Wind, sediment transport and surface morphological data collected at Sand Creek during a month long field campaign in March and April 2019 to investigate protodune development under bimodal winds. Data is used in the accepted paper ‘Dune initiation in a bimodal wind regime’, Journal of Geophysical Research: Earth Surface, by Delorme, P., Wiggs, G.F.S., Baddock, M.C., Claudin, P., Nield, J.M. and Valdez, A. (accepted 18th September 2020, article reference number 2020JF005757R; https://repository.lboro.ac.uk/articles/Dune_initiation_in_a_bimodal_wind_regime/12973817) Surface morphological data: This is terrestrial laser scanned (TLS) data collected of the creek sand surface during multiple visits. The data is raw point cloud format in text columns of x, y and z coordinate data. It has been orientation in local format (the origin is located at 13UTM 443152, 4184478). *_full_lowres cover the whole creek surface and the banks on either side. * is the date that the data was collected in yymmdd format. All other data is high resolution section of the actual creek surface within the channel. Each data set uses the same coordinate system. Data can be viewed in any spatial software. Wind and sediment data were collected from a fixed point on the eastern edge of the creek channel. The data is in csv file format with column titles and can be viewed in any text or database software. See Delorme et al. (accepted) for more details.

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

This dataset provides digital spatial information on the location of mineral resources in Wales at a scale of 1:50 000. The term ‘mineral resources’ has a definition under international standards that includes both an economic and geological dimension. These data are based primarily on mapped geology with limited assessment of economics. Therefore, the term ‘mineral resources’ is used here in a broad sense. The dataset allows users to visualise the extent and distribution of mineral resources and to relate them to other forms of land-use (such as urban areas or designated environmentally sensitive areas) or to other factors (such as transport infrastructure and conservation information). The British Geological Survey (BGS) was awarded a grant from the Welsh Assembly Government Aggregates Levy Fund in 2009 to provide a comprehensive, relevant and accessible information base to enhance the sustainability of mineral resources for Wales. BGS co-funded this project through its Sustainable Mineral Solutions project. This work was completed in 2010. This dataset comprises the digital GIS files which were produced through this project. The major elements of minerals information presented on the maps are; the geological distribution of all onshore mineral resources in Wales, the location of mineral extraction sites, the recorded occurrences of metallic minerals, the recorded location of former slate quarries and significant areas of slate waste and the recorded location of historic building stone quarries. In 2020 minor revisions to geometry and attributes were made in in response to minor corrections that were required. The paper maps were not re-released with these data updates. Point data for mineral occurrence and mine site data has not been included in this revision as these data are superseded by other BGS datasets, such as the BGS BritPits database of mines and quarries. The BGS Mineral Resource data does not determine mineral reserves and therefore does not denote potential areas of extraction. Only onshore, mainland mineral resources are included in the dataset. This dataset has been produced by the collation and interpretation of mineral resource data principally held by the British Geological Survey. The mineral resource data presented are based on the best available information, but are not comprehensive and their quality is variable. The dataset should only be used to show a broad distribution of those mineral resources which may be of current or potential economic interest.

Data from laboratory experiments conducted as part of project NE/K011464/1 (associated with NE/K011626/1) Multiscale Impacts of Cyanobacterial Crusts on Landscape stability. Soils were collected from two sites in eastern Australia and transferred to a laboratory at Griffith University, Queensland for conduct of experiments. Soils were A, a sandy loam, and B a loamy fine sand. Trays 120 mm x 1200 mm x 50 mm were filled with untreated soil that contained a natural population of biota. Soils were either used immediately for experiments (physical soil crust only: PC) or were placed in a greenhouse and spray irrigated until a cyanobacterial crust has grown from the natural biota. Growth was for a period of 5 days (SS), c.30 days (MS2) or c.60 days (MS1). Following the growing period (if applicable) trays were placed in a temperature/humidity controlled room at 35° and 30% humidity until soil moisture (measured 5 mm below the surface) was 5%. Trays were then subject to rainfall simulation. Rainfall intensity of 60 mm hr-1 was used and rainfall was applied for 2 minutes (achieving 2 mm application), 8 minutes (achieving 8 mm application) or 15 minutes (achieving 15 mm application). Following rainfall, trays were returned to the temperature/humidity-controlled room under UV lighting until soil moisture at 5 mm below the surface was 5%. A wind tunnel was then placed on top of each tray in turn and a sequential series of wind velocities (5, 7, 8.5, 10, 12 m s-1) applied each for one minute duration. On each tray the five wind velocities were run without saltation providing a cumulative dust flux. For the highest wind speed, an additional simulation run was conducted with the injection of saltation sands. Three replicates of each rainfall treatment were made. Variables measured include photographs, spectral reflectance, surface roughness, fluorescence, penetrometry, chlorophyll content, extracellular polysaccharide content, Carbon, Nitrogen and splash erosion and particle-size analysis (of wind eroded material). Details of rainfall simulator, growth of cyanobacteria, laser soil surface roughness characterisation and wind tunnel design and deployment in Strong et al., 2016; Bullard et al. 2018, 2019. Bullard, J.E., Ockelford, A., Strong, C.L., Aubault, H. 2018a. Impact of multi-day rainfall events on surface roughness and physical crusting of very fine soils. Geoderma, 313, 181-192. doi: 10.1016/j.geoderma.2017.10.038. Bullard, J.E., Ockelford, A., Strong, C.L., Aubault, H. 2018b. Effects of cyanobacterial soil crusts on surface roughness and splash erosion. Journal of Geophysical Research – Biogeosciences. Doi: 10.1029/2018. Strong, C.S., Leys, J.F., Raupach, M.R., Bullard, J.E., Aubault, H.A., Butler, H.J., McTainsh, G.H. 2016. Development and testing of a micro wind tunnel for on-site wind erosion simulations. Environmental Fluid Mechanics, 16, 1065-1083.

Shallow overland flows in steady state can become unstable and break up into destructive surges. The following data documents maximum growth rates for disturbances to uniform steady flows on a fixed slope in a one-dimensional shallow-layer model that incorporates the mechanics of erosion and deposition of monodisperse sediment, documented in sections 2 and 4 of the following freely available preprint: https://arxiv.org/abs/2007.15989. The data comprises the following 4 columns, separated by spaces: grain diameter, Froude number, solid fraction and maximum growth rate. Grain diameter refers to the characteristic diameter of erodible particles, non-dimensionalised by the steady flow depth h0. Froude number, Fr, is a dimensionless constant defined as Fr = u0 / sqrt(h0 * g'), where u0 is the velocity of the steady flow and g' is gravitational acceleration resolved perpendicular to the slope. Solid fraction is a number between 0 and 1 that describes the proportion of solid particles in the flowing mixture. A solid fraction of 0 denotes a purely fluid flow and a solid fraction of 1 denotes a saturated mixture containing a maximum packing of solid particles. Maximum growth rate refers to the largest linear growth rate for perturbations to a uniform flowing layer with the corresponding properties given in the prior 3 columns. The model formulation describes the dynamics of 4 unknown observables: flow height, flow velocity, solids concentration and bed height. By taking the 'maximum' in this case, we mean the maximum over these 4 flow fields that may be perturbed by an environmental disturbance and also the maximum over all possible wavelengths of disturbance. We note that in this dataset, flows with a maximum growth rate equal to zero or small positive values (e.g. up to machine precision) are stable; flows with strictly positive growth rate are unstable. Zero growth rate indicates that the maximum growth rate is given by a neutrally stable perturbation and such perturbations always exist for reasons of symmetry in the model. For each grain diameter and Froude number in the dataset, there exist two steady uniform states with different solid fractions. Therefore two files are supplied - one containing data for the more dilute states and the other containing data for the more concentrated states. These various technical details, as well as full documentation of the model and the parameters used are explained more fully in the aforementioned paper.

The RiftVolc microgravity network was comprised of a total of 4 benchmarks including a reference benchmark. Benchmark locations, observed gravity changes, dg14 -16, from 2014-2016, corresponding vertical deformation, Uz, free-air effect, and resultant residual gravity changes gr of the microgravity and GNSS network at Corbetti.

This document is the drillers log of strata encountered during site investigation work. The log was made in the field during drilling at Prees, Shropshire on 8th to 10th January 2020. The log includes basic information on lithology and drilling equipment used and depths of the individual core runs.

These data comprise four phases of geophysical survey carried out in 2002, 2007, 2008 and 2011, covering various areas within the Thames Estuary as part of an overarching archaeological investigation called the London Gateway project (2001-2020) ahead of planned dredging works. 88635_49575_2002 - Area of sidescan sonar data within the Thames Estuary (shell haven) area. The data were acquired in OSGB36 British National Grid coordinates and covers the area: Top Left - 570395.312500 E, 183444.953125 N, Top Right - 604097.250000 E, 183444.953125 N, Bottom Right - 604097.250000 E, 177590.140625 N, Bottom Left - 570395.312500 E, 177590.140625 N. 88635_61207_2007 - Geophysical survey comprising sidescan sonar data acquired over 13 separate wreck sites. Corresponding MBES data were acquired previously in 2005 and are deposited with the UKHO. SSS data comprise a total of 83 .xtf files with 2 channels. Acquired in WGS84 UTMz31N coordinates. 400 KhZ frequency. Range 50 m. Sensor positions rather than ship positions for each line in metadata. Each wreck location is centred on (UTMz31N): Amethyst - 364468 E, 5708659 N; Ancient - 325490 E, 5708230 N; Argus - 359499 E, 5706071 N; Ash - 360905 E, 5706497 N; Atherton - 359708 E, 5706186 N; Dynamo - 401449 E, 5743755 N; EastOaze - 362786 E, 5707385 N; ErnaBoldt - 403551 E, 5746997 N; Letchworth - 357544 E, 5705592 N; London - 343115 E, 5707365 N; Pottery - 346619 E, 5706276 N; SS Storm - 406001 E, 5747115 N; Unknown wreck - 375530 E, 5714052 N. 88635_61208_2008 - Geophysical survey comprising sidescan sonar and multibeam echosounder data over a single wreck site. SSS data comprise a total of 7 .xtf files with 2 channels. 400 KhZ frequency. Range 50 m. Acquired in WGS84 Geographic coordinates. Sensor positions rather than ship positions for each line in metadata. Wreck location in WGS84 UTMz31N: Aisha - 363982 E, 5707656 N. 88635_79800_2011 - Geophysical survey comprising magnetometer, sidescan sonar and multibeam echosounder data undertaken over three separate blocks; Area 9to11, Area 26to36 and Area 105. Area9to11:332980 E, 5708675 N; 332980 E, 5708226 N; 338586 E, 5707813 N; 338681 E, 5708242 N. Area26to36:339693 E, 5708096 N; 339571 E, 5707680 N; 345670 E, 5706229 N; 350490 E, 5706371 N; 350338 E, 5706838 N; 345680 E, 5706685 N. Area105:383734 E, 5719704 N; 384035 E, 5719369 N; 385920 E, 5720804 N; 385619 E, 5721134 N. Where corresponding multibeam echosounder data were acquired, these data have been archived with the United Kingdom Hydrographic Office (UKHO). Overarching full archaeological investigation, including results of the assessment of these data, and technical reports are archived with the Archaeology Data Service (ADS) (https://doi.org/10.5284/1083494).

This dataset contains a summary of the weekly volumetric output of pumps monitored using Smart Handpump sensors for 2014 and 2015. Grants that permitted the data collection include: Groundwater Risk Management for Growth and Development project (NE/M008894/1) funded by NERC/ESRC/DFID’s UPGro programme; New mobile citizens and waterpoint sustainability in rural Africa (ES/J018120/1) ESRC-DFID; Groundwater Risks and Institutional Responses for Poverty Reduction in Rural Africa (NE/L001950/1) funded by NERC/ESRC/DFID’s UPGro programme Notes: 1. The accuracy of these volume figures should be considered to be +/- 20%. 2. The dataset has gaps due to variable signal, and some attrition due to damage and vandalism. 3. Not all pumps in the study area were under monitoring. References: [1] P. Thomson, R. Hope, and T. Foster, “GSM-enabled remote monitoring of rural handpumps: a proof-of-concept study,” Journal of Hydroinformatics, vol. 14, no. 4, pp. 829–839, 05 2012. [Online]. Available: https://doi.org/10.2166/hydro.2012.183 [2] Behar, J., Guazzi, A., Jorge, J., Laranjeira, S., Maraci, M.A., Papastylianou, T., Thomson, P., Clifford, G.D. and Hope, R.A., 2013. Software architecture to monitor handpump performance in rural Kenya. In Proceedings of the 12th International Conference on Social Implications of Computers in Developing Countries, Ochos Rios, Jamaica. pp. 978 (Vol. 991).

Multibeam bathymetric Echosounder (MBES) and Sparker seismic acquired August 2019. NSFGEO-NERC Grant: Tsunamis from large volume eruptions