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65 results for “Water transport”
Lagrangian Decomposition of the Meridional Heat Transport at 26.5N - Water Parcel Crossings of the RAPID 26.5N Array
<p>This dataset contains the initial and final positions and properties of Lagrangian trajectories evaluated using 5-day mean velocity and tracer fields output from the ORCA0083-N06 ocean sea-ice model hindcast (1958-2015). Numerical water parcels are initialised to sample the full-depth southward transport across the RAPID 26.5N array every month during 2004-2015. Water parcels are advected backwards-in-time using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool which enables users to specify a custom domain using a mask netCDF file.</p><p>Particles are initialised on the first-available day of each month (based on the centre of the model 5-day mean field windows) between 2004 and 2015 (inclusive) before being advected backwards-in-time within the North Atlantic Ocean until any one of four termination conditions are met: (1) water parcels reach the RAPID 26.5N array, (2) water parcels reach the OSNAP (West or East) arrays in the subpolar North Atlantic, (3) water parcels reach either the English Channel or Gibraltar Strait, or (4) particles reach the maximum advection time of 25-years. The 25-year maximum advection time ensures that we adequately resolve the subtropical gyre circulation north to the RAPID 26.5N array. The pathway transporting dense North Atlantic Deep Water from the OSNAP arrays to RAPID at 26.5N is not fully resolved in this Lagrangian experiment since these water parcels transit on multi-decadal timescales.</p><p>The number of water parcels initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 5.0 mSv (mSv == 10-3 Sv), enabling the calculation of robust Lagrangian statistics. In reality, the average. water parcel has an associated volume transport of 3.3 mSv which is conserved throughout its circulation.</p><p>Water parcel locations (converted to geographical coordinates) and properties (conservative temperature, absolute salinity, potential density [TEOS-10]) are output on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points. Here, we provide the initial and final locations and properties of all water parcels initialised from RAPID 26.5N.</p><p>All Lagrangian experiments were completed using the JASMIN High-Performance Computing facility (<a href="https://jasmin.ac.uk">https://jasmin.ac.uk</a>).</p><p><strong>For a complete description of the ORCA0083-N06 hindcast configuration see:</strong> Moat et al. (2016).</p><p><strong>For a complete description of TRACMASS v7.1 see</strong>: <a href="https://www.tracmass.org">https://www.tracmass.org</a></p>
Data for Li et al., Waterborne virus transport and the associated risks in a large lake, Water Research; DOI: 10.1016/j.watres.2022.119437
<p>Experimental data (virus concentrations measured in sewage, in units of genome copies per mL sewage) </p> <p>Code (written in R) used to model virus concentration distributions</p> <p>Code (Delft3D and Matlab) used for hydrodynamic, water quality and QMRA simulations. Input files are for the "winter scenario" shown in the publication.</p>
NACLIM - Fluxes: Faroe Shetland Channel volume transport of Atlantic Water
<p><strong>Last update: 14 January 2015</strong></p> <p><strong>Data set:</strong> Faroe Shetland Channel volume transport of Atlantic Water</p> <p><strong>Description: </strong>Monthly mean volume transport of Atlantic Water</p> <p><strong>Period: </strong>December 1992 – March 2014 </p> <p><strong>Location: </strong>61° N 4° W </p> <p><strong>Instruments: </strong>CTD, moored current meters and ADCPs, altimeter </p> <p><strong>Variables:</strong> Volume transport of Atlantic water based on ADCP and altimeter observations</p> <p><strong>Source: </strong>Barbara Berx (MARLAB) </p>
Leaf habit affects the distribution of drought sensitivity but not water transport efficiency in the tropics
<p>Considering the global intensification of aridity in tropical biomes due to climate change, we need to understand what shapes the distribution of drought sensitivity in tropical plants. We conducted a pantropical data synthesis representing 1117 species to test whether xylem-specific hydraulic conductivity (K<sub>S</sub>), water potential at leaf turgor loss (Ψ<sub>TLP</sub>), and water potential at 50% loss of K<sub>S</sub> (ΨP50) varied along climate gradients. The Ψ<sub>TLP</sub> and ΨP<sub>50</sub> increased with climatic moisture only for evergreen species, but K<sub>S</sub> did not. Species with high Ψ<sub>TLP</sub> and Ψ<sub>P50</sub> values were associated with both dry and wet environments. However, drought-deciduous species showed high Ψ<sub>TLP</sub> and ΨP<sub>50</sub> values regardless of water availability whereas evergreen species only in wet environments. All three traits showed a weak phylogenetic signal and a short half-life. These results suggest that environmental controls on trait variance, which in turn is modulated by leaf habit along climatic moisture gradients in the tropics.</p>
Water will find its way: transport through narrow tunnels in hydrolases (Epx protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (Epx protein)</strong></p> <p><br>The input files and results used for the paper “<em>Water will find its way: transport through narrow tunnels in hydrolases</em>” are separated in the different folders depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the minimum sphere event is present. Also the txt files with the results from the H-bond and contact analyses are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>Epx.dat</strong> Parsed database of transport events for Epx system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/Epx_tt.ini -o Epx.dat</em></li> </ol>
Water will find its way: transport through narrow tunnels in hydrolases (hEpx protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (hEpx protein)</strong></p> <p>The input files and results used for the paper "<em>Water will find its way: transport through narrow tunnels in hydrolases</em>" are separated in the different files depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the "<em>Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results for hEpx and E470G systems together, since we employed the comparative approach to analyze the results with TransportTools (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the *minimum sphere event* is present. Also the txt files with the results from the H-bond analysis are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>hEpx_E470G.dat</strong> Parsed database of transport events for hEpx_E470G system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/hEpx_E470G_tt.ini -o hEpx_E470G.dat</em></li> </ol> <p> </p>
Water will find its way: transport through narrow tunnels in hydrolases (Lip protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (Lip protein)</strong></p> <p><br>The input files and results used for the paper “<em>Water will find its way: transport through narrow tunnels in hydrolases</em>” are separated in the different folders depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the minimum sphere event is present. Also the txt files with the results from the H-bond analysis are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>Lip.dat</strong> Parsed database of transport events for Lip system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/Lip_tt.ini -o Lip.dat</em></li> </ol>
Water will find its way: transport through narrow tunnels in hydrolases (Hal protein with different MD simulation settings)
<h1>Hal_2fs.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (Hal_2fs protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.<br> 2. <em>Hal_2fs.dat</em>: Parsed database of transport events for Hal system. Command used: <br><em>python3 01_build_database.py -c 04_TransportTools/Hal_2fs.ini -o Hal_2fs.dat</em></p> <p> </p> <h1>Hal_300K.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (Hal_300K protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.<br> 2. <em>Hal_300K.dat:</em> Parsed database of transport events for Hal system. Command used:<br><em>python3 01_build_database.py -c 04_TransportTools/Hal_300K.ini -o Hal_300K.dat</em></p> <p> </p> <h1>Hal_TIP3P.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (TIP3P protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</p> <p> 2. <em>Hal_TIP3P.dat</em>: Parsed database of transport events for Hal system. Command used:<br><em>python3 01_build_database.py -c 04_TransportTools/Hal_TIP3P.ini -o Hal_TIP3P.dat</em></p>
Full Inverse Velocity Fields for "Transport of Antarctic Bottom Water entering the Brazil Basin in a Planetary Geostrophic Inverse Model"
<p>Velocity fields on all approximate neutral surfaces from the inverse model presented in "Transport of Antarctic Bottom Water entering the Brazil Basin in a Planetary Geostrophic Inverse Model". The pressure of the approximate neutral surface is contoured in the background. The number in the title represents the pressure of the approximate neutral surface at the reference station in the Hunter Channel.</p>
Lattice Boltzmann simulation of liquid water transport in gas diffusion layers of proton exchange membrane fuel cells: Impact of gas diffusion layer and microporous layer degradation on effective transport properties
<p><span>Underlying data to publication Sarkezi-Selsky et al., <em>J. Pow. Sour.</em> 556 (2023) 232415,<span> https://doi.org/10.1016/j.jpowsour.2022.232415</span> <br><br>Polymer Electrolyte Membrane Fuel Cells (PEMFCs) represent a promising technology for clean drivetrain solutions, in particular for heavy-duty applications. However, lifetime requirements demand high durability of each cell component.<br></span><span>In this work, transport of liquid water through pristine and degraded gas diffusion layers (GDL) was simulated with a 3D Color-Gradient Lattice Boltzmann model. The GDL microstructure was reconstructed </span><span>from high-resolution X-ray micro-computed tomography (</span><span>μ</span><span>-CT) of an impregnated Freudenberg H14. The </span><span>effect of a microporous layer (MPL) was considered by reconstruction of an impregnated and MPL-coated H14. Aged microstructures were generated artificially, assuming loss of polytetrafluoroethylene (PTFE) within the GDL and increase of MPL macroporosity as main degradation mechanisms. Liquid water transport within aged microstructures was simulated by imposing a liquid phase flow rate until breakthrough was reached. Subsequently, the GDL microstructures were analyzed for their breakthrough characteristics by means of saturation and effective gas transport properties. When the MPL was pristine, no distinct GDL degradation effect was observable, this was attributed to the MPL dominating capillary transport. MPL aging, however, led to increased saturations and thus to a deterioration of the effective gas transport. With a partially degraded MPL, aging of the GDL then appeared to affect the breakthrough characteristics.</span></p>
Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange: Neural network potentials training data
<h1>Neural network potentials of an excess proton in bulk water, training data</h1> <p>This dataset contains 2188 configurations labeled at two hybrid DFT levels (revPBE0-D3 and B3LYP-D3).</p> <p>The configurations are given as a single XYZ file: configurations.xyz</p> <p>The box dimensions are written in box.txt</p> <p>The energies for all configurations at a given level of theory are written in energies_LEVEL.txt (one configuration per line)</p> <p>The atomic forces for each configuration at a given level of theory are gathered in a XYZ file: forces_LEVEL.xyz</p> <p>The relative displacements of the Wannier centroids, with respect to the closest oxygen atom, for each configuration at a given level of theory, are in the following XYZ file: wannier-centroids-displacements_LEVEL.xyz</p>
Fig. 1 in Transport of jundiá Rhamdia quelen juveniles at different loading densities: water quality and blood parameters
Fig. 1. Plasma cortisol and blood glucose of jundiá juveniles transported at different loading densities. Different letters over the bars indicate significant differences (P <0.05) among treatments within each experimental time. Asterisks show significant differences (P <0.001) when compared to the initial value.
Describing ion transport and water splitting in an electrodialysis stack with bipolar membranes by a 2-D model: Experimental validation
<p>Electrodialysis with bipolar membranes (EDBM) has drawn attention motivated by their application in gener- ating reagents from salts. Due to the water splitting (WS) occurring at the junction of the bipolar membranes (BPMs), where the anion and cation layers are in strict contact, H+ and OH- are released from the BPM producing acid and alkali on the respective compartment. Considering this application, the interest of this work is to provide further understanding of the mechanisms of WS and transport of species in EDBM. This work develops and utilizes, for the first time, an experimentally validated two-dimensional (2-D) computational model, in which the Navier-Stokes and Nernst-Planck equations are coupled with the description of WS given by the Second Wien effect. In addition, a 1-D geometry is also proposed to perform a comparison between electroneutrality and Poisson charge conservation. The model is computationally solved using COMSOL Multiphysics. According to simulations, electroneutrality is valid for 2-D geometries. Moreover, the semipermeable characteristics of the membranes are assessed by means of evidencing a polarization effect resulting in a double-electric layer. The model proposed predicts a significant proton leakage, and facilitates the study of WS within the BPMs.</p>
Leaf habit affects the distribution of drought sensitivity but not water transport efficiency in the tropics
Open the record for dataset details and reuse information.
Data for: Wintertime coastal upwelling in Lake Geneva: An efficient transport process for deep-water renewal in a large, deep lake
<p>Combining field measurements, 3D numerical modeling and Lagrangian particle tracking,<br> we investigated wind-driven, Ekman-type coastal upwelling during the weakly stratified winter period 2017/2018<br> in Lake Geneva, a large and deep lake in western Europe. The data include measurements from<br> moored Acoustic Doppler Current Profilers (ADCP) and vertical temperature profiles<br> along with the corresponding 3D modeling and particle tracking results.<br> The three-dimensional model used in this study is based on the MIT General<br> Circulation Model (MITgcm, http://mitgcm.org/, https://doi.org/10.1029/96JC02775).<br> The particle tracking code is based on ctracker (https://doi.org/10.5281/zenodo.1034118).</p>
Probing structural superlubricity of two-dimensional water transport with atomic resolution.
<p>Here lies the tabulated data used to create the Figures for the Science manuscript ado1544 titled "Probing structural superlubricity of two-dimensional water transport with atomic resolution".</p>
Water will find its way: transport through narrow tunnels in hydrolases (water-protein interaction analysis)
<p>Repository including information on the analyses of water-protein interactions by MM/GBSA.</p> <p><strong>hal_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results for Hal.<br><strong>epx_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results for Epx.<br><strong>lip_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results<strong> </strong>for Lip<strong>.</strong></p> <p>In each archive structure is:<br> {supercluster_id}: - supercluster directory<br> {event_nr}_{rounded_radius}: - transport event directory<br> renum.pdb -pdb file containing protein, water of interest and 8 Angstrom radius sphere of surrounding waters<br> water.txt - file denoting water ID for water of interest in the renum.pdb file<br> binding_[implicit|explicit].dat - file containing MMGBSA results for full implicit and partial explicit system<br> decomposition_[implicit|explicit].dat - file containing MMGBSA decomposition results for full implicit and partial explicit system<br> info_{supercluster_id}.csv - file containing descriptions of transport events in supercluster (columns: event_name, radius, frame, simulation, water_id)</p> <p><strong>results.tar.gz </strong>- contains csv files generated by <em>script collect_data_minimal.py</em> for each protein for full implicit and partial explicit solvent. Columns in those csv file are: event_name, supercluster, radius, frame, simulation, water_id, delta_total, delta_van_der_waals, delta_electrostatic, delta_G_solv</p> <p><strong>scripts.tar.gz</strong> - contains following scripts<br> read_transport_events.py<br> prep_inputs.py<br> run_MMGBSA_single.sh<br> collect_data_minimal.py<br> plot_bin.py</p> <p>To recreate MMGBSA calculations with provided inputs one should:<br>$ python read_transport_events.py [Hal.dat|Epx.dat|Lip.dat] > selection.txt<br>Prepare transport events information in a compatible format. Binary transport events databases are available in the repository.</p> <p>$ python prep_inputs.py selection.txt - {absolute output path} {supercluster_id} 1 <br>Prepare parm and coordinate file for each supercluster id included in computation. Last argument 1 point script to resume preparations from renum.pdb files.</p> <p>$ bash run_MMGBSA_single.sh {residue_count}<br>Runs MMGBSA for both partial explicit and fully implicit systems. Needs to be run for each event in each supercluster.<br>residue_count should be set to 293 for hal, 319 for epx, and 534 for lip.</p> <p>$ python collect_data_minimal.py [hal|epx|lip]/{supercluster_id} [hal|epx|lip]/info_{supercluster_id}.csv [0|1] >> output.csv<br>Gathers data from MMGBSA calculations for a single supercluster into csv file. <br>Last argument should be 0 for fully implicit system, 1 for partial explicit system.</p> <p>$ python plot_bin.py {hal_implicit_csv} {hal_explicit_csv} {epx_implicit_csv} {epx_explicit_csv} {lip_implicit_csv} {lip_explicit_csv}<br>Results visualization. Produces "combined.png".</p>
Simulations of FLEXPART-WRF used for testing TRansport Of water VApor (TROVA) software (Part II)
<p>Necessary data for carrying out the tests using TRansport Of water VApor (TROVA) software for backward in time. The TROVA software, was developed in Python and Fortran for the study of moisture sources and sinks. <span><span><span>In addition, the Python code is provided for the representation of the results.</span></span></span></p>
Data sets for "Formation of an Al-rich niccolite-type silica in subducted oceanic crust: implications for water transport to the deep lower mantle"
<p>This is the XRD and IR datasets for the article "Formation of an Al-rich niccolite-type silica in subducted oceanic crust: implications for water transport to the deep lower mantle" by Liu and Yuan et al.</p>
FESOM model data and particle tracking data used in publication 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean'
<p>FESOM model data and particle tracking data used in the paper 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean" by Andreas Rogge at al.</p> <p>1) FESOM velocity fields averaged over the top 200 m water depth, averaged over the time period 2015-2018.</p> <p>2) FESOM transect at 95°E in the Arctic Ocean (temperature, salinity and velocity), averaged over the time period 2015-2018.</p> <p>3a) Particle back-tracking data based on daily FESOM velocity fields in netcdf format. Particles were released at 95°E every 14 days during the year 2018 and tracked until they reached the surface. Three different constant sinking velocities were used, representative for small and large non-ballasted particles and small ballasted particles.</p> <p>3b) Distribution of particles at the surface for the experiments with three different sinking velocities as mat files. </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.