David Joyner
Impact in
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- Analytic Number Theory Research
Papers in
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- Radiomics and Machine Learning in Medical Imaging 2
- Surgery 4
- Head and Neck Surgical Oncology 3
- Co-authors
- Majid Khan (8 shared papers)Robert Kraft (1 shared paper)Ann M. Peiffer (1 shared paper)Christina E. Hugenschmidt (1 shared paper)Joseph A. Maldjian (1 shared paper)Jennifer L. Mozolic (1 shared paper)Paul J. Laurienti (1 shared paper)Seth T. Lirette (3 shared papers)
- Journals
- Neuroradiology (2 papers)Abdominal Radiology (2 papers)World Neurosurgery (1 paper)BMC Neurology (1 paper)American Journal of Neuroradiology (1 paper)
- Partner nations
- United StatesUnited KingdomIndia
In The Last Decade
David Joyner
20 papers receiving 288 citations
Peers
Comparison fields: 5 of 81
- Algebra and Number Theory 29
- Sensory Systems 24
- Cognitive Neuroscience 82
- Experimental and Cognitive Psychology 41
- Genetics 33
Countries citing papers authored by David Joyner
This map shows the geographic impact of David Joyner'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 David Joyner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Joyner more than expected).
Fields of papers citing papers by David Joyner
This network shows the impact of papers produced by David Joyner. 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 David Joyner. The network helps show where David Joyner may publish in the future.
Co-authors
The 25 scholars most cited alongside David Joyner, 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 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2008 | 94 | |
| 2 | 2016 | 36 | |
| 3 | Distribution theorems of L-functions | 1986 | 29 |
| 4 | 2019 | 21 | |
| 5 | 2018 | 15 | |
| 6 | 2008 | 15 | |
| 7 | 2022 | 14 | |
| 8 | 2022 | 11 | |
| 9 | 2020 | 10 | |
| 10 | 2018 | 9 | |
| 11 | 2002 | 9 | |
| 12 | 2013 | 8 | |
| 13 | 2018 | 7 | |
| 14 | 2018 | 5 | |
| 15 | 2018 | 5 | |
| 16 | 2016 | 3 | |
| 17 | 2021 | 3 | |
| 18 | 1996 | 3 | |
| 19 | 2024 | 2 | |
| 20 | 2018 | 1 |
About David Joyner
David Joyner is a scholar working on Radiology, Nuclear Medicine and Imaging, Surgery, Genetics, Neurology and Molecular Biology, having authored 24 papers that have together received 301 indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (4 papers), Cerebrospinal fluid and hydrocephalus (3 papers), Vascular Malformations Diagnosis and Treatment (3 papers), Head and Neck Surgical Oncology (3 papers), Intracranial Aneurysms: Treatment and Complications (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Cerebrovascular and Carotid Artery Diseases (1 paper) and Hepatocellular Carcinoma Treatment and Prognosis (1 paper). The work is most often cited by research in Algebra and Number Theory (29 citations), Sensory Systems (24 citations), Cognitive Neuroscience (82 citations), Experimental and Cognitive Psychology (41 citations) and Genetics (33 citations). David Joyner has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include Majid Khan, Robert Kraft, Ann M. Peiffer, Christina E. Hugenschmidt, Joseph A. Maldjian, Jennifer L. Mozolic, Paul J. Laurienti, Seth T. Lirette, Thomas H. Mosley and Andrew D. Smith. Their work appears in journals such as Neuroradiology, Abdominal Radiology, World Neurosurgery, BMC Neurology and American Journal of Neuroradiology.
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.