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- Published by:
- Marine Environmental Data & Information Network
- Last updated:
- 10 July 2024
Spatial predictions of the fractions of mud, sand and gravel as continuous
response variables for the north-west European continental shelf. Mud, sand
and gravel fractions range from 0-1 (i.e....
- Published by:
- Environment Agency
- Last updated:
- 01 August 2025
PLEASE NOTE: This record has been retired. It has been superseded by: https://environment.data.gov.uk/dataset/b5aaa28d-6eb9-460e-8d6f-43caa71fbe0e
This dataset is not suitable for identifying...
- Published by:
- British Geological Survey (BGS)
- Last updated:
- 31 December 2025
This project will use the techniques of stereolithography and PIV directly to measure the fluid velocity field in complex, 2D, geologically realistic media with multi-scaled heterogeneities. The...
- Published by:
- Scottish Government SpatialData.gov.scot
- Last updated:
- 19 June 2024
These layers are the outputs of research which developed a national river temperature model for Scotland capable of predicting both daily maximum river temperature and sensitivity to climate...
- Published by:
- North Sea Transition Authority
- Last updated:
- 13 June 2025
Carbon capture and storage (CCS) refers to a number of techniques and processes which capture carbon dioxide emissions, generally from industrial processes. The carbon dioxide (CO2) can then...
- Published by:
- North Sea Transition Authority
- Last updated:
- 13 June 2025
Carbon capture and storage (CCS) refers to a number of techniques and processes which capture carbon dioxide emissions, generally from industrial processes. The carbon dioxide (CO2) can then...
- Published by:
- North Sea Transition Authority
- Last updated:
- 15 June 2025
Carbon capture and storage (CCS) refers to a number of techniques and processes which capture carbon dioxide emissions, generally from industrial processes. The carbon dioxide (CO2) can then...
- Published by:
- Joint Nature Conservation Committee
- Last updated:
- 01 July 2019
Prediction of the presence of rock at outcrop or subcrop at the seabed across the UK shelf area. This shapefile was produced through a semi-automated approach, using a Random Forest model combined...
- Published by:
- Marine Environmental Data & Information Network
- Last updated:
- 10 July 2024
Spatial prediction of the sediment accumulation rate, provided as a .geotif
file (as an average linear rate in cm yr-1 since 1986) for the Baltic Sea
created using a machine learning approach....
- Published by:
- Marine Environmental Data & Information Network
- Last updated:
- 30 September 2025
Spatial prediction of the sediment accumulation rate, provided as a .geotif
file (as an average linear rate in cm yr-1 since 1986) for the Baltic Sea
created using a machine learning approach....
- Published by:
- Environmental Information Data Centre
- Last updated:
- 19 November 2025
This dataset presents predicted soil erosion rates (t ha-1 yr-1) and its impact on topsoils, including lifespans (yr) assuming erosion rates remain constant and there is no replacement of soil;...
- Published by:
- Environmental Information Data Centre
- Last updated:
- 19 November 2025
A spatial approach was developed to interpret qualitatively expressed scenarios, and predict the probability and amount of change for 10 land-cover types across 127 sub-catchments in upland Wales....
- Published by:
- Environmental Information Data Centre
- Last updated:
- 19 November 2025
[This dataset is embargoed until February 2, 2026]. For the Bislak River, the Philippines, this dataset contains: (i) topography and orthoimagery in 2019 and 2020; (ii) Digital Elevation Models...
- Published by:
- British Geological Survey (BGS)
- Last updated:
- 07 January 2026
Os isotopes, highly siderophile element abundance measurements, lithophile trace element data, major element data, electron microprobe and LA-ICP-MS mineral chemistry of Cr-spinel and sulphide -...
- Published by:
- Marine Environmental Data & Information Network
- Last updated:
- 10 September 2024
This dataset contains predicted seahorse habitat distributions for two species
(*Hippocampus hippocampus *and *H. guttulatus*) and the genus combined
(Hippocampus hippocampus MAXENT.asc,...
- Published by:
- Environmental Information Data Centre
- Last updated:
- 19 November 2025
The data deposited here underlie an assessment of the exposure of UK habitats to climate change, and a linked assessment of how well current UK plant monitoring schemes cover these exposure...
- Published by:
- British Geological Survey (BGS)
- Last updated:
- 31 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:
- Environmental Information Data Centre
- Last updated:
- 19 November 2025
Data from whole transcriptome sequencing of the four European pine species - Pinus sylvestris (Scots pine), P. mugo (Dwarf mountain pine), P. uliginosa (Mountain pine) and P. uncinata (Peat-bog...
- Published by:
- British Geological Survey (BGS)
- Last updated:
- 07 January 2026
Matlab m-file code to generate a probabilistic model of aquifer-body occurrence in the subsurface of the Indo-Gangetic foreland basin, northwestern India. The accompanying ArcGIS ASCII matrix files...
- 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...