Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Tech Mining× | Бібліометричне картографування за допомогою bibliometrix× | |
|---|---|---|
| Галузь≠ | Science Technology Studies | Наукометрія |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2005 | 2017 |
| Автор методу≠ | Alan L. Porter & Scott W. Cunningham | Massimo Aria & Corrado Cuccurullo (bibliometrix R package) |
| Тип≠ | Text-mining methodology for competitive technical intelligence | Computational bibliometric pipeline |
| Основоположне джерело≠ | Porter, A. L., & Cunningham, S. W. (2005). Tech Mining: Exploiting New Technologies for Competitive Advantage. Wiley. ISBN: 9780471475675 | Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. DOI ↗ |
| Інші назви≠ | Technology mining, S&T text mining, Technical intelligence mining | bibliometrix science mapping, R-based science mapping, bibliometrix bibliometric mapping, bibliometrix-driven knowledge mapping |
| Пов'язані≠ | 4 | 6 |
| Підсумок≠ | Tech mining is the text mining of science and technology information—the publication, patent, and proposal databases that record the world's research and invention—to extract competitive technical intelligence. Coined by Alan Porter and Scott Cunningham, it turns large, fielded bibliographic corpora into actionable answers about who is doing what, where, with whom, and along which trajectories. By extracting entities such as authors, institutions, countries, keywords, and assignees and analysing their co-occurrence over time, tech mining profiles emerging technologies, maps research landscapes, and supports R&D management and innovation policy decisions. | bibliometrix-assisted science mapping is a computational approach that uses the bibliometrix R package to retrieve, clean, and analyze large bibliographic datasets, producing structured visual maps of how knowledge in a field is organized, interconnected, and evolving over time. It combines descriptive bibliometrics with network analysis and strategic clustering techniques to reveal intellectual structure, thematic frontiers, and influential actors in a research domain. |
| ScholarGateНабір даних ↗ |
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