Daniel J. Gans
Impact in
- Nephrology top 5%
- Chronic Kidney Disease and Diabetes
- Dialysis and Renal Disease Management
-
- Blood Pressure and Hypertension Studies
- Renin-Angiotensin System Studies
Papers in
-
- Computational Drug Discovery Methods 4
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- Advanced Statistical Methods and Models 3
- Statistical Methods in Clinical Trials 2
- Co-authors
- Lori M. Laffel (1 shared paper)Janet B. McGill (1 shared paper)James W. McFarland (4 shared papers)Ivan G. Otterness (3 shared papers)Edward H. Wiseman (1 shared paper)Jerome G. Porush (2 shared papers)Tomás Berl (2 shared papers)Edmund J. Lewis (2 shared papers)
- Journals
- Journal of Medicinal Chemistry (2 papers)The Journal of Interdisciplinary History (1 paper)Inflammation Research (1 paper)Statistics in Medicine (1 paper)Technometrics (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
Daniel J. Gans
16 papers receiving 457 citations
Peers
Comparison fields: 5 of 107
- Nephrology 152
- Cardiology and Cardiovascular Medicine 149
- Endocrinology, Diabetes and Metabolism 95
- Statistics and Probability 41
- Pharmacology 52
Countries citing papers authored by Daniel J. Gans
This map shows the geographic impact of Daniel J. Gans'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 Daniel J. Gans with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel J. Gans more than expected).
Fields of papers citing papers by Daniel J. Gans
This network shows the impact of papers produced by Daniel J. Gans. 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 Daniel J. Gans. The network helps show where Daniel J. Gans may publish in the future.
Co-authors
The 24 scholars most cited alongside Daniel J. Gans, 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 | 1995 | 192 | |
| 2 | 2004 | 93 | |
| 3 | 1981 | 30 | |
| 4 | 1986 | 29 | |
| 5 | 1979 | 28 | |
| 6 | 1988 | 25 | |
| 7 | 1994 | 24 | |
| 8 | 1987 | 20 | |
| 9 | 2004 | 19 | |
| 10 | 1985 | 13 | |
| 11 | 1984 | 10 | |
| 12 | 1981 | 6 | |
| 13 | 1982 | 5 | |
| 14 | 1990 | 5 | |
| 15 | 1991 | 4 | |
| 16 | 1975 | 1 |
About Daniel J. Gans
Daniel J. Gans is a scholar working on Computational Theory and Mathematics, Statistics and Probability, Pharmacology, Nephrology and Cardiology and Cardiovascular Medicine, having authored 16 papers that have together received 504 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Advanced Statistical Methods and Models (3 papers), Chronic Kidney Disease and Diabetes (2 papers), Inflammatory mediators and NSAID effects (2 papers), Statistical Methods in Clinical Trials (2 papers), Analytical Chemistry and Chromatography (2 papers), Cardiac Imaging and Diagnostics (1 paper) and Cardiac electrophysiology and arrhythmias (1 paper). The work is most often cited by research in Nephrology (152 citations), Cardiology and Cardiovascular Medicine (149 citations), Endocrinology, Diabetes and Metabolism (95 citations), Statistics and Probability (41 citations) and Pharmacology (52 citations). Daniel J. Gans has collaborated with scholars based in United States and Canada. Frequent co-authors include Lori M. Laffel, Janet B. McGill, James W. McFarland, Ivan G. Otterness, Edward H. Wiseman, Jerome G. Porush, Tomás Berl, Edmund J. Lewis, Jean‐Lucien Rouleau and Julia B. Lewis. Their work appears in journals such as Journal of Medicinal Chemistry, The Journal of Interdisciplinary History, Inflammation Research, Statistics in Medicine and Technometrics.
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.