Cody J. Smith

3.9k total citations
46 papers, 1.4k citations indexed

About

Cody J. Smith is a scholar working on Developmental Neuroscience, Cellular and Molecular Neuroscience and Biophysics. According to data from OpenAlex, Cody J. Smith has authored 46 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Developmental Neuroscience, 11 papers in Cellular and Molecular Neuroscience and 10 papers in Biophysics. Recurrent topics in Cody J. Smith's work include Neurogenesis and neuroplasticity mechanisms (15 papers), Advanced Fluorescence Microscopy Techniques (10 papers) and Neuroinflammation and Neurodegeneration Mechanisms (9 papers). Cody J. Smith is often cited by papers focused on Neurogenesis and neuroplasticity mechanisms (15 papers), Advanced Fluorescence Microscopy Techniques (10 papers) and Neuroinflammation and Neurodegeneration Mechanisms (9 papers). Cody J. Smith collaborates with scholars based in United States, United Kingdom and Israel. Cody J. Smith's co-authors include David M. Miller, Ev L. Nichols, Sarah Kucenas, Joseph D. Watson, Millet Treinin, Scott S. Howard, Yide Zhang, William C. Spencer, Timothy O’Brien and Siyuan Zhang and has published in prestigious journals such as Nature Communications, Neuron and Journal of Neuroscience.

In The Last Decade

Cody J. Smith

45 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Cody J. Smith United States 19 387 337 309 233 196 46 1.4k
Christoph Kirst United States 14 315 0.8× 589 1.7× 51 0.2× 117 0.5× 58 0.3× 23 1.5k
Netta Cohen United Kingdom 19 550 1.4× 405 1.2× 456 1.5× 134 0.6× 169 0.9× 48 1.6k
Hiroshi Kori Japan 23 474 1.2× 731 2.2× 53 0.2× 98 0.4× 170 0.9× 71 2.3k
Thomas M. Morse United States 14 693 1.8× 472 1.4× 324 1.0× 31 0.1× 22 0.1× 27 1.7k
Bing Ye United States 22 1.2k 3.1× 1.3k 3.8× 170 0.6× 871 3.7× 172 0.9× 57 2.4k
Andrew S. Yoo United States 28 878 2.3× 2.9k 8.5× 247 0.8× 156 0.7× 510 2.6× 48 3.8k
Tomoko Ohyama United States 23 1.2k 3.2× 1.0k 3.1× 81 0.3× 510 2.2× 53 0.3× 40 2.5k
Sean Simmons United States 15 205 0.5× 1.1k 3.4× 66 0.2× 69 0.3× 241 1.2× 31 1.8k
Zizhen Yao United States 38 570 1.5× 4.4k 13.0× 83 0.3× 176 0.8× 180 0.9× 58 5.5k
С. В. Козлов United States 24 405 1.0× 1.9k 5.6× 57 0.2× 359 1.5× 350 1.8× 85 3.1k

Countries citing papers authored by Cody J. Smith

Since Specialization
Citations

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

Fields of papers citing papers by Cody J. Smith

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cody J. Smith

This figure shows the co-authorship network connecting the top 25 collaborators of Cody J. Smith. A scholar is included among the top collaborators of Cody J. Smith 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 Cody J. Smith. Cody J. Smith 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.
Hasel, Philip, Melissa Cooper, Rachel D. Kim, et al.. (2025). Defining the molecular identity and morphology of glia limitans superficialis astrocytes in vertebrates. Cell Reports. 44(3). 115344–115344. 4 indexed citations
2.
Smith, Cody J., et al.. (2023). Improving fluorescence lifetime imaging microscopy phasor accuracy using convolutional neural networks. SHILAP Revista de lepidopterología. 3. 1335413–1335413. 6 indexed citations
3.
Zhang, Yide, Yinhao Zhu, Ev L. Nichols, et al.. (2022). Real-time image denoising of mixed Poisson–Gaussian noise in fluorescence microscopy images using ImageJ. Optica. 9(4). 335–335. 44 indexed citations
4.
O’Dea, Michael R., et al.. (2022). The embryonic zebrafish brain is seeded by a lymphatic-dependent population of mrc1+ microglia precursors. Nature Neuroscience. 25(7). 849–864. 14 indexed citations
5.
Smith, Cody J.. (2022). Members of the majority need to actively promote diversity, equity, inclusion, and belonging. PLoS Biology. 20(12). e3001902–e3001902. 1 indexed citations
6.
Smith, Cody J., et al.. (2022). Diabetes and Impaired Fracture Healing: A Narrative Review of Recent Literature. Current Osteoporosis Reports. 20(5). 229–239. 42 indexed citations
7.
Zhang, Yide, Ian H. Guldner, Ev L. Nichols, et al.. (2021). Instant FLIM enables 4D in vivo lifetime imaging of intact and injured zebrafish and mouse brains. Optica. 8(6). 885–885. 26 indexed citations
8.
Nichols, Ev L., et al.. (2021). Pioneer Axons Utilize a Dcc Signaling-Mediated Invasion Brake to Precisely Complete Their Pathfinding Odyssey. Journal of Neuroscience. 41(31). 6617–6636. 5 indexed citations
9.
Smith, Cody J., et al.. (2021). Object Detection Accuracy Enhancement in Color based Dynamic Sorting using Robotic Arm. Digital Commons - University of South Florida (University of South Florida).
10.
DeSantis, Dana F. & Cody J. Smith. (2021). Tetris in the Nervous System: What Principles of Neuronal Tiling Can Tell Us About How Glia Play the Game. Frontiers in Cellular Neuroscience. 15. 734938–734938. 6 indexed citations
11.
Smith, Cody J., et al.. (2019). Actin assembly and non-muscle myosin activity drive dendrite retraction in an UNC-6/Netrin dependent self-avoidance response. PLoS Genetics. 15(6). e1008228–e1008228. 19 indexed citations
12.
Smith, Cody J., et al.. (2019). Microglia exit the CNS in spinal root avulsion. PLoS Biology. 17(2). e3000159–e3000159. 40 indexed citations
13.
Nichols, Ev L. & Cody J. Smith. (2019). Synaptic-like Vesicles Facilitate Pioneer Axon Invasion. Current Biology. 29(16). 2652–2664.e4. 14 indexed citations
14.
Done, Aaron J., et al.. (2019). A low-cost high-fidelity model for abscess simulation. The American Journal of Surgery. 219(4). 628–631. 1 indexed citations
15.
Smith, Cody J., et al.. (2018). Single-cell Photoconversion in Living Intact Zebrafish. Journal of Visualized Experiments. 6 indexed citations
16.
Smith, Cody J., Michael A. Wheeler, Lindsay Marjoram, et al.. (2017). TNFa/TNFR2 signaling is required for glial ensheathment at the dorsal root entry zone. PLoS Genetics. 13(4). e1006712–e1006712. 17 indexed citations
17.
Wheeler, Michael A., Cody J. Smith, Matteo Ottolini, et al.. (2016). Genetically targeted magnetic control of the nervous system. Nature Neuroscience. 19(5). 756–761. 172 indexed citations
18.
Smith, Cody J., et al.. (2014). Contact-Mediated Inhibition Between Oligodendrocyte Progenitor Cells and Motor Exit Point Glia Establishes the Spinal Cord Transition Zone. PLoS Biology. 12(9). e1001961–e1001961. 49 indexed citations
19.
Smith, Cody J., Joseph D. Watson, Miri K. VanHoven, Daniel A. Colón‐Ramos, & David M. Miller. (2012). Netrin (UNC-6) mediates dendritic self-avoidance. Nature Neuroscience. 15(5). 731–737. 75 indexed citations
20.
Smith, Cody J., Marios Chatzigeorgiou, Dror G. Feitelson, et al.. (2010). C. elegans multi-dendritic sensory neurons: Morphology and function. Molecular and Cellular Neuroscience. 46(1). 308–317. 122 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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