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887 results for “tunnels”
Numerical code and data for: Suppressed Charge Dispersion via Resonant Tunneling in a Single-Channel Transmon
<p>The numerical code and data accompanying the analysis of Figs. 4 and 5 of Suppressed Charge Dispersion via Resonant Tunneling in a Single-Channel Transmon, Phys. Rev. Lett. (2020)</p>
How to reveal metastable skyrmionic spin structures by spin-polarized scanning tunneling microscopy
<p>We predict the occurrence of metastable skyrmionic spin structures such as antiskyrmions and<br> higher-order skyrmions in ultra-thin transition-metal films at surfaces using Monte Carlo simulations<br> based on a spin Hamiltonian parametrized from density functional theory calculations.Weshow that<br> such spin structures will appear with a similar contrast in spin-polarized scanning tunneling<br> microscopy images. Both skyrmions and antiskyrmions display a circular shape for out-of-plane<br> magnetized tips and a two-lobe butterfly contrast for in-plane tips. An unambiguous distinction can<br> be achieved by rotating the tip magnetization direction without requiring the information of all<br> components of the magnetization.</p>
Data and Mathematical notebook for "Fractional-statistics-induced entanglement from Andreev-like tunneling"
<p>The uploaded files "SourceRightON_full.txt", "SourceLeftON_full.txt" and "BothSourcesON_full.txt" contain data for the work entitled "Fractional-statistics-induced entanglement from Andreev-like tunneling".</p> <p> </p> <p>The other file "New_Anyonic_data_fittings v2.nb" is the Mathematica notebook with which we perform the data analysis. When using it, please place three data files (mentioned above) in the Download folder.</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>
Measured and analyzed raw data for publication "Nanoscale spin ordering and spin screening effects in tunnel ferromagnetic Josephson junctions" (doi: https://doi.org/10.1038/s43246-024-00497-1)
<p>The data provided by this dataset are the raw data published in the paper "Nanoscale spin ordering and spin screening effects in tunnel ferromagnetic Josephson junctions" (doi: https://www.nature.com/articles/s43246-024-00497-1). </p> <p>It can be found:</p> <p>-In the folder figure2_IV, the current-voltage characteristics (IV) of standard Superconductor-Insulator-Superconductor Josephson Junctions (SIS JJ) and of Superconductor-Insulator-Ferromagnet-thin superconductor- Superconductor Josephson Junctions (SIsFS JJ) at 10 mK </p> <p>-In the folder figure2_IVH, the magnetic dependence of the critical current of the SIsS and SIsFS at 10 mK</p> <p>-In the folder figure3_IVHT, the magnetic dependence of the critical current of the SIsFS as a function of the temperature T</p> <p>-In the figure4_gamma, the experimental and theoretical dependence of \gamma, i.e., the magnetic moment of the S-layers normalized to the F-layer in absolute value, as a function of the characteristic energy of the inverse proximity effect</p>
Measured and analyzed raw data for publication "Phase dynamics of tunnel Al-based ferromagnetic Josephson junctions"(https://doi.org/10.1063/5.0211006)
<p>The dataset provided here reports raw data published in June 2024 (Phase dynamics of tunnel Al-based ferromagnetic Josephson junctions): current-voltage I-V characteristics as a function of the temperature T; switching current distributions (SCD) as a function of T and calculated mean switching currents, standard deviations and skewness from the SCDs and superconducting branch resistance R0 as a function of the temperature. All the data for magnetic and non-magnetic Josephson junctions have been acquired, as highlighted in the corresponding reference.</p>
The initial stages of Ag fluorination: a scanning tunneling microscopy investigation
<p>Low Temperature scanning tunneling microscopy images of Ag(100) and Ag(110) crystals exposed at room temperature for different amounts of time to a fluorine-rich atmosphere. The temperature used for acquiring the STM images is written within the title of the file. </p> <p>The calibration factors for the STM images are 1.12 in X and Y, 0.867 in Z. </p> <p>These STM data are the raw images of the results shown in the paper published in ArXiv with the doi number 10.48550/arXiv.2410.04858.</p>
SBF-SEM datasets related to the manuscript "Trans-cellular tunnels induced by the fungal pathogen Candida albicans facilitate invasion through successive epithelial cells without host damage" by Lachat et al, 2022.
<p>11 serial block face- scanning electron microscopy (SBF-SEM) datasets described in the manuscript "Trans-cellular tunnels induced by the fungal pathogen Candida albicans facilitate invasion through successive epithelial cells without host damage" by Lachat et al, 2022.</p> <p>Resolution: 10 nm x,y, 100 nm Z.</p> <p>Datasets description and quantification can be found in the Supplementary information.</p>
Anonymized GTP Tunnel Trace in Mobile IoT
<p>Extensive dataset containing one whole month of create and delete events as well as the total, received, and transmitted volume of devices. We obtained data tunnel related events and volume values over 30 days in October 2021. In total the dataset contains a sample of 500000 unique devices that generate 155 million individual data tunnels.</p>
"Wind turbine wakes on escarpments: A wind-tunnel study"
<p>Dar, Arslan Salim, and Fernando Porté-Agel. "Wind turbine wakes on escarpments: A wind-tunnel study." <em>Renewable Energy</em> 181 (2022): 1258-1275.</p>
Text-fig. 13. Zoophycos isp. a: BK 17, Layer No. 6; b: BK 27, Layer No. 8; c: lateral tunnel continuing from spreite side to the surrounding rock, BK 28, Layer No. 23; d: BK 22, Layer No. 1; e: "juvenile" stage of the structure on a horizontal winding tunnel, BK 21, Layer No. 26; f: BK 24, Layer No. 17; g: broad winding tunnel adjacent to spreite, BK 26, Layer No. 6; h: BK 15, Layer No. 18; i: BK 23, Layer No. 2. Scale bar = 1 cm. in Early Complex Tiering Pattern: Upper Ordovician, Barrandian Area, The Czech Republic
Text-fig. 13. Zoophycos isp. a: BK 17, Layer No. 6; b: BK 27, Layer No. 8; c: lateral tunnel continuing from spreite side to the surrounding rock, BK 28, Layer No. 23; d: BK 22, Layer No. 1; e: "juvenile" stage of the structure on a horizontal winding tunnel, BK 21, Layer No. 26; f: BK 24, Layer No. 17; g: broad winding tunnel adjacent to spreite, BK 26, Layer No. 6; h: BK 15, Layer No. 18; i: BK 23, Layer No. 2. Scale bar = 1 cm.
Text-fig. 6. Zoophycos showing spreiten structure with a continuously meandering tunnel. Schematic drawing of the specimen from unidentified layer from a block out of the measured profile. Scale in centimetres. in Early Complex Tiering Pattern: Upper Ordovician, Barrandian Area, The Czech Republic
Text-fig. 6. Zoophycos showing spreiten structure with a continuously meandering tunnel. Schematic drawing of the specimen from unidentified layer from a block out of the measured profile. Scale in centimetres.
Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations
<p># *"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"*</p> <p>The input files and data used for the paper *"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"* are separated in the different folders depending stage they belong to.</p> <p>## Folders</p> <p> 1. **01_inputs:** The MD trajectory of DhaA used (only protein atoms present)<br> 2. **02_sliced_trajectory:** The CAVER3 results for the sliced trajectory (eight parts)<br> 3. **03_sliced_filtered:** Filtered CAVER3 results and results from the divide-and-conquer approach<br> 4. **04_full_trajectory:** The CAVER3 results for the full trajectory analysis<br> 5. **05_guided_example:** Guided example for the divide-and-conquer approach<br> </p>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (inputs, outputs, analysis)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt: contains raw data that are used for analysis, also conatin folder for GaMD testing.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. GaMD-testing :</p> <p> 1. Input file of GaMD used to run testing and output gamd.log files for multiple run of σOP 1.2 - 1.4 and σOD 2.5.</p> <p> 4. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant: contains raw data that are used for analysis.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant: contains raw data that are used for analysis.</li> </ul> <p><br> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>03_TT_analysis.tar.gz - TransportTools: contains config file and all the raw data from all set and subset of reclustered (using in-house python script) caver calculations used for running TT.</li> </ul> <p> 1. Caver input data for comparison between 500ns, 1 us, 2.5 us and 5us between LinB-Wt and it’s mutants.<br> 2. TransportTools log file.<br> 3. Main statistics result of comparative analysis.</p> <ul> <li>04_reweighting.tar.gz: directory contains reweighted .csv files after running in-house reweighting protocol.<br> <br> 1. GaMD log files from each simulation of LinB-Wt and it’s mutants.<br> 2. CSV files from TT result folder.<br> 3. Result *.csv file contained reweighted tunnel properties in folder reweighted_filtered_new.</li> <li>05_caverdock.tar.gz: contains raw data for caverdock calculations uisng 100 best tunnels with four ligands 2-bromoethanol (be), 1,2-dibromoethane (dbe), Bromide ion (br-) and water (h2o).</li> </ul> <p> 1. Top 100 tunnels present in tunnel folder for all three tunnels ST, p1b and p3 with subdirectory containing three variants and four ligand, whichare used for running caverdock.<br> 2. Ligand *.pdbqt file and receptor *.pdbqt are present in each 100 tunnel folder of respective caverdock calculation.<br> 3. Inside each variant and each ligand, there is respective result of migration analysis with energy barrier calculation of respective tunnels *energy_barriers-new.log* and further simplied *.csv files that was used for preparing figure in manuscript.</p> <p> </p>
Wind Tunnel Testing of Tethered Inflatable Wings
<p>This dataset consists of all the data collected and presented in the AIAA Journal of Aircraft titled "Wind Tunnel Testing of Tethered Inflatable Wings". The attached zipped folder contains a README file that explains the dataset. </p>
High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range - data
<div> <p>Experimental and simulation data from the paper "High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range".</p> <p> </p> </div>
Fig. 529. Kouhrang Tunnel, which discharge annually 320 million m3 in Fig. 4 in Fig. 4 in Fig. 3 in Fig. 21. Sesarmops mora n in Paralbunea dayriti
Fig. 529. Kouhrang Tunnel, which discharge annually 320 million m3 water annually from the Tigris to Esfahan basin.
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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.