CLARA: citation and similarity-based author ranking

Bedru, Hayat Dino; Zhang, Chen; Xie, Feng; Yu, Shuo; Hussain, Iftikhar

doi:10.1007/s11192-022-04590-5

Cited by 4 publications

(5 citation statements)

References 55 publications

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“…This facilitates cross-discipline use of the calibrated author bias metric as several cultural differences may exist between fields in terms of publication and citation behaviour (Bornmann and Daniel 2008; Zhou and Leydesdorff 2010). At present ABCal, and the novel author bias metric, also provides enhanced utility in comparison to existing options (Bedru et al 2023; Keirstead 2016; Kozlowski 2019). For example, ABCal uses provided information and does not rely on the indexing of papers on a specific database (Kozlowski 2019) or the existence of author profiles on a specific platform (Keirstead 2016).…”

Section: Discussionmentioning

confidence: 99%

“…This is particularly important for included studies from publishers that don't index their articles on all databases or when including preprints from e.g., bioRxiv. ABCal is also freely available to the community for implementation in other studies while several similar algorithms are not (Bedru et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…This method emerged as a practical approach in response to the various complexities of assessing the contribution levels within scholarly works attributed to specific authors. While this method has proven useful in illuminating prominent contributors in differing fields (Bedru et al, 2023;Small & Garfield, 1985) and in citation network analyses (Perianes-Rodriguez et al, 2016), no clear link has been made between scores for individual authors from fractional counting and bias introduced in reviews from contribution levels. Furthermore, many of the methods that have been described still lack available software that implements the method (Bedru et al, 2023), or are available with very limited functionality (Keirstead, 2016;Kozlowski, 2019).…”

mentioning

confidence: 99%

“…While this method has proven useful in illuminating prominent contributors in differing fields (Bedru et al, 2023;Small & Garfield, 1985) and in citation network analyses (Perianes-Rodriguez et al, 2016), no clear link has been made between scores for individual authors from fractional counting and bias introduced in reviews from contribution levels. Furthermore, many of the methods that have been described still lack available software that implements the method (Bedru et al, 2023), or are available with very limited functionality (Keirstead, 2016;Kozlowski, 2019). To address the current need to quantitate author contribution levels as a measure of bias, and perform scientometric checks on publication year and location, ABCal (version 1.0.2) was created to compute author bias and plot scientometric aspects of studies included in systematic reviews and meta-analyses.…”

mentioning

confidence: 99%

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ABCal: a Python package for Author Bias Computation and Scientometric Plotting for Reviews and Meta-Analyses

Le Clercq

2023

Preprint

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Systematic reviews are critical summaries of the exiting literature on a given subject and, when combined with meta-analysis, provides a quantitative synthesis of evidence to direct and inform future research. Such reviews must, however, account for complex sources of between study heterogeneity and possible sources of bias, such as publication bias. This paper presents the methods and results of a research study using a newly developed software tool called ABCal (version 1.0.2) to compute and assess author bias in the literature, providing a quantitative measure for the possible effect of overrepresented authors introducing bias to the overall interpretation of the literature. ABCal includes a new metric referred to as author bias, which is a measure of potential biases per paper when the frequency or proportions of contributions from specific authors are considered. The metric is able to account for a significant portion of the observed heterogeneity between studies included in meta-analyses. A meta-regression between observed effect measures and author bias values revealed that higher levels of author bias were associated with higher effect measures while lower author bias was evident for studies with lower effect measures. Furthermore, the software’s capabilities to analyse authorship contributions and produce scientometric plots was able to reveal distinct patterns in both the temporal and geographic distributions of publications, which may relate to any evident publication bias. Thus, ABCal can aid researchers in gaining a deeper understanding of the research landscape and assist in identifying both key contributors and holistic research trends.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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ABCal: a Python package for Author Bias Computation and Scientometric Plotting for Reviews and Meta-Analyses

Le Clercq

2023

Preprint

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“…We have chosen to measure scientific collaboration primarily through the analysis of co-authorship in published research papers. This is a common and widely accepted method for assessing scientific collaboration [17,[27][28][29] as follows.…”

Section: Backgrounds Of Scientometric Analysismentioning

confidence: 99%

The Impact of a National Crisis on Research Collaborations: A Scientometric Analysis of Ukrainian Authors 2019–2022

Damaševičius,

Zailskaitė-Jakštė

2023

Publications

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This paper analyzes the impact of the ongoing war in Ukraine on the productivity and collaboration networks of Ukrainian academics. As a case study, we analyze the publication patterns in open-access MDPI journals using bibliographic analysis methods and compare the research output published in 2022 with research papers published in the three preceding years (2019–2021) with at least one author having an Ukrainian affiliation. A total of 2365 publications were analyzed. The identified publication trends provide an interesting insight into the dynamics of the research network of Ukrainian researchers, which demonstrated a decline in diversity of international collaborations in 2022. The findings of this study emphasize the necessity of international research collaboration in a variety of fields in order to mitigate the detrimental effects of national crises and emergencies.

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ABCal: a Python package for author bias computation and scientometric plotting for reviews and meta-analyses

Le Clercq

2023

Scientometrics

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Systematic reviews are critical summaries of the exiting literature on a given subject and, when combined with meta-analysis, provides a quantitative synthesis of evidence to direct and inform future research. Such reviews must, however, account for complex sources of between study heterogeneity and possible sources of bias, such as publication bias. This paper presents the methods and results of a research study using a newly developed software tool called ABCal (version 1.0.2) to compute and assess author bias in the literature, providing a quantitative measure for the possible effect of overrepresented authors introducing bias to the overall interpretation of the literature. ABCal includes a new metric referred to as author bias, which is a measure of potential biases per paper when the frequency or proportions of contributions from specific authors are considered. The metric is able to account for a significant portion of the observed heterogeneity between studies included in meta-analyses. A meta-regression between observed effect measures and author bias values revealed that higher levels of author bias were associated with higher effect measures while lower author bias was evident for studies with lower effect measures. Furthermore, the software's capabilities to analyse authorship contributions and produce scientometric plots was able to reveal distinct patterns in both the temporal and geographic distributions of publications, which may relate to any evident publication bias. Thus, ABCal can aid researchers in gaining a deeper understanding of the research landscape and assist in identifying both key contributors and holistic research trends.

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CLARA: citation and similarity-based author ranking

Cited by 4 publications

References 55 publications

ABCal: a Python package for Author Bias Computation and Scientometric Plotting for Reviews and Meta-Analyses

ABCal: a Python package for Author Bias Computation and Scientometric Plotting for Reviews and Meta-Analyses

The Impact of a National Crisis on Research Collaborations: A Scientometric Analysis of Ukrainian Authors 2019–2022

ABCal: a Python package for author bias computation and scientometric plotting for reviews and meta-analyses

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