On May 8, Clarivate Analytics released the latest ESI statistical data (covering the period from January 1, 2015, to February 28, 2025), marking another milestone for SCMU as Computer Science joined the ranks of disciplines in the global ESI top 1%. The discipline has published 290 papers with a total citation count of 5,411 and four highly cited papers. This achievement brings SCMU’s total number of disciplines in the global ESI top 1% to five, ranking first among ethnic universities nationwide.
Computer Science discipline enters global ESI top 1%. Photo by Wu Qiong
The five SCMU disciplines that have entered the ESI top 1%. Photo by Wu Qiong
SCMU now has five disciplines in the ESI top 1%: Chemistry, Engineering, Materials Science, Environment/Ecology, and Computer Science, with global rankings of 703/2,069 (≈33.98%), 1,333/2,684 (≈49.66%), 970/1,524 (≈63.65%), 1,751/2,009 (≈87.16%), and 820/836 (≈98.09%) respectively. (Note: Percentage ranking = institutional ranking/total number of institutions; a lower percentage indicates a higher ranking)
The Essential Science Indicators (ESI) database serves as a crucial tool for measuring scientific research performance and tracking scientific trends, and represents one of the key indicators for evaluating the academic level and influence of universities, academic institutions, and countries/regions. Whether a discipline can enter the global ESI top 1% is an important benchmark for determining if a discipline ranks among the world’s elite and whether a university possesses core research competitiveness.
In recent years, SCMU has placed significant emphasis on world-class discipline construction, achieving new breakthroughs in ESI discipline development through strengthened management, deepened academic content, and cultivation of new quality productive forces. The university library has simultaneously established an ESI dynamic monitoring mechanism, regularly publishing discipline analysis reports and research output and impact benchmarking analysis reports to provide analysis and forecasting for related promising disciplines.
