Сравнение на методи

	Анализ на наукометрията, разделена по времеви периоди	Анализ на съвместно цитиране
Област	Наукометрия	Библиометрия
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1980s–1990s	1973
Създател	Derived from scientometrics tradition; temporal slicing formalized in longitudinal bibliometric studies from the 1980s onward	Henry Small
Тип	Quantitative longitudinal analysis	Method
Основополагащ източник	Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799-813.	Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.
Други названия	temporal scientometrics, period-based scientometric analysis, time-window scientometrics, longitudinal scientometric analysis	co-citation mapping, historiograph, direct citation, citation pair analysis
Свързани	6	5
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.	Co-citation analysis is a method that identifies the intellectual structure of a research domain by examining how frequently pairs of documents are cited together in other publications. When two papers are frequently cited together in the literature, they are considered co-cited, indicating they are conceptually related or influential within the same research community. Developed by Henry Small in 1973, co-citation analysis maps the 'invisible colleges' of science—networks of researchers working on related problems—and reveals how knowledge domains evolve over time.