Da Shi
Impact in
- Animal Science and Zoology top 0.5%
- Animal Virus Infections Studies
- Infectious Diseases top 1%
- Viral gastroenteritis research and epidemiology
- Antimicrobial Resistance in Staphylococcus
- SARS-CoV-2 and COVID-19 Research
Papers in
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- Animal Virus Infections Studies 54
-
- Viral gastroenteritis research and epidemiology 44
- SARS-CoV-2 and COVID-19 Research 12
- Antimicrobial Resistance in Staphylococcus 4
- Co-authors
- Hongyan Shi (54 shared papers)Jianfei Chen (53 shared papers)Li Feng (27 shared papers)Li Feng (12 shared papers)Xiaobo Wang (16 shared papers)Wataru Higuchi (5 shared papers)Tomomi Takano (5 shared papers)Tatsuo Yamamoto (5 shared papers)
In The Last Decade
Da Shi
73 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 89
- Animal Science and Zoology 876
- Infectious Diseases 1.1k
- Genetics 549
- Clinical Biochemistry 97
- Aquatic Science 101
Countries citing papers authored by Da Shi
This map shows the geographic impact of Da Shi'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 Da Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Da Shi more than expected).
Fields of papers citing papers by Da Shi
This network shows the impact of papers produced by Da Shi. 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 Da Shi. The network helps show where Da Shi may publish in the future.
Co-authors
The 25 scholars most cited alongside Da Shi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 76 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 147 | |
| 2 | 2010 | 120 | |
| 3 | 2013 | 81 | |
| 4 | 2017 | 75 | |
| 5 | 2012 | 65 | |
| 6 | 2011 | 59 | |
| 7 | 2015 | 54 | |
| 8 | 2021 | 53 | |
| 9 | 2019 | 50 | |
| 10 | 2015 | 44 | |
| 11 | 2012 | 44 | |
| 12 | 2020 | 44 | |
| 13 | 2017 | 41 | |
| 14 | 2020 | 41 | |
| 15 | 2019 | 41 | |
| 16 | 2020 | 41 | |
| 17 | 2017 | 35 | |
| 18 | 2023 | 35 | |
| 19 | 2014 | 34 | |
| 20 | 2015 | 33 |
About Da Shi
Da Shi is a scholar working on Animal Science and Zoology, Infectious Diseases, Genetics, Molecular Biology and Cardiology and Cardiovascular Medicine, having authored 76 papers that have together received 1.6k indexed citations. Recurring topics across this work include Animal Virus Infections Studies (54 papers), Viral gastroenteritis research and epidemiology (44 papers), Virus-based gene therapy research (30 papers), SARS-CoV-2 and COVID-19 Research (12 papers), Viral Infections and Immunology Research (10 papers), interferon and immune responses (4 papers), Antimicrobial Resistance in Staphylococcus (4 papers) and Animal Disease Management and Epidemiology (3 papers). The work is most often cited by research in Animal Science and Zoology (876 citations), Infectious Diseases (1.1k citations), Genetics (549 citations), Clinical Biochemistry (97 citations) and Aquatic Science (101 citations). Da Shi has collaborated with scholars based in China, Belgium and Japan. Frequent co-authors include Hongyan Shi, Jianfei Chen, Li Feng, Li Feng, Xiaobo Wang, Wataru Higuchi, Tomomi Takano, Tatsuo Yamamoto, Xin Zhang and Shizuka Yabe. Their work appears in journals such as Journal of Virology, Veterinary Microbiology, Archives of Virology, Virus Research and Viruses.
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.