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  • This data contains high-resolution XRF data scanned from IODP cores recovered from Expedition 369, IODP Sites U1513, U1514 and U1516. Sietske Batenburg was responsible for scanning the Cenomanian-Turonian interval at Sites U1513 and U1516, and the lower half of the Eocene at U1514. Data is available from IODP database: http://web.iodp.tamu.edu/LORE/

  • (I) Handpump Vibration Data For each handpump, data is organized in one CSV file per day. These files are grouped together over batches, where each batch approximately corresponds to three months. (II) Borehole Water Level Data Water level data at the borehole of each handpump is recorded in one CSV file per handpump. Both uncompensated (raw) and compensated (with respect to atmospheric pressure) data are available. (III) Data Time Logs A separate Excel file lists the locations of the monitoring sites and the time logs corresponding to both (I) and (II) per handpump. 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] F. Colchester, “Smart handpumps: a preliminary data analysis,” IET Conference Proceedings, pp. 7–7(1). [Online]. Available: https://digital-library.theiet.org/content/conferences/10.1049/cp.2014.0767 [3] H. Greeff, A. Manandhar, P. Thomson, R. Hope, and D. A. Clifton, “Distributed inference condition monitoring system for rural infrastructure in the developing world,” IEEE Sensors Journal, vol. 19, no. 5, pp.1820–1828, March 2019. [4] F. E. Colchester, H. G. Marais, P. Thomson, R. Hope, and D. A. Clifton, “Accidental infrastructure for groundwater monitoring in africa,” Environmental Modelling Software, vol. 91, pp. 241 – 250, 2017. [Online]. Available:http://www.sciencedirect.com/science/article/pii/S1364815216308325 [5] A. Manandhar, H. Greeff, P. Thomson, R. Hope, and D. A. Clifton, “Shallow Aquifer Monitoring Using Handpump Vibration Data,” In-review, 2019.

  • The dataset consists of daily rainfall data for 23 manual rain gauge stations installed by Gro for GooD project within and about the study area. The installed stations covering four river catchments name Ramisi River, Mukurumudzi River, Mtawa River and Mwachema River in Kwale County. The dataset period is from January 2016 to November 2018. Gro for GooD: Groundwater Risk Management for Growth and Development

  • The file contain groundwater level/depth (WL), Groundwater and Surface Water Quality data (EC (micro-siemens per centimetre or µS/cm), Temperature (degrees C) and pH) for 49 points under fortnightly monitoring relevant to Gro for GooD research project in Kwale County, Kenya. Blank - Data not available. Note this is same dataset as NGDC record number 118189 with extended time series. Gro for GooD: Groundwater Risk Management for Growth and Development

  • Sequence data from blood samples, taken from a wild population of long-lived birds (mute swans; Cygnus olor) that has experienced three outbreaks of related H5 HPAIVs in the past decade, specifically, H5N1 (2007), H5N8 (2016) and H5N6 (2017). Detailed demographic data were available and intense sampling was conducted before and after the outbreaks. This data is NERC-funded but not held by the EIDC. This data is archived in NCBI Genbank at https://www.ncbi.nlm.nih.gov/nuccore with accession numbers MH819084 to MH819175 (inclusive)

  • The dataset consist of daily rainfall data for 22 manual rain gauge stations installed by Gro for GooD project within and about the study area. The installed stations covering four river catchments name Ramisi River, Mukurumudzi River, Mtawa River and Mwachema River in Kwale County. The dataset period is from January 2016 to September 2017. Gro for GooD: Groundwater Risk Management for Growth and Development

  • Here, we provide data corresponding to the experimental conditions used, the results gained via electron microprobe for natural and experimental volcanic samples. Mass balance calculations and a compilation of monitoring data for recent explosive eruptions.

  • Dataset of material characterisation (X-ray diffraction) analyses to constrain the nature of materials produced during Fe(II)-silicate precipitation experiments from seawater in the presence of various metals.

  • Geological observations during field walks, with coordinates, photographs and descriptions of rocks/geological materials and features at the various stops.

  • The project is aimed at understanding how a number of economically and geologically important chemical elements partition themselves between the silicates of the outer parts of the Earth and sulphides, minerals and liquids rich in sulphur. Although sulphur is not very abundant in the Earth, it has a powerful impact on the behaviour of a wide range of elements in Earth's crust and underlying mantle. For example, the majority of ore bodies rich in nickel, copper, gold and platinum are sulphides. Many of them are formed when sulphides separate from molten silicates in volcanic areas. A principal aim of my project is to experimentally reproduce the conditions under which sulphides separate and to determine how they extract the economically important elements from the host volcanic rocks. A second aim is to use my experimental results to determine whether or not a large mass of sulphide was extracted from the molten earth early in its history (4500 million years ago) and dissolved into the metallic core. In order to study how elements are distributed into sulphide I perform experiments at high pressures and temperatures, typically 15000 atmospheres pressure and 1400 degrees C in a large hydraulic press. After treatment at high pressure and temperature, the samples (typically about 1x1x1 millimeters) are rapidly cooled to room temperature and pressure and examined using a range of microanalytical techniques. The latter enables me to resolve chemical composition on the scale of 10 microns (or 10 millionth's of a meter).