Chad A. Cowan
- Molecular Biology top 0.2%
- Cellular and Molecular Neuroscience top 0.5%
- Surgery top 1%
- Genetics top 1%
- Physiology top 2%
- Co-authors
- Tim AhfeldtMark HenkemeyerDouglas A. MeltonGeorge Q. DaleyNimet MaheraliKonrad HochedlingerNatasha AroraFrank H. Lau
- Topics
- CRISPR and Genetic Engineering (30 papers)Pluripotent Stem Cells Research (30 papers)Adipose Tissue and Metabolism (9 papers)
- Partner nations
- United StatesJapanNetherlands
In The Last Decade
Chad A. Cowan
81 papers receiving 12.2k citations
Hit Papers
Peers
Comparison fields: 5 of 147
- Molecular Biology 10.1k
- Cellular and Molecular Neuroscience 1.9k
- Surgery 1.9k
- Genetics 1.6k
- Physiology 1.3k
Countries citing papers authored by Chad A. Cowan
This map shows the geographic impact of Chad A. Cowan'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 Chad A. Cowan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chad A. Cowan more than expected).
Fields of papers citing papers by Chad A. Cowan
This network shows the impact of papers produced by Chad A. Cowan. 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 Chad A. Cowan. The network helps show where Chad A. Cowan may publish in the future.
Co-authorship network of co-authors of Chad A. Cowan
This figure shows the co-authorship network connecting the top 25 collaborators of Chad A. Cowan. A scholar is included among the top collaborators of Chad A. Cowan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chad A. Cowan. Chad A. Cowan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 34 | |
| 3 | 2 | |
| 4 | 239 | |
| 5 | 62 | |
| 6 | 27 | |
| 7 | 20 | |
| 8 | 80 | |
| 9 | Genome editing : from modeling disease to novel therapeutics | 0 |
| 10 | 380 | |
| 11 | 5 | |
| 12 | Disease-Specific Induced Pluripotent Stem Cellsbreakdown → | 1616 |
| 13 | Marked differences in differentiation propensity among human embryonic stem cell linesbreakdown → | 628 |
| 14 | 426 | |
| 15 | 11 | |
| 16 | 3 | |
| 17 | 14 | |
| 18 | Derivation of Embryonic Stem-Cell Lines from Human Blastocystsbreakdown → | 733 |
| 19 | 104 | |
| 20 | 262 |
About Chad A. Cowan
Chad A. Cowan is a scholar working on Aging, Molecular Biology and Business and International Management, having authored 83 papers that have together received 12.5k indexed citations. Recurring topics across this work include CRISPR and Genetic Engineering (30 papers), Pluripotent Stem Cells Research (30 papers) and Adipose Tissue and Metabolism (9 papers). The work is most often cited by research in Molecular Biology (10.1k citations), Developmental Neuroscience (594 citations) and Aging (197 citations). Chad A. Cowan has collaborated with scholars based in United States, Japan and Netherlands. Frequent co-authors include Tim Ahfeldt, Mark Henkemeyer, Douglas A. Melton, George Q. Daley, Nimet Maherali, Konrad Hochedlinger, Natasha Arora, Frank H. Lau, M. William Lensch and Hongguang Huo. Their work appears in journals such as Nature, Science and New England Journal of Medicine.
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.