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Deformation of Sandstone under Crustal Pressure and Temperature Conditions (NERC Grant NE/L002485/1)
Data generated at UCL on a conventional triaxial apparatus used to deform three different sandstones at room temperature and 150 °C. The data includes the raw mechanical data (time, load, displacement, pore pressure, pore pressure volume and confining pressure) and the meaningful processed data used to plot figures and draw main conclusions (stress, strain, pore volume change, effective mean stress, inelastic strain, yield points and Youngs modulus). The three sandstones used were Bleursville, Locharbriggs and Boise Sandstone, and are denoted by data files SS, L and MBO respectively. This dataset is used in the paper: M. Jefferd, N. Brantut, P.G. Meredith and T.M. Mitchell, Compactive Deformation of Sandstone under Crustal Pressure and Temperature Conditionsserpentinite, submitted to J. Geophys. Res. And in the UCL PhD Thesis M.Jefferd, Sandstone under Crustal Pressure and Temperature

Electronic Supplementary Material: "Event trees and epistemic uncertainty in longterm volcanic hazard assessment of rift volcanoes: the example of Aluto (Central Ethiopia)", by Tierz, P., Clarke, B., Calder, E. S., Dessalegn, F., Lewi, E., Yirgu, G., Fontijn, K., Crummy, J. M., and Loughlin, S. C., submitted to Geochemistry, Geophysics, Geosystems. The datasets contain volcanological data on analogue (i.e. similar) volcanoes of Aluto volcano (Ethiopia), including conditional probabilities of eruption size, number of eruptions with specific volcanic phenomena reported and values of volcano analogy calculated using the VOLCano ANalogues Search tool (VOLCANS, Tierz et al., 2019, https://doi.org/10.1007/s0044501913363). These type of data can be used to parameterise event tree models (e.g. Newhall and Hoblitt, 2002, https://doi.org/10.1007/s004450100173; Marzocchi et al., 2010, https://doi.org/10.1007/s0044501003578) and, thus, quantify volcanic hazard at a particular volcano of interest, including the relevant sources of uncertainty. The production of the datasets was supported by the UK Natural Environment Research Council project: Rift Volcanism: Past, Present and Future (RiftVolc). Grant NE/L013460/1.

Data are distances (in cm) to water measured by an experimental nearinfrared lidar sensor in six different setups (2017–9). Laboratory tests conducted at Imperial College London include quantifying the effect of (i) distance, (ii) sensor inclination, (iii) turbidity/clarity of the water, and (iv) ambient temperature on measurement bias. Outdoor tests at three locations in London interrogated the effect of varying water surface roughness on the measurements. A dataset of highfrequency measurements is also included, from which the effects of sample autocorrelation were interrogated.

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 [100] crystallographic axis and the x direction of the EBSD map, 'angle100Y' is the angle between [100] crystallographic axis and the y direction of the EBSD map, and 'angle100Z' is the angle between the [100] crystallographic axis and the z direction in the EBSD map etc. 'angle100P' is the angle between the EPMA profile and the [100] crystallographic axis, 'angle010P' is the angle between the EPMA profile and the [010] crystallographic axis, and 'angle100P' is the angle between the EPMA profile and the [001] 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 Mginplagioclase 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).

Data produced during three BGS ground gas surveys (August 2018, and May and October 2019) of up to 83 point measurements across four predetermined locations within the UK Geoenergy Observatories (UKGEOS) Glasgow site, located to the south of the Cuningar Loop Woodland Park. The dataset includes measurements of CH4 and CO2 flux between the ground surface and lower atmosphere, along with concentrations of CO2, O2, H2, H2S and ‘residual balance’ in near surface ground gas measured at c.70 cm below ground level. Further details about the dataset can be found in the accompanying report. http://nora.nerc.ac.uk/id/eprint/528737/

Seismic waveforms from an explosion catalogue from a seismic network at Santiaguito volcano between November 2014 and December 2018. A network of 6 broadband and 6 shortperiod stations was used to record explosive volcanic activity. Waveforms from 18,895 explosions have been automatically been detected and extracted.

This data set characterises the seismicity occurring within 30 km of the Bora  TulluMoye volcanic field between 2016 and 2017. It also provides a description of key geologic features in this region. See the README file for a full explanation of the data set. These data were originally published as supplementary material in gcubed article: Seismicity of the Bora – TulluMoye Volcanic field 20162017: Greenfield et al (2018), https://doi.org/10.1029/2018GC007648