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.
62
datasets available to search
ShareScore release 0.9.0
Dataset results
62 results for “Liquid water”
Data sets for "Superionic water with stiffness between molecular ice and liquid"
<p>This is the source data shown in Figs. 1, 3A, and Figs. S2, S3, S4, S5, S6, S7, and S12 for the article "Superionic water with stiffness between molecular ice and liquid" by Kimura and Murakami.</p>
Data sets for "Superionic water with stiffness between molecular ice and liquid"
<p>This is the source data shown in Figs. 1, 3A, and Figs. S2, S3, S4, S5, S6, S7, and S12 for the article "Superionic water with stiffness between molecular ice and liquid" by Kimura and Murakami.</p>
Experimental and simulation data from Stransky et al. "Ionization by XFEL radiation produces distinct structure in liquid water"
<p>This deposition contains two separate gzipped tarballs:</p> <ol> <li><strong>preprocessed_diffraction_data.tar.gz</strong><br>pre-processed diffraction data, specifically I(q) traces for each X-ray pulse along with associated metadata</li> <li><strong>XMDYN_simulations.tar.gz</strong><br>simulation trajectories produced by the XMDYN code that describe water ionization following an intense X-ray pulse</li> </ol> <p>In more detail, the <strong>preprocessed_diffraction_data.tar.gz</strong> contains 36 HDF5 files, each corresponding to a single LCLS run. Each HDF5 contains the following data arrays:</p> <pre><code>ebeam Group event_time Dataset {8674/Inf} evr Group fiducials Dataset {8674/Inf} gas_detector Group phase_cav Group probe_energy Dataset {8674/Inf} probe_mag Dataset {8674/Inf} pump_energy Dataset {8674/Inf} pump_mag Dataset {8674/Inf} radial_profile Dataset {8674/Inf, 500} radial_profile_qvalues Dataset {500/8192}</code></pre> <p>The X contains trajectories for 7 different simulations, each in a separate directory. These top level directories are named according to simulation parameters:</p> <div>sim<BOX_SIZE>A_<probe_delay[fs]/singl>_<fraction_of_nominal_fluence></div> <div> </div> <div>Box sizes are in [Å], probe delay is in [fs] or there is only single pulse, 1.00 is the nominal fluence.</div> <div> </div> <div>Each of these directories contain subdirectories recording snapshots every 5 fs.</div> <div>The subdirectory name is the snapshot time in attoseconds.</div> <div>The simulation starts at 0 fs, the pump pulse is centered at the time 30 fs.</div> <div>That means that if there is a probe pulse with delay of 110 fs, it is centered</div> <div>at 140 fs.</div> <div> </div> <div>The snapshot directories contain 6 text files:</div> <ul> <li><strong>econf.dat</strong> : list of all atomic configurations observed in the simulation snapshot, each line starts with the atomic Z, followed by the electron configuration</li> <li><strong>T.dat</strong> : the electronic configuration for each particle in the simulation, in the form of a list of indices. The n-th element of the list indicates the electronic configuration of the n-th particle in the simulation by specifying a line in econf.dat. For instance, if the 10th element is "0", it indicates the 10th simulated particle has the electronic configuration written on the first line of econf.dat</li> <li><strong>Z.dat</strong> : sequence of atomic numbers for all simulated atoms.</li> <li><strong>q.dat</strong> : charge of atoms in elementary charge units (redundant data).</li> <li><strong>r.dat</strong> : cartesian position in [m], each atom position on one line.</li> <li><strong>v.dat</strong> : velocity components in [m/s], each atom on one line.</li> </ul>
Reproducibility of electrochemical measurements with ionic liquids: Is the water content the decisive parameter?
<p>This project contains all the data shown in the figures of the manuscript (and the supporting information) entitled:<br>'Reproducibility of electrochemical measurements with ionic liquids: Is the water content the decisive parameter?'<br>(doi:10.26434/chemrxiv-2024-05d56).</p> <p>The data to each figure is provided in a subfolder where each curve is stored as a single CSV.<br>The filenames contain labels describing the curves.</p>
Supporting Data for the paper titled"Diurnal Variation of Liquid Water Path Derived from Two Polar-Orbiting FengYun-3 Microwave Radiation Imagers"
<p>The dataset contains:</p> <p>a) 4 files with name indicating collocated satellite, sensor, latitude,longitude and brightness temperature; format</p> <p>b) annual mean liquid water path derived from FY-3B/C Level 1 data; satellite,sensor, region; format</p> <p>c) annual mean amplitude and phase</p> <p>d) four seasons (winter,spring,summer, and autumn) mean amplitude</p> <p>d) 24 files with name indicating sensor, lwp, region, local solar time hour, format</p> <p>All files can be read by using matlab language.</p> <p> </p> <p>The first author was supported by National Natural Science Foundation of China Grant 91337218, and the second author was supported by NOAA grant NA14NES4320003 (Cooperative Institute for Climate and Satellites-CICS) at the University of Maryland/ESSIC.</p> <p>Corresponding author: Xiaolei Zou,xzou1@umd.edu</p>
Pilot scale on-site demonstration and seasonality assessment of nitrogen recovery and water reclamation from pig's slurry liquid fraction
Open the record for dataset details and reuse information.
Raw data for "Impact of nuclear quantum effects on the structural inhomogeneity of liquid water"
<p>Raw data for "Impact of nuclear quantum effects on the structural inhomogeneity of liquid water"</p>
Liquid water content and vertical velocity from RICO-based LES simulations
<p>The RICO-based simulations by implementing the LES module of WRF model 4.0 generate the raw data. The simulation continued for 40 hours with a time step of one second. The domain size is 12.8×12.8×4 km3, with a resolution of 100 m and 40 m in the horizontal and vertical orientation, respectively. The outputs during 8~40 hour were retained every 10 minutes. Two parameters (LWC and vertical velocity) were then extracted by using MATLAB to create the following dataset. The "q" and "w" in the filename represent LWC and vertical velocity, respectively, and the "a"~"e" in the filename represents the corresponding minute in each hour (e.g. "q24a" means LWC value at 24h10m). The unit of "q" and "w" are "kg/kg" and "m/s' respectively. </p>
Greenland liquid water discharge from 1950 through...
<p>Observational validation data sets used in <a href="https://doi.org/10.5194/essd-12-2811-2020">https://doi.org/10.5194/essd-12-2811-2020</a></p>
Dispersive liquid-liquid microextraction with extractant removal by magnetic nanoparticles for chloramphenicol preconcentration in water samples
Open the record for dataset details and reuse information.
Data from: Combining ground‐penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks
Open the record for dataset details and reuse information.
Raw data accompanying "A supramolecular and liquid crystalline water‐based alignment medium based on azobenzene‐substituted 1,3,5‐benzenetricarboxamides"
<p>Raw spectral data files for NMR, CD and UV/VIs measurements.</p>
Proteinaceous Matter and Liquid Water in Fine Aerosols in Nanchang, Eastern China: Seasonal Variations, Sources, and Potential Connections
<p>DATA-JGR-A</p>
Are neural network potentials trained on liquid states transferable to crystal nucleation? A test on ice nucleation in the mW water model
<p>Dataset used to train the neural network potential for reproducing mW nucleation.</p>
Multisensor Advanced Climatology Mean Liquid Water Path L3 Monthly 1 degree X 1 degree V1 (MACLWP_mean) at GES DISC
The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP) data set contains monthly 1.0-degree ocean-only estimates of cloud liquid water path (MACLWP_mean), total water path (MACTWP_mean) which includes both cloud and rain water, and monthly climatologies of cloud liquid water path diurnal cycle amplitudes and phases (MACLWP_diurnal). The MACTWP_mean field can also be used as a quality-control screen for the MACLWP_mean field as discussed in Elsaesser et al. (2017), where uncertainty increases as the ratio of cloud to total water path increases. The MAC-LWP algorithm uses as input the Remote Sensing Systems (RSS) Version 7 0.25 degree-resolution retrieval products (produced using the SSM/I, AMSR-E, TMI, AMSR-2, GMI, SSMIS, and WindSat satellite sensors), and performs a bias correction on all input RSS cloud water path products based on AMSR-E matchups to clear-sky MODIS scenes. The MAC-LWP algorithm ensures that spurious trends and variability in the cloud fields arising from drifting satellite overpass times are mitigated by simultaneously solving for the monthly average cloud and total water paths and monthly-mean diurnal cycles, as discussed in O’Dell et al. (2008). Additional details on the algorithm and data fields can be found in Elsaesser et al. (2017).
Multisensor Advanced Climatology Mean Liquid Water Path Diurnal Cycle L3 Monthly 1 degree x 1 degree V1 (MACLWP_diurnal) at GES DISC
The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP) data set contains monthly 1.0-degree ocean-only estimates of cloud liquid water path (MACLWP_mean), total water path (MACTWP_mean) which includes both cloud and rain water, and monthly climatologies of cloud liquid water path diurnal cycle amplitudes and phases (MACLWP_diurnal). The MACTWP_mean field can also be used as a quality-control screen for the MACLWP_mean field as discussed in Elsaesser et al. (2017), where uncertainty increases as the ratio of cloud to total water path increases. The MAC-LWP algorithm uses as input the Remote Sensing Systems (RSS) Version 7 0.25 degree-resolution retrieval products (produced using the SSM/I, AMSR-E, TMI, AMSR-2, GMI, SSMIS, and WindSat satellite sensors), and performs a bias correction on all input RSS cloud water path products based on AMSR-E matchups to clear-sky MODIS scenes. The MAC-LWP algorithm ensures that spurious trends and variability in the cloud fields arising from drifting satellite overpass times are mitigated by simultaneously solving for the monthly average cloud and total water paths and monthly-mean diurnal cycles, as discussed in O’Dell et al. (2008). Additional details on the algorithm and data fields can be found in Elsaesser et al. (2017).
Multisensor Advanced Climatology Total Liquid Water Path L3 Monthly 1 degree x 1 degree V1 (MACTWP_mean) at GES DISC
The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP) data set contains monthly 1.0-degree ocean-only estimates of cloud liquid water path (MACLWP_mean), total water path (MACTWP_mean) which includes both cloud and rain water, and monthly climatologies of cloud liquid water path diurnal cycle amplitudes and phases (MACLWP_diurnal). The MACTWP_mean field can also be used as a quality-control screen for the MACLWP_mean field as discussed in Elsaesser et al. (2017), where uncertainty increases as the ratio of cloud to total water path increases. The MAC-LWP algorithm uses as input the Remote Sensing Systems (RSS) Version 7 0.25 degree-resolution retrieval products (produced using the SSM/I, AMSR-E, TMI, AMSR-2, GMI, SSMIS, and WindSat satellite sensors), and performs a bias correction on all input RSS cloud water path products based on AMSR-E matchups to clear-sky MODIS scenes. The MAC-LWP algorithm ensures that spurious trends and variability in the cloud fields arising from drifting satellite overpass times are mitigated by simultaneously solving for the monthly average cloud and total water paths and monthly-mean diurnal cycles, as discussed in O’Dell et al. (2008). Additional details on the algorithm and data fields can be found in Elsaesser et al. (2017).
Coupled cycling of carbon and water in the form of hydrous carbonatitic liquids in the subarc region
<p>We determined the phase relations of subducted ophicarbonate. Chemical composition of run products are listed in this file.</p>
Liquid water content and vertical velocity from RICO-based LES simulations
<p>The RICO-based simulations by implementing the LES module of WRF model 4.0 generate the raw data. The simulation continued for 40 hours with a time step of 0.5 second. The domain size is 12.8×12.8×4 km<sup>3</sup>, with a resolution of 50 m and 40 m in the horizontal and vertical orientation, respectively. The outputs during 8~40 hour were retained every 20 minutes. Two parameters (LWC and vertical velocity) were then extracted by using MATLAB to create the following dataset. Additionally, LWC at 1 min before each moment was also uploaded for tracking. The "q" and "w" in the filename represent LWC and vertical velocity, respectively, and the number in the filename represents the corresponding hour and minute (e.g. "q24_20" means LWC value at 24h20m). The unit of "q" and "w" are "kg/kg" and "m/s' respectively.</p>
A Randomized Controlled Trial of a Water Protocol for Clients With Thin Liquid Dysphagia
ClinicalTrials.gov study NCT00616512. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.