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484 results for “water quality”
Fig. 3 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas
Fig. 3. Activity (mean ± SEM, n = 8) of catalase in liver (A) and gills (B) of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P <0.05).
Fig. 4 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas
Fig. 4. Content (mean ± SEM, n = 8) of glutathione in liver (A) and gills (B) of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P<0.05).
Figure 4 in Diatom responses to river water quality in the Kruger National Park, South Africa
Figure 4. The relationship between SPI and two physicochemical water quality parameters. Open symbols represent rivers with large catchments outside the KNP. Closed represent rivers with relatively small parts of their catchments outside the KNP. In both cases, the dark- er the symbol, the larger the catchment.
Figure 3 in Diatom responses to river water quality in the Kruger National Park, South Africa
Figure 3. Two-dimension MDS plot of diatom community composition in 1983 and 2015 at different sites along the Olifants (OM and OC), Letaba (LC and LK) and Sabie rivers (SL and SB). Note that all sites' diatom composition shifted significantly from 1983 to 2015 irrespective of whether the index values changed or not (see Table 4). Sample site abbreviations also in Table 4.
Figure 2 in Diatom responses to river water quality in the Kruger National Park, South Africa
Figure 2. Long-term changes in two selected river chemical parameters in the Olifants, Letaba and Sabie rivers between 1983 and 2015. The closed symbols represent data extracted from the Department of Water Affairs and Sanitation. Open symbols represent the values recorded at sites within the three focal rivers during the present study in 2015.
Figure 1 in The influence of land use-impacted tributaries on water quality and phytoplankton in the Mooi River, North West Province, South Africa
Figure 1: Catchment area of the Mooi River from its source to the confluence with the Vaal River, showing the position of sampling sites in the main stream (1–8) and in the tributaries (WFS, GM and WS). Different types of land use activities in the catchment are also indicated on the map.
Figure 1. A in Diatom responses to river water quality in the Kruger National Park, South Africa
Figure 1. A map of the Kruger National Park, showing the location of the study area and diatom sampling sites.
Figure 3 in The influence of land use-impacted tributaries on water quality and phytoplankton in the Mooi River, North West Province, South Africa
Figure 3: Canonical Correspondence Analysis (CCA) of the physico-chemical environmental variables, natural log of the phytoplankton data, and the different sites located in the Mooi River for 2015.
Figure 1 in Water quality assessment of the Demetrio stream: an affluent of the Gravataí River in the South of Brazil
Figure 1. Satellite view of the area and water sampling points of Demétrio stream: point 1 source, point 2 and point 3, upstream near the area with the highest urban density and from the downstream near the meeting point of the Demétrio stream with the Gravataí River.
Figure 4 in Water quality assessment of the Demetrio stream: an affluent of the Gravataí River in the South of Brazil
Figure 4. Integrated analysis of physicochemical and microbiological factors (PCA) of three water samples of Demétrio stream: point 1, point 2 and point 3. Component 1, with 78.9% affinity, separates component 2 with 21.1% affinity.
Figure 3 in Water quality assessment of the Demetrio stream: an affluent of the Gravataí River in the South of Brazil
Figure 3. Al, Fe, Mn and Cu analysis of three water samples of Demétrio stream: point 1 (P1), point 2 (P2) and point 3 (P3).
Marcellus shale water and air quality data
<p>Data summary for water and air quality of the Marcellus shale</p>
EOMORES earth observation and in situ data of water quality in lakes and coastal areas - year 1
<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 first project year (2017), 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.<br> Data sets of the second (2018) and third (2019) year of EOMORES will also be submitted.</p> <p>The following is included:<br> - Estonia lakes and coast: in situ data 2017<br> - Finland: links to repositories of EO data<br> - Italy Trasimeno: sample of EO data 2017<br> - Lithuania Curonian Lagoon: sample of EO data 2017<br> - Netherlands Lake Markermeer: sample of EO data 2017<br> - Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017<br> - UK Scotland: in situ data Loch Leven and Loch Lomond 2017<br> - UK WCO Sentinel2A match ups: Western Channel Observatory match ups with Sentinel-2 satellite 2016, 2017</p> <p>http://eomores-h2020.eu</p>
Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers
<p>Hydraulic and tracer data used in the Chillan case study presented in "Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers"</p>
Dataset for RANAS and water quality survey in Malawi
<p>Data outlines behaviour factors and water quality results used in the co-design and development of educational materials for Malawi WaterSPOUTT prototype</p>
Water Quality Classes - Recommended Water Quality Based on Guideline and Typical Wastewater Qualities
<p>This dataset compiles water quality standards for different end-uses based on most prominent guidelines. The value "-1" signifies no limit specified or no data available. The dataset also contains a list of typical wastewater qualities for several types of wastewater to be reused. The dataset contains the following two document:</p> <ul> <li>Water Quality Classes - Typical Wastewater Qualities and Recommended Water Quality Based on international Guidelines(Dataset) - PDF</li> </ul>
Water quality data with nitrate
<p>Water quality data in a drinking water distribution network (Helsinki and Vantaa, in Finland). This data includes all the samples that have nitrate analysis. Monochloramine is used in the distribution network as secondary disinfection chemical. The water originates from Lake Päijänne. The titles of the Excel file are in Finnish.</p> <p>Included are an Excel file, an abstract and a poster, where this data was partly utilized. The reference of the poster and abstract: <a href="https://research.aalto.fi/portal/pirjo.rantanen.html">Rantanen, P</a> 2015, <a href="https://research.aalto.fi/en/publications/nitrification-in-drinking-water-distribution-network-in-helsinki-and-vantaa(c7db018f-3225-4e9f-9c05-237533d3669a).html">Nitrification in drinking water distribution network in Helsinki and Vantaa</a>. in Finnish Conference of Environmental Sciences 12th May 2015, Jyväskylä. ed. / Elijah Ngumba; Tuula Tuhkanen; Matti Leppanen; Jaakko Mannio; Sanna Pynnonen. Jyväskylä : FCES, 2015. p. 51.</p>
Figure 3 in Water quality assessment in an irrigation pond based on adult caddisfly (Insecta: Trichoptera) assemblages
Figure 3. Canonical Correspondence Analysis (CCA) showing correlation between caddisflies species and physicochemical variables. Abbreviations for taxonomy are shown in Table 2.
Figure 2 in Water quality assessment in an irrigation pond based on adult caddisfly (Insecta: Trichoptera) assemblages
Figure 2. The total number of species and individuals caught at an irrigation pond in the Kasetsart University, Thailand.
Fig. 2 in Oxidative stress biomarkers in the African sharptooth catfish, Clarias gariepinus, associated with infections by adult digeneans and water quality
Fig. 2. Monthly variation of physico-chemical parameters during the fish collection period, October 2016–September 2017. A– pH; B– Electrical conductivity; C– Temperature; D– Dissolved oxygen; E– Salinity; F– Turbidity; G– Total dissolved solids.
ScienceDex guides
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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.