Porównaj metody

	Analiza bibliometryczna w przekrojach czasowych	Analiza współcytowań
Dziedzina	Naukometria	Bibliometria
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2000s–2010s (as an explicit methodological variant)	1973
Twórca	Derived from classical bibliometrics (Price, Garfield); explicitly formalised in longitudinal studies by Zhao & Strotmann (2008) and others	Henry Small
Typ	Quantitative scientometric analysis	Method
Źródło pierwotne	Zhao, D., & Strotmann, A. (2008). Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic-coupling analysis. Journal of the American Society for Information Science and Technology, 59(13), 2070–2086.	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.
Inne nazwy	longitudinal bibliometrics, temporal bibliometric analysis, diachronic bibliometrics, time-window bibliometric analysis	co-citation mapping, historiograph, direct citation, citation pair analysis
Pokrewne	6	5
Podsumowanie	Time-sliced bibliometric analysis partitions a literature corpus into consecutive time windows and applies standard bibliometric indicators (publication counts, citation patterns, co-authorship networks, keyword frequencies) within each window. By comparing results across slices, researchers can document how a field's productivity, intellectual structure, and thematic focus have shifted over time — providing a diachronic rather than static view of scholarly output.	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.