Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mapitio ya mfumo wa fasihi kwa msaada wa bibliometrix× | Mapitio ya Kitaalam ya Vitabu× | |
|---|---|---|
| Nyanja | Saintometriki | Saintometriki |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2017 | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| Mwanzilishi≠ | Massimo Aria & Corrado Cuccurullo (bibliometrix R package) | Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004) |
| Aina≠ | Software-assisted systematic review | Evidence synthesis methodology |
| Chanzo asilia≠ | Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. DOI ↗ | Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401. link ↗ |
| Majina mbadala | bibliometrix SLR, R-bibliometrix systematic review, bibliometrix-based literature review, bibliometrix-enhanced SLR | SLR, systematic review, evidence synthesis review, structured literature review |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | A bibliometrix-assisted systematic literature review integrates the R package bibliometrix — developed by Aria and Cuccurullo (2017) — into the standard systematic review pipeline to automate and visualize bibliometric performance and science-mapping analyses. It combines the transparency and reproducibility of a protocol-driven systematic search with quantitative tools for tracking publication trends, author collaboration networks, keyword co-occurrence, and thematic evolution across a field. | A systematic literature review (SLR) is a structured, reproducible method for identifying, appraising, and synthesizing all relevant studies on a research question. Unlike a narrative review, it follows an explicit, pre-specified protocol — from database search strings through inclusion criteria to data extraction — so that the process is transparent, auditable, and replicable by other researchers. It is widely used in medicine, education, software engineering, and the social sciences to produce the most comprehensive possible evidence base on a topic. |
| ScholarGateSeti ya data ↗ |
|
|