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 the results of 211 household surveys conducted in Mambwe District, Zambia, as part of a wider study looking at human and animal trypanosomiasis and changing settlement patterns in the area. The interviews were conducted from June 2013 to August 2013. The objective of the survey was to set the health of people and their animals in the context of overall household wellbeing, assets and access to resources. The topics covered included household demographics, human and animal health, access to and use of medical and veterinary services, livestock and dog demographics, livestock production, human and animal contacts with wildlife, crop and especially cotton production, migration, access to water and fuel use, household assets and poverty, resilience and values. The dataset has been anonymised by removing names of respondents, Global Positioning Satellite (GPS) location of their homes and names of interviewers. Household numbers were retained. Written consent was obtained prior to commencing all interviews. This research was part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC), and these data contributed to the research carried out by the consortium. The research was funded by NERC 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/b1647138-49f5-4777-a39d-e7359bf7b98d