Сравнение на методи

	Анализ на библиометрични данни по времеви интервали	Научно картографиране
Област	Наукометрия	Библиометрия
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2000s–2010s (as an explicit methodological variant)	2000s
Създател	Derived from classical bibliometrics (Price, Garfield); explicitly formalised in longitudinal studies by Zhao & Strotmann (2008) and others	Katy Börner, Chaomei Chen, and others
Тип	Quantitative scientometric analysis	Method
Основополагащ източник	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.	Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255.
Други названия	longitudinal bibliometrics, temporal bibliometric analysis, diachronic bibliometrics, time-window bibliometric analysis	knowledge mapping, domain mapping, research landscape visualization
Свързани	6	5
Резюме	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.	Science mapping is a bibliometric visualization method that creates visual representations of research domains, showing the structure, development, and relationships of scientific fields. Using bibliographic data (citations, keywords, authors, journals), science mapping algorithms generate network diagrams where nodes represent documents, concepts, or authors and edges represent relationships (citation, collaboration, semantic similarity). The resulting maps make invisible intellectual structures visible, enabling researchers to understand field topology, identify emerging areas, and navigate disciplinary landscapes. Pioneered by Börner, Chen, and Boyack in the 2000s, science mapping has become a standard tool in research evaluation and strategic planning.