Automated Organization ProfileZhejiang AF University
Zhejiang AF University
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization'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: 1.5 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Understanding thespatial correlation between industrial agglomeration and the optimization ofthe wood industry structure is imperative for propelling the wood industrytoward higher value-added and sustainable development. The research resultsindicate a discernible positive spatial correlation between the optimizationlevel in the wood industry structure and different regions. The correlation inthe optimization level of industrial structures in spatial areas is notablyhigh, revealing characteristics of both high-level regional clustering andlow-level regional clustering.
Authors
- xia, jingjing
Understanding thespatial correlation between industrial agglomeration and the optimization ofthe wood industry structure is imperative for propelling the wood industrytoward higher value-added and sustainable development. The research resultsindicate a discernible positive spatial correlation between the optimizationlevel in the wood industry structure and different regions. The correlation inthe optimization level of industrial structures in spatial areas is notablyhigh, revealing characteristics of both high-level regional clustering andlow-level regional clustering.
Authors
- xia, jingjing