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Co-mention network of R packages: Scientific impact and clustering structure

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  • Li, Kai
  • Yan, Erjia

Abstract

Despite its rising position as a first-class research object, scientific software remains a marginal object in studies of scholarly communication. This study aims to fill the gap by examining the co-mention network of R packages across all Public Library of Science (PLoS) journals. To that end, we developed a software entity extraction method and identified 14,310 instances of R packages across the 13,684 PLoS journal papers mentioning or citing R. A paper-level co-mention network of these packages was visualized and analyzed using three major centrality measures: degree centrality, betweenness centrality, and PageRank. We analyzed the distributive patterns of R packages in all PLoS papers, identified the top packages mentioned in these papers, and examined the clustering structure of the network. Specifically, we found that the discipline and function of the packages can partly explain the largest clusters. The present study offers the first large-scale analysis of R packages’ extensive use in scientific research. As such, it lays the foundation for future explorations of various roles played by software packages in the scientific enterprise.

Suggested Citation

  • Li, Kai & Yan, Erjia, 2018. "Co-mention network of R packages: Scientific impact and clustering structure," Journal of Informetrics, Elsevier, vol. 12(1), pages 87-100.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:1:p:87-100
    DOI: 10.1016/j.joi.2017.12.001
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    1. Li, Kai & Chen, Pei-Ying & Yan, Erjia, 2019. "Challenges of measuring software impact through citations: An examination of the lme4 R package," Journal of Informetrics, Elsevier, vol. 13(1), pages 449-461.
    2. Yuzhuo Wang & Kai Li, 2024. "How do official software citation formats evolve over time? A longitudinal analysis of R programming language packages," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3997-4019, July.
    3. Wang, Yuzhuo & Zhang, Chengzhi, 2020. "Using the full-text content of academic articles to identify and evaluate algorithm entities in the domain of natural language processing," Journal of Informetrics, Elsevier, vol. 14(4).
    4. Yuzhuo Wang & Chengzhi Zhang & Kai Li, 2022. "A review on method entities in the academic literature: extraction, evaluation, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2479-2520, May.
    5. Esther Prieto-Jiménez & Luis López-Catalán & Blanca López-Catalán & Guillermo Domínguez-Fernández, 2021. "Sustainable Development Goals and Education: A Bibliometric Mapping Analysis," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    6. Enrique Orduña-Malea & Rodrigo Costas, 2021. "Link-based approach to study scientific software usage: the case of VOSviewer," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 8153-8186, September.
    7. Alsudais, Abdulkareem, 2021. "In-code citation practices in open research software libraries," Journal of Informetrics, Elsevier, vol. 15(2).
    8. Xiao Han & Chu Wei, 2021. "Household energy consumption: state of the art, research gaps, and future prospects," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 12479-12504, August.
    9. Xiaotong Hu & Ziwei Chen & Lingyun Situ & Xuelian Pan & Jin Shi, 2025. "Identification of important software based on software dependency graph," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(7), pages 3961-3985, July.

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