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society

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  • 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

  • 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

  • This is a theoretical model of leadership in warfare by exploitative individuals who reap the benefits of conflict while avoiding the costs. In this model we extend the classic hawk-dove model to consider pairwise interactions between groups in which a randomly chosen leader decides whether the group will collectively adopt aggressive or peaceful tactics. We allow for unequal sharing of fitness payoffs among group members such that the leader can obtain either a larger share of the benefits, or pay a reduced share of costs, from fighting compared to their followers. Our model shows that leadership of this kind can explain the evolution of severe collective violence in certain animal societies. Full details about this application can be found at https://doi.org/10.5285/7aab999e-cef9-41c2-8400-63f10af798ec

  • 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

  • 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

  • Point locations of Antarctic historic sites and monuments. Data compiled using ''''Revised List of Historic Sites and Monuments Measure 9 (2016) Annex'' pdf from Antarctic Treaty Secretariat (https://www.ats.aq/e/protected.html), for adding to Antarctic Digital Database (ADD) visualisation, by the Mapping and Geographic Information Centre, British Antarctic Survey.

  • [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

  • Surveys of wellbeing, nature connectedness and pro-nature conservation behaviour scores from adult human participants before and after taking part in nature-based activities, including citizen science, in 2020 are presented. Participants were recruited via a public campaign and were randomly allocated into groups: citizen science, noticing nature (three good things in nature activity), combined citizen science and three good things in nature, and a wait list control. They were invited to take part in activities up to five times in the following eight days. Online surveys of wellbeing and nature connectedness were undertaken at people’s sign up to the project and after the eight days of activities. Demographic characteristics and people’s engagement with the project and responses to the pathways to nature connectedness were recorded after the eight days of activities. The research was carried out to investigate concern about the negative impacts of COVID-19 movement restrictions and social distancing on people's wellbeing and mental health. Research was funded through NERC grant NE/V009656/1 - COVID 19 - Does nature-based citizen science enhance well-being and mitigate negative effects of social isolation? Full details about this dataset can be found at https://doi.org/10.5285/56d4b055-c66b-42b9-8962-a47dfcf3b8b0

  • 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 nitrogen data from nitrate, ammonium and nitrite, total nitrogen and carbon data, and elemental composition data from anaerobic digestate and biomass ash from UK bioenergy production. Anaerobic digestate was sampled 8 times from different industrial scale plants across the UK between January 2015 and January 2018 and biomass ash was sampled in January 2015 and June 2016. Anaerobic digestate was sourced from segregated food waste (mainly household waste), pig slurry, maize silage, vegetables waste, sweet corn waste, aerobically treated food waste, food manufacturer waste and other biodegradable sludge from within the UK. Biomass ash, both fly and bottom ash, from virgin and recycled wood was sourced from three sites within the UK and one from Spain. All laboratory analyses were undertaken at Lancaster University using standardised methods. The data were collected as part of the research grant, Developing a suite of novel land conditioners and plant fertilizers from the waste streams of biomass energy generation. The research was funded by NERC, award NE/L014122/1. Full details about this dataset can be found at https://doi.org/10.5285/990c54f6-5c92-4054-8bfa-953533a89149