Daniel B. Szymanski

4.8k total citations
72 papers, 3.5k citations indexed

About

Daniel B. Szymanski is a scholar working on Molecular Biology, Plant Science and Cell Biology. According to data from OpenAlex, Daniel B. Szymanski has authored 72 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Molecular Biology, 47 papers in Plant Science and 16 papers in Cell Biology. Recurrent topics in Daniel B. Szymanski's work include Plant Reproductive Biology (33 papers), Plant Molecular Biology Research (33 papers) and Polysaccharides and Plant Cell Walls (22 papers). Daniel B. Szymanski is often cited by papers focused on Plant Reproductive Biology (33 papers), Plant Molecular Biology Research (33 papers) and Polysaccharides and Plant Cell Walls (22 papers). Daniel B. Szymanski collaborates with scholars based in United States, Germany and Canada. Daniel B. Szymanski's co-authors include M. David Marks, Eileen L. Mallery, Jie Le, Chunhua Zhang, Dipanwita Basu, Susan M. Wick, Salah El‐Din El‐Assal, Daniel J. Cosgrove, Alan Lloyd and Makoto Yanagisawa and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and The Plant Cell.

In The Last Decade

Daniel B. Szymanski

64 papers receiving 3.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel B. Szymanski United States 34 2.8k 2.7k 693 105 96 72 3.5k
Jordi Chan United Kingdom 26 1.6k 0.6× 1.5k 0.5× 808 1.2× 43 0.4× 64 0.7× 33 2.0k
Guido Großmann Germany 30 1.7k 0.6× 1.8k 0.6× 556 0.8× 110 1.0× 37 0.4× 49 2.9k
Arun Sampathkumar Germany 26 2.1k 0.7× 1.5k 0.5× 225 0.3× 61 0.6× 122 1.3× 50 2.5k
Kentaro Tamura Japan 33 2.1k 0.7× 2.8k 1.0× 1.1k 1.5× 56 0.5× 13 0.1× 73 3.6k
Gui‐Xian Xia China 32 2.2k 0.8× 1.9k 0.7× 1.0k 1.5× 94 0.9× 8 0.1× 64 3.5k
Jie Le China 26 1.8k 0.6× 1.4k 0.5× 260 0.4× 49 0.5× 44 0.5× 48 2.1k
Moritz K. Nowack Belgium 30 3.4k 1.2× 2.7k 1.0× 296 0.4× 200 1.9× 24 0.3× 63 3.9k
Heather Cartwright United States 14 1000 0.4× 1.0k 0.4× 234 0.3× 50 0.5× 31 0.3× 39 1.6k
Muthugapatti K. Kandasamy United States 33 2.3k 0.8× 2.6k 0.9× 366 0.5× 448 4.3× 15 0.2× 74 3.1k
Matyáš Fendrych Czechia 26 2.1k 0.7× 1.6k 0.6× 321 0.5× 79 0.8× 19 0.2× 38 2.4k

Countries citing papers authored by Daniel B. Szymanski

Since Specialization
Citations

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

Fields of papers citing papers by Daniel B. Szymanski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel B. Szymanski

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel B. Szymanski. A scholar is included among the top collaborators of Daniel B. Szymanski 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 B. Szymanski. Daniel B. Szymanski 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.
Mallery, Eileen L., et al.. (2025). A cell fractionation and quantitative proteomics pipeline to enable functional analyses of cotton fiber development. The Plant Journal. 121(4). e17246–e17246.
2.
Swaminathan, Sivakumar, Corrinne E. Grover, Pengcheng Yang, et al.. (2024). Daily glycome and transcriptome profiling reveals polysaccharide structures and correlated glycosyltransferases critical for cotton fiber growth. The Plant Journal. 120(5). 1857–1879. 3 indexed citations
3.
Mallery, Eileen L., Makoto Yanagisawa, Chunhua Zhang, et al.. (2022). Tandem C2 domains mediate dynamic organelle targeting of a DOCK family guanine nucleotide exchange factor. Journal of Cell Science. 135(7). 3 indexed citations
4.
Okita, Thomas W., et al.. (2021). A co-fractionation mass spectrometry-based prediction of protein complex assemblies in the developing rice aleurone-subaleurone. The Plant Cell. 33(9). 2965–2980. 10 indexed citations
5.
Belteton, Samuel A., et al.. (2021). Real-time conversion of tissue-scale mechanical forces into an interdigitated growth pattern. Nature Plants. 7(6). 826–841. 32 indexed citations
6.
Kwon, Taegun, et al.. (2021). Spatial and temporal localization of SPIRRIG and WAVE/SCAR reveal roles for these proteins in actin-mediated root hair development. The Plant Cell. 33(7). 2131–2148. 16 indexed citations
7.
8.
Li, Wenlong, et al.. (2021). Protocol for mapping the variability in cell wall mechanical bending behavior in living leaf pavement cells. PLANT PHYSIOLOGY. 188(3). 1435–1449. 7 indexed citations
9.
Szymanski, Daniel B., et al.. (2021). Multimerization variants as potential drivers of neofunctionalization. Science Advances. 7(13). 15 indexed citations
10.
Chen, Donglai, et al.. (2019). A Label-free Mass Spectrometry Method to Predict Endogenous Protein Complex Composition*. Molecular & Cellular Proteomics. 18(8). 1588–1606. 26 indexed citations
11.
Wong, Jeh Haur, T. KATO, Samuel A. Belteton, et al.. (2019). Basic Proline-Rich Protein-Mediated Microtubules Are Essential for Lobe Growth and Flattened Cell Geometry. PLANT PHYSIOLOGY. 181(4). 1535–1551. 21 indexed citations
12.
Belteton, Samuel A., Megan G. Sawchuk, Bryon S. Donohoe, Enrico Scarpella, & Daniel B. Szymanski. (2017). Reassessing the Roles of PIN Proteins and Anticlinal Microtubules during Pavement Cell Morphogenesis. PLANT PHYSIOLOGY. 176(1). 432–449. 60 indexed citations
13.
Wu, Tzu‐Ching, et al.. (2016). LobeFinder: A Convex Hull-Based Method for Quantitative Boundary Analyses of Lobed Plant Cells. PLANT PHYSIOLOGY. 171(4). 2331–2342. 33 indexed citations
14.
15.
Bociąga, E. & Daniel B. Szymanski. (2013). Wpływ warunków wtryskiwania na właściwości mechaniczne oraz użytkowe wyprasek z obszarem łączenia strumieni tworzywa. Przetwórstwo Tworzyw.
16.
Bociąga, E. & Daniel B. Szymanski. (2011). Wpływ warunków wtryskiwania na przebieg procesu wtryskiwania kaskadowego. Przetwórstwo Tworzyw. 121–127.
17.
Basu, Dipanwita, et al.. (2008). A SPIKE1 signaling complex controls actin-dependent cell morphogenesis through the heteromeric WAVE and ARP2/3 complexes. Proceedings of the National Academy of Sciences. 105(10). 4044–4049. 120 indexed citations
18.
Basu, Dipanwita, Jie Le, Shanjin Huang, et al.. (2005). DISTORTED3/SCAR2 Is a Putative Arabidopsis WAVE Complex Subunit That Activates the Arp2/3 Complex and Is Required for Epidermal Morphogenesis. The Plant Cell. 17(2). 502–524. 101 indexed citations
19.
Basu, Dipanwita, Salah El‐Din El‐Assal, Jie Le, Eileen L. Mallery, & Daniel B. Szymanski. (2004). Interchangeable functions of Arabidopsis PIROGI and the human WAVE complex subunit SRA1 during leaf epidermal development. Development. 131(17). 4345–4355. 104 indexed citations
20.
Qiu, Jin‐Long, et al.. (2002). The Arabidopsis SPIKE1 Gene Is Required for Normal Cell Shape Control and Tissue Development. The Plant Cell. 14(1). 101–118. 171 indexed citations

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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