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University of Brighton

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From 1 - 9 / 9
  • The data set contains the qualitative results from fieldwork from the ‘sense of place’ and 'contemporary social representations' workpackage components of the WetlandLIFE project. Fieldwork included two discussant focus groups and thirty semi-structured interviews with specialist users of wetlands. The University of Brighton's social science qualitative fieldwork seeks to capture the different perspectives of people whose lives are intimately connected to particular English wetlands, in order to understand the range and diversity of wellbeing practices in these spaces. The target cohort are those groups of people, or organisations, that are particularly drawn to wetlands, or who could be expected to make regular use of these spaces, particularly for their health and wellbeing. Such Specialist Interest Groups (SIGs) would include birders, walkers, wildlife photographers, artists and anglers alongside educators, naturalists, spiritual practitioners and ecologists. They may not live close to the wetland sites but their field of interest, or sense of place, would be expected to include them. These interviews and focus groups took place at the case study sites in the Somerset Levels (Westhay Moor and Shapwick Heath), Bedfordshire (Priory Country Park and Millennium Country Park) and North Lincolnshire (Alkborough Flats) between January 2018 and September 2018. This data is NERC-funded but not held by the EIDC. This data is archived in the UK Data Service ReShare repository.

  • 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

  • [This dataset is embargoed until September 1, 2020]. Data were generated to investigate the influence of bed roughness on the dynamics of large sand-bed rivers like the South Saskatchewan, Canada. The influence of roughness was investigated by using a numerical model to simulate the evolution of the river bed for a hypothetical sand-bed river modelled on the South Saskatchewan. The model generated information on the evolving river bed topography, water depth, flow velocities and sediment transport rates, over a period of 28 years as part of NERC project NE/L00738X/1 Full details about this dataset can be found at https://doi.org/10.5285/790e507c-ce99-47ca-99b4-c97a684ee8c6

  • Data were collected in 2015, 2016 and 2017 to provide high resolution imagery for two sections of the South Saskatchewan River, Canada. Photographs were acquired using conventional aerial plane images with a 0.06m ground resolution, captured at a height of approximately 1500m from a fixed-wing aeroplane with an UltraCamXp sensor. Imagery was obtained on four occasions (13th May 2015; 2nd Sept 2016; 8th June 2017; and 12th June 2017). The dataset consists of eight orthomosaics; one for each of the two river sections on each of the four dates. Images were collected as part of NERC project NE/L00738X/1. Full details about this dataset can be found at https://doi.org/10.5285/7473d4f9-c9a7-40ad-9f58-e58e25997fc5

  • Data were collected in 2015, 2016 and 2017 to provide Digital Surface Models (DSM) for two sections of the South Saskatchewan River, Canada. DSMs were generated using aerial plane images with a 0.06m ground resolution, captured at a height of c. 1500 m from a fixed-wing aeroplane with an UltraCamXp sensor. DSMs were generated as part of NERC project NE/L00738X/1. DSMs were constructed using imagery obtained on four occasions (13th May 2015; 2nd Sept 2016; 8th June 2017; and 12th June 2017). The dataset consists of eight DSMs; one for each of the two river sections on each of the four dates. Full details about this dataset can be found at https://doi.org/10.5285/13695138-227f-4d85-9049-0a9cba9e1867

  • [This dataset is embargoed until July 1, 2020]. Datasets consists of the results of Computational Fluid Dynamics (CFD) flow simulations for a section of the South Saskatchewan River, Canada. The aim of these CFD simulations was to investigate the effect of dunes on the depth-averaged and near-bed flow fields. Modelling was carried out using the open source CFD package OpenFOAM to solve the three-dimensional Navier-Stokes equations. The dataset consists of two files, one with simulation results for a river bed characterised by alluvial bedforms (dunes) and one for a smooth river bed without dunes. This work was part of NERC project NE/L00738X/1. Digital Surface Models (DSMs) were constructed using imagery obtained on four occasions (13th May 2015; 2nd Sept 2016; 8th June 2017; and 12th June 2017). Full details about this dataset can be found at https://doi.org/10.5285/7db04405-2f5e-4543-aa94-948ddbcd588a

  • Data were collected in 2017, to provide information on spatial patterns of dune migration rates and associated water flow characteristics, at locations on the South Saskatchewan River, Canada. Dune migration rates were measured using repeat aerial imagery. Bedform crests were digitised in individual images, and average dune migration rates were calculated from the mean migration distance between image pairs, divided by the time between image collection. Water depth and velocity data were collected using a Sontek M9 acoustic Doppler current profiler (aDcp) mounted on a small zodiac boat. The position of the aDcp was recorded using a RTK dGPS system. Data were collected on 12th June 2017 as part of NERC project NE/L00738X/1 Full details about this dataset can be found at https://doi.org/10.5285/864434b7-2102-4edc-802d-ebdbfe9ff766

  • Data were collected in 2015 and 2016 to provide information about spatial variations in water depth and river bed morphology (including bedform height) on the South Saskatchewan River, Canada. Water depth measurements were obtained with a Navisound NS 215 system and a Reson TC 2024 200kHz high-resolution dual frequency single beam echo sounder (SBES) operating at a sampling frequency of 10hz. Data were geolocated via a Leica 1230 Real-Time Kinematic (RTK) dGPS system. Data were collected in 2015 (between 7th and 9th September) and 2016 (between 2nd and 14th September) as part of NERC project NE/L00738X/1. Full details about this dataset can be found at https://doi.org/10.5285/14c80b71-6eb6-4dba-a298-b95a37059f55

  • Data were collected in 2015, 2016 and 2017 to provide information on the distribution of flow depth and depth-averaged flow velocity at cross-sections on the South Saskatchewan River, Canada. Data were obtained using a Sontek M9 acoustic Doppler current profiler (aDcp) mounted onto either a small zodiac boat or a SonTek Hydroboard. Data for each cross-section is recorded in a single file. Individual points within each file represent single locations on the particular cross-section. Data were collected as part of NERC project NE/L00738X/1. Full details about this dataset can be found at https://doi.org/10.5285/e4fe2ebe-b207-47d5-8c77-9873afc63da9