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19,393 results for “water”
Surface water quality from the Seagrass Recovery Experiment, South Bay, VA 2020-2022
To understand intra-meadow stability, the Seagrass Recovery Experiment was designed to ask 1) is recovery faster at sites with less thermal stress owing to greater exchange with cooler oceanic water at the meadow edge? 2) what is the shape of recovery? and 3) what are the recovery mechanisms? To conduct this experiment, aboveground seagrass biomass was removed from 28.3 m2 plots within the interior and along an edge of a restored seagrass meadow in South Bay, VA. Sites 1-3 correspond to the meadow interior while sites 4-6 correspond to the northern edge. Each site was comprised of a control (i.e., C) where no seagrass was disturbed and a treatment (i.e., T) where seagrass was removed (n = 12 sites total, e.g., 1C, 1T, 2C...). To further characterize differences between the meadow interior and edge, surface water quality samples were also collected and include turbidity, total suspended solids (TSS) concentration, TSS ash-free dry weight, TSS percent organic matter, pelagic chlorophyll concentration, dissolved oxygen saturation, dissolved oxygen concentration, salinity, water temperature, and specific conductivity. These discrete samples were collected monthly between June-October 2020, May-October 2021, and April-October 2022 using a 1-L Nalgene bottle and a handheld YSI Pro Plus Multiparameter meter.
Water Temperatures and Climatology for Wachapreague, VA, 1982-2021
Daily average measured near-surface water temperature from 1982-2021 constructed for the site in the Virginia coastal bays where the NOAA Wachapreague station is located (Site CBW (Coastal Bay Wachapreague); 37.61N, 75.69W; water depth 1m) and for a site in the coastal ocean just outside the bays (Site COC (Coastal Ocean CHLV2); 36.91N, 75.71W; water depth 15m). Daily climatological mean temperature and marine heatwave threshold (90th percentile) calculated using 30 years of the record (1987-2016) are also provided.
Dataset of "Cobalt and nickel doped WSe2 as efficient electrocatalysts for water splitting and as cathodes in hydrogen evolution reaction PEM water electrolysis"
<p>Efficient electrocatalysts are crucial for water splitting and fuel cells. Using cheap alternatives that can improve reaction kinetics is essntial for advancing fuel cell technology. Although, tungsten diselinide (WSe2) is promising for electrocatalysis is not fully explored, especially in oxygen evolution and in applications such as polymer electrolyte membrane water electrolyzer.<br>In this work, we used a simple approach to dope WSe2 with cobalt and/or nickel atoms. The doped material was subsequently tested for hydrogen evolution reaction and oxygen evolution reaction. Accordingly, the two electrocatalysts are highly active and stable, affording low overpotentials comparable to those of noble metals. The effective introduction of heteroatoms causes the retention of coordination vacancies, furnishing active catalytic sites that enhanced electrocatalytic performance both in activity and charge transfer. Moreover, both doped materials show excellent performance and stability as cathode electrocatalysts in the polymer electrolyte membrane water electrolyzer with great promise for real-world applications.</p>
Data to 'The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0'
<p>This dataset includes the AWARE2.0 characterization factors as documented in the article "The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0" (<a href="https://www.doi.org/10.1111/jiec.70023" target="_blank" rel="noopener">DOI: 10.1111/jiec.70023</a>). When using the dataset in your own work, please cite the article and provide reference to this zenodo repository.</p> <p>For importing the country-level characterization factors into LCA software, please see the AWARE2.0 implementations (openLCA, SimaPro, brightway2) in IMPACT World+, version 2.1: <a title="IMPACT World+ version 2.1" href="https://doi.org/10.5281/zenodo.14041258">https://doi.org/10.5281/zenodo.14041258</a></p> <h3>Content</h3> <p><strong>- native resolution (monthly, watershed scale):</strong></p> <ul> <li><strong>AWARE20_Native_CFs_geospatial.gpkg</strong>: Geospatial file containing the AWARE2.0 basins as polygons with associated monthly and annual CFs</li> <li><strong>AWARE20_Native_CFs_geospatial.kmz</strong>: Version of <em>AWARE20_Native_CFs_geospatial.gpkg </em>for GoogleEarth</li> <li><strong>AWARE20_Native_CFs.xlsx</strong>: AWARE2.0 CFs on basin level (monthly and annual) and associated water consumption used for weighting</li> <li><strong>AWARE20_Intermediate_Variables.xlsx</strong>: Intermediate Variables from the calculation of the AWARE2.0 CFs, such as the longterm average natural and actual water availability, the AMDs, the EFRs, etc.</li> <li><strong>figures_AWARE_AWARE20_comparison_all_basins.zip</strong>: Figures comparing CFs, AMDs, Natural and Actual Availability, EWRs, and EFR coefficients between AWARE and AWARE2.0, for each of the 8149 basins individually. Consult these figures for a visual impression of how and why CFs might have changed between AWARE and AWARE2.0.</li> </ul> <p><strong>- spatiotemporal aggregations:</strong></p> <ul> <li><strong>AWARE20_Countries_and_Regions.xlsx</strong>: AWARE2.0 CFs aggregated according to geography definitions of GLAM and ecoinvent <a href="https://geography.ecoinvent.org/#version-2-5-ecoinvent-3-10" target="_blank" rel="noopener">(version 2.5, applicable to ecoinvent 3.10) </a></li> <li><strong>AWARE20_Subnational_Resolution.xlsx</strong>: AWARE2.0 CFs aggregated to subnational resolution, using the GADM dataset version 4.1 (<a href="https://gadm.org/old_versions.html" target="_blank" rel="noopener">https://gadm.org/old_versions.html</a>)</li> <li><strong>AWARE20_Crop_Specific.xlsx</strong>: AWARE2.0 CFs aggregated according to geography definitions of ecoinvent <a href="https://geography.ecoinvent.org/#version-2-5-ecoinvent-3-10" target="_blank" rel="noopener">(version 2.5, applicable to ecoinvent 3.10)</a>, using crop-specific irrigation water consumption for 27 crop classes as spatiotemporal weights. See readme sheet in Excel file for more information.</li> </ul> <p> </p> <h3><strong>Changes:</strong></h3> <ul> <li>v1.0.1: <ul> <li>addition of crop-specific spatiotemporal aggregations (AWARE20_Crop_Specific.xlsx)</li> </ul> </li> <li>v1.0.0 (corresponding to published article): <ul> <li>update of readme sheets with appropriate references to corresponding article</li> <li>update of reference "Müller Schmied et al. (2024)"</li> <li>added file: AWARE20_Subnational_Resolution.xlsx</li> </ul> </li> <li> v0.0.3: <ul> <li>use bug-fixed WaterGAP2.2e data from Sept 2023</li> <li>added country and subnational aggregations</li> <li>changed "NoData" to "NotDefined" in the tables</li> <li>added gridcell pHWC to intermediate variables</li> <li>corrected table of water consumption data without post-processing in "Intermediate_Variables"</li> </ul> </li> </ul> <p> </p> <h3><strong>Caveats:</strong></h3> <ul> <li>Spatial CF aggregations for treaties: <ul> <li>Due to the creation date of the data set, the <strong>BRICS aggregations </strong>in<strong> </strong><em>AWARE20_Countries_and_Regions.xlsx</em> do not include the states that joined after 2023. In <em>AWARE20_Crop_Specific.xlsx</em>, the 10-member BRICS is labeled BRICS+.</li> </ul> </li> </ul>
Summaries of temperature and water table depth prior to peat sampling in Stordalen Mire, 2011-2017
<div> <p>This dataset provides summaries of temperature (T) and water table depth (WTD) conditions prior to the collection of peat samples from Stordalen Mire, Sweden, in July of 2011-2017. These summaries include the following files:</p> <h2><strong>t_wtd_summaries_July2011-2017samplings.csv</strong></h2> </div> <p>This file gives summary statistics over various time intervals for the following environmental measurements:</p> <ul> <li><strong>AirTemperature</strong>: Mean daily air temperature (°C), obtained from automatic sensors at the nearby Abisko Scientific Research Station (ANS) (station ID 188790; the source file [ANS_Daily_Wx_Jul84_Dec17.txt] is not included due to sharing restrictions).</li> <li><strong>WTD</strong>: Water table depths (cm), obtained from <a href="https://doi.org/10.5281/zenodo.10420396">Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)</a> (from Patrick Crill et al.).</li> </ul> <p>The time intervals for these summaries are defined relative to the peat sampling date at each site (see <a href="https://doi.org/10.5281/zenodo.12827096">EMERGE Sample Metadata Sheet for Samples with Microbiomes</a>), which varies by site and year. The specific intervals are defined as follows:</p> <ul> <li><strong>7d</strong>: 7 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>14d</strong>: 14 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>21d</strong>: 21 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>28d</strong>: 28 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>growing</strong>: Time from beginning of growing season (defined as June 1) until (and including) the sampling date.</li> <li><strong>all_growing</strong>: Entire growing season (June 1 – Sept. 30).</li> </ul> <p>For clarity, the start and end dates for each time interval (inclusive) are also given under the columns <strong>Start_Date</strong> and <strong>End_Date</strong>, where End_Date=<strong>Sampling_Date</strong> for all intervals except all_growing.</p> <p>Summary statistics for each interval include: measurement count (<strong>n</strong>), median (<strong>median</strong>), mean (<strong>mean</strong>), and standard deviation (<strong>sd</strong>), and are given under the column names beginning with these statistic labels.</p> <p><em>IMPORTANT NOTE: </em>For temperature, these statistics are calculated based on the average temperature measured on each day, meaning that<strong> </strong><em>the standard deviations do NOT account for within-day temperature variation.</em> To provide short-term (1 day) temperature variation context for each sampling date, the within-day mean, minimum, and maximum air temperatures for the sampling date only (taken directly from the corresponding row & columns in the source ANS data file) are provided in the columns <strong>samplingdate_mean_AirTemperature</strong>, <strong>samplingdate_min_AirTemperature</strong>, and <strong>samplingdate_max_AirTemperature</strong>.</p> <div> <div> <h2><strong>wtd_summaries_July2011-2017samples.csv</strong></h2> </div> <p>This file gives the percentage of time that each peat sample's depth midpoint (<strong>DepthAvg__</strong>) was at or below the water table depth (WTD), over each of the longer time intervals (≥21 days) defined above for the temperature & WTD summaries. (Intervals <21 days are not included due to the lower frequency of WTD measurements, which results in low <em>n</em> for shorter intervals.)</p> <p>The first few columns are taken directly from the <a href="https://doi.org/10.5281/zenodo.12827096">EMERGE Sample Metadata Sheet for Samples with Microbiomes</a>, for the samples collected in July of 2011-2017 from the MainAutochamber sites. The last set of columns include the following, with the time interval labels (defined as in the above temperature summaries) appended at the end of each column name:</p> <ul> <li><strong>n_WTD_*</strong>: Number of WTD measurements used in the calculation.</li> <li><strong>pct_time_below_WTD_*</strong>: Fraction (relative to 1) of measured WTDs over the given time interval that were at or above the DepthAvg__ for each sample, which equates to the fraction of measurement timepoints during which the given sample was at or below the WTD. This is the same method used for calculating "% Time below water table" in Figure 6 of <a href="https://doi.org/10.1038/s41396-018-0065-5">Singleton et al. (2018)</a>. For palsa sites, this value is automatically set to 0 based on the lack of a water table at all timepoints in the analysis.)</li> </ul> <p>As above, the WTD values used for these calculations were obtained from <a href="https://doi.org/10.5281/zenodo.10420396">Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)</a> (Patrick Crill et al.).</p> <h1>Funding acknowledgments</h1> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>This research was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.</p> <p>The temperature summary has been made possible by data provided by Abisko Scientific Research Station and the Swedish Infrastructure for Ecosystem Science (SITES).</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.</p> </div>
Historical and future water demand for households and industry for the STARS4Water river basins
<pre>This repository contains the data related to the deliverable D2.5 "Data sets on scenario narratives" prepared within the STARS4Water project ("Supporting STakeholders for Adaptive, Resilient and Sustainable Water Management").</pre> <p>The data spans historical years (2000-2020) and projections under different Shared Socioeconomic Pathways (SSP1-5) scenarios for the years 2020-2050.</p> <p>The repository contains historical and future water demand for households and industry for the STARS4Water river basins divided into two items packed in zip file:<br>1. STARS4Water_Domestic_and_Industrial_Water_Demands_historical.zip for years 2000-2020<br>2. STARS4Water_Domestic_and_Industrial_Water_Demands_projections.zip for years 2020-2050 (SSP1-SSP5)<br><br>The data in the repository was prepared based on Python scripts developed by Stephanie E. Lips and described in <em>Towards a global high </em><em>resolution water demand dataset. Effect of data quality and downscaling techniques - the case for Europe</em>, Utrecht University, 2020 as well as open source databases of WorldPop, WorldBank, UNCTADstat, EIA, Eurostat, Aquastat, UNEP an others. </p>
Dataset of "Molecular dynamics of evaporative cooling of water clusters"
<p>The cooling of water clusters through evaporation into a vacuum is studied using classical molecular dynamics with the SPC water model, and the results are compared with semimacroscopic theory. A model based on the Hertz–Knudsen equation underestimates the cooling rates. A modified approach, which accounts for the Kelvin equation, provides better results. While the rotational temperature of the clusters is in equilibrium with their internal temperature, the translational temperature of the clusters “as individual particles” remains unchanged.</p>
Dataset of "MoO3-xNiMoO4 nanorods synthetized using NiO nanoparticles for hydrogen evolution in anion exchange membrane water electrolysis"
<p>Novel method of Mo-Ni catalyst for hydrogen evolution reaction in anion exchange membrane water electrolysis was used. Complete physico-chemical and electrochemical characterization was done. Prepared material showed enhanced performance when compared to the similar Ni based materials. Physico-chemical characterization showed, that final material is formed by NiMoO4 nanorods coverd on the surface by the layer of the MoO3-x.</p>
Dataset of "Microporous electrode binders for anion exchange membrane water electrolyzers"
<p>Membranes made of SEBS/DABCO/PIM-1 blends were prepared and characterized. In the next step, the several blends were used as polymer binder's of the catalysts layers. Characterization of SEBS-DABCO/PIM-1 blends in the form of the catalyst layer revealed the significance of the catalyst layer porosity, which controls the permeation of gasses.</p>
Water availability parameters for area around Figueres for 1985 - 2015
<p>Water availability modelling results from the Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model model giving surface water parameters such as evapotranspiration, baseflow and runoff for an area around Figueres up to the Gulf of Roses, between the years 1985 - 2015.</p> <p>This dataset is deliverable D4.6 for the E4Warning project "<strong>Potential breeding areas - 1st draft version</strong>". These water availability parameters indicate the hydrological conditions to enable mosquitoes to breed and, as such, are an important covariate in determining the introduction and spread of mosquito borne diseases.</p> <p>Area of interest is given in terms of Subbasins of river sections classified according to modified Pfafstetter subbasins slopes to the Mediterranean I north.</p>
Dataset of "High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH"
<p>Novel simple and efficient method for synthesis of high entropy sulfides of iron group metals (Cr, Fe, Ni, Co, Zn) is describedThe created material was investigated as a catalyst for electrochemical water splitting in acidic, neutral and alkaline pH. Investigation of the electrocatalytic activity of the synthesized material shows its high efficiency for overall water splitting in alkaline media. </p>
3-D velocity field of the Tibetan Plateau due to land water loading
<h3>Basic information:</h3> <p>This dataset includes a series of 3-D loading deformation velocity fields, which are supplements to the GRL paper entitled "Present-Day Three-Dimensional Crustal Deformation Velocity of the Tibetan Plateau Due to Multi-Component Land Water Loading" [<a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a>]. The deformation velocities are fitted using long time span data during 2000-2020, and the detailed description of the data processing and calculation methods can be found through the GRL paper. There are results of three different grid resolutions (0.5x0.5, 0.25x0.25, 0.1x0.1), and for distinction, different file naming suffixes are used. For example, '0point5grids' indicates the results are in 0.5-degree grid resolutions (0.5x0.5), and so forth.</p> <h3>Application scenario:</h3> <p>The velocity fields here can be directly used for the analysis of crustal deformation or used for the separation of land water-induced loading deformation within geodetic deformation velocity fields over Tibetan Plateau. There are results of all the six main land water components, including soil moisture (SM) [Table S1], snow water equivalent (SWE) [Table S2], glacier [Table S3], lake [Table S4], permafrost (PM) [Table S5] and groundwater storage (GWS) [Table S6], thus users can choose one or some they focus on, or directly choose the sum of all the six main components (i.e., GRACE-inferred total terrestrial water storage [Table S7]).</p> <h3>Citation: </h3> <p>When using this dataset, please cite the GRL paper: Jiao, J., Pan, Y., Ren, D., & Zhang, X. (2024). Present-day three-dimensional crustal deformation velocity of the Tibetan Plateau due to multi-component land water loading. <em>Geophysical Research Letters</em>, 51, e2024GL108684. <a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a></p> <h3>Contents:</h3> <p>Table S1. 3-D velocity field of the Tibetan Plateau due to the loading of soil moisture (SM).</p> <p>Table S2. 3-D velocity field of the Tibetan Plateau due to the loading of snow water equivalent (SWE).</p> <p>Table S3. 3-D velocity field of the Tibetan Plateau due to the loading of glacier.</p> <p>Table S4. 3-D velocity field of the Tibetan Plateau due to the loading of lake.</p> <p>Table S5. 3-D velocity field of the Tibetan Plateau due to the loading of permafrost (PM).</p> <p>Table S6. 3-D velocity field of the Tibetan Plateau due to the loading of groundwater storage (GWS).<br>Table S7. 3-D velocity field of the Tibetan Plateau due to the loading of GRACE-inferred total terrestrial water storage (TWS).</p>
Majadas de Tietar: Ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean tree-grass ecosystem
<p>This dataset contains a subset of measurements collected at the experimental site Majadas de Tietar. We collected ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean Savanna using the eddy covariance technique and a series of meteorological sensors for the time period December 2015 - February 2018. The dataset is used for the development of a series of R packages including 'bigleaf' (Knauer et al., 2018).</p> <p>The experimental site is collected in Majadas de Tietar (Casals et al., 2009) located in western Spain (39°56′25″N 5°46′29″W). The ecosystem is a typical “Iberic Dehesa”, which is characterized by an herbaceous stratum of native pasture and sparse trees, for the majority (~98%) Quercus ilex. The tree density is about 20–25 trees/ha, the fractional cover of trees is about 20%, mean DBH of 46 cm, and a canopy height of about 8 m. (El-Madany et al., 2018). The herbaceous layer is composed of native annual species of the three main functional plant forms (grasses, forbs and legumes), whose fractional cover varies seasonally and is characterized by important inter-annual variations in the seasonal dynamics related to the onset of the dry period.</p> <p>Fluxes were measured with the eddy covariance technique with two different systems, one at ecosystem scale to characterize the fluxes of the whole ecosystem (15.5 m above ground), and one at 1.65 m above ground in an open space to measure the fluxes of the well-established understory grass layer.</p> <p>The description of the set-up, equipment and processing used to calculate ecosystem scale fluxes are described in El-Madany et al., (2018), while for the understory tower can be found in Perez-Priego et al., (2017).</p> <p>The dataset is composed of two files: 'ESLMa_MainTower', which is the ecosystem eddy covariance system, and 'ESLMa_SubCanopy', which is the understory eddy covariance system. The dataset contains half-hourly, processed eddy covariance of the ecosystem and understory tower, as well as the main biometeorological data used in the big-leaf package (net radiation, soil heat fluxes, horizontal wind velocity, atmospheric pressure, precipitation, air temperature). All the processing was conducted with EddyPro software (version 5.2.0, LI-COR Biosciences Inc., Lincoln, NE, USA) and the ustar filtering, gap-filling and partitioning with the R package REddyProc (Wutzler et al., 2018). The variables and the units are described in the Readme.txt file released with the dataset.</p> <p><strong>References</strong></p> <p>Casals, P. et al., 2009. Soil CO2 efflux and extractable organic carbon fractions under simulated precipitation events in a Mediterranean Dehesa. Soil Biol. Biochem. 41, 1915–1922. <a href="https://doi.org/10.1016/j.soilbio.2009.06.015">https://doi.org/10.1016/j.soilbio.2009.06.015</a>.</p> <p>El-Madany, T.S.,et al., 2018. Drivers of spatio-temporal variability of carbon dioxide and energy fluxes in a Mediterranean savanna ecosystem 21. <a href="https://doi.org/10.1016/j.agrformet.2018.07.010">https://doi.org/10.1016/j.agrformet.2018.07.010</a></p> <p>Knauer, J., et al., 2018. bigleaf - An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PLOS ONE, doi:10.1371/journal.pone.0201114</p> <p>Perez-Priego O, et al., 2017. Evaluation of eddy covariance latent heat fluxes with independent lysimeter and sapflow estimates in a Mediterranean savannah ecosystem. Agricultural and Forest Meteorology. 236: 87-99. doi: 10.1016/j.agrformet.2017.01.009.</p> <p>Wutzler, T., et al., 2018. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences Discuss., p. 1-39.</p> <p> </p>
Dataset of "Strain-Engineered Ir Shell Enhances Activity and Stability of Ir-Ru Catalysts for Water Electrolysis: An Operando Wide-Angle X-Ray Scattering Study"
<p>Ir-Ru alloys with high Ru content serve as stable and highly active catalysts for the oxygen evolution reaction (OER) in Proton Exchange Membrane Water Electrolyzers (PEM-WEs), enabling efficient operation with remarkably low Ir loadings (150 µg cm-²). Despite this, the mechanisms behind their enhanced stability remain unclear. In this study, we employ operando Wide-Angle X-ray Scattering (WAXS) and complementary ex-situ techniques to investigate the structural evolution of these magnetron-sputtered alloys within a PEM-WE cell. Our results reveal that, upon potential application, Ru is leached from the surface, leading to the formation of a bimetallic Ir-Ru@IrOx core-shell structure. The Ir shell, significantly strained by the underlying Ir-Ru core, exhibits substantially higher catalytic activity than pure Ir. Notably, the Ir-Ru 25:75 catalyst shows superior stability over Ir-Ru 50:50, despite its higher Ru content, due to a more robust Ir shell that protects subsurface Ir and Ru from oxidation and dissolution. This study not only clarifies the performance-enhancing mechanisms of Ir-Ru catalysts but also suggests that other, more economical materials such as Co, Os, or Ti could serve as effective cores in Ir-M systems, offering a pathway to more cost-effective catalysts for PEM-WE applications.</p>
Concentration of nanoparticles per mL for water samples collected from Venice Lagoon
<p>The concentration of nanoparticles from surface seawater collected from the three sites of Venice Lagoon, Venice-Lido Port Inlet, Grand Canal under Rialto Bridge, and Saint Marc basin was analyzed via the Nanoparticle Tracking Analysis technique. Five replications were tested for each sample. Sampling locations: Venice-Lido Port Inlet, GPS coordinates: latitude: 45.431508, longi-tude: 12.406952; Grand Canal under Rialto Bridge, GPS coordinates: latitude: 45.438350, longitude: 12.336311; and Saint Marc basin, GPS coordinates: latitude: 45.431962, longitude: 12.340953.</p>
Extended data for the paper: "SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters"
<p>Extended data 1 to 4 for the software article:<br>SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters. </p> <p>The extended data is tables and a Figure output and input from/to SentemQC runs relevant for the SentemQC paper.</p>
Knowledge base for NBS for water treatment and stormwater management
<p>Five tables containing:</p> <ol> <li>nbs_catalog.csv: A catalogue of nature-based solutions for wastewater treatment and stormwater management. For each solution there is information on its performance, types of water, cobenefits, barriers and cost.</li> <li>sci_publications.csv: A list of scientific publications focused on one or several technologies of the above catalogue.</li> <li>sci_publications_treatment_details: For solutions for water treatment, a second table containing data about treatment performance extracted from previous scientific publications.</li> <li>description_nbs_catalog.csv: Descriptors for the catalogue.</li> <li>description_sci_publications_treatment_details.csv: Descriptors for the treatment performance data.</li> </ol> <p>The most updated version of each table can be queried from https://snappapi-v2.icradev.cat/</p>
Dissolved Cr concentration and stable isotope data presented in "Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ53Cr distributions in the ocean interior" (Janssen et al., 2021, EPSL).
<p>This dataset presents all of the dissolved Cr data included and discussed in “Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ<sup>53</sup>Cr distributions in the ocean interior” (Janssen et al., 2021, EPSL). Three primary datasets are included:</p> <ol> <li>Dissolved [Cr], [Cr(III)] and d53Cr in samples from shipboard particle regeneration incubations conducted in the subantarctic Southern Ocean.</li> <li>Dissolved [Cr] in porewater samples from a sediment core collected in the Tasman Sea in primarily calcareous sediments, along with [Cr] and δ<sup>53</sup>Cr in overlying bottom waters.</li> <li>3. A compilation of intermediate and deep water dissolved [Cr] and δ<sup>53</sup>Cr from seawater samples from the Southern, Pacific and Atlantic Oceans</li> </ol>
Surface water and flooding dynamics data set based on seasonally continuous Landsat data (1986-2011) in a dryland river basin
<p>Animations of the data are available here: <a href="https://doi.org/10.5281/zenodo.2438110">https://doi.org/10.5281/zenodo.2438110</a></p> <p>If you are using this data set, please cite the following publication:</p> <p>Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466 </p> <p>The data represent statistically validated surface water and flooding extent dynamics derived from seasonally continous Landsat TM/ETM+ data and random forest models, and summarised to the maximum extent of surface water per season between 1986-2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157</p> <p>URL: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p> <p>Data are provided in GeoTIFF format per season per year. File naming convention is as follows:<br> yy_inund_freq_season_SamplingMethod. For example, "99_inund_freq_winter_max" will represent inundation frequency for winter 1999 resampled using a maximum resampling method. </p> <p>Inundation frequency represents the number of times a pixel has been flagged as flooded out of the times that pixel had valid observations * 100. Valid observation exclude no data values and clouds. The valid range of inundation frequency is 0-100 [%], with 255 indicating no data values. Data type is eight bit unsigned integer (uint8). </p> <p>The data were resampled to 120m resolution to reduce file size. The resampling methods used include max (e.g. selects the max value of all non-NODATA contributing 30m pixels) and mean (median and min can be provided upon request). If you are unsure which resampling to use, you may want to start with the mean. </p>
Meter-Scale Magma-Water Interaction Experiments
<p>These are video and other sensor data of experiments in which "magma" — that is: volcanic rock, re-melted at ca. 1300°C — interacts with liquid water. The experiments aim to better understand the escalation behavior of the processes involved when magma comes into contact with liquid water.</p> <p>The dataset will grow over time as data of new experiments is added.</p> <p><strong>Changes</strong></p> <ul> <li>Version 1.0: Add the <code>pr06</code> experiment.</li> <li>Version 0.11: Add the <code>pr05</code> experiment.</li> <li>Version 0.10: Add the <code>ir16</code> experiment.</li> <li>Version 0.9: Add the <code>ir15</code> experiment.</li> <li>Version 0.8: Add the <code>ir14</code> experiment.</li> <li>Version 0.7: Add the <code>ir13</code> experiment.</li> <li>Version 0.6: Add the <code>ir12</code> experiment.</li> <li>Version 0.5: Add the <code>ir07</code> experiment.</li> <li>Version 0.4: Add the <code>ir06</code> experiment.</li> <li>Version 0.3: Add the <code>ir05</code> experiment.</li> <li>Version 0.2: Add the <code>ir04</code> experiment.</li> <li>Version 0.1: Start with experiment <code>ir03</code>.</li> </ul>
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.