This dataset presents predicted soil erosion rates (t ha-1 yr-1) and its impact on topsoils, including lifespans (yr) assuming erosion rates remain constant and there is no replacement of soil; flux rates of soil organic carbon via erosion (t SOC ha-1 yr-1); flux rates of soil nitrogen via erosion (t N ha-1 yr-1); and flux rates of soil phosphorous via erosion (t P ha-1 yr-1). The dataset comes in the form of 3 multi-band raster GeoTiff files, structured as follows: LC16_Results.tif: Model predictions generated under the 2016 Copernicus Land Cover Map at 30-metre resolution (5 bands) Mitigation_scenarios.tif: Predicted reductions in erosion rates in the event of implementing mitigation scenarios described in 16 different scenarios (16 bands). PNV_Results.tif: Same structure as LC16_Results.tif, but stores predictions generated under the Potential Natural Vegetation cover map for East Africa at 30-metre resolution (5 bands) Full details about this dataset can be found at https://doi.org/10.5285/86d07d98-2956-4395-8b02-29dd5d98e6be

The NCEO Kenya forest aboveground biomass map shows aboveground woody biomass (AGB) in Kenyan forests. Forest areas include vegetated wetlands and wooded grassland for the year 2015. The map was generated by combining field inventory plots (KFS) with Advanced Land Observing Satellite (ALOS-2), Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) and multispectral optical data (NASA Landsat 8), by means of a Random Forests algorithm within a k-Fold calibration/validation framework. The characterization of carbon stocks and dynamics at the national level is critical for countries engaging in climate change mitigation and adaptation strategies. However, several tropical countries, including Kenya, lack the essential information typically provided by a complete national forest inventory. These data were produced by the National Centre for Earth Observation (NCEO), University of Leicester, in collaboration with the Kenya Forest Service (KFS) with funding from the NCEO ODA Programme. Known Issues: Residual scan line corrector (SLC) effects due to the use of the SLEEK land cover product as a retrieval mask (derived from Landsat imagery) are visible in some areas

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

This data describes the recovering and isolation processes of Bacteroides spp. strains from human and cattle faecal sources from rural areas in Siaya County (Kenya), and occurred between 7th and 28th of June 2018. The data also includes the detection of bacteriophages (infecting these Bacteroides spp. host strains) in conjunction with traditional faecal indicator organisms in water sources from Kisumu and Siaya County (Kenya) occurring between June 18th 2018 and June 13th 2019. Exact location (coordinates) of the sample points are also described in the data set. A microbiological technique using Bile Esculin Bacteroides (BBE) agar was used for the recovering and isolation processes of Bacteroides spp. strains. Standard ISO (7899-2, 9308-1, 10705-2 and 10705-4) techniques, such as membrane filtration and the double-agar-layer methods, were used for the detection of bacteriophages and traditional faecal indicator organisms. The purpose of data collection was to develop new markers that could identify cattle and/or human sources of faecal contamination, which could be used as part of a Microbial Source Tracking (MST) tool box. Technicians and researchers from the University of Brighton (UK), University of Southampton (UK), from the Victoria Institute for Research on Environment and Development (VIRED) (KE) and from the Kenya Medical Research Institute (KEMRI) (KE) were responsible for the collection and interpretation of data. Full details about this dataset can be found at https://doi.org/10.5285/02c8a6b0-e59e-4278-b9a2-9958cd5a2c3c

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

The data comprises of two datasets. The first consists of text files of anonymised transcripts from focus group discussions (FGDs) on livelihood activities, ecosystem services and the prevalent human and animal health problems in irrigated and non-irrigated areas in northeastern Kenya. The second comprises of scores from proportional piling exercises which showed the distribution of wealth categories and livestock species kept. The study was conducted between August and October, 2013 and the data were collected as open-ended meeting notes and audio clips captured using digital recorders. Written/thumb print consent was always obtained from each individual in the group. All the discussions were also recorded, with the participant's permission. Thirteen FGDs were held in the irrigated areas in Bura and Hola, Tana River County involving farmers who grew a variety of crops for subsistence and commercial purposes. The others were held in Ijara and Sangailu, Garissa County inhabited by transhumance pastoralists. Each group comprised of 10 to 12 people and the discussions were guided by a check list. The transcribed documents were formatted in Microsoft Word (2013) and saved as text files in preparation for analysis. The aim of the study was to collate perceptions of land use change and their effects on ecosystem services. The data were collected by enumerators trained by experienced researchers from the University of Nairobi 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 NE-J001570-1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Additional funding was provided by the CGIAR Research Program Agriculture for Nutrition and Health. Full details about this dataset can be found at https://doi.org/10.5285/4f569d73-30c5-4b12-bca7-8901fb567594

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

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

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 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