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484 results for “water quality”
Lake water quality, chemistry and zooplankton composition for 16 lakes surrounding the Green Lakes Valley, 2016
This dataset contains water chemistry measurements made from 16 different lakes north and south of LTER site in the green lakes valley. At each visit water samples were collected for analysis of chlorophyll a and nutrient analysis and field measurements were recorded for pH, temperature, specific conductivity, dissolved oxygen (DO), % saturation, secchi depth, PAR. Secchi depth was measured using a 30cm disk to the nearest 0.25 meter and here recorded at the 0m row in the data file however it is a measurement of depth and so the units are meters. Most samples were collected between 0900 and 1300 MST. The first sampling date occurred shortly after lake had become ice free and then if the lake was at least 3 meters deep it was re-surveyed at approximately 2 week intervals. All data are collected from an inflatable raft at the point of deepest depth. All water samples were taken at the surface (0m), and the hypolimnion (about 2m above the lake sediment) using a Van Dorne sampler. Chlorophyll-a was extracted from water sample filters and absorbance was measured before and after acidification to quantify chlorophyll a concentration. Field measurements were conducted using a YSI either DO or multiple probe meter (YSI MPS 556) and a Li-Cor meter with a quantum sensor. Zooplankton samples were collected opportunistically 3 times throughout the summer, once immediately after ice-out and two more times with at least 14 days bewteen each subsequent visit. Zooplankton were sampled at the deepest location of each lake by pulling a conical net (Wisconsin net) vertically through the water column (i.e., vertical tow sample). The Wisconsin net is 0.9m long with an opening of an inner diameter of 0.2m and a mesh size of 80um. The vertical tow samples are taken from approximately 0.5 m off the lake bottom. After the net is raised and the sides rinsed, the sample is transferred to a 250 mL jar and preserved in a solution of 80% Ethanol. Jars are labeled with date (yyyy-mm-dd), time hh:
EOMORES earth observation and in situ data of water quality in lakes and coastal areas - year 2 and 3
<p>EOMORES is a European innovation project aiming to develop commercial services for monitoring the quality of inland and coastal water bodies, using data from Earth Observation (EO) satellites and in situ sensors to measure, model and forecast water quality parameters.</p> <p>The current data set is a sample of the data generated within the second and third project years (2018, 2019), and consists of Earth Observation (EO) data and in situ data from lakes and coastal areas. For full data sets, please contact the respective contact point listed for each area. Data sets of the first (2017) year of EOMORES have also be submitted.</p> <p>The following is included:<br> - Estonia lakes and coast: in situ data 2019<br> - Finland: links to repositories of EO data<br> - Italy CNR: sample of EO data 2018 and 2019<br> - Italy Trasimeno: sample of WISPstation in situ spectral data and water quality parameters data<br> - Lithuania Curonian Lagoon: sample of WISPstation in situ spectral data and satellite based water quality maps<br> - Netherlands Lake Markermeer: sample of EO data 2018 and 2019 (Chl, Secchi, SPM)<br> - Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017<br> - UK Scotland: links to relevant repositories of in situ data</p> <p>http://eomores-h2020.eu</p>
Data from: Effects of macrophytes on lake‐water quality across latitudes: a meta‐analysis
Macrophytes are widely recognized for improving water quality and stabilizing the desirable clear‐water state in lakes. The positive effects of macrophytes on water quality have been noted to be weaker in the (sub)tropics compared to those of temperate regions. We conducted a global meta‐analysis using 47 studies that met our set criteria to assess the overall effects of macrophytes on water quality (measured by phytoplankton chlorophyll a concentration, total nitrogen concentration, total phosphorus concentration, Secchi depth, and the trophic state index) and to investigate how these effects correlate with latitude using meta‐regressions. We also examined if the effects of macrophytes on lake‐water quality differ with growth form in (sub)tropical and temperate areas by grouping the data and then comparing the effect sizes. We found that macrophytes significantly reduced phytoplankton chlorophyll a concentration, total nitrogen concentration, total phosphorus concentration, as well as the trophic state index, but they did not have a significant overall effect on Secchi depth. The effects of macrophytes on reducing phytoplankton chlorophyll a concentration, total nitrogen concentration, and the trophic state index did not differ with latitude. However, the reduction of total phosphorus concentration was greater at lower latitudes. We showed that at lower latitudes, the positive effects of macrophytes on water quality are similar to or greater than those at higher latitudes, thus challenging the prevailing paradigm of macrophytes being less effective at enhancing lake‐water quality in the (sub)tropics. Furthermore, our data showed that the macrophyte effects vary by growth forms, and the growth forms that positively affect water quality differ between the (sub)tropical and temperate areas. We showed a lack of significant macrophyte effects in surveys within and outside macrophyte stands, suggesting difference in the sensitivities of study designs or possibly weaker effects of macrophytes in lakes compared to experimental settings.
Innovations in water quality assessment
<p>Dataset accompanying BSc thesis "Innovations in water quality assessment" by Henok Tesfai, University of Amsterdam.</p>
Light and water quality observations from neutrally buoyant drifters in rivers
<p><span>Vertical motion is an important driver of sunlight exposure in aquatic environments, shaping the growth and fate of materials and organisms. We derive a simple model accounting for turbulent depth fluctuations of particles to predict the depth that contributes the most sunlight exposure (effective depth) as well as the single depth that, if measured at one place over time, produces the same total sunlight exposure as a moving particle (functional depth). Field measurements of light and depth in rivers using neutrally buoyant drifters and buoys validate our model. Effective depth varied from 0.1-1.5 m below the water surface and was ~30% of the overall water depth on average. Functional depth varied from 0.67-2.3 m and was ~50% of the overall water depth on average. Functional and effective depth are physically based concepts incorporating turbulent motion, spatial variability, and water clarity offering new approaches to characterize light exposure in aquatic environments.</span></p> <p class="MsoNormal"><span> </span></p>
Surface Water Quality Parameters Data of Khadakwasala Reservoir Pune, India
<p>This dataset contains water quality parameters collected from Khadakwasla Reservoir, India, between October 20, 2022, and April 22, 2023. The sampling location coordinates are 18.4390° N and 73.7720° E.</p> <p><strong>Parameters:</strong></p> <ul> <li>Date of sample collection</li> <li>pH</li> <li>Water Temperature (°C)</li> <li>Dissolved Oxygen (DO) (mg/L)</li> <li>Biochemical Oxygen Demand (BOD) (mg/L)</li> <li>Chemical Oxygen Demand (COD) (mg/L)</li> <li>Chlorophyll-a (Chl-a) (µg/L)</li> <li>Turbidity (NTU)</li> </ul> <p><strong>Data collection:</strong></p> <p>Physical water samples were collected from the reservoir. Turbidity was measured onsite using a standard turbidity meter. All the parameters are measured following the American Public Health Association (APHA) protocol. </p> <p><strong>Data format:</strong></p> <p>The data will be provided in a comma-separated values (Excel) file.</p> <p><strong>Quality control:</strong></p> <p>It is not possible to determine the quality control procedures from the information provided.</p> <p><strong>Additional notes:</strong></p> <ul> <li>The data may be useful for researchers studying water quality in Khadakwasla Reservoir or the surrounding area.</li> <li>Users of the data should be aware of the limitations of the dataset, including the relatively short sampling period and the lack of information on quality control procedures.</li> </ul> <p><strong>Acknowledgement:</strong></p> <p>The authors would like to express their sincere gratitude to the Water Resource Department of Maharashtra, Khadakwasala Division for granting permission to collect water samples from the Khadakwasala Reservoir. We appreciate the Department's cooperation and guidance. All security instructions were strictly followed during the sampling process.</p> <p><strong>Contact:</strong></p> <p>[DR.Rushikesh Kulkarni] [rushikeshk@sitpune.edu.in]</p>
Dataset for "Toward polarization-enhanced water quality remote sensing measurements from UAVs"
<p>Dataset used in publication "Toward polarization-enhanced water quality remote sensing measurements from UAVs," published and presented at the 2024 SPIE Future Sensing Technologies (FST) conference.</p>
An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters: Data and R code
<p>These are the data and R code that accompany the Forest Ecology and Management publication titled "An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters". </p>
Online water quality monitoring data from full scale CS#3 DWDN for the DBP prediction model
<p>Online water quality data though the drinking water distribution network. More than 1 year of data.</p> <p>SCADA data source.</p> <p>Provide water quality of the whole system at selected locations.</p>
Supplementary Data and Codes for "Machine Learning Based Long-term Water Quality in the Turbid Pearl River Estuary, China"
<p>The file "Ma_AGUSupplementary.xlsx" contains the in situ data used to develop the ANN model.</p> <p>The files "example_Chla.zip" and "example_TSS.zip" contain codes for retrieving the concentration of Chl-a and TSS on the sea surface of the Pearl River Estuary.</p> <p> </p>
Reservoir water quality data
<span><span><span><span>Potable source-water reservoirs are the main water supplies in many urbanizing regions, yet their long-term responses to cultural eutrophication are poorly documented in comparison to natural lakes, creating major management uncertainties. </span></span><span><span>Here, long-term discrete data (</span></span><span><span>June 2006–June 2018</span></span><span><span>) for classical eutrophication water quality indicators, continuous depth-profile data for dissolved oxygen (DO), and an enhanced hybrid statistical trend analysis model were used to evaluate the eutrophication status of a potable source-water reservoir. Based on classical indicators (nitrogen - N and phosphorus - P concentrations and ratios; phytoplankton biomass as chlorophyll a, chla; and trophic state indices), the reservoir was eutrophic to hypereutrophic and stoichiometrically imbalanced. Anoxia/hypoxia occurred for 7-8 months annually system-wide, even throughout the water column for days to weeks in some years; and elevated total ammonia (up to ~900 µg tNH<sub>3 </sub>L<sup>-1</sup>) in surface waters from late summer/fall through late winter/early spring suggested substantial internal legacy nutrient loading. These surprising DO and tNH<sub>3 </sub>phenomena may characterize many reservoirs in urbanizing areas, and the associated cascade of negative impacts may increasingly affect them under global warming. Total organic carbon (TOC), seasonally influenced by phytoplankton biomass, commonly exceeded 6 mg L<sup>-1</sup> which is problematic for potable water treatment, and significantly trended up over time. Wet-year inflow dilution influenced an apparent decreasing trend in nutrients within the hypereutrophic upper reservoir, which receives most tributary inputs. Nevertheless, significant reservoir-wide trends (increasing TP, phytoplankton chla, TOC) and mid- and/or lower region trends (increasing TN, tNH<sub>3</sub>, decreasing TN:TP ratios) suggest that water quality degradation from eutrophication has worsened over time. </span></span><span><span>These findings support broadly applicable recommendations to strengthen protection of potable source-water reservoirs in urbanizing watersheds: (i) Protective numeric water quality criteria are needed for TOC as well as TN, TP, and chla; (ii) Continuous diel data capture more realistic DO conditions than traditional sampling, and can provide important insights for water treatment managers; and (iii) Assessment of reservoir eutrophication status to track management progress over time should emphasize classic indicators equally as statistical trends, which are highly sensitive to short-term meteorological forcing.</span></span></span></span>
Mississippi River Baton Rouge water quality
<p>We investigated how physical and chemical factors affect Chlorophyll a concentrations in the Mississippi River, the largest river in North America, by sampling 878 times from February 1997 to December 2018 near its terminus at Baton Rouge, Louisiana. Measured values included temperature, light (secchi disk), suspended sediments, and nutrients (nitrate+nitrite, phosphate, TP, TN, ammonium, silicate) that may affect phytoplankton communities. The data are available for unrestricted use.</p>
Submerged aquatic vegetation, water quality (pH, salinity, and turbidity) and waterfowl abundance data from 1991-2017 in Back Bay, Virginia
<p><span>Back Bay, Virginia, has been documented as an important foraging area for waterfowl since at least the mid-1800s. Expansive submerged plant beds historically supported diverse assemblages of non-breeding waterfowl, however coastal development and other anthropogenic influences have since led to fluctuations in submerged aquatic vegetation (SAV) and an associated decline in waterfowl abundance in the bay. To gain insight into the effects of environmental drivers on waterfowl foraging guilds, our study explores the effects of SAV frequency and water quality on the abundance of dabbling ducks, diving ducks, and swans and geese in Back Bay. We use 8 years of SAV, water quality, and waterfowl monitoring data collected by state and federal agencies to model the effects of salinity, turbidity, pH, and percent frequency of SAV on the relative abundance of waterfowl by foraging guild in Back Bay. The appropriateness of the data and reasonability of the preliminary results were then evaluated through semi-structured interviews with 11 local informants representing state, federal, and non-governmental organizations. Quantitative results indicated that dabbling ducks are affected differently than other guilds by water quality and percent frequency of SAV. Thematic analysis of the interview data revealed a number of potential explanations for the model results, as well as highlighted areas of uncertainty in need of further research. In a test of face validity, participants demonstrated a significant degree of belief in turbidity, salinity, and SAV as drivers of waterfowl abundance, but were not convinced by the potential effects of pH as demonstrated by the model. This mixed methods study provides insights that could potentially influence the management and conservation of non-breeding waterfowl populations by challenging the assumption that particular environmental conditions serve all foraging groups equally.</span></p>
Data from: Integrating water quality monitoring and diatom community trends to determine landscape-level change in protected lakes.
<p>These data represent water quality measurements and diatom counts from surface sediments collected from lakes in five National Park units in the Great Lakes Region (USA) between 2005 and 2018. Water quality measurements were made three times annually during the growing season; data are presented as growing season means. Surface sediment samples were collected from the same lakes every 3-5 years over more than a decade to analyze diatom community turnover. Diatom data are presented as proportions relative to total diatom counts in each sample; only taxa occuring at 2% or greater in a given park were included. </p>
Kings and Anderson Lakes water quality
<p>These are data of various water quality parameters measured thought the water column at 1m intervals for both Kings and Anderson Lakes.</p>
Kings and Anderson Lakes water quality
<p>These data were collected in association with an invasive northern pike eradication project that occurred in Kings and Anderson Lakes October 2020. Data were collected at the deepest spot in each lake at 1 m increments from the surface to the bottom. In Kings Lake, samples were taken at this location: 61.617537 -149.359134. At Anderson lake, the data were collected here: 61.620188 -149.340946. The data collected was water temperature, conductivity, dissolved oxygen, and pH.</p>
Machine Learning Constructs Color Features to Accelerate Development of Long-Term Continuous Water Quality Monitoring
<p>This is a machine learning method for predicting the concentration of colored pollutants based on RGB and kmeans methods. This dataset includes raw images of pollutants as well as characteristic data of pollutants, as well as code for the model. You can see the contents of the zip file for details.</p>
Lakefront property owners' willingness to accept easements for conservation of water quality and habitat survey
<p>This dataset describes properties and property owners on inland lakes in the Lower Peninsula of Michigan, USA. Further information can be found at:</p> <p>Nohner, J. K., F. Lupi, and W. W. Taylor. <em>In review</em>. Lakefront property owners’ willingness to accept easements for conservation of water quality and habitat. Water Resources Research.</p>
Water Quality monitoring
Open the record for dataset details and reuse information.
Data: The effects of glucose addition and water table manipulation on peat quality of drained peatland forests with different management practices
<p>The file contain data on peat chemical quality and peat decomposition. We studied how glucose addition, water table and forest harvesting affect the chemical composition of peat and decomposition rate (carbon dioxide fluxes) and soil water quality. Experiments and results are presented in:</p> <p>Aaltonen H., Zhu X., Khatun R., Laurén A., Palviainen M., Könönen M., Peltomaa E., Berninger F., Köster K., Ojala A., Pumpanen J. 2022. The effects of glucose addition and water table manipulation on peat quality of drained peatland forests with different management practices. Soil Science Society of America Journal 86:1625–1638. <a href="https://doi.org/10.1002/saj2.20419">https://doi.org/10.1002/saj2.20419</a></p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.