Hao Ding

836 citations
66 papers · 625 · h-index 15

Impact in

Papers in

Hao Ding

59 papers receiving 615 citations

Peers

Hao Ding
Comparison fields: 5 of 85
  • Industrial and Manufacturing Engineering 112
  • Civil and Structural Engineering 234
  • Environmental Engineering 116
  • Mechanics of Materials 147
  • Neurology 78
Replace Shinya Tachibana with:
Shinya Tachibana Japan
John R. Buchanan United States
Jonas Meyer Switzerland
Yi Fang China
Yanling Zhang China
Xinzhuang Cui China
Ali M. Rajabi Iran
Qiong Wang China
Zhenhua Zhang China
Quanming Li China
Hao Ding relative to Shinya Tachibana Japan Shinya Tachibana's profile →
Citations per field
00.5×9.2×
Shinya Tachibana · 1×
Citations per year

Countries citing papers authored by Hao Ding

Since Specialization
Citations

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

Fields of papers citing papers by Hao Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Hao Ding, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hao Ding Line = papers co-authored together Hao Ding links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 66 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201877
2 202139
3 202039
4 202134
5 202233
6 202127
7 200825
8 202322
9 202122
10 201521
11 202218
12 202218
13 200617
14 200015
15 200214
16 201814
17 202013
18 200512
19 202211
20 202211

About Hao Ding

Hao Ding is a scholar working on Civil and Structural Engineering, Industrial and Manufacturing Engineering, Neurology, Mechanics of Materials and Surgery, having authored 66 papers that have together received 625 indexed citations. Recurring topics across this work include Neuroblastoma Research and Treatments (11 papers), Landfill Environmental Impact Studies (10 papers), Statistical Methods and Inference (8 papers), Soil and Unsaturated Flow (6 papers), Geotechnical Engineering and Underground Structures (6 papers), Groundwater flow and contamination studies (6 papers), Glioma Diagnosis and Treatment (6 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (112 citations), Civil and Structural Engineering (234 citations), Environmental Engineering (116 citations), Mechanics of Materials (147 citations) and Neurology (78 citations). Hao Ding has collaborated with scholars based in China, United Kingdom and Canada. Frequent co-authors include Haijian Xie, Huaxiang Yan, Haijin Wang, Weiqiu Chen, Zhao Hu, Ling He, Xiuling Wang, Siyue He, Hao Wang and Jinxing Lai. Their work appears in journals such as Abdominal Radiology, Journal of Hydrology, Academic Radiology, Acta Mechanica and Computers and Geotechnics.

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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