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484 results for “water quality”
Fig. 1 in Oxidative stress biomarkers in the African sharptooth catfish, Clarias gariepinus, associated with infections by adult digeneans and water quality
Fig. 1. Various maps of the Incomati River showing the position of the sampling site. A– Mozambique shaded on the African continent; B– shows position of Maputo Province in Mozambique; C– indicates the position of the Incomati River and the sampling site.
Fig. 5 in Oxidative stress biomarkers in the African sharptooth catfish, Clarias gariepinus, associated with infections by adult digeneans and water quality
Fig. 5. Principal Component Analysis (PCA) of physico-chemical variables, biomarkers and parasitism in Clarias gariepinus collected in the Incomati River in Mozambique. Two principal components (PC1 and PC2) explained 45.45% of the total variation between water variables, biomarkers and occurrence of parasites. The EC, TDS and salinity (SAL) are associated with Component 1 while LPX, CAT, SOD, turbidity (TB) and temperature (T) are negatively associated with these variables. CI = co-infection; IM = M. nkomatiensis intensity; IG = G. pedatum intensity, UN = uninfected.
Figure 1 in Impact of Drought and Land - Use Changes on Surface - Water Quality and Quantity: The Sahelian Paradox
Figure 1. - Location of the sampling site (star) of flying gurnard Dactylopterus volitans in the Eastern English Channel.
Figure 3 in Impact of Drought and Land - Use Changes on Surface - Water Quality and Quantity: The Sahelian Paradox
Figure 3. - Specimen of flying gurnard Dactylopterus volitans (MNHN 2013-0612; 47 cm TL) caught in the Eastern English Channel in 2011.
Figure 2 in Impact of Drought and Land - Use Changes on Surface - Water Quality and Quantity: The Sahelian Paradox
Figure 2. - Whole otolith of flying gurnard (Dactylopterus volitans) with annotation of growth rings (red stars).
Linked collectors and determiners for: Freshwater samples in MZNA-INV-FRW: Macroinvertebrate samples from the water quality monitoring network along the Ebro Basin.
Natural history specimen data linked to collectors and determiners held within, "Freshwater samples in MZNA-INV-FRW: Macroinvertebrate samples from the water quality monitoring network along the Ebro Basin". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9">https://bionomia.net/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9">https://gbif.org/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9</a>. Formatted as a Frictionless Data package.
Figure 1 in Colony site choice of blue-tailed bee-eaters: influences of soil, vegetation, and water quality
Figure 1. Distribution of blue-tailed bee-eater colony and soil sampling sites on Kinmen Island 2000–2002. Population estimates are given in parentheses for colonies active in 2002.
Figure 2 in Colony site choice of blue-tailed bee-eaters: influences of soil, vegetation, and water quality
Figure 2. Vegetation height profile comparisons between used (solid lines) and abandoned (dashed lines) bluetailed bee-eater nest cavities within colony (X) and between colonies (L and M).
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>
Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use
<p>Changes in climate and land-use and land-cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (1) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios to altered nutrient loads, (2) sensitivity (i.e., how responsive estuaries are to altered nutrient loads), and (3) adaptive capacity (i.e., how the socio-ecological system can use existing resources to reduce the impacts associated with increased exposure). The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increase is expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Our findings illustrate the benefits of integrating natural and socio-ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries. A<a href="https://lisemontefiore.shinyapps.io/estuarine_vulnerability/"> web-based application</a> has been developed to visualize and download the data.</p>
Figure 11 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 11 An example of how water mites appear under a stereoscope. a – water mites of mixed taxa in a single sample ready to be sorted through and identified; b – water mites belonging to the genusTestudacarus and their easily observable characteristic dorsal plates; c – water mites belonging to the genusKongsbergia and their easily distinguishable posterior body shape.
Figure 6 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 6 The sieving process proposed in this manuscript. a – collector fills the composited sample container with water and shakes the container for approximately 10 seconds; b – immediately after shaking the collector removes the lid and pours the water through the 3mm and 250 μm sieves; c and d – the 3mm will be placed on top of the 250 μm sieve so it can filter out larger sized substrate and debris. This process is repeated 10x.
Figure 5 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 5 An example of the collector emptying the net contents from the first of four site collections into sample container; the next three site
Figure 2 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 2 Collection nets discussed in this manuscript. a – a standard, commercially available, truncated D-frame net with 500 μm mesh; b – our custom made, non-truncated D-frame net with 250 μm mesh.
Figure 1 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 1 Examples of impaired and attaining riffle-run stream habitats in central Pennsylvania. a – Warriors Mark Run, impaired stream; b – Muddy Run, impaired stream; c – Spruce Run, attaining stream; d – Laurel Run, attaining stream.
Figure 3 What a in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 3 What a single collection site should look like after the proposed three-minute collection effort. The blue arrows indicate stacked rocks that were stacked upstream of the net to better direct flow into the net. Large rocks were moved before digging into the substrate, while medium-sized and smaller rocks were placed as the substrate was dug up.
Figure 10 A in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 10 A final, picked water mite sample preserved in 80% ethanol ready for identification and enumeration.
Figure 7 After sieving, the collector removes the contents from the 250 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 7 After sieving, the collector removes the contents from the 250 μm sieve and places them into the final sample jar. a – fine sediment
Figure 4 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 4 An example of the collection process proposed in this manuscript. a – the collector stands upstream of the net and disturbs the substrate; b – a typical collection setup that includes one person shoveling and another holding the net; c – water mites become suspended in the water column and flow downstream into the net.
ScienceDex guides
Understand access before you commit
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.