Jonathan M. Levitt

3.2k citations
96 papers · 2.4k · h-index 30

Impact in

Papers in

Jonathan M. Levitt

94 papers receiving 2.3k citations

Peers

Jonathan M. Levitt
Comparison fields: 5 of 191
  • Statistics, Probability and Uncertainty 450
  • Biophysics 258
  • Acoustics and Ultrasonics 35
  • Information Systems and Management 165
  • Immunology 348
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Thomas Schlange Germany
Richard B. Rosen United States
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Citations per field
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Citations per year

Countries citing papers authored by Jonathan M. Levitt

Since Specialization
Citations

This map shows the geographic impact of Jonathan M. Levitt'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 Jonathan M. Levitt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan M. Levitt more than expected).

Fields of papers citing papers by Jonathan M. Levitt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan M. Levitt. 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 Jonathan M. Levitt. The network helps show where Jonathan M. Levitt may publish in the future.

Co-authors

The 25 scholars most cited alongside Jonathan M. Levitt, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jonathan M. Levitt Line = papers co-authored together Jonathan M. Levitt links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 96 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008119
2 2013107
3 2009104
4 2012104
5 202098
6 200891
7 202170
8 199059
9 200657
10 201755
11 201754
12 201053
13 200752
14 200850
15 201149
16 200749
17 202048
18 201645
19 201044
20 201142

About Jonathan M. Levitt

Jonathan M. Levitt is a scholar working on Statistics, Probability and Uncertainty, Immunology, Molecular Biology, Oncology and Pulmonary and Respiratory Medicine, having authored 96 papers that have together received 2.4k indexed citations. Recurring topics across this work include scientometrics and bibliometrics research (25 papers), Immunotherapy and Immune Responses (17 papers), Prostate Cancer Treatment and Research (9 papers), Meta-analysis and systematic reviews (8 papers), T-cell and B-cell Immunology (8 papers), Cancer Immunotherapy and Biomarkers (8 papers), Immune Cell Function and Interaction (8 papers) and Spectroscopy Techniques in Biomedical and Chemical Research (7 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (450 citations), Biophysics (258 citations), Acoustics and Ultrasonics (35 citations), Information Systems and Management (165 citations) and Immunology (348 citations). Jonathan M. Levitt has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Mike Thelwall, Irene Georgakoudi, Kevin M. Slawin, William K. Decker, Vanaja Konduri, David M. Spencer, David L. Kaplan, Mamatha Seethammagari, Karl Münger and Jonathan Vazquez‐Perez. Their work appears in journals such as Scientometrics, Journal of the Association for Information Science and Technology, The Journal of Urology, Cancer Research and Journal of Clinical Oncology.

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.

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