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

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

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

This data set includes a range of physico-chemical properties measured from topsoil within a wide range of land use types across Wales, collected as part of the Glastir Monitoring and Evaluation Programme (GMEP). The properties included are: soil organic matter (loss on ignition (LOI)), derived carbon concentration, total soil organic carbon (SOC), nitrogen, total soil phosphorous, Olsen-phosphorous (within improved land only), pH, electrical conductivity, soil bulk density of fine earth, fine earth volumetric water content when sampled and soil water repellency - water drop penetration time. The monitoring programme was set up by the Welsh Government in 2013 to monitor the effects of the Glastir agri-environment scheme on the environment and ran from 2013 to 2016. The field survey element was based on a stratified random sampling design of 300 x 1km square sites across Wales, and was managed by the Centre for Ecology & Hydrology. Full details about this dataset can be found at https://doi.org/10.5285/0fa51dc6-1537-4ad6-9d06-e476c137ed09

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

The data contains location and x,y,z accelerometer readings from trackers embedded into 23 boulders in the upper Bhote Koshi catchment, Nepal before the 2019 monsoon season. The data was transmitted in real time via a long-range wide-area network (LoRaWAN®) gateway to a server. The data presented cover the period May 2019 to October 2019. The data from this study was used to demonstrate how cost-effective technology can be used to monitor boulder movement in hazard-prone sites, and to show the potential for active sensors connected through a long-range wide-area network (LoRaWAN®) to be used in an early warning system in the future. Data was collected by the data authors. This was carried out as part the BOULDER: Accounting for BOUlders in Landslide-flood Disaster Evaluation and Resilience project, funded by the Natural Environment Research Council (NERC), Award reference NE/S005951/1 Full details about this dataset can be found at https://doi.org/10.5285/93518ac3-4ded-47fa-b260-38184c09dfc8

This is a spatial dataset containing polygons representing different geology types in the Moor House National Nature Reserve, northern Pennines, England. The survey was undertaken by G.A.L. Johnson under a grant by The Nature Conservancy in the 1950s and 1960s. Full details about this dataset can be found at https://doi.org/10.5285/0e3aefb2-ce86-4d09-8ff0-6d165dfd48db

This dataset contains baseline soil carbon and nitrogen content within a native forest creation site on the Beldorney Estate, Aberdeenshire, Scotland. 17 samples were collected on a 100 m grid at the site prior to planting. The 100 m grid was extended into adjacent grassland that won’t be planted and 8 additional samples were collected. The 100 m grid samples were all collected in September 2022. Within the planting area 17 plots were left unplanted, these will be used to track natural tree regeneration, and additional soil samples were collected here in November 2022. Soil carbon and nitrogen content will be tracked at the site as the planted and naturally regenerating trees establish. The work was supported by Natural Environment Research Council (NE/W004976/1) as part of the Agile Initiative at the Oxford Martin School and Leverhulme Trust as part of the Leverhulme Centre for Nature Recovery at the University of Oxford. Full details about this dataset can be found at https://doi.org/10.5285/75fc1418-b0ff-4dca-9b78-70c3c82d94b7

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

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