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19,365 results for “watering”
Hourly LC impacts - Fresh water Eutrophication - current mix and future scenarios
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Fresh water Eutrophication, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Data and code for: Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent
<p>Code and data to reproduce figures in manuscript entitled "Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent" published in Hydrology and Earth System Sciences (https://hess.copernicus.org/preprints/hess-2022-136/).</p> <p>The contents include three folders, "Codes", "Data", and "Figures". In "Codes" folder, R scripts are listed in the order needed to reproduce the figures. All code is written in R version 4.2.0. Data sets needed to reproduce figures are provided in "Data" folder (Rdata format). The pdf files in "Figures" folder are outputs generated from the corresponding R scripts. Note that final figures in the article were produced by combining multiple figures using a vector graphics software (Inkscape) or PowerPoint. Please contact Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>) with any questions. </p> <p>Preferred citation: Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., Kim, R. S., and Jacobs, J. M. (2022). Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent, Hydrol. Earth Syst. Sci., https://doi.org/10.5194/hess-2022-136.</p> <p>Corresponding author: Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>; <a href="mailto:escho@umd.edu">escho@umd.edu</a>)</p>
Estimating surface water availability in high mountain rock slopes using a numerical energy balance model
<p>Model output, forcing data and physical parameters used to estimate water and energy balance. The model was calibrated with field measurements from a study site in the Mont-Blanc massif, at 3842 m a.s.l, at a slope of 55 deegrees and aspect azimut of 150 degrees (south-east). The different ModelOutput files are from simulations at different elevastions (from 4800 m to 2700 m at steps of 300 m). We used the CryoGrid community model (version 1.0) toolbox (Westermann et al., 2022) to simulate the 1D ground thermal regime and ice/water balance, and estimate the availability of surface water and its potential for infiltration in rock fractures. The S2M-SAFRAN dataset combines output from a numerical weather prediction model and <em>in situ</em> observations, and was originally developed for operational needs to estimate avalanche hazard in mountainous areas (Durand et al., 1993). The S2M-SAFRAN dataset that we used is available for various mountain areas, at elevation steps of 300 m, and with an hourly resolution between the years 1958 to 2021 (Vernay et al., 2022). It includes most parameters that are required for modeling with CryoGrid: Relative humidity, air T, incoming long wavelength radiation, incoming short wavelength solar radiation, and wind speed. To complete the forcing data we used top of the atmosphere incident solar radiation from ERA5 global reanalysis dataset (Hersbach et al., 2020).</p>
Bottom water acidification and warming on the western Eurasian Arctic shelves: Dynamical downscaling projections. Data archive.
<p>This archive includes one .mat file (MATLAB format) containing all the data and interpolated SINMOD model used for skill assessment and bias correction, and several NetCDF files containing the SINMOD SRES A1B projections (bias corrected where possible) for the bottom water in the pan-Arctic model domain for years 2001-2099 inclusive. Temporal resolution is biweekly and spatial resolution is 20km (see grid info in NetCDF files).</p>
IN02086 Minanatha water conduit stone Inscription
<p>Gnoli, Raniero. <em>Nepalese Inscriptions in Gupta Characters</em>. Roma: Is. M. E. O. 1956. Plate 77, Inscription 80, pp. 113-114.</p>
GRAiCE: Terrestrial water storage anomalies reconstructions
<p>Terrestrial Water Storage (TWS) is the total amount of freshwater stored on and below the Earth’s land surface, including surface water, groundwater, soil moisture, snow, and ice. As a result, TWS is a crucial variable of the global hydrologic cycle, representing an essential indicator of water availability.</p> <p>Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on (GRACE-FO) have been measuring temporal and spatial variations of TWS, namely the Terrrestrial Water Storage Anomalies (TWSA), enabling the monitoring of global hydrological changes over the last two decades. However, the lack of observations prior to 2002 along with the temporal gaps in GRACE/GRACE-FO time series limit our understanding of long-term variations of global freshwater availability.</p> <p>In this study, we use Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) neural networks and two sets of predictors to develop four global monthly reconstructions of TWSA from 1984 to 2021 at 0.5º spatial resolution (GR<em>Ai</em>CE). The first set of predictors is given by a combination of five fundamental meteorological forcings and data on vegetation dynamics, whereas the second set of predictors includes the five meteorological forcings only. Specifically, the meteorological predictors are monthly averaged data of total precipitation, snow depth water equivalent, surface net solar radiation, surface air temperature, and surface air relative humidity. We derive data on vegetation dynamics from a long-term reconstruction of solar-induced fluorescence (SIF), which represents a proxy for photosynthesis. Each model is trained with monthly TWSA data from the GRACE JPL mascon dataset. The GR<em>Ai</em>CE dataset accurately reproduces GRACE/GRACE-FO observations at the global scale and across different climatic regions. Moreover, we found that our models predict observed TWSA better than a previous reference reconstruction and produce reliable estimates of the water budget at the river basin scale. Beyond generating long-term continuous TWSA time series, our models allow us to detect and examine TWS changes due to climate variability/change.</p> <p>This repository contains the GR<em>Ai</em>CE dataset and includes four files in netCDF format. The dataset provides monthly TWSA estimates from 1984 to 2021 at a 0.5º spatial resolution. TWSA values are expressed in terms of cm of equivalent water thickness. GRAiCE_LSTM.nc and GRAiCE_BiLSTM.nc files contain TWSA reconstructions obtained from LSTM and BiLSTM models fed with all predictors (i.e., including SIF data), respectively. GRAiCE_LSTMnoSIF.nc and GRAiCE_BiLSTMnoSIF.nc files contain TWSA reconstructions obtained from LSTM and BiLSTM models fed with meteorological forcings only (i.e., without SIF data).</p>
Winter cumulated water presence of "Marais Breton nord" calculated with the WIW
<p><span>The WIW was calculated for all SENTINEL 2 cloudless images available within the period from January to April from 2017 to 2024. All WIW rasters were then added to provide the cumulated water presence ranging from 1 to 40 corresponding to the number of dates related to available cloudless images at that period. </span>The table provides the dates of the images used. EPSG 3857</p>
Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome
<p>Demultiplexed fastq files used for sequencing analysis, processed taxonomy read data for all samples and controls, and relevant environmental metadata as described in "Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome".</p>
IODP Expedition 379 ICP-AES elemental analysis (interstitial water)
Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
IODP Expedition 379 Interstitial water composite report
This composite report includes data from numerous interstitial water (IW) analyses (e.g., ICP-LIQUIDS and IONCHROM) in a variable set of columns. Each row combines the data from several tests (groups of measurements made on the same sample at the same time) for a particular IW sample. If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate analysis (ALKALINITY, ICP-LIQUIDS, IONCHROM, SALINITY, SPEC, TITRATOR, and potentially others). Major and minor elements were determined by inductively coupled plasma-atomic emission spectrocsopy (ICP-LIQUIDS). Cations and anions were measured by ion chromatography (IONCHROM). Complex ions were measured by colorimetric measurements using a UV-VIS spectrometer (SPEC). Alkalinity and chloride was measured by titration (ALKALINITY and TITRATOR). Dissolved carbon (DC), dissolved inorganic carbon (DIC), and dissolved organic carbon (DOC) were measured by combustion in a total organic carbon (TOC) analyzer. Salinity was measured by refractometer (SALINITY). pH was measured by ion-selective electrode as part of the alkalinity procedure and is reported in the ALKALINITY analysis.
Data set: Variations in water economy traits in two Sphagnum species across their distribution boundaries
<p><em>Sphagnum</em> trait data collected (2016-2017) across a climatic gradient in Sweden. Trait data for both shoot and canopy traits. Data for <em>Sphagnum cuspidatum</em> and <em>Sphagnum lindbergii</em>. Also contains data on species occurrence records in Sweden and output from speceis distribution modelling. See published paper for more information.</p> <p>Files contain (i) processed data ("calculated_trait_data...cvs"), (ii) raw data ("Campbell_etal_clim_traits_...cvs"), (iii) their readme files, and (iv) R-scripts to run the analyses. Note that you need the files in the zip-file to run the analyses in the R-script. The zip-file contains all raw data (climate, traits, species occurences), MaxEnt output, and raster files from photogrammetry.</p> <p>More info in paper: <a href="https://doi.org/10.1002/ajb2.16347" target="_blank" rel="noopener">https://doi.org/10.1002/ajb2.16347</a></p>
Molecular simulations of nanoscale two-phase Couette flow of a water-hexane system on a hydrophobic substrate
<p>This dataset contains the output of Molecular Dynamics simulations (MD) of two-phase Couette flow of water/hexane biphasic systems, in terms of density, velocity and temperature fields. Instructions on how to read and analyze the output files in the <code>.tar.gz</code> archives can be found in these previously-published datasets: <a href="https://doi.org/10.5281/zenodo.8077915">https://doi.org/10.5281/zenodo.8077915</a>, <a href="https://doi.org/10.5281/zenodo.6541983">https://doi.org/10.5281/zenodo.6541983</a></p> <p>The run output files are labeled using the following pattern: <code>hex-ca<capillary-number>-q<partial-charge>.tar.gz</code>. It is possible to obtain the wall speed/contact line speed from the capillary number using the following formula: <code>u_w = U_0*<capillary-number></code>, with <code>U_0 = 37.246 m/s</code>.</p> <p>To reproduce the runs it is necessary to use a specific version of Gromacs that allows for a special algorithm of pressure scaling with position restraints. The code can be obtained by cloning <a href="https://github.com/MicPellegrino/gromacs-flow-field.git">https://github.com/MicPellegrino/gromacs-flow-field.git</a>, and switching to the <code>flow-field-grid-visco-coms-deform</code> branch.</p> <p>The folder <code>conf-wat-hex.zip</code> contains the configuration files to reproduce MD simulations. To prepare the equilibration runs at constant pressure, run after having installed Gromacs:</p> <p><code>gmx grompp -f npt.mdp -p topology.top -c before-npt.gro -r before-npt.gro -o system-npt.tpr</code></p> <p>while to prepare the shear runs:</p> <p><code>gmx grompp -f shear.mdp -p topology.top -c after-npt.gro -r lambda0.gro -rb lambda1.gro -o system-shear.tpr</code></p> <p>Simulations are launched by running:</p> <p><code>gmx mdrun -v -s <tpr-file-name>.tpr <possibly-other-mdrun-flags></code></p> <p>Have fun simulating!</p>
Molecular simulations of nanoscale two-phase Couette flow of a water-hexane system on a hydrophilic substrate
<p>This dataset contains the output of Molecular Dynamics simulations (MD) of two-phase Couette flow of water/hexane biphasic systems, in terms of density, velocity and temperature fields. Instructions on how to read and analyze the output files in the <code>.tar.gz</code> archives can be found in these previously-published datasets: <a href="https://doi.org/10.5281/zenodo.8077915">https://doi.org/10.5281/zenodo.8077915</a>, <a href="https://doi.org/10.5281/zenodo.6541983">https://doi.org/10.5281/zenodo.6541983</a></p> <p>The run output files are labeled using the following pattern: <code>hex-ca<capillary-number>-q<partial-charge>.tar.gz</code>. It is possible to obtain the wall speed/contact line speed from the capillary number using the following formula: <code>u_w = U_0*<capillary-number></code>, with <code>U_0 = 37.246 m/s</code>.</p> <p>To reproduce the runs it is necessary to use a specific version of Gromacs that allows for a special algorithm of pressure scaling with position restraints. The code can be obtained by cloning <a href="https://github.com/MicPellegrino/gromacs-flow-field.git">https://github.com/MicPellegrino/gromacs-flow-field.git</a>, and switching to the <code>flow-field-grid-visco-coms-deform</code> branch.</p> <p>The folder <code>conf-wat-hex.zip</code> contains the configuration files to reproduce MD simulations. To prepare the equilibration runs at constant pressure, run after having installed Gromacs:</p> <p><code>gmx grompp -f npt.mdp -p topology.top -c before-npt.gro -r before-npt.gro -o system-npt.tpr</code></p> <p>while to prepare the shear runs:</p> <p><code>gmx grompp -f shear.mdp -p topology.top -c after-npt.gro -r lambda0.gro -rb lambda1.gro -o system-shear.tpr</code></p> <p>Simulations are launched by running:</p> <p><code>gmx mdrun -v -s <tpr-file-name>.tpr <possibly-other-mdrun-flags></code></p> <p>Have fun simulating!</p>
IODP Expedition 371 ICP-AES elemental analysis (interstitial water)
Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
IODP Expedition 371 Interstitial water composite report
This composite report includes data from numerous interstitial water (IW) analyses (e.g., ICP-LIQUIDS and IONCHROM) in a variable set of columns. Each row combines the data from several tests (groups of measurements made on the same sample at the same time) for a particular IW sample. If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate analysis (ALKALINITY, ICP-LIQUIDS, IONCHROM, SALINITY, SPEC, TITRATOR, and potentially others). Major and minor elements were determined by inductively coupled plasma-atomic emission spectrocsopy (ICP-LIQUIDS). Cations and anions were measured by ion chromatography (IONCHROM). Complex ions were measured by colorimetric measurements using a UV-VIS spectrometer (SPEC). Alkalinity and chloride was measured by titration (ALKALINITY and TITRATOR). Dissolved carbon (DC), dissolved inorganic carbon (DIC), and dissolved organic carbon (DOC) were measured by combustion in a total organic carbon (TOC) analyzer. Salinity was measured by refractometer (SALINITY). pH was measured by ion-selective electrode as part of the alkalinity procedure and is reported in the ALKALINITY analysis.
Samuel S. Waters (w3224)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Samuel S. Waters<br><u>musiXplora-ID</u>: w3224<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/w3224">https://musixplora.de/mxp/w3224</a><br><u>Gender</u>: m<br><u>First Mentioned</u>: 1910<br><u>Sectors</u>: Automatenbau<br><u>Professions (Historical)</u>: Patentinhaber<br><u>Other Places of Activity</u>: Washington<br><br><br><u>Institutionen:</u><br><table><tbody><tr><th>Role</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Aeolian</td><td><a href="https://musixplora.de/mxp/3030162">3030162</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)</p> <p>Source data:<br>- CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m</a> <br>- CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m</a></p> <p>Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management.<br>The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices.</p> <p>Processing steps:<br>To cover the complete time period from 2019 to 2023 two data products of the Water Bodies product group are processed. Up to December of 2020 the Water Bodies at 10-daily resolution have been used, from January 2021 the Water Bodies at monthly resolution have been used. Both original datasets have been downloaded for the area of Mauritania (NUTS MR) within Latitude-Longitude/WGS84 spatial reference system. Then both datasets have been downsampled to 30 arc seconds (ca. 1000 meter) using the most frequent occuring value. The 10-daily data have been aggregated to monthly resolution using the most frequent occurring value.</p> <p>File naming:<br>Until December 2020: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_2020_12_01T00_00_00.tif</code> <br>From January 2021 on: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_2023_12_01T00_00_00.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.<br>NOTE: data for 2023-04 are missing, since they are not available from CLMS</p> <p>Pixel values:<br>0: Sea<br>70: Water<br>255: No water</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 27:17:30N<br>south: 14:43:30N<br>west: 17:04:30W<br>east: 04:48:00W</p> <p>Temporal extent:<br>January 2019 - December 2023 (except: April 2023)</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original dataset license:<br>Generated using European Union's Copernicus Land Monitoring Service information</p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
IODP Expedition 397 ICP-AES elemental analysis (interstitial water)
Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
IODP Expedition 397 Interstitial water composite report
This composite report includes data from numerous interstitial water (IW) analyses (e.g., ICP-LIQUIDS and IONCHROM) in a variable set of columns. Each row combines the data from several tests (groups of measurements made on the same sample at the same time) for a particular IW sample. If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate analysis (ALKALINITY, ICP-LIQUIDS, IONCHROM, SALINITY, SPEC, TITRATOR, and potentially others). Major and minor elements were determined by inductively coupled plasma-atomic emission spectrocsopy (ICP-LIQUIDS). Cations and anions were measured by ion chromatography (IONCHROM). Complex ions were measured by colorimetric measurements using a UV-VIS spectrometer (SPEC). Alkalinity and chloride was measured by titration (ALKALINITY and TITRATOR). Dissolved carbon (DC), dissolved inorganic carbon (DIC), and dissolved organic carbon (DOC) were measured by combustion in a total organic carbon (TOC) analyzer. Salinity was measured by refractometer (SALINITY). pH was measured by ion-selective electrode as part of the alkalinity procedure and is reported in the ALKALINITY analysis.
High water temperature significantly influences swimming performance of New Zealand migratory species
<p>Our study examined how temperature variations affect critical swimming speeds of four migratory species. Higher temperatures (26°C) significantly reduced swimming speeds for three species, emphasising the need for fish passage solutions that consider temperature fluctuations. This is crucial for habitat restoration and freshwater fish preservation, especially in a changing climate. </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.