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 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 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

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

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 dataset includes synthetically produced data from 10 different cities (Istanbul, Nablus, Chattogram, Cox’s Bazaar, Nairobi, Nakuru, Quito, Kokhana, Rapti and Darussalam) for a future urban context. The data includes physical elements in a city such as buildings, roads, and power networks, as well as social elements such as households and individuals. The dataset contains a maximum of 9 different data types, described below. For some cities power and road network data were not considered due to context specific priorities. landuse: The land use plan data depicting how the land will be zoned and used in the next fifty years within the area or interest. The attributes include the land use type, areal coverage in hectares, maximum population density and existing population. building: Data representing the building footprints that will emerge as a result of the future exposure generation procedure. It includes the attributes of the building such as its identifier number, construction type, number of floors, footprint area, occupation type and construction code level. road nodes: Data representing the points where road segments (edges) are connected to each other, including the identifier number for each node. road edges: Data representing the road segments, including the ID numbers of the starting and ending point (node). power nodes: Data representing the points where power lines (edges) are connected to each other, including the identifier number for each node. power edges: Data representing the power segments, including the including the ID numbers of the starting and ending point (node). household: Data that contains social attributes of a household living in a building. The attributes include number of individuals, income level and commonly used facility ID (such as hospital). individual: Data that contains the attributes of the individuals that are a part of a household. The attributes are age, gender, school ID (if relevant), workplace ID (if relevant) and last attained education level. Distribution table: The future projections for each city that identifies the socio-demographic changes and expected physical development in the next 50 years. The data can be used in geospatial platforms. The nomenclature for the data is as follows: “CitynameFutureExposureDataset/Cityname_CommunityCode_DataType”. This dataset was created as case studies for the Tomorrows Cities: Tomorrowville virtual testbed. It is supported by NERC as part of the GCRF Urban Disaster Risk Hub (NE/S009000/1). Full details about this dataset can be found at https://doi.org/10.5285/cdfea06f-d47c-4967-99d4-cc71bddea45d

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

The dataset comprises two elements: 1) logs of nine sedimentary profiles recorded in three locations in southern Iceland and 2) geochemical analyses of tephra samples taken from these profiles. The three locations were Heimaey (V), an island in the Vestmannaeyjar archipelago (four profiles); Seljaland (S) in southern Iceland (two profiles), and Húshólmi (H) on the Reykjanes peninsula (three profiles). All three locations are associated with the earliest phases of the settlement of Iceland by the Norse in the ninth century CE. The datasets were collected to establish a tephrochronological framework for the three sites. The logs are based on field observations made by Prof. Andrew Dugmore (University of Edinburgh) and Dr Richard Streeter (University of St Andrews) in June 2023. The geochemical analyses were carried out by Dr Streeter and conducted using an Electron Probe Micro Analyser (EPMA). Full details about this dataset can be found at https://doi.org/10.5285/dd570900-245c-4586-82b7-e548cbdc4ac5