Kai Song

1.1k citations
38 papers · 639 · h-index 14

Impact in

Papers in

    • Genomics and Phylogenetic Studies 6
    • Advanced biosensing and bioanalysis techniques 3
    • Wildlife Ecology and Conservation 8

Kai Song

37 papers receiving 633 citations

Peers

Kai Song
Comparison fields: 5 of 94
  • Aquatic Science 129
  • Ecological Modeling 55
  • Global and Planetary Change 228
  • Ecology 146
  • Nature and Landscape Conservation 68
Replace Yongpu Zhang with:
Yongpu Zhang China
Meng Xu China
Chiara Manfrin Italy
Thomas Orrell United States
Giovanni Scillitani Italy
Rebekah A. Oomen Canada
Irene de los A. Barriga-Sosa Mexico
Pedro Cruz‐Hernández Mexico
Xuena Huang China
Jose A. Domingos Australia
Kai Song relative to Yongpu Zhang China Yongpu Zhang's profile →
Citations per field
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Citations per year

Countries citing papers authored by Kai Song

Since Specialization
Citations

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

Fields of papers citing papers by Kai Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201762
2 201758
3 201756
4 201649
5 201248
6 201146
7 201841
8 201837
9 201837
10 201735
11 202326
12 201718
13 201714
14 202013
15 202411
16 202011
17 20219
18 20198
19 20188
20 20218

About Kai Song

Kai Song is a scholar working on Molecular Biology, Ecology, Global and Planetary Change, Ecological Modeling and Genetics, having authored 38 papers that have together received 639 indexed citations. Recurring topics across this work include Marine Bivalve and Aquaculture Studies (12 papers), Species Distribution and Climate Change (9 papers), Wildlife Ecology and Conservation (8 papers), Genomics and Phylogenetic Studies (6 papers), Ecology and Vegetation Dynamics Studies (6 papers), Genetic diversity and population structure (4 papers), Ocean Acidification Effects and Responses (4 papers) and Advanced biosensing and bioanalysis techniques (3 papers). The work is most often cited by research in Aquatic Science (129 citations), Ecological Modeling (55 citations), Global and Planetary Change (228 citations), Ecology (146 citations) and Nature and Landscape Conservation (68 citations). Kai Song has collaborated with scholars based in China, Sweden and Norway. Frequent co-authors include Li Li, Guofan Zhang, Busu Li, Ao Li, Jie Meng, Chunyan Li, Wei Wang, Guofan Zhang, Lı Wang and Jinxing Lin. Their work appears in journals such as Animals, Marine Biotechnology, BMC Genomics, PLoS ONE and Scientific Reports.

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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