In this mesocosm study, three types of wet wipes (plastic containing, and home and commercially compostable) colonised with E. coli were buried in beach sand and their degradation, tensile strength, and concentration of E. coli was quantified over 15 weeks. Wipes were firstly passed through a series of treatments to simulate their journey from the bathroom to the beach. Water characteristics (pH, salinity) of these water types were measured. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/19a8083b-afa4-40b9-91bf-7a529eea870d

[This dataset is embargoed until December 15, 2025]. This data set represents field-based monitoring of insect pollinator communities found within soya (Glycine max L. Merril) crops located along a latitudinal gradient ranging from -37.669486 to -24.495121 covering both Argentina and Brazil. Yield data was also collected from these same sites to elucidate the dependencies of this crop on insect pollination with a focus on managed and wild pollinators. Data was collected over multiple seasons between 2020 and 2022. Soybean is one of the most traded agricultural commodities and is of significant economic importance in South America. Full details about this dataset can be found at https://doi.org/10.5285/2bd21042-ebbc-4454-8ca3-96e18333ccd2

This dataset comprises operational data for a point-of-use drinking water treatment system (POU DWTS) located on the University of the West of England’s Frenchay campus. This dataset covers 34 months (November 2019 to September 2022). The POU DWTS was continuously operational throughout this period with parameters presented in this dataset including oxidation reduction potential, free available chlorine, flow rate, discharge pressure, filtration flux, ultrafiltration membrane permeability. Telemetry data is collected over a 5-minute period provide a rolling 5-minute average and is available in a .csv format. Full details about this dataset can be found at https://doi.org/10.5285/d3daab67-dc02-43a0-82c0-afff709c43f4

[This dataset is embargoed until October 1, 2024]. The Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) dataset is a global hydrological dataset containing publicly available daily flow data for 2,418 gauging stations across the globe which have natural or near-natural catchments. Metadata is also provided alongside these stations for the Full ROBIN Dataset consisting of 3,060 gauging stations. Data were quality controlled by the central ROBIN team before being added to the dataset, and two levels of data quality are applied to guide users towards appropriate the data usage. Most records have data of at least 40 years with minimal missing data with data records starting in the late 19th Century for some sites through to 2022. ROBIN represents a significant advance in global-scale, accessible streamflow data. The project was funded the UK Natural Environment Research Council Global Partnership Seedcorn Fund - NE/W004038/1 and the NC-International programme [NE/X006247/1] delivering National Capability Full details about this dataset can be found at https://doi.org/10.5285/3b077711-f183-42f1-bac6-c892922c81f4

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 contains over 4000 faecally-contaminated environmental samples collected over 2 years across 53 dairy farms in England. The samples were analysed for E. coli resistance to amoxicillin, streptomycin, cefalexin, tetracycline and ciprofloxacin and detection of resistant strains is presented in the dataset as a binary result, along with mechanisms of resistance to third generation cephalosporins where relevant. In addition there is comprehensive farm management data including antibiotic usage data. Full details about this dataset can be found at https://doi.org/10.5285/c9bc537a-d1c5-43a0-b146-42c25d4e8160

The dataset contains borehole groundwater levels and physico-chemical parameters for the period May 2017 to June 2018 including; (1) near-monthly measurements of water table depth, groundwater temperature, pH, electrical conductivity and total dissolved solids obtained from manual sampling of 22 boreholes; and (2) higher temporal resolution (5-min time-step) timeseries of water table depth, groundwater temperature and electrical conductivity obtained from automatic dataloggers in 3 of the abovementioned boreholes. Full details about this dataset can be found at https://doi.org/10.5285/40a80d95-5a8a-4586-aa24-d6c87f9968b6

This dataset contains measurements from smart sensors monitoring the motion state of a physical analogue of a Large Wood (LW) dam in laboratory experiments. The smart sensors were equipped with an accelerometer, gyroscope and magnetometer and they recorded angular velocity, total acceleration, and the magnetic field. These data were used to calculate the linear acceleration and gravity acceleration which are also provided in this dataset. Three factors were changed during the experiment: inclination, constraint configuration, and flow conditions. The experiments were repeated 10 times. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/94aa98b2-b7f7-42c8-81ad-3085d0151eff

This dataset contains soil hydraulic measurement data from the Climoor field site in the Clocaenog forest, in North Wales. The collection contains five data sets. 1) soil bulk density (0-5 centimetre) and saturated water content 2) Unsaturated hydraulic conductivity measured in the field at tensions of -2 and -6 centimetre using a mini disk infiltrometer 3) Unsaturated hydraulic conductivity measured using a HYPROP (TM) instrument, an instrument which determines the hydraulic properties of soil samples, on soil cores taken from the field plots 4) Soil water release curves for wet soil corresponding to the hydraulic conductivity measurements made using a HYPROP in the laboratory on cores from the field 5) Soil water release curve data for dry soil measured using a WP4 potentiometer. The data has been quality checked, and incorrect or missing values removed, data has not been infilled and not available (NA's) have been added where there is no data. Data was collected between the end of 2010 and early 2012. Data sets 1, 3 and 4 were collected in April and September, 2011, data set 2 in May, 2012 and data set 5 in November, 2010. The Climoor field experiment intends to answer questions regarding the effects of warming and drought on ecosystem processes. The reported data were collected to monitor site specific soil properties at a specific reference time. Full details about this dataset can be found at https://doi.org/10.5285/e75620c0-547a-4756-a617-daf6180974d9

The UK Checklist of Freshwater Species is a collation of all the species (apart from algae) known to be found in association with fresh waters in the United Kingdom. The following eight major groups were identified as being associated with fresh waters in the UK: algae, amphibians, birds, fish, invertebrates, macrophytes, mammals and reptiles. Algae (except stoneworts) were not included in the UK Checklist of Freshwater Species as they are currently undergoing a major revision. Other microorganisms (bacteria, fungi and viruses) are also not included in this species list. The checklist was compiled to allow querying of freshwater species data in the Biological Records Centre (BRC) but to also to query freshwater species data from the BRC via the UK Lakes Portal (https://eip.ceh.ac.uk/apps/lakes/), as well as to update the freshwater species list supplied to the UK Species Inventory (UKSI) partners, such as Recorder 6, National Biodiversity Network (NBN) Atlas and iRecord. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/57653719-434b-4b11-9f0d-3bd76054d8bd