Danielle G. May
Impact in
- Cell Biology top 5%
- Biotin and Related Studies
- Cellular transport and secretion
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- Ubiquitin and proteasome pathways
- RNA Research and Splicing
- Protein Degradation and Inhibitors
- RNA and protein synthesis mechanisms
Papers in
-
- Ubiquitin and proteasome pathways 5
- Protein Degradation and Inhibitors 3
- Genomics and Chromatin Dynamics 2
- Epigenetics and DNA Methylation 1
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- Biotin and Related Studies 5
- Cellular transport and secretion 4
- Co-authors
- Kyle J. Roux (15 shared papers)Dae In Kim (2 shared papers)Brian Burke (1 shared paper)Rhiannon M. Sears (1 shared paper)Kelsey L. Scott (2 shared papers)Alexandre Rosa Campos (1 shared paper)Alexandre Rosa Campos (2 shared papers)Paul J. Galardy (3 shared papers)
- Journals
- Biomedicines (1 paper)iScience (1 paper)Molecular Biology of the Cell (1 paper)Scientific Reports (1 paper)Journal of Biological Chemistry (1 paper)
- Partner nations
- United StatesUnited KingdomNetherlands
In The Last Decade
Danielle G. May
14 papers receiving 578 citations
Peers
Comparison fields: 5 of 66
- Cell Biology 272
- Molecular Biology 358
- Aging 6
- Organic Chemistry 95
- Immunology and Allergy 14
Countries citing papers authored by Danielle G. May
This map shows the geographic impact of Danielle G. May'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 Danielle G. May with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danielle G. May more than expected).
Fields of papers citing papers by Danielle G. May
This network shows the impact of papers produced by Danielle G. May. 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 Danielle G. May. The network helps show where Danielle G. May may publish in the future.
Co-authors
The 25 scholars most cited alongside Danielle G. May, 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 | 2018 | 203 | |
| 2 | 2020 | 115 | |
| 3 | 2019 | 99 | |
| 4 | 2017 | 40 | |
| 5 | 2018 | 31 | |
| 6 | 2022 | 30 | |
| 7 | 2022 | 27 | |
| 8 | 2019 | 11 | |
| 9 | 2019 | 11 | |
| 10 | 2022 | 5 | |
| 11 | 2020 | 2 | |
| 12 | 2025 | 2 | |
| 13 | 2024 | 1 | |
| 14 | 2023 | 1 | |
| 15 | 2024 | 0 |
About Danielle G. May
Danielle G. May is a scholar working on Molecular Biology, Cell Biology, Organic Chemistry, Genetics and Infectious Diseases, having authored 15 papers that have together received 578 indexed citations. Recurring topics across this work include Ubiquitin and proteasome pathways (5 papers), Biotin and Related Studies (5 papers), Click Chemistry and Applications (4 papers), Cellular transport and secretion (4 papers), Protein Degradation and Inhibitors (3 papers), Genomics and Chromatin Dynamics (2 papers), Epigenetics and DNA Methylation (1 paper) and SARS-CoV-2 detection and testing (1 paper). The work is most often cited by research in Cell Biology (272 citations), Molecular Biology (358 citations), Aging (6 citations), Organic Chemistry (95 citations) and Immunology and Allergy (14 citations). Danielle G. May has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Kyle J. Roux, Dae In Kim, Brian Burke, Rhiannon M. Sears, Kelsey L. Scott, Alexandre Rosa Campos, Alexandre Rosa Campos, Paul J. Galardy, Tibor Bedekovics and Sajjad Hussain. Their work appears in journals such as Biomedicines, iScience, Molecular Biology of the Cell, Scientific Reports and Journal of Biological 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.