Automated Author ProfileWang, Hongbin
Wang, Hongbin
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 7.8 (sum of 11 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Although artificial intelligence (AI) has rapidly advanced with transformer-based large language models (LLMs) inspired by human attention mechanisms, whether the artificial attention in transformers has implemented some fundamental human attentional functions is elusive. Using the classic cognitive task of the color Stroop effect, we found that state-of-the-art LLMs failed in performing this task as the word list length increased. This study highlights that executive control is lacking in artificial attention architecture, which may limit AI's ability to acquire adaptive behavior that is essential for coping with conflict. The data provided here support the analyses and figures presented in the main study.
Authors
- Patel, Suketu ;
- Fan, Jin ;
- Wang, Hongbin
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Authors
- Yu, Yanwu ;
- Miao, Shuyue ;
- Jia, Kanghui ;
- Wang, Hongbin ;
- Qin, Wuli ;
- Jing, Suming
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Authors
- Yu, Yanwu ;
- Miao, Shuyue ;
- Jia, Kanghui ;
- Wang, Hongbin ;
- Qin, Wuli ;
- Jing, Suming
The source data of the relevant graphs in the manuscript
Authors
- Wang, Hongbin ;
- Zhou, Cheng ;
- Li, Peng ;
- Yang, Lin ;
- Ma, Jiangang ;
- Akaike, Ryota ;
- Akaike, Ryota ;
- Xu, Haiyang ;
- Liu, Yichun
The source data of the relevant graphs in the manuscript
Authors
- Wang, Hongbin ;
- Zhou, Cheng ;
- Li, Peng ;
- Yang, Lin ;
- Ma, Jiangang ;
- Akaike, Ryota ;
- Akaike, Ryota ;
- Xu, Haiyang ;
- Liu, Yichun
Although artificial intelligence (AI) has rapidly advanced with transformer-based large language models (LLMs) inspired by human attention mechanisms, whether the artificial attention in transformers has implemented some fundamental human attentional functions is elusive. Using the classic cognitive task of the color Stroop effect, we found that state-of-the-art LLMs failed in performing this task as the word list length increased. This study highlights that executive control is lacking in artificial attention architecture, which may limit AI's ability to acquire adaptive behavior that is essential for coping with conflict. The data provided here support the analyses and figures presented in the main study.
Authors
- Patel, Suketu ;
- Fan, Jin ;
- Wang, Hongbin
This dataset includes all the figures(Figures 1-8, Figures S1-S3) and tables (Table1-Table3) of the manuscript: "New Insights into the Eocene Structure in the Yinggehai Basin and Its Tectonic Implications, South China Sea: Evidence from Scaled Physical Models"
Authors
- Yang, Gengxiong ;
- Yin, Hongwei ;
- Jia, Dong ;
- Wang, Hongbin ;
- Wang, Wei ;
- Xu, Wenqiao
This dataset includes all the figures(Figures 1-8, Figures S1-S3) and tables (Table1-Table3) of the manuscript: "New Insights into the Eocene Structure in the Yinggehai Basin and Its Tectonic Implications, South China Sea: Evidence from Scaled Physical Models"
Authors
- Yang, Gengxiong ;
- Yin, Hongwei ;
- Jia, Dong ;
- Wang, Hongbin ;
- Wang, Wei ;
- Xu, Wenqiao
Table S1 The u values and the z values for all gypsy moth samples Table S2 The u values of some samples in this study Table S3 The z values of some samples in this study Table S4 The ratio of u values and the ratio of z values for the folded and spread specimens Table S5 The ratio of the u values and the ratio of z values for formalin-fixed larvae and air-dried adult specimens Table S6 Allprimers used in this study
Authors
- Xu, Yao ;
- Ren, XueYu ;
- Wang, Hongbin ;
- Li, GuoHong
Table S1 The u values and the z values for all gypsy moth samples Table S2 The u values of some samples in this study Table S3 The z values of some samples in this study Table S4 The ratio of u values and the ratio of z values for the folded and spread specimens Table S5 The ratio of the u values and the ratio of z values for formalin-fixed larvae and air-dried adult specimens Table S6 Allprimers used in this study
Authors
- Xu, Yao ;
- Ren, XueYu ;
- Wang, Hongbin ;
- Li, GuoHong