Antibiotic susceptibility tests are presented as the zone of inhibition using the disc-diffusion method, and categorized as resistant, intermediate or susceptible. DNA samples from antibiotic-resistant bacteria were analysed for the presence or absence of resistance genes using polymerase chain reaction (PCR). Laboratory analyses were conducted by trained staff at the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b). The aim of the study was to identify the antibiotic-susceptibility profiles and resistance genes of bacteria (Escherichia coli) obtained from humans, poultry and the environment. Bacterial isolates previously identified with resistance to third-generation cephalosporins or carbapenems were included in the analysis. Bacterial samples originated from rural households and poultry farms (broiler chickens) in Mirzapur, Tangail district; and urban food markets in Dhaka city, Bangladesh. Environmental samples included surface water, water supply, wastewater, soil, animal faeces (poultry and cattle) and solid waste. The survey was part of a wider research project, Spatial and Temporal Dynamics of Antimicrobial Resistance 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/dda6dd55-f955-4dd5-bc03-b07cc8548a3d

[This dataset is embargoed until January 2, 2024]. This dataset contains Leptospirosis case numbers for a number of place studies in Brazil, Malaysia, Philippines, Argentina, China and Sri Lanka. Leptospirosis case numbers are provided as weekly or monthly case numbers and cover the period 1978 to 2020, although timelines vary within place studies. Area-weighted daily average hydrometeorological variables are also included: precipitation, river discharge and soil moisture. The data have been collected and collated for a global analysis of the effect of hydrometeorological extremes on leptospirosis infection risk. Also included are the spatial polygons for each of the place studies. Full details about this dataset can be found at https://doi.org/10.5285/56f42170-3678-4586-b8c8-9b05f03125e1

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

Mosquito trap data from Kilombero Valley in Tanzania - a global hotspot for malaria. Since 2007, field entomologists working at Ifakara Health Institue (IHI) and at the University of Glasgow have been trapping and collecting primary malaria vectors for four villages in the Kilombero Valley: Lupiro, Kidugalo, Minepa and Sagamaganga. Trapped mosquitoes were identified to species level (Anopheles gambiae and A funestus), their sex recorded (male or female) and their abdominal status (fed or unfed) noted. When available, the daily mosquito data was consistently linked to micro climate data logger data (weather conditions on site, including averaged, minimum and maximum daytime and night time values for temperature, humidity and vapour pressure deficit). This long record allows exploring the relationship between malaria vector dynamics and related environmental conditions. Full details about this dataset can be found at https://doi.org/10.5285/89406b06-d0aa-4120-84db-a5f91b616053

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

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

These dataset files show the calibration of a sensor for mercury (II) ions using a Fluorimeter and either HgCl2 or HgNO3. A range of different sample conditions are tested, including sensor concentrations and relative proportions of water and a methanol co-solvent (required for solubility of the probe). Also tested was the ability of acid to affect the probes sensitivity to mercury as nitric acid is needed for the stability of HgNO3 as an analyte. File names listed show the concentration of sensor and the ratio of water to methanol tested. Inductively coupled plasma mass spectrometry (ICP-MS) data are also given these are used to validate the sensors calibration and also to monitor the levels of soluble mercury content of dental amalgam samples held at either (11⁰C or 37⁰C) in water and saliva. The supernatant of these suspensions is filtered and measured using ICP-MS to give the data as reported. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/bc82f15b-8db6-4398-bfec-655a1eecf2d7

This dataset contains pH, turbidity, conductivity and viral concentration information in river and estuarine water, wastewater, sediment and mussel samples collected in the Conwy River and estuary. The aim of data collection was to monitor wastewater contamination in the freshwater-marine continuum. Samples were collected by trained members of staff from Bangor University at four weekly between March 2016 and August 2017. Treated and untreated wastewater samples were collected at four wastewater treatment plants along the Conwy River. Surface water samples were collected at four sites, sediments at three sites and mussels at two sites. 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/10.5285/5d19f6e2-1383-41ed-92d2-138d95bf4c72

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

A cross-sectional, interviewer-administered survey was conducted in 2017 in rural households, poultry farms and urban food markets. Survey data for each setting comprise three datafiles. The rural households and poultry farms (broiler chickens) were located in Mirzapur, Tangail district; urban food markets were located in Dhaka city, Bangladesh. In each setting, the survey included participants that had high exposure to poultry, and a comparison group that had lower exposure to poultry. The aim of the survey was to assess potential sources of exposure to antibiotic-resistant bacteria, particularly commensal bacteria that colonise the gastrointestinal tract of humans and poultry. The survey also assessed the use of antibiotics for human participants and practices relating to their poultry such as type of feed, housing, use of antibiotics for poultry and hygiene practices before and after being in contact with poultry. The survey was part of a wider research project, Spatial and Temporal Dynamics of Antimicrobial Resistance 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 nonGeographicDataset can be found at https://doi.org/10.5285/b4a90182-8b9c-4da8-8b95-bcd5acc727d1