[This dataset is embargoed until March 1, 2026]. Vitamin D (25(OH)D3 and 1,25(OH)D3) concentrations of incrementally-sampled preserved human hair from non-mortuary contexts at the Yup’ik archaeological site of Nunalleq Alaska (~1650 AD). Multi-isotope isotope data (carbon, nitrogen, sulphur, oxygen and hydrogen) for the same samples are found in accompanying files. Full details about this dataset can be found at https://doi.org/10.5285/8d5af777-384c-4114-af6b-69cdac239810

This dataset includes the transcript of discussion group activities on Human Wildlife conflict, conducted with ten rural communities in Marrupa District, Niassa (Northern Mozambique). It also comprises the results of semi-structured interviews conducted individually in three of the ten selected communities. The ten villages were selected from a forest cover gradient running from villages with a higher forest cover to those within degraded forest areas and consequently low cover. The villages had similar infrastructure, soils, rainfall, and vegetation types. The dataset contains information on the occurrence of conflict with both vertebrate and invertebrate wild species, mitigation strategies, conflict seasonality and trends, but also its impact on agricultural production and livestock rearing. The discussion groups were conducted with six to ten people and the presence of the leader of each village, between May and July 2015. Data were collected as part of a project funded under the Ecosystem Services for Poverty Alleviation (ESPA) programme. Full details about this dataset can be found at https://doi.org/10.5285/7bd2e230-c219-4017-9914-b5cfd83a4eae

This dataset contains responses to a set of evaluation questions on flood resilience improvement within communities in the Katakwi District, Uganda. This data were created as part of the NIMFRU project (National-Scale Impact Based Forecasting of Flood Risk in Uganda) and consists of 21 semi-structured interviews. These have been completed by community members from the project target communities of Anyangabella, Agule and Kaikamosing which are all found in the Katakwi district. Five of the interviews were completed by local district officers. The data were collected in December 2020. These data were collected to understand how communities resilience had changed as a result of the NIMFRU project. Full details about this dataset can be found at https://doi.org/10.5285/d5043ca4-5451-42f1-ae38-69e084bfad80

Results of a survey undertaken in 2018 involving a range of open and closed questions intended to elicit local residents’ values they attach to the importance of coastal attributes and their perceptions of various tidal and wave energy development characteristics. Three case study sites were selected: Weston-super-Mare, Minehead, and the Taw-Torridge Estuary, South-West UK. Full details about this dataset can be found at https://doi.org/10.5285/e5190fd0-2995-42aa-aca0-80714abde768

This dataset contains the gridded estimates per 1 km2 for mean and median ensemble outputs from 4-6 individual ecosystem service models for Sub-Saharan Africa, for above ground Carbon stock, firewood use, charcoal use and grazing use. Water use and supply are identically supplied as polygons. Individual model outputs are taken from previously published research. Making ensembles results in a smoothing effect whereby the individual model uncertainties are cancelled out and a signal of interest is more likely to emerge. Included ecosystem service models were: InVEST, Co$ting Nature, WaterWorld, Monetary value benefits transfer, LPJ-GUESS and Scholes models. Ensemble outputs have been normalised, therefore these ensembles project relative levels of service across the full area and can be used, for example, for optimisation or assignment of most important or sensitive areas. The work was completed under the "EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models" project (NE/T00391X/1) funded by the UKRI Landscape Decisions programme. Full details about this dataset can be found at https://doi.org/10.5285/11689000-f791-4fdb-8e12-08a7d87ad75f

This dataset contains a digital urban scenario, named Tomorrowville, that is developed as a testbed for multi-hazard risk assessments and to evaluate the performance of urbanisation scenarios. Tomorrowville was created to represent a global-south urban setting by means of its socio-economic and physical aspects. It covers an area of 500ha located south of Kathmandu (Nepal). The dataset consists of 5 different data types: - Buildings: Data representing the building footprints for today and 50 years from now including specific attributes to be used within multi-hazard risk assessments. - Land uses: Data representing the land use information for today and 50 years from now. - Vulnerability: Tabular files that contain vulnerability functions for buildings under earthquake and flood hazards. - Household: Data that contains social attributes of the Tomorrowville, such as the level of education, age, gender and working status of the individuals and their states in the households. - Hazards: Data representing the hazards (earthquake (eq), floods (fl) and debris flows (df) that may impact the case study areas of Tomorrowville. Observational data of the built environment and socio-economical properties of Kathmandu and Nairobi were used in addition to synthetic social data to create the initial scenario. This is a synthetic social dataset, meaning it was derived from existing population projections and distributions for the testbed but does not reflect the reality on the ground. It is synthetically created using specific algorithms in a GIS environment to represent a Global South social context. For the building data, Open Street Map (OSM) database is used as a basis. The data is scraped from OSM and modified to represent an urban context for Tomorrowville. The attributes are also modified to be able to use in a multi-hazard risk computation. A taxonomy string is generated for each building that represents an acronym for its building code level, number of storeys, occupation type and structural system. The hazards that were existing in the selected spatial extent were earthquake, flood, and debris flow. Hazard data represents an intensity measure for the relevant hazard type (ground acceleration for earthquake, flow velocity for the flood and debris flow hazards). The following hazard input data are included: - For the flood simulations, the discharge and rainfall time series are generated based on moderate to peak daily data based on recorded data from the Department of Hydrology and Meteorology, Nepal. - Earthquake hazard sources are generated and simulated by Jenkins et al. (2023). - For the debris-flow and flood simulations tri-stereo Pleiades satellite imagery is used to produce a 2m resolution Digital Elevation Model. The work to create this dataset 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/10.5285/8b5834a5-ae8a-4f24-836c-16fab961aeb3

This dataset was constructed to understand the perceptions of respondents about pine tree invasion in three communes in central-southern Chile: Santa Juana, Constitución and Tucapel. In addition, the factors that influence the perception of the species and the interest of each community to participate in community control strategies were identified. Face-to-face interviews were conducted in two communities affected by megafires (Santa Juana and Constitución) and one community not affected by such an event (Tucapel), in order to check if there are differences in the willingness of the respondents. The variables evaluated include: (a) demographic data; with information on location, gender, education, age, economic activities and sectors of the respondents; (b) beliefs; whether they think that alien species damage the ecosystem; benefit people; and whether they think that the pine tree harms the traditions of the community; (c) what uses they give to wild pine trees; as fuel, construction material, economic, recreational and cultural purposes; (d) relationship between pine trees and forest fires; if they think that wild pine favours intense and frequent fires, if all vegetation has the same fire risk, and if they think that pine trees can grow back easily after fires, and (e) responsibilities associated with management; if they have ever controlled wild pine in their sector, personal, community, business and government responsibilities associated with management, and how likely they are to participate in strategies to control wild pine. Data were collected between November 2023 and January 2024. Full details about this dataset can be found at https://doi.org/10.5285/63e72aa5-6ea3-4e9f-93fa-311605d3d290

[THIS DATASET HAS BEEN WITHDRAWN]. This dataset contains gridded population with a spatial resolution of 1 km x 1 km for the UK based on Census 2011 and Land Cover Map 2007 input data. Data on population distribution for the United Kingdom is available from statistical offices in England, Wales, Northern Ireland and Scotland and provided to the public e.g. via the Office for National Statistics (ONS). Population data is typically provided in tabular form or, based on a range of different geographical units, in file types for geographical information systems (GIS), for instance as ESRI Shapefiles. The geographical units reflect administrative boundaries at different levels of detail, from Devolved Administration to Output Areas (OA), wards or intermediate geographies . While the presentation of data on the level of these geographical units is useful for statistical purposes, accounting for spatial variability for instance of environmental determinants of public health requires a more spatially homogeneous population distribution. For this purpose, the dataset presented here combines 2011 UK Census population data on Output Area level with Land Cover Map 2007 land-use classes 'urban' and 'suburban' to create a consistent and comprehensive gridded population data product at 1 km x 1 km spatial resolution. The mapping product is based on British National Grid (OSGB36 datum). Full details about this dataset can be found at https://doi.org/10.5285/61f10c74-8c2c-4637-a274-5fa9b2e5ce44

This dataset contains gridded human population with a spatial resolution of 1 km x 1 km for the UK based on Census 2021 (Census 2022 for Scotland) and Land Cover Map 2021 input data. Data on population distribution for the United Kingdom is available from statistical offices in England, Wales, Northern Ireland and Scotland and provided to the public e.g. via the Office for National Statistics (ONS). Population data is typically provided in tabular form or, based on a range of different geographical units, in file types for geographical information systems (GIS), for instance as ESRI Shapefiles. The geographical units reflect administrative boundaries at different levels of detail, from Devolved Administration to Output Areas (OA), wards or intermediate geographies. While the presentation of data on the level of these geographical units is useful for statistical purposes, accounting for spatial variability for instance of environmental determinants of public health requires a more spatially homogeneous population distribution. For this purpose, the dataset presented here combines 2021/2022 UK Census population data on Output Area level with Land Cover Map 2021 land-use classes 'urban' and 'suburban' to create a consistent and comprehensive gridded population data product at 1 km x 1 km spatial resolution. The mapping product is based on British National Grid (OSGB36 datum). Full details about this dataset can be found at https://doi.org/10.5285/7beefde9-c520-4ddf-897a-0167e8918595

This dataset contains gridded human population with a spatial resolution of 1 km x 1 km for the UK based on Census 2011 and Land Cover Map 2015 input data. Data on population distribution for the United Kingdom is available from statistical offices in England, Wales, Northern Ireland and Scotland and provided to the public e.g. via the Office for National Statistics (ONS). Population data is typically provided in tabular form or, based on a range of different geographical units, in file types for geographical information systems (GIS), for instance as ESRI Shapefiles. The geographical units reflect administrative boundaries at different levels of detail, from Devolved Administration to Output Areas (OA), wards or intermediate geographies. While the presentation of data on the level of these geographical units is useful for statistical purposes, accounting for spatial variability for instance of environmental determinants of public health requires a more spatially homogeneous population distribution. For this purpose, the dataset presented here combines 2011 UK Census population data on Output Area level with Land Cover Map 2015 land-use classes 'urban' and 'suburban' to create a consistent and comprehensive gridded population data product at 1 km x 1 km spatial resolution. The mapping product is based on British National Grid (OSGB36 datum). Full details about this dataset can be found at https://doi.org/10.5285/0995e94d-6d42-40c1-8ed4-5090d82471e1