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The data represent ground motion results obtained from Interferometric Synthetic Aperture Radar (InSAR) for the UKGEOS – Glasgow site. The InSAR techniques used is called Interferometric Point Target Analysis (IPTA) and the BGS processing is based on Sentinel-1 radar satellite data for the period August 2015 - June 2017. The results include time series of displacement (in mm) during this interval and average velocity across the whole period (in mm/yr) along the satellite Line of Sight (Hanssen, 2001). InSAR has provided information on the baseline conditions of ground stability ahead of any underground activity planned in at the Glasgow Geothermal Energy Research Field Site (GGERFS) as described in Bateson and Novellino (2019). References: Bateson, L.; Novellino, A.. 2019 Glasgow Geothermal Energy Research Field Site : ground motion survey report. Nottingham, UK, British Geological Survey, 35pp. (OR/18/054) (Unpublished). Available at http://nora.nerc.ac.uk/id/eprint/524555/ Hanssen, R., 2001. Radar Interferometry: Dordrecht Kluwer Academic Publishers, The Netherlands (2001) (308 pp.)
Vesicularity (phi) as a function of time for samples of natural hydrated silicate glass (obsidian) from optical dilatrometric analysis. Also numerical model for analysis of dataset and associated user guide.
Two categories of data are presented: 1) Experimental data of catalyst performance under conditions for a Blast Furnace Gas (BFG) to methanol to process, comprising the monitored gas phase species evolution in a single channel micro reactor. 2) Process simulation and techno-economic analysis of the BFG-to-methanol process, comprising Aspen Plus V10 anotated process flowsheet, process model summary, stream results, reactor performances and cost analysis calculation. Funded by UKCCSRC 2018 Flexible Funding Call
The property subsidence assessment dataset provides an understanding of the shrink-swell hazard at both the individual property and/or postcode level for England and Wales. It builds upon the BGS GeoSure shrink-swell data by mapping the hazard to the individual building polygon and considering the other susceptibility factors of building type, foundation depth, and drainage and tree proximity. The data consist of GIS building polygons with an overall susceptibility to subsidence score between 1-100. Scores are also classified from non-plastic to very high. Each building polygon is also scored from 1-10 for each subsidence factor (geology, foundation, drainage, building type, building storey and tree proximity). Postcode data is also available as a table showing the ‘average’ PSA score for all buildings within the postcode. The identification of shrink-swell related subsidence prone areas, alongside the inclusion of potential sources to exacerbate these phenomena, can better inform insurers and homeowners and form the basis to make decisions concerning prevention and remediation. The product enhances geological information obtained from GIP (BGS GeoSure Insurance Product) and GeoSure via the inclusion of the crucial shrink-swell susceptibility factors (proximity to trees and foundation depth). This therefore allows the derivation of a risk element for the housing stock at Building level, which is then generalised to Postcode level. BGS GeoSure - a series of GIS digital maps identifying areas of potential natural ground movement hazard in Great Britain
This data set comprises broadband magnetotelluric (MT) and transient electromagnetic (TEM) data collected during three field seasons in 2008, 2009 and 2010 by a team of researchers from the University of Edinburgh, UK, IMAGIR, Brest, France and the Institute for Geophysics, Space Science and Astronomy at Addis Ababa University, Ethiopia. The MT dataset includes the original time series and processed transfer functions. The time series data are provided in the original raw data format with files to convert them to ascii. Raw and processed TEM data, collected with a Geonics PROTEM system, are included. We provide information on the locations and the processing and include the necessary instrument response functions and metadata to reproduce our results from the raw data.
The data set presents major and trace element geochemical data obtained from ICP-MS measurements on micro-drilled subsamples of ferromanganese (Fe-Mn) crusts from Tropic Seamount, north-east Atlantic Ocean. The data represent detailed stratigraphic analysis of Fe-Mn crust samples 078_019 and 085_004. These samples were collected at 3100 and 1100 meters beneath sea level, respectively, during the JC142 expedition of the RRS James Cook for the MarineE-Tech project in 2016.
This is supporting data for the manuscript entitled 'DFENS: Diffusion chronometry using Finite Elements and Nested Sampling' by E. J. F. Mutch, J. Maclennan, O. Shorttle, J. F. Rudge and D. Neave. Preprint here: https://doi.org/10.1002/essoar.10503709.1 Data Set S1. ds01.csv Electron probe microanalysis (EPMA) profile data of olivine crystals used in this study. Standard deviations are averaged values of standard deviations from counting statistics and repeat measurements of secondary standards. Data Set S2. ds02.csv Plagioclase compositional profiles used in this study, including SIMS, EPMA and step scan data. Standard deviations for EPMA analyses are averaged values of standard deviations from counting statistics and repeat measurements of secondary standards. Standard deviations for SIMS and step scan analyses are based on analytical precision of secondary standards. Data Set S3. ds03.csv Angles between the EPMA profile and the main olivine crystallographic axes measured by electron backscatter diffraction (EBSD). 'angle100X' is the angle between the  crystallographic axis and the x direction of the EBSD map, 'angle100Y' is the angle between  crystallographic axis and the y direction of the EBSD map, and 'angle100Z' is the angle between the  crystallographic axis and the z direction in the EBSD map etc. 'angle100P' is the angle between the EPMA profile and the  crystallographic axis, 'angle010P' is the angle between the EPMA profile and the  crystallographic axis, and 'angle100P' is the angle between the EPMA profile and the  crystallographic axis. All angles are in degrees. Data Set S4. ds04.csv Median timescales and 1 sigma errors from the olivine crystals of this study. The +1 sigma (days) is the quantile value calculated at 0.841 (i.e. 0.5 + (0.6826 / 2)). The -1 sigma (days) is therefore the quantile calculated at approximately 0.158 (which is 1 - 0.841). The 2 sigma is basically the same but it is 0.5 + (0.95/2). The value quoted as the +1 sigma (error) is the difference between the upper 1 sigma quantile and the median. Likewise the -1 sigma (error) is the difference between the median and the lower 1 sigma quantile. Data Set S5. ds05.xlsx Median timescales and 1 sigma errors from the plagioclase crystals of this study. Results from each of the parameterisations of the Mg-in-plagioclase diffusion data are included: Faak et al, (2013), Van Orman et al., (2014) and a combined expression. Data Set S6. ds06.xlsx Spreadsheet containing the regression parameters and covariance matrices used in this study and in Mutch et al. (2019). Additional versions of the olivine regressions where the ln fO2 is expressed in Pa have been made for completeness. We recommend using the versions where ln fO2 is expressed in its native form (bars).
These images were acquired using micro computed tomographic imaging of 7 sandstone plugs taken at various depths in the Sellafield borehole 13B. SF696 (63.8 m), SF697 (76.1 m), SF698 (96.98 m), SF699 (126.27 m), SF700 (144.03 m), SF701 (172.16 m) and SF702 (181.39 m). These samples are further detailed and analysed in the following article: http://dx.doi.org/10.1144/petgeo2020-092
These images were acquired using micro computed tomographic imaging of 4 sandstone plugs taken at various depths in the Glasgow UKGEOS borehole GGC01. GG496 (170.07 m), GG497 (168.66 m), GG498 (73.37 m) and GG499 (135.06 m). These samples are further detailed and analysed in the following article: http://dx.doi.org/10.1144/petgeo2020-092.
Water concentration as a function of position in silicates from diffusion couple experiments. Data can be used to determine diffusivity of water in the silicate melt. Also numerical model for analysis of dataset and associated user guide.