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.

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[THIS DATASET HAS BEEN WITHDRAWN].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 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

The dataset contains concentrations of total soil organic carbon, soil carbon fractions, soil CO2 fluxes, soil temperature and moisture in the Peruvian Andes. Measurements and sampling took place between 2010 and 2013. Data were generated as part of a larger NERC project: 'Are tropical uplands regional hotspots for methane and nitrous oxide' Full details about this dataset can be found at https://doi.org/10.5285/3813aef3-71cc-49e6-ba21-495a43363001

These data are GIS shapefiles which contain geospatial information describing the location and condition of bridges, buildings and roads in Chamoli District, Uttarakhand, India, following the 7th February 2021 avalanche and debris flow hazard cascade (the so-called ‘Chamoli event’). The dataset also contains a GIS shapefile which contains polygon outlines supporting geomorphological analysis of change in river valleys between the avalanche source and the town of Joshimath. The latter is designed to be used in conjunction with the other data resources contained in this data collection. Full details about this dataset can be found at https://doi.org/10.5285/a763e254-c249-4934-b0fb-c3b808b37db6

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

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

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