Jinling Tang

3.9k citations
46 papers · 2.8k indexed · 1 hit paper · h-index 21

Jinling Tang

43 papers receiving 2.7k citations

Hit Papers

Traditional Chinese medicine6312008202620142020200400600

Peers

Jinling Tang
Comparison fields: 5 of 163
  • Complementary and alternative medicine 514
  • Applied Psychology 128
  • Physiology 610
  • Pharmacology 168
  • Endocrinology, Diabetes and Metabolism 305
Replace Vincent CH Chung with:
Vincent CH Chung Hong Kong
Dean Ornish United States
Lara Hilton United States
Jaime Gahche United States
Shigeru Hisamichi Japan
C H Hennekens United States
Shannon Rhodes United States
Laura C Morgan United States
Zubin Punthakee Canada
Elizabeth D. Kantor United States
Jinling Tang relative to Vincent CH Chung Hong Kong Vincent CH Chung's profile →
Citations per field
00.5×3.0×
Vincent CH Chung · 1×
Citations per year

Countries citing papers authored by Jinling Tang

Since Specialization
Citations

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

Fields of papers citing papers by Jinling Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20242
3 20241
4 20241
5 20241
6 20205
7 20181
8 201750
9 201735
10 201641
11 201619
12 20159
13 201413
14 20143
15 2013238
16 20134
17 201362
18 2010310
19 200918
20
Traditional Chinese medicinebreakdown →
2008631

About Jinling Tang

Jinling Tang is a scholar working on Complementary and alternative medicine, Statistics, Probability and Uncertainty and Applied Psychology, having authored 46 papers that have together received 2.8k indexed citations. Recurring topics across this work include Acupuncture Treatment Research Studies (5 papers), Health Systems, Economic Evaluations, Quality of Life (4 papers), Meta-analysis and systematic reviews (4 papers), Clinical practice guidelines implementation (3 papers), Pancreatic and Hepatic Oncology Research (3 papers), Hormonal and reproductive studies (3 papers), Health Promotion and Cardiovascular Prevention (3 papers) and Primary Care and Health Outcomes (3 papers). The work is most often cited by research in Complementary and alternative medicine (514 citations), Applied Psychology (128 citations) and Physiology (610 citations). Jinling Tang has collaborated with scholars based in China, Hong Kong and United Kingdom. Frequent co-authors include Baoyan Liu, Kan-Wen Ma, Zu‐Yao Yang, Chen Mao, Kate Cahill, Ying Qin, Nicholas Wald, Matthew Law, Catherine A. Scott and Andrew Farmer. Their work appears in journals such as The Lancet, PLoS ONE and Stroke.

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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