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62 results for “Liquid water”
Ab initio molecular dynamics trajectories of liquid water at the interface with graphene sheets
<p>Ab initio molecular dynamics trajectories of water in contact with graphene sheets for different confinement widths and system sizes</p> <p>This repository contains data supporting the findings of the paper: </p> <p>G. Tocci, M. Bilichenko, L. Joly, M. Iannuzzi, <em>Ab initio</em> nanofluidics: disentangling the role of the energy landscape and of density correlations on liquid/solid friction, Nanoscale, 12, 10994-11000 (2020). DOI: 10.1039/D0NR02511A.</p>
Ab initio molecular dynamics trajectories of liquid water at the interface with MoS2 sheets
<p>Ab initio molecular dynamics trajectories of water confined between MoS2 sheets for different confinement widths and system sizes.</p> <p>This repository contains data supporting the findings of the paper: </p> <p>G. Tocci, M. Bilichenko, L. Joly, M. Iannuzzi, <em>Ab initio</em> nanofluidics: disentangling the role of the energy landscape and of density correlations on liquid/solid friction, Nanoscale, 12, 10994-11000 (2020). DOI: 10.1039/D0NR02511A.</p>
Isobaric specific heat for liquid water at different temperatures
<p><strong>Isobaric specific heat for liquid water at different temperatures</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Heat capacity is the ratio of heat absorbed by a material to the temperature change. It is usually expressed as calories per degree in terms of the actual amount of material being considered, most commonly a mole (the molecular weight in grams). The heat capacity in calories per gram is called specific heat. The definition of the calorie is based on the specific heat of water, defined as one calorie per degree Celsius. At sufficiently high temperatures, the heat capacity per atom tends to be the same for all elements. For metals of higher atomic weight, this approximation is already a good one at room temperature, giving rise to the law of Dulong and Petit.</p> <p>Temperature (degrees Celsius), Isobaric specific heat (joules per kelvin per mole), Isobaric specific heat (kilojoules per kelvin per kilogram), Isobaric specific heat (kilowatts-hour per kelvin per kilogram), Isobaric specific heat (kilocalories per kelvin per kilogram)</p> <p>0.01 76.026 4.2199 0.001172 1.0079</p> <p>10 75.586 4.1955 0.001165 1.0021</p> <p>20 75.386 4.1844 0.001162 0.9994</p> <p>25 75.336 4.1816 0.001162 0.9988</p> <p>30 75.309 4.1801 0.001161 0.9984</p> <p>40 75.300 4.1796 0.001161 0.9983</p> <p>50 75.334 4.1815 0.001162 0.9987</p> <p>60 75.399 4.1851 0.001163 0.9996</p> <p>70 75.491 4.1902 0.001164 1.0008</p> <p>80 75.611 4.1969 0.001166 1.0024</p> <p>90 75.763 4.2053 0.001168 1.0044</p> <p>100 75.950 4.2157 0.001171 1.0069</p> <p>110 76.177 4.2283 0.001175 1.0099</p> <p>120 76.451 4.2435 0.001179 1.0135</p> <p>140 77.155 4.2826 0.001190 1.0229</p> <p>160 78.107 4.3354 0.001204 1.0355</p> <p>180 79.360 4.4050 0.001224 1.0521</p> <p>200 80.996 4.4958 0.001249 1.0738</p> <p>220 83.137 4.6146 0.001282 1.1022</p> <p>240 85.971 4.7719 0.001326 1.1397</p> <p>260 89.821 4.9856 0.001385 1.1908</p> <p>280 95.285 5.2889 0.001469 1.2632</p> <p>300 103.60 5.7504 0.001597 1.3735</p> <p>320 117.78 6.5373 0.001816 1.5614</p> <p>340 147.88 8.2080 0.002280 1.9604</p> <p>360 270.31 15.004 0.004168 3.5836</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Isochoric specific heat for liquid water at different temperatures
<p><strong>Isochoric specific heat for liquid water at different temperatures</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Specific heat is the quantity of heat required to raise the temperature of one gram of a substance by one Celsius degree. The units of specific heat are usually calories or joules per gram per Celsius degree. For example, the specific heat of water is one calorie (or 4.186 joules) per gram per Celsius degree. The Scottish scientist Joseph Black, in the 18th century, noticed that equal masses of different substances needed different amounts of heat to raise them through the same temperature interval, and, from this observation, he founded the concept of specific heat. Measurements of specific heats of substances allow calculation of their atomic weights.</p> <p>Temperature (degrees Celsius), Isochoric specific heat (joules per kelvin per mole), Isochoric specific heat (kilojoules per kelvin per kilogram), Isochoric specific heat (kilowatts-hour per kelvin per kilogram), Isochoric specific heat (kilocalories per kelvin per kilogram)</p> <p>0.01 75.981 4.2174 0.001172 1.0073</p> <p>10 75.505 4.1910 0.001164 1.0010</p> <p>20 74.893 4.1570 0.001155 0.9929</p> <p>25 74.548 4.1379 0.001149 0.9883</p> <p>30 74.181 4.1175 0.001144 0.9834</p> <p>40 73.392 4.0737 0.001132 0.9730</p> <p>50 72.540 4.0264 0.001118 0.9617</p> <p>60 71.644 3.9767 0.001105 0.9498</p> <p>70 70.716 3.9252 0.001090 0.9375</p> <p>80 69.774 3.8729 0.001076 0.9250</p> <p>90 68.828 3.8204 0.001061 0.9125</p> <p>100 67.888 3.7682 0.001047 0.9000</p> <p>110 66.960 3.7167 0.001032 0.8877</p> <p>120 66.050 3.6662 0.001018 0.8757</p> <p>140 64.306 3.5694 0.000992 0.8525</p> <p>160 62.674 3.4788 0.000966 0.8309</p> <p>180 61.163 3.3949 0.000943 0.8109</p> <p>200 59.775 3.3179 0.000922 0.7925</p> <p>220 58.514 3.2479 0.000902 0.7757</p> <p>240 57.381 3.1850 0.000885 0.7607</p> <p>260 56.392 3.1301 0.000869 0.7476</p> <p>280 55.578 3.0849 0.000857 0.7368</p> <p>300 55.003 3.0530 0.000848 0.7292</p> <p>320 54.819 3.0428 0.000845 0.7268</p> <p>340 55.455 3.0781 0.000855 0.7352</p> <p>360 59.402 3.2972 0.000916 0.7875</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Liquid water content, ice water content and density measured via the SnowMelt Instrument at Auberge station
<pre><span><span>Liquid water content, ice water content and density measured via the SnowMelt Instrument nearby the Auberge station during 3 consecutive winters, from <span>2021-11-05</span> to <span>2024-03-21, for the bottom snow layer (0-7cm). Data are aggregated at the hourly time step. The instrument was unmounted during no-snow periods. The liquid water content of the bottom snow layer can be used as a proxy for snow melt at the measurement point.</span></span></span></pre>
Raw Picarro L2140i data - Support to "A versatile water vapor generation module for vapor isotope calibration and liquid isotope measurements"
<p>Support data to reproduce Figure 3 - Figure 10 from the article:</p> <p><em>A versatile water vapor generation module for vapor isotope calibration and liquid isotope measurements</em></p> <p>by Hans Christian Steen-Larsen and Daniele Zannoni</p> <p>Accepted in Atmospheric Measurement Techniques on 25/05/2024 (Preprint available at <a href="https://doi.org/10.5194/amt-2023-160" rel="nofollow">https://doi.org/10.5194/amt-2023-160</a>)</p> <p>Figure numbers refer to the final (peer-reviewed and accepted) version of the manuscript.</p> <p>To reproduce the figures, the data can be used in conjunction with the code available at <a href="https://github.com/danielez83/AMT-2023-160" target="_blank" rel="noopener">https://github.com/danielez83/AMT-2023-160 </a>(https://zenodo.org/doi/10.5281/zenodo.12741980)</p> <p><strong>File description</strong></p> <p>Figure numbers are referring to the final (peer-reviewd and accepted) version of the manuscript.</p> <ul> <li>ADEV_BER17k_withmemory_CALIBRATED_R1.csv <ul> <li>Allan Deviation data without removing memory effect, used in FIgure 3</li> </ul> </li> <li>Cal_Pulses_MultiOven_new20230609.csv <ul> <li>Data obtained with the multioven configuration, used in Figure 9</li> </ul> </li> <li>Cal_Pulses_Selector.csv<br> <ul> <li>Data obtained with the VICI selector configuration (only one oven working), used in Figure 9</li> </ul> </li> <li>HKDS2092.zip<br> <ul> <li>Compressed archive of the Picarro L2140i (HKDS2092) raw data.</li> </ul> </li> <li>HKDS2156.zip<br> <ul> <li>Compressed archive of the Picarro L2140i (HKDS2156) raw data.</li> </ul> </li> <li>HKDS2156_IsoWater_20221116_165037.csv<br> <ul> <li>Results of liquid injections with Picarro vaporizer, used in Figure 4 and Figure 10</li> </ul> </li> <li>SP_BER_inj_time.csv <ul> <li>Date and times of injections, used in Figure 4 and Figure 10</li> </ul> </li> <li>Timings_Picarro.xlsx <ul> <li>Excel spreadsheet with time and dates of experiment. It is used as a lookup table to retrieve the raw data correctly</li> </ul> </li> </ul>
Data files and electromagnetic simulation software used in the paper "Radar evidence of subglacial liquid water on Mars" By Orosei et al. (2018)
<p>This archive contains radargrams, geometric information and visualizations of MARSIS radar observations over the area centered at 193°E, 81°S within Planum Australe, Mars, together with scripts and results of electromagnetic propagation simulations used to interpret the data. This archive contains everything needed to reproduce the results presented in the paper "Radar evidence of subglacial liquid water on Mars" by Orosei et al. (2018). The software made available in this archive consists of scripts written in the Matlab© computing language, and is provided "as is" without warranty of any kind, either express or implied. Queries on the content of the archive can be sent to Roberto Orosei (roberto.orosei@inaf.it).</p> <p>Three types of MARSIS data files are contained in this archive:</p> <p>* orbit_XXXXX_frequency_Y_MHz_radargram.csv, where XXXXX is the orbit number, and Y the frequency at which the radar was operating, in MHz. The file contains an ASCII table of real numbers separated by commas. The table has as many columns as the number of radar echoes acquired during the orbit (usually 3200), and 980 lines, one for each echo sample. Values are samples of the uncalibrated echo voltage, without phase information (i.e. positive real numbers instead of complex echo samples). Echo samples are acquired every 0.3571 microseconds (2.8 MHz sampling rate). The first sample of an echo is located at a round-trip time corresponding to an altitude of 25 km above the Martian IAU ellipsoid.</p> <p>* orbit_XXXXX_frequency_Y_MHz_geometry.csv, where XXXXX is the orbit number, and Y the frequency at which the radar was operating, in MHz. The file contains an ASCII table of real numbers separated by commas. The table has as many rows as the number of radar echoes acquired during the orbit (usually 3200), and contains the following auxiliary parameters:</p> <p> - EPHEMERIS TIME - Number of seconds elapsed since Jan 1, 2000, 12:00 UTC corresponding to the time at which data collection for the current echo started.</p> <p> - MARS SOLAR LONGITUDE - Angle between the Mars-Sun line at the time corresponding to EPHEMERIS TIME and the Mars-Sun line at the vernal equinox, in degrees.</p> <p> - MARS SUN DISTANCE - Distance from the centre of Mars to centre of the Sun at the time corresponding to EPHEMERIS TIME, in Km.</p> <p> - SPACECRAFT ALTITUDE - Distance from the Mars Express spacecraft to the reference surface of the target body measured normal to the surface at the time corresponding to EPHEMERIS TIME, expressed in Km.</p> <p> - SUB-SPACECRAFT LONGITUDE - East longitude of the point on the target body that lies closest to the Mars Express spacecraft at the time corresponding to EPHEMERIS TIME, expressed in degrees and in the [ 0 -360 ] range.</p> <p> - SUB-SPACECRAFT LATITUDE - Planetocentric latitude of the point on the target body that lies directly beneath the Mars Express spacecraft at the time corresponding to EPHEMERIS TIME, expressed in degrees.</p> <p> - RADIAL VELOCITY - Radial component of the Mars Express spacecraft velocity vector in the reference frame of the target body at the time corresponding to EPHEMERIS TIME, expressed in Km/s.</p> <p> - TANGENTIAL VELOCITY - Tangential component of the Mars Express spacecraft velocity vector in the reference frame of the target body at the time corresponding to EPHEMERIS TIME, expressed in Km/s.</p> <p> - LOCAL TRUE SOLAR TIME - Angle between the extension of the vector from the Sun to Mars and the projection on Mars' ecliptic plane of a vector from the center of the target body and the point on the target body surface that lies directly beneath the Mars Express spacecraft at the time corresponding to EPHEMERIS TIME, expressed on a 24-hour clock with decimal fractions of the hour.</p> <p>* orbit_XXXXX_frequency_Y_MHz.png, where XXXXX is the orbit number, and Y the frequency at which the radar was operating, in MHz. The file is a visualization of the corresponding radargram, of the spacecraft ground track during the observation, and of surface and subsurface echo power.</p> <p>Electromagnetic propagation model and results consist of several files:</p> <p>* model_Pss_over_Ps_ratio.m is a script written in the Matlab© computing language. It simulates the propagation of a MARSIS radar pulse through a model stratigraphy representing the Martian South Polar Layered Deposits (SPLD). The simulation is based on the solution of Maxwell's equations for a plane parallel stratigraphy, and it is thus one-dimensional. The SPLD are represented as an uniform layer of ice mixed with dust.</p> <p>* figure_S5.m is a script written in the Matlab© computing language. It simulates the propagation of a MARSIS radar pulse through a model stratigraphy representing the Martian South Polar Layered Deposits (SPLD). The simulation is based on the solution of Maxwell's equations for a plane parallel stratigraphy, and it is thus one-dimensional. The SPLD are represented as an uniform layer of ice mixed with dust, either overlaid by or overlaying a layer of pure CO2 ice of variable thickness.</p> <p>* fftvars.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes vectors containing the correct time and frequency values for FFT computations.</p> <p>* genrefl.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes the frequency-dependent complex electromagnetic reflectivity of a plane parallel stratigraphy at normal incidence.</p> <p>* GPR_1D_simulation.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It simulates the radar echo produced by the propagation of a broadband signal in a plane parallel stratigraphy.</p> <p>* matzler.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes the relative dielectric permittivity of pure water ice (ice Ih) for any temperature and frequency according to formulas provided in Matzler (1998).</p> <p>* maxgarmix.m is a function written in the Matlab© computing language and used by both "model_Pss_over_Ps_ratio.m" and "figure_S5.m". It computes the effective dielectric constant of a medium containing intrusions of different dielectric properties according to the Maxwell-Garnett dielectring mixing model.</p> <p>* Pss_over_Ps_ratio_at_X_MHz_and_Y.YY_dust_fraction.csv is a comma-separated value file containing model results for subsurface to surface echo power ratio as a function of central frequency, basal temperature, dust content and basal permittivity. One file is produced for every value of the central frequency and of the dust fraction. The value of these two parameters for a given files are reported in the file name, where X is the frequency in MHz, and Y.YY is the dust fraction. Each file contains a matrix in which there is a row for every value of basal temperature from 170 K to 270 K, and a column for every value of basal permittivity from 4 to 100 with logarithmic spacing. Basal temperature and permittivity values are reported in different comma-separated value files called "basal_temperatures.csv" and "basal_permittivities.csv".</p> <p>* basal_permittivities.csv is a comma-separated value file containing the values of basal permittivity corresponding to the columns of the model result matrixes contained in the files "Pss_over_Ps_ratio_at_X_MHz_and_Y.YY_dust_fraction.csv".</p> <p>* basal_temperatures.csv is a comma-separated value file containing the values of basal temperature corresponding to the rowso of the model result matrixes contained in the files "Pss_over_Ps_ratio_at_X_MHz_and_Y.YY_dust_fraction.csv".</p>
MiniMPL data for 'Supercooled liquid water cloud classification using lidar backscatter peak properties'
<p>This depository contains MiniMPL data collected in Christchurch, New Zealand from May 2021 to December 2022 for 'Supercooled liquid water cloud classification using lidar backscatter peak properties' by Whitehead et al. (2024). The dataset contains:</p> <ul> <li> MiniMPL data processed with the Automatic Lidar and Ceilometer Framework (ALCF; Kuma et al., 2021)</li> <li>Reference cloud phase mask</li> <li>G22-Christchurch model-generated cloud phase mask</li> <li>Figures comparing the G22-Davis and G22-Christchurch masks to the reference mask</li> </ul>
Data for: Imaging the short-lived hydroxyl-hydronium pair in ionized liquid water
<p>The radiolysis of water is ubiquitous in nature and plays a critical role in numerous biochemical and technological applications. Although the elementary reaction pathways for the ionized water have been studied, the short-lived intermediate complex and structural dynamic response after the proton transfer reaction remain poorly understood. Using liquid-phase ultrafast electron diffraction technique to measure the intermolecular O··O and O··H bonds, we captured the short-lived radical-cation complex OH(H<sub>3</sub>O<sup>+</sup>) that was formed within 140 femtoseconds through a direct oxygen-oxygen bond contraction and proton transfer, followed by the radical-cation pair dissociation and the subsequent structural relaxation of water within 250 femtoseconds. These measurements provide direct evidence of capturing this metastable radical-cation complex before separation, thereby improving our fundamental understanding of elementary reaction dynamics in ionized liquid water.</p>
The Collective Burst Mechanism in Liquid Water
<p>This dataset contains essential data for reproducing results in the Paper</p> <p>"The Collective Burst Mechanism in Liquid Water".</p> <p>These are the files</p> <p>Nature_data_original.zip</p> <p>original_dp_space_1.mat (Dipole time series)</p> <p>original_hh_space_1.mat (HH vector time series)</p> <p> </p> <p>Nature_data_original.zip contains files </p> <p>the defect_*.mat which are time series of the defects for 1019 molecule used in the simulation.</p> <p>each number corresponds to 500ps of the full 2ns trajectory.</p> <p> </p> <p>The files gd_angle_vs_time_time_dp*.mat/gd_angle_vs_time_time_hh*.mat/ report on jumps detected in </p> <p>each of the 500ps blocks of the full 2ns trajectory </p> <p>Also contained is the code used for generating the data. See the Readme.txt file for more information</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Electrofreezing of liquid water at ambient conditions - trajectories from ab initio molecular dynamics simulations
Open the record for dataset details and reuse information.
Structure and dynamics of liquid water from ab initio simulations: Adding Minnesota density functionals to Jacob's ladder
<p>Supporting data and analysis script for the work</p><p><i>Structure and dynamics of liquid water from ab initio simulations: Adding Minnesota density functionals to Jacob's ladder</i></p>
Water Content in E-Liquids
ClinicalTrials.gov study NCT05257109. IPD Sharing: NO. Countries: 1. Publications: 0.
Data for: Imaging the short-lived hydroxyl-hydronium pair in ionized liquid water
Open the record for dataset details and reuse information.
Deep eutectic solvent-based emulsification liquid-liquid microextraction coupled with HPLC-UV for the analysis of phenoxy acid herbicides in paddy field water samples
Open the record for dataset details and reuse information.
Data for "Attosecond spectroscopy of liquid water"
<p>Data sets underlying Figs. 2, 4 and S1-S12 of the paper entitled "Attosecond Spectroscopy of Liquid Water"</p>
Data from: Combining ground‐penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks
Many communities and ecosystems around the world rely on mountain snowpacks to provide valuable water resources. An important consideration for water resources planning is runoff timing, which can be strongly influenced by the physical process of water storage within and release from seasonal snowpacks. The aim of this study is to present a novel method that combines light detection and ranging with ground‐penetrating radar to nondestructively estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring snowmelt. We develop these methods in a manner to be applicable within a short time window, making it possible to spatially observe rapid changes that occur to this property at subdaily timescales. We applied these methods at two experimental plots in Colorado, showing the high variability of liquid water content in snow. Volumetric liquid water contents ranged from near zero to 19%vol within the scale of meters. We also show rapid changes in bulk liquid water content of up to 5%vol that occur over subdaily timescales. The presented methods have an average uncertainty in bulk liquid water content of 1.5%vol, making them applicable for future studies to estimate the complex spatio‐temporal dynamics of liquid water in snow.
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>
Small Variations in Ice Composition and Layer Thickness Explain Bright Reflections Below Martian Polar Cap Without Liquid Water
<p>These files include the model results used in Lalich et al. 2024 as well as the code necessary to analyze and reprooduce those results. See README for more detail. </p>
Image Dataset for 'AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water'
<p>This dataset is presented in the following publication. Please cite this publication if you use the dataset.</p> <p><em>Jiyoon Yi, Nicharee Wisuthiphaet, Pranav Raja, Nitin Nitin, J. Mason Earles. (2023). AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water. Water Research, 120258. doi: <a href="https://doi.org/10.1016/j.watres.2023.120258">10.1016/j.watres.2023.120258</a></em></p>
ScienceDex guides
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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.