This dataset is a sequential list of all the terrestrial snow and ice samples taken at Robert Island, Antarctica from January 2023 to March 2023. Sample date, location, bloom colour and what the sample was subsequently used for (metabolomics, pigment, DNA, DOC, FTIR, CN), along with cell counts is provided. Cell counts and biometric measurements of Ancylonema are included in the dataset. The samples were taken by small 15ml or 50ml plastic tube sampling in the snow pack or on the icecap hard ice. This was to study the habitat and ecosystem progression over the season and linking this to other satellite or drone or vegetation data. A remote sensing output (red snow algae vector for Robert Island) was also produced. The expedition and sampling was carried out by Matthew Davey (PI), Alex Thomson, Andrew Gray, Hannah Moulton, Charlotte Walshaw with support from INACH, Chile and BAS. This was part of a wider project with Claudia Colesie, Naomi Thomas, Peter Convey, Alison G. Smith, Peter Fretwell, Lloyd Peck. Samples were transferred to UK (SAMS) for further analysis. This project was funded by Standard NERC References: NE/V000764/1 and NE/V000896/1. The past, present and future of snow algae in Antarctica: a threatened terrestrial ecosystem?

This annotated dataset comprises locational data of beluga whales along the eastern shore of the Yugor Penisula and in the inner part of Baydaratskaya Bay in the southern Kara Sea (Russia) as detected from Very High Resolution (VHR) satellite imagery on July 4th and 9th, 2016. Images were manually scanned and whales detected through the use of grids. Additional metadata includes information on image type and model, and whale distinctive characteristics (e.g., fluke or blow). This work supports the ''training'' of machine learning algorithms for automatic detection of whales from satellite imagery. This study was possible thanks to imagery support from MAXAR Technologies/Digital Globe Foundation for the VHR images and core funding from British Antarctic Survey, Natural Environment Research Council, as part of the ''Wildlife from Space'' project.

This annotated dataset comprises locational data of grey whales in lagoons San Ignacio and Ojo de Liebre in Baja California Sur as detected from Very High Resolution (VHR) satellite imagery in January 2009, 2013 and 2015. Images were manually scanned and whales detected through the use of grids. Additional metadata includes information on image type and model, and whale distinctive characteristics (e.g., fluke or blow). This work supports the '' training'' of machine learning algorithms for automatic detection of whales from satellite imagery. This study formed part of the Ecosystems component of the British Antarctic Survey Polar Science for Planet Earth Programme, funded by The Natural Environment Research Council. The work was supported by the UKRI Centre for Doctoral Training in Application of Artificial Intelligence to the study of Environmental Risks (reference EP/S022961/1).