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	Tidssegmenterad bibliometrisk analys – Spåra forskningens utveckling över tid	Vetenskaplig analys
Ämnesområde	Scientometri	Scientometri
Familj	Process / pipeline	Process / pipeline
Ursprungsår	2000s–2010s (as an explicit methodological variant)	1969 (term); 1963 (Price's foundational work)
Upphovsperson	Derived from classical bibliometrics (Price, Garfield); explicitly formalised in longitudinal studies by Zhao & Strotmann (2008) and others	V. V. Nalimov and Z. M. Mulchenko (term coined); Derek J. de Solla Price (foundational methods)
Typ	Quantitative scientometric analysis	Quantitative literature analysis
Ursprungskälla	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.	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.
Alias	longitudinal bibliometrics, temporal bibliometric analysis, diachronic bibliometrics, time-window bibliometric analysis	scientometrics, science of science, quantitative science studies, research evaluation analysis
Närliggande	6	6
Sammanfattning	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.	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.