Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Оглядовий аналіз із застосуванням bibliometrix× | Бібліометричний аналіз за допомогою bibliometrix× | |
|---|---|---|
| Галузь | Наукометрія | Наукометрія |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2017 (bibliometrix tool); mapping review approach formalised c. 2010s | 2017 |
| Автор методу≠ | Aria & Cuccurullo (bibliometrix, 2017); mapping review methodology developed in evidence synthesis community (~2000s) | Massimo Aria and Corrado Cuccurullo (bibliometrix R package) |
| Тип≠ | Tool-assisted evidence mapping review | Quantitative review method with software toolkit |
| Основоположне джерело | 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 ↗ |
| Інші назви | bibliometrix mapping review, R-bibliometrix evidence map, bibliometric-assisted systematic map, bibliometrix evidence synthesis map | bibliometrix bibliometric analysis, R-based bibliometric analysis, bibliometrix workflow, bibliometrix package analysis |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | A bibliometrix-assisted mapping review combines the structured scope-and-search logic of an evidence mapping review with the analytical power of the bibliometrix R package. Instead of manually categorising studies, the researcher leverages bibliometrix functions — keyword co-occurrence networks, thematic clustering, and yearly trend analysis — to chart the landscape of a research field systematically and at scale, producing an interactive, reproducible evidence map. | 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. |
| ScholarGateНабір даних ↗ |
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