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  • [This dataset is embargoed until June 1, 2022]. This dataset contains half-hourly output data (for the year 2011) generated by a preliminary version of the Shrubland Ecosystem Assessment (SEcA) model. SEcA calculates the ecosystem processes for a semi-arid shrubland system, for this dataset the model has been configured for a Caatinga ecosystem. The model generates 4 output files, those generating the aerodynamic and surface resistances, state variables, energy balance fluxes, and carbon flux-related outputs. The data provided here relate to model runs with the JULES Farquhar model, with the Sinclair plant water stress switched on. This work was funded by Newton/NERC/FAPESP Nordeste project: NE/N012488/1. Full details about this dataset can be found at https://doi.org/10.5285/ad86c0d3-624c-4ae3-9280-8d1ccbe14929

  • These data are the simulated peat heights and water-table depths (both in cm) from a DigiBog run. The virtual peatland was configured as a 2-D transect of 100 x 2m x 2m columns. The data were generated for each year of a 5,100-year run. After 4,900 years, six ditches were added and the model allowed to run for a further 100 years. After this time, the ditches were ‘restored’ and the simulation continued until a total runtime of 5,100 years had elapsed. Full details about this dataset can be found at https://doi.org/10.5285/1d16b303-ca1d-43b4-93b1-c8172adc9792

  • This dataset contains biogeochemical and edaphic information from burned peat soil on the Stalybridge estate located near Manchester (UK), commonly referred to as Saddleworth moor. This study was conducted after a wildfire fire on the Saddleworth moor in June 2018. The sample plots included areas with deep and shallow peat burn. The data includes geographical information (location, elevation and slope), soil temperature and soil chemical composition (carbon, nitrogen and 22 other elements). The dataset is the result of research funded by a NERC Urgency grant entitled 'RECOUP-Moor: Restoring Ecosystem CarbOn Uptake of Post-fire Moorland' (NE/S011943/1, led by Dr. Bjorn Robroek of the University of Southampton (now Radboud University Nijmegen, the Netherlands). Full details about this dataset can be found at https://doi.org/10.5285/1fa8d605-b996-4687-ace2-1fa59cd5c6dd

  • [This dataset is embargoed until March 31, 2022]. This dataset contains particulate and dissolved organic carbon concentrations, nutrients (ammonia, nitrates, phosphate), alkalinity, pH, particulate organic nitrogen, delta-C-13 and delta-15-N isotopes, fluorescence and absorbance from river water samples. Data come from 41 rivers from around Great Britain, sampled on a monthly basis during 2017. LOCATE (Land Ocean CArbon TransfEr) is a multi-disciplinary project that undertakes coordinated sampling of the major rivers in Great Britain to establish how much carbon from soils is getting into rivers and estuaries and to determine what is happening to it. LOCATE is a multidisciplinary NERC project involving the National Oceanography Centre, the British Geological Survey, the Centre for Ecology and Hydrology and the Plymouth Marine Laboratory, with assistance from the University of Lancaster, University of Durham, University of Hull, the University of the Highlands and Islands and the Environment Agency. Full details about this dataset can be found at https://doi.org/10.5285/08223cdd-5e01-43ad-840d-15ff81e58acf

  • Data comprise soil organic carbon content from a simulation using the ECOSSE model; a pool-based carbon and nitrogen turnover model. Simulations were performed using input data from the Sunjia research farm in southeast China (Jianxi province). Data here is from simulations using the global version of the ECOSSE model, a package which applies the regular model spatially. Input data for the simulations were provided by the soil science department of the Chinese Academy of Sciences. Simulations were conducted in 2018. Full details about this dataset can be found at https://doi.org/10.5285/876fa724-c3d3-4091-8de2-8140b7c973eb

  • Data were collected in 2015, 2016 and 2017 to provide Digital Surface Models (DSM) for two sections of the South Saskatchewan River, Canada. DSMs were generated using aerial plane images with a 0.06m ground resolution, captured at a height of c. 1500 m from a fixed-wing aeroplane with an UltraCamXp sensor. DSMs were generated as part of NERC project NE/L00738X/1. DSMs were constructed using imagery obtained on four occasions (13th May 2015; 2nd Sept 2016; 8th June 2017; and 12th June 2017). The dataset consists of eight DSMs; one for each of the two river sections on each of the four dates. Full details about this dataset can be found at https://doi.org/10.5285/13695138-227f-4d85-9049-0a9cba9e1867

  • Data consists of gene expression estimates and encapsulation rates in wild caught Drosophila melanogaster larvae following exposure to different treatments. Treatments include injection with wasp homogenate, injection with oil and no injection. Also provided are functional enrichment categories for differentially expressed genes and library generation and read mapping metrics. The data were produced under the grant: NE/P00184X/1 Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/2998b066-6a35-4e4f-ae39-16838781856b

  • The data set comprises single channel seismic from the Sunda Strait, Indonesia. The data were acquired in 2019 to research the 1883, Krakatau volcanic eruption.

  • The datasets contains species presence and background points, and their associated environmental data for non-native common wall lizard (Podarcis muralis). These data are included for local and national scale modelling of likelihood of species presence, as used in the modelling software MaxEnt. The .asc files included are the raw spatial data of parameters (i.e., distance to nearest road) used in modelling at various local regions, from which SWD 'samples with data' were extracted. Outputs from the local MaxEnt models produced the .txt files included. These serve as landscape layer inputs (habitat suitability and movement cost layers) for modelling population growth and spatial spread in the Individual based modelling platform, RangeShifter. Subsequent outputs of projected population growth (number of individuals per landscape cell) and x/y coordinates for each cell, are presented in files with the prefix Pop.csv and avg.csv (averaged data over 50 replicate runs). Full details about this dataset can be found at https://doi.org/10.5285/8ae3f9ef-9a75-4237-afbd-e01abe02e75b

  • Scanned and annotated thin sections, in plane-polarised and cross-polarised light. Derivative statistical data for mineral grainsize and spatial distribution.