From 1 - 6 / 6
  • 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.

  • 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.

  • 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).

  • 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

  • Auroral oval boundary locations derived from IMAGE (Imager for Magnetopause-to-Aurora Global Exploration) satellite FUV (Far Ultra Violet imager) data covering the period from May 2000 until October 2002. Three sets of boundary data were derived separately from the WIC (Wideband Imaging Camera) and SI12/SI13 (Spectrographic Imager 121.8/135.6 nm) detectors. For each image, the position of each pixel in AACGM (Altitude Adjusted Corrected Geomagnetic) coordinates was established. Each image was then divided into 24 segments covering 1 hour of magnetic local time (MLT). For each MLT segment, an intensity profile was constructed by finding the average intensity across bins of 1 degree magnetic latitude in the range of 50 to 90 degrees (AACGM). Two functions were fit to each intensity profile: a function with one Gaussian component and a quadratic background, and a function with two Gaussian components and a quadratic background. The function with a single Gaussian component should provide a reasonable model when the auroral emission forms in a continuous oval. When the oval shows bifurcation, the function with two Gaussian components may provide a better model of the auroral emission. Of the two functions fit to each intensity profile, we determine the one with the lower reduced chi-square goodness-of-fit statistic to be the better model for that profile. For the version 1.1 boundary location data, the fitting process was performed over 200 iterations to achieve each fit. The auroral boundaries were then determined to be the position of the peak of the poleward Gaussian curve, plus its FWHM (full-width half-maximum) value of the Gaussian, to the peak of the equatorward Gaussian, minus its FWHM. In the case of the single Gaussian fit, the same curve is used for both boundaries. A number of criteria were applied to discard poorly located auroral boundaries arising from either poor fitting or incomplete data. A further correction can be applied to the data, to estimate the location of the Earth''s magnetic field''s OCB (open-close boundary). These corrections have been tabulated in a separate file; if this correction is required the adjustments should be made to the poleward boundary value.