Type of resources
Contact for the resource
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
[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
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.
Scanned and annotated thin sections, in plane-polarised and cross-polarised light. Derivative statistical data for mineral grainsize and spatial distribution. Younger Giant Dyke, Tugtutoq, South Greenland.