This is a dataset of spot gauged river flows (m3 s-1) at multiple sites along the River Frome, Dorset, UK, conducted during the year 2022. All sites are contained within the stretch of river between the Environment Agency gauging stations located at Dorchester and East Stoke, i.e. the lower part of the River Frome. The monitoring sites included the major tributaries along this river reach, which are: the South Winterbourne, Tadnoll Brook, and the River Win. In total, 19 river channels were spot gauged at 11 river cross-sectional locations. Due to the braided nature of the river, some locations required multiple channels to be measured to produce a total cross-sectional flow for that part of the river. The river cross-sectional locations were evenly spaced, approximately every 3 km along the river reach. Measurements were taken on multiple flow accretion survey days between 12/04/2022 and 05/11/2022. On each day, as many of the sites were spot gauged as possible, working upstream to downstream. Full details about this dataset can be found at https://doi.org/10.5285/0d5c7e45-2c43-4276-af0d-8d941db2e124

This dataset is a compilation of water quality data for the River Frome catchment, Dorset, UK. The data have been sourced from the Environment Agency and from Wessex Water Ltd., the water utility company for the catchment area. The monitoring sites are specifically located in the lower part of the River Frome, between the Environment Agency gauging stations at Dorchester and East Stoke. The dataset includes water quality measurements for boreholes, sewage treatment work (STW) final effluents, tributaries, and main river channels. In total there are 21 monitoring sites. Water quality measurements date from 1976 to 2022. The main river channel and tributary sites are typically monitored on a monthly basis and the STW final effluents typically on a weekly basis. The borehole data can vary from weekly to monthly, depending on the determinand and borehole measured. Full details about this dataset can be found at https://doi.org/10.5285/9d98e1a7-7602-490f-9896-0eeed6eb1d40

The data comprise outcomes from questionnaire surveys conducted with greenspace users on their perceptions of experimentally manipulated urban meadows (varying levels of diversity and vegetation height of sown wildflower meadows), and associated socio-economic data of respondents to the questionnaire surveys. The experimental meadows were located in Bedford and Luton. Data was collected by the data authors, and participants gave informed consent before completing the questionnaires. The work was initially completed under the Fragments, functions and flows NERC BESS project in 2014. The scaling of biodiversity and ecosystem services in urban ecosystems was funded by grant NE/J015369/1 from the Biodiversity and Ecosystem Service Sustainability (BESS) programme. Subsequent analysis was carried out under the NERC grant ‘Location, Configuration, Distribution: the Role of Landscape Pattern and Diversity in Ecosystem Services’ (NE/K015508/1). Full details about this dataset can be found at https://doi.org/10.5285/29d6345f-9f53-4894-8f60-80843f49c017

The data describe vegetation outlines and tree tops above 1m in height as polylines and points. Data have been processed from a digital terrain model (DTM) and digital surface model (DSM), converted from raw LiDAR data. The LiDAR dataset was acquired for Cornwall and Devon (all the land west of Exmouth) during the months of July and August 2013. The data were created as part of the Tellus South West project. Full details about this dataset can be found at https://doi.org/10.5285/78dba959-989b-43d4-b4da-efd2506e0c8e

This is a dataset generated from information extracted from previously published studies, for the purpose of a meta-analysis investigating fitness benefits of different migratory strategies in partially migratory populations. Each line of data includes a mean and associated variance for a given fitness metric for both migrants and residents extracted from a study, in addition to information concerning population location, study species, type of fitness metric, year data were collected, and details on the publication from which the data were obtained. Data were collected as part of a NERC-funded PhD project, grant number NE/L002582/1. Full details about this dataset can be found at https://doi.org/10.5285/1a4e8d59-e112-4de6-a06b-9ea47ff15815

These data consist of relative telomere length (RTL) measures from quantitative polymerase chain reaction, of Seychelles warbler birds on Cousin Island, Seychelles. The data were collected by the Seychelles Warbler Project in 1995-2014. Data include bird identity, sex, age, birth period, qPCR plate identity, RTL, technician, territory, field period, mum ID, dad ID, mum age at conception, dad age at conception, dominant female ID in the natal territory, dominant male ID in the natal territory Full details about this dataset can be found at https://doi.org/10.5285/8a8240a2-e8ed-495d-ae93-c35200956764

Data comprise plot details and radionuclide activity concentrations for Sr-90, Cs-137, Am-241, Pu-238, Pu-239 and Pu-240 in ‘grassy’ vegetation and soil. These radionuclide activity concentrations have been used to make estimations of total weighted absorbed doses to grassy vegetation, deciduous trees and bacteria; no dose rate estimates for grassy vegetation have been made for those sites where grassy vegetation was absent. Radiation from the 1986 Chernobyl nuclear power plant accident killed coniferous trees in a 4-6 km2 area of forest to the west of the power plant. This area is now known as the 'Red Forest’ and it has subsequently regenerated with understorey vegetation and deciduous trees; it is the most anthropogenically contaminated radioactive ecosystem on Earth. In July 2016 a severe fire burnt (to varying degrees) c. 80 percent of the Red Forest; this presented a unique opportunity to study the impact of radiation on the recovery of forest ecosystems exposed to a secondary stressor (fire). To investigate this, in September 2017 the RED FIRE project set up sixty study plots in the Red Forest (in burnt and unburnt areas) with a further nine plots established close to Buriakivka village (approximately 8 km from the Red Forest). Vegetation samples from each plot were harvested using shears in September 2017. Each sample was sorted into ‘grassy’ and ‘other’ vegetation; these were air-dried (20-25 degrees Celsius) and the grassy vegetation samples homogenised prior to radionuclide analyses. Soil core samples collected in September 2017 were bulked, homogenised and sub-samples taken for determination of pH and percentage moisture determined by oven drying (approximately 60 degrees Celsius) to a constant mass. The remaining soil sample was used for the determination of radionuclide activity concentrations; prior to analyses, these samples were dried at approximately 80 degrees Celsius. This work was funded by the NERC, Grant Ref: NE/P015212/1 (RED FIRE: Radioactive Environment Damaged by fire: a Forest In Recovery) Full details about this dataset can be found at https://doi.org/10.5285/60782622-7bfa-4615-a9e3-0a802a9f4674

The dataset includes information on antibiotic-resistance and resistance genes in bacteria (Escherichia coli) from humans, poultry and the environment in rural households, poultry farms and urban food markets. The rural households and poultry farms (broiler chickens) were located in Mirzapur, Tangail district; and urban food markets were located in Dhaka city, Bangladesh. Environmental samples were collected from surface water, water supply, wastewater, soil, animal faeces (poultry and cattle) and solid waste between February 2017 and October 2018 . DNA samples from antibiotic-resistant bacteria found in all samples were analysed for quantitative assessment of two resistance genes. Trained staff from the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) undertook sample collection and laboratory analysis. The aim of the study was to assess the prevalence and abundance of antibiotic-resistant bacteria and associated genes among humans, poultry and environmental compartments in Bangladesh. 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/10.5285/0239cdaf-deab-4151-8f68-715063eaea45

The data include temperature and relative humidity (RH) values recorded every minute inside and outside whole-tree, passive heating, open top chambers. Respiration and photosynthesis rates were recorded (at incremental controlled leaf temperatures) on leaves on study individuals of Erythroxylum suberosum growing inside and outside the chambers. Temperature, RH, and solar irradiance were measured every fifteen minutes by local weather station are also included for the whole testing period, June to September 2020, in an area of typical Cerrado, Bacaba Park, Nova Xavantina, Brazil. The data were collected to enable development of methodology and testing of, a novel in situ passive heating method for evaluating whole-tree responses to daytime warming in remote environments using an open top chamber. Full details about this dataset can be found at https://doi.org/10.5285/2dcc08e9-d8e6-4675-b78d-a318efc799d8

Data presented here include imagery with ground-sampling distances of 3 cm and 7 cm for March 2019, May 2019 and July 2019. Also included are the corresponding ground-truth training and verification data presented as shapefiles, as well as the classification output and other data relevant to the project such as the width of floral units. The imagery was acquired by Spectrum Aviation using A6D-100c (50mm) Hasselblad cameras with bayer filters, mounted on a Sky Arrow 650 manned aircraft. Ground-truth data for training maximum likelihood classifications and for verifying the accuracy of classifications were gathered within eight days of imagery acquisition. Ground-truth data were acquired from sown field margins and hedgerow surrounding one study field. This dataset was acquired from March to July 2019 at a farm in Northamptonshire, UK. Data were acquired as part of a NERC funded iCASE PhD studentship (NERC grant NE/N014472/1) based at the University of East Anglia and in collaboration with Hutchinsons Ltd. The aim of the research was to map the floral units of five nectar-rich flowering plant species using very high resolution multispectral imagery. Each species constitutes an important food resource for pollinators. The plant species in question were Prunus spinosa, Crataegus monogyna, Silene dioica, Centaurea nigra and Rubus fruticosus. Full details about this dataset can be found at https://doi.org/10.5285/cf68be0c-e969-4190-8ec6-abeedb51b42c