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396 results for “Surface water”
Dataset for "Unconventional structural evolution of the oxide surface in water unveiled by in situ sum frequency spectroscopy"
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Data from: A review of the defining chemical properties of soda lakes and pans: an assessment on a large geographic scale of Eurasian inland saline surface waters
The aim of this study is to evaluate the definition of water chemical type, with particular attention to soda brine characteristics by assessing ionic composition and pH values on a large geographic scale and broad salinity (TDS) range of Eurasian inland saline surface waters, in order to rectify the considerable confusion about the exact chemical classification of soda lakes and pans. Data on pH and on the concentration of eight major ions were compiled into a database drawn from Austria, China, Hungary, Kazakhstan, Mongolia, Russia, Serbia, and Turkey. The classification was primarily based on dominant ions exceeding an equivalent percentage of 25 (> 25e%) of the total cations or anions, and the e% rank of dominant ions was also identified. We identified four major types: waters dominated by (1) Na-HCO3 (10.0%), (2) Na-HCO3 + CO3 (31.4%), (3) Na-Cl (45.9%), and (4) Na-SO4 (12.7%), considering only the first ion by e% rank. These major types can be divided into 30 subtypes in the dataset, taking into account the e% rank of all dominant ions. The major and subtypes of soda brine can be divided into "Soda" and "Soda-Saline" types. "Soda type" when Na+ and HCO3– + CO32– are the first in the rank of dominant ions (> 25e%), and "Soda-Saline type" when Na+ is the first in the rank of dominant cations and the sum of HCO3– + CO32– concentration exceeds 25e%, but it is not the first in the rank of dominant anions. Soda-saline type can be considered as a separate evolutionary stage between Soda and Saline types respect to the geochemical interpretation by saturation indexes of brines. The obtained overlapping ranges in distribution demonstrate that a pH measurement alone is not a reliable indicator to classify the permanent alkaline "soda type" and various other types of temporary alkaline waters.
Figure 4 in Shell surface adaptations in relation to water management in rockdwelling land snails, Albinaria (Pulmonata: Clausiliidae)
Figure 4. Means ¡95% confidence intervals of activation time for smooth and ribbed Albinaria snails.
Figure 3 in Shell surface adaptations in relation to water management in rockdwelling land snails, Albinaria (Pulmonata: Clausiliidae)
Figure 3. Means ¡95% confidence intervals of (a) water gain; (b) water loss and (c) proportion of retained water, for smooth and ribbed Albinaria shells after removing the effect of length (and water gain in b).
Figure 2 in Shell surface adaptations in relation to water management in rockdwelling land snails, Albinaria (Pulmonata: Clausiliidae)
Figure 2. (a) Relation between shell length and dry weight for ribbed and smooth Albinaria snails; (b) means ¡95% confidence intervals of dry weight for smooth and ribbed Albinaria shells after removing the effect of length.
DSM Water Level: An UAV photogrammetry dataset for determination of river surface level using machine learning
<p>Orthophotos and digital surface models (DSMs) obtained using UAV photogrammetry can be used to determine the water surface level of a river. However, this task is difficult due to disturbances of the water surface on DSMs caused by limitations of photogrammetric algorithms. Machine Learning can be used to correct these disturbances as well as to extract a single water surface elevation value. The presented dataset contains raw photogrammetric orthophotos and DSMs of areas representing parts of a small river and the corresponding DSMs with corrected water surface disturbances. Also a single ground truth value of mean water surface level for each DSM sample is provided. This allows the dataset to be used for supervised training of a neural network performing a denoising or regression task.</p> <p>Acknowledgement: some of the samples were extracted from photogrammetric data acquired by Bandini et. al (https://doi.org/10.5281/zenodo.3519888)</p>
Figure 11. Sea surface temperatures for 15 March 2011 in Delineating the fishes of the Clinus superciliosus species complex in southern African waters (Blennioidei: Clinidae: Clinini), with the validation of Clinus arborescens Gilchrist & Thompson, 1908 and Clinus ornatus Gilchrist & Thompson, 1908, and with descriptions of two new species
Figure 11. Sea surface temperatures for 15 March 2011, showing usual summer temperature gradient and upwelling areas on the west coast. (The MODIS Sea Surface Temperature data were downloaded from the Marine Sensing Unit website http://www.afro-sea.org.za).
Global lake surface water temperature layers
<p>In modeling species distributions and population dynamics, spatially-interpolated climatic data are often used as proxies for real, on-the-ground measurements. In shallow freshwater systems, this practice may be problematic as interpolations used for surface waters are generated from terrestrial sensor networks measuring air temperatures. Using these may therefore bias statistical estimates of species' environmental tolerances or population projections -- particularly among pleustonic and epilimnetic organisms. I used a global database of satellite-derived lake surface water temperatures (LSWT) to assess and correct for the statistical correspondence between air and LSWT as a function of atmospheric and topographic predictors, resulting in the creation of monthly high-resolution global maps of air-LSWT offsets, corresponding uncertainty measures, and derived LSWT-based bioclimatic layers for use by the scientific community.</p>
Data for Structural Features of Interfacial Water Predict the Hydrophobicity of Chemically Heterogeneous Surfaces
<p>Scripts, raw and processed data, Jupyter notebooks, and force field files for the simulations performed in:</p> <p>B. C. Dallin, A. S. Kelkar, and R. C. Van Lehn. “Structural Features of Interfacial Water Predict the Hydrophobicity of Chemically Heterogeneous Surfaces.” <em>Chemical Science </em><strong>2023</strong>.</p>
Surface water of Qinghai Province during 1986 to 2018
<p>This dataset contains a product of yearly surface water in Qinghai Province from 1986 to 2018 at 30m×30m spatial resolution. More details on this dataset are presented in the manuscript titled “Spatial-temporal variations of surface water area during 1986-2018 in the Qinghai Province, northwestern China based on Google Earth Engine”.</p>
Surface water supply allocation, crop, and disadvantaged community data for the San Joaquin Valley, CA, 2016
<p>Societies globally are struggling to meet freshwater demands while agencies attempt to address water access inequities under a rapidly changing climate and growing population. An understanding of dynamic interactions between people and water, known as sociohydrology, regionally could provide approaches to addressing local water mismanagement and water access inequity. In semi-arid California, local water agencies, primarily agricultural irrigation districts, are at the intersection of rethinking approaches to balance freshwater demands. More than 150 years of complex water governance and management have defined San Joaquin Valley irrigation districts and the region's water access inequities and sociohydrologic instability.</p> <p>Data in this dataset supported analysis of water governance, specifically including surface water and groundwater dependence within and outside of irrigation districts. Additional data includes disadvantaged community designation, allowing for assessment of inequities between water users and the relationship to broader societal inequities.</p>
Water uptake, cloud condensation nuclei and surface tension: results from the MadFACTS campaign
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Surface water supply allocation, crop, and disadvantaged community data for the San Joaquin Valley, CA, 2016
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Data from: Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011
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Trade-off between vegetation type, soil erosion control and surface water in global semi-arid regions: A meta-analysis
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Ultrafast water permeation through nanochannels with a densely fluorous interior surface
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Data from: An affordable and reliable assessment of aquatic decomposition: tailoring the Tea Bag Index to surface waters
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Data from: A review of the defining chemical properties of soda lakes and pans: an assessment on a large geographic scale of Eurasian inland saline surface waters
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Data from: Predicting nitrate retention at the groundwater- surface water interface in sandplain streams
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Water quality and spatial parameters from the main channel of a 6th order stream collected with an uncrewed surface vehicle
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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)
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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.