The data comprises river section, zone and test site delineation, winter Season average NDVI by section and zone 1989-2020, land cover maps seasonally 1989-2020, and derived land cover fractions by section and zone 1989-2020. The data was produced as part of a study to determine how changes in geomorphic form and dynamics due to human alteration to river flows and riparian land management relate to changes in vegetation communities in the Sutlej and Beas Rivers, India. Vegetated and other land cover, including water area, were quantified by winter season NDVI trends (in the plains of Punjab) and seasonal supervised classification of Landsat data for over a 30-year period. The work was supported by the Natural Environment Research Council (Grant NE/S01232X/1). Full details about this dataset can be found at https://doi.org/10.5285/9a96e199-34d0-46f9-9a64-140d300a2531

Two scripts for classifying remotely sensed data used to produce maps of peatland distribution and predicted peat thickness, using random forest classification and regression. Written in JavaScript for use with Google Earth Engine. These are versions of the scripts used in Hastie et al. (2022), https://doi.org/10.1038/s41561-022-00923-4. Users should also cite Rodríguez-Veiga et al. (2020), https://doi.org/10.3390/rs12152380 . Full details about this application can be found at https://doi.org/10.5285/e337de58-df5e-4412-8aef-28875870f965

This dataset provides input files for LAMMPS open access molecular dynamics software ( https://www.lammps.org/ ) and contains simulation details, force field parameters, and topology information for polymer crystallisation at a surface that will enable a researcher to replicate the molecular dynamics simulations. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/25fc1140-07bf-424a-a32c-87dbba9c426a

Measurements of sediment properties (incl. organic and carbonate content), radionuclides (210Pb, 137Cs, 241Am) and elements (including mercury, nickel, copper, zinc, and lead) in lake sediment successions. Radionuclide dating provides a reliable chronology of sediment ages from the mid-19th century (sometimes only 20th century) to the present (2016). The dataset comprises a standardised matrix of multiple measured sediment variables (element values per mass) against stratigraphic depth for 8 lakes. In these water bodies multiple core datasets exist, one collected from the littoral zone, one of intermediate depth and one from the deepest area. The deepest core was used for 210Pb dating. The intermediate and littoral depth cores are not dated, except at Esthwaite where the littoral core (29328_ESTH_LITT.csv) had been previously collected, 210Pb dated and measured for organic and carbonate content. Full details about this dataset can be found at https://doi.org/10.5285/87dec506-ca7f-4b57-a605-486ec9d8cca2

This R application is an implementation of state tagging approach for improved quality assurance of environmental data. The application returns state-dependent prediction intervals on input data. The states are determined based on clustering of auxiliary inputs (such as meteorological data) made on the same day. The method provides contextual information to assess the quality of observational data and is applicable to any point-based, daily time series observational data. To use this application, the user will need to input two separate csv files: one for state variables and the other for observations. 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 application can be found at https://doi.org/10.5285/1de712d3-081e-4b44-b880-b6a1ebf9fcd8

This dataset contains the results of a laboratory study investigating the dissolution of UO3•nH2O particles in dynamic sediment/groundwater column systems, representative of the shallow subsurface at the Sellafield Ltd. site, UK. Measurements were carried out to determine the extent of uranic particle dissolution and the speciation of dissolved uranium within the columns under contrasting biogeochemical conditions (oxic and electron-donor amended). Columns effluents were analysed periodically for key biogeochemical indicators (nitrate, sulfate) and trace metals (iron, manganese, uranium) and systems were sacrificed after 6 and 12 months of groundwater flow. Upon sacrifice, columns were cross-sectioned, and the sediment structure preserved for synchrotron micro-focus X-ray Fluorescence (XRF) mapping, and uranium L-edge X-ray Absorption Spectroscopy (XAS) measurements. Sub-samples of column sediments were also analysed for acid extractable metals, microbial abundance and classification and bioavailable Fe(II) concentrations. Experiments were performed between March 2016 and March 2017. Subsequent analyses were performed between March 2017 and December 2018. This data was collected as part of the project: Understanding radioactive ‘hot’ particle evolution in the environment funded by the UK Natural Environment Research Council (grant NE/M014088/1). Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/2702e1b0-13df-4ae4-9f91-4ac4bd07bbf1

This data set provides a spatial stratification of forest cover into discrete vegetation classes according to the High Carbon Stock (HCS) Approach. The data set covers the Stability of Altered Forest Ecosystems (SAFE) project site located in Sabah, Malaysian Borneo. Data were collected in 2015 during a project which was included in the NERC Human-modified tropical forest (HMTF) programme. Full details about this dataset can be found at https://doi.org/10.5285/81cad1ef-b5cc-4592-a71f-204a5d04b700

This dataset presents modelled estimates of soil carbon concentration (g kg-1) at 1km2 resolution across Great Britain. A Generalized Additive Model approach was used with Countryside Survey soil carbon data from 2007 and including climate, atmospheric deposition, habitat, soil and spatial predictors. The model is based on soil carbon data from 2446 locations across Great Britain and is representative of 0-15 cm soil depth. Loss-on-ignition (LOI) was determined by combustion of 10g dry soil at 375 degrees Celsius for 16 hours; carbon concentration was estimated by multiplying LOI by a factor of 0.55. The Countryside Survey looks at a range of physical, chemical and biological properties of the topsoil from a representative sample of habitats across the UK. 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/10.5285/3aaa52d3-918a-4f95-b065-32f33e45d4f6

[THIS DATASET HAS BEEN WITHDRAWN]. This dataset consists of measures of topsoil (0-15cm) physico-chemical properties from soils sampled from 49 x 1km squares across Great Britain in 2020 as part of a rolling soil and vegetation monitoring program of 500 1km squares repeated every 5 years. The properties included are: soil organic matter (loss on ignition (LOI)), derived carbon concentration, total soil organic carbon (SOC), nitrogen, Olsen-phosphorous, pH, electrical conductivity, soil bulk density of fine earth and fine earth volumetric water content. The UKCEH Countryside Survey is a unique study or 'audit' of the natural resources of the UK's countryside. The sample sites are chosen from a stratified random sample, based on a 15 by 15 km grid of GB. Surveys have been carried out in 1978, 1984, 1990, 1998 and 2007 by the UK Centre for Ecology & Hydrology (UKCEH) and predecessors, with repeated visits to the majority of squares. 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. In addition to soil data, vegetation species data are also gathered by the current phase of the Countryside Survey. 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/cc2aa8f3-95cb-4b85-b883-8ac26e69bdbe

This dataset presents modelled estimates of soil nitrogen concentration (% dry weight soil) at 1km2 resolution across Great Britain. A Generalized Additive Model approach was used with Countryside Survey soil nitrogen data from 2007 and including climate, atmospheric deposition, habitat, soil and spatial predictors. The model is based on soil nitrogen data from 913 locations across Great Britain and is representative of 0-15 cm soil depth. Soil N concentration was determined using a total elemental analyser. The Countryside Survey looks at a range of physical, chemical and biological properties of the topsoil from a representative sample of habitats across the UK. 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/10.5285/8ec2d5ae-5d19-4b58-8cf6-aafdad485bb2