A single Excel spreadsheet giving augite-plagioclase-plagioclase dihedral angle populations in cumulates from the Rustenberg Layered Suite of the Bushveld Igneous Complex. A document providing the background information and location of the samples used in the study. The data have been published: Holness et al. (2017) Contributions to Mineralogy and Petrology, 172:102 doi.org/10.1007/s00410-017-1423-4

Data derived from NERC Grant NE/J022632/1, Sequence alignments and resulting phylogenetic hypotheses from Harrington et al. (2016) BMC Evolutionary Biology.

A seismic dataset of 70 temporary and 3 permanent seismic stations deployed from 05/2012 to 10/2013 in northern Turkey. Three-component seismic data were collected at each location. Stations were deployed across the North Anatolian Fracture Zone (NAFZ) in the region of the 1999 Izmit and Duzce earthquakes. The network covered a footprint of ~35 by 70 km with a nominal station spacing of 7 km. Continuous seismic data were collected to study the crustal structure of the NAFZ to better understand the structure and dynamics of the NAFZ and it seismic hazard for the region. Funding for the project was provided through NERC Standard grant NE/I028017/1 and 63 stations were provided by the GEF. Additional stations were provided by the Kandilli Observatory and Earthquake Research Institute. Seismic stations were a mixture of Guralp CMG-6TD and CMG-3T. Further information can be found in GEF report for loan 947 - http://gef.nerc.ac.uk/documents/report/947. Link to data: http://ds.iris.edu/gmap/YH?timewindow=2012/5/01-2013/10/01

The data were produced by Joe Emmings, NERC-funded PhD student at the University of Leicester and British Geological Survey, between 2014 and 2017. Authors of these data: Joe Emmings a, b; Sarah Davies a; Christopher Vane b; Melanie Leng b, c; Vicky Moss-Hayes b; Michael Stephenson b a School of Geography, Geology and the Environment, University of Leicester, University Road, Leicester, LE1 7RH, UK. b British Geological Survey, Keyworth, Nottingham, NG12 5GG, UK. c School of Biosciences, Centre for Environmental Geochemistry, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD, UK. Data include: 1) A range of photographs from the outcrop Hind Clough and boreholes MHD4 and Cominco S9, sample photographs, thin section scans, microphotographs (transmitted light and scanning electron microscopy) and hand specimen descriptions; 2) The results of 100 analyses from the outcrop Hind Clough and boreholes MHD4 and Cominco S9; x-ray fluorescence major and trace element concentrations, RockEval pyrolysis measurements, x-ray diffraction traces and LECO elemental C and S data. These data were interpreted together with 20 drill-core samples previously acquired from Hind Clough ('HC01' prefix). See http://dx.doi.org/10.5285/c39a32b2-1a30-4426-8389-2fae21ec60ad for further information regarding this drill-core dataset. Acknowledgements: This study was funded by NERC grant NE/L002493/1, a part of the Central England Training Alliance (CENTA). This study also received CASE funding from the BGS. Nick Riley (Carboniferous Ltd) is thanked for sharing his expertise, particularly regarding the field identification of marine faunas. Charlotte Watts is thanked for providing field assistance. Nick Marsh, Tom Knott and Cheryl Haidon are thanked for providing expertise and assistance during inorganic geochemical and mineralogical analyses.

A worldwide compilation of 189 analyses of U and Pb concentrations in olivine-hosted melt inclusions from ocean island magmas. These data were used in Delavault et al. (2016, Geology 44, 819-822) to calculate the present-day distribution of the U/Pb ratios in magmas generated in intraplate setting.

[THIS DATASET HAS BEEN WITHDRAWN]. Standardised Precipitation Index (SPI) data for Integrated Hydrological Units (IHU) groups (Kral et al. [1]). SPI is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al. [2]. SPI is calculated for different accumulation periods: 1, 3, 6, 12, 18, 24 months. Each of these is in turn calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1862 to 2015. NOTE: the difference between this dataset with the previously published dataset 'Standardised Precipitation Index time series for IHU Groups (1961-2012)' [SPI_IHU_groups] (Tanguy et al., 2015 [3]), apart from the temporal extent, is the underlying rainfall data from which SPI was calculated. In the previously published dataset, CEH-GEAR (Keller et al., 2015 [4], Tanguy et al., 2014 [5]) was used, whereas in this new version, Met Office 5km rainfall grids were used (see supporting information for more details). Within Historic Droughts project (grant number: NE/L01016X/1), the Met Office has digitised historic rainfall and temperature data to produce high quality historic rainfall and temperature grids, which motivated the change in the underlying data to calculate SPI. The methodology to calculate SPI is the same in the two datasets. [1] Kral, F., Fry, M., Dixon, H. (2015). Integrated Hydrological Units of the United Kingdom: Groups. NERC-Environmental Information Data Centre doi:10.5285/f1cd5e33-2633-4304-bbc2-b8d34711d902 [2] McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California. [3] Tanguy, M.; Kral., F.; Fry, M.; Svensson, C.; Hannaford, J. (2015). Standardised Precipitation Index time series for Integrated Hydrological Units Groups (1961-2012). NERC Environmental Information Data Centre. https://doi.org/10.5285/dfd59438-2170-4472-b810-bab33a83d09f [4] Keller, V. D. J., Tanguy, M., Prosdocimi, I., Terry, J. A., Hitt, O., Cole, S. J., Fry, M., Morris, D. G., and Dixon, H.: CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological use, Earth Syst. Sci. Data Discuss., 8, 83-112, doi:10.5194/essdd-8-83-2015, 2015. [5] Tanguy, M.; Dixon, H.; Prosdocimi, I.; Morris, D. G.; Keller, V. D. J. (2014). Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2012) [CEH-GEAR]. NERC Environmental Information Data Centre. https://doi.org/10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e Full details about this dataset can be found at https://doi.org/10.5285/047d914f-2a65-4e9c-b191-09abf57423db

These data represent a massive synchrotron based programme to study ancient life. Not all of these data have been processed yet, nor have we published all of the results that we intend to. These data are still very much a work in progress. NERC grant abstract: Building on our previous successes with identifying and mapping the chemical residues of eumelanin and beta keratin, herein we propose an analytical and experimental plan to enhance our ability to detect and image key components of soft tissue. First of all we will perform a series of experiments with extant soft tissue so that we can monitor and determine the breakdown reactions of organic compounds as a function of host lithology, moisture content, and trace metal inventory. Secondly, we will complete an analytical programme, including SRS-XRF imaging, which will include these experimental run products as well as a series of time-stepped fossil samples of varying ages and host lithology so that we may build up a database which allows us to refine our general understanding of reaction paths during fossil degradation. Because the techniques we have developed are non-destructive we now have opened up the possibility for detailed analysis of extremely rare specimens which hold important information but cannot be destructively sampled. Finally, these experimental and analytical results from fossils and comparable extant species will be combined in order to answer several critically important questions in palaeontology, biology, and geochemistry. Project partners: University of Nancy, CNRS, Prof. R. Michels Feather degradation experiments SLAC Linear Accelerator Center, Linac Coherent Light Source, Dr. U. Bergmann SRS-XRF scans of large objects and x-ray spectroscopy SLAC Linear Accelerator Center, Stanford Synchrotron Radiation Lightsource, Prof. C. Kao SRS-XRF scans of large objects DIAMOND Lightsource, Prof. Fred Mosselmans XAS spectroscopy.

1. Rainfall (0.2 mm tipping bucket), accelerometer (OEM) and extensometer (string potentiometer) data from 10 instruments deployed across ground cracks in Sindhupalchok, Nepal in the aftermath of the 2015 earthquake. Data is provided in *.csv format, to include for each instrument: date, rainfall, extension(raw/min/max/mean/std), x(raw/min/max/mean/std), y(raw/min/max/mean/std), z(raw/min/max/mean/std). 2. Shapefiles of landslide mapping in the Upper Bhote Kosi valley, Sindhupalchok, Nepal, Including immediately post-earthquake (pre-monsoon), and post-2015 monsoon.

FeS polymorphs are of significant relevance to condensed matter physics and planetary science. In particular, they are thought to form the cores of Earth and Mars, which is suggested by their presence in many meteorites. Data are plain text files containing the relative volume expansion, molar heat capacity and molar entropy of the FeS phases at different pressures as a function of temperature. Research results based upon these data are published at https://doi.org/10.1016/j.jpcs.2017.07.033

The 5km Hex GS Landslides dataset shows a generalised view of the GeoSure Landslides v7 dataset to a hexagonal grid resolution of 64.95km coverage area (side length of 5km). This dataset indicates areas of potential ground movement in a helpful and user-friendly format. The rating is based on a highest level of susceptibility identified within that Hex area: Low (1), Moderate (2), Significant (3). Areas of localised significant rating are also indicated. The summarising process via spatial statistics at this scale may lead to under or over estimation of the extent of a hazard. The supporting GeoSure reports can help inform planning decisions and indicate causes of subsidence. The methodology is based on the BGS Digital Map (DiGMapGB-50) and expert knowledge of the behaviour of the formations so defined. This dataset provides an assessment of slope instability. Landslide hazard occurs due to particular slope characteristics (such as geology, gradient, sources of water, drainage, man-made constructions) combining to cause the slope to become unstable. Downslope movement of materials, such as a landslide or rockfall may lead to a loss of support and damage to buildings. Complete Great Britain national coverage is available.