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  • The dataset collates the relative concentration of nearly 300 antimicrobial resistance (AMR) genes found in soil locations across Scotland. Soils were obtained from the National Soils Inventory of Scotland (NSIS2), from which the total community DNA were extracted and provided to assess AMR gene content. Sampling of the NSIS2 was conducted between 2007-2010 at 183 soil locations representing intersections of a 20km grid across all of Scotland. For each sample, nearly 300 AMR genes were assessed representing major antibiotic classes, and included many resistance traits: aminoglycosides, beta-lactams, FCA (fluoroquinolone, quinolone, chloramphenicol, florfenicol and amphenicol resistance genes), MLSB (macrolide, lincosamide, streptogramin B), tetracycline, vancomycin, sulphonamide, efflux pumps and integron genes. The data represent relative gene abundance, i.e., the amount of genes per “total bacteria.” Full details about this dataset can be found at

  • The dataset details the biological characteristics and concentrations of toxic metals/semi-metals in liver tissue from 278 Eurasian otters (Lutra lutra) that died across England and Wales during the period 2006-2017. For each otter carcass, location (National Grid Reference) was recorded. These locations were used to collate data describing spatial variation in stream and soil biochemistry, weather, and potential anthropogenic sources of contaminants in the otter’s habitat from a range of sources. This data was collected as part of the Cardiff University Otter Project. This project was supported in part by a grant from the Esme Fairbairn Foundation. The CEH contribution was supported the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCaPE programme delivering National Capability. This study contains model estimates of topsoil properties [Countryside Survey] and British Geological Survey data owned by © NERC as well as Ordnance Survey data owned by © Crown copyright and database right 2007. Full details about this dataset can be found at

  • This dataset contains polylines depicting non-woodland linear tree and shrub features in Cornwall and much of Devon, derived from lidar data collected by the Tellus South West project. Data from a lidar (light detection and ranging) survey of South West England was used with existing open source GIS datasets to map non-woodland linear features consisting of woody vegetation. The output dataset is the product of several steps of filtering and masking the lidar data using GIS landscape feature datasets available from the Tellus South West project (digital terrain model (DTM) and digital surface model (DSM)), the Ordnance Survey (OS VectorMap District and OpenMap Local, to remove buildings) and the Forestry Commission (Forestry Commission National Forest Inventory Great Britain 2015, to remove woodland parcels). The dataset was tiled as 20 x 20 km shapefiles, coded by the bottom-left 10 km hectad name. Ground-truthing suggests an accuracy of 73.2% for hedgerow height classes. Full details about this dataset can be found at

  • This dataset provides UK maps of baseline prior uncertainty (UQ) in fluxes of Greenhouse Gases (GHGs) carbon dioxide, CO2 (2014-15) and methane, CH4 (2015). Spatial maps of these GHG emissions are produced annually in the National Atmospheric Emissions Inventory (NAEI) but it is important to quantify uncertainty in these maps. These uncertainty estimates come from sectoral uncertainty data provided by the NAEI. Here, we propagate the uncertainty in the maps for each of the sectors contributing to the emissions using a Monte Carlo method, in order to quantify the uncertainty in the total emissions spatially. The Monte Carlo method employed here uses a novel approach (Nearest Neighbour Gaussian Process) to make calculations computationally affordable. These estimate the influence on the overall uncertainty of unknown errors in the model structure. Further details of the methodology used here can be found in the supporting documentation included with this data. In the near term, this methodology will be used and developed further in the NERC-funded project, DARE-UK (NE/S003614/1), to update UQ in maps of CO2 and CH4 for the UK. For that work and in general, it is useful to have a baseline prior uncertainty quantification against which future UK maps of uncertainty in CO2 and CH4 fluxes can be compared. Full details about this dataset can be found at