Shengchun Kong

27 total papers · 777 total citations
15 papers, 511 citations indexed

About

Shengchun Kong is a scholar working on Endocrinology, Diabetes and Metabolism, Statistics and Probability and Pathology and Forensic Medicine. According to data from OpenAlex, Shengchun Kong has authored 15 papers receiving a total of 511 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Endocrinology, Diabetes and Metabolism, 6 papers in Statistics and Probability and 4 papers in Pathology and Forensic Medicine. Recurrent topics in Shengchun Kong's work include Statistical Methods and Inference (6 papers), Hormonal and reproductive studies (5 papers) and Statistical Methods and Bayesian Inference (4 papers). Shengchun Kong is often cited by papers focused on Statistical Methods and Inference (6 papers), Hormonal and reproductive studies (5 papers) and Statistical Methods and Bayesian Inference (4 papers). Shengchun Kong collaborates with scholars based in United States, France and Germany. Shengchun Kong's co-authors include Bin Nan, Bin Nan, Robert M. Jotte, Federico Cappuzzo, Francesco Orlandi, Daniil Stroyakovskiy, Naoyuki Nogami, Shelley Coleman, Christian A. Thomas and Makoto Nishio and has published in prestigious journals such as Journal of the American Statistical Association, The Journal of Clinical Endocrinology & Metabolism and Biometrika.

In The Last Decade

Shengchun Kong

15 papers receiving 506 citations

Hit Papers

IMpower150 Final Overall ... 2021 2026 2022 2024 2021 100 200 300

Author Peers

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

Author Last Decade Papers Cites
Shengchun Kong 301 222 72 72 57 15 511
E. E. Holdener 188 0.6× 128 0.6× 93 1.3× 45 0.6× 256 4.5× 16 611
M.K. Gospodarowicz 103 0.3× 429 1.9× 13 0.2× 19 0.3× 83 1.5× 15 583
R. E. Millikan 182 0.6× 135 0.6× 55 0.8× 30 0.4× 302 5.3× 13 594
Mirat Shah 256 0.9× 152 0.7× 71 1.0× 132 1.8× 103 1.8× 18 525
Byar Dp 78 0.3× 298 1.3× 11 0.2× 40 0.6× 83 1.5× 11 596
Donald I. Twito 362 1.2× 233 1.0× 42 0.6× 11 0.2× 122 2.1× 13 603
D Perrault 382 1.3× 120 0.5× 24 0.3× 14 0.2× 56 1.0× 16 610
Marina Pulido 314 1.0× 273 1.2× 133 1.8× 15 0.2× 73 1.3× 25 520
P. Stattin 195 0.6× 266 1.2× 17 0.2× 14 0.2× 192 3.4× 18 529
Jennifer Ruhl 298 1.0× 144 0.6× 24 0.3× 9 0.1× 68 1.2× 14 580

Countries citing papers authored by Shengchun Kong

Since Specialization
Citations

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

Fields of papers citing papers by Shengchun Kong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shengchun Kong

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