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Loughborough University

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  • This dataset includes sediment trap diatom captures and water column temperature profiles from a PhD research project at Rostherne Mere in Cheshire. These data were collected to show the relationship between climate, especially short-term climatic perturbations, and diatom assemblages. The sediment trap data cover the period from October 2004 to January 2017, while the thermal profiles cover October 2005 to December 2016. Diatom data is presented with date, percentage taxa abundance and diatom fluxes based on total sediment yield. Temperature profiles are presented as mean daily figures. The work was carried out as part of a Natural Environment Research Council (NERC) funded PhD [grant number NE/L002493/1], with the temperature data funded by the UKLEON (UK Lake Ecological Observatory Network) project via a NERC small grant [grant number NE/I007261/1]. Full details about this dataset can be found at https://doi.org/10.5285/16f52064-a19d-4cf5-a388-aff04a592179

  • This dataset includes catchment stream inflow and outflow rates, secchi depth, chlorophyll, phytoplankton counts and nutrient concentrations for the lake, inflow, outflow and groundwater spring. The measurements are from a PhD research project at Rostherne Mere in Cheshire. These data were collected to show the relationship between the catchment hydrology and in-lake nutrient loads for assessment of the current catchment nutrient budget. The monitoring study covered a period from January 2016 to January 2017. All data is presented with date, flow rate, nutrient and chlorophyll concentrations and phytoplankton species abundance. The work was carried out as part of a Natural Environment Research Council (NERC) funded PhD [grant number NE/L002493/1]. Full details about this dataset can be found at https://doi.org/10.5285/5c6b2bcb-6b10-4c57-a595-ce94a655e709

  • [This dataset is embargoed until December 31, 2023]. This dataset contains meteorological and atmospheric dust concentration, deposition and particle-size data from Kangerlussuaq, southwest Greenland, 2017-2019. Meteorological and dust concentration data measured at two locations and dust deposition data measured at 5 locations on an east to west transect between 1.8 and 37.4 km from the 2017 Greenland Ice Sheet western margin. The work was supported by the Natural Environment Research Council (grant NE/P011578/1) Full details about this dataset can be found at https://doi.org/10.5285/ea6ff6d0-a2da-418f-ac6f-7e6b777e40c5

  • The dataset describes the data needed for and results produced by the flood risk assessment framework under different development strategies of Luanhe river basin under a changing climate. The Luanhe river basin is located in the northeast of the North China Plain (115°30′ E-119°45′ E, 39°10′ N-42°40′ N) of China, is an essential socio-economic zone on its own in North-Eastern China, and also directly contributes to and influences the socio-economic development of the Beijing-Tianjin-Hebei region. The dataset here used for investigating the flood risk includes (1) uplifts of future climate scenarios to 2030 (2) the validation results of a historical event that happened in 2012; (3) the flood inundation prediction under different development strategies and climate scenarios to 2030; (4) and the spatial resident density map in Luanhe river basin to 2030. Wherein, the uplifts of the future climate change is generated based on the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and will be applied to the future design rainfall to represent the future climate scenarios; a 2012 event is select to validate the flood model, and the remote sensing data is adopted as real-world observation data; considering the uplifts and future land use data as input, the validated flood model is applied to produce flood inundation prediction under different development strategies and climate scenarios to 2030; and the inundation results are used to overlay the Gridded Population of the World, Version 4 (GPWv4) and then calculate the flood risk map of the local resident. These data are mainly open data or produced by authors. With all these data, the flood risk of the Luanhe river basin in the near future (2030) can be assessed. Full details about this dataset can be found at https://doi.org/10.5285/82055942-386a-4a8b-b2a1-0c3eea12b168

  • This dataset includes sediment trap, sediment core and loss-on-ignition to total organic carbon measurements from a PhD research project at Rostherne Mere in Cheshire. These data were collected to show the relationship between the changing nutrient loads and subsequent organic carbon burial over the last 120 years. The sediment trap data cover the period from May 2010 to August 2016, while the sediment core was taken in September 2011 and has been 210Pb dated to circa 1360AD. All data is presented for date, loss-on-ignition (LOI) and calcium carbonate (CaCO3), with sediment trap data converted into net flux measurements and sediment core data calculated for net sedimentation rate following 210Pb dating. The conversion from LOI to total organic carbon was measured using mass spectrometry and applied to the trap and core data. The work was carried out as part of a Natural Environment Research Council (NERC) funded PhD [grant number NE/L002493/1], with part of the work also funded by the NERC small grant [grant number NE/H011978/1]. Full details about this dataset can be found at https://doi.org/10.5285/8616c1a0-6c6d-441c-9b10-8464dc4ee346

  • This dataset includes the PROTECH validation output against a yearlong monitoring study conducted during 2016 in the lake and catchment of Rostherne Mere and the PROTECH output files following changes in internal and external nutrient loads and future climate scenarios based on the UK Climate Projections (UKCP09) data. These data were collected to demonstrate the future possible trajectories of change with alterations in air temperature, internal nutrient loads and external nutrient loads. Validation data is presented as daily model outputs, while all future projection data is presented as collated annual average model output data for each future change scenario. The PROTECH model (Phytoplankton RespOnses To Environmental CHange) simulates the in situ dynamics of phytoplankton in lakes and reservoirs, specialising in predicting phytoplankton species, particularly Cyanobacteria (blue-green algae) The work was carried out as part of a Natural Environment Research Council (NERC) funded PhD [grant number NE/L002493/1]. Full details about this dataset can be found at https://doi.org/10.5285/2f0eae1c-1512-4823-9cbe-cb54f05ee996

  • Data comprise resistance of aquatic bioconstructions (Trichoptera cases), and the loose sediment they were built from, to hydraulic forces measured in a hydraulic flume. S1_Velocimetry_data contains the output after post-processing the acoustic Doppler velocimetry data for each flow stage. This includes the mean and SD of velocity (m/s) for each direction (U – downstream, V – vertical and W- cross stream), the quality of the data recorded by the ADV (Signal to noise ratio and correlation) and calculated values of turbulent kinetic energy and bed shear stress. An explanation of each variable and units are included in the file. S2_Case-grain-size-distribution_data contains the grain size distributions for each caddisfly case obtained by sieving the case sediment. Includes the mass of sediment in half phi size intervals from 0.063 mm to 8 mm diameter. An explanation of each variable and units are included in the file. S3_Entrainment_data contains the percentage of sediment entrained during each flow stage (1-11). Percentage of sediment measured using photography of sediment remaining on the measurement platform. An explanation of each variable and units are included in the file. S4_Case-and-entrainment-threshold_data contains the caddisfly case characteristics (mass and a,b&c axes), critical entrainment thresholds and general movement data for each case and loose sediment. An explanation of each variable and units are included in the file. Full details about this dataset can be found at https://doi.org/10.5285/cce26277-0b17-4c25-aa08-dd1486f89d9b

  • This dataset contains maximum water depth and maximum water velocity for 12 different Glacial Lake outburst floods (GLOFs) scenarios of the Tsho Rolpa Lake, Nepal. Also included is the water depth of dam breach flow and discharge of dam breach flow under each scenario. The GLOFs scenarios were created using a simple dam breach model. A high-performance hydrodynamic model was then used to simulate the resulting flood hydrodynamics. Full details about this dataset can be found at https://doi.org/10.5285/f4292d99-de93-4a28-a821-b2a6a826df4c

  • The data consists of identified exposed objects subject to flooding risk from the Tsho Rolpa Lake. The Tsho Rolpa Lake is the largest moraine-dammed proglacial lake in Nepal and was identified as one of the country’s most dangerous glacier lakes with a high possibility of outburst. Full details about this dataset can be found at https://doi.org/10.5285/3834d477-7a1d-4ad3-8a41-d38fc727dbd8

  • A cross-sectional, interviewer-administered survey was conducted in 2017 in rural households, poultry farms and urban food markets. Survey data for each setting comprise three datafiles. The rural households and poultry farms (broiler chickens) were located in Mirzapur, Tangail district; urban food markets were located in Dhaka city, Bangladesh. In each setting, the survey included participants that had high exposure to poultry, and a comparison group that had lower exposure to poultry. The aim of the survey was to assess potential sources of exposure to antibiotic-resistant bacteria, particularly commensal bacteria that colonise the gastrointestinal tract of humans and poultry. The survey also assessed the use of antibiotics for human participants and practices relating to their poultry such as type of feed, housing, use of antibiotics for poultry and hygiene practices before and after being in contact with poultry. 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 nonGeographicDataset can be found at https://doi.org/10.5285/b4a90182-8b9c-4da8-8b95-bcd5acc727d1