Lin Jun-fen

31 papers receiving 509 citations

Peers

Lin Jun-fen
Comparison fields: 5 of 98
  • Health 97
  • Neuropsychology and Physiological Psychology 17
  • Biological Psychiatry 27
  • Health, Toxicology and Mutagenesis 102
  • Geriatrics and Gerontology 22
Replace Yujia Zhai with:
Yujia Zhai China
Dayoon Kwon United States
Zhenggang Bai China
Ninghao Huang China
Abdonas Tamošiūnas Lithuania
Kevin McCarroll Ireland
Nina Rautio Finland
Helena Hörder Sweden
Kaori Kitamura Japan
Stephen Vida Canada
Lin Jun-fen relative to Yujia Zhai China Yujia Zhai's profile →
Citations per field
00.5×2.7×
Yujia Zhai · 1×
Citations per year

Countries citing papers authored by Lin Jun-fen

Since Specialization
Citations

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

Fields of papers citing papers by Lin Jun-fen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lin Jun-fen. 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 Lin Jun-fen. The network helps show where Lin Jun-fen may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201577
2 201840
3 201639
4 201539
5 201537
6 201733
7 201832
8 201828
9 201622
10 202122
11 201820
12 202019
13 202018
14 201910
15 20169
16 20208
17 20227
18 20217
19 20227
20 20216

About Lin Jun-fen

Lin Jun-fen is a scholar working on Public Health, Environmental and Occupational Health, Psychiatry and Mental health, Health, Toxicology and Mutagenesis, Physiology and Health, having authored 32 papers that have together received 517 indexed citations. Recurring topics across this work include Nutritional Studies and Diet (9 papers), Dementia and Cognitive Impairment Research (6 papers), Health disparities and outcomes (3 papers), Air Quality and Health Impacts (3 papers), Obesity, Physical Activity, Diet (3 papers), Frailty in Older Adults (3 papers), Climate Change and Health Impacts (3 papers) and Eating Disorders and Behaviors (2 papers). The work is most often cited by research in Health (97 citations), Neuropsychology and Physiological Psychology (17 citations), Biological Psychiatry (27 citations), Health, Toxicology and Mutagenesis (102 citations) and Geriatrics and Gerontology (22 citations). Lin Jun-fen has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Fudong Li, Fan He, Yujia Zhai, Wei Shen, Xiaopeng Shang, Xinyi Wang, Yuanyuan Xiao, Xinyi Wang, Wei Ma and Xue Gu. Their work appears in journals such as Journal of Affective Disorders, The journal of nutrition health & aging, BMJ Open, Frontiers in Psychiatry and International Journal of Environmental Research and Public Health.

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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