Automated Organization ProfileSLU
SLU
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: 79.8 (sum of 74 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
:unav
Authors
- Sharma, Himani ;
- Vågen, Tor-Gunnar ;
- Winowiecki, Leigh Ann ;
- Tobella-Bargues, Aida
The dataset is based on ecological field data collection carried out in prioritized common land areas in 288 treated and matched control villages in the states of Andhra Pradesh, Karnataka, Odisha, and Rajasthan. It is based on the Land Degradation Survelliance Framework (LDSF) methodology. The LDSF methodology typically follows a standardized sampling design, where clusters are selected within 10-by-10-kilometer sentinel sites and then 10 0.1 hectare plots are randomly selected within each cluster. Each plot is then divided into four 0.01-hectare sub-plots from which field measurements are subsequently taken. We adapted this sampling approach to meet the unique needs of our study. Specifically, we treated each prioritized common land area as a sampling cluster. We then randomly sampled 10 plots (including their associated four sub-plots) within each. We then took inventories of all the trees, shrubs, and saplings in each of the 40 sampled sub-plots. We further randomly selected two of the plots for water infiltration measurement, which took place in the central sub-plot of the sampled plots in question. Given the time and effort involved, it was not possible to take these measurements in all 10 plots.
Authors
- Sharma, Himani ;
- Vågen, Tor-Gunnar ;
- Winowiecki, Leigh Ann ;
- Tobella-Bargues, Aida
:unav
Authors
- Sharma, Himani ;
- Vågen, Tor-Gunnar ;
- Winowiecki, Leigh Ann ;
- Tobella-Bargues, Aida
:unav
Authors
- Sharma, Himani ;
- Vågen, Tor-Gunnar ;
- Winowiecki, Leigh Ann ;
- Tobella-Bargues, Aida
:unav
Authors
- Sharma, Himani ;
- Vågen, Tor-Gunnar ;
- Winowiecki, Leigh Ann ;
- Tobella-Bargues, Aida
:unav
Authors
- Sharma, Himani ;
- Vågen, Tor-Gunnar ;
- Winowiecki, Leigh Ann ;
- Tobella-Bargues, Aida
:unav
Authors
- Sharma, Himani ;
- Vågen, Tor-Gunnar ;
- Winowiecki, Leigh Ann ;
- Tobella-Bargues, Aida
:unav
Authors
- Sharma, Himani ;
- Vågen, Tor-Gunnar ;
- Winowiecki, Leigh Ann ;
- Tobella-Bargues, Aida
Data on phosphorus, color, cyanobacteria biovolume, total phytoplankton biovolume and cyanobacteria proportion of the total phytoplankton biovolume used for GAMM
Authors
- Moe, Jannicke ;
- Gundersen, Hege ;
- Solheim, Anne Lyche ;
- Drakare, Stina ;
- Järvinen, Marko ;
- Carvalho, Laurence ;
- Phillips, Geoff
Data on phosphorus, color, cyanobacteria biovolume, total phytoplankton biovolume and cyanobacteria proportion of the total phytoplankton biovolume used for GAMM
Authors
- Moe, Jannicke ;
- Gundersen, Hege ;
- Solheim, Anne Lyche ;
- Drakare, Stina ;
- Järvinen, Marko ;
- Carvalho, Laurence ;
- Phillips, Geoff