Beijing Institute of Big Data Research

53.3k citations
3.0k papers ·

Impact in

Papers in

Beijing Institute of Big Data Research

2.5k papers receiving 50.7k citations

Peers

Beijing Institute of Big Data Research
Comparison fields: 5 of 237
  • Artificial Intelligence 13.4k
  • Computer Vision and Pattern Recognition 8.5k
  • Statistical and Nonlinear Physics 3.4k
  • Environmental Engineering 3.4k
  • Media Technology 2.0k
Replace Jingdong (China) with:
Jingdong (China) China
Shandong University of Finance and Economics China
China Mobile (China) China
Communication University of China China
Shijiazhuang Tiedao University China
China Electronics Technology Group Corporation China
Institute of Information Engineering China
Institute of Electronics China
Computer Network Information Center China
Shandong Institute of Business and Technology China
Beijing Institute of Big Data Research relative to Jingdong (China) China Jingdong (China)'s profile →
Citations per field
00.5×3.8×
Jingdong (China) · 1×
Citations per year

Countries citing scholars working at Beijing Institute of Big Data Research

Since Specialization
Citations

This map shows the geographic impact of research produced by authors working at Beijing Institute of Big Data 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 papers produced at Beijing Institute of Big Data Research with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Beijing Institute of Big Data Research more than expected).

Fields of papers published by authors at Beijing Institute of Big Data Research

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers affiliated with Beijing Institute of Big Data Research at the time of their publication. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers affiliated with Beijing Institute of Big Data Research at the time of their publication.

About Beijing Institute of Big Data Research

In recent decades, authors affiliated with Beijing Institute of Big Data Research have published 3.0k papers, which have received a total of 53.3k indexed citations . Scholars at this organization have produced 436 papers in Computer Vision and Pattern Recognition, 455 papers in Global and Planetary Change, 682 papers in Artificial Intelligence, 264 papers in Environmental Engineering and 296 papers in Atmospheric Science on the topics of Remote Sensing in Agriculture (176 papers), Land Use and Ecosystem Services (164 papers), Topic Modeling (146 papers), Remote-Sensing Image Classification (111 papers), Remote Sensing and Land Use (102 papers), Advanced Image and Video Retrieval Techniques (94 papers), Remote Sensing and LiDAR Applications (93 papers) and Cryospheric studies and observations (93 papers). Their work is cited by papers focused on Artificial Intelligence (13.4k citations), Computer Vision and Pattern Recognition (8.5k citations), Statistical and Nonlinear Physics (3.4k citations), Environmental Engineering (3.4k citations) and Media Technology (2.0k citations). Authors at Beijing Institute of Big Data Research collaborate with scholars in China, United States and Hong Kong and have published in prestigious journals including Remote Sensing, International Journal of Digital Earth, Sustainability, Remote Sensing of Environment and International Journal of Applied Earth Observation and Geoinformation. Some of Beijing Institute of Big Data Research's most productive authors include E Weinan, Jiequn Han, Yingjie Tian, Linfeng Zhang, Dongkuan Xu, Arnulf Jentzen, Yong Shi, Qingming Huang, Ji-Rong Wen and Bing Yu.

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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