Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| библиометрический анализ связей по библиографии× | Библиометрический анализ с использованием bibliometrix× | |
|---|---|---|
| Область | Наукометрия | Наукометрия |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | Base method 1963; R-package workflow 2017 | 2017 |
| Автор метода≠ | 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 |
| Основополагающий источник | 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 ↗ |
| Другие 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 |
| Связанные≠ | 5 | 6 |
| Сводка≠ | 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 данных ↗ |
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