Módszerek összehasonlítása

	Time-sliced Bibliometric Analysis	Témaváltozási analízis – Kutatási témák időbeli változásainak nyomon követése
Tudományterület	Tudománymetria	Tudománymetria
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2000s–2010s (as an explicit methodological variant)	2011
Megalkotó	Derived from classical bibliometrics (Price, Garfield); explicitly formalised in longitudinal studies by Zhao & Strotmann (2008) and others	Manuel J. Cobo and colleagues (University of Granada)
Típus	Quantitative scientometric analysis	Quantitative bibliometric technique
Alapmű	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.	Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.
Alternatív nevek	longitudinal bibliometrics, temporal bibliometric analysis, diachronic bibliometrics, time-window bibliometric analysis	TEA, thematic development analysis, temporal thematic mapping, longitudinal theme analysis
Kapcsolódó	6	6
Összefoglaló	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.	Thematic evolution analysis is a bibliometric technique that divides a body of literature into consecutive time periods and tracks how research themes emerge, consolidate, split, merge, or disappear across those periods. By combining co-word analysis, clustering, and strategic diagrams for each time slice, it produces a dynamic picture of a field's intellectual development rather than a static snapshot.