Chan‐Won Kim

43 papers receiving 1.4k citations

Peers

Chan‐Won Kim
Comparison fields: 5 of 114
  • Endocrinology, Diabetes and Metabolism 244
  • Experimental and Cognitive Psychology 205
  • Epidemiology 433
  • Hepatology 78
  • Physiology 251
Replace Tanefa A. Apekey with:
Tanefa A. Apekey United Kingdom
Phyliss Sholinsky United States
Qingshan Geng China
Ji Eun Lee South Korea
Doug Case United States
John A. Linton South Korea
Tamara S. Hannon United States
Yoko Hori Japan
Helena Furberg United States
Rasa Kazlauskaite United States
Chan‐Won Kim relative to Tanefa A. Apekey United Kingdom Tanefa A. Apekey's profile →
Citations per field
00.5×1.5×1.9×
Tanefa A. Apekey · 1×
Citations per year

Countries citing papers authored by Chan‐Won Kim

Since Specialization
Citations

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

Fields of papers citing papers by Chan‐Won Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chan‐Won Kim. 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 Chan‐Won Kim. The network helps show where Chan‐Won Kim may publish in the future.

Co-authors

The 25 scholars most cited alongside Chan‐Won Kim, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Chan‐Won Kim Line = papers co-authored together Chan‐Won Kim links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015166
2 2016165
3 2013154
4 201688
5 201277
6 201575
7 201254
8 201452
9 201549
10 201548
11 201343
12 201339
13 201436
14 201836
15 201830
16 201229
17 201728
18 200325
19 202221
20 201021

About Chan‐Won Kim

Chan‐Won Kim is a scholar working on Epidemiology, Experimental and Cognitive Psychology, Physiology, Public Health, Environmental and Occupational Health and Cardiology and Cardiovascular Medicine, having authored 50 papers that have together received 1.5k indexed citations. Recurring topics across this work include Sleep and related disorders (7 papers), Liver Disease Diagnosis and Treatment (6 papers), Obesity, Physical Activity, Diet (3 papers), Adipose Tissue and Metabolism (3 papers), Nutritional Studies and Diet (2 papers), Nutrition and Health in Aging (2 papers), Liver Disease and Transplantation (2 papers) and Cancer Risks and Factors (2 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (244 citations), Experimental and Cognitive Psychology (205 citations), Epidemiology (433 citations), Hepatology (78 citations) and Physiology (251 citations). Chan‐Won Kim has collaborated with scholars based in South Korea, United States and Malaysia. Frequent co-authors include Seungho Ryu, Yoosoo Chang, Hyun Suk Jung, Kyung Eun Yun, Ho Cheol Shin, Min‐Jung Kwon, Juhee Cho, Eunju Sung, Eun Chul Chung and Yuni Choi. Their work appears in journals such as Circulation Journal, Journal of Hepatology, PLoS ONE, Arteriosclerosis Thrombosis and Vascular Biology and Journal of Korean Medical Science.

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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