Abstract
With the advances of all research fields, the volume of scientific literature has grown exponentially over the past decades, and the management and exploration of scientific literature is becoming an increasingly complicated task. It calls for a tool that combines scientific impacts and social focuses to visualize relevant papers from a specific research area and time period, and to find important and interesting papers. Therefore, we propose a graphical article-level metric (gALM), which captures the impact and popularity of papers from scientific and social aspects. These two dimensions are combined and visualized graphically as a circular map. The map is divided into sectors of papers belonging to a publication year, and each block represents a paper’s journal citations by block size and readerships in Mendeley by block color. In this graphical way, gALM provides a more intuitive comparison of large-scale literatures. In addition, we also design an online Web server, Science Navigation Map (SNM), which not only visualizes the gALM but provides it with interactive features. Through an interactive visualization map of article-level metrics on scientific impact and social popularity in Mendeley, users can intuitively make a comparison of papers as well as explore and filter important and relevant papers by these metrics. We take the journal PLoS Biology as an example and visualize all the papers published in PLoS Biology during 2003 and 2014 by SNM. From this map, one can easily and intuitively find basic statistics of papers, such as the most cited papers and the most popular papers in Mendeley during a time period. SNM on the journal PLoS Biology is publicly available at http://www.linkscholar.org/plosbiology/.
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This work was supported by grants from the Natural Science Foundation of China (No.U1233110) and the Fundamental Research Funds for the Central Universities (No. DUT13JR01).
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Ling, X., Liu, Y., Huang, Z. et al. A graphical article-level metric for intuitive comparison of large-scale literatures. Scientometrics 106, 41–50 (2016). https://doi.org/10.1007/s11192-015-1782-4
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DOI: https://doi.org/10.1007/s11192-015-1782-4