This record comprises nine datasets related to the South Georgia and the South Sandwich Islands (SGSSI) Marine Protected Area (MPA) review undertaken in 2019. The SGSSI MPA is one of the world's largest MPAs, and the 2019 review led to a significant extension of the MPA area to cover all of the SGSSI Maritime Zone totalling 1.24 million km2. Details of the new measures can be found within the legislation (SG Gazette No 1. Dated 31 January 2019: https://laws.gov.gs/gazettes), and the datasets here relate directly to these measures. The following datasets are published here, for which further information can be found in the legislation linked above: -The boundary of the SGSSI MPA/Maritime Zone -No Take Zones for South Georgia -No Take Zones for Clerke and Shag rocks -No Take Zones for South Sandwich Islands -No Take Zones for South Sandwich Islands trench -No Take Zones for South of 60degS -Benthic Closed Areas -Pelagic Closed Areas -Heavy Fuel Oil Prohibition line The datasets are available as shapefiles. This work has been funded directly by the Government of South Georgia and the South Sandwich Islands.

This record comprises six datasets related to the South Georgia and the South Sandwich Islands (SGSSI) Marine Protected Area (MPA) review undertaken in 2025. The SGSSI MPA is one of the world's largest MPAs, and in April 2025 the area of the MPA in which fishing is prohibited was greatly expanded following the second five-year MPA review in 2024. Details of the new measures can be found within the latest legislation (SGSSI Gazette No 2 dated 22 April 2025: https://laws.gov.gs/gazettes/), and the datasets here relate directly to the areas and boundaries referred to in these measures. These datasets are: -The boundary of the SGSSI MPA -No Take Zones -General Benthic Closed Areas -Research Benthic Closed Areas -Pelagic Closed Areas -General Fisheries Zones The datasets are available as shapefiles. This work has been funded directly by the Government of South Georgia and the South Sandwich Islands with additional financial support from the UK Government Blue Belt programme.

A coastline of Kalaallit Nunaat/ Greenland covering all land and islands, produced in 2017 for the BAS map 'Greenland and the European Arctic'. The dataset was produced by extracting the land mask from the Greenland BedMachine dataset and manually editing anomalous data. Some missing islands were added and glacier fronts were updated using 2017 satellite imagery. The dataset can be used for cartography, analysis and as a mask, amongst other uses. At very large scales, the data will appear angular due to the nature of being extracted from a raster with 150 m cell size, but the dataset should be suitable for use at most scales and can be edited by the user to exclude very small islands if required. The projection of the dataset is WGS 84 NSIDC Sea Ice Polar Stereographic North, EPSG 3413. The dataset does not promise to cover every island and coastlines were digitised using the data creator's interpretation of the landforms from the images.

A vector polyline at 60 deg S which is the northern limit for ADD datasets.

These catchment boundaries define upland catchments and subcatchments at the headwaters of rivers Upper Hafren (Severn) and Upper Gwy (Wye). They identify the area of study of the Plynlimon research catchment project.

Topographic contours of Signy Island with 10 m intervals, derived from a digital elevation model originally created using stereoscopic photogrammetry from VHR (very high resolution) In-Track stereo satellite imagery collected during March 2015. The topographic contours were created to support the updated release of the British Antarctic Survey (BAS) Signy Island map (BAS, 2024). The dataset is available as a polyline shapefile and a GeoPackage. WorldView-3 satellite images (c) 2015 Maxar Technologies.

This dataset consists of soil data for 64 field sites on paired farm sites, with 29 variables measured for soil texture and structural condition, aggregate stability, organic matter content, soil shear strength, fuel consumption, work rate, infiltration rate, water quality and hydrological condition (HOST) data. The study is part of the NERC Rural Economy and Land Use (RELU) programme. A move to organic farming can have significant effects on wildlife, soil and water quality, as well as changing the ways in which food is supplied, the economics of farm business and indeed the attitudes of farmers themselves. Two key questions were addressed in the SCALE project: what causes organic farms to be arranged in clusters at local, regional and national scales, rather than be spread more evenly throughout the landscape; and how do the ecological, hydrological, socio-economic and cultural impacts of organic farming vary due to neighbourhood effects at a variety of scales. The research was undertaken in 2006-2007 in two study sites: one in the English Midlands, and one in southern England. Both are sites in which organic farming has a 'strong' local presence, which we defined as 10 per cent or more organically managed land within a 10 km radius. Potential organic farms were identified through membership lists of organic farmers provided by two certification bodies (the Soil Association and the Organic Farmers and Growers). Most who were currently farming (i.e. their listing was not out of date) agreed to participate. Conventional farms were identified through telephone listings. Respondents' farms ranged in size from 40 to 3000 acres, with the majority farming between 100 and 1000 acres. Most were mixed crop-livestock farmers, with dairy most common in the southern site, and beef and/or sheep mixed with arable in the Midlands. In total, 48 farms were studied, of which 21 were organic farmers. No respondent had converted from organic to conventional production, whereas 17 had converted from conventional to organic farming. Twelve of the conventional farmers defined themselves as practicing low input agriculture. Farmer interview data from this study are available at the UK Data Archive under study number 6761 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

This dataset contains a national-scale geodatabase of stream network and river catchment characteristics in the Philippines. It presents detailed information on 128 medium- to large-sized catchments (catchment area > 250 km2). The quantitative descriptions provide context for enabling geomorphologically-informed sustainable river management. The geodatabase provides a baseline understanding of fundamental topographic characteristics in support of varied geomorphological, hydrological and geohazard susceptibility applications. Data sets include: 1) GIS shapefiles with river catchment properties; 2) GIS shapefiles with stream network properties; 3) spreadsheets containing morphometric and topographic characteristics (n = 91); 4) example MATLAB code and topographic data to replicate the analysis for a selected catchment. 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/S003312/1. Full details about this dataset can be found at https://doi.org/10.5285/49ae11ec-e4e5-4e4a-b091-976d18c4ee3e

A modelled dataset derived from a range of national datasets, describing the distribution of woody linear feature boundaries in Great Britain. The dataset presents linear features which have a high likelihood of being a woody linear feature. The dataset was created by a predictive model developed at the Centre for Ecology & Hydrology, Lancaster in 2016. Full details about this dataset can be found at https://doi.org/10.5285/d7da6cb9-104b-4dbc-b709-c1f7ba94fb16

This dataset consists of shapefile outlines of winter roads and ice roads in Canada, verified for the 2022-2023 winter road season. It focuses on the public winter roads leading to remote First Nations communities which have no permanent land access. The line data also includes private winter roads, community-built winter roads where information is available, and feeder roads connecting to the permanent road network. First Nations communities connected solely by winter roads are included as point locations. Their local roads were likewise verified, updated, or newly digitised if not included in Canada's National Road Network (NRN) data. Features were traced by hand and information was extracted from Canada''s NRN open datasets and then modified using Esri Imagery Basemap, Planet Labs and provincial, municipal and federal information. This dataset aims to provide a temporally and spatially consistent record of varying provincial datasets to support respective infrastructure departments and environmental research of surface and climatic conditions surrounding winter roads. The dataset was produced and funded through a NERC QUADRAT DTP studentship (NE/S007377/1).