Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Bibliometrix-Ondersteunde Thematische Evolutie-Analyse× | VOSviewer-Assisted Thematische Evolutieanalyse× | |
|---|---|---|
| Vakgebied | Scientometrie | Scientometrie |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2017 (bibliometrix package); thematic evolution approach ~2011 | 2010–2011 |
| Grondlegger≠ | Massimo Aria & Corrado Cuccurullo (bibliometrix package); thematic evolution method from Cobo et al. | Nees Jan van Eck & Ludo Waltman (VOSviewer); thematic evolution methodology associated with Cobo et al. |
| Type≠ | Computational scientometric workflow | Scientometric workflow / bibliometric visualization pipeline |
| Oorspronkelijke bron≠ | Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. DOI ↗ | van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. DOI ↗ |
| Aliassen | bibliometrix thematic map analysis, R-based thematic evolution analysis, bibliometrix strategic diagram analysis, thematic evolution analysis with bibliometrix | VOSviewer thematic mapping, keyword co-occurrence thematic evolution, science mapping thematic evolution, VOSviewer longitudinal thematic analysis |
| Verwant | 6 | 6 |
| Samenvatting≠ | 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. | VOSviewer-assisted thematic evolution analysis is a scientometric pipeline that uses the VOSviewer software to build keyword co-occurrence networks across chronological time slices of a bibliographic dataset, revealing how research themes emerge, converge, fragment, or disappear over time within a scientific field. By coupling VOSviewer's density-based clustering with period-by-period comparison, researchers obtain a visual and quantitative account of a field's intellectual trajectory. |
| ScholarGateGegevensset ↗ |
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