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Xinjiang Branch of CAS Sees Four Scientists Named to 2024 "Highly Cited Chinese Researchers" List
       Updatetime: 2025-07-17 Printer      Text Size:A A A 

Recently, Elsevier released the 2024 "Highly Cited Chinese Researchers" list, featuring 6,388 scholars from 547 universities, enterprises, and research institutions across 83 first-level disciplines within 10 academic fields defined by the Ministry of Education. Among them, four scientists from the Chinese Academy of Sciences (CAS) Xinjiang Branch system were selected.

The honored scientists include Academician Xiao Wenjiao from CAS Xinjiang Branch and Researcher Chen Yaning from the Xinjiang Institute of Ecology and Geography, CAS, who were listed in the disciplines of Geology and Geography, respectively. Researcher Pan Shilie and Researcher Yang Zhihua from the Xinjiang Technical Institute of Physics and Chemistry, CAS, were recognized in the disciplines of Materials Science and Engineering and Electronic Science and Technology, respectively. Additionally, from Xinjiang universities, Professor Ma Xinbin, Professor Teng Zhidong, and Professor Huang Haibao of Xinjiang University were listed in the disciplines of Chemical Engineering and Technology, Mathematics, and Environmental Science and Engineering, respectively, while Professor Jiang Haijun of Xinjiang Normal University was named in the Mathematics discipline.

The list is based on Scopus, a globally authoritative citation and indexing database, and employs a methodology developed by Shanghai Ranker Consultancy. Elsevier provided data support and technical implementation for the list, marking the 11th annual release since the collaboration began in 2015.

By analyzing multidimensional scholar metrics and optimizing discipline classification, the list highlights research contributions and value. Through visualized citation data analysis, it aids scholars in building academic profiles and enhancing recognition. Serving as a benchmark for institutional academic influence, the list reflects core competitiveness and offers data-driven decision-making support for discipline development and talent strategies.


 
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