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A Survey of Scholarly Data Visualization
2018
IEEE Access
Scholarly information usually contains millions of raw data, such as authors, papers, citations, as well as scholarly networks. With the rapid growth of the digital publishing and harvesting, how to visually present the data efficiently becomes challenging. Nowadays, various visualization techniques can be easily applied on scholarly data visualization and visual analysis, which enables scientists to have a better way to represent the structure of scholarly data sets and reveal hidden patterns
doi:10.1109/access.2018.2815030
fatcat:2mpe5imlxnag7omqku25r3jrma