Daniel T. Kaplan

10 papers receiving 349 citations

Peers

Daniel T. Kaplan
Comparison fields: 5 of 72
  • Statistical and Nonlinear Physics 145
  • Signal Processing 72
  • Economics and Econometrics 136
  • Cognitive Neuroscience 90
  • Cardiology and Cardiovascular Medicine 106
Replace D. Y. Qianli with:
D. Y. Qianli China
Danuta Makowiec Poland
A. Passamante United States
M.A. Jiménez-Montaño Mexico
О. Н. Павлова Russia
Yonghong Chen China
Ruoxi Xiang Hong Kong
Srimonti Dutta India
Christopher W. Kulp United States
Daniel T. Kaplan relative to D. Y. Qianli China D. Y. Qianli's profile →
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Citations per year

Countries citing papers authored by Daniel T. Kaplan

Since Specialization
Citations

This map shows the geographic impact of Daniel T. Kaplan'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 T. Kaplan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel T. Kaplan more than expected).

Fields of papers citing papers by Daniel T. Kaplan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel T. Kaplan. 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 T. Kaplan. The network helps show where Daniel T. Kaplan may publish in the future.

Co-authors

The 10 scholars most cited alongside Daniel T. Kaplan, 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 Daniel T. Kaplan Line = papers co-authored together Daniel T. Kaplan links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1 1994125
2 199396
3 199663
4 199633
5 199819
6 200212
7 199612
8 19915
9
Google's PageRank and Beyond: The Science of Search Engine Rankings by Amy N. Langville; Carl D. Meyer.
20082
10 19832
11
Review of "Google's PageRank and Beyond: The Science of Search Engine Rankings''
20081

About Daniel T. Kaplan

Daniel T. Kaplan is a scholar working on Statistical and Nonlinear Physics, Economics and Econometrics, Cardiology and Cardiovascular Medicine, Signal Processing and Computer Networks and Communications, having authored 11 papers that have together received 370 indexed citations. Recurring topics across this work include Chaos control and synchronization (6 papers), Complex Systems and Time Series Analysis (5 papers), Genetic diversity and population structure (1 paper), Analog and Mixed-Signal Circuit Design (1 paper), Time Series Analysis and Forecasting (1 paper), Quantum chaos and dynamical systems (1 paper), Marine and coastal ecosystems (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (145 citations), Signal Processing (72 citations), Economics and Econometrics (136 citations), Cognitive Neuroscience (90 citations) and Cardiology and Cardiovascular Medicine (106 citations). Daniel T. Kaplan has collaborated with scholars based in Canada, Germany and United States. Frequent co-authors include Leon Glass, Thomas Schreiber, M. Eiselt, M.I. Furman, Steve Pincus, Hal Caswell, Mercedes Pascual, Michael G. Neubert, Stephen P. Ellner and Timothy Sauer. Their work appears in journals such as Physica D Nonlinear Phenomena, Chaos An Interdisciplinary Journal of Nonlinear Science, Bulletin of the Atomic Scientists, American Mathematical Monthly and Journal of Electrocardiology.

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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