Put research into context with this tutorial on VOSviewer, an application that provides an overview of the scientific landscape by aggregating relevant publications. It helps you find more accurate search terms, partners, seminal papers and knowledge gaps

background

VOSviewer was originally launched in 2009 as a free bibliometric mapping tool by Nees Jan van Eck and Ludo Waltman of the Center for Science and Technology Studies (CWTS) at Leiden University.

VOSviewer is a tool using VOS mapping technology invented by the same author. VOS stands for Visualization of Similarity and aims to “provide low-dimensional visualizations in which objects are positioned in such a way that the distance between any pair of objects reflects their similarity as accurately as possible” (van Eck & Waltman, 2007, p .299) .

In other words, VOSviewer generates “distance-based maps” where “the distance between two items reflects the strength of the relationship between the items” (van Eck & Waltman, 2010), in contrast to “graph-based maps” Contrast, where distances between items may not represent relationships or similarities, but instead lines or edges between items are used to show relationships. CiteSpace, which we introduced in another ResearchRadar article, generates such a map.

VOSviewer is not the first or only bibliometric software to build distance-based maps, but since VOS mapping technology shows good performance compared to other mapping techniques for generating distance-based maps, VOSviewer is able to generate over 5,000 sizable Scaled maps, for example, appear over a relatively short period of time with items in the cited map.

It is probably the most popular scientific mapping/bibliometric mapping tool currently in use, followed by CiteSpace and Bibliometrix/Biblioshiny.

General characteristics

Accepts input from a variety of bibliometric sources, including Web of Science, Scopus, PubMed, Lens.org, Dimensions, Semantic Scholar, COCI, and more.
Accepts input as standardized formats and popular web tools (such as GML, JSON, P

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Origin blog.csdn.net/iCloudEnd/article/details/133392686