Yuan Lv

35 total papers · 708 total citations
18 papers, 301 citations indexed

About

Yuan Lv is a scholar working on Endocrinology, Diabetes and Metabolism, Cardiology and Cardiovascular Medicine and Epidemiology. According to data from OpenAlex, Yuan Lv has authored 18 papers receiving a total of 301 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Endocrinology, Diabetes and Metabolism, 4 papers in Cardiology and Cardiovascular Medicine and 4 papers in Epidemiology. Recurrent topics in Yuan Lv's work include Diabetes Management and Research (3 papers), Neonatal and fetal brain pathology (2 papers) and Diabetes Treatment and Management (2 papers). Yuan Lv is often cited by papers focused on Diabetes Management and Research (3 papers), Neonatal and fetal brain pathology (2 papers) and Diabetes Treatment and Management (2 papers). Yuan Lv collaborates with scholars based in China, United States and Belgium. Yuan Lv's co-authors include Yang Wang, Rong Zhang, Jiachang Cai, Jun Li, Congming Wu, Jianzhong Shen, Andrea T. Feßler, Tao He, Yingbo Shen and Štefan Schwarz and has published in prestigious journals such as PLoS ONE, Antimicrobial Agents and Chemotherapy and Journal of Antimicrobial Chemotherapy.

In The Last Decade

Yuan Lv

16 papers receiving 296 citations

Author Peers

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

Author Last Decade Papers Cites
Yuan Lv 150 80 78 77 45 18 301
Kévin Alexandre 61 0.4× 28 0.3× 106 1.4× 80 1.0× 65 1.4× 20 310
Hamsa T. Tayeb 86 0.6× 18 0.2× 98 1.3× 91 1.2× 38 0.8× 14 339
Peter Hedlin 49 0.3× 33 0.4× 91 1.2× 69 0.9× 59 1.3× 15 295
Gabriel Haddad 60 0.4× 26 0.3× 15 0.2× 93 1.2× 26 0.6× 22 223
Arta Karruli 107 0.7× 18 0.2× 102 1.3× 35 0.5× 67 1.5× 18 275
Qiwen Yang 36 0.2× 26 0.3× 131 1.7× 61 0.8× 68 1.5× 22 274
Adriano Anesi 107 0.7× 15 0.2× 38 0.5× 56 0.7× 32 0.7× 21 278
Atsuko Sekiguchi 67 0.4× 28 0.3× 29 0.4× 81 1.1× 19 0.4× 17 363
Zahra Ghafouri 35 0.2× 39 0.5× 156 2.0× 65 0.8× 60 1.3× 17 307
Lucia Mališová 53 0.4× 33 0.4× 25 0.3× 76 1.0× 124 2.8× 26 292

Countries citing papers authored by Yuan Lv

Since Specialization
Citations

This map shows the geographic impact of Yuan Lv'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 Yuan Lv with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuan Lv more than expected).

Fields of papers citing papers by Yuan Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuan Lv. 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 Yuan Lv. The network helps show where Yuan Lv may publish in the future.

Co-authorship network of co-authors of Yuan Lv

This figure shows the co-authorship network connecting the top 25 collaborators of Yuan Lv. A scholar is included among the top collaborators of Yuan Lv 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 Yuan Lv. Yuan Lv 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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