Automated Author ProfileSailley, Sévrine
Plymouth Marine Laboratory0000-0003-0401-3506
Sailley, Sévrine
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: 1.7 (sum of 6 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The folder Include results from the H2020 project CERES (POLCOM-ERSEM.rar) and runs that were generated for the Copernicus Climate Data Store (http://doi.org/10.24381/cds.39c97304; the catalogue entry is deprecated and data cannot be accessed there anymore).The data were used within the MSPACE project (UK research council) and the FutureMARES H2020 project.Note these are the raw outputs from the SS-DBEM model not the results from analysis done for any of the linked work (past, present, or future)
Authors
- Sailley, Sevrine
The folder Include results from the H2020 project CERES (POLCOM-ERSEM.rar) and runs that were generated for the Copernicus Climate Data Store (http://doi.org/10.24381/cds.39c97304; the catalogue entry is deprecated and data cannot be accessed there anymore).The data were used within the MSPACE project (UK research council) and the FutureMARES H2020 project.Note these are the raw outputs from the SS-DBEM model not the results from analysis done for any of the linked work (past, present, or future)
Authors
- Sailley, Sevrine
No description available
Authors
- Andersen, Ken Haste ;
- Jacobsen, Nis Sand ;
- Sailley, Sevrine ;
- Steenbeek, Jeroen
No description available
Authors
- Andersen, Ken Haste ;
- Jacobsen, Nis Sand ;
- Merillet, Laurene ;
- Millington, Rebecca ;
- Powley, Helen ;
- Sailley, Sevrine
The dataset contain Projection from the Size-Spectra Bioclimatic Envelop Model (SS-DBEM), this work was part of the GCRF Blue communities Programme (www.blue-communities.org). The model provides distribution and abundance and/or biomass of fish and other species of commercial interest under climate change and fishing pressure. The model outputs are yearly abundance/biomass on a 0.5-by-0.5 degree grid, covering the period from 2000 to 2098. Further description of the model and relevant references are listed in the following file: Guide-fish-model-output-use.docx The model was run under two climate scenario: RCP4.5 and RCP8.5, with different combinations of fishing pressure expressed as the Maximum Sustainable Yield (MSY) for the following values: 0 (no fishing, climate change alone will cause variation in fish biomass), 1 (sustainable fishing), 2, 3 (overfishing), and, 4 (overfishing with destructive practice). The intent is not to reproduce current fishing level but to provide a range of scenarios with which the future of fisheries can be explored. We projected fish species that were identified as key in the South East Asia seas region by our regional partners.The full list is provided in document: Fish-list-modelguide.xlsx There are 4 zip files that contain the model outputs of in either abundance (number of fish) or biomass grams of fish) for the two climate scenario. For example Biomass-RCP45.zip will contain model outputs in biomass for projections under RCP4.5 and all MSY. within the zip files are .csv files of the outputs for each species under the 5 MSY (0 to 4), the individual file names identify the species (identified by a 6digit code), the output provided (abundance or biomass), the RCP (8.5 or 4.5), and the MSY (0, 1, 2, 3, or 4). For example the file labelled 600107-Abundance-rcp85-msy4.csv contains the outputs for species 600107 (Skipjack tuna, Katsuwonnus pelamis), as abundance, under RCP8.5 with MSY4. Headers indicate what is in each column (latitude, longitude and year). Note: some knowledge of Python, R, or a similar software is recommended to ensure easy of use.
Authors
- Sailley, Sévrine
The dataset contain Projection from the Size-Spectra Bioclimatic Envelop Model (SS-DBEM), this work was part of the GCRF Blue communities Programme (www.blue-communities.org). The model provides distribution and abundance and/or biomass of fish and other species of commercial interest under climate change and fishing pressure. The model outputs are yearly abundance/biomass on a 0.5-by-0.5 degree grid, covering the period from 2000 to 2098. Further description of the model and relevant references are listed in the following file: Guide-fish-model-output-use.docx The model was run under two climate scenario: RCP4.5 and RCP8.5, with different combinations of fishing pressure expressed as the Maximum Sustainable Yield (MSY) for the following values: 0 (no fishing, climate change alone will cause variation in fish biomass), 1 (sustainable fishing), 2, 3 (overfishing), and, 4 (overfishing with destructive practice). The intent is not to reproduce current fishing level but to provide a range of scenarios with which the future of fisheries can be explored. We projected fish species that were identified as key in the South East Asia seas region by our regional partners.The full list is provided in document: Fish-list-modelguide.xlsx There are 4 zip files that contain the model outputs of in either abundance (number of fish) or biomass grams of fish) for the two climate scenario. For example Biomass-RCP45.zip will contain model outputs in biomass for projections under RCP4.5 and all MSY. within the zip files are .csv files of the outputs for each species under the 5 MSY (0 to 4), the individual file names identify the species (identified by a 6digit code), the output provided (abundance or biomass), the RCP (8.5 or 4.5), and the MSY (0, 1, 2, 3, or 4). For example the file labelled 600107-Abundance-rcp85-msy4.csv contains the outputs for species 600107 (Skipjack tuna, Katsuwonnus pelamis), as abundance, under RCP8.5 with MSY4. Headers indicate what is in each column (latitude, longitude and year). Note: some knowledge of Python, R, or a similar software is recommended to ensure easy of use.
Authors
- Sailley, Sévrine