Daniel T. Kaplan
- Statistical and Nonlinear Physics top 5%
- Economics and Econometrics top 10%
- Cardiology and Cardiovascular Medicine
- Cognitive Neuroscience
- Signal Processing top 10%
- Co-authors
- Leon GlassThomas SchreiberM. EiseltSteve PincusM.I. FurmanStephen P. EllnerHal CaswellMercedes Pascual
- Topics
- Chaos control and synchronization (6 papers)Complex Systems and Time Series Analysis (5 papers)ECG Monitoring and Analysis (1 paper)
- Journals
- Physica D Nonlinear PhenomenaAmerican Mathematical MonthlyChaos An Interdisciplinary Journal of Nonlinear Science
- Partner nations
- CanadaGermanyUnited States
In The Last Decade
Daniel T. Kaplan
10 papers receiving 349 citations
Peers
Comparison fields: 5 of 72
- Statistical and Nonlinear Physics 145
- Economics and Econometrics 136
- Cardiology and Cardiovascular Medicine 106
- Cognitive Neuroscience 90
- Signal Processing 72
Countries citing papers authored by Daniel T. Kaplan
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
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-authorship network of co-authors of Daniel T. Kaplan
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel T. Kaplan. A scholar is included among the top collaborators of Daniel T. Kaplan 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 Daniel T. Kaplan. Daniel T. Kaplan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Google's PageRank and Beyond: The Science of Search Engine Rankings by Amy N. Langville; Carl D. Meyer. | 2 |
| 2 | Review of "Google's PageRank and Beyond: The Science of Search Engine Rankings'' | 1 |
| 3 | 12 | |
| 4 | 19 | |
| 5 | 63 | |
| 6 | 33 | |
| 7 | 12 | |
| 8 | 125 | |
| 9 | 96 | |
| 10 | 5 | |
| 11 | 2 |
About Daniel T. Kaplan
Daniel T. Kaplan is a scholar working on Statistical and Nonlinear Physics, Signal Processing and Economics and Econometrics, 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) and ECG Monitoring and Analysis (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (145 citations), Signal Processing (72 citations) and Economics and Econometrics (136 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, Steve Pincus, M.I. Furman, Stephen P. Ellner, Hal Caswell, Mercedes Pascual, Michael G. Neubert and Timothy Sauer. Their work appears in journals such as Physica D Nonlinear Phenomena, American Mathematical Monthly and Chaos An Interdisciplinary Journal of Nonlinear Science.
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.