Patrick D. Shipman

2.0k total citations
54 papers, 1.0k citations indexed

About

Patrick D. Shipman is a scholar working on Molecular Biology, Materials Chemistry and Plant Science. According to data from OpenAlex, Patrick D. Shipman has authored 54 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 12 papers in Materials Chemistry and 11 papers in Plant Science. Recurrent topics in Patrick D. Shipman's work include Plant Molecular Biology Research (10 papers), Plant Reproductive Biology (9 papers) and Ion-surface interactions and analysis (9 papers). Patrick D. Shipman is often cited by papers focused on Plant Molecular Biology Research (10 papers), Plant Reproductive Biology (9 papers) and Ion-surface interactions and analysis (9 papers). Patrick D. Shipman collaborates with scholars based in United States, Germany and Türkiye. Patrick D. Shipman's co-authors include R. Mark Bradley, Alan C. Newell, Richard G. Finke, Francis C. Motta, Saim Özkâr, Christopher B. Whitehead, Benjamin F. Miller, Alain Goriely, Karyn L. Hamilton and D. Ghose and has published in prestigious journals such as Journal of the American Chemical Society, Physical Review Letters and The Journal of Chemical Physics.

In The Last Decade

Patrick D. Shipman

51 papers receiving 1.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Patrick D. Shipman United States 19 330 308 275 273 183 54 1.0k
Akitoshi Okino Japan 22 112 0.3× 210 0.7× 130 0.5× 632 2.3× 61 0.3× 147 1.7k
David Schaefer United States 16 74 0.2× 244 0.8× 132 0.5× 190 0.7× 41 0.2× 45 996
Michael J. Taormina United States 8 283 0.9× 103 0.3× 139 0.5× 142 0.5× 16 0.1× 11 883
Jan Kierfeld Germany 23 117 0.4× 233 0.8× 237 0.9× 87 0.3× 26 0.1× 74 1.5k
Pavel Horváth Czechia 13 85 0.3× 134 0.4× 71 0.3× 140 0.5× 40 0.2× 73 560
Nobuhiko J. Suematsu Japan 20 88 0.3× 242 0.8× 118 0.4× 81 0.3× 35 0.2× 72 1.1k
Adriana I. Pesci United Kingdom 20 315 1.0× 380 1.2× 133 0.5× 123 0.5× 12 0.1× 44 1.3k
Satoshi Sugimoto Japan 14 169 0.5× 109 0.4× 172 0.6× 297 1.1× 22 0.1× 99 858
Hans Jörg Limbach Germany 16 81 0.2× 393 1.3× 267 1.0× 84 0.3× 20 0.1× 24 1.2k
Toshio Mogi Japan 34 654 2.0× 133 0.4× 1.1k 4.1× 41 0.2× 57 0.3× 81 3.6k

Countries citing papers authored by Patrick D. Shipman

Since Specialization
Citations

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

Fields of papers citing papers by Patrick D. Shipman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Patrick D. Shipman. 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 Patrick D. Shipman. The network helps show where Patrick D. Shipman may publish in the future.

Co-authorship network of co-authors of Patrick D. Shipman

This figure shows the co-authorship network connecting the top 25 collaborators of Patrick D. Shipman. A scholar is included among the top collaborators of Patrick D. Shipman 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 Patrick D. Shipman. Patrick D. Shipman 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
1.
Shipman, Patrick D., et al.. (2023). Molecular transformations and self-association in anthocyanin pigment patterns. Journal of Biosciences. 49(1). 2 indexed citations
2.
Kobak, Kamil, Marcus M. Lawrence, Gavin Pharaoh, et al.. (2021). Determining the contributions of protein synthesis and breakdown to muscle atrophy requires non‐steady‐state equations. Journal of Cachexia Sarcopenia and Muscle. 12(6). 1764–1775. 19 indexed citations
3.
Shipman, Patrick D., et al.. (2020). Particle Size Distributions via Mechanism-Enabled Population Balance Modeling. The Journal of Physical Chemistry.
4.
Shipman, Patrick D., et al.. (2019). Mechanism-Enabled Population Balance Modeling of Particle Formation en Route to Particle Average Size and Size Distribution Understanding and Control. Journal of the American Chemical Society. 141(40). 15827–15839. 55 indexed citations
5.
Miller, Benjamin F., Karyn L. Hamilton, Zana R. Majeed, et al.. (2017). Enhanced skeletal muscle regrowth and remodelling in massaged and contralateral non‐massaged hindlimb. The Journal of Physiology. 596(1). 83–103. 60 indexed citations
6.
Shipman, Patrick D., et al.. (2016). Highly ordered square arrays of nanoscale pyramids produced by ion bombardment of a crystalline binary material. Physical review. E. 93(3). 32207–32207. 2 indexed citations
7.
Motta, Francis C., et al.. (2016). Optimally Topologically Transitive Orbits in Discrete Dynamical Systems. American Mathematical Monthly. 123(2). 115–115.
8.
Pearson, Daniel A., R. Mark Bradley, Francis C. Motta, & Patrick D. Shipman. (2015). Producing nanodot arrays with improved hexagonal order by patterning surfaces before ion sputtering. Physical Review E. 92(6). 62401–62401. 11 indexed citations
9.
Strickland, Christopher, Gerhard Dangelmayr, Patrick D. Shipman, Sunil Kumar, & Thomas J. Stohlgren. (2015). Network spread of invasive species and infectious diseases. Ecological Modelling. 309-310. 1–9. 5 indexed citations
10.
Strickland, Christopher, Gerhard Dangelmayr, & Patrick D. Shipman. (2013). Modeling the presence probability of invasive plant species with nonlocal dispersal. Journal of Mathematical Biology. 69(2). 267–294. 4 indexed citations
11.
Shipman, Patrick D., Sérgio H. Faria, & Christopher Strickland. (2013). Towards a continuous population model for natural language vowel shift. Journal of Theoretical Biology. 332. 123–135. 3 indexed citations
12.
Thompson, Stephen P. & Patrick D. Shipman. (2013). Patterns, Oscillations, and Microtornadoes: Extreme Events in Vapor-to-particle Reaction Zones. Procedia IUTAM. 9. 138–164. 5 indexed citations
13.
Thompson, Stephen P. & Patrick D. Shipman. (2013). Topochemical diffusion-reaction-convection dynamics in vapor-to-particle aerosol nucleation and growth. AIP conference proceedings. 75–78. 1 indexed citations
14.
Shipman, Patrick D.. (2010). Discrete and continuous invariance in phyllotactic tilings. Physical Review E. 81(3). 31905–31905. 7 indexed citations
15.
Bradley, R. Mark & Patrick D. Shipman. (2010). Spontaneous Pattern Formation Induced by Ion Bombardment of Binary Compounds. Physical Review Letters. 105(14). 145501–145501. 112 indexed citations
16.
Newell, Alan C., et al.. (2008). Phyllotaxis as an example of the symbiosis of mechanical forces and biochemical processes in living tissue. Plant Signaling & Behavior. 3(8). 586–589. 13 indexed citations
17.
Tuckwell, Henry C., Patrick D. Shipman, & Alan S. Perelson. (2008). The probability of HIV infection in a new host and its reduction with microbicides. Mathematical Biosciences. 214(1-2). 81–86. 29 indexed citations
18.
Shipman, Patrick D. & Alan C. Newell. (2005). Polygonal planforms and phyllotaxis on plants. Journal of Theoretical Biology. 236(2). 154–197. 42 indexed citations
19.
Shipman, Patrick D. & Alan C. Newell. (2004). Phyllotactic Patterns on Plants. Physical Review Letters. 92(16). 168102–168102. 59 indexed citations
20.

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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