Daniel J. Thompson

742 citations
20 papers · 331 indexed · h-index 11
Topics
Mathematical Dynamics and Fractals (14 papers)Geometric Analysis and Curvature Flows (4 papers)Machine Learning in Bioinformatics (2 papers)
Partner nations
United StatesJapan

In The Last Decade

Daniel J. Thompson

18 papers receiving 304 citations

Peers

Daniel J. Thompson
Comparison fields: 5 of 55
  • Mathematical Physics 246
  • Statistical and Nonlinear Physics 116
  • Geometry and Topology 65
  • Computational Theory and Mathematics 36
  • Food Science 33
Replace N. I. Gerasimenko with:
N. I. Gerasimenko Russia
Herbert Ziezold Germany
Michael Fuchs Taiwan
Toby Hall United Kingdom
Didier Arnal France
Arkady Tempelman United States
Alain Jacquemard France
Yun Zhao China
Keonhee Lee South Korea
Cymra Haskell United States
Daniel J. Thompson relative to N. I. Gerasimenko Russia N. I. Gerasimenko's profile →
Citations per field
00.5×4.3×
N. I. Gerasimenko · 1×
Citations per year

Countries citing papers authored by Daniel J. Thompson

Since Specialization
Citations

This map shows the geographic impact of Daniel J. Thompson'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. Thompson 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. Thompson more than expected).

Fields of papers citing papers by Daniel J. Thompson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel J. Thompson

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel J. Thompson. A scholar is included among the top collaborators of Daniel J. Thompson 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 J. Thompson. Daniel J. Thompson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 10
3 4
4 0
5 13
6 14
7 21
8 16
9 35
10 7
11 71
12 16
13
Topological Pressure and Coding Sequence Density Estimation in the Human Genome
1
14 1
15 5
16 44
17 41
18 7
19 1
20
The life history of Lestes sponsa (Hansemann): larval growth (Zygoptera: Lestidae)
24

About Daniel J. Thompson

Daniel J. Thompson is a scholar working on Mathematical Physics, Geometry and Topology and Applied Mathematics, having authored 20 papers that have together received 331 indexed citations. Recurring topics across this work include Mathematical Dynamics and Fractals (14 papers), Geometric Analysis and Curvature Flows (4 papers) and Machine Learning in Bioinformatics (2 papers). The work is most often cited by research in Mathematical Physics (246 citations), Statistical and Nonlinear Physics (116 citations) and Geometry and Topology (65 citations). Daniel J. Thompson has collaborated with scholars based in United States and Japan. Frequent co-authors include Vaughn Climenhaga, Ruifu Yuan, Todd Fisher, John H. Lawton, Tom Fisher, Keith Burns, Jean‐François Lafont, Kenichiro Yamamoto, David Koslicki and Anthony A. Frank. Their work appears in journals such as Carbohydrate Polymers, Transactions of the American Mathematical Society and Toxicology Letters.

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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