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 a statistical benchmark. This dataset includes three types of data from the study. Firstly, IceNet's SIP forecasts from 2012/1 - 2020/9. Secondly, the 25 neural network files underlying the IceNet model. Thirdly, CSV files of results from the study. The codebase associated with this work includes a script to download this dataset and reproduce all the paper's figures. This dataset is supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the "AI for Science" theme within that grant and The Alan Turing Institute. The dataset is also supported by the NERC ACSIS project (grant NE/N018028/1).

The dataset is the output of a statistical model which downscales ERA5 monthly precipitation data using gauge measurements from the Upper Beas and Sutlej Basins in the Western Himalayas. Multi-Fidelity Gaussian Processes (MFGPs) are used to generate more accurate precipitation values between 1980 and 2012, including over ungauged areas of the basins. MFGPs are a probabilistic machine learning method that provides principled uncertainty estimates via the prediction of probability distributions. These predictions can therefore be used to estimate the likelihood of extreme precipitation events which have led to droughts, floods, and landslides. Funding from UK Engineering and Physical Sciences Research Council [grant number: 2270379].

This dataset presents point annotations of stranded whale (Sperm whales, Physeter macrocephalus) and dolphin (Pilot whales, Globicephala melas edwardii) species identified in very high-resolution (VHR) optical and SAR satellite imagery, along offshore islands of New Zealand and Tasmania, between 2018-2023. Cetacean strandings offer significant conservation value for the assessment of ecosystems and serve as early warning of emerging concerns regarding animal, ocean, and human health. However stranding monitoring programmes are scarce or non-existent along minimally populated areas, coastlines with limited economic resources, geographically remote areas, complex coastlines and areas of geopolitical unrest. VHR satellite imagery offers the prospect of improving monitoring in these regions. While VHR satellite imagery is able to detect large baleen whale strandings, mass strandings are predominantly smaller-sized odontocetes (toothed whale and dolphin species). Detecting odontocetes is therefore crucial for VHR satellites to be useful for monitoring strandings globally. In addition, scaling up the use of VHR optical satellite imagery is limited by cloud cover, the primary environmental condition governing successful imagery collection. Synthetic Aperture Radar (SAR) satellites enable VHR imaging of Earth in cloudy regions and in darkness. This approach could facilitate strandings detection in cloudy regions and independent of daylight hours, which is critical for enabling timely emergency responses to unfolding stranding events. Here, we present data from four smaller odontocete mass strandings of long-finned pilot whale (LFPW), on Chatham, Pitt and Stewart Island, New Zealand, and one large odontocete (sperm whale) mass stranding on King Island, Tasmania, Australia between 2018-2023, to successfully detect and quantify large and small odontocete strandings in VHR optical and SAR satellite imagery. This research has been supported by the Natural Environment Research Council (NERC) through a SENSE CDT studentship (grant no. NE/T00939X/1). The research was further supported by additional funding provided through, the British Antarctic Survey (BAS) Innovation Voucher, Sentinel Hub and their #30MapChallenge competition, BAS Ecosystems, and the support and cooperation of Airbus and Maxar Technologies Ltd, for their rapid response and efforts to enable successful collection of the imagery analysed here.

Monitoring whales in remote regions is important for their conservation, using traditional survey platforms (boat and plane) is logistically difficult. The use of very high-resolution satellite imagery to survey whales, particularly in remote regions, is gaining interest and momentum. However, development is hindered by the lack of automated systems to detect whales. Such a system requires an open source library containing examples of whales and confounding features in satellite imagery. Here we present such a database, created by surveying 6,300 km2 of satellite imagery in various regions across the globe, which allowed us to detect 633 whale objects and 120 confounding features. Funding was provided from a BAS Innovation Voucher.

This dataset contains information about the locations and local environmental conditions of 123 Malaise trap samples collected in November-December 2021 in the 908 km2 forested ‘leakage belt’ buffer zone of the Gola Rainforest National Park (GRNP) in eastern Sierra Leone, where cocoa, a driver of deforestation, is the main cash crop. Each trap was set out for five days with >99% ethanol. The samples were transported from Sierra Leone to the UK, where they have been sent for metabarcoding for arthropods (using Leray2 PCR primers). The work was supported by the Natural Environment Research Council (Grant NE/S014063/1). Full details about this dataset can be found at https://doi.org/10.5285/161315e4-71c1-481d-906c-149ab2e9705c

Monitoring whales in remote regions is important for their conservation, using traditional survey platforms (boat and plane) is logistically difficult. The use of very high-resolution satellite imagery to survey whales, particularly in remote regions, is gaining interest and momentum. However, development is hindered by the lack of automated systems to detect whales. Such a system requires an open source library containing examples of whales and confounding features in satellite imagery. Here we present such a database, created by surveying 6,300 km2 of satellite imagery in various regions across the globe, which allowed us to detect 633 whale objects. This dataset contains image chips as png files. Funding was provided from a BAS Innovation Voucher.