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| Анализ на наукометрията, разделена по времеви периоди× | Наукометричен анализ× | |
|---|---|---|
| Област | Наукометрия | Наукометрия |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1980s–1990s | 1969 (term); 1963 (Price's foundational work) |
| Създател≠ | Derived from scientometrics tradition; temporal slicing formalized in longitudinal bibliometric studies from the 1980s onward | V. V. Nalimov and Z. M. Mulchenko (term coined); Derek J. de Solla Price (foundational methods) |
| Тип≠ | Quantitative longitudinal analysis | Quantitative literature analysis |
| Основополагащ източник≠ | Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799-813. link ↗ | Nalimov, V. V., & Mulchenko, Z. M. (1969). Naukometriya: Izucheniye razvitiya nauki kak informatsionnogo protsessa [Scientometrics: The Study of the Development of Science as an Information Process]. Nauka. link ↗ |
| Други названия | temporal scientometrics, period-based scientometric analysis, time-window scientometrics, longitudinal scientometric analysis | scientometrics, science of science, quantitative science studies, research evaluation analysis |
| Свързани | 6 | 6 |
| Резюме≠ | Time-sliced scientometric analysis divides a bibliographic corpus into discrete temporal windows — commonly five- or ten-year periods — and applies standard scientometric indicators (publication counts, citation rates, h-index, collaboration networks, keyword co-occurrence) within each slice. By comparing results across slices, researchers can reconstruct how a scientific field has grown, shifted focus, formed new collaborations, or declined in influence over time. The approach combines the rigor of quantitative scientometrics with an explicit longitudinal dimension. | Scientometric analysis applies statistical and computational methods to publication and citation data to measure the growth, structure, and impact of scientific fields. Drawing on databases such as Web of Science, Scopus, or OpenAlex, it quantifies output trends, identifies leading authors and institutions, maps intellectual networks, and evaluates research impact — transforming large bibliographic corpora into evidence-based portraits of how knowledge develops and spreads. |
| ScholarGateНабор от данни ↗ |
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