Cory D. Hirsch

2.7k total citations
39 papers, 1.7k citations indexed

About

Cory D. Hirsch is a scholar working on Plant Science, Molecular Biology and Genetics. According to data from OpenAlex, Cory D. Hirsch has authored 39 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Plant Science, 9 papers in Molecular Biology and 7 papers in Genetics. Recurrent topics in Cory D. Hirsch's work include Plant Disease Resistance and Genetics (13 papers), Chromosomal and Genetic Variations (11 papers) and Genomics and Phylogenetic Studies (6 papers). Cory D. Hirsch is often cited by papers focused on Plant Disease Resistance and Genetics (13 papers), Chromosomal and Genetic Variations (11 papers) and Genomics and Phylogenetic Studies (6 papers). Cory D. Hirsch collaborates with scholars based in United States, China and Australia. Cory D. Hirsch's co-authors include Nathan M. Springer, Candice N. Hirsch, Brian J. Steffenson, C. Robin Buell, Ce Yang, Wen‐Hao Su, Irina Makarevitch, Jiming Jiang, Jaclyn M Noshay and Amanda J. Waters and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Cory D. Hirsch

38 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Cory D. Hirsch United States 20 1.5k 636 284 167 164 39 1.7k
Candice Gardner United States 17 1.3k 0.9× 303 0.5× 809 2.8× 86 0.5× 132 0.8× 29 1.6k
Dilip R. Panthee United States 27 1.9k 1.3× 335 0.5× 210 0.7× 31 0.2× 99 0.6× 85 2.1k
Jeremy D. Edwards United States 16 1.4k 1.0× 502 0.8× 518 1.8× 33 0.2× 41 0.3× 42 1.7k
M. Isabel Vales United States 25 1.8k 1.2× 373 0.6× 610 2.1× 44 0.3× 220 1.3× 85 2.0k
Zahoor Ahmad Mir India 17 1.1k 0.8× 385 0.6× 163 0.6× 21 0.1× 46 0.3× 36 1.4k
Nnadozie Oraguzie United States 21 1.0k 0.7× 395 0.6× 175 0.6× 32 0.2× 86 0.5× 63 1.3k
Wolfgang Schipprack Germany 29 2.1k 1.4× 472 0.7× 1.5k 5.2× 82 0.5× 61 0.4× 52 2.4k
Primetta Faccioli Italy 20 868 0.6× 416 0.7× 160 0.6× 20 0.1× 129 0.8× 43 1.1k
François Laurens France 27 1.8k 1.2× 518 0.8× 194 0.7× 77 0.5× 155 0.9× 79 2.0k
Nabila Yahiaoui France 31 2.8k 1.9× 1.0k 1.6× 282 1.0× 28 0.2× 117 0.7× 53 3.2k

Countries citing papers authored by Cory D. Hirsch

Since Specialization
Citations

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

Fields of papers citing papers by Cory D. Hirsch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cory D. Hirsch

This figure shows the co-authorship network connecting the top 25 collaborators of Cory D. Hirsch. A scholar is included among the top collaborators of Cory D. Hirsch 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 Cory D. Hirsch. Cory D. Hirsch 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.
Hirsch, Cory D., et al.. (2025). Current methods and future needs for visible and non-visible detection of plant stress responses. Frontiers in Plant Science. 16. 1585413–1585413. 1 indexed citations
2.
Murphy, Katherine M., et al.. (2024). Maize Abiotic Stress Treatments in Controlled Environments. Cold Spring Harbor Protocols. 2025(6). pdb.prot108620–pdb.prot108620. 1 indexed citations
3.
Lima, Leonardo W., et al.. (2024). Optimized Methods for Applying and Assessing Heat, Drought, and Nutrient Stress of Maize Seedlings in Controlled Environment Experiments. Cold Spring Harbor Protocols. 2025(6). pdb.top108467–pdb.top108467. 2 indexed citations
4.
Martin, Frank N., et al.. (2022). A Reference Genome Sequence Resource for the Sugar Beet Root Rot Pathogen Aphanomyces cochlioides. Molecular Plant-Microbe Interactions. 35(8). 706–710. 1 indexed citations
5.
Springer, Nathan M., et al.. (2022). Opportunities and challenges in phenotyping row crops using drone‐based RGB imaging. SHILAP Revista de lepidopterología. 5(1). 31 indexed citations
6.
Zhang, Jiajing, Min An, Brian J. Steffenson, et al.. (2022). Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model. Frontiers in Plant Science. 13. 834938–834938. 13 indexed citations
7.
Henson, Cynthia A., et al.. (2021). Description and functional analysis of the transcriptome from malting barley. Genomics. 113(5). 3310–3324. 11 indexed citations
8.
Hirsch, Cory D., et al.. (2020). Evaluating and Mapping Grape Color Using Image-Based Phenotyping. Plant Phenomics. 2020. 8086309–8086309. 18 indexed citations
9.
Li, Zhi, Peng Zhou, Rafael Della Coletta, et al.. (2020). Single‐parent expression drives dynamic gene expression complementation in maize hybrids. The Plant Journal. 105(1). 93–107. 21 indexed citations
10.
Su, Wen‐Hao, et al.. (2020). Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision. Remote Sensing. 13(1). 26–26. 102 indexed citations
11.
12.
Qiu, Ruicheng, et al.. (2019). Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging. Remote Sensing. 11(22). 2658–2658. 77 indexed citations
13.
Anderson, Sarah N., Michelle C. Stitzer, Peng Zhou, et al.. (2019). Dynamic Patterns of Transcript Abundance of Transposable Element Families in Maize. G3 Genes Genomes Genetics. 9(11). 3673–3682. 25 indexed citations
14.
15.
Li, Feng, Narayana M. Upadhyaya, Jana Sperschneider, et al.. (2019). Emergence of the Ug99 lineage of the wheat stem rust pathogen through somatic hybridisation. Nature Communications. 10(1). 5068–5068. 114 indexed citations
16.
Anderson, Sarah N., Michelle C. Stitzer, Alex B. Brohammer, et al.. (2019). Transposable elements contribute to dynamic genome content in maize. The Plant Journal. 100(5). 1052–1065. 66 indexed citations
17.
Miller, Marisa E., Ying Zhang, Vahid Omidvar, et al.. (2018). De Novo Assembly and Phasing of Dikaryotic Genomes from Two Isolates of Puccinia coronata f. sp. avenae , the Causal Agent of Oat Crown Rust. mBio. 9(1). 51 indexed citations
18.
Hirsch, Candice N., Cory D. Hirsch, Alex B. Brohammer, et al.. (2016). Draft Assembly of Elite Inbred Line PH207 Provides Insights into Genomic and Transcriptome Diversity in Maize. The Plant Cell. 28(11). 2700–2714. 107 indexed citations
19.
Hirsch, Cory D. & Nathan M. Springer. (2016). Transposable element influences on gene expression in plants. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms. 1860(1). 157–165. 177 indexed citations
20.
Li, Qing, Jonathan I. Gent, Greg Zynda, et al.. (2015). RNA-directed DNA methylation enforces boundaries between heterochromatin and euchromatin in the maize genome. Proceedings of the National Academy of Sciences. 112(47). 14728–14733. 161 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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