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- Published by:
- British Geological Survey (BGS)
- Last updated:
- 17 December 2025
The raster provide the output of a machine-learning random forest algorithm modelling the occurrence of ferromanganese (Fe-Mn) crust deposits in the world ocean. This raster constitutes a...
- Published by:
- Centre for Environmental Data Analysis
- Last updated:
- 19 June 2018
"Improving our ability to predict rapid changes in the El Nino Southern Oscillation climatic phenomenon" project, which was a Natural Environment Research Council (NERC) RAPID Climate Change...
- Published by:
- Centre for Environmental Data Analysis
- Last updated:
- 17 July 2017
"Improving our ability to predict rapid changes in the El Nino Southern Oscillation climatic phenomenon" project, which was a Natural Environment Research Council (NERC) RAPID Climate Change...
- Published by:
- Marine Environmental Data & Information Network
- Last updated:
- 10 September 2024
In December 2020, a recent focus on the marine environment (Marine special protection areas selection process) included the classification of 12 marine special protection areas (SPAs) in Scotland...
- Published by:
- Marine Environmental Data & Information Network
- Last updated:
- 20 July 2024
In December 2020, a recent focus on the marine environment (Marine special protection areas selection process) included the classification of 12 marine special protection areas (SPAs) in Scotland...
- Published by:
- British Geological Survey (BGS)
- Last updated:
- 17 December 2025
EPSRC grant EP/L012227/1: Development of Unified Experimental and Theoretical Approach to Predict Reactive Transport in Subsurface Porous Media. The effect of pore-scale heterogeneity on non-Darcy...
- Published by:
- Marine Environmental Data & Information Network
- Last updated:
- 10 July 2024
Input data used to predict sedimentation rate for the Baltic Sea. Observation
data includes: Sedimentation rate data derived from isotope 137Cs tracers and
pseudo-samples of zero sedimentation...
- Published by:
- Marine Environmental Data & Information Network
- Last updated:
- 30 September 2025
Input data used to predict sedimentation rate for the Baltic Sea. Observation
data includes: Sedimentation rate data derived from isotope 137Cs tracers and
pseudo-samples of zero sedimentation...
- Published by:
- Centre for Environmental Data Analysis
- Last updated:
- 19 June 2018
PalaeoQUMP was headed by Prof Sandy Harrison of the University of Bristol, with co-investigators at the University of Southampton and Durham University, as part of QUEST (Quantifying and...
- Published by:
- Centre for Environmental Data Analysis
- Last updated:
- 17 July 2017
PalaeoQUMP was headed by Prof Sandy Harrison of the University of Bristol, with co-investigators at the University of Southampton and Durham University, as part of QUEST (Quantifying and...
- Published by:
- Centre for Environmental Data Analysis
- Last updated:
- 19 June 2018
The Quantifying Flood Risk of Extreme Events using Density Forecasts Based on a New Digital Archive and Weather Ensemble Predictions Project is a Natural Environment Research Council (NERC) Flood...
- Published by:
- Centre for Environmental Data Analysis
- Last updated:
- 17 July 2017
The Quantifying Flood Risk of Extreme Events using Density Forecasts Based on a New Digital Archive and Weather Ensemble Predictions Project is a Natural Environment Research Council (NERC) Flood...
- Published by:
- Environmental Information Data Centre
- Last updated:
- 19 November 2025
This dataset contains information about predicted future erosion hazards to electricity transmission towers at a site in the Mersey River valley. River channel change and floodplain erosion rates...
- Published by:
- Marine Environmental Data & Information Network
- Last updated:
- 10 July 2024
Abundance and species composition were determined for phytoplankton in 361
water samples collected at 12 sites: five transects from 488N to 508S in the
Atlantic Ocean, the Benguela upwelling, the...
- Published by:
- Marine Environmental Data & Information Network
- Last updated:
- 01 December 2024
Abundance and species composition were determined for phytoplankton in 361
water samples collected at 12 sites: five transects from 488N to 508S in the
Atlantic Ocean, the Benguela upwelling, the...
- Published by:
- British Geological Survey (BGS)
- Last updated:
- 17 December 2025
There are two components to this dataset: (1) fault analyses used to estimate underlying dyke properties, imaged in 3D seismic reflection data; and (2) dimension measurements and calculations of...
- Published by:
- British Geological Survey (BGS)
- Last updated:
- 17 December 2025
Grant: ACT ELEGANCY, Project No 271498. This repository includes CMG simulation input and output files, processed micro-CT data, figure 3 data and plot, pore network modeling sensitivity examples
- Published by:
- British Geological Survey (BGS)
- Last updated:
- 17 December 2025
Grant: ACT ELEGANCY, Project No 271498. This repository includes CMG simulation input and output files, processed micro-CT data, and pore network modelling sensitivity examples. CMG simulation,...
- Published by:
- Marine Management Organisation
- Last updated:
- 14 September 2016
This data represents the presence and absence of various life stages of fish species and the relative confidence of the probability based on the outputs of statistical modelling. Confidence is...
- Published by:
- British Geological Survey (BGS)
- Last updated:
- 17 December 2025
Fault analyses used to estimate underlying dyke properties, imaged in 3D seismic reflection data. The seismic reflection data are located offshore NW Australia and image a series of Late Jurassic...