4 Works

Peat properties from the Flow Country, Scotland, 2020, following wildfire in 2019

P. Fernandez-Garcia, R. Andersen, R. Schmidt, P.P.J. Gaffney, P. Gilbert, D.J. Large, C. Marshall, D. Mayor, A. Pickard & B. Williamson
Location of peat cores and peat properties including moisture, bulk density, ash and organic matter content for short cores (50 cm) collected 10 month post-fire in high, medium and low severity areas within a drained and a near natural area in the footprint of a severe wildfire that impacted >6500 ha of blanket bog and wet heath in the Flow Country of Northern Scotland.

Isis remotely-operated vehicle (ROV) bathymetry data in Marguerite Trough, Antarctica, from James Clark Ross cruise JR157, 2007

Julian Dowdeswell, Robert Larter, Riko Noormets, Gwyn Griffiths & Kelly Hogan
On cruise JR157 the Isis ROV was deployed on 15 dives in the Marguerite Trough area in January and February 2007. Dives 10 and 11 targeted a bedrock channel system on the inner continental shelf to investigate channel incision processes and the history of glaciation in the area. The plan was to map parts of the channel walls and thalweg, and then to use these data to locate the best coring sites within the channel...

Time-lapse imagery of a highly active submarine channel and its implications for seafloor geohazards

Maarten Heijnen , Michael Clare , Matthieu Cartigny, Sophie Hage, Gwyn Lintern , Cooper Stacey , Daniel Parsons , Stephen Simmons , Ye Chen , Esther Sumner, Justin Dix & John Huges Clarke
National Oceanography Centre (1); Ocean and earth Sciences, University of Southampton (2); Departments of geography and Earth Sciences, Durham University (3); Department of Geoscience, University of Calgary (4); Natural Resources Canada (5); Energy and Environment Institute, University of Hull (6); Center for Coastal Ocean Mapping, University of New Hampshire (7)

With increasing energy and communication demands, and recent advances in technology, seafloor infrastructure becomes more abundant. For instance, a global network of seafloor...

Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning'

Tom R. Andersson & J. Scott Hosking
This dataset encompasses data produced in the study 'Seasonal Arctic sea ice forecasting with probabilistic deep learning', published in Nature Communications. The study introduces a new Arctic sea ice forecasting AI system, IceNet, which predicts monthly-averaged sea ice probability (SIP; probability of sea ice concentration > 15%) up to 6 months ahead at 25 km resolution. The study demonstrated IceNet's superior seasonal forecasting skill over a state-of-the-art physics-based sea ice forecasting system, ECMWF SEAS5, and...

Registration Year

  • 2021

Resource Types

  • Dataset
  • Other


  • National Oceanography Centre
  • British Antarctic Survey
  • Natural Environment Research Council, UK Research & Innovation
  • National Oceanography Centre, University of Southampton
  • University of Washington
  • UK Centre for Ecology & Hydrology
  • University of Glasgow
  • University of Cambridge
  • European Centre for Medium-Range Weather Forecasts
  • University of Nottingham