William D. Beavis

6.9k total citations
60 papers, 3.3k citations indexed

About

William D. Beavis is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, William D. Beavis has authored 60 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Plant Science, 35 papers in Genetics and 10 papers in Molecular Biology. Recurrent topics in William D. Beavis's work include Genetic Mapping and Diversity in Plants and Animals (30 papers), Genetics and Plant Breeding (28 papers) and Genetic and phenotypic traits in livestock (16 papers). William D. Beavis is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (30 papers), Genetics and Plant Breeding (28 papers) and Genetic and phenotypic traits in livestock (16 papers). William D. Beavis collaborates with scholars based in United States, Brazil and Japan. William D. Beavis's co-authors include David Grant, O. S. Smith, Jean‐Luc Jannink, M. Paul Scott, Franco G. Asoro, Mark A. Newell, Réka Howard, Marc C. Albertsen, Alicia L. Carriquiry and James M. Schupp and has published in prestigious journals such as Bioinformatics, PLoS ONE and Nature Reviews Drug Discovery.

In The Last Decade

William D. Beavis

59 papers receiving 3.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
William D. Beavis United States 26 2.6k 1.8k 568 288 125 60 3.3k
Xuecai Zhang Mexico 28 3.4k 1.3× 2.5k 1.3× 488 0.9× 359 1.2× 63 0.5× 82 3.9k
Chenwu Xu China 27 2.2k 0.8× 1.0k 0.5× 744 1.3× 182 0.6× 79 0.6× 119 2.5k
Hongliang Zhang China 34 3.2k 1.2× 1.6k 0.9× 1.2k 2.1× 190 0.7× 97 0.8× 124 3.9k
Zichao Li China 37 4.1k 1.6× 2.0k 1.1× 1.2k 2.1× 196 0.7× 123 1.0× 169 4.7k
David Grant United States 26 3.5k 1.3× 1.3k 0.7× 1.2k 2.1× 244 0.8× 110 0.9× 41 4.2k
Mark H. Wright United States 21 2.8k 1.1× 1.9k 1.0× 930 1.6× 89 0.3× 155 1.2× 29 3.5k
H. Friedrich Utz Germany 36 3.5k 1.3× 2.7k 1.5× 462 0.8× 387 1.3× 52 0.4× 76 3.9k
Jamie Sherman United States 30 2.1k 0.8× 780 0.4× 696 1.2× 525 1.8× 111 0.9× 87 2.7k
Ramil Mauleon Philippines 28 3.1k 1.2× 1.2k 0.6× 1.0k 1.8× 108 0.4× 98 0.8× 70 3.5k
Weiming He China 16 1.7k 0.7× 1.2k 0.7× 853 1.5× 170 0.6× 176 1.4× 54 2.9k

Countries citing papers authored by William D. Beavis

Since Specialization
Citations

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

Fields of papers citing papers by William D. Beavis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of William D. Beavis

This figure shows the co-authorship network connecting the top 25 collaborators of William D. Beavis. A scholar is included among the top collaborators of William D. Beavis 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 William D. Beavis. William D. Beavis 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.
Diers, Brian W., James E. Specht, George L. Graef, et al.. (2023). Genetic architecture of protein and oil content in soybean seed and meal. The Plant Genome. 16(1). e20308–e20308. 6 indexed citations
3.
Piepho, Hans‐Peter, et al.. (2023). Models to estimate genetic gain of soybean seed yield from annual multi-environment field trials. Theoretical and Applied Genetics. 136(12). 252–252. 15 indexed citations
4.
Montes, Christopher M., Carolyn M. Fox, Álvaro Sanz‐Sáez, et al.. (2022). High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population. Genetics. 221(2). 13 indexed citations
5.
Xu, Zhanyou, Steven B. Cannon, & William D. Beavis. (2022). Applying Spatial Statistical Analysis to Ordinal Data for Soybean Iron Deficiency Chlorosis. Agronomy. 12(9). 2095–2095. 3 indexed citations
6.
Beavis, William D., et al.. (2021). Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean. Frontiers in Genetics. 12. 675500–675500. 6 indexed citations
7.
Andorf, Carson M., William D. Beavis, Matthew B. Hufford, et al.. (2019). Technological advances in maize breeding: past, present and future. Theoretical and Applied Genetics. 132(3). 817–849. 121 indexed citations
8.
Akdemir, Deniz, William D. Beavis, Roberto Fritsche‐Neto, Asheesh K. Singh, & Julio Isidro y Sánchez. (2018). Multi-objective optimized genomic breeding strategies for sustainable food improvement. Heredity. 122(5). 672–683. 73 indexed citations
9.
Beavis, William D., et al.. (2017). Numericware i: Identical by State Matrix Calculator. Evolutionary Bioinformatics. 13. 1608434967–1608434967. 5 indexed citations
10.
Zhang, Jiaoping, et al.. (2017). Leveraging genomic prediction to scan germplasm collection for crop improvement. PLoS ONE. 12(6). e0179191–e0179191. 26 indexed citations
11.
Cameron, J. N., et al.. (2017). Systematic design for trait introgression projects. Theoretical and Applied Genetics. 130(10). 1993–2004. 10 indexed citations
12.
Xu, Zhanyou, Jing Yu, R. J. Kohel, et al.. (2015). Distribution and evolution of cotton fiber development genes in the fibreless Gossypium raimondii genome. Genomics. 106(1). 61–69. 11 indexed citations
13.
Wang, Daolong, et al.. (2013). Family-based association mapping in crop species. Theoretical and Applied Genetics. 126(6). 1419–1430. 15 indexed citations
14.
Newell, Mark A., Franco G. Asoro, M. Paul Scott, et al.. (2012). Genome-wide association study for oat (Avena sativa L.) beta-glucan concentration using germplasm of worldwide origin. Theoretical and Applied Genetics. 125(8). 1687–1696. 57 indexed citations
15.
Beavis, William D., et al.. (2012). Large Homogeneous Genome Regions (Isochores) in Soybean [Glycine Max (L.) Merr.]. Frontiers in Genetics. 3. 98–98. 1 indexed citations
16.
Kingsmore, Stephen F., Ingrid Lindquist, Joann Mudge, Damian D. G. Gessler, & William D. Beavis. (2008). Genome-wide association studies: progress and potential for drug discovery and development. Nature Reviews Drug Discovery. 7(3). 221–230. 90 indexed citations
17.
Kingsmore, Stephen F., Neil Kennedy, Henry L. Halliday, et al.. (2008). Identification of Diagnostic Biomarkers for Infection in Premature Neonates. Molecular & Cellular Proteomics. 7(10). 1863–1875. 43 indexed citations
18.
Vigouroux, Yves, Jennifer Jaqueth, Yoshihiro Matsuoka, et al.. (2002). Rate and Pattern of Mutation at Microsatellite Loci in Maize. Molecular Biology and Evolution. 19(8). 1251–1260. 265 indexed citations
19.
Keim, Paul, et al.. (1992). Evaluation of soybean RFLP marker diversity in adapted germ plasm. Theoretical and Applied Genetics. 85-85(2-3). 205–212. 96 indexed citations
20.
Beavis, William D. & David Grant. (1991). A linkage map based on information from four F2 populations of maize (Zea mays L.). Theoretical and Applied Genetics. 82(5). 636–644. 123 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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