Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| бібліометричне зв'язування на основі бібліометрикс× | Картографування науки× | |
|---|---|---|
| Галузь≠ | Наукометрія | Бібліометрія |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | Base method 1963; R-package workflow 2017 | 2000s |
| Автор методу≠ | Bibliographic coupling: M. M. Kessler (1963); bibliometrix package: Aria & Cuccurullo (2017) | Katy Börner, Chaomei Chen, and others |
| Тип≠ | Quantitative scientometric network analysis | Method |
| Основоположне джерело≠ | Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. DOI ↗ | Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255. DOI ↗ |
| Інші назви≠ | bibliometrix bibliographic coupling, R-based bibliographic coupling, coupling analysis via bibliometrix, biblioNetwork coupling | knowledge mapping, domain mapping, research landscape visualization |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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. | Science mapping is a bibliometric visualization method that creates visual representations of research domains, showing the structure, development, and relationships of scientific fields. Using bibliographic data (citations, keywords, authors, journals), science mapping algorithms generate network diagrams where nodes represent documents, concepts, or authors and edges represent relationships (citation, collaboration, semantic similarity). The resulting maps make invisible intellectual structures visible, enabling researchers to understand field topology, identify emerging areas, and navigate disciplinary landscapes. Pioneered by Börner, Chen, and Boyack in the 2000s, science mapping has become a standard tool in research evaluation and strategic planning. |
| ScholarGateНабір даних ↗ |
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