Simon Schiml

1.8k total citations · 1 hit paper
9 papers, 1.3k citations indexed

About

Simon Schiml is a scholar working on Molecular Biology, Plant Science and Biotechnology. According to data from OpenAlex, Simon Schiml has authored 9 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 8 papers in Plant Science and 1 paper in Biotechnology. Recurrent topics in Simon Schiml's work include CRISPR and Genetic Engineering (9 papers), Chromosomal and Genetic Variations (8 papers) and Plant Virus Research Studies (4 papers). Simon Schiml is often cited by papers focused on CRISPR and Genetic Engineering (9 papers), Chromosomal and Genetic Variations (8 papers) and Plant Virus Research Studies (4 papers). Simon Schiml collaborates with scholars based in Germany and United States. Simon Schiml's co-authors include Holger Puchta, Friedrich Fauser, Veit Schubert, Michael Florian Mette, Evgeny Gladilin, Patrick Schindele, Twan Rutten, Steven Dreißig, Oda Weiß and Andreas Houben and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Plant Journal and Plant Cell Reports.

In The Last Decade

Simon Schiml

9 papers receiving 1.3k citations

Hit Papers

Both CRISPR/Cas‐based nucleases and nickases can be used ... 2014 2026 2018 2022 2014 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Simon Schiml Germany 9 1.2k 947 194 93 87 9 1.3k
Claudia Corvalán South Korea 7 985 0.8× 814 0.9× 141 0.7× 92 1.0× 92 1.1× 7 1.1k
Bastian Minkenberg United States 6 1.1k 0.9× 870 0.9× 169 0.9× 78 0.8× 146 1.7× 6 1.4k
Je Wook Woo South Korea 4 1.0k 0.8× 700 0.7× 170 0.9× 110 1.2× 110 1.3× 5 1.1k
Aimee A. Malzahn United States 13 1.9k 1.5× 1.4k 1.4× 343 1.8× 147 1.6× 170 2.0× 17 2.1k
Kai Hua China 13 942 0.8× 1.1k 1.1× 125 0.6× 55 0.6× 212 2.4× 15 1.4k
Patrick Schindele Germany 15 724 0.6× 559 0.6× 99 0.5× 46 0.5× 83 1.0× 23 820
Yuming Lu China 13 763 0.6× 708 0.7× 72 0.4× 57 0.6× 83 1.0× 21 971
Tom Lawrenson United Kingdom 10 752 0.6× 762 0.8× 57 0.3× 46 0.5× 69 0.8× 13 964
Hongge Jia United States 16 1.2k 1.0× 1.4k 1.5× 407 2.1× 152 1.6× 64 0.7× 22 1.8k
Si Nian Char United States 11 542 0.4× 698 0.7× 57 0.3× 54 0.6× 80 0.9× 21 854

Countries citing papers authored by Simon Schiml

Since Specialization
Citations

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

Fields of papers citing papers by Simon Schiml

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simon Schiml

This figure shows the co-authorship network connecting the top 25 collaborators of Simon Schiml. A scholar is included among the top collaborators of Simon Schiml 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 Simon Schiml. Simon Schiml 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.
Dreißig, Steven, Simon Schiml, Patrick Schindele, et al.. (2017). Live‐cell CRISPR imaging in plants reveals dynamic telomere movements. The Plant Journal. 91(4). 565–573. 102 indexed citations
2.
Schiml, Simon, Friedrich Fauser, & Holger Puchta. (2017). CRISPR/Cas-Mediated In Planta Gene Targeting. Methods in molecular biology. 1610. 3–11. 8 indexed citations
3.
Schiml, Simon, et al.. (2016). Homology-based double-strand break-induced genome engineering in plants. Plant Cell Reports. 35(7). 1429–1438. 67 indexed citations
4.
Schiml, Simon & Holger Puchta. (2016). Revolutionizing plant biology: multiple ways of genome engineering by CRISPR/Cas. Plant Methods. 12(1). 8–8. 102 indexed citations
5.
Schiml, Simon, Friedrich Fauser, & Holger Puchta. (2016). Repair of adjacent single-strand breaks is often accompanied by the formation of tandem sequence duplications in plant genomes. Proceedings of the National Academy of Sciences. 113(26). 7266–7271. 49 indexed citations
6.
Schiml, Simon, Friedrich Fauser, & Holger Puchta. (2016). CRISPR/Cas-Mediated Site-Specific Mutagenesis in Arabidopsis thaliana Using Cas9 Nucleases and Paired Nickases. Methods in molecular biology. 111–122. 25 indexed citations
7.
Schiml, Simon, et al.. (2015). Highly efficient heritable plant genome engineering using Cas9 orthologues from Streptococcus thermophilus and Staphylococcus aureus. The Plant Journal. 84(6). 1295–1305. 188 indexed citations
8.
Schiml, Simon, Friedrich Fauser, & Holger Puchta. (2014). The CRISPR/Cas system can be used as nuclease for in planta gene targeting and as paired nickases for directed mutagenesis in Arabidopsis resulting in heritable progeny. The Plant Journal. 80(6). 1139–1150. 251 indexed citations
9.
Fauser, Friedrich, Simon Schiml, & Holger Puchta. (2014). Both CRISPR/Cas‐based nucleases and nickases can be used efficiently for genome engineering in Arabidopsis thaliana. The Plant Journal. 79(2). 348–359. 544 indexed citations breakdown →

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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