Manki Song

83 total papers · 1.8k total citations
42 papers, 826 citations indexed

About

Manki Song is a scholar working on Epidemiology, Immunology and Infectious Diseases. According to data from OpenAlex, Manki Song has authored 42 papers receiving a total of 826 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Epidemiology, 17 papers in Immunology and 15 papers in Infectious Diseases. Recurrent topics in Manki Song's work include Respiratory viral infections research (11 papers), Influenza Virus Research Studies (11 papers) and SARS-CoV-2 and COVID-19 Research (7 papers). Manki Song is often cited by papers focused on Respiratory viral infections research (11 papers), Influenza Virus Research Studies (11 papers) and SARS-CoV-2 and COVID-19 Research (7 papers). Manki Song collaborates with scholars based in South Korea, United States and United Kingdom. Manki Song's co-authors include Sang Hwan Seo, Jung-ah Choi, Dong Wook Kim, Eunji Yang, Sun‐Je Woo, Hyoungjoon Moon, John D. Clemens, Jeong-Ki Kim, Daesub Song and Nam‐Chul Ha and has published in prestigious journals such as PLoS ONE, Analytical Chemistry and Journal of Virology.

In The Last Decade

Manki Song

41 papers receiving 818 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Manki Song 285 260 245 191 156 42 826
Nancy Walters 160 0.6× 276 1.1× 155 0.6× 156 0.8× 158 1.0× 22 734
J U Que 253 0.9× 196 0.8× 315 1.3× 177 0.9× 330 2.1× 28 1.0k
Sae‐Hae Kim 290 1.0× 410 1.6× 110 0.4× 49 0.3× 313 2.0× 37 920
Yukari Hagiwara 192 0.7× 477 1.8× 363 1.5× 113 0.6× 158 1.0× 17 862
P.H. Lambert 178 0.6× 213 0.8× 103 0.4× 88 0.5× 173 1.1× 27 718
Diane Williamson 161 0.6× 243 0.9× 85 0.3× 85 0.4× 527 3.4× 19 993
S J Cryz 277 1.0× 239 0.9× 301 1.2× 249 1.3× 192 1.2× 26 904
Jin-Yong Noh 264 0.9× 112 0.4× 346 1.4× 21 0.1× 149 1.0× 48 760
Jae-Keun Park 335 1.2× 159 0.6× 497 2.0× 20 0.1× 177 1.1× 52 902
Jun‐ichi Maeyama 149 0.5× 369 1.4× 207 0.8× 89 0.5× 218 1.4× 33 717

Countries citing papers authored by Manki Song

Since Specialization
Citations

This map shows the geographic impact of Manki Song's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Manki Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manki Song more than expected).

Fields of papers citing papers by Manki Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Manki Song. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Manki Song. The network helps show where Manki Song may publish in the future.

Co-authorship network of co-authors of Manki Song

This figure shows the co-authorship network connecting the top 25 collaborators of Manki Song. A scholar is included among the top collaborators of Manki Song based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Manki Song. Manki Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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