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83 results for “water properties”
Water properties of Arco Lake, Budd Lake, Deming Lake, and Josephine Lake in Itasca State Park from 2006-2009 and 2019-et seq.
Depth profiles of water column chemical and physical properties were assessed with seasonal-scale frequency from four lakes in the Itasca State Park from 2006-2009 and from 2019-et seq. The data was used to assess the mixing status and major geochemical constituents within the lakes. Several parameters were routinely measured with deployable probes at meter or sub-meter resolution at the deepest location in each lake. Water samples were also collected for laboratory analysis. Bathymetry data collected in 2022 is supplied as rasters.
Water properties of Brownie Lake, MN and Canyon Lake, MI from 2015-2022
Depth profiles of water column chemical and physical properties were assessed with seasonal-scale frequency from two meromictic lakes in the upper Midwest, U.S.A. from 2015 to 2022. Brownie Lake in Minneapolis, MN and Canyon Lake in the Huron Mountains of MI both contain elevated hypolimnetic dissolved iron (i.e. “ferruginous”). Several parameters were routinely measured with deployable probes at meter or sub-meter resolution at the deepest location in each lake. Water samples were also collected for laboratory analysis.
Dissolved Organic Carbon Concentration, Dissolved Organic Matter Optical Properties, and Water Quality Indicators in the Plum Island Estuary (PIE), Massachusetts, USA (2018-2023)
This is a data set of paired in situ measurements of water quality parameters, total suspended solids concentration, and concentration and optical properties (absorption coefficient spectra and fluorescence indices) of dissolved organic matter (DOM) collected between 2018 and 2023 in the Plum Island Estuary and nearshore waters. In situ water quality measurements (salinity, temperature, optical dissolved oxygen saturation, turbidity, and dissolved organic matter fluorescence) were collected with a water quality sonde from the surface (top 1 m of water column), along with corresponding samples that were processed and analyzed in the lab for dissolved organic carbon (DOC) concentration, chromophoric DOM (CDOM), absorption coefficient spectra, DOM excitation-emission matrix (EEM) fluorescence, and total suspended sediment (TSS) concentration. The data were used in multiple studies (see manuscripts listed below) focusing on the dynamics of DOC and CDOM in the Plum Island Estuary.
Optical properties of marine aerosols with varying water content at wavelengths 532 and 1064 nm, modelled with a morphologically realistic aerosol model
<p>The data contain computational results obtained with the ADDA program at wavelengths 532 nm and 1064 nm, for particle sizes 0.04, 0.06, ..., 1.5 micrometers (where size = volume-equivalent dry radius), and for salt mass fractions 0.91, 0.94, 0.97, 1.00. The content of the data files is described in the README file.</p>
Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses sampled during the Tara Pacific Expedition 2016-2018
<p>In order to provide a description of the dynamical properties of the water masses sampled, different Eulerian and Lagrangian diagnostics were calculated. </p> <p>For each of the 246 stations sampled, we proceeded as follows.</p> <p>We identified the water mass sampled at the given station. This was considered as a stadium shape with the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the stadium semi-circles was considered 0.1°, which is in accordance with previous studies25,29,30. The stadium was filled with virtual particles separated by 0.01°.</p> <p>For each virtual particle inside the stadium shape, we calculated an Eulerian or Lagrangian diagnostic (described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sampling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward in time for an amount of time 𝞽 from the day of sampling day_S. For the Lagrangian betweenness, the advection was performed between day_S+𝞽/2 and day_S-𝞽/2, so that the advective time window was centered on the sampling day (details in25).</p> <p>For the Lagrangian diagnostics, we used the following advective times 𝞽: 5, 10, 15, 20, 30, and 60 days. The only exception is the retention time, which, by construction, was calculated only with the largest advective time, namely 𝞽=60 days.</p> <p>Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.</p> <p> Furthermore, two different velocity fields were used, which are described as follows. </p> <p>Both the velocity fields were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used was MULTIOBS_GLO_PHY_REP_015_004 [GlobEkmanDt]. This was produced by combining the altimetry derived geostrophic velocities and modeled Ekman surface currents. It had a spatial resolution of 0.25° and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_030 [GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial resolution of 1/12° and a temporal resolution of 1 day.</p> <p>The following Eulerian diagnostics were calculated:</p> <ul> <li> <p>Absolute velocity ([Uabs], m s-1): sqrt(u2+v2), where u and v are the zonal and meridional components of the horizontal velocity field used (described below)</p> </li> <li> <p>Kinetic energy ([Ekin], m2 .s-2): 0.5*(u2+v2)</p> </li> <li> <p>Divergence ([EulerDiverg], d-1): du/dx + dv/dy</p> </li> <li> <p>Vorticity ([Vorticity], d-1): dv/dx - du/dy</p> </li> <li> <p>Okubo-Weiss ([OW], d-2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx+du/dy)2. If negative, it indicates that the station sampled was inside an eddy.</p> </li> </ul> <p>The following Lagrangian diagnostics were calculated:</p> <ul> <li> <p>Finite-Time Lyapunov Exponents ([Ftle], d-1): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. barriers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far away backward in time.</p> </li> <li> <p>Lagrangian betweenness ([betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory26. It can identify regions which act as bottlenecks for the circulation, in that they receive waters coming from different origins, and that are then spread over several different destinations. These can represent possible hotspots driving biodiversity25.</p> </li> <li> <p>Lagrangian Divergence ([LagrDiverg], d-1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling.</p> </li> <li> <p>Retention Time ([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the previous period. If the water mass is outside an eddy, then its retention time is set to zero.</p> </li> </ul>
Stability in water and electrochemical properties of the Na3V2(PO4)2F3 – Na3(VO)2(PO4)2F solid solution
<p>Graphitical Abstract of the publication "Stability in water and electrochemical properties of the Na<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F<sub>3</sub> – Na<sub>3</sub>(VO)<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F solid solution" <a href="https://www-sciencedirect-com.docelec.u-bordeaux.fr/science/journal/24058297/20/supp/C">Energy Storage Materials Volume 20</a>, 2019, p. 324-334 - DOI : <a href="https://doi-org.docelec.u-bordeaux.fr/10.1016/j.ensm.2019.04.010">https://doi.org/10.1016/j.ensm.2019.04.010</a></p> <p>Abstract : Polyanionic materials have been intensively studied as promising active materials for <a href="https://www-sciencedirect-com.docelec.u-bordeaux.fr/topics/engineering/positive-electrode">positive electrodes</a> in Na-ion batteries thanks to their excellent stability upon cycling and the fast ionic mobility in their structural framework. Among them, Na<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F<sub>3</sub> and Na<sub>3</sub>(VO)<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F are two of the most promising ones due to their high voltages for Na<sup>+</sup>-ion extraction and their high energy densities: 500 mWh g<sup>−1</sup> and 495 mWh g<sup>−1</sup>, respectively. Here, we study the formation mechanism as well as the stability of these phases in <a href="https://www-sciencedirect-com.docelec.u-bordeaux.fr/topics/engineering/aqueous-medium">aqueous media</a> and the possible use of a washing step in water in order to remove undesirable <a href="https://www-sciencedirect-com.docelec.u-bordeaux.fr/topics/materials-science/impurity">impurities</a> formed during the synthesis. Furthermore, the origin of the extra capacity observed at the high voltage region for Na<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F<sub>3</sub> and Na<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F<sub>1.5</sub>O<sub>1.5</sub> was studied by <em>operando</em> <a href="https://www-sciencedirect-com.docelec.u-bordeaux.fr/topics/materials-science/x-ray-absorption-spectroscopy">X-ray absorption spectroscopy</a>.</p> <p> </p>
Thermodynamic properties of ammonia-water (NH3H2O mixture). In Esperanto
<p>Thermodynamic data for the ammonia-water mixture are adapted from: Ibrahim, O. M. (1993). Thermodynamic properties of ammonia-water mixtures. In ASHRAE Transactions: Symposia (Vol. 93, p. 1495). <br> <br> </p>
A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?
<p>Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials.<br> In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram.<br> We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties.<br> This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions.<br> An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties.<br> These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process.<br> The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.</p>
Vertical profiles of stable water isotopes and thermodynamic properties from research flights during the L-WAIVE field campaign in June 2019
<p>This datasets contains the measurements of stable water isotopes conducted during the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021). The measurements were conducted using a Picarro laser spectrometer L2130-i that was installed on an ultralight aircraft. The Picarro measurements of atmospheric humidity are merged measurements of thermodynamic properties by a fast-response temperature and humidity probe (iMet XQ-2; see also Chazette et al. 2021) interpolated on 10s temporal resolution.</p> <p>The data is provided on a one file per flight. All variables are described in README.</p> <p>This dataset has been used in Thurnherr et al. (submitted) for a comparison study of stable water isotopes measurements from various platforms and COSMOiso model simulations.</p>
Chemical, optical, and oxidizing properties of three kinds of water-soluble organic matter in PM2.5 from biomass and coal combustion in rural areas in Northwest China
<p>this data set is about the molecular carbon content, light absorption, infrared spectra, and oxidation activity in PM2.5</p>
Fig 2 in Monitoring and assessing the physico-chemical water properties and planktonic communities in tilapia nursing pond
Fig 2: Percentages of different phytoplankton communities during the study period (a, b c, are indicating the size category as small, medium and large)
Fig 1 in Monitoring and assessing the physico-chemical water properties and planktonic communities in tilapia nursing pond
Fig 1: Percentages of total phytoplankton and total zooplankton in all size categorized pond during the sampling period
Lattice Boltzmann simulation of liquid water transport in gas diffusion layers of proton exchange membrane fuel cells: Impact of gas diffusion layer and microporous layer degradation on effective transport properties
<p><span>Underlying data to publication Sarkezi-Selsky et al., <em>J. Pow. Sour.</em> 556 (2023) 232415,<span> https://doi.org/10.1016/j.jpowsour.2022.232415</span> <br><br>Polymer Electrolyte Membrane Fuel Cells (PEMFCs) represent a promising technology for clean drivetrain solutions, in particular for heavy-duty applications. However, lifetime requirements demand high durability of each cell component.<br></span><span>In this work, transport of liquid water through pristine and degraded gas diffusion layers (GDL) was simulated with a 3D Color-Gradient Lattice Boltzmann model. The GDL microstructure was reconstructed </span><span>from high-resolution X-ray micro-computed tomography (</span><span>μ</span><span>-CT) of an impregnated Freudenberg H14. The </span><span>effect of a microporous layer (MPL) was considered by reconstruction of an impregnated and MPL-coated H14. Aged microstructures were generated artificially, assuming loss of polytetrafluoroethylene (PTFE) within the GDL and increase of MPL macroporosity as main degradation mechanisms. Liquid water transport within aged microstructures was simulated by imposing a liquid phase flow rate until breakthrough was reached. Subsequently, the GDL microstructures were analyzed for their breakthrough characteristics by means of saturation and effective gas transport properties. When the MPL was pristine, no distinct GDL degradation effect was observable, this was attributed to the MPL dominating capillary transport. MPL aging, however, led to increased saturations and thus to a deterioration of the effective gas transport. With a partially degraded MPL, aging of the GDL then appeared to affect the breakthrough characteristics.</span></p>
Figure 6. Caspase 3 in Physical characterization and wound healing properties of Zamzam water
Figure 6. Caspase 3 levels of treatment groups. ***Extremely high significant at p <0.001 when compared to Group 1; **Highly significant lesser at p <0.01 on comparing with Group 2; ns: nonsignificant when compared to group 3 at p <0.05. Group 1: Normal control; Group 2: Disease control (wound without treatment); Group 3: Standard control (treatment with povidoneiodine cream); Group 4: Zamzam water treatment group.
Figure 7. A comparative wound healing study. A1 in Physical characterization and wound healing properties of Zamzam water
Figure 7. A comparative wound healing study. A1: Control animals, dorsal view of wound soon after creating on the 1st day; B1: Dorsal view of the wound after treating with povidone-iodine cream on 3rd day; C1: Dorsal view of the wound after treating with Zamzam water on 3rd day; A2: Control animals, dorsal view of the wound on 6th day; B2: Dorsal view of the wound after treating with povidoneiodine cream on 6th day; C2: Dorsal view of the wound after treating with Zamzam water on 6th day; A3: Control animals, dorsal view of the wound on 12th day; B3: Dorsal view of the wound after treating with povidone-iodine cream on 12th day; C3: dorsal view of the wound after treating with Zamzam water on 12th day.
Figure 2. Serum IL-1 in Physical characterization and wound healing properties of Zamzam water
Figure 2. Serum IL-1β level of treatment groups. *Significantly lesser at p <0.05 on comparing with Group 2; **Highly significant lesser at p <0.01 on comparing with group 2; ns: nonsignificant when compared to Group 3. Group 1: Normal control; Group 2: Disease control (wound without treatment); Group 3: Standard control (treatment with povidone-iodine cream); Group 4: Zamzam water treatment group.
Figure 1 in Physical characterization and wound healing properties of Zamzam water
Figure 1. Zeta potential analysis of Zamzam water. (A) Before exposure to open-air; (B) After exposure to open-air.
Figure 3. Serum IL-6 in Physical characterization and wound healing properties of Zamzam water
Figure 3. Serum IL-6 level of treatment groups. ***Extremely high significant at p <0.001; **Extremely significant at p <0.01 on comparing with Group 2; ns: nonsignificant when compared to Group 3. Group 1: Normal control; Group 2: Disease control (wound without treatment); Group 3: Standard control (treatment with povidone iodine cream); Group 4: Zamzam water treatment group.
Figure 5. Caspase 9 in Physical characterization and wound healing properties of Zamzam water
Figure 5. Caspase 9 levels of treatment groups. ***Extremely high significant at p <0.001 when compared to Group 1; **Significantly lesser at p <0.01 on comparing with Group 2; ns: nonsignificant on comparing with group 3 at p <0.05. Group 1: Normal control; Group 2: Disease control (wound without treatment); Group 3: Standard control (treatment with povidone-iodine cream); Group 4: Zamzam water treatment group.
FIG. 7. — Dendrograms resulting from Q in Micropaleontological parameters as proxies of late Miocene surface water properties and paleoclimate in Gavdos Island, eastern Mediterranean
FIG. 7. — Dendrograms resulting from Q-mode cluster analysis and the assemblages identified in each section.
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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.