Automated Author ProfileStock, Jason
National Institute of Oceanography and Applied Geophysics
Stock, Jason
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: 1.4 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
ARCO-OCEAN is an analysis-ready cloud-optimized dataset providing physical properties of the ocean, waves, and sea ice for a period of about 28 years between the 1st of January 1993 and the 30th of June 2021. The dataset includes also atmospheric and hydrological variables that would be needed as boundary conditions and used to drive a numerical simulation. The dataset is the result of collecting, processing, merging and optimizing for the cloud different data sources, all retrospective analyses (reanalyses) or hindcasts of different Earth system components. The dataset has been designed with machine learning in mind, and takes inspiration from similar datasets derived from ERA5.
Authors
- Campanella, Stefano ;
- Stock, Jason ;
- Salon, Stefano ;
- Querin, Stefano ;
- Bortolussi, Luca
ARCO-OCEAN is an analysis-ready cloud-optimized dataset providing physical properties of the ocean, waves, and sea ice for a period of about 28 years between the 1st of January 1993 and the 30th of June 2021. The dataset includes also atmospheric and hydrological variables that would be needed as boundary conditions and used to drive a numerical simulation. The dataset is the result of collecting, processing, merging and optimizing for the cloud different data sources, all retrospective analyses (reanalyses) or hindcasts of different Earth system components. The dataset has been designed with machine learning in mind, and takes inspiration from similar datasets derived from ERA5.
Authors
- Campanella, Stefano ;
- Stock, Jason ;
- Salon, Stefano ;
- Querin, Stefano ;
- Bortolussi, Luca