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19,365 results for “watering”
Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment
<p>Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment<br> Prepared by Natalie Ceperley, February 2020. </p> <p><br> All methods associated with this data are available in the manuscript: Elvira Mächler, Anham Salyani, Jean-Claude Walser, Annegret Larsen, Bettina Schaefli, Florian Altermatt, and Natalie Ceperley. 2019. Water tracing with environmental DNA in a high-Alpine catchment, Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-2019-551. <br> Related data sets are and will be published in the Vallon de Nant Community on Zenodo. Associated sequencing data are publicly available on European Nucleotide Archive (Mächler et al., 2020). </p> <p>All isotope data analyzed in the laboratory of Torsten W. Vennemann at the University of Lausanne. </p> <p> </p> <p><br> All Files:<br> ▪ NaN - No measurement or sample<br> ▪ Details regarding measurement are available in paper or supplement. </p> <p>Files: <br> 1) climate_hydro_2017_daily.csv <br> ⁃ 16 columns: <br> ⁃ 1. day of year with January 1, 2017 = 1<br> ⁃ 2-5. Q: daily mean, min, max, and baseflow discharge as measured at outlet (location ER/MR), in liters / day <br> ⁃ 6. P: mean mm of rain across catchment per day<br> ⁃ 7. SR: total solar radiation per day in W/hr/m2 as median of 4 meteorological stations<br> ⁃ 8-10. SCA: mean, min, and max snow covered area on days with satellite imagery available for whole catchment area, in %<br> ⁃ 11-13. water temperature, mean, min, and max, at outlet (location ER/MR), in degrees C<br> ⁃ 14-16. air temperature, mean, min, and max at 4 meteorological stations, in degrees C</p> <p>2) delta-18-O_permil.csv <br> ⁃ stable isotopes of water (delta 18-O) in per mil<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>3) delta-2-H_permil.csv <br> ⁃ stable isotopes of water (delta 2-H) in per mil<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>4) dqdt_outlet_prev48hrs.csv<br> - dq/dt determined at the outlet for the previous 48 hours at sampling moment (TimeOfSamples_HR.csv) for each sampling site<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p><br> 5) ednasamplecount.csv <br> - this is the tally of samples (1 sample includes 4 replicates)<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>6) electricalconductivity_instrument.csv <br> ⁃ Code: <br> 108 - post-analyzed using a glass bodied 6 mm probe in the laboratory (Jenway 4510, Staffordshire, UK). <br> 102 - hand measurement with WTW (multi-3510 with a IDS-tetracon-925, Xylem Analytics, Germany)<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p><br> 7) electricalconductivity_uScm.csv <br> - this is the electrical conductivity in micro siemens per cm, according to the instruments coded in electricalconductivity_instrument.csv<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>8) LC-excess.csv <br> - this is the line control execss from the meteoric water line as determined by the samples in the file: precipitationistopemetadata.csv<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>9) locations.csv <br> ⁃ Location codes used in other files. <br> - Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl. </p> <p>10) precipitationisotopemetadata.csv <br> - This is the sampling information for the isotope data that was used to calculate the meteoric water line. <br> - The full data set will become available in a subsequent publication on Zenodo linked to the same community. <br> - 4 columns: <br> - 1. code: rain (1) or snow (2)<br> - 2. collection date and time<br> - 3. elevation in m. asl. <br> - 4. in the case of rain, this is the depth of collection in mm (area normalized volume), in the case of snow, this is the mean depth below the surface that the sample was taken from in cm. <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>11) sampledates.csv <br> - These are the sample dates in day, month, year and day of year corresponding to the rows in other files</p> <p>12) stationlocations.csv<br> - These are the locations of four meteorological stations and discharge measurement station. <br> - Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl. </p> <p>13) TimeOfSamples_HR.csv <br> - This is the time of the sample in hours and decimals correspond to minutes past hour<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>14) watertemperature_degC.csv <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)<br> - measure in degrees C<br> - instrument in watertemperature_instrument.csv</p> <p>15) watertemperature_instrument.csv <br> ⁃ Code: <br> 1 = hand measurement with WTW (multi-3510 with a IDS-tetracon-925, Xylem Analytics, Germany)<br> 2 = HOBO Pendant Temperature/Light Data Logger 64K - UA-002-64", Onset (Bourne, MA, USA)<br> 3 = Continually logging WTW (IDS-tetracon-325, Xylem Analytics, Germany)<br> 4 = Continually logging (10min) HOBO U24-001 Conductivity, Onset (Bourne, MA, USA) <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p>
Random-Phase Approximation in Many-Body Noncovalent Systems: Methane in a Dodecahedral Water Cage
<p>Supplementary information and raw data for Random-Phase Approximation in Many-Body Noncovalent Systems: Methane in a Dodecahedral Water Cage. </p>
Data for "How Nitrogen and Phosphorus Availability Change Water Use Efficiency in a Mediterranean Savanna Ecosystem"
<p>These are flux and meteorological data for the measurement sites ES-LMa (CT; control treatment), ES-LM1 (NT; nitrogen treatment), and ES-LM2 (nitrogen + phosphorus treatment) for the period from 2014-03-20 to 2020-02-01.</p> <p>These data were used for the manuscript:</p> <p>El-Madany, et al. (2021) "How Nitrogen and Phosphorus Availability Change Water Use Efficiency in a Mediterranean Savanna Ecosystem" submitted to Journal of Geophysical Research - Biogeoscience.</p>
Water vapor database for atmospheric correction of Landsat imagery
<p>Atmospheric correction is a crucial preprocessing step for the analysis of optical satellite imagery like Landsat. Among the radiance-modifying gases, atmospheric water vapor is spatially and temporally variable, and cannot be measured reliably from the Landsat sensors. As such, atmospheric correction of Landsat data requires spatially and temporally explicit auxiliary information about atmospheric water vapor content.</p> <p>We have compiled a water vapor dataset that can be readily used to perform atmospheric correction of Landsat images. The dataset was generated by a global processing of the MODIS MOD05/MYD05 collection 6.1 products (<a href="https://doi.org/10.1029/2002JD003023">https://doi.org/10.1029/2002JD003023</a>; MODIS Atmosphere L2 Water Vapor Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA: <a href="https://doi.pangaea.de/10.5067/MODIS/MOD05_L2.006">doi:10.5067/MODIS/MOD05_L2.006</a>, <a href="https://doi.pangaea.de/10.5067/MODIS/MYD05_L2.006">doi:10.5067/MODIS/MYD05_L2.006</a>). The dataset is comprised of daily global water vapor data for February 2000 to December 2020 for each land-intersecting Worldwide Reference System 2 (WRS-2) scene, as well as a monthly climatology that can be used if no daily value is available.</p> <p>The dataset was generated by the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE v. 2.0 / 3.6, <a href="https://github.com/davidfrantz/force">https://github.com/davidfrantz/force</a>, <a href="https://doi.org/10.3390/rs11091124">https://doi.org/10.3390/rs11091124</a>), which is freely available software under the terms of the GNU General Public License v. >= 3. The water vapor dataset can be readily ingested into the FORCE Level 2 Processing System (Frantz et al. 2016, <a href="https://doi.pangaea.de/10.1109/TGRS.2016.2530856">doi:10.1109/TGRS.2016.2530856</a>) to perform atmospheric correction of Landsat imagery. This dataset is an update of <a href="https://doi.pangaea.de/10.1594/PANGAEA.893109">https://doi.pangaea.de/10.1594/PANGAEA.893109</a> and should be used from now on.</p>
UWSCatCH: Urban Water Supply Catchment Contributions and Hydrological Statistics for large cities of the conterminous United States.
<p>UWSCatCH extends and enhances the Urban Water Blueprint (McDonald et al., 2014) for a selection of 116 cities (population > 150,000) and their associated surface water supply catchments in the conterminous United States. The two major enhancements to the Urban Water Blueprint are: [1] estimates of the relative contributions of each surface water catchment to each city's average water supply (as well as updated estimates of any contributions from groundwater); [2] NHDplusV2 reach codes for each water supply intake stream location and associated average flow estimates (regulated and unregulated) (local upstream USGS gage IDs are also provided). UWSCatCH also features a raster file with spatially distributed (1/24° grid) runoff (average of 1980 - 2012 reanalysis simulation) which is masked to watershed polygons (included as a shapefile) to explore spatial distribution of average runoff generation affecting each city. UWSCatCH is designed for use in the R package "gamut" (https://github.com/IMMM-SFA/gamut), and may be applied in a variety of regional and national scale research studies concerning drinking water supply to major US cities.</p>
The ground-based MUSICA dataset: Tropospheric water vapour isotopologues (H216O, H218O and HD16O) as obtained from NDACC/FTIR solar absorption spectra
<p>MUSICA (“MUlti-platform remote sensing of Isotopologues for investigating the Cycle of Atmospheric water”, http://www.imk-asf.kit.edu/english/musica.php) is a European Research Council (ERC) project. The project has developed tropospheric water vapour isotopologue retrievals (H2O and H2O-δD pairs) using ground-based FTIR spectra as well as thermal nadir spectra measured by the satellite sensor IASI. H2O-δD pairs allow studying tropospheric water transport pathways and in combination with models they can improve our understanding of important climate feedback mechanisms (see also WCRP Grand Challenges: http://www.wcrp-climate.org/grand-challenges).<br /> <br /> For MUSICA, the FTIR spectra have been analysed centrally at KIT using uniform and consistent retrieval settings, thereby guaranteeing ultimate consistency of the retrieval products generated for different FTIR stations. The FTIR products are H2O profiles for the lower, middle and upper troposphere as well as H2O-δD pairs for the lower and middle troposphere. The data have been produced for 12 FTIR stations and date back to 1996.</p> <p>The dataset has been extensively characterized and validated (theoretically and empirically). Furthermore, the spectra have been used to perform uniform retrievals of XCO<sub>2</sub>, which is then used for documenting the long-term stability of these kind of FTIR data. The data are provided in the form of two data types. The first type ("ftir.iso.h2o") is best-suited for tropospheric water vapour distribution studies that disregard the different isotopologues (comparison with radiosonde data, analyses of water vapour variability and trends, etc.). The second type ("ftir.iso.post.h2o") is needed for analysing moisture pathways by means of H<sub>2</sub>O-δD pair distribution.</p> <p>The data format is hdf4 and the files have been generated in compliance with GEOMS (Generic Earth Observation Metadata Standard). The complete MUSICA NDACC/FTIR dataset is also publicly available via the NDACC database (ftp://ftp.cpc.ncep.noaa.gov/ndacc/MUSICA).</p> <p>Details on the characteristics of the dataset are described in the paper "Tropospheric water vapour isotopoloque data (H<span class="math-tex">\(_{2}^{16}\)</span>O, H<span class="math-tex">\(_{2}^{18}\)</span>O and HD<sup>16</sup>O) as obtained from NDACC/FTIR solar absorption spectra" that has been prepared for ESSD in the context of the special issue “25th anniversary of NDACC”.</p>
Aukana, Sri Lanka. Water tank near inscription.
<p>Aukana (near Kekirawa) Sri Lanka. Water tank near inscription, as documented in 02/2012. Located at <strong> </strong>8°0'41"N 80°30'46"E.</p>
Water quality and diarrhoea bibliometric data and visualisation
<p>This repository contains bibliometric data and its visualisations:</p> <p>Bibliometric data</p> <ul> <li>Keyword: "water quality" AND diarrhoea</li> <li>Database: Scopus</li> <li>Date taken: 28 June 2017</li> <li>Formats: bib, csv, ris</li> <li>Reference manager: Jabref and Zotero</li> </ul> <p>Visualisations</p> <ul> <li>Tools: VosViewer (http://VosViewer.com)</li> <li>Tool's citation: Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. (paper, preprint, supplementary material) (http://dx.doi.org/10.1007/s11192-009-0146-3)</li> <li>Mindmap of analysis procedures using Freeplane https://www.freeplane.org/wiki/index.php/Main_Page</li> </ul>
Global River Water Quality Archive (GRQA)
<p>A major problem related to large-scale water quality modeling has been the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing models. In addition to the observation data itself, insufficient or poor quality metadata has also discouraged researchers to integrate the already available datasets. Therefore, improving both the availability and quality of open water quality data woould increase the potential to implement predictive modeling on a global scale. We aim to address the aforementioned issues by presenting the new Global River Water Quality Archive (GRQA) by integrating data from five existing global and regional sources: Canadian Environmental Sustainability Indicators program (CESI), Global Freshwater Quality Database (GEMStat), GLObal RIver Chemistry database (GLORICH), European Environment Agency (Waterbase) and USGS Water Quality Portal (WQP). The resulting dataset covering the timeframe 1898 - 2023 contains a total of over 17 million observations for 43 different forms of some of the most important water quality parameters, focusing on nutrients, carbon, oxygen and sediments. Supplementary metadata and statistics are provided with the observation time series to improve the usability of the dataset.</p> <p>GRQA <strong>data processing scripts</strong> are available at <a href="https://doi.org/10.5281/zenodo.5082147" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5082147</a>.</p> <p><strong>Last update: 2025-05-04</strong></p> <p><strong>Changes since GRQA_v1.3</strong></p> <p>The updated dataset now includes the observations from the latest versions of GEMStat and Waterbase. These additions extend the time series to 2023 in many sites, particularly in Europe.</p> <p>An overview of all the files in the dataset can be found in README_v1.4.md.</p> <p>Statistical overview of all 43 parameters is given in the data catalog file GRQA_data_catalog_v1.4.pdf.</p> <p>For more information about the development of this dataset look for Virro, H., Amatulli, G., Kmoch, A., Shen, L., and Uuemaa, E.: GRQA: Global River Water Quality Archive, Earth Syst. Sci. Data, 13, 5483–5507, <a href="http://doi.org/10.5194/essd-13-5483-2021">https://doi.org/10.5194/essd-13-5483-2021</a>, 2021.</p>
Wildfires drive multi-year water quality degradation over the western U.S.
<p>Information on the 245 burned basins, 293 unburned basins, and 356 associated fires from across the U.S. West which were used in statistical analyses of post-wildfire water quality response. Included are physiographic characteristics, as well as ESRI Shapefile polygons representing delineations for each basin and fire. Additionally, daily carbon, nitrogen, phosphorus, sediment, and turbidity data sampled from the basins' outlets are provided from 1974-2022. R coding scripts used in data processing and modeling also included, as well as data directly used in generating manuscript and "Supplementary Information" plots.</p> <p>Water quality data used to create this dataset are from the Water Quality Portal and wildfire burn perimeters are from the Monitoring Trends in Burn Severity database.</p>
GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present
<p>Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional investigation of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940-2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent datasets such as the land-ocean mass budget, large-scale water balance in 341 large river basins, and streamflow measurements at 10,168 gauges. We find that the proposed approach performs overall as well as or is more reliable than previous TWS datasets. Moreover, our reconstructions successfully reproduce the impact of climate variability, such as strong El Niño events. GTWS-MLrec dataset consists of three reconstructions based on JPL, CSR and GSFC mascons, three detrended and de-seasonalized reconstructions, and six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a broad range of applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management.</p><p>Please cite the reference: <strong>Yin J, Slater L, Khouakhi A, et al. GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present. Earth System Science Data. 2023.</strong></p><p>For any inquiry about the dataset, welcome to contact Dr. Jiabo Yin (jboyn@whu.edu.cn).</p>
Mars Thermospheric Water Abundance in Mars Years 32-36
<p>This file contains the derived thermospheric water abundances and mixing ratios along with the associated solar longitude, latitude, solar zenith angle, local time and global dust optical depth.</p>
Future monthly discharge and water temperature simulations under global change (CMIP6)
<p>Monthly discharge (m3 s-1) and water temperature (K) simulated by a global hydrological model coupled to a surface water quality model (<i>PCR-GLOBWB2-DynQual)</i> for the time period 2005 - 2100, for an ensemble of 15 projections based on three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and five general circulation models (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0)</p><p>Output data are provided at 10km resolution and are averaged at monthly temporal resolution.</p><p>These datasets were generated as part of the work presented in: Jones, E.R., Bierkens, M.F.P., van Puijenbroek, P.J.T.M. <i>et al.</i> Sub-Saharan Africa will increasingly become the dominant hotspot of surface water pollution. <i>Nat Water</i> <strong>1</strong>, 602–613 (2023). <a href="https://www.nature.com/articles/s44221-023-00105-5#citeas">https://doi.org/10.1038/s44221-023-00105-5</a></p><p>Relevant model description papers can be found at the following links:</p><ul><li><i>PCR-GLOBWB2</i>: Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, <i>Geoscientific Model Development</i>, 11, 2429–2453, <a href="https://gmd.copernicus.org/articles/11/2429/2018/gmd-11-2429-2018.html">https://doi.org/10.5194/gmd-11-2429-2018</a>, 2018.</li><li><i>DynQual</i>: Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., and van Vliet, M. T. H.: DynQual v1.0: a high-resolution global surface water quality model, <i>Geoscientific Model Development</i>, 16, 4481–4500, <a href="https://gmd.copernicus.org/articles/16/4481/2023/gmd-16-4481-2023.html">https://doi.org/10.5194/gmd-16-4481-2023</a>, 2023.</li></ul><p>Additional information on the water temperature modelling can also be found at:</p><ul><li>Wanders, N., van Vliet, M. T. H., Wada, Y., Bierkens, M. F. P., & van Beek, L. P. H. (Rens): High-resolution global water temperature modeling. <i>Water Resources Research</i>, 55, 2760–2778, <a href="https://doi.org/10.1029/2018WR023250">https://doi.org/10.1029/2018WR023250</a>, 2019</li><li>van Beek, L. P. H., Eikelboom, T., van Vliet, M. T. H., and Bierkens, M. F. P.: A physically based model of global freshwater surface temperature, <i>Water Resources. Research</i>, 48, W09530, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2012WR011819">https://doi.org/10.1029/2012WR011819</a> , 2012.</li></ul>
Data from: Chronic Rapamycin administration via drinking water mitigates the pathological phenotype in a Krabbe disease mouse model through autophagy activation.
<p>ABSTRACT </p><p>Krabbe disease (KD) is a rare disorder caused by a deficiency of the lysosomal enzyme galactosylceramidase (GALC), resulting in the accumulation of the cytotoxic metabolite psychosine (PSY) in the nervous system. This accumulation triggers demyelination and neurodegeneration. Despite ongoing research, the underlying pathogenic mechanisms remain incompletely understood, and there is currently no cure available.</p><p>Previous studies from our lab revealed the presence of autophagy dysfunctions in KD pathogenesis, as evidenced by the presence of p62-tagged protein aggregates in the brains of KD mice and increased p62 levels in the KD sciatic nerve. We also demonstrated that the autophagy inducer Rapamycin (RAPA) can partially restore the wild-type (WT) phenotype in KD primary cells by reducing the number of p62 aggregates.</p><p>In this study, we tested RAPA in the Twitcher (TWI) mouse, a spontaneous KD mouse model. We administered the drug ad libitum via drinking water (15 mg/L) starting from post-natal day (PND) 21-23. We longitudinally monitored the motor performance of the mice through grip strength and rotarod tests, along with various biochemical parameters related to KD pathogenesis (i.e. autophagy markers expression, myelination, astrogliosis, and PSY accumulation).</p><p>Our findings demonstrate that RAPA significantly enhances motor functions at specific treatment time points and reduces astrogliosis in TWI brain, spinal cord, and sciatic nerves. Using western blot and immunohistochemistry, we observed a decrease in p62 aggregates in TWI nervous tissues, which corroborates our earlier in-vitro results. Furthermore, RAPA treatment partially reduces PSY levels in the spinal cord.</p><p>In conclusion, our results support the consideration of RAPA as a supportive therapy for KD. Importantly, as RAPA is already available in pharmaceutical formulations for clinical use, its potential for KD treatment can be promptly evaluated in clinical trials.</p>
MPAS-Ocean Shallow Water Meshes
<p>The MPAS_Ocean_Shallow_Water_Meshes directory contains planar hexagonal mesh files in NetCDF format necessary for running a verification suite of shallow water test cases for the barotropic solver of ocean models using a mimetic finite volume spatial discretization based on the TRiSK scheme. It also contains mesh plots showing the cell centers, edge centers, vertices, and orientation of the normal vectors at the edges; plots of high resolution meshes superimposed on low resolution ones; plots of state variables interpolated from edges and vertices to cell centers along with the interpolation error; plots of state variables interpolated from a high resolution mesh to a low resolution one; and plots of various spatial operators of the TRiSK scheme applied to the state variables along with their error and convergence plots. The associated code can be cloned from the Github repository <a href="https://github.com/siddharthabishnu/Rotating_Shallow_Water_Verification_Suite.git">Rotating_Shallow_Water_Verification_Suite</a>. Please download the MPAS_Ocean_Shallow_Water_Meshes.zip file, unzip it, and place the resulting directory within the meshes directory of Rotating_Shallow_Water_Verification_Suite.</p>
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>
IODP Expedition 391 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 383 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 383 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.
FESOM2.1 model data used in the paper "Atlantic Water warming increases melt below Northeast Greenland's last floating ice tongue"
<p><span>This data set includes the minimal data necessary to reproduce the findings of Wekerle et al., in revision. Output of model simulations with the global ocean sea ice model FESOM2.1 is provided. In particular, the data set includes:</span></p> <p><span>a) long term means of potential temperature, salinity, velocity and basal melt of the 79N Glacier averaged over 1970-2021 (</span>Wekerle2024_FESOM2_ltm_REF.nc<span>)</span></p> <p><span>b) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from the reference experiment REF for the years 1970-2021 (</span>Wekerle2024_FESOM2_annual_avg_REF.nc<span>)</span></p> <p><span>c) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from experiment CLIM for the years 2000-2021 (</span>Wekerle2024_FESOM2_annual_avg_CLIM.nc<span>)</span></p> <p><span>d) daily mean basal melt rates of experiments with varying subglacial discharge averaged over the years 2010-2014 (</span>Wekerle2024_FESOM2_daily_avg_EXP_subglacial_discharge.nc<span>)</span></p> <p><span>e) daily mean basal melt rates of experiments with varying drag coefficients for the year 2000 (</span>Wekerle2024_FESOM2_daily_EXP_basal_drag.nc<span>)</span></p> <p><span>Each netcdf file includes information on the model grid (longitude and latitude of nodes, depths of the vertical layers, elements, nodal areas).</span></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.