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    This data set contains land cover/land use data for the year 1990 and 2015 obtained through processing of Landsat images of US Geological Survey. These data sets were obtained through a supervised classification carried out with Landsat 8 image for 2015; Landsat 4 and 5 were used for land use classification of 1990. Gro for GooD: Groundwater Risk Management for Growth and Development

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    The data set contains Soil Data used in the Gro for GooD Project in Kwale, Kenya based on KENSOTER database and soil survey in study area. The KENSOTER dataset, specific for Kenya, was compiled by the Kenya Soil Survey (KSS) and ISRIC and released in 2006 where ISRIC plays a lead role in methodology development and programme implementation (http://www.isric.org/projects/soil-and-terrain-soter-database-programme). The dataset includes over 600 soil components, including synthetic profiles, which have been derived from soil survey reports and expert knowledge. The second version of the dataset which has been made available includes additional soil profile database and is also used for the assessment of soil carbon stocks. The gaps in the measured soil profile data have been filled using a step-wise procedure which includes three main stages: (1) collate additional measured soil analytical data where available; (2) fill gaps using expert knowledge and common sense; (3) fill the remaining gaps using a scheme of taxotransfer rules. Parameter estimates are presented by soil unit for fixed depth intervals of 0.2 m to 1 m depth for: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), bulk density, content of sand, silt and clay, content of coarse fragments, and available water capacity. The data have recently been used for the Green Water Credit (GWC) programme in the Upper Tana River Valley. This dataset was prepared for the Gro for GooD project by Mike Thomas, Rural Focus Ltd., Kenya; John Gathenya, JKUAT, Kenya. Gro for GooD: Groundwater Risk Management for Growth and Development

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    Direct geological observations made during field work, tied to positional information collected by hand-held GPS.

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    The file contain groundwater level/depth (WL), Groundwater and Surface Water Quality data (EC (micro-siemens per centimetre or µS/cm), Temperature (°C) and pH) for 49 points under fortnightly monitoring relevant to Gro for GooD research project in Kwale County, Kenya. Blank - Data not available. Gro for GooD: Groundwater Risk Management for Growth and Development

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    The newGeoSure Insurance Product (newGIP) provides the potential insurance risk due to natural ground movement. It incorporates the combined effects of the 6 GeoSure hazards on (low-rise) buildings. This data is available as vector data, 25m gridded data or alternatively linked to a postcode database – the Derived Postcode Database. A series of GIS (Geographical Information System) maps show the most significant hazard areas. The ground movement, or subsidence, hazards included are landslides, shrink-swell clays, soluble rocks, running sands, compressible ground and collapsible deposits. The newGeoSure Insurance Product uses the individual GeoSure data layers and evaluates them using a series of processes including statistical analyses and expert elicitation techniques to create a derived product that can be used for insurance purposes such as identifying and estimating risk and susceptibility. The Derived Postcode Database (DPD) contains generalised information at a postcode level. The DPD is designed to provide a ‘summary’ value representing the combined effects of the GeoSure dataset across a postcode sector area. It is available as a GIS point dataset or a text (.txt) file format. The DPD contains a normalised hazard rating for each of the 6 GeoSure themes hazards (i.e. each GeoSure theme has been balanced against each other) and a combined unified hazard rating for each postcode in Great Britain. The combined hazard rating for each postcode is available as a standalone product. The Derived Postcode Database is available in a point data format or text file format. It is available in a range of GIS formats including ArcGIS (*.shp), ArcInfo Coverages and MapInfo (*.tab). More specialised formats may be available but may incur additional processing costs. The newGeoSure Insurance Product dataset has been created as vector data but is also available as a raster grid. This data is available in a range of GIS formats, including ArcGIS (*.shp), ArcInfo coverage’s and MapInfo (*.tab). More specialised formats may be available but may incur additional processing costs. Data for the newGIP is provided for national coverage across Great Britain. The newGeoSure Insurance Product dataset is produced for use at 1:50 000 scale providing 50 m ground resolution. This dataset has been specifically developed for the insurance of low-rise buildings. The GeoSure datasets have been developed to identify the potential hazard for low-rise buildings and those with shallow foundations of less than 2 m deep. The identification of ground instability and other geological hazards can assist regional planners; rapidly identifying areas with potential problems and aid local government offices in making development plans by helping to define land suited to different uses. Other users of these data may include developers, homeowners, solicitors, loss adjusters, the insurance industry, architects and surveyors. Version 7 released June 2015.

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    Data identifying landscape areas (shown as polygons) attributed with geological names and rock type descriptions. The scale of the data is 1:25 000 scale. Onshore coverage is partial and BGS has no intention to create a national coverage at this scale. Areas covered are essentially special areas of 'classic' geology and include Llandovery (central Wales), Coniston (Lake District) and Cuillan Hills (Isle of Skye). Superficial deposits are the youngest geological deposits formed during the most recent period of geological time, the Quaternary, which extends back about 2.58 million years from the present. They lie on top of older deposits or rocks referred to as bedrock. Superficial deposits were laid down by various natural processes such as action by ice, water, wind and weathering. As such, the deposits are denoted by their BGS lexicon name, which classifies them on the basis of mode of origin (lithogenesis) with names such as, 'glacial deposits', 'river terrace deposits' or 'blown sand'; or on the basis of their composition such as 'peat'. Most of these superficial deposits are unconsolidated sediments such as gravel, sand, silt and clay. The digital data includes attribution to identify each deposit type (in varying levels of detail) as described in the BGS Rock Classification Scheme (volume 4). The data are available in vector format (containing the geometry of each feature linked to a database record describing their attributes) as ESRI shapefiles and are available under BGS data licence.

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    These files contain ground penetrating radar (GPR) data collected from the glacier margins and forelands of Falljökull and of Kvíárjökull, south-east Iceland, between 2012 and 2014. The data were collected using a Sensors and Software PulseEKKO Pro GPR system. For each glacier the data are stored in folders that indicate the month and year in which the surveys were conducted. Each GPR profile has a Sensors and Software GPR (.DT1) file, and associated header (.HD) and GPS (.GPS) files. The .HD files (which can be opened as text files) give the parameters and equipment used for each profile. GPS files are not available for some of the profiles collected on Falljökull in April 2013 (due to damage that occurred to the GPS linked with the PulseEKKO Pro system). For these profiles start, finish, and mid profile positions were recorded using differential GPS, and locations of these profiles are instead given by GIS shapefiles in the relevant folders. These datasets have been used in the publications listed below. Further information relating to the data collection methodology can be found therein. Phillips, Emrys; Everest, Jez; Evans, David J.A.; Finlayson, Andrew; Ewertowski, Marek; Guild, Ailsa; Jones, Lee. 2017 Concentrated, ‘pulsed’ axial glacier flow: structural glaciological evidence from Kvíárjökull in SE Iceland. Earth Surface Processes and Landforms, 42 (13). 1901-1922. https://doi.org/10.1002/esp.4145 Phillips, Emrys; Finlayson, Andrew; Bradwell, Tom; Everest, Jez; Jones, Lee. 2014 Structural evolution triggers a dynamic reduction in active glacier length during rapid retreat: evidence from Falljökull, SE Iceland. Journal of Geophysical Research: Earth Surface, 119 (10). 2194-2208. https://doi.org/10.1002/2014JF003165 Phillips, Emrys; Finlayson, Andrew; Jones, Lee. 2013 Fracturing, block-faulting and moulin development associated with progressive collapse and retreat of a polar maritime glacier: Virkisjokul-Falljokull, SE Iceland. Journal of Geophysical Research: Earth Surface, 118 (3). 1545-1561. https://doi.org/10.1002/jgrf.20116 Flett, Verity; Maurice, Louise; Finlayson, Andrew; Black, Andrew; MacDonald, Alan; Everest, Jez; Kirkbride, Martin. 2017. Meltwater flow through a rapidly deglaciating glacier and foreland catchment system: Virkisjökull, SE Iceland. Hydrology Research, 48 (6). 1666-1681. https://doi.org/10.2166/nh.2017.205

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    This datasets contains 323 observations of borehole breakouts across and drilling induced tensile fractures from borehole imaging used to re-characterise the UK stress field orientation in 2016. This was published in the Journal of Marine and Petroleum Geology and is openly available using doi:10.1016/j.marpetgeo.2016.02.012 The observations relate to 39 wells from Central and Northern England and are provided with links to screen grabs of the images for clarity. The basic well meta data is supplied along with a description of the dataset. The Images were generated in the IMAGE DISPLAY module of the Landmark RECALL software. and are supplied on an “as shown” basis. Descriptions of the tools and the techniques used are listed in the accompanying paper: KINGDON, A., FELLGETT, M. W. & WILLIAMS, J. D. O. 2016. Use of borehole imaging to improve understanding of the in-situ stress orientation of Central and Northern England and its implications for unconventional hydrocarbon resources. Marine and Petroleum Geology, 73, 1-20.

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    This summary borehole information release is for ‘as-built’ mine water and environmental baseline monitoring boreholes of the UK Geoenergy Observatories (UKGEOS) Glasgow facility at Cuningar Loop. The information pack from BGS contains a brief report with summary tables of locations, drilled depths, screened intervals and illustrative interpretations, and is accompanied with a spreadsheet and shapefile of the borehole locations and drilled depths. The purpose is to give potential users of the Glasgow Observatory an outline of the as-built infrastructure including condition of the target mine workings, as a summary of the more detailed information packs for each borehole. This dataset was updated in June 2020. Further details can be found in the accompanying report http://nora.nerc.ac.uk/id/eprint/526889

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    The UK Geoenergy Observatories (UKGEOS) Glasgow baseline surface water chemistry dataset1 released from the BGS comprises an excel file with two spreadsheets. The first spreadsheet contains information on the chemical composition of 98 surface water samples (84 samples and 14 field duplicates) collected monthly for 14 months between February 2019 and March 2020 from six sampling locations. These comprised three on the River Clyde at the UKGEOS Glasgow Cuningar Loop borehole cluster and three from control sites (two on the River Clyde and one on the Tollcross Burn). Field measurements of pH, redox potential, specific electrical conductance, temperature, dissolved oxygen and alkalinity and laboratory chemical data for concentrations of 71 inorganic and 10 organic substances in the surface water samples are presented. The dataset contains locational and descriptive information about the samples also. The analyte name, element chemical symbols, analytical method, units of measurement and long-term limits of detection are recorded in header rows at the top of the spreadsheet. The limits of detection/quantification for each monthly batch of samples are documented in rows at the head of each batch. The dataset includes abbreviations documenting quality control issues such as missing values. A guide to abbreviations used in the dataset is provided in the second excel spreadsheet released with the data. Further details about the dataset can be found in the accompanying report http://nora.nerc.ac.uk/id/eprint/529818.