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| библиометрично свързване на библиографии, подпомогнато от bibliometrix× | Библиометричен анализ, подпомогнат от bibliometrix× | |
|---|---|---|
| Област | Наукометрия | Наукометрия |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | Base method 1963; R-package workflow 2017 | 2017 |
| Създател≠ | Bibliographic coupling: M. M. Kessler (1963); bibliometrix package: Aria & Cuccurullo (2017) | Massimo Aria and Corrado Cuccurullo (bibliometrix R package) |
| Тип≠ | Quantitative scientometric network analysis | Quantitative review method with software toolkit |
| Основополагащ източник | Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. DOI ↗ | Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. DOI ↗ |
| Други названия | bibliometrix bibliographic coupling, R-based bibliographic coupling, coupling analysis via bibliometrix, biblioNetwork coupling | bibliometrix bibliometric analysis, R-based bibliometric analysis, bibliometrix workflow, bibliometrix package analysis |
| Свързани≠ | 5 | 6 |
| Резюме≠ | Bibliometrix-assisted bibliographic coupling applies the open-source R package bibliometrix to construct and analyse bibliographic coupling networks, in which two documents are linked by the number of references they share. The workflow automates record import, network construction, community detection, and summary statistics within a single reproducible R environment, making it accessible to researchers without dedicated scientometric software licences. | bibliometrix-assisted bibliometric analysis is a structured quantitative approach to mapping a scientific field using the bibliometrix R package. Developed by Aria and Cuccurullo (2017), it provides an integrated environment for importing bibliographic records from Scopus or Web of Science, computing performance indicators, building co-authorship and citation networks, and generating thematic maps — all within a reproducible R or Shiny workflow. |
| ScholarGateНабор от данни ↗ |
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