This dataset summarises cetacean sightings made during January and February 2019 by an experienced team of cetacean researchers doing regular small-scale surveys in coastal waters close to Cumberland Bay, South Georgia. Some surveys were just within Cumberland Bay, and others include locations to the west and east of Cumberland Bay, as far as Stromness Bay (west) and St Andrews Bay (east). The dataset includes survey tracks, survey effort periods, species sighted and numbers of animals encountered. Over the survey period, Cumberland Bay alone was surveyed six times. Cumberland Bay plus adjoining waters were surveyed nine times, a total of 25:12 hr of search effort for all surveys. Nine expeditions were carried out in January (13:39 hr effort, with whales sighted on six surveys) and six in February (11:33 hr effort, with whales sighted on three surveys). A total of 43 whales (41 humpback whales) were observed during 26 sighting events, nine of which were within Cumberland Bay; a further 10 humpback whales were sighted at the entrance to the Bay (Right Whale Rocks), making a total of 19 humpback whale sightings within or at the entrance to Cumberland Bay. EU BEST 2.0 Medium Grant 1594, DARWIN PLUS award DPLUS057 and funding from the South Georgia Heritage Trust and Friends of South Georgia Island.

This dataset summarises the raw GPS locations obtained by satellite tracking of two southern right whales tagged at South Georgia island on 28th January 2020. One whale, genetically identified as a female, was tracked for 117 days (4,860 tag locations provided) and travelled ~5818km including a short period of time at the ice edge. The second whale, genetically identified as a male, was tracked for 238 days (8,492 tag locations provided) and travelled ~9,885km, including migration through the national waters of Argentina, Uruguay and Brazil. Funding: EU BEST 2.0 Medium Grant 1594, DARWIN PLUS award DPLUS057 and funding from the South Georgia Heritage Trust and Friends of South Georgia Island.

This data set contains satellite-derived information on geomorphic river mobility for ten catchments in the Philippines. We applied the locational probability approach to map the proportion of time that a river channel occupies a particular location. We quantified satellite-derived locational probabilities for 600 km2 of riverbed. The information is useful for predicting and developing resilience to river-related hazards in dynamic landscapes. We provide example Google Earth Engine (GEE) and MATLAB codes to replicate satellite-derived locational probability analyses, and provide outputs for each catchment. Data sets include: (1) example GEE codes to run satellite imagery analyses; (2) example MATLAB codes and data to generate locational probabilities; (3) example MATLAB codes and data to produce longitudinal analyses; and, (4) processed locational probability outputs for the ten catchments. The work was supported by the Natural Environment Research Council (NERC) and Department of Science and Technology - Philippine Council for Industry, Energy and Emerging Technology Research and Development (DOST-PCIEERD) – Newton Fund grant NE/S003312. Full details about this dataset can be found at https://doi.org/10.5285/a2bcc66e-4dcc-4ed1-897d-cdf36dde246d

The shapefiles contain the classification and locations of each river style determined by the authors. The data were used to characterise the river styles in Bislak River, Philippines. Shapefiles were clipped to the catchment boundary from different national government agencies to produce different thematic maps. Catchment properties such as land use (from the National Mapping and Resource Information Authority (NAMRIA)), geology (from the Mines and Geosciences Bureau), fault (from Philippine Institute of Volcanology and Seismology, rainfall isohyets, slope map, and the digital elevation model (also from NAMRIA) were used for regional and catchment analysis. The data only covers the whole Bislak catchment, Philippines. The CSV contains data used for the stream power analysis where stream power is a factor of slope and discharge. Full details about this dataset can be found at https://doi.org/10.5285/31ae71aa-74a9-466b-9a3a-25d2b1a9406e