Ranjan K. Shaw

463 total citations
34 papers, 279 citations indexed

About

Ranjan K. Shaw is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Ranjan K. Shaw has authored 34 papers receiving a total of 279 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Plant Science, 24 papers in Genetics and 5 papers in Molecular Biology. Recurrent topics in Ranjan K. Shaw's work include Genetic Mapping and Diversity in Plants and Animals (21 papers), Genetics and Plant Breeding (16 papers) and Genetic and phenotypic traits in livestock (9 papers). Ranjan K. Shaw is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (21 papers), Genetics and Plant Breeding (16 papers) and Genetic and phenotypic traits in livestock (9 papers). Ranjan K. Shaw collaborates with scholars based in China, India and Italy. Ranjan K. Shaw's co-authors include S. Senthilvel, Xiaoguang Sheng, Honghui Gu, Jiansheng Wang, Yusen Shen, Huifang Yu, V. Rajaram, M. Lakshmi Narasu, Ram C. Yadav and C. Tom Hash and has published in prestigious journals such as Journal of Agricultural and Food Chemistry, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Ranjan K. Shaw

30 papers receiving 272 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ranjan K. Shaw China 10 232 105 74 28 21 34 279
Wuming Xiao China 12 445 1.9× 172 1.6× 130 1.8× 4 0.1× 20 1.0× 35 484
Achala Bakshi India 9 368 1.6× 27 0.3× 252 3.4× 20 0.7× 12 0.6× 19 437
Pei Du China 14 428 1.8× 56 0.5× 147 2.0× 10 0.4× 14 0.7× 32 496
Jerwin R. Undan Philippines 6 444 1.9× 85 0.8× 189 2.6× 9 0.3× 54 2.6× 16 482
Xiaoshan Zeng China 7 522 2.3× 130 1.2× 266 3.6× 25 0.9× 72 3.4× 13 596
Kaoru Tonosaki Japan 9 295 1.3× 51 0.5× 204 2.8× 7 0.3× 7 0.3× 18 357
Maren Livaja Germany 6 519 2.2× 42 0.4× 189 2.6× 12 0.4× 74 3.5× 10 564
Jung‐Heon Han South Korea 14 440 1.9× 92 0.9× 150 2.0× 4 0.1× 29 1.4× 31 488
Xiumei Yu China 15 388 1.7× 25 0.2× 168 2.3× 6 0.2× 48 2.3× 28 440
L. Malysheva Germany 6 319 1.4× 39 0.4× 177 2.4× 22 0.8× 40 1.9× 7 381

Countries citing papers authored by Ranjan K. Shaw

Since Specialization
Citations

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

Fields of papers citing papers by Ranjan K. Shaw

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ranjan K. Shaw. 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 Ranjan K. Shaw. The network helps show where Ranjan K. Shaw may publish in the future.

Co-authorship network of co-authors of Ranjan K. Shaw

This figure shows the co-authorship network connecting the top 25 collaborators of Ranjan K. Shaw. A scholar is included among the top collaborators of Ranjan K. Shaw 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 Ranjan K. Shaw. Ranjan K. Shaw 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.
2.
Yang, Xiaoping, et al.. (2025). Unraveling the genetic basis of maize ear diameter in a multi-parent RIL population derived from tropical and temperate germplasms. Theoretical and Applied Genetics. 138(8). 181–181.
3.
Liu, Meichen, Yudong Zhang, Ranjan K. Shaw, et al.. (2024). Genome-Wide Association Study and Prediction of Tassel Weight of Tropical Maize Germplasm in Multi-Parent Population. International Journal of Molecular Sciences. 25(3). 1756–1756. 2 indexed citations
4.
Sun, Jiachen, et al.. (2024). Mining of Oil Content Genes in Recombinant Maize Inbred Lines with Introgression from Temperate and Tropical Germplasm. International Journal of Molecular Sciences. 25(19). 10813–10813. 2 indexed citations
5.
Li, Shaoxiong, Xing‐Jie Zhang, Jinfeng Li, et al.. (2024). Utilizing Two Populations Derived from Tropical Maize for Genome-Wide Association Analysis of Banded Leaf and Sheath Blight Resistance. Plants. 13(3). 456–456. 1 indexed citations
6.
7.
Yang, Xiaoping, et al.. (2024). Discovery of candidate genes enhancing kernel protein content in tropical maize introgression lines. BMC Plant Biology. 24(1). 1110–1110.
8.
Shaw, Ranjan K., et al.. (2024). Genetic dissection of resistance to gray leaf spot by genome-wide association study in a multi-parent maize population. BMC Plant Biology. 24(1). 10–10. 5 indexed citations
9.
10.
Shen, Yusen, Jiansheng Wang, Xiaoguang Sheng, et al.. (2024). Fine mapping of a major co-localized QTL associated with self-incompatibility identified in two F2 populations (broccoli × cauliflower and cauliflower × Chinese kale). Theoretical and Applied Genetics. 137(12). 264–264. 1 indexed citations
11.
Zhang, Yudong, Ziwei Li, Ranjan K. Shaw, et al.. (2024). Identification of a novel marker and its associated laccase gene for regulating ear length in tropical and subtropical maize lines. Theoretical and Applied Genetics. 137(4). 94–94. 5 indexed citations
12.
13.
Shaw, Ranjan K., et al.. (2023). Mapping and Functional Analysis of QTL for Kernel Number per Row in Tropical and Temperate–Tropical Introgression Lines of Maize (Zea mays L.). Current Issues in Molecular Biology. 45(5). 4416–4430. 8 indexed citations
14.
Sun, Jiachen, Yudong Zhang, Jinfeng Li, et al.. (2023). Hotspot Regions of Quantitative Trait Loci and Candidate Genes for Ear-Related Traits in Maize: A Literature Review. Genes. 15(1). 15–15. 10 indexed citations
15.
Liu, Li, Ziwei Li, Jing Wang, et al.. (2023). Identification of Candidate QTLs and Genes for Ear Diameter by Multi-Parent Population in Maize. Genes. 14(6). 1305–1305. 9 indexed citations
16.
Yu, Huifang, Jiansheng Wang, Yusen Shen, et al.. (2023). A 43 Bp-Deletion in the F3′H Gene Reducing Anthocyanins Is Responsible for Keeping Buds Green at Low Temperatures in Broccoli. International Journal of Molecular Sciences. 24(14). 11391–11391. 4 indexed citations
17.
Shaw, Ranjan K., Yusen Shen, Zhenqing Zhao, et al.. (2021). Molecular Breeding Strategy and Challenges Towards Improvement of Downy Mildew Resistance in Cauliflower (Brassica oleracea var. botrytis L.). Frontiers in Plant Science. 12. 667757–667757. 14 indexed citations
18.
Shen, Yusen, Jiansheng Wang, Ranjan K. Shaw, et al.. (2021). Development of GBTS and KASP Panels for Genetic Diversity, Population Structure, and Fingerprinting of a Large Collection of Broccoli (Brassica oleracea L. var. italica) in China. Frontiers in Plant Science. 12. 655254–655254. 33 indexed citations
19.
Shaw, Ranjan K., Yusen Shen, Jiansheng Wang, et al.. (2021). Advances in Multi-Omics Approaches for Molecular Breeding of Black Rot Resistance in Brassica oleracea L.. Frontiers in Plant Science. 12. 742553–742553. 14 indexed citations
20.
Senthilvel, S., Nepolean Thirunavukkarasu, V. Rajaram, et al.. (2011). Development of a molecular linkage map of pearl millet integrating DArT and SSR markers. Theoretical and Applied Genetics. 123(2). 239–250. 59 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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