ycf1, the most promising plastid DNA barcode of land plants

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This paper, published in 1950, received 412 indexed citations. Written by Wenpan Dong, Chao Xu, Changhao Li, Jiahui Sun, Yunjuan Zuo, Shuo Shi, Tao Cheng, Junjie Guo and Shiliang Zhou covering the research area of Molecular Biology and Ecology, Evolution, Behavior and Systematics. It is primarily cited by scholars working on Molecular Biology (333 citations), Ecology, Evolution, Behavior and Systematics (180 citations) and Plant Science (113 citations). Published in Scientific Reports.

Countries where authors are citing ycf1, the most promising plastid DNA barcode of land plants

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Citations

This map shows the geographic impact of ycf1, the most promising plastid DNA barcode of land plants. 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 ycf1, the most promising plastid DNA barcode of land plants with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites ycf1, the most promising plastid DNA barcode of land plants more than expected).

Fields of papers citing ycf1, the most promising plastid DNA barcode of land plants

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of ycf1, the most promising plastid DNA barcode of land plants. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the ycf1, the most promising plastid DNA barcode of land plants.

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.

This paper is also available at doi.org/10.1038/srep08348.

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