Automated Organization ProfileInstitute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
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: 0.3 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
About:The dataset constitutes the first reconstructed global water use data product at sub-annual and sub-national/gridded resolution that is derived from different models and data sources; it was generated by spatially and temporally downscaling country-scale estimates of sectoral water withdrawals from FAO AQUASTAT (and state-scale estimates of USGS for the US). In addition, the industrial sector was disaggregated into manufacturing, mining and cooling of thermal power plants by using historical estimates from GCAM. Downscaling was performed using the output of various models and new modeling approaches, which includes the spatial and temporal downscaling methodologies for water withdrawal in previous studies (Wada et al., 2011; Voisin et al., 2013; Hejazi et al., 2014). For the consumptive water use, irrigation water consumption is reconstructed based on estimates by 4 GHMs and consumptive water use efficiency (the proportion of water consumption to water withdrawal), which is calculated based on simulation of Flörke et al (2013) and USGS estimates, is used to generated global consumptive water use for the remaining sector. Therefore, a global monthly gridded (0.5 degree) sectoral water use dataset for the period 1971–2010, which distinguishes six water use sectors, i.e. irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing, was reconstructed. The detailed descriptions for this dataset are presented in Huang et al. (in review).
Authors
- Huang, Zhongwei ;
- Hejazi, Mohamad ;
- Li, Xinya ;
- Tang, Qiuhong ;
- Vernon, Chris ;
- Leng, Guoyong ;
- Liu, Yaling ;
- Döll, Petra ;
- Eisner, Stephanie ;
- Gerten, Dieter ;
- Hanasaki, Naota ;
- Wada, Yoshihide