Matthew Kim

44 papers receiving 1.4k citations

Matthew Kim's Hit Papers

The National Sleep Research Resource: towards a sleep data commons 2018 · 528 citations
5280+2+5Years since publication100200300400500

Peers

Matthew Kim
Comparison fields: 5 of 126
  • Endocrine and Autonomic Systems 177
  • Experimental and Cognitive Psychology 293
  • Cognitive Neuroscience 419
  • Physiology 526
  • Aging 27
Replace Hongbao Cao with:
Hongbao Cao United States
Yu‐Ting Kuo Taiwan
Richard Li United States
Hyun-Seok Kim South Korea
Jianguo Liu China
Manuel Campos Spain
Benjamin Vandendriessche Belgium
Frank J. Jacono United States
Licong Cui United States
Andraž Stožer Slovenia
Matthew Kim relative to Hongbao Cao United States Hongbao Cao's profile →
Citations per field
00.5×8.8×
Hongbao Cao · 1×
Citations per year

Countries citing papers authored by Matthew Kim

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Matthew 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 Matthew Kim Line = papers co-authored together Matthew Kim links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1
The National Sleep Research Resource: towards a sleep data commons
Hit paper breakdown →
2018528
2 2016224
3 2004178
4 201050
5 200247
6 200742
7 201833
8 201632
9 201828
10 202121
11 202218
12 201816
13 201115
14 201315
15 201515
16 201115
17 201013
18 201813
19 201010
20 20118

About Matthew Kim

Matthew Kim is a scholar working on Molecular Biology, Epidemiology, Pediatrics, Perinatology and Child Health, Physiology and Public Health, Environmental and Occupational Health, having authored 47 papers that have together received 1.4k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (5 papers), Autophagy in Disease and Therapy (4 papers), Obstructive Sleep Apnea Research (4 papers), Prenatal Screening and Diagnostics (4 papers), Advanced Glycation End Products research (4 papers), Scientific Computing and Data Management (4 papers), CRISPR and Genetic Engineering (3 papers) and Vaccine Coverage and Hesitancy (3 papers). The work is most often cited by research in Endocrine and Autonomic Systems (177 citations), Experimental and Cognitive Psychology (293 citations), Cognitive Neuroscience (419 citations), Physiology (526 citations) and Aging (27 citations). Matthew Kim has collaborated with scholars based in United States, United Kingdom and South Africa. Frequent co-authors include Susan Redline, Michael Rueschman, Guo‐Qiang Zhang, Remo Mueller, Licong Cui, Daniel Mobley, Shiqiang Tao, Sara Mariani, Satya S. Sahoo and Ido Solt. Their work appears in journals such as American Journal of Obstetrics and Gynecology, The Journal of Maternal-Fetal & Neonatal Medicine, Journal of Ultrasound in Medicine, Obstetrics and Gynecology and Infection and Drug Resistance.

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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