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This data is NERC-funded but not held by the EIDC. This data is archived in the UK Data Service ReShare repository. The dataset contains data from a randomised controlled trial study which aimed to provide an evaluation of a smartphone app-based wellbeing intervention. The data comprise participant demographics and questionnaire responses supplied at the pre-, post- and follow-up phases of the study and geolocation data returned by the app as the participants entered areas designated as urban green spaces.
These data include results from serological analysis carried out on serum collected from randomly recruited subjects, merged with household and subject level data about the subjects. The subject and household data collected included occupation of the household head, size of the household, and occupation, gender and age of the subject. Samples were collected from 303 people based in irrigated areas, 728 people from pastoral areas and 81 people from riverine areas along River Tana in Tana River and Garissa counties, Kenya. Field surveys were implemented in December 2013 to February 2014 and laboratory analyses were completed in June 2015. Serum samples were harvested from blood samples obtained from randomly recruited subjects and screened for anti-RVF virus immunoglobulin G using inhibition ELISA (enzyme-linked immunosorbent assay) immunoassay. The household and subject metadata was collected using Open Data Kit (ODK) (https://opendatakit.org) loaded into smart phones. The aim of the project was to determine the risk of Rift Valley Fever virus exposure in people living in areas with different land use and socio-ecological settings. The data were collected by experienced researchers from the International Livestock Research Institute (Kenya), the Department of Disease Surveillance and Response, Kenyatta National Hospital This dataset is part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC). The research was funded by NERC project no NE/J001570/1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Additional funding was provided by the Consultative Group on International Agricultural Research (CGIAR) Research Program Agriculture for Nutrition and Health. Full details about this dataset can be found at https://doi.org/10.5285/8a668a4f-3526-4443-9e77-cea67f04ca19
The resource consists of genome sequence data for the Drosophila C virus that has been serially passaged through different species of Drosophila in the laboratory. The genomes were sequenced and aligned to the reference genome. The frequency of variants at both biallelic and triallelic sites was then calculated. We also generated a phylogeny of the species involved using published data. This data was generated to understand how viruses adapt to new host species by Francis Jiggins and his co workers. The work was carried out between July 2016 and September 2017 and was funded by NERC under award reference NE/L004232/1 Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/4434a27d-5288-4f2e-88ac-4b1372e4d073
This resource contains anonymised policy interviews on trypanosomiasis in Zambia from 2013 conducted by Catherine Grant (Institute of Development Studies) and Noreen Machila (University of Zambia, Department of Disease Control). These interviews explore the differing opinions of various stakeholders in relation to trypanosomiasis, a widespread and potentially fatal disease spread by tsetse flies which affects both humans and animals. It is an important time to examine this issue as human population growth and other factors have led to migration into new areas which are populated by tsetse flies and this may affect disease levels. This means that there is a greater risk to people and their livestock. Opinions on the best way to manage the disease are deeply divided (Source: Author Summary- Grant, C, Anderson, N and Machila, N [Accepted] Stakeholder narratives on trypanosomiasis, their effect on policy and the scope for One Health, Public Library of Science Neglected Tropical Diseases (PLOS NTD). This was part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC) and these interviews contributed to this consortium. The research was funded by NERC project no NE/J001570/1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Full details about this dataset can be found at https://doi.org/10.5285/727c1c4e-e097-44a4-abc7-74a4cc9acbfc
Concentrations of SARS-CoV-2 RNA and physichochemical data on wastewater samples collected from six sites across England and Wales between March and July 2020. Also included are the number of COVID-19 positive tests and COVID-19 related deaths for the same period collated from publicly available records. COVID-19 data relate to the lower tier local authority that the wastewater treatment plant was located within. Full details about this dataset can be found at https://doi.org/10.5285/ce40e62a-21ae-45b9-ba5b-031639a504f7
This dataset contains pH, turbidity and viral concentration information in untreated and treated wastewater samples at wastewater discharge points and wastewater treatment plants along the Conwy River. The aim of the data collection was to investigate diurnal changes in enteric virus concentrations in wastewater and to investigate any correlation with wastewater pH and turbidity. Untreated wastewater samples were collected at one wastewater treatment plant for two events. Treated wastewater samples were collected at two wastewater discharge points for two and three sampling events, respectively. All the sampling took place between July 2016 and March 2017. During a sampling events, samples were collected every two hours for 72 hours using autosamplers. Samples were collected by trained members of staff from Bangor University and Centre for Ecology & Hydrology (CEH). The data were collected as part of the VIRAQUA project was funded by the Natural Environment Research Council (NERC) under the Environmental Microbiology and Human Health (EMHH) Programme (NE/M010996/1). Full details about this dataset can be found at https://doi.org/10.5285/61640ba9-ffdd-4eda-9e83-dafc01ba8cc7
These data provide results from serological analysis carried out on serum collected from cattle (sample number = 460), goats (sample number = 949) and sheep (Sample number = 574) combined with data collected at the household and subject/animal levels at the time of serum sampling. The data collected at the household and subject/animal levels were: the total number of livestock owned by a household, altitude, geographical coordinates of the sampling sites; and breed, age, sex and body condition score of an animal. The research was carried out in irrigated and non-irrigated areas in Tana River County, Kenya. Field surveys were implemented in August to November 2013 and laboratory analyses were completed in June 2015. Serum samples were harvested from blood samples obtained from animals and screened for anti-Rift Valley Fever (RVF) virus immunoglobulin G using inhibition (enzyme-linked immunosorbent assay) ELISA immunoassay. The household data was collected using Open Data Kit (ODK) loaded into smart phones. The serological analysis was performed to determine the risk of Rift Valley Fever virus exposure in cattle, sheep and goats. The aim of the survey was to investigate whether land use change, specifically the conversion of rangeland into cropland, affected RVF exposure pattern in livestock. The data were collected by experienced researchers from the Ministry of Livestock Development Nairobi, Kenya and the International Livestock Research Institute (Kenya). This dataset is part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC). The research was funded by NERC project no NE-J001570-1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Additional funding was provided by Consultative Group on International Agricultural Research (CGIAR) Research Program Agriculture for Nutrition and Health led by International Food Policy Research Institute (IFPRI). Full details about this dataset can be found at https://doi.org/10.5285/b9756c4c-9894-4147-a260-a79067604a06
The dataset provides observational information on events when humans are in contact with poultry in rural and urban Bangladesh. Data were collected during observation periods of three hours duration in three settings where humans and poultry have close interactions: rural households with domestic poultry and small-scale commercial farms in rural areas of Tangail district and market stalls that sell, slaughter and process live poultry in Dhaka city. Observations on hygiene or handwashing behaviours that take place before or after contact with poultry, poultry products (eggs, meat) or poultry waste (bedding, faeces or carcasses) were also recorded. A structured observation sheet was used to record the number of occurrences of pre-defined activities. The objective was to record the types of contact behaviours and proportion of human-poultry interactions that could result in human exposure to antibiotic-resistant bacteria carried by poultry. The research was part of a wider research project, Spatial and Temporal Dynamics of Antimicrobial Resistance (AMR) Transmission from the Outdoor Environment to Humans in Urban and Rural Bangladesh. The research was funded by NERC/BBSRC/MRC on behalf of the Antimicrobial Resistance Cross-Council Initiative, award NE/N019555/1. Full details about this dataset can be found at https://doi.org/10.5285/76f52a38-7a2c-49a3-b86f-cc40205459ef
These data comprise apparent densities, species and sex and of mosquitos collected in irrigated and non-irrigated areas in Bura, Tana River County Kenya, between September 2013 and November 2014. Sampling was repeated four times over the period to cover the wet season, dry season, irrigation season and fallow periods. Mosquitoes were trapped using carbon dioxide-baited (CDC) light traps. Mosquitoes harvested from each of these traps were immobilized using 99.5% triethyleamine (Sigma-Aldrich, St. Louis, Missouri) and transferred to distinct bar-coded centrifuge tubes or cryogenic vials. The samples were transported in liquid nitrogen to the entomology section of Arbovirus/Viral haemorrhagic fever (VHF) laboratory at the Kenya Medical Research Institute (KEMRI) where they were sorted by species, sex, village, collection date and counted. The study was implemented to assess the impact of land use change (specifically the conversion of pastoral rangeland into crop land) on the suitability of the habitats to mosquito development and colonization. It also aimed to identify relative abundance of mosquitoes associated with Rift Valley fever virus transmission. The data were collected and analysed by experienced researchers from the International Centre of Insect Physiology and Ecology (Kenya), the International Livestock Research Institute (Kenya) and the Kenya Medical Research Institute. This dataset is part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC). The research was funded by NERC project no NE-J001570-1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Additional funding was provided by the Consultative Group on International Agricultural Research (CGIAR) Program Agriculture for Nutrition and Health. Full details about this dataset can be found at https://doi.org/10.5285/813f99c4-d07a-42dc-993a-1c35df9f028e
This data is NERC-funded but not held by the EIDC. This data is archived in the UK Data Service ReShare repository. This dataset contains material from work package 4.1 from the IWUN project: ‘A new green paradigm for wellbeing: an integrated approach to GBI (green and blue infrastructure) planning, health and social care’. The qualitative research for this work package took place between late 2017 and mid 2018. The central element of this research was a process of identifying greenspace interventions to improve wellbeing, and engaging stakeholders through a survey, interviews and focus groups to shortlist those interventions considered most practicable in Sheffield. The interviews and focus groups also discussed how such interventions could be implemented, what benefits were associated with them, and the processes involved in deciding whether or not to invest in the chosen actions. The dataset contains (a) anonymised interview and focus group transcripts to identify stakeholder preferences for greenspace interventions to improve wellbeing; (b) anonymised notes from associated public events; (c) notes from a thematic analysis of the interview and focus group material; (d) records of voting preferences from stakeholder groups in selecting possible greenspace interventions. This work was supported by the Natural Environment Research Council award number NE/N013565/1 as part of the Valuing Nature programme.