Two simple new bibliometric indexes to better evaluate research in disciplines where publications typically receive less citations

Anania, Giovanni; Caruso, Assunta

doi:10.1007/s11192-013-0951-6

Cited by 11 publications

(8 citation statements)

References 18 publications

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“… Indicators are distinguished according to the goals being pursued by amending well-established procedures such as years from publication rather than academic age (i.e., the duration of a researcher's career at the time of the analysis; Egghe, 2013b), and they can be applied at different levels of aggregation (e.g., at department or university levels).  Indicators are based on information that is available at an individual level, including citations that would be disregarded by the original H index (Anania & Caruso, 2013), and they can be easily computed.  Rankings can also be obtained when the publication period is prior to the citation period under consideration (e.g., neglecting citations older than 22 years rather than articles published more than 22 years ago).…”

Section: Discussionmentioning

confidence: 99%

“…Note that the obtained H indices are continuous and do not change abruptly when the number of citations of a single article changes (i.e., they solve the problem of the discontinuity that could potentially be created by an additional citation received by the marginal article) (Thelwall & Wilson, 2014), because they account for the citations received by the entire set of published articles (Anania & Caruso, 2013). Figures 1 and 2 show the H indices for the total and core scientific production based on linear and cubic interpolation, respectively (i.e., interceptions between the 45° lines and the linear and cubic interpolations, respectively).…”

Section: Scientific Production and Productivitymentioning

confidence: 99%

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Scientific Production and Productivity in Curriculum Vitae Characterisation: Simple and Nested H Indices that Support Cross-Disciplinary Comparisons

Zagonari

2017

SSRN Journal

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In this study, I developed operational versions of Hirsch's H index that can be applied to each researcher's curriculum vitae (CV) to allow cross-disciplinary comparisons. The revised indices account for anomalies that potentially arise from tactical or opportunistic citation and publication behaviours by authors and editors, and can be calculated from readily available information. I split the original H index into nested indices to isolate networking activity, distinguish scientific production and productivity, and used nested Gini indices to identify intentional and successful inter-topical and inter-disciplinary research. I applied the most popular normalisations (i.e., per author and per year) using simple methodologies (i.e., least-squares linear and cubic interpolation fitting, whole-career vs. sub-periods, two-dimensional graphs) to solve empirical problems (e.g., sensitivity to citations, the "fashion" effect, attribution to disciplines, life cycle of articles) as well as open questions (e.g., the attribution of an article to a given discipline) associated with the original H index. I provided three numerical examples based on a representative heterodox, a representative orthodox multi-disciplinary, and a representative orthodox uni-disciplinary CV: the first CV includes 17 Scopus publications, and shows a highly heterodox (i.e., 5.8%), but no interdisciplinary research career, with a tiny networking component (i.e., 0.9%); the second CV includes 24 Scopus publications, and shows a slightly heterodox (i.e., 0.3%), but highly interdisciplinary (i.e., 53.9%) research career, with a small networking component (i.e., 14.3%); the third CV includes 16 Scopus publications, and shows slightly heterodox (i.e., 0.1%) and no interdisciplinary research career, with no networking component.

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Section: Discussionmentioning

confidence: 99%

Section: Scientific Production and Productivitymentioning

confidence: 99%

Scientific Production and Productivity in Curriculum Vitae Characterisation: Simple and Nested H Indices that Support Cross-Disciplinary Comparisons

Zagonari

2017

SSRN Journal

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“…Note that a fitting based on x such that y = 1/(a 0 − a 1 x) = x (i.e., two parameters) would produce similar results. Moreover, the obtained H values are continuous and do not change abruptly when the number of citations of a single article changes; that is, they solve the problem of the discontinuity that could potentially be created by an additional citation received by the marginal article [69], because they account for the citations received by the entire set of published articles [70]. Finally, linear fitting gives too much weight to fashionable articles (i.e., articles with many citations in a few years), whereas a cubic fitting disregards them by giving more weight to articles with few citations in many years.…”

Section: Scientific Production and Productivitymentioning

confidence: 99%

“…Indicators are based on information that is available at an individual level, including citations that would be disregarded by the original H index [70], and the indicators can be easily computed. • Rankings can also be obtained when the publication period is prior to the citation period under consideration (e.g., neglecting citations older than 22 years rather than articles published more than 22 years ago).…”

mentioning

confidence: 99%

Scientific Production and Productivity for Characterizing an Author’s Publication History: Simple and Nested Gini’s and Hirsch’s Indexes Combined

Zagonari

2019

Publications

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In this study, I developed operational versions of Gini’s and Hirsch’s indexes that can be applied to characterize each researcher’s publication history (PH) as heterodox, orthodox, and interdisciplinary. In addition, the new indicators warn against anomalies that potentially arise from tactical or opportunistic citation and publication behaviors by authors and editors, and can be calculated from readily available information. I split the original Hirsch index into nested indexes to isolate networking activity, as well as to distinguish scientific production (number of articles) from scientific productivity (rate of production), and used nested Gini indexes to identify intentional and successful intertopical and interdisciplinary research. I applied the most popular standardizations (i.e., per author and per year), and used simple methodologies (i.e., least-squares linear and cubic fitting, whole-career vs. subperiods, two-dimensional graphs). I provide three representative numerical examples based on an orthodox multidisciplinary PH, a heterodox PH from the social sciences, and an orthodox unidisciplinary PH from the physical sciences. Two additional numerical examples based on PHs from the life and health sciences show that the suggested PH characterization can be applied to different disciplines where different publication and citation practices prevail. Software is provided to help readers explore the use of these indicators.

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“…K-Index Centrality. Giovanni et al proposed K-index [30]. First, compare the value of H-index [31,32]; when the H-index is equal, the citations (the sum of citations of h core papers) are used to further distinguish the size of influence.…”

Section: Closeness Centralitymentioning

confidence: 99%

Study on Discovery Method of Cooperative Research Team Based on Improved Louvain Algorithm

Liu

Huang

Zhang

et al. 2021

Scientific Programming

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According to the needs of scientific research project research and development, the research of cooperative team excavation methods was carried out. Aiming at the current difficulties in accurately and reliably defining and identifying cooperative research teams from co-author network, an improved Louvain algorithm that integrates core node recognition was proposed: Louvain-LSCR algorithm. Based on the analysis of the Louvain algorithm, considering the local topology of the node in the network and the communication range of the node, a new algorithm LSCR for core node identification was constructed. The LSCR algorithm and Louvain algorithm were merged to obtain a new and improved algorithm, Louvain-LSCR. In this algorithm, the leaf nodes in phase 1 of Louvain algorithm were first pruned to reduce calculations; then, seed nodes were selected according to the LSCR algorithm in phase 2. The experimental results on related datasets show that LSCR algorithm has certain advantages in identifying core nodes. The modularity of Louvian-LSCR algorithm is better than other algorithms, and the community structure is more reasonable. It was verified that the algorithm can mine potential cooperative research teams in co-author network.

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Two simple new bibliometric indexes to better evaluate research in disciplines where publications typically receive less citations

Cited by 11 publications

References 18 publications

Scientific Production and Productivity in Curriculum Vitae Characterisation: Simple and Nested H Indices that Support Cross-Disciplinary Comparisons

Scientific Production and Productivity in Curriculum Vitae Characterisation: Simple and Nested H Indices that Support Cross-Disciplinary Comparisons

Scientific Production and Productivity for Characterizing an Author’s Publication History: Simple and Nested Gini’s and Hirsch’s Indexes Combined

Study on Discovery Method of Cooperative Research Team Based on Improved Louvain Algorithm

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