Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Аналіз еволюції тем за допомогою бібліометрикс× | Картографування науки× | |
|---|---|---|
| Галузь≠ | Наукометрія | Бібліометрія |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2017 (bibliometrix package); thematic evolution approach ~2011 | 2000s |
| Автор методу≠ | Massimo Aria & Corrado Cuccurullo (bibliometrix package); thematic evolution method from Cobo et al. | Katy Börner, Chaomei Chen, and others |
| Тип≠ | Computational scientometric workflow | 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 thematic map analysis, R-based thematic evolution analysis, bibliometrix strategic diagram analysis, thematic evolution analysis with bibliometrix | knowledge mapping, domain mapping, research landscape visualization |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | Bibliometrix-assisted thematic evolution analysis uses the bibliometrix R package to trace how research themes emerge, mature, decline, or transform across successive time periods within a scientific field. By combining co-word analysis with strategic diagram visualisation, the workflow maps the intellectual structure of a field and reveals longitudinal shifts in topic centrality and development, producing reproducible, publication-ready outputs within a single R environment. | 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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