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University of Surrey

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  • This dataset consists of tick sampling and microclimate data from Exmoor, Richmond and New Forest study sites; as well as ARCGIS risk maps that model tick abundance driven by climate surfaces and host abundance. Tick sampling data (91 files, each representing a day of sampling) indicate tick abundance (distinguishing larvae, nymphs, adult males and adult females), vegetation height, soil moisture, temperature and relative humidity. Static risk map files indicate modeled tick abundance: 251 landcover files for the three sites, as well as 36 ArcView map files. The study is part of the NERC Rural Economy and Land Use (RELU) programme. Many people take pleasure from activities in forests and wild lands in the UK and others are being encouraged to participate. Unfortunately, there are risks and one of the most insidious is the possibility (albeit tiny) of acquiring a disease from wild animals; for example, ticks can be vectors of the bacterial infection leading to Lyme Disease. Both diagnosis and treatment can be problematic so prevention of acquiring such disease is highly desirable. Surprisingly little is known about how best to warn countryside users about the potential for disease without scaring them away or spoiling their enjoyment. Answering such questions was the goal of this project, and required the integration of a diverse set of scientific skills, and an understanding of the views of those who manage countryside, those who have contracted zoonotic diseases and those who access the land. This project combined knowledge from three strands of work, namely risk assessment, risk perception and communication, and scenario analysis. The study sites were selected to provide a range of environmental conditions and countryside use. Peri-urban parkland, accessible lowland forest and heath and remote upland forest were chosen as represented by Richmond Park on the fringe of Greater London, the New Forest in Southern England, and Exmoor in South West England. The following additional data from this same research project are available at the UK Data Archive under study number 6892 (see online resources): Lyme disease risk perception data resulting from tick imagery vignette experiments, Lyme disease patient interviews and surveys, residents and countryside staff focus groups, forest manager interviews, and multiple scoring procedures of animal social representation; as well as Lyme and tick risk communication data resulting from interviews with organisations and content analysis of risk warning information leaflets, Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

  • Universities Weather Research Network (UWERN) Urban Meteorology Programme (URBMET) was a Natural Environment Research Council (NERC) Urban Regeneration and the Environment (URGENT) Air project (GST/02/2231 - Duration: 1/01/1999 - 30/6/2002) led by Dr Stephen Belcher, University of Reading. This dataset contains wind tunnel model data from the University of Surrey.

  • This dataset contains free residual chlorine, turbidity, nitrate, chloride, sulphate, fluoride, phosphate and thermatolerant coliform concentrations in groundwater from a variety of sources within two neighbourhoods of Kisumu, Kenya. A total of 73 groundwater sources were tested between February and March 2014. The data were collected as part of the Groundwater2030 project, which aims to reduce the health problems that result from consumption of contaminated groundwater in urban areas of Africa. The project was co-ordinated by the University of Southampton, with partners at the University of Surrey, the Victoria Institute of Research on Environment and Development (VIRED) International, and the Jaramogi Oginga Odinga University of Science and Technology. The project was funded by the Natural Environment Research Council and the Department for International Development as part of the Unlocking the Potential of Groundwater for the Poor (UPGro) programme. Full details about this dataset can be found at https://doi.org/10.5285/4062e6d9-2e90-4775-87f1-179dea283ef1

  • This datasets contains the anonymised results of a survey of well owners in Kisumu, Kenya. Data includes information on the amount of water abstracted daily from the well and ways in which this water was used and handled, information on other sources of water (e.g. piped utility water and rainwater) and how this is used, and the assets and services that the well owner has access to. Answers from questions to assess food poverty are also included. The surveys were carried out during February and March 2014 and include data from 51 well owners. The data were collected as part of the Groundwater2030 project, which aims to reduce the health problems that result from consumption of contaminated groundwater in urban areas of Africa. The project was co-ordinated by the University of Southampton, with partners at the University of Surrey, the Victoria Institute of Research on Environment and Development (VIRED) International, and the Jaramogi Oginga Odinga University of Science and Technology. The project was funded by the Natural Environment Research Council and the Department for International Development as part of the Unlocking the Potential of Groundwater for the Poor (UPGro) programme. Full details about this dataset can be found at https://doi.org/10.5285/4ca855a3-752c-4492-8e26-3438652dd35c

  • This dataset contains the anonymised results of a survey of customers who buy groundwater for consumption in Kisumu, Kenya. Data includes information on the amount of water bought and ways in which this water was used and handled, as well as their use of water from other sources. Data about assets and services, including access to food, are also included. The surveys were carried out during February and March 2014 and include data from 137 well customers. The data were collected as part of the Groundwater2030 project, which aims to reduce the health problems that result from consumption of contaminated groundwater in urban areas of Africa. The project was co-ordinated by the University of Southampton, with partners at the University of Surrey, the Victoria Institute of Research on Environment and Development (VIRED) International, and the Jaramogi Oginga Odinga University of Science and Technology. The project was funded by the Natural Environment Research Council and the Department for International Development as part of the Unlocking the Potential of Groundwater for the Poor (UPGro) programme. Full details about this dataset can be found at https://doi.org/10.5285/6f3f1d06-4e6b-435e-a770-af7549993b88

  • This dataset contains the results of a sanitary risk inspection for different groundwater sources in Kisumu, Kenya. A total of 70 groundwater sources were surveyed between February and March 2014. The survey took the form of an observation checklist that identified contamination hazards at well heads and in their immediate surroundings. Data on well depth, electro-conductivity, pH and temperature were also collected. The data were collected as part of the Groundwater2030 project, which aims to reduce the health problems that result from consumption of contaminated groundwater in urban areas of Africa. The project was co-ordinated by the University of Southampton, with partners at the University of Surrey, the Victoria Institute of Research on Environment and Development (VIRED) International, and the Jaramogi Oginga Odinga University of Science and Technology. The project was funded by the Natural Environment Research Council and the Department for International Development as part of the Unlocking the Potential of Groundwater for the Poor (UPGro) programme. Full details about this dataset can be found at https://doi.org/10.5285/bc1a979b-7cc9-4c9d-9fe4-a8510cd62f8e