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162 results for “Water quality data”
Newport Bay Water Quality TMDL Annual Data Reports 2001 - 2013
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Vermont Department of Environmental Conservation. Vermont Water Quality Data Archive and Lake Inventory Database from 1977 to 2010. Waterbury, VT
VT’s long-term archive of lake physical and chemical water quality. Maintained on a Sequel Server platform. Database integrates measurements from 1977 to present, and is continually being updated and augmented. Numerous parameters are archived within the database.
Experimental data generated to study the quality-dependent performance of hydrophobic ZIF-67 upon water intrusion–extrusion process
<p><span>Open access to experimental data generated by the project <span><a href="https://www.electro-intrusion.eu/en"><span>Electro-Intrusion</span></a></span><span> (101017858, Horizon 2020, European Union) </span>along with the research on the hydrophobic porous materials to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2).</span></p> <p><span>Underlying data for the publication Amayuelas, E. et al. Quality-dependent performance of hydrophobic ZIF-67 upon high-pressure water intrusion–extrusion process. Physical Chemistry Chemical Physiscs 2024, 267, 2440-2448. <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3CP03519K">https://doi.org/10.1039/D3CP03519K</a>. Data related to Figures 1, 2 and 4 in the article.</span></p>
Long-term water quality monitoring data of Shenzhen Bay
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Water quality data and isotope data of groundwater, river water and seawater in the Pearl River Estuary
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Lupi et al data for Disentangling water quality indices to enhance the valuation of divergent ecosystem services PNAS
<p>State dataset for PNAS article, in press, Lupi, F., J.A. Herriges, R.J. Stevenson, and H. Kim. <em>In Press</em>, Disentangling water quality indices to enhance the valuation of divergent ecosystem services. <em>Proceedings of the National Academy of Science.</em></p>
Water quality data and processing scripts
<ul> <li>Python scripts used to process eight water quality databases.</li> <li>Output files for Glorich (Global), Waterbase (EU), WQP (USA) and DWS (South Africa).</li> </ul> <p>References</p> <p>Graham, D.J., Bierkens, M.F.P., van Vliet, M.T.H., 2024. Impacts of droughts and heatwaves on river water quality worldwide. Journal of Hydrology 629, 130590, https://doi.org/10.1016/j.jhydrol.2023.130590</p> <p>Hartmann, J., Lauerwald, R., Moosdorf, N., 2014. A Brief Overview of the GLObal RIver Chemistry Database. GLORICH. Procedia Earth Planet. Sci. 10, 23–27. https://doi.org/10.1016/J.PROEPS.2014.08.005</p> <p>European Environment Agency: Waterbase – Water Quality ICM, available at: https://www.eea.europa.eu/data-and-maps/data/waterbase-water-quality-icm-2, Accessed 01/01/2022</p> <p>Water Quality Portal. Washington (DC): National Water Quality Monitoring Council, United States Geological Survey (USGS), Environmental Protection Agency (EPA); 2021. doi: 10.5066/P9QRKUVJ</p> <p>Department of Water and Sanitation, South Africa. Research Quality Information Services, available at: https://www.dws.gov.za/iwqs/report.aspx, Accessed 01/01/2022</p>
Data Repository for "Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin"
<p>Data for "Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin". This repository contains all the processed data used in the simulation (in "processed" folder), part of the raw data (in "raw" folder), and the SWAT simulation results (in "SWAT" folder). The code for processing the raw data, which are either provided here or publicly available online, is provided in the <a href="https://doi.org/10.5281/zenodo.8132662">code repository</a>. The links to the publicly available raw data are also provided in the code repository.</p>
Data from: Native forest cover safeguards stream water quality under a changing climate
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Data from: Assessment of grain quality in terms of functional group response to elevated [CO2], water and nitrogen using a meta-analysis: Grain protein, zinc and iron under future climate
The increasing [CO2] in the atmosphere increases crop productivity. However, grain quality of cereals and pulses are substantially decreased and consequently compromise human health. Meta-analysis techniques were employed to investigate the effect of elevated [CO2] (e[CO2]) on protein, zinc (Zn) and iron (Fe) concentrations of major food crops (542 experimental observations from 135 studies) including wheat, rice, soybean, field peas and corn considering different levels of water and nitrogen (N). Each crop, except soybean, had decreased protein, Zn and Fe concentrations when grown at e[CO2] concentration ( ≥550 μmol mol-1) compared ambient [CO2] (a[CO2]) concentration (≤ 380 μmol mol-1). Grain protein, Zn and Fe concentrations were reduced under e[CO2], however, the responses of protein, Zn and Fe concentrations to e[CO2 ] were modified by water stress and N. There was an increase in Fe concentration in soybean under medium N and wet conditions but non-significant. The reductions in protein concentrations for wheat and rice were ~ 5-10%, and the reductions in Zn and Fe concentrations were ~ 3-12%. For soybean, there was a small and non-significant increase of 0.37% in its protein concentration under medium N and dry water, while Zn and Fe concentrations were reduced by ~ 2-5%. The protein concentration of field peas decreased by 1.7%, and the reductions in Zn and Fe concentrations were ~ 4-10%. The reductions in protein, Zn and Fe concentrations of corn were ~ 5-10%. Bias in the dataset was assessed using a regression test and rank correlation. The analysis indicated that there are medium levels of bias within published meta-analysis studies of crops responses to Free Air [CO2]Enrichment (FACE). However, the integration of the influence of reporting bias did not affect the significance or the direction of the [CO2] effects.
Data from: Long-term agroecosystem research in the Central Mississippi River Basin: Goodwater Creek Experimental Watershed and regional herbicide water quality data
PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN ANOTHER PUBLICATION. PLEASE SEE http://doi.org/10.2134/jeq2013.12.0518. Goodwater Creek Experimental Watershed (GCEW) has been the focus area of a long-term effort to document the extent of and to understand the factors controlling herbicide transport. We document the datasets generated in the 20-yr-long research effort to study the transport of herbicides to surface and groundwater in the GCEW. This long-term effort was augmented with a spatially broad effort within the Central Mississippi River Basin encompassing 12 related claypan watersheds in the Salt River Basin, two cave streams on the fringe of the Central Claypan Areas in the Bonne Femme watershed, and 95 streams in northern Missouri and southern Iowa. Details of the analytical methods, periods of record, number of samples, study locations, and means of accessing these data are provided. In addition, a brief overview of significant findings is presented. A key finding was that near-surface restrictive soil layers, such as argillic horizons of smectitic mineralogy, result in greater herbicide transport than soils with high percolation and low clay content. Because of this, streams in the claypan soil watersheds of northeastern Missouri have exceptionally high herbicide concentrations and relative loads compared with other areas of the Corn Belt.
Water Quality Data for Narragansett Bay, RI (USA) from 2014 to 2022
<p>This dataset includes results of a monthly monitoring program conducted in Narragansett Bay, RI (USA)<br>between January 2014 and December 2022. Eight stations were sampled at three depths (surface, mid-depth,<br>and bottom) using a combination of sondes, secchi disk, and discrete water collections. Water<br>samples were processed in laboratory immediately after collection to be either filtered, preserved, or<br>stored and then later analyzed for chlorophyll-a, dissolved inorganic nutrients (nitrate+nitrite, nitrite,<br>ammonium, and orthophosphate), dissolved organic carbon, stable isotopes of particulate carbon and<br>nitrogen, and total suspended solids. These water quality data are being made available to use, in<br>conjunction with pre-existing datasets collected by others, to better understand the ecological dynamics<br>of the Narragansett Bay ecosystem.</p>
Laboratory water quality monitoring data from full scale CS#3 DWDN for the DBP prediction model
<p>Lab measurements of water quality based on manual sampling including ordinary monitoring and special monitoring performed in SafeCREW project.</p> <p>Laboratory LIMS database.</p> <p>Provide water quality measurements based on the planned monitoring schedule. Schedule tailored to SafeCREW project needs plus usual legal water sampling.</p>
Lake water quality monitoring data from 8287 northern lakes
<p>This dataset contains water quality monitoring data and lake morphometry data from 8287 lakes collected between 1960 and 2022 in Finland, Sweden, Norway, Canada and the United States (Alaska). Data was sourced from governmental databases and non-profit organizations via data portals and data requests (see Metadata). The dataset contains over 2.6 million standardized and quality-controlled observations of dissolved oxygen, water temperature, total nitrogen, total phosphorus, water colour and ammonia, as well as sampling location, sampling date, sampling depth and key lake properties including lake altitude, surface area, mean depth and maximum depth. We excluded lakes undergoing physical or chemical treatments against hypoxia, lakes in which experiments have been conducted (e.g. nutrient addition). and reservoirs.</p> <p>The consumer of these data ("Data User" herein) should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is". The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors, original data providers and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. The data user may not create derivatives of this dataset or re-distribute these data to a third party.</p> <p><strong>Note: </strong>if you would like to use this data in your own study, you must contact the author (joachim.jansen@helsinki.fi) to ensure compliance with requirements of the contributors to this synthesis dataset, </p>
Data from: Assessment of grain quality in terms of functional group response to elevated [CO2], water and nitrogen using a meta-analysis: Grain protein, zinc and iron under future climate
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Data from: The low but uncertain measured benefits of U.S. water quality policy
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Data from: Long-term agroecosystem research in the Central Mississippi River Basin: Goodwater Creek Experimental Watershed and regional herbicide water quality data
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Data from: Effects of land use, topography, climate and socio-economic factors on geographical variation pattern of inland surface water quality in China
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Data from: Water quality assessment of Australian ports using water quality evaluation indices
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Indicators of Coastal Water Quality: Ancillary Data
The Ancillary Data component of the Indicators of Coastal Water Quality Collection includes a 5 arc-minute (approximately 9 x 9 km at the equator) sequence grid, grid cell centroids that relate to the grid cells in the tabular "Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007" data set, and a country buffer data set that is divided by exclusive economic zones (EEZ). The data are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.