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18 results for “water quality parameters”
Measuring water quality parameters to estimate Nitrate concentration in surface water in Bonet catchment, Sligo, Ireland
<p><span>Time series data of surface water quality (temperature, pH, dissolved oxygen, oxidation-reduction potential and electrical conductivity) collected from May 2024 to September 2024 at 1m intervals. The file contains tabular data with the following columns: Date and time, Battery (%), temperature (ºC), fix Quality in fix code (Fix), Latitude (in deg), Longitude (in deg), pH,<span> </span>electrical conductivity (µS/cm), TDS (in ppm),<span> </span>Salinity in PSU(ppt), Specific Gravity (in SG), Dissolved Oxygen (in mg/L), Oxygen Saturation (in %), ORP (in mV), Altitude (in meters), Ground Speed (in m/s), Horizontal dilution, Satellites in number.</span></p>
PRISMA-derived water quality parameters for Lake Hume (Australia) (2020/04/22)
<p>This dataset contains PRISMA-derived water quality (WQ) products of Lake Hume (Australia) for the 22 April 2020. Available parameters are: True-color image (RGB), Colored Dissolved Organic Matter (CDOM), Chlorophyll-a (CHL), and Suspended Particulate Matter (SPM). WQ parameters have been calculated using CNR’s bio-optical model BOMBER parameterized with the inherent optical properties specific of the case study. The data are available as GeoTiff files in WGS 84 / UTM zone 32N (EPSG: 32632). PRISMA data courtesy of the Italian Space Agency (ASI, 2020).</p>
DESIS-derived water quality parameters for Lake Hume (Australia) (2020/02/26)
<p>This dataset contains DESIS-derived water quality (WQ) products of Lake Hume (Australia) for the 26 February 2020. Available parameters are: True-color image (RGB), Colored Dissolved Organic Matter (CDOM), Chlorophyll-a (CHL), and Suspended Particulate Matter (SPM). WQ parameters have been calculated using CNR’s bio-optical model BOMBER parameterized with the inherent optical properties specific of the case study. The data are available as GeoTiff files in WGS 84 / UTM zone 32N (EPSG: 32632). DESIS data courtesy of the German Aerospace Center (DLR, 2020).</p>
DESIS-derived water quality parameters for Lake Mulargia (Sardinia, Italy) (2020/08/17)
<p>This dataset contains DESIS-derived water quality (WQ) products of Lake Mulargia (Sardinia, Italy) for the 17 August 2020. Available parameters are: True-color image (RGB), Colored Dissolved Organic Matter (CDOM), Chlorophyll-a (CHL), and Suspended Particulate Matter (SPM). WQ parameters have been calculated using CNR’s bio-optical model BOMBER parameterized with the inherent optical properties specific of the case study. The data are available as GeoTiff files in WGS 84 / UTM zone 32N (EPSG: 32632). DESIS data courtesy of the German Aerospace Center (DLR, 2020).</p>
PRISMA-derived water quality parameters for Lake Mulargia (Sardinia, Italy) (2020/07/08)
<p>This dataset contains PRISMA-derived water quality (WQ) products of Lake Mulargia (Sardinia, Italy) for the 8 July 2020. Available parameters are: True-color image (RGB), Colored Dissolved Organic Matter (CDOM), Chlorophyll-a (CHL), and Suspended Particulate Matter (SPM). WQ parameters have been calculated using CNR’s bio-optical model BOMBER parameterized with the inherent optical properties specific of the case study. The data are available as GeoTiff files in WGS 84 / UTM zone 32N (EPSG: 32632). PRISMA data courtesy of the Italian Space Agency (ASI, 2020).</p>
Dataset of water quality parameters (WP6 Cure4Aqua)
<p>Dataset of water quality parameters (alkalinity, ammonia, nitrates, nitrites, dissolved oxygen, pH, salinity, water temperature) in two flow-through (G1, G2) and two RAS (G3, G4) tanks </p>
High-frequency water quality and air parameters from large lake Võrtsjärv, Estonia: 2010-2019
This high frequency water-air dataset was collected from large shallow Lake Võrtsjärv (Estonia) with an automated lake monitoring buoy system and was used in the analyses described in the manuscript by Thayne, M.W., B. Kraemer, J.P. Mesman, A. Laas, E. de Eyto, B. W. Ibelings, R. Adrian. “Trophic state effects on antecedent lake characteristics shape the resistance and resilience of lakes following extreme storms”. Dataset contains every 10-minute measurements from the open water periods in years 2010-2019. Underwater measurements were conducted with Yellow Springs Instrument multiparameter sonde model YSI 6600 V2-4. Underwater measurements contain the collected values of pH, dissolved oxygen, dissolved oxygen saturation, water temperature, specific conductance, water turbidity, chlorophyll concentration (calculated by the sensor from fluorescence values), and the abundance of the bluegreen algae (calculated by the sensor from phycocyanin fluorescence values). Air parameters were collected with the Vaisala multiparameter weather station model WXT520 and contain measurements of wind direction, wind speed, air temperature, atmospheric pressure, and cumulative amount of rain between measurement periods. All air measurements were collected 2 m above from the water surface. Additionally, solar irradiance measurements were collected from the automated buoy system with the Licor pyranometer model LI-200, those measurements contain values in two different columns: average solar irradiance in W/m2 and average photon flux density values in micromoles per square meter per second.
Fig. 1 in Transport of jundiá Rhamdia quelen juveniles at different loading densities: water quality and blood parameters
Fig. 1. Plasma cortisol and blood glucose of jundiá juveniles transported at different loading densities. Different letters over the bars indicate significant differences (P <0.05) among treatments within each experimental time. Asterisks show significant differences (P <0.001) when compared to the initial value.
EO-derived water quality parameters using aerial imaging spectrometry for Lake Mulargia (Sardinia, Italy) (2020/09/24)
<p>This dataset contains Hyspex-derived water quality (WQ) products of Lake Mulargia (Sardinia, Italy) for the 24 September 2020. The acquisition was done by CGR Spa (Italy). Available parameters are: True-color image (RGB), Colored Dissolved Organic Matter (CDOM), Chlorophyll-a (CHL), and Suspended Particulate Matter (SPM). WQ parameters have been calculated using CNR’s bio-optical model BOMBER parameterized with the inherent optical properties specific of the case study. The data are available as GeoTiff files in WGS 84 / UTM zone 32N (EPSG: 32632).</p>
Dataset for the paper submitted for peer-review with the title "Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model"
<p>The proposed dataset is related to the following article submitted for peer review:</p> <p>Hasanyar, M., Flipo, N., Romary, T., Wang, S. (2023), Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model, UNDER PEER-REVIEW</p> <p>It consists of command files for the prose-pa0.74 software available here: https://gitlab.com/prose-pa/prose-pa </p> <p>To run the model :</p> <p>1. Compile prose-pa0.74</p> <p>2. Copy the executable in the current directory</p> <p>3. In a terminal launch</p> <p>> ./prose-pa0.74 simulation.COMM test.log</p> <p>The “simulation.COMM” holds the settings for the ProSe-PA simulation related to the paper mentioned in the front head of the current file. </p> <p>The information on different parameters of “simulation.COMM” are included in “bathymetrie”, “Cmds”, “Inflows”, “layers”, “meteo”, “o2_obs”, “param_bio”, “Reaches” and “Singularities” folders.</p> <p>The “bathymetrie” folder holds the geometric information of several cross-sections along the river. </p> <p>The Cmds folder holds the “simulation.COMM” file. </p> <p>The “Inflows” folder the information about the boundary condition inflows to the river such as discharge, concentration of organic carbon, etc.</p> <p>The layer folder holds data of the initial conditions of the model (Table 2 in the article).</p> <p>The “meteo” folder holds the meteorological information.</p> <p>The “o2_obs” folder holds the observed oxygen data needed to do data assimilation. </p> <p>The “param_bio” folder holds information on the physiology of bacteria, phytoplankton, and other model species.</p> <p>The “Reaches” folder holds information about river reaches and their manning coefficient. </p> <p>The “param_range” file holds the variation range of model parameters considered in data assimilation together with their perturbation percentage.</p> <p>The output files are written in $HOME/Outputs folder. It is possible to change it directly in simulation.COMM, last entry “Output_folder”.</p>
Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks
<pre>The data are part of the doctoral work entitled: Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks.</pre>
Bio-physicochemical Parameters of Water Quality Analysis in Dass LGA, Northeastern Nigeria
<p>The data consist of 20 groundwater samples collected randomly in Dass LGA for water quality assessment. The data has 14 biophysicochemical parameters including:<br>1.pH = The pH value of the samples</p> <p>2. Temp = Temperature of the samples</p> <p>3. TBD = Turbidity of the samples</p> <p>4. Colr = Colour of the water</p> <p>5. TDS = Total Dissolved Solids</p> <p>6. EC = Electrical Conductivity</p> <p>7. TH = Total Hardness</p> <p>8. Al- = Aluminum</p> <p>9. F- = Fluoride</p> <p>10. Fe- = Iron</p> <p>11. NO3 = Nitrate</p> <p>12. Mn = Manganese</p> <p>13. TCC = Total Coliform Count</p> <p>14. FCC = Fecal Coliform Count </p> <p>Other parametrs recorded include Location (Lat/Lon), Name of community, Sample Name, Altitude, Depth, Source of pollution, Distance to pollution source </p>
Water Quality parameters in Odesa, Ukraine from February to May 2024
<p>The TOPCOAT Data.xlsx file contains parameters of seawater at the Odesa coast of the Black Sea as two sheets:</p> <p>A. WISP-3 - data measured by WISP-3 instrument</p> <ol> <li>Chl - Chlorophyll-a in μg/l</li> <li>TSS - Total Suspended Matter in mg/l</li> <li>Kd - Transparency in 1/m</li> <li>CPC - Phycocyanin in μg/l</li> </ol> <p>B. Values for the phytoplankton community</p> <ol> <li>N - Abundance in 10^6/m^3</li> <li>B - Wet Biomass in mg/m^3</li> <li>Bc - Carbon Biomass in mg/m^3</li> <li>S - Surface Area in µm^2/m^3</li> <li>S/W - Specific Surface in m^2/kg</li> <li>SI - Surface Index in 1/m</li> </ol> <p>S, S/W, and SI were calculated by (Minicheva H.G., Zotov A.B., Kosenko M.N., 2003. Methodological recommendations for determining the complex of morpho-functional indicators for unicellular and multicellular forms of aquatic vegetation. Odesa, 37 рр.)</p> <p>Both sheets also contain the name of the Station together with its latitude and longitude, and the date when samples were collected. The WISP-3 sheet additionally contains Time (UTC), Water Temperature (°C) and Salinity (‰).</p> <p> </p>
Predicting the density of zooplankton subsidy to a stream with multiple impoundments using water quality parameters
<p>Damming a stream inserts a lentic system (an impoundment or reservoir) into a lotic system, changing downstream hydrological, biogeochemical, and ecological processes. One such ecological effect of damming is to create a resource subsidy of easily captured and consumed zooplankton, which are preyed upon by filter-feeders and visual predators. The data included here were used to predict the density of lentic zooplankton subsidizing downstream habitats with water quality parameters as an alternative to microscopy. We also used this data to detect three different water quality regimes (high conductivity, high-CDOM, and a remainder) that are associated with differences in the density of zooplankton. This dataset is contained in two parts, both of which are focused on zooplankton density in the effluent of a series of tributary-impoundment reservoirs: 1) zooplankton density for a single summer season with water quality parameters and 2) zooplankton density for a series of three summers without water quality parameters.</p>
Predicting the density of zooplankton subsidy to a stream with multiple impoundments using water quality parameters
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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>
Water quality and spatial parameters from the main channel of a 6th order stream collected with an uncrewed surface vehicle
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FLUID: Bio-optical water quality parameters (in-situ 2017-2019) in 4 Baltic lakes
<p>The database consists bio-optical measurements done under FLUID<sup>1</sup> project in 4 different lakes (Burtnieks, Lubans, Razna and Võrtsjärv) during 2017-2019.</p> <p>The listed parameters are: </p> <ul> <li>LAKE (name)</li> <li>DATE </li> <li>TIME (local time)</li> <li>LAT (latitude)</li> <li>LON (longitude)</li> <li>Air T (air temperature in Celcius)</li> <li>Water T (water temperature in 0.5 m depth in Celcius)</li> <li>DO (dissolved oxygen in ppm)</li> <li>O2 (O2 saturation in %)</li> <li>Secchi (in meters)</li> <li>Chl a (chlorophyll-a in mg/m3)</li> <li>Pheo (pheopigments in mg/m3) </li> <li>TSS (total suspended sediments in mg/l)</li> <li>OSS (organinc suspended sediments in mg/l)</li> <li>MSS (mineral suspended sediments in mg/l)</li> <li>CDOM (absorption of coloured dissolved organic matter at 400 nm)</li> <li>TN (total nitrogen in mg/l)</li> <li>TP (total phosphorus in mg/l)</li> <li>CO2 (carbon dioxide in mg/l)</li> </ul> <p><sup>1</sup>FLUID is funded by ERDF, Latvian state budget and IES proposal No.1.1.1.2/VIAA/1/16/137, Contract No. 1.1.1.2/16/I/001 “Innovative tool for lake monitoring using remote sensing data"</p>
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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.