عنوان مقاله [English]
Identifying the characteristics of scientific publications of small data in the Web of Science and its application in the main branches of science.
Methodology: This research is a descriptive study based on scientometric approach and content analysis method, which has been done by using the common techniques of co-word analysis and social network analysis.
Results: Publications of small data has had an increasing trend with an average annual growth rate of 15.59%. The main language of these works is English. Although the National Cheng Kung University (Taiwan) ranked the first of organizations, the United States, China and Germany recognized the top countries , overall. More than 90% of these products are in the fields of Computer Science (8 clusters), Engineering (6 clusters), Mathematics (7 clusters), Telecommunications (5 clusters) and Physics (3 clusters). The greatest degree of centrality belongs to Machine Learning, the Internet of Things, and Universal existence; the most closeness centrality belongs to Adaptation, Bipartite Graph and Machine Learning; and the most betweenness centrality belongs to Machine Learning, Long-Term Evolution Technology, and Global Existence.
Conclusion: Theoretical discussions of smalldata have further evolved in the mathematics and physics, and its applications in computer science and other fields are expanding.