เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การสร้างแผนที่วิทยาศาสตร์ด้วย VOSviewer× | การทบทวนวรรณกรรมอย่างเป็นระบบ (Systematic Literature Review× | |
|---|---|---|
| สาขาวิชา | วิทยาศาสตรมิติ | วิทยาศาสตรมิติ |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | 2010 | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| ผู้ริเริ่ม≠ | Nees Jan van Eck & Ludo Waltman (Leiden University) | Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004) |
| ประเภท≠ | Bibliometric mapping technique | Evidence synthesis methodology |
| แหล่งต้นตำรับ≠ | van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. DOI ↗ | Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401. link ↗ |
| ชื่อเรียกอื่น | VOSviewer science mapping, bibliometric science mapping with VOSviewer, VOS-based science mapping, VOSviewer network mapping | SLR, systematic review, evidence synthesis review, structured literature review |
| ที่เกี่ยวข้อง≠ | 6 | 5 |
| สรุป≠ | VOSviewer-assisted science mapping uses the VOSviewer software — developed at Leiden University — to construct and visualize bibliometric networks from publication metadata. It applies the VOS (Visualization of Similarities) mapping technique to reveal intellectual structures in a research field: co-authorship networks, citation landscapes, keyword clusters, and thematic frontiers, all rendered as interactive, color-coded network maps that expose how concepts, authors, and journals are relationally positioned within a discipline. | A systematic literature review (SLR) is a structured, reproducible method for identifying, appraising, and synthesizing all relevant studies on a research question. Unlike a narrative review, it follows an explicit, pre-specified protocol — from database search strings through inclusion criteria to data extraction — so that the process is transparent, auditable, and replicable by other researchers. It is widely used in medicine, education, software engineering, and the social sciences to produce the most comprehensive possible evidence base on a topic. |
| ScholarGateชุดข้อมูล ↗ |
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