Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
19,365
datasets available to search
ShareScore release 0.9.0
Dataset results
19,365 results for “watering”
MeanDRS River Width Sampling: Data products corresponding to "Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all input and output files that were used in the study reported in:</p> <ul> <li>Wade, J., David, C.H., Collins, E.L., Denbina, M., Cerbelaud, A., Tom, M., Reager, J.T., Frasson, R.P.M., Famiglietti, J.S., Lee, T., Gierach, M.M. (In Review), Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary</strong></p> <p>The Earth’s rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Using monthly simulations of river stores and fluxes from the MeanDRS river routing dataset, we sample global rivers by a range of estimated river width thresholds to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale-dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes.</p> <p>We aim to answer two questions:</p> <p>1. What is the intrinsic spatial resolution of global river dynamics?</p> <p>2. How can the spatial scale of river processes be used to inform efficient monitoring and modeling strategies of global river stores and fluxes?</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. <a href="../records/10013744">https://zenodo.org/records/10013744</a>. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5</li> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> </ul> <p><strong>Software</strong></p> <p>The software that was used to produce files in this dataset are available at https://github.com/jswade/meandrs-width-sampling.</p> <p><strong>Data Products</strong></p> <p>The following files represent the primary outputs of the analysis. Each file class generally has 61 files, corresponding to the 61 global hydrologic regions (region ii).</p> <p><strong>Riv_coast.zip</strong> contains shapefiles of corrected and uncorrected MeanDRS river reaches that intersect with the global coast and are inferred to drain to the ocean.</p> <p><strong>· </strong><strong>riv_coast.zip</strong></p> <p><strong> o </strong><strong>cor:</strong> riv_coast_pfaf_ii_COR.shp</p> <p><strong> o </strong><strong>uncor: </strong>riv_coast_pfaf_ii_UNCOR.shp</p> <p><strong> </strong></p> <p><strong>Qout_rivwidth.zip </strong>contains csv files of the aggregate river discharge to the ocean (km<sup>3</sup>/yr) of under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>Qout_rivwidth.zip: </strong>Qout_pfaf_ii_rivwidth.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_low.zip</strong> contains csv files of the aggregate river storage (km<sup>3</sup>) for the low residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_low.zip:</strong> V_pfaf_ii_rivwidth_low.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_nrm.zip </strong>contains csv files of the aggregate river storage (km<sup>3</sup>) for the normal (medium) residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_nrm.zip: </strong>V_pfaf_ii_rivwidth_nrm.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_hig.zip </strong>contains csv files of the aggregate river storage (km<sup>3</sup>) for the high residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_hig.zip: </strong>V_pfaf_ii_rivwidth_hig.csv</p> <p><strong> </strong></p> <p><strong>Largest_rivs.zip </strong>contains files related to our analysis of the relative contributions of discharge to the ocean from the 10 largest global river basins.</p> <p><strong>· </strong><strong>largest_rivs.zip</strong></p> <p><strong> o </strong><strong>cat: </strong>cat_dis_top10_nxx.shp – dissolved catchments of reaches draining from the 10 largest basins</p> <p><strong> o </strong><strong>csv:</strong> Q_df_top10.csv – total discharge contributed by each basin</p> <p><strong> o </strong><strong>riv:</strong> riv_top10_nxx.shp – river reaches that drain the 10 largest basins</p> <p><strong> </strong></p> <p><strong>Smallest_rivs.zip </strong>contains files related to our analysis of the relative contributions of discharge to the ocean from global rivers narrower than 100 m.</p> <p><strong>· </strong><strong>smallest_rivs.zip</strong></p> <p><strong> o </strong><strong>cat: </strong>cat_pfaf_pfaf_ii_small_100m.shp – dissolved catchments of narrow reaches draining to the ocean for each region ii</p> <p><strong> o </strong><strong>csv:</strong> Q_df_top10.csv – total discharge to the ocean from each narrow river reach</p> <p><strong> o </strong><strong>riv: </strong>riv_pfaf_ii_small_100m.shp – river reaches narrower than 100 m that drain to the ocean for each region ii</p> <p><strong> </strong></p> <p><strong>Global_summary.zip </strong>contains files related to the global aggregation of our region-specific river width sampling estimates for discharge to the ocean and river storage.</p> <p><strong>· </strong><strong>global_summary.zip</strong></p> <p><strong> o </strong><strong>Qout_rivwidth: </strong>global summary files for discharge to the ocean (km<sup>3</sup>/yr) under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_low:</strong> global summary files for total river storage (km<sup>3</sup>) for the low residence time scenario under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_nrm:</strong> global summary files for total river storage (km<sup>3</sup>) for the normal (medium) residence time scenario under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_hig: </strong>global summary files for total river storage (km<sup>3</sup>) for the hig residence time scenario under river width sampling</p> <p><strong> o </strong><strong>cat_small_gl: </strong>cat_dis_global_small_100m.shp – global dissolved catchments contributing to all rivers narrower than 100 m that drain to the ocean</p> <p><strong> </strong></p> <p><strong>Rivwidth_sens.zip </strong>contains files related to our supplemental analysis of the sensitivity of our width estimation approach to choice of input discharge dataset. Here, we compute estimated river widths using 3 versions of MeanDRS discharge outputs (VIC, CLSM, NOAH) and compare the results of river width sampling from those runs to that of the primary analysis. The file formats and explanations follow those presented above, with added information for the land surface model used to generate those discharge simulations.</p> <p><strong>· </strong><strong>Rivwidth_sens.zip</strong></p> <p><strong> o </strong><strong>riv_coast</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_VIC</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_CLSM</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_NOAH</strong></p> <p><strong> o </strong><strong>global_summary_VIC</strong></p> <p><strong> o </strong><strong>global_summary_CLSM</strong></p> <p><strong> o </strong><strong>global_summary_NOAH</strong></p> <p><strong> </strong></p> <p><strong>Cor_sens.zip </strong>contains files related to our supplemental analysis of the sensitivity use of corrected ensemble MeanDRS discharge and volume simulations as opposed to uncorrected ensemble simulations. Here, we repeat our primary analysis using only uncorrected simulations throughout, rather than performing river width sampling using corrected simulations. The file formats and explanations follow those presented above, with the files using uncorrected ensemble (ENS) discharge and storage values in contrast to the primary analysis.</p> <p><strong>· </strong><strong>Cor_sens.zip</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_ENS</strong></p> <p><strong> o </strong><strong>global_summary_ENS</strong></p> <p><strong> </strong></p> <p><strong>Width_val.zip </strong>contains files related to our supplemental validation of river widths estimated from MeanDRS discharge simulations through comparison with optical measurements of widths from the Global River Widths from Landsat (GRWL) Databse (Allen & Pavelsky, 2018).</p> <p><strong>· Width_val.zip: </strong>width_validation_pfaf_ii.csv</p> <p> </p> <p><strong>Known bugs in this dataset or the associated manuscript</strong></p> <p>No bugs have been identified at this time.</p> <p> </p> <p><strong>References</strong></p> <p>Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. <em>Science</em>, <em>361</em>(6402), 585-588. https://doi.org/10.1126/science.aat0636</p> <p>Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M. (2024). Global patterns in river water storage dependent on residence time. <em>Nature Geoscience</em>, 1–7. https://doi.org/10.1038/s41561-024-01421-5</p> <p>Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., & Wood, E. F. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. <em>Water Resources Research</em>, <em>55</em>(8), 6499–6516. https://doi.org/10.1029/2019WR025287</p> <p>Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). <em>Bulletin of the American Meteorological Society</em>, <em>102</em>(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1</p>
Water Body Checklists 2019: Adriatic Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Adriatic Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: Ceram Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Ceram Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Map of standing water habitat types and regionally important biotopes in Flanders
<p>This map is a combination of the <a href="https://zenodo.org/records/13865531">standardized habitat map of Flanders</a> (version habitatmap_stdized_2023_v1) and <a href="https://zenodo.org/records/14203168">the watersurface map of Flanders</a> (version watersurfaces_2024). It contains standing water Natura 2000 habitat types (2190_a and 31xx) and regionally important biotopes (rbbah) in Flanders.</p> <p>The polygons with 2190_a habitat (dune slack ponds) are generated by selecting all watersurface polygons that overlap with dune habitat polygons (21xx) of the standardized habitat map.</p> <p>For each of the other aquatic habitat types (31xx and rbbah) we select the watersurface polygons that overlap with the selected habitat type polygons of the standardized habitat map. We also select polygons of the standardized habitat map containing standing water types but that do not overlap with polygons of the watersurface map.</p> <p>The <code>watersurfaces_hab.gpkg</code> file is a GeoPackage that contains:</p> <ul> <li><code>watersurfaces_hab_polygons</code>: a spatial layer with the selected polygons that contain standing water habitat types or regionally important biotopes. </li> <li><code>watersurfaces_hab_types</code>: a table with information on standing water habitat types and regionally important biotopes in each watersurface polygon.</li> </ul> <p>The R-code for creating the <code>watersurfaces_hab</code> data source can be found in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/58138a8/src/generate_watersurfaces_hab">'n2khab-preprocessing' at commit 58138a8</a>.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://github.com/inbo/n2khab">n2khab</a>.</p>
Senator Beck Basin Corrected Radiation (Water Years 2005-2023)
<p>This is qa/qc'd hourly data from two snow energy balance instrumentation towers in Senator Beck Basin Study Area, San Juan Mountains, CO. Data processed by McKenzie Skiles (Snow HydRO Lab, University of Utah). </p> <p>All data originates from the Center for Snow and Avalanche Studies, if used they should be recognized, please see their data policy: <a href="https://snowstudies.org/csas-archival-data/">https://snowstudies.org/csas-archival-data</a><a href="https://snowstudies.org/csas-archival-data/">/</a> </p> <p>Correction of radiation data is described in <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012WR011985">Painter et al., 2012.</a></p>
Time-lapse electrical resistivity tomography and seismic reflection imaging of a shallow ground-water aquifer (0-50 m): Mississippi River levee seepage across the Duncan Point bar, Baton Rouge, Louisiana, U.S.A.
<p>The electrical resisitivity raw data files are slightly processed to remove bad data points but can be inverted using tomographic inversion code. </p> <p>The seismic data were assembled in Seismic Unix format, a shortened version of the SEG-Y format (Society of Exploration Geophysicists Exchange Format-Y https: //seg. org/Publications/SEG-Technical-Standards), that has the 3200-byte EBCDIC and 400-byte tape header removed. The data uploaded online (<a href="https://zenodo.org/records/14776025">https://zenodo.org/records/14776025</a>) is a CMP brute-stacked seismic section. </p> <p>During data collection, shotpoint location changed proceeding along a 136-degree azimuth (south-easterly direction), and spaced every 1 m.</p> <p>A total of 48, horizontal-component 28-Hz nominal geophones were placed every one meter and shotpoints were located half-way between geophones. Geophones remained fixed at their locations throughout the survey and so the CMP spacing is nominally 0.5-m but fold varies linearly from a value of 1 from either side of the survey to a central maximum of 24. The seismic source consisted of a partially buried 20-lb steel I-beam struck repeatedly on either side three times by an 8-lb sledge hammer. Data of the same striking polarity were added in-phase in the field. Data with opposing polarity at each shotpoint location were subtracted later to enhance SH-wave data and suppress converted SH-to-P waves.</p> <p>Seismic processing is minimal and consists of standard surface-wave muting, elimination of bad seismic traces, normal moveout, bandpass filtering (between 12 Hz and 50 Hz) and preliminary stacking with trace mixing every 3 CMPs. The data were stacked with a single velocity throughout that ranged from 80 m/s (Vs) at 0.2 s, to 100 m/s at 0.35 s and reached 180 m/s at 0.5 s of two-way traveltime.</p> <p> </p>
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
'Subglacial Water Amplifies Antarctic Contributions to Sea-Level Rise' by Chen Zhao et al.
<p>Code and data used to produce figures in "Subglacial Water Amplifies Antarctic Contributions to Sea-Level Rise" by Chen Zhao et al.</p> <p>We are happy to help with anybody with any problems with this code, please get in touch (chen.zhao@utas.edu.au) or raise an issue.</p>
Supplementary dataset for "Potential of Wastewater Reuse to Alleviate Water Scarcity under Future Warming Scenarios"
<p>The folder contains water gap data relative to the paper:<br>Kahn, M., Sangiorgio, M., and Rosa, L. (2025) Potential of wastewater reuse to alleviate water scarcity under future warming scenarios. Environmental Research Letters, 20, 034012<br>https://doi.org/10.1088/1748-9326/adb31d</p> <p>All water gaps data are in km3/yr.</p> <p><br>Gridded data(NetCDF at 0.5°)</p> <ul> <li>baseline (2001-2010) <ul> <li>Water_gap_baseline_no_wastewater_reuse: Water gap under baseline climate scenario with no wastewater reuse.</li> <li>Water_gap_baseline_treated_wastewater_reuse: Water gap under baseline climate scenario with treated wastewater reuse.</li> <li>Water_gap_baseline_full_wastewater_reuse: Water gap under baseline climate scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>1.5°C warming <ul> <li>Water_gap_15_no_wastewater_reuse: Water gap under 1.5°C warming scenario with no wastewater reuse.</li> <li>Water_gap_15_treated_wastewater_reuse: Water gap under 1.5°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_15_full_wastewater_reuse: Water gap under 1.5°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>3°C warming (5 models + average) <ul> <li>Water_gap_3_no_wastewater_reuse: Water gap under 3°C warming scenario with no wastewater reuse.</li> <li>Water_gap_3_treated_wastewater_reuse: Water gap under 3°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_3_full_wastewater_reuse: Water gap under 3°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <p>Aggregated data (.xlsx)</p> <ul> <li>country_level_water_gaps.xlsx: Water gap aggregated by country for all the considered scenarios.</li> <li>city_water_gaps.xlsx: 0.5° pixels with populations greater than 5,000,000 and non-zero water gaps corresponding to urban center.</li> <li>seasonal_variations.xlsx: Monthly water gaps of the 5 most water scarce countries.</li> </ul> <p><br>Note: the global water gap obtained by summing all the countries is not completely equivalent to the sum of all the pixels because some pixels' center is outside the polygon of the corresponding country.</p>
Data set associated to the manuscript entitled Carbon emissions from inland waters may be underestimated: evidence from European river networks fragmented by drying by López-Rojo et. al
<p>CO2 and CH4 emissions and several associated environmental variables were taken in 6 European drying river networks, in 20 river reaches per river network. The field work was carried across 3 sampling campaigns in 2021, coinciding with 3 hydrological seasons (pre-dry, dry and post-rewetting) to encompass most of the hydrological variability. Each time, measures were taken in the habitats available (flowing water, dry riverbeds, isolated pools).</p>
Data set for the integrated Climate, Land, Energy and Water systems modelling exercise RCLEWs in OSeMOSYS
<p>This dataset refers to the modelling exercise (version01_210616RCLEWs). The dataset contains the OSeMOSYS code used to run the modelling exercise, the model input data, the scenarios model data files, and the results. The code for the results visualization is available at https://github.com/KTH-dESA/teaching-CLEWs_visualization.</p> <p>This is an update of version 01_210827 available at: https://doi.org/10.5281/zenodo.5293834</p>
Seasonal Terrestrial Water Load Modulation of Seismicity at the Southeastern Margin of the Tibetan Plateau Constrained by GNSS and GRACE Data
<p>Data Set S1. The earthquake catalog is used to decluster aftershocks and background events, and the time range is from July 2004 to July 2021. This data set includes 672585 events in the study area.</p> <p>Data Set S2. Focal mechanism solutions of M ≥ 4 earthquakes at the southeastern margin of the Tibetan Plateau. The data set includes 634 solutions of earthquakes M ≥ 4, and the time range is from 2009 to 2017.</p>
The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?
<p>This is a reproduction package for the paper "The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?" by Tazaki et al. (2021). In this repository, you will find the data files used to make figures in the paper. Source codes and scripts are included as well.</p>
Water risks to hydropower projects in the face of climate change
<p>This repository hosts the main outputs from an analysis using the <a href="https://waterriskfilter.org/">WWF Water Risk Filter</a> to demonstrate how one such tool can be used to screen for a variety of risks at a global scale, including risks to riverine ecosystems from both climate change and hydropower as well as risks to hydropower projects — and operators, owners, and investors — from climate change and potential regulatory or reputational risk arising from negative impacts to ecosystems. The study <a href="https://www.mdpi.com/2073-4441/14/5/721">Using the WWF Water Risk Filter to Screen Existing and Projected Hydropower Projects for Climate and Biodiversity Risks (DOI 10.3390/w14050721) </a>was published in the special issue of the MDPI journal Water: <a href="https://www.mdpi.com/journal/water/special_issues/hydrometeorological_hazards">"Hydro-Meteorological Hazards under Climate Change"</a>.</p> <p>This product incorporates data from the GRanD v1.3 database which is © Global Water System Project (2011), and from the FHReD database beta version, both datasets available at <a href="http://globaldamwatch.org/">globaldamwatch.org</a> . The source code used in this study is available at <a href="https://github.com/rafaexx/hydropowerClimateChange">https://github.com/rafaexx/hydropowerClimateChange</a></p> <p>See the interactive maps using this data at <a href="https://rcamargo.shinyapps.io/HydropowerClimateChange">https://rcamargo.shinyapps.io/HydropowerClimateChange</a></p>
Supporting Data - Double Ridge Formation over Shallow Water Sills on Jupiter's Moon Europa
<p>This archive contains data produced in support of R. Culberg, D. M. Schroeder, G. Steinbrügge, Double Ridge Formation Over Shallow Water Sills on Jupiter’s Moon Europa, <em>Nature Communications</em>, 2022. This includes the WorldView imagery in Figure 1, the reprocessed radargrams underlying the radar analysis and inversion, and outputs of all inversion and sensitivity test runs. See the README file for a complete description of the files available in this archive.</p>
S2 | STOFFIDENT | HSWT/LfU STOFF-IDENT Database of Water-Relevant Substances
<p>This is the collection associated with list S2 STOFFIDENT<strong> HSWT/LfU STOFF-IDENT Database of Water-Relevant Substances</strong> on the NORMAN Suspect List Exchange</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>The database enables the search for exact masses from target or unknown lists and the automatic use of a Retention Time Index. See: <a href="https://www.lfu.bayern.de/stoffident/#!home">https://www.lfu.bayern.de/stoffident/#!home</a> (single search for free; batch search after free registration).</p> <p>Nov 17 2019 update: added CSV for PubChem upload.<br> Jun 18, 2020 update: fixed synonym for CAS=1245526-82-2 after feedback from PubChem (Jeff).<br> Jan 18, 2022 update: added IUPAC name instead of anakinra & deleted incorrect CAS (reported by Leon)</p>
Discrete Land-Water Tiles Dataset
<p>A quad-tree based dataset of land and water tiles with world-wide coverage in WGS84 coordinate system (EPSG:4326) and a resolution of up to quad-level 18 (~150m height and width at the equator). This data separates the world into land and water tiles with any 4-tuple of tiles of the same classification value being recursively aggregated into their respective larger parent tiles. E.g. the quadkeys "3120", "3121", "3122", "3123" are aggregated into "312" if (and only if) they all share the same classification and would again be aggregated into "31" if "310", "311" and "313" also share that classification.</p> <p>The dataset is provided as a comma separated value (CSV) file and a spatialite database file with roughly 133 million entries and consists of the following fields:</p> <p>- classification - classification of tiles into land (0) or water (5)<br> - quadkey - the unique identifying quadkey of a tile<br> - size_tile_area_aea - the tile size in meter in Albers Equal Area (AEA) projection<br> - size_land_area_aea - the size of the land parts of a tile in AEA projection (only in coastal tiles)<br> - size_water_area_aea - the size of the water parts of a tile in AEA projection (only in coastal tiles)<br> - geometry - the tile geometry in EPSG:4326<br> - land_area - the land polygon geometry in EPS:4326 (only in coastal tiles)<br> - water_area - the water polygon geometry in EPSG:4326 (only in coastal tiles)</p>
Water content in the upper 1 meter of the Martian regolith, estimated from FREND experiment data
<p>This dataset contains a raw map of neutron suppression and a smoothed map of water content in the upper meter of the Martian subsurface derived from FREND instrument data, between latitudes 50° north and south. FREND is a high resolution neutron telescope onboard ExoMars’ TGO orbiter (see Mitrofanov, I. et al. Fine Resolution Epithermal Neutron Detector (FREND) Onboard the ExoMars Trace Gas Orbiter. Space Sci Rev 214, 86 (2018). <a href="https://doi.org/10.1007/s11214-018-0522-5">https://doi.org/10.1007/s11214-018-0522-5</a>).</p> <p>Neutron Suppression is defined as ratio between neutron count rate (counts per second) at each pixel and a reference count rate (average count rate over Solis Planum area between -115° and -65° longitude and -55° and -15° latitude). It is a dimensionless parameter, showing how much the neutron flux is suppressed relative to the reference Solis Planum area, known to be one of the driest on Mars with water equivalent hydrogen (WEH) estimated as 2.78 weight percent (wt%, see Boynton, W.V. et al. Concentration of H, Si, Cl, K, Fe, and Th in the low- and mid-latitude regions of Mars. Journal of Geophysical Research 112, (2007) E12S99. <a href="https://doi.org/10.1029/2007JE002887">https://doi.org/10.1029/2007JE002887</a>). Statistical errors are given in each pixel. Map of neutron suppression can be used to locate regions of high or low water content.</p> <p>Map of water is derived from the neutron suppression map in the following way: neutron suppression map is smoothed with a gaussian 12° (full width half maximum) filter, considering statistical errors in each pixel. Transition from smoothed neutron suppression values to WEH values is done through numerical modeling of all physical process from neutron emission from the Martian regolith to detection of neutrons by FREND instrument, depending on the WEH content in the regolith. Map of water contains three values for each pixel: minimum, mean and maximum WEH in wt%: mean values are derived from neutron suppression map, maximum and minimum values correspond to plus and minus error of the neutron suppression. These three values represent the range of WEH in each pixel within 1 error margin of neutron suppression values.</p>
Data and ancillary data for publication: Natural infrastructure and water erosion mitigation in the Andes
<p>The data contain information on the effectiveness of natural infrastructure to mitigate soil erosion. Data were compiled from 118 case studies from the Andean region, whereby information on natural infrastructure interventions, soil erosion and soil quality were tabulated and analysed.</p> <p>The data contains the following documents:<br> -Database with data on soil erosion, soil quality for different types of natural infrastructure (118 case studies)<br> -Metadata<br> -Summary of terms used in the systematic review of the literature (in Spanish and English)<br> -List of bibliographic data sources that were searched with the search terms<br> -Full bibliographic references of all 118 case studies</p> <p><strong>Full reference </strong></p> <p><em>Vanacker V, Molina A, Rosas-Barturen M, Bonnesoeur V, Román-Dañobeytia F, Ochoa-Tocachi B, Buytaert W (2022). The effect of natural infrastructure on water erosion mitigation in the Andes. </em></p> <p> </p> <p> </p>
Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses sampled during the Tara Pacific Expedition 2016-2018
<p>In order to provide a description of the dynamical properties of the water masses sampled, different Eulerian and Lagrangian diagnostics were calculated. </p> <p>For each of the 246 stations sampled, we proceeded as follows.</p> <p>We identified the water mass sampled at the given station. This was considered as a stadium shape with the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the stadium semi-circles was considered 0.1°, which is in accordance with previous studies25,29,30. The stadium was filled with virtual particles separated by 0.01°.</p> <p>For each virtual particle inside the stadium shape, we calculated an Eulerian or Lagrangian diagnostic (described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sampling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward in time for an amount of time 𝞽 from the day of sampling day_S. For the Lagrangian betweenness, the advection was performed between day_S+𝞽/2 and day_S-𝞽/2, so that the advective time window was centered on the sampling day (details in25).</p> <p>For the Lagrangian diagnostics, we used the following advective times 𝞽: 5, 10, 15, 20, 30, and 60 days. The only exception is the retention time, which, by construction, was calculated only with the largest advective time, namely 𝞽=60 days.</p> <p>Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.</p> <p> Furthermore, two different velocity fields were used, which are described as follows. </p> <p>Both the velocity fields were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used was MULTIOBS_GLO_PHY_REP_015_004 [GlobEkmanDt]. This was produced by combining the altimetry derived geostrophic velocities and modeled Ekman surface currents. It had a spatial resolution of 0.25° and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_030 [GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial resolution of 1/12° and a temporal resolution of 1 day.</p> <p>The following Eulerian diagnostics were calculated:</p> <ul> <li> <p>Absolute velocity ([Uabs], m s-1): sqrt(u2+v2), where u and v are the zonal and meridional components of the horizontal velocity field used (described below)</p> </li> <li> <p>Kinetic energy ([Ekin], m2 .s-2): 0.5*(u2+v2)</p> </li> <li> <p>Divergence ([EulerDiverg], d-1): du/dx + dv/dy</p> </li> <li> <p>Vorticity ([Vorticity], d-1): dv/dx - du/dy</p> </li> <li> <p>Okubo-Weiss ([OW], d-2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx+du/dy)2. If negative, it indicates that the station sampled was inside an eddy.</p> </li> </ul> <p>The following Lagrangian diagnostics were calculated:</p> <ul> <li> <p>Finite-Time Lyapunov Exponents ([Ftle], d-1): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. barriers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far away backward in time.</p> </li> <li> <p>Lagrangian betweenness ([betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory26. It can identify regions which act as bottlenecks for the circulation, in that they receive waters coming from different origins, and that are then spread over several different destinations. These can represent possible hotspots driving biodiversity25.</p> </li> <li> <p>Lagrangian Divergence ([LagrDiverg], d-1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling.</p> </li> <li> <p>Retention Time ([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the previous period. If the water mass is outside an eddy, then its retention time is set to zero.</p> </li> </ul>
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.