade acadêmica, aliados à compreensão geopopulacional. O objetivo deste estudo foi analisar a distribuição espacial de pesquisas em Biotecnologia no Brasil, verificando possível correlação entre índice-h e desenvolvimento social. A amostra foi composta por dados de 959 docentes e 59 programas strictu senso em Biotecnologia do país. Dados autorreferenciados de cada programa traçaram seus perfis descritivos. Foram construídos mapas de distribuição e de autocorrelação local (MoranMap) dos programas e do índice-h pela densidade populacional. Análise bivariada (LISA) considerou variáveis dependentes (taxa de programas e índice-h pela densidade populacional) e independente (IDHM). Como resultados, houve prevalência de doutorado acadêmico, com maior tempo de criação, carga horária, nota CAPES, número de docentes e índice-h médio, com maioria de programas na Região Sudeste, exceto mestrados acadêmicos, na Nordeste. A distribuição de programas pela densidade populacional foi maior em RIAUs de Palmas, Araraquara e Alfenas, enquanto de índice-h em RIAUs de Palmas, Belo Horizonte e Rio de Janeiro. Houve autocorrelação positiva de programas aglomerados em RIAUs de SP e Porto Alegre e entre programas e índice-h em RIAUs de Porto Alegre, Caxias do Sul e Ribeirão Preto. Análise bivariada IDHM-programas exibiu aglomerados alto-alto em RIAUs de SP e Porto Alegre e IDHM-índice-h aglomerados alto-alto em RIAUs de SP, MG e RJ. O mapeamento do índice-h e IDHM ajudou na compreensão da produtividade acadêmica da Biotecnologia no Brasil, contribuindo para discussão de modelos de avaliação em realidades desiguais no cenário nacional.
... It is well known that the h-index is a significant indicator of the research impact realised by one author or a team, based on citation measurement, along with the c-index and c'index indexes (Ciaccio et al., 2019). Out of these considerations of paper ranking use, we will concentrate in this pilot study only on the one or more of the authors on the search results page, by checking the h-index for each of them. ...
In this paper, we discuss the issue of data credibility, an important one that is specific not only to digital media. We present the main actors and projects concerning the assessment of online information credibility. Then we underline the recent research approaches in assessing credibility of online information. As a result, we have pointed out that an important aspect constitutes the availability of comprehensive dataset for experiments assessing the credibility of information. Not all datasets available for research are developed from the same types of source of information. Some are collected from online news sites, others from social media messages. We note that all datasets presented in the paper are for English, without identifying suitable datasets for research for other languages. In the end, we explore the possibilities of the tools for assessing the credibility of information. The majority of tools are focused mainly on information evaluations based on experts from journalism.
... In 2008, Ronald Rousseau [12] developed two more metric i.e h 2 indices and R indices which only included more paper to evaluate the ranking of the author. In [3], authors developed a novel method to evaluate the author ranking. They took the mean of h-index, c-index and c' -index. ...
Cyberattacks are increasing rapidly with rapid Internet advancement and, the cybersecurity situation is not optimistic. Anomaly detection is one of the challenging sectors of network security, which shows a significant role in any organization. Many anomaly detection systems identify malicious activities by deploying machine learning and deep learning techniques. The major contribution of this research is to develop an anomaly detection model for networks using a homogenous ensemble of Long‐Short‐Term‐Memory integrated with Genetic Algorithm (GA) utilized for feature extraction. An extensive literature on anomaly detection, which utilizes deep learning algorithms, is studied. NSL‐KDD and UNSW‐NB datasets are deployed for evaluating the proposed network anomaly model. The experimental analysis shows that the proposed ensemble is superior to other ensembles with a maximum accuracy of 99.9% and a minimum false alarm rate of 1.56% on NSL‐KDD dataset and a maximum accuracy of 99.3% is obtained on UNSW‐NB15 dataset with false alarm rate of 1.7%. Hence, the proposed model performs fair on both the datasets.
Purpose:
The impact of academic publications is often characterized by the total number of future citations. However, this metric does not adequately characterize the true impact in terms of changing practices or paradigms. A new metric called the "disruption score" (DS) has been developed and validated in nonsurgical publications. This study aims to use the DS to identify the most disruptive publications in plastic surgery.The DS, a ratio of 2 numbers, varies between -1 and +1. Scores closer to -1 are developing papers that summarize the known literature while papers closer to +1 are disruptive-they result in a paradigm shift in the field of study.
Methods:
A search was performed for all articles from 1954 to 2014 in the following journals: Plastic and Reconstructive Surgery; Aesthetic Surgery Journal; Journal of Plastic, Reconstructive, and Aesthetic Surgery; Annals of Plastic Surgery; Aesthetic Plastic Surgery; Clinics in Plastic Surgery; and Plastic Surgery. The disruptive score was calculated for each article.The top 100 papers ranked by DS were examined and any editorials/viewpoints, publications with less than 26 citations, or less than 3 references were excluded because of their subjective nature and smaller academic contribution. The remaining 64 publications were analyzed for topic, study type, and citation count.
Results:
A total of 32,622 articles were found with a DS range from 0.385 to 0.923. The mean score of the top 64 articles was 0.539 with an average citation count of 195 and 9 references. Plastic and Reconstructive Surgery had the most disruptive papers with 50. There were no randomized controlled trials with a majority of the studies being technical descriptions or case series.
Conclusions:
There are many ways to measure academic success, but there are fewer ways to measure the impact of academic contributions. The DS is a novel measurement that can demonstrate when an article results in a paradigm shift as opposed to just total citation count. When applied to the plastic surgery literature, the DS demonstrates that technical innovation and creativity are the most academically impactful. Future evaluations of academic success should include the DS to measure the quality of academic contributions.
Bibliometric parameters are used as the yardstick for judging the productivity and scientific impact of the scholarship of scholars, including the viability and prestige of academic programs, universities, and nations. Despite the recent proliferation of bibliometric indices, their actual meaning and relevance are poorly understood and often misused and abused. This chapter introduces the fundamentals of bibliometrics and discusses the primary literature search platforms. Additionally, it appraises the H-index, and its variants, primary bibliometric databases, and journal prestige metrics. It also examines the misuse and abuses of bibliometric databases, journal impact factors, and authorship, including controversies on using the H-index to assess research output and scientific impact. Finally, the chapter presents valuable data on research productivity in low-/middle-/high-income countries, vis-à-vis global investment in scientific development, including research output and scientific impact rankings.
