Automated Author ProfileChamberlain, Samuel
Chamberlain, Samuel
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: 5.0 (sum of 11 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We are a large BAG of research groups with varied interests and technical requirements, from routine rapid collection of diffraction data from well behaved systems for ligand studies to challenging microcrystals grown using cubic phase methods. Our BAG is focussed around a large group of experimenters who work on a diverse range of biological and physical problems. Whilst this makes it difficult to write an overall summary of the science it makes it easy to share the beamtime very efficiently and ensure that all time granted is used to the fullest extent.
Authors
- LOWE, Edward ;
- CHAMBERLAIN, Samuel
Our BAG is focussed around a large group of experimenters who work on a diverse range of biological and physical problems. Whilst this makes it difficult to write an overall summary of the science it makes it easy to share the beamtime very efficiently and ensure that all time granted is used to the fullest extent. The interests of the groups involved in the BAG include repair of DNA damage, chromatin assembly and organisation, metallo-protein chemistry, membrane transporter proteins, novel protein antibiotics, cell adhesion and the study of parisitic diseases with a view to vaccine development.
Authors
- LOWE, Edward ;
- CHAMBERLAIN, Samuel
No description available
Authors
- Lutz, Nina ;
- Chamberlain, Samuel ;
- Goodyer, Ian ;
- Bhardwaj, Anupam ;
- Sahakian, Barbara ;
- Jones, Peter ;
- Wilkinson, Paul
Water retention data for 5-year experimental pasture study where water control structures were installed on 4 pastures (SP1-4), and no structures were installed on adjacent pastures (SP5-8). Riser and no_riser columns are means of each treatment.
Authors
- Chamberlain, Samuel
Daily sums for NEE, CH4 and mean data from meteorological variables. Also, included are flux rates converted to mass units and cumulative fluxes used to create annual greenhouse gas budgets.
Authors
- Chamberlain, Samuel
Water retention data for 5-year experimental pasture study where water control structures were installed on 4 pastures (SP1-4), and no structures were installed on adjacent pastures (SP5-8). Riser and no_riser columns are means of each treatment.
Authors
- Chamberlain, Samuel
Description: Environmental (air temperature and precipitation) and soil variables (soil temperature and moisture) along with CO2 and CH4 fluxes for both grazed and ungrazed pastures.
Environmental variables: Air temperature and precipitation values provided are daily averages.
Soil variables: Soil temperature and moisture values provided are daily averages measured at 10 and 15-cm depth, respectively.
CO2 and CH4 fluxes provided are half-hour gap filled values. Fluxes of CO2 were gap filled using the Eddy covariance gap-filling and flux partitioning online tool (http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/index.php), and the fluxes of CH4 were gap filled replacing missing values by the mean of specific half-hour of four adjacent days. Details of post-processing and gap filling methods are provided in Material and Methods.
Authors
- Gomez-Casanovas, Nuria ;
- DeLucia, Nicholas ;
- Bernacchi, Carl J ;
- Boughton, Elizabeth ;
- Sparks, Jed ;
- Chamberlain, Samuel ;
- DeLucia, Evan H
Description: Environmental (air temperature and precipitation) and soil variables (soil temperature and moisture) along with CO2 and CH4 fluxes for both grazed and ungrazed pastures.
Environmental variables: Air temperature and precipitation values provided are daily averages.
Soil variables: Soil temperature and moisture values provided are daily averages measured at 10 and 15-cm depth, respectively.
CO2 and CH4 fluxes provided are half-hour gap filled values. Fluxes of CO2 were gap filled using the Eddy covariance gap-filling and flux partitioning online tool (http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/index.php), and the fluxes of CH4 were gap filled replacing missing values by the mean of specific half-hour of four adjacent days. Details of post-processing and gap filling methods are provided in Material and Methods.
Authors
- Gomez-Casanovas, Nuria ;
- DeLucia, Nicholas ;
- Bernacchi, Carl J ;
- Boughton, Elizabeth ;
- Sparks, Jed ;
- Chamberlain, Samuel ;
- DeLucia, Evan H
Daily sums for NEE, CH4 and mean data from meteorological variables. Also, included are flux rates converted to mass units and cumulative fluxes used to create annual greenhouse gas budgets.
Authors
- Chamberlain, Samuel
Half-hourly eddy covariance data from subtropical pastures in south Florida, including CO2 fluxes, CH4 fluxes, gap-filled data, and meteorological data. This raw data was used to compute daily sums and annual budgets.
Authors
- Chamberlain, Samuel