Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi wa Kibibliometri kwa kutumia bibliometrix× | Ramani ya Kisayansi× | |
|---|---|---|
| Nyanja≠ | Saintometriki | Bibliometriki |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2017 | 2000s |
| Mwanzilishi≠ | Massimo Aria and Corrado Cuccurullo (bibliometrix R package) | Katy Börner, Chaomei Chen, and others |
| Aina≠ | Quantitative review method with software toolkit | Method |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala≠ | bibliometrix bibliometric analysis, R-based bibliometric analysis, bibliometrix workflow, bibliometrix package analysis | knowledge mapping, domain mapping, research landscape visualization |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | 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. | 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. |
| ScholarGateSeti ya data ↗ |
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