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.
182
datasets available to search
ShareScore release 0.9.0
Dataset results
182 results for “Hydrological Data”
Global hydrology and water quality data from 1980-2019, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<p>Global ~10km (5 arcmin) output data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual and monthly temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Discharge (m3 s-1)</li> <li>Channel storage (m3) </li> <li>Water temperature (K)</li> <li>Total dissolved solids (TDS) load (g s-1)</li> <li>Biological oxygen demand (BOD) load (g s-1)</li> <li>Fecal coliform (FC) load (million cfu s-1)</li> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Note. a minimum discharge threshold of 0.1 m3 s-1 was used when computing salinity (TDS), organic (BOD) and pathogen (FC) concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Thus, if the the average discharge for the month was below 0.1 m3 s-1, concentrations are not calculated (assigned as NA).</p> <p>In-stream water quality aggregated to 0.5 degree (i.e. 30 arcmin) spatial resolution (daily, monthly and annual) can be found at: <a href="https://zenodo.org/records/14675270">https://zenodo.org/records/14675270</a>. </p>
Data for: Freeze tolerance influenced forest cover and hydrology during the Pennsylvanian
<p><span>Global forest cover affects the Earth system by altering surface mass and energy exchange. Physiology determines plant environmental limits and influences geographical vegetation distribution. Ancient plant physiology, therefore, likely affected vegetation-climate feedbacks. We combine climate modeling and ecosystem-process modeling to simulate arboreal vegetation in the late Paleozoic ice age. Using GENESIS V3 GCM simulations, varying <i><span>p</span></i>CO<sub><span>2</span></sub>, <i><span>p</span></i>O<sub><span>2</span></sub>, and ice extent for the Pennsylvanian, and fossil-derived leaf C:N, maximum stomatal conductance, and specific conductivity for several major Carboniferous plant groups, we simulated global ecosystem processes at a 2-degree (longitude, latitude)</span><span> resolution with </span><i>Paleo</i>-BGC<span>. Based on leaf water constraints, Pangaea could have supported widespread arboreal plant growth and forest cover. However, these models do not account for the impacts of freezing on plants. According to our interpretation, freezing would have affected plants in 89% of unglaciated land during peak glacial periods, and 65% during the warmer interglacials. Comparing forest cover, minimum temperatures, and paleo-locations of Pennsylvanian-aged plant fossils from the Paleobiology Database supports restriction of global forest extent due to freezing. Many genera were limited to </span>25% <span>of unglaciated land where temperatures remained above −</span>4°C<span>. Freeze-intolerance of Pennsylvanian arboreal vegetation had the potential to alter surface runoff, silicate weathering, CO<sub><span>2</span></sub><span> levels, and</span> climate forcing. As a bounding case, we assume total plant mortality at </span>−4°C <span>and estimate that contracting forest cover increased net global surface runoff by up to 6.1%. Repeated freezing likely influenced freeze- and drought-tolerance evolution in lineages like the coniferophytes, which became increasingly dominant in the Permian and early Mesozoic.</span></p>
Data archive for 'Opportunities to curb hydrological alterations via dam re-operation in the Mekong'
<p>This repository contains the data used in the paper '<a href="https://www.nature.com/articles/s41893-022-00971-z">Opportunities to curb hydrological alterations via dam re-operation in the Mekong</a>'.</p> <p>We first use VIC-Res to simulate daily river discharge and available hydropower generation of the Mekong basin from 1996 to 2016 under 32 scenarios (NAT (natural flow conditions), BAU (business as usual), MAX_MB (dams kept at full storage in Mekong), MAX_LMB (dams kept at full storage in Lower Mekong), and 28 OPT (optimized re-operation strategies) scenarios). The 'VIC-Res' folder contains the daily discharge at Stung Treng and hydropower production in Cambodia, Laos, and Thailand. The hydropower outputs are then used in PowNet, a unit commitment/economic dispatch model for the Cambodian, Laotian, and Thai power systems. 'PowNet' folder contains the relevant input and output files for the three scenarios that are elaborated on in the paper (BAU, MAX_LMB, and OPT).</p> <p>For more information on the PowNet models, refer to the following GitHub repositories: <a href="https://github.com/kamal0013/PowNet">PowNet-Cambodia</a>, <a href="https://github.com/kamal0013/PowNet-Laos">PowNet-Laos</a>, <a href="https://github.com/kamal0013/PowNet-Thailand">PowNet-Thailand</a>.</p>
Neutron and LIBS data behind figures in Gabriel et al. (2022). On an extensive late hydrologic event in Gale crater as indicated by water-rich fracture halos. JGR-Planets.
<p>This repository contains datasets that allow for the reproduction of certain figures and analysis by Gabriel et al. (2022), a peer-reviewed journal article accepted in the Journal of Geophysical Research: Planets. Below are brief descriptions of the datasets.</p> <p> </p> <p>File: TGabriel_JGR-P_DAN_Passive_NoMobility_Raw_Data_sol350-400_FigureS10.txt</p> <p>Description: These are raw neutron counts from the thermal and epithermal neutron detectors as part of the Dynamic Albedo of Neutrons instrument. Only data from rover stops for sols 350 to 400 are included. Data from rover stops allows them to be readily colocated rover localization data, which includes 'site' and 'drive' numbers that are specific to each stop.</p> <p><br> File: TGabriel_JGR-P_DAN_Passive_NoMobility_Raw_Data_sol900-1500_Figure5.txt</p> <p>Description: This is similar data to the product above, however for the sol range 900 to 1500.</p> <p> </p> <p>File: TGabriel_JGR-P_DAN_Passive_withMobility_Raw_Data_sol350-420_FigureS13.txt</p> <p>Description: This is similar data to the products above, however the dataset includes passive neutron count rates acquired while the rover was traversing, smoothed over 3 meters of lateral distance traveled. This dataset allows for the analysis of environments that may be present between rover stops, and thus not detected in 'no mobility' datasets.</p> <p> </p> <p>TGabriel_JGR-P_Kukri_CCAM_MajorOxideComposition_FigureS19TableS1.xlsx</p> <p>Description: This is the result of the Major Oxide Quantification pipeline developed by the ChemCam instrument team (sPDL Tool v2.0, 25 July 2015) as run by William Rapin. Additional H quantification in Figure S19 of Gabriel et al. (2022) is not included in this dataset, but is provided in the manuscript.</p>
Making the Most out of a Hydrological Model Dataset: Sensitivity Analyses to Open the Model Black-Box (data and code)
<p>This is "data and code" repository for the Water Resources Research Article 2017WR020401 by Borgonovo et al. (2017): "Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box". Each sub-directory contains the Matlab or R scripts to reproduce all paper plots. </p> <p>Note, that the data of this repository (i.e. under ./data_input ) are identical to the data analysed by Rakovec et al. (2014).</p> <p>References:</p> <ul> <li>Borgonovo, E., Lu, X., Plischke, E., Rakovec, O. and Hill, M. C. (2017), Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box. Water Resour. Res.. Accepted Author Manuscript. doi:10.1002/2017WR020767</li> <li>Rakovec, O., M. C. Hill, M. P. Clark, A. H. Weerts, A. J. Teuling, and R. Uijlenhoet (2014), Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models, Water Resour. Res., 50, 409–426, doi:10.1002/2013WR014063.</li> </ul>
Hydrologic and Isotopic Data from the Montepaldi Experimental Vineyard, Tuscany, Italy
<div> <div>This dataset includes soil moisture and water stable isotope data collected at the Montepaldi farm (Tuscany, Italy) in the summer 2021. The data is discussed in a paper which was published in 2024 on the journal Ecohydrology:</div> <div> </div> <div>Benettin, P., Tagliavini, M., Andreotti, C., Manca di Villahermosa, F., Verdone, M., Dani, A. and Penna, D. (2024), Ecohydrological Dynamics and Temporal Water Origin in a European Mediterranean Vineyard. Ecohydrology e2711. <a href="https://doi.org/10.1002/eco.2711">https://doi.org/10.1002/eco.2711</a></div> <div> </div> </div>
Data from: Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios
<p>Datasets for manuscript "Andres, K. J., Chien, H., and Knouft, J. H. Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios. Science of the Total Environment. <a href="https://doi.org/10.1016/j.scitotenv.2019.03.292">https://doi.org/10.1016/j.scitotenv.2019.03.292</a>"</p> <p>landmarks.zip: landmarks digitized on images of 1081 specimens using TpsDig2 software.</p> <p>streamflow_estimates.csv: Contemporary (1980-2009) and future (2070-2099) streamflow estimates [avg: average annual streamflow discharge (m3 s-1); cv: coefficient of variation of annual discharge] in sub-basins containing populations of 6 minnow species in IL, USA</p>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </p>
SRTM derived hydrological data for the SAFE Project
<b>Description: </b><p>These files contain flow direction and flow accumulation data for the area surrounding the SAFE Project. The data were derived using compiled Shuttle Radar Topograpy Mission (SRTM) 30m resolution elevation data for the region (<a href="https://zenodo.org/record/630004">https://zenodo.org/record/630004) using the GRASS GIS <code>r.watershed</code> tool.<br><br>Further details of the geoprocessing can be found here: </a><a href="https://www.safeproject.net/dokuwiki/safe_gis/hydrology">https://www.safeproject.net/dokuwiki/safe_gis/hydrology</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3490687">here</a></p><p><b>Files: </b>This dataset consists of 3 files: SAFE_SRTM_hydrology_metadata.xlsx, SRTM_Flow_Direction.tif, SRTM_Log_Flow_Accum.tif</p><p><b>SAFE_SRTM_hydrology_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>SRTM_Flow_Direction.tif</b></p><p>Description: Flow direction data in UTM 50N geotiff</p><p><b>SRTM_Log_Flow_Accum.tif</b></p><p>Description: Flow accumulation data in UTM50N geotiff</p><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.0223 to 5.9761</p><p><b>Longitudinal extent: </b>116.0242 to 117.9758</p>
Data and R-Scripts for: Value of crowd-based water level class observations for hydrological model calibration
<p>This dataset corresponds to the study<br> "Value of crowd-based water level class observations for hydrological model calibration"<br> submitted to Water Resources Research in August 2019.</p> <p>Please use the R-Scripts in ascending numbers and adapt the paths to where you stored the files.<br> The helpfunctions.R will be used by some of the scripts and you might<br> want to adapt a path in line 356 for it to be used correctly with the scripts 8a and 8b.</p> <p>The parameter ranges used for the HBV calibration can be found in the "Parameters and parameter ranges.pdf"</p> <p>If you do not wish to calibrate the model, and just perform some statistics<br> start with script 7 and use the<br> - CrossValidation_stats_all.txt in the LUT Tables folder which contains<br> all model performances.<br> - CrossValidation_stats_WP1.txt contains also results of the upper benchmark<br> (only those labelled with no error and hourly).<br> - RandomParamPerformance_Validation.txt contains the results of the random parameters<br> (lower benchmark).<br> - The folders Validation Results and Calibration Results contain the files in HBV-format after the model<br> calibration and validatin were completed. The results of the Calibration and Validation files are also summarized<br> in the aforementioned txt-files within script 6 -HBV CrossValidation.R.<br> Please be aware that for the study only the catchments Murg, Guerbe, Mentue, and Verzasca were used!</p> <p><br> If you run into trouble using the data please contact simon.etter[at]outlook.com.</p> <p>Co-authors are:<br> Prof. Dr. Jan Seibert - jan.seibert[at]geo.uzh.ch<br> Dr. Ilja (H.J.) van Meerveld - ilja.vanmeerveld[at]geo.uzh.ch<br> Barbara Strobl - barbara.strobl[at]geo.uzh.ch</p>
Data to reproduce the results presented in Lake et al. 2024. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2024.131930. ("High-frequency spatial sediment source fingerprinting using in situ absorbance data")
<p>This repository contains data on the used absorbance data, measured at the field site, as described in Lake et al., 2024 (<span>h</span><span>t</span><span>t</span><span>p</span><span>s</span><span>:</span><span>/</span><span>/</span><span>d</span><span>o</span><span>i</span><span>.</span><span>o</span><span>r</span><span>g</span><span>/</span><span>1</span><span>0</span><span>.</span><span>1</span><span>0</span><span>1</span><span>6</span><span>/</span><span>j</span><span>.</span><span>j</span><span>h</span><span>y</span><span>d</span><span>r</span><span>o</span><span>l</span><span>.</span><span>2</span><span>0</span><span>2</span><span>4</span><span>.</span><span>1</span><span>3</span><span>1</span><span>9</span><span>3</span><span>0).</span> Furthermore, data on the turbidity, used calibration curves and R code to prepare the input data for the MixSIAR model are included in the data repository.</p>
Data for: Antarctic wide subglacial hydrology modeling
Open the record for dataset details and reuse information.
Data for "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover"
<p>Dataset for the "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover". Dataset 01 includes site locations, basin area, dissolved organic carbon (DOC), dissolved lignin concentration and relevant references. Dataset 03 includes mean/discharge-weighted DOC, mean/discharge-weighted dissolved lignin concentrations. Dataset 03 includes geomorphological, climatic, hydrological and land-cover data for the 25 rivers. Dataset 04 includes the reconstructed yield of dissolved lignin and basin area of the 79 rivers.</p>
The exemplary basin modeling with Global Hydrologic Data Cloud (GHDC) and SHUD model.
<p><strong>GHDC</strong> = Global Hydrological Data Cloud</p> <p><strong>SHUD</strong> = Simulator of Hydrologic Unstructured Domains</p> <p> </p> <p>Files in the package:</p> <p> </p> <ul> <li> <p>Conestoga - The data retrived from GHDC and the modeling result by SHUD model, for Gonestoga, Pennsylvania, USA.</p> </li> <li> <p>Gummara - The data retrived from GHDC and the modeling result by SHUD model, for Gummara, Ethiopia</p> </li> <li> <p>heihe - The data retrived from GHDC and the modeling result by SHUD model, for Heihe Headwater, Gansu, China</p> </li> <li> <p>threeBasin - The R analysis code for the three basin simulations, including loading data and visualization.</p> </li> </ul> <p> </p> <p>The file structure in each basin folder:</p> <table> <thead> <tr> <th>FOLDER</th> <th>FILE OR SUBFOLDER(BOLD)</th> <th>DESCRIPTION</th> </tr> </thead> <tbody> <tr> <td>ETV</td> <td>-</td> <td>General ETV data.</td> </tr> <tr> <td> </td> <td>dem.tif</td> <td>DEM subset of ASTER Global DEM.</td> </tr> <tr> <td> </td> <td>Soil.csv</td> <td>Soil texture of soil layer reclassified from HWSD subset.</td> </tr> <tr> <td> </td> <td>Geol.csv</td> <td>Soil texture of geology layer reclassified from HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.Geol.csv</td> <td>Soil texture, organic matter and bulk density of geology layer HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.Soil.csv</td> <td>Soil texture, organic matter and bulk density of soil layer HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.tif</td> <td>HWSD data subset.</td> </tr> <tr> <td> </td> <td>buff.shp, .dfb, .prj, .shx</td> <td>Shapefile of buffered polygon from user-provided watershed.</td> </tr> <tr> <td> </td> <td>outlets.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>stm_dem.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>wbd_dem.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td><strong>TSD</strong></td> <td>Time-series forcing files.</td> </tr> <tr> <td> </td> <td><strong>GCS</strong></td> <td>Subfolder, terriestial data in GCS</td> </tr> <tr> <td> </td> <td><strong>PCS</strong></td> <td>Subfolder, terriestial data in PCS.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/landuse.tif</td> <td>Landuse raster subset.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/soil.tif</td> <td>Soil classification raster subset.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/geology.tif</td> <td>Geology classification raster subset.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong></td> <td>The figures during pre-processing.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_watershed_delineation</td> <td>Watershed delineation, include boundary, river, pourpoint.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/dem_buf</td> <td>The DEM, and buffer zone.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_LDAS</td> <td>The coverage of reanalysis grid.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_Landuse</td> <td>The landuse classification of the research area</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_Soil</td> <td>The soil classification of the research area</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_Geol</td> <td>The geology classification of the research area</td> </tr> <tr> <td>Modeling</td> <td>-</td> <td> </td> </tr> <tr> <td> </td> <td><strong>Figure</strong></td> <td>The figures during model deployment</td> </tr> <tr> <td> </td> <td><strong>input</strong></td> <td>Model input folder.</td> </tr> <tr> <td> </td> <td>GCS</td> <td>Spatial data for model deployment in GCS.</td> </tr> <tr> <td> </td> <td>PCS</td> <td>Spatial data for model deployment in PCS.</td> </tr> <tr> <td> </td> <td>deployConfig.txt</td> <td>The configuration file for model deployment script.</td> </tr> <tr> <td>StaticFiles</td> <td>-</td> <td>Static files, include the model, citation, description and executable model file.</td> </tr> <tr> <td> </td> <td><strong>SHUD_model</strong></td> <td>Source code of SHUD model</td> </tr> <tr> <td> </td> <td>Citation.bib</td> <td>Citation of Data and models.</td> </tr> <tr> <td> </td> <td>ReadMe_cn.html</td> <td>The readme file in Chinese.</td> </tr> <tr> <td> </td> <td>ReadMe_en.html</td> <td>The readme file in English.</td> </tr> <tr> <td> </td> <td>shud.exe</td> <td>The executable files of SHUD model, for Windows platform only.</td> </tr> <tr> <td>UserData</td> <td>-</td> <td>The files uploaded by user</td> </tr> </tbody> </table>
Supporting Data: Characterizing Sub-Glacial Hydrology Using Radar Simulations
<p>Raw data from "A Simulation Approach to Characterizing Sub-Glacial Hydrology". </p> <p>THW2_UBH0c_X243a.M1D1 is the 1 dimensionally focused, high gain radargram from the IPR radar survey. </p> <p>Actual along-track IPR pick data for THW2_UBH0c_X243a are contained in file X243a_rad_data_foc.csv. Metadata are as follows:</p> <ul> <li>pst: flight line name</li> <li>long: observation longitude in decimal degrees (EPSG:4326)</li> <li>lat: observation latitude in decimal degrees (EPSG:4326)</li> <li>bed: bed pick elevation in [m]</li> <li>year: timestamp year</li> <li>day: timestamp day</li> <li>t_ms: timestamp miliseconds</li> <li>srf: surface pick elevation in [m]</li> <li>echo: bed echo strength [dB]</li> <li>thk: ice thickness [m]</li> <li>Lice: estimated 2-way attenuation loss [dB]</li> <li>R_bed: bed absolute reflectivity [dB]</li> <li>L_unc: 2-way attenuation loss uncertainty [dB]</li> </ul> <p>Simulated data: A folder for each simulation contains the files listed below.</p> <ul> <li>Inputs: <ul> <li>[INPUT] Trajectory_MRS.mat: values for simulated aircraft trajectory, including total # of rangelines (TrackLengthTot), aircraft position at each rangeline in [m] (sc_position_x, sc_position_y, sc_position_z), and aircraft velocity in [m/s] (sc_vel_x, sc_vel_y, sc_vel_z)</li> <li>bed.csv: lists all z-values in [m] for the bed surface</li> <li>chan.csv: lists all z-values in [m] for the channel surface</li> <li>params.txt: lists all pertinent parameters from the simulation</li> <li>surf.csv: lists all z-values in [m] for the ice surface</li> <li>xx.csv: lists all x-values in [m] for the ice surface</li> <li>xx_b.csv: lists all x-values in [m] for the bed and channel surface</li> <li>yy.csv: lists all y-values in [m] for the ice surface</li> <li>yy_b.csv: lists all y-values in [m] for the bed and channel surface</li> </ul> </li> <li>Radargrams: <ul> <li>RgramFocPWR_RM3_hamm.mat: Focused radargram power [dB]</li> <li>RgramRawPWR.mat: Raw radargram power [dB]</li> <li>RgramRCPWR.mat: Range-compressed radargram power [dB]<br> </li> </ul> </li> </ul>
Data from: A multi-year case study highlighting the influence of hydrological conditions on epidemic dynamics in a natural plant pathosystem
Open the record for dataset details and reuse information.
Data for: Freeze tolerance influenced forest cover and hydrology during the Pennsylvanian
Open the record for dataset details and reuse information.
Snow depth, air temperature, humidity, soil moisture and temperature, and solar radiation data from the basin-scale wireless-sensor network in American River Hydrologic Observatory (ARHO)
Open the record for dataset details and reuse information.
Habitat suitability and the distribution of species: Polygonatum biflorum demography data from the Coweeta Hydrologic Laboratory from 1998 to 2006
Metapopulation theory posits that suitable habitat may frequently be unoccupied because it is isolated and has never been colonized or has been colonized followed by local extinction and has not yet been recolonized. This research addresses the question of how to identify suitable, unoccupied habitat and distinguish it from unsuitable habitat. We are studying a group of six species of forest understory herbs chosen to represent a broad range of habitat distribution and dispersal characteristics. Our aim is to quantify the fundamental niche of these species (sensu Hutchinson 1957), in terms of variables such as soil moisture and temperature, by developing a set of habitat specific demographic stage transition models (i.e. conditional on such environmental variables) for these species. These models, in combination with data from field surveys of the local distribution of the species, will be used to develop testable predictive maps of the distribution of suitable habitat which can be compared to the observed distribution of the plants. We hypothesize that both dispersal ability and the distribution of suitable habitat are important determinants of the actual distribution of species. The goal of this research is both to further our conceptual understanding of the relationships between habitat requirements and species distributions, and to provide a practical approach to operationalizing the concept of "suitable habitat."
Tree census, demography, and exposed canopy area data at the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrological Laboratory, Otto, NC from 1993 to 2016
The five Terrestrial Gradient sites were established in the early 1990s as part of the 1990 Coweeta LTER Renewal. The original terrestrial gradient sites were 20 x 40-m. In the late 1990s the plots were expanded to 80 x 80-m and later (around 1998) they were slope-corrected by Clark's lab using survey equipment. Much of the Coweeta LTER “core” datasets have been collected from the gradient plots. This study is one of the long-term studies that are ongoing with defined sampling intervals. The tree demography and census study consists of trees census every two years and seeds collected ~5 x each year.
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.