This dataset is a characterisation of the soil and rocks and the potential bulking factor (likely excavated volume increases) at Formation (local to regional) level for Great Britain. The data is categorised into Class, characteristics of similar soils and rocks and Bulking Factor, range or ranges of % bulking. The excavation of rocks or soils is usually accompanied by a change in volume. This change in volume is referred to as ‘bulking’ and the measure of the change is the ‘bulking factor’. The bulking factor is used to estimate the likely excavated volumes that will need to be moved, stored on site, or removed from site. It is envisaged that the 'Engineering Properties: Bulking of soils and rocks' dataset will be of use to companies involved in the estimation of the volume of excavated material for civil engineering operations. These operations may include, but are not limited to, resource estimation, transportation, storage, disposal and the use of excavated materials as engineered fill. It forms part of the DiGMap Plus dataset series of GIS layers which describe the engineering properties of materials from the base of pedological soil down to c. 3m depth (ie the uppermost c.2m of geology). These deposits display a variable degree of weathering, but still exhibit core engineering characteristics relating to their lithologies.

Data are presented showing change in saltmarsh extent along 25 estuaries/embayments in six regions across Great Britain, between 1846 and 2016. Data were captured from maps and aerial photographs. Marsh extent was delineated a scale of 1:7,500 by placing vertices every 5 m along the marsh edge. Error introduced from: (i) inaccuracies in the basemap used to georeference maps and aerial photographs; (ii) the georeferencing procedure itself; (iii) the interpreter when placing vertices on the marsh edge; and (iv) map and photo distortions that occurred prior to digitisation were calculated and used to estimate the root mean square error (RMSE) in areal extent of each marsh complex. Measures of marsh extent were only recorded if maps and aerial photographs were available for the entire estuary/embayment. Data was collected as part of a study on the large-scale, long-term trends and causes of lateral saltmarsh change. The data was used in the analysis for Ladd et al. (2019). C. Ladd and M.F. Duggan-Edwards carried out the collection and processing of the saltmarsh extent data. All authors contributed to the interpretation of the data. The work was carried out under the NERC programme - Carbon Storage in Intertidal Environment (C-SIDE), NERC grant reference NE/R010846/1. Full details about this dataset can be found at https://doi.org/10.5285/03b62fd0-41e2-4355-9a06-1697117f0717

This dataset provides stream networks for three river basins in eastern Sri Lanka (Mundeni Aru, Maduru Oya and Miyangolla Ela). The stream networks were developed for use in hydrologic modelling and are provided as shapefiles. The work was supported by the Natural Environment Research Council (Grant NE/S005838/1). Full details about this dataset can be found at https://doi.org/10.5285/0537af26-5cab-4381-aca0-d997db421111

This dataset consists of change data for areas of Broad Habitats across Great Britain between 1990 and 1998. The data are national estimates generated by analysing the sample data from up to 569 1km squares and scaling up to a national level. The data are summarized as percentage increase or decrease in habitat area per Land Class (areas of similar environmental characteristics) and are in a vector format. The sample sites are chosen from a stratified random sample, based on a 15 by 15 km grid of GB and using the 'ITE Land Classification' as a method of stratification. The data were collected as part of Countryside Survey, a unique study or 'audit' of the natural resources of the UK's countryside. The Survey has been carried out at regular intervals since 1978 by the Centre for Ecology & Hydrology. The countryside is sampled and surveyed using rigorous scientific methods, allowing us to compare new results with those from previous surveys. In this way we can detect the gradual and subtle changes that occur in the UK's countryside over time. Surveys have been carried out in 1978, 1984, 1990, 1998 and 2007 with repeated visits to the majority of squares. In addition to habitat areas, vegetation species data, soil data, linear habitat data, and freshwater habitat data are also gathered by Countryside Survey. Full details about this dataset can be found at https://doi.org/10.5285/2bfdede9-8008-4ba3-ac8e-af4e6ab9888b

Erosion risk mapping showing river channel concentrations modelled using SCIMAP for the Yorkshire River Derwent, UK. Scenario mapping has been carried out and the dataset includes the following scenarios to assess variation in model output: 1) traditional land use map; 2) satellite derived land use maps; 3) long term rainfall averages; 4) integrating the artificial drainage network and 5) incorporating future climate change. Full details about this dataset can be found at https://doi.org/10.5285/331dd8ca-a4ff-40e6-b753-1b68468d8996

This dataset consists of a vector layer (based on 1 by 1° grid), of modelled daily surface nitrogen dioxide (NO2, ug m-3). A seasonal average value per grid cell was calculated for the grassland growing season (mid-April to mid-July), for the USA and UK, in 2018. Full details about this dataset can be found at https://doi.org/10.5285/d2524c77-c0b6-4228-a743-ec6f16623d80

These spatial layers contain the predicted occurrence and abundance of three heathland shrubs, Arctostaphylos uva-ursi, Vaccinium myrtillus and Vaccinium vitis-idaea identified as susceptible host species for Phytophthora ramorum and Phytophthora kernoviae in Scotland. The distribution models were developed from quadrat vegetation data kindly provided by Scottish Natural Heritage combined with data on climate and soil conditions as well as deer abundance and were fitted using a Bayesian Generalised Mixed Modelling approach adapted for input data on the DOMIN scale. This research was funded by the Scottish Government under research contract CR/2008/55, 'Study of the epidemiology of Phytophthora ramorum and Phytophthora kernoviae in managed gardens and heathlands in Scotland' and involved collaborators from St Andrews University, Science and Advice for Scottish Agriculture (SASA), Scottish Natural Heritage (SNH), Forestry Commission, the Food and Environment Research Agency (FERA) and the Centre for Ecology & Hydrology (CEH). Full details about this dataset can be found at https://doi.org/10.5285/5749df3d-000c-445e-a37f-dc0763b4d5ec

This dataset consists of a vector layer (based on 1 by 1° grid), of modelled surface ozone concentrations (ppb). The values per cell are daily mean surface ozone for the period 6am – 6pm. The seasonal average has been calculated for the grassland growing season, for the period spanning mid-April to mid-July, for the UK and the USA, for 2018. Full details about this dataset can be found at https://doi.org/10.5285/4b0871a9-196a-48e1-a0c8-c5f53e17e9a7

This dataset is a model output from the European Monitoring and Evaluation Programme (EMEP) model applied to the UK (EMEP4UK) driven by Weather and Research Forecast model meteorology (WRF). It provides annual averages of vegetation specific atmospheric deposition of oxidised sulphur, oxidised nitrogen, and reduced nitrogen on a 1x1 km2 grid for the year 2018. The EMEP4UK model version used here is rv4.36, and the WRF model version is the 4.1.1. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/2adc10bf-e6f4-4e8d-b268-ee5d58d31c50

This dataset models positive plant habitat condition indicators across Great Britain (GB). This data provides a metric of plant diversity weighted by the species that you would expect and desire to have in a particular habitat type so indicates habitat condition. In each Countryside Survey 2007 area vegetation plot the number of positive plant habitat indicators (taken from a list created from Common Standards Monitoring Guidance and consultation with the Botanical society of the British Isles (BSBI)) for the habitat type in which the plot is located are counted. This count is then divided by the possible indicators for that habitat type (and multiplied by 100) to get a percentage value. This is extrapolated to 1km squares across GB using a generalised additive mixed model. Co-variables used in the model are Broad Habitat (the dominant broad habitat of the 1km square), air temperature, nitrogen deposition, sulphur deposition, precipitation and whether the plot is located in a Site of Special Scientific Interest (SSSI) (presence or absence data). Full details about this dataset can be found at https://doi.org/10.5285/cc5ae9b1-43a0-475e-9157-a9b7fccb24e7