Margaret Evers

1.3k total citations · 1 hit paper
9 papers, 916 citations indexed

About

Margaret Evers is a scholar working on Plant Science, Molecular Biology and Genetics. According to data from OpenAlex, Margaret Evers has authored 9 papers receiving a total of 916 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Plant Science, 3 papers in Molecular Biology and 3 papers in Genetics. Recurrent topics in Margaret Evers's work include Chromosomal and Genetic Variations (4 papers), Genetic diversity and population structure (2 papers) and Genetics and Plant Breeding (2 papers). Margaret Evers is often cited by papers focused on Chromosomal and Genetic Variations (4 papers), Genetic diversity and population structure (2 papers) and Genetics and Plant Breeding (2 papers). Margaret Evers collaborates with scholars based in France, Australia and United Kingdom. Margaret Evers's co-authors include Andrzej Kilian, Eric Huttner, Vanessa Caig, Grzegorz Uszyński, Ling Xia, Katarzyna Heller-Uszyńska, Jason Carling, Damian Jaccoud, Peter Wenzl and Kaiman Peng and has published in prestigious journals such as PLoS ONE, Theoretical and Applied Genetics and Molecular Breeding.

In The Last Decade

Margaret Evers

9 papers receiving 892 citations

Hit Papers

Diversity Arrays Technology: A Generic Genome Profiling T... 2012 2026 2016 2021 2012 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Margaret Evers France 6 478 466 164 147 147 9 916
Katarzyna Heller-Uszyńska Australia 11 694 1.5× 556 1.2× 229 1.4× 142 1.0× 151 1.0× 18 1.2k
Vanessa Caig Australia 8 958 2.0× 717 1.5× 201 1.2× 172 1.2× 151 1.0× 11 1.4k
Anne‐Laure Besnard France 16 508 1.1× 325 0.7× 158 1.0× 77 0.5× 219 1.5× 28 987
Grzegorz Uszyński Poland 9 1.0k 2.2× 790 1.7× 220 1.3× 166 1.1× 150 1.0× 12 1.5k
César Daniel Petroli Mexico 13 768 1.6× 779 1.7× 262 1.6× 120 0.8× 106 0.7× 28 1.3k
H. H. Hattemer Germany 14 279 0.6× 321 0.7× 171 1.0× 193 1.3× 104 0.7× 39 667
Ling Xia Australia 9 1.3k 2.6× 963 2.1× 272 1.7× 168 1.1× 156 1.1× 15 1.8k
К. В. Крутовский United States 11 285 0.6× 317 0.7× 251 1.5× 197 1.3× 87 0.6× 21 651
Carolina Sansaloni Mexico 21 1.4k 2.9× 1.1k 2.4× 297 1.8× 125 0.9× 115 0.8× 48 1.9k
B. D. Dow United States 5 297 0.6× 524 1.1× 218 1.3× 344 2.3× 227 1.5× 7 859

Countries citing papers authored by Margaret Evers

Since Specialization
Citations

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

Fields of papers citing papers by Margaret Evers

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Margaret Evers. 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 Margaret Evers. The network helps show where Margaret Evers may publish in the future.

Co-authorship network of co-authors of Margaret Evers

This figure shows the co-authorship network connecting the top 25 collaborators of Margaret Evers. A scholar is included among the top collaborators of Margaret Evers 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 Margaret Evers. Margaret Evers is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Kilian, Andrzej, Peter Wenzl, Eric Huttner, et al.. (2012). Diversity Arrays Technology: A Generic Genome Profiling Technology on Open Platforms. Methods in molecular biology. 888. 67–89. 685 indexed citations breakdown →
2.
Heller-Uszyńska, Katarzyna, Grzegorz Uszyński, Eric Huttner, et al.. (2010). Diversity Arrays Technology effectively reveals DNA polymorphism in a large and complex genome of sugarcane. Molecular Breeding. 28(1). 37–55. 48 indexed citations
3.
Heller-Uszyńska, Katarzyna, Grzegorz Uszyński, Margaret Evers, et al.. (2010). Diversity arrays technology (DArT) and statistical tools for genome profile-based molecular breeding of sugarcane : [Abstract W305]. Agritrop (Cirad). 1 indexed citations
4.
Risterucci, Ange-Marie, Isabelle Hippolyte, Xavier Perrier, et al.. (2009). Development and assessment of Diversity Arrays Technology for high-throughput DNA analyses in Musa. Theoretical and Applied Genetics. 119(6). 1093–1103. 52 indexed citations
5.
James, Karen E., Harald Schneider, Stephen W. Ansell, et al.. (2008). Diversity Arrays Technology (DArT) for Pan-Genomic Evolutionary Studies of Non-Model Organisms. PLoS ONE. 3(2). e1682–e1682. 46 indexed citations
6.
Wenzl, Peter, Eric Huttner, Jason Carling, et al.. (2008). Diversity Arrays Technology (DArT): a generic high-density genotyping platform.. 1–7. 3 indexed citations
7.
Huttner, Eric, Ange-Marie Risterucci, Isabelle Hippolyte, et al.. (2007). Establishment of diversity arrays technology for whole-genome profiling of banana. Agritrop (Cirad). 2 indexed citations
8.
Kilian, Andrzej, Eric Huttner, Peter Wenzl, et al.. (2005). The fast and the cheap: SNP and DArT-based whole genome profiling for crop improvement. 68 indexed citations
9.
Huttner, Eric, Peter Wenzl, Mona Akbari, et al.. (2005). Diversity Arrays Technology: A Novel Tool for Harnessing the Genetic Potential of Orphan Crops. 11 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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