Jason E. Donald
Impact in
- Virology top 5%
- HIV Research and Treatment
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- Protein Structure and Dynamics
- RNA and protein synthesis mechanisms
- Lipid Membrane Structure and Behavior
Papers in
-
- Protein Structure and Dynamics 7
- Machine Learning in Bioinformatics 3
- RNA and protein synthesis mechanisms 3
- Biochemical and Structural Characterization 2
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- Enzyme Structure and Function 4
- Co-authors
- William F. DeGrado (7 shared papers)Daniel W. Kulp (2 shared papers)Eugene I. Shakhnovich (4 shared papers)Robert A. Lamb (2 shared papers)William W. Chen (1 shared paper)Brett T. Hannigan (2 shared papers)Gevorg Grigoryan (2 shared papers)Theodore S. Jardetzky (1 shared paper)
- Journals
- Nucleic Acids Research (3 papers)Computer applications in the biosciences (2 papers)Proceedings of the National Academy of Sciences (2 papers)Journal of Virology (1 paper)Journal of Computational Chemistry (1 paper)
- Partner nations
- United StatesIsraelCanada
In The Last Decade
Jason E. Donald
14 papers receiving 782 citations
Peers
Comparison fields: 5 of 92
- Virology 76
- Molecular Biology 504
- Infectious Diseases 72
- Epidemiology 124
- Spectroscopy 43
Countries citing papers authored by Jason E. Donald
This map shows the geographic impact of Jason E. Donald'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 Jason E. Donald with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason E. Donald more than expected).
Fields of papers citing papers by Jason E. Donald
This network shows the impact of papers produced by Jason E. Donald. 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 Jason E. Donald. The network helps show where Jason E. Donald may publish in the future.
Co-authors
The 25 scholars most cited alongside Jason E. Donald, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 308 | |
| 2 | 2012 | 63 | |
| 3 | 2011 | 58 | |
| 4 | 2011 | 53 | |
| 5 | 2008 | 52 | |
| 6 | 2007 | 51 | |
| 7 | 2012 | 51 | |
| 8 | 2011 | 38 | |
| 9 | 2005 | 29 | |
| 10 | 2012 | 22 | |
| 11 | 2005 | 18 | |
| 12 | 2010 | 16 | |
| 13 | 2008 | 15 | |
| 14 | 2005 | 11 |
About Jason E. Donald
Jason E. Donald is a scholar working on Molecular Biology, Materials Chemistry, Epidemiology, Virology and Cell Biology, having authored 14 papers that have together received 785 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (7 papers), Enzyme Structure and Function (4 papers), Virology and Viral Diseases (3 papers), Machine Learning in Bioinformatics (3 papers), RNA and protein synthesis mechanisms (3 papers), Cellular transport and secretion (2 papers), HIV Research and Treatment (2 papers) and Biochemical and Structural Characterization (2 papers). The work is most often cited by research in Virology (76 citations), Molecular Biology (504 citations), Infectious Diseases (72 citations), Epidemiology (124 citations) and Spectroscopy (43 citations). Jason E. Donald has collaborated with scholars based in United States, Israel and Canada. Frequent co-authors include William F. DeGrado, Daniel W. Kulp, Eugene I. Shakhnovich, Robert A. Lamb, William W. Chen, Brett T. Hannigan, Gevorg Grigoryan, Theodore S. Jardetzky, Chaim A. Schramm and Chen Keasar. Their work appears in journals such as Nucleic Acids Research, Computer applications in the biosciences, Proceedings of the National Academy of Sciences, Journal of Virology and Journal of Computational Chemistry.
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.