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19,365 results for “watering”
Raman spectra of rhenium heptoxide Re2O7 and its water adducts: Re2O7.2H2O and Re2O7.2D2O
<p>Raw .txt files of the Raman spectra of Re2O7, Re2O7.2H2O and Re2O7.2D2O obtained by the controlled hydration of Re2O7.</p>
A dataset of physicochemical water parameters from Lima and Douro estuaries (Northwest Portugal)
<p>This dataset provides physicochemical water data collected during five sampling campaigns at two temperate North Atlantic estuaries, the Lima and Douro, in northern Portugal, between 2021 and 2022. The dataset is described in the <em>Data in Brief </em>article “Physicochemical dataset from Lima and Douro estuaries (Northwest Portugal)”. It includes: a) in-situ vertical profiles of water parameters (temperature, salinity, pH, turbidity, dissolved oxygen), acquired with a multiparameter probe along the water column, from the surface to the bottom, at 8 stations in the Lima estuary (during ebb and flood tides) and 11 stations in the Douro estuary (during flood tide); and b) chemical analyses of nutrients, chlorophyll <em>a</em>, total particulate matter, particulate organic matter, and metal concentrations from 5 representative stations in each estuary. Additional dissolved metal data are provided for the Lima estuary.</p>
Data from: A robust model for the assessment of oil spill hazards over land and water bodies
<p>This repository contains all the data required to generate the results and figures reported in the article:</p> <p><strong>A robust model for the assessment of oil spill hazards over land and water bodies. </strong><br>Pablo Vallés, Sergio Martínez-Aranda, Reinaldo García & Pilar García-Navarro <br>Fluid Dynamic Technologies TFD-I3A, Universidad de Zaragoza, Spain, 2024</p> <p><strong>Author:</strong> Sergio Martínez Aranda<br><strong>Email: </strong>sermar@unizar.es</p> <p><strong>Summary of the content:</strong></p> <p>*FILE* BSLmodel_code.c : Implementation of the BSL model in the software OILFlow2D (Hydronia LLC)</p> <p>*ZIP-FOLDER* testOilChannel : Synthetic test 1: Oil spill over water channel with parabolic velocity profile<br> Contains:<br> *FILE* plotter2D.m : Matlab file for plotting the article figures<br> *FILE* readVTK_hu.m : Ad-hoc Matlab function for reading VTK files and extract arrays of x, y, h, modU variables at cells<br> *FOLDER* graphics : Contains the output figures for the article<br> *FILE* free_surface_profiles_impCent.mat : Matlab structure containing the water level results along the longitudinal center profile for all the cases tested<br> *FILE* vel_profiles_impCent.mat : Matlab structure containing the velocity results along the cross-section x=900m for all the cases tested<br> *FOLDER* hydro_shear_layer : Folder with the 2D hydrodynamics fields for the Bottom Shear Layer used in the simulations<br> *FOLDER* BSL_disabled : Folders containing the raw simulation results with the BSL model disabled <br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges <br> *FOLDERS* BSL_impCent_CdXpXXXX : Folders containing the raw simulation results with the BSL model enabled for different drag coefficients Cd<br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges<br> </p> <p>*ZIP-FOLDER* testOilBay : Synthetic test 2: Oil spill from land to a rotating water bay <br> Contains:<br> *FILE* plotter2D.m : Matlab file for plotting the article figures<br> *FILE* readVTK_zhvel.m : Ad-hoc Matlab function for reading VTK files and extract arrays of x, y, z, h, u, v variables at cells<br> *FOLDER* graphics : Contains the output figures for the article.<br> *FOLDER* hydro_shear_layer : Folder with the 2D hydrodynamics rotating fields, including VTK files, for the Bottom Shear Layer used in the simulations<br> *FOLDER* BSL_disabled : Folders containing the raw simulation results with the BSL model disabled <br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges <br> *FOLDERS* BSL_impCent_CdXpXXXX : Folders containing the raw simulation results with the BSL model enabled for different drag coefficients Cd<br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges<br> <br> <br>*ZIP-FOLDER* caseSpillTilenga : Realistic case: Oil spill hazard assessment in the White Nile - Tilenga Project <br> Contains:<br> *FILE* Qgis_project.qgz : Portable QGIS project for plotting the article figures<br> *FOLDER* geoData : Contains the georeferenced data used for the simulation setup<br> *FOLDER* images : Contains the output figures for the article<br> *FOLDER* hydro_shear_layer : Folder with the 2D hydrodynamics fields for the Bottom Shear Layer used in the simulations<br> *FOLDER* spills : Folders containing the OilFlow2D project files to perform the simulation of the six spill scenarios reported in the article <br> *FOLDERS* spill_XXX_XX : Folders containing raster files with the oil spreading results at different times for the six spill scenarios reported in the article </p>
Data from: Habitat suitability models reveal extensive distribution of deep warm water coral frameworks in the Red Sea
<p>Deep-sea coral frameworks are understudied in the Red Sea, where conditions in the deep are conspicuously warm and saline compared to other basins. Habitat suitability models can be used to predict the distribution pattern of species or assemblages where direct observation is difficult. Here we show how coral frameworks, built by species within the families Caryophylliidae and Dendrophylliidae, are distributed between water depths of 150 m and 700 m in the northern Red Sea and Gulf of Aqaba. To extrapolate the known (ground-truthed) positions of these deep frameworks, we use environmental and geomorphometric variables to inform well-performing maximum entropy models. Over 250 km2 of seafloor in our study area are identified as suitable for such frameworks, equivalent to at least 35% of the area of photic-zone coral reefs in the same region. We hence contend that deep-water coral frameworks are an important and underappreciated repository of Red Sea biodiversity.</p>
IODP Expedition 356 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.
IODP Expedition 356 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 353 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.
IODP Expedition 353 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.
XRDs of Materials used in the Supplementary Information file of A. Lowe et al Exploring the Heat of Water Intrusion ... ACS Appl. Mater. Interfaces 2024, 16, 5286−5293
<p>Data plots were limited to 2theta range from 5 degrees to 50 degrees. CuKa</p>
IODP Expedition 359 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.
IODP Expedition 359 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.
Measuring water quality parameters to estimate Nitrate concentration in surface water in Bonet catchment, Sligo, Ireland
<p><span>Time series data of surface water quality (temperature, pH, dissolved oxygen, oxidation-reduction potential and electrical conductivity) collected from May 2024 to September 2024 at 1m intervals. The file contains tabular data with the following columns: Date and time, Battery (%), temperature (ºC), fix Quality in fix code (Fix), Latitude (in deg), Longitude (in deg), pH,<span> </span>electrical conductivity (µS/cm), TDS (in ppm),<span> </span>Salinity in PSU(ppt), Specific Gravity (in SG), Dissolved Oxygen (in mg/L), Oxygen Saturation (in %), ORP (in mV), Altitude (in meters), Ground Speed (in m/s), Horizontal dilution, Satellites in number.</span></p>
EyeOnWater training dataset for assessing the inclusion of water images
<h1>Training dataset</h1> <p>The EyeOnWater app is designed to assess the ocean's water quality using images captured by regular citizens. In order to have an extra helping hand in determining whether an image meets the criteria for inclusion in the app, the YOLOv8 model for image classification is employed. With the help of this model all uploaded pictures are assessed. If the model deems a water image unsuitable, it is excluded from the app's online database. In order to train this model a training dataset containing a large pool of different images is required. The dataset contains a total of 13,766 images, categorized into three distinct classes: “water_good,” “water_bad,” and “other.” The “water_good” class includes images that meet the requirements of EyeOnWater. The “water_bad” class comprises images of water that do not fulfill these requirements. Finally, the “other” class consists of miscellaneous images that users submitted, which do not depict water. This categorization enables precise filtering and analysis of images relevant to water quality assessment.</p>
HOLSEA-NL: Holocene water level and sea-level indicator dataset for the Netherlands
<p>This dataset contains an assembly of geological water-level indicators, relevant for studying relative sea level rise (RSLR), regional subsidence quantification and causal breakdown, coastal prism accommodation and Holocene aggradation chronology of the Holocene Netherlands. It gives a sources-referenced, uniform overview of 658 basal geological water-level indicators collected from original research of various type and application (140 primary references). From the indicators, 59% was collected in 1950-2000, mainly in academic studies and survey mapping campaigns; 37% was collected in 2000-2020 in academic studies and archaeological surveying projects, 4% was newly collected (this study), the latter mainly in previously under sampled central and northern Netherlands regions. 117 are true sea-level indicators (so-called SLIPs), the majority of datapoints (536) are inland water level indicators that are upper limiting to sea-level. The total number of entries is 712, because we included some literature mentioned rejected samples and deep positioned intercalated water level indicators.</p> <p>The dataset is compiled in the so-called HOLSEA workbook format. It covers measured, calculated and classification fields defining the geological observational data and its uncertainties, allowing to document and assess indicative meaning adapted to specific use variants. Hereto, the workbook contains expansions to the original format. See Related Works (ESSD paper: De Wit et al. 2024).</p>
Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers [Data set].
<p>Data set covering the meta data of the 39 well fields, the macro chemistry data and the data of the noble gases and carbon, hydrogen and oxygen isotope tracers used for assessing the paleoclimate signals and age distributions in the publication in Water Resources Research (2021)</p> <p><strong>Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers</strong></p> <p>Hans Peter Broers, Jürgen Sültenfuß<sup> </sup>, Werner Aeschbach, Arne Kersting,, Armin Menkovich, Jasperien de Weert and Jeroen Castelijns</p>
Global CHIRPS MCWD (Maximum Cumulative Water Deficit) Dataset
<p><strong>Global CHIRPS MCWD Dataset</strong></p> <p>The MCWD (Maximum Cumulative Water Deficit) is a measure of drought severity, which corresponds to the maximum value of the monthly accumulated water deficit reached for each pixel within the year. The MCWD is a useful indicator of meteorologically induced water stress without taking into account local soil conditions and plant adaptations, which are poorly understood in Amazonia. The full method of MCWD is described in Aragão et al. (2007; <a href="https://doi.org/10.1029/2006GL028946">https://doi.org/10.1029/2006GL028946</a>). Detail about CHIRPS (Rainfall Estimates from Rain Gauge and Satellite Observations) can be found in Funk et al. (2015; <a href="https://doi.org/10.1038/sdata.2015.66">https://doi.org/10.1038/sdata.2015.66</a>).</p> <p> </p> <p><strong>Coverage:</strong> Spanning 50°S-50°N (and all longitudes/land areas)</p> <p><strong>Period:</strong> 1981 to 2020</p> <p><strong>Spatial resolution:</strong> 0.05-degree</p> <p><strong>Temporal resolution:</strong> Annual</p> <p><strong>Coordinate reference system:</strong> Geographic Coordinate System (Datum WGS84)</p> <p><strong>File format:</strong> The zip file containing 40 files (one per year) in compressed TIFF format.</p> <p><strong>Code:</strong> <a href="https://zenodo.org/record/5034650">https://zenodo.org/record/5034650</a></p> <p><strong>Dataset usage</strong>: It is free to use, but if you use this dataset in your work, please make sure to cite the repository and our paper properly. We also welcome users to invite us for collaboration.</p> <p><strong>For the use of this dataset, please cite:</strong></p> <p>Silva Junior, C.H.L. et al. Global CHIRPS MCWD (Maximum Cumulative Water Deficit) Dataset. <em>Zenodo</em> (2021). DOI: 10.5281/zenodo.4903340. <a href="https://doi.org/10.1038/s41597-020-00600-4">https://doi.org/10.5281/zenodo.4903340</a></p> <p>Silva Junior, C.H.L. et al. Fire Responses to the 2010 and 2015/2016 Amazonian Droughts. <em>Front. Earth Sci</em>. (2019). DOI: 10.3389/feart.2019.00097. <a href="https://doi.org/10.3389/feart.2019.00097">https://doi.org/10.3389/feart.2019.00097</a></p> <p>Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. <em>Scientific Data</em> (2015). DOI: 10.1038/sdata.2015.66. <a href="https://doi.org/10.1038/sdata.2015.66">https://doi.org/10.1038/sdata.2015.66</a></p>
Polyelectrolyte in a water cavity
<p>The compressed file provide 400 snapshots of a molecular dynamics simulation performed at ambient condition using the POLARIS(MD) code (http://biodev.cea.fr/polaris/) and polarizable force fields of a polyelectrolyte in compact quasi spherical conformation as embedded in a spherical cavity comprising about 7 500 water molecules. </p>
Areas of global importance for conserving terrestrial biodiversity, carbon, and water
<p><strong>Content:</strong><br> This data repository contains the results of the NatureMap ( naturemap.earth/) conservation prioritization effort. The maps were created by jointly optimizing biodiversity and NCPs such as carbon and/or water.</p> <p><strong>Usage notes:</strong><br> Maps are supplied at both 10km and 50km resolution unless specified differently in the manuscript.<br> All maps that aim to find priority areas for all species considered in the analysis, utilize a series of representative sets.<br> The ranks for each layer are area-specific and can be used to extract summary statistics by simple subsetting.<br> For example:<br> To obtain the top 30% of land area for biodiversity and carbon, one needs to create a mask of all areas lower than a value of 30 from the respective ranked layers.</p> <p>For convenience two files are supplied that contain the fraction of land area per grid cell times 1000. Multiplying those with the cell area (100km2, respectively 2500km2) gives the exact amount of land area in a given grid cell.<br> These are labelled " globalgrid_mollweide_**km.tif " can be used to create masks for the priority maps.</p> <p><strong>Spatial resolution:</strong></p> <p>10 and 50 km</p> <p><strong>Geographic projection:</strong><br> World Mollweide Equal Area projection<br> PROJ4 ( +proj=moll +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs )</p> <p><strong>Filename suffix description:</strong></p> <p><em>'minshort_speciestargets'</em><br> =- Problem formulation where targets were achieved by minimzing a shortfall</p> <p><em>'repruns10'</em><br> =- The number of representative that were used to create the ranked layer</p> <p><em>'biome.id'</em><br> =- Species distribution were split by biome, thus creating separate targets for subpopulation</p> <p><em>'withPA'</em><br> =- Fractions of current protected areas (Date: WDPA 2019) were locked in as baseline and starting budget. Approximately 15% of the globe. Note that not entire grid cells, but fractions were locked in and build opon!</p> <p><em>'carbon'</em><br> =- Carbon was included in the prioritization and jointly optimized together with the other assets by giving it equal weighting (see manuscript)</p> <p><em>'water'</em><br> =- Water was included in the prioritization and jointly optimized together with the other assets by giving it equal weighting (see manuscript)</p> <p><strong>License:</strong><br> CC-BY-SA 4.0</p> <p><strong>Citation:</strong><br> Jung, Martin, Andy Arnell, Xavier De Lamo, Shaenandhoa Garcia-Rangel, Matthew Lewis, Jennifer Mark, Cory Merow et al. (2021) "Areas of global importance for terrestrial biodiversity, carbon, and water." Nature Ecology & Evolution</p>
Sub-national Water-Food-Labour Nexus in Colombia
<p>Poorer countries often face a severe trade-off: the need to improve socio-economic conditions is hard to balance with the maintenance of key ecological processes. As a case study, we select Colombia, a Latin American country with almost 10% of its inhabitants living in extreme poverty. We elaborate a<strong> water-food-labour (WFL) nexus</strong> grounded on a sub-national Environmentally Extended Input-Output (EEIO) analysis to assess the virtual water trade (VWT) and virtual informal labour (VIL) flows across administrative departments and economic sectors related to domestic trade.</p> <p>The main results are the following: high cross-departmental resource interdependence both in terms of VWT and VIL, rich departments highly depend on the resources of their neighbouring trading partners, extreme poverty conditions are shown by economically isolated departments, and considerable income inequality in the food production sectors. Moreover, departments that are net exporters of virtual water suffer from water stress that might be exacerbated by future high rainfall variability due to climate change. </p> <p>These results suggest that strategies to attain sustainable development goals (SDGs) must deal with the biophysical constraints and the economic and political feasibility of the proposed solutions. In this vein, we argue that a holistic framework, grounded on quantitative analyses, is necessary to support informed policy decisions for the simultaneous achievement of multiple (possibly contrasting) goals. Moreover, severe spatial imbalances call for local policy responses coordinated at the national level.</p>
PRISMA-derived water quality parameters for Lake Hume (Australia) (2020/04/22)
<p>This dataset contains PRISMA-derived water quality (WQ) products of Lake Hume (Australia) for the 22 April 2020. 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). PRISMA data courtesy of the Italian Space Agency (ASI, 2020).</p>
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Allen Brain Atlas
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OpenNeuro
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