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669 results for “ATOM”
OPLS-UA POPE Simulations (versions 1 and 2) 303 K with vdW on H atoms
<p>Two OPLS-UA POPE bilayer simulations performed using GROMACS 4.5.7 for 200 ns with different starting velocities. Simulations were performed with a 1.0 nm cut-off with PME for the Coulombic and a 1.0 nm cut-off for the van der Waals interactions interactions. These simulations were performed at 303 K with a 128 lipid bilayer. The full trajectories are provided bar the initial 100 ns. The starting structure was made through the conversion of an equilibrated OPLS-UA POPC membrane. The PE parameters were constructed by modifying the OPLS-UA POPC of Ulmschneider and Ulmschnider with the standard OPLS lysine parameters and the addition of LJ parameters on the hydrogen atoms in the head group (designed to increase the area per lipid).</p>
ATOM: Model-Driven Autoscaling for Microservices
<p>This dataset release supports the results presented in the paper<br> "ATOM: Model-Driven Autoscaling for Microservices", by A. U. Gias, G. Casale and M. Woodside, accepted in IEEE International Conference on Distributed Computing Systems (ICDCS), 2019. </p> <p>When referring to the dataset please cite the paper above.</p> <p> </p>
Data presented in "Microwave trap for atoms and molecules"
<p>Data presented in figures 2-5 of our paper "Microwave trap for atoms and molecules"</p>
Data for probing gravity by holding atoms for 20 seconds
<p>Data for figures.</p>
Raw Data for "Combining experiments and relativistic theory for establishing accurate radiative quantities in atoms: the lifetime of the $^2$P$_{3/2}$ state in $^{40}$Ca$^+$"
<p>Raw data and analysis files (Matlab) for the paper: "Combining experiments and relativistic theory for establishing accurate radiative quantities in atoms: the lifetime of the $^2$P$_{3/2}$ state in $^{40}$Ca$^+$".</p>
Higher-order and fractional discrete time crystals in Floquet-driven Rydberg atoms
<div>These folders contain data used to reproduce figures in main text.</div> <div> </div> <div>The file "fig2b.xlsx" contains the phase diagram in Figure 2 of the main text. The columns of fig2b.xlsx represent the detuning and the rows represent the frequencies of the Fourier spectrum. The values in the table represent transimission.</div> <div> </div> <div>The files "fig3a.xlsx" and "fig3c.xlsx" contain the phase diagrams in Figure 3 of the main text. The columns of fig3a.xlsx and fig3c.xlsx represent the detuning and the rows represent the frequencies of the Fourier spectrum. The values in the table represent transimission.</div> <div> </div> <div>The file "fig4a2.xlsx" contains the phase diagram in Figure 4 of the main text. The columns of fig4a2.xlsx represent represent the detuning and the rows represent the frequencies of the Fourier spectrum. The values in the table represent transimission.</div> <div> </div> <div>The files "fig5a.xlsx", "fig5b.xlsx" and "fig5c.xlsx" contain the phase diagrams in Figure 5 of the main text. The columns in fig5a.xlsx, fig5b.xlsx and fig5c.xlsx represent represent the detuning and the rows represent the frequencies of the Fourier spectrum. The values in the table represent transimission.</div> <div> </div> <div>The files "fig8a.xlsx" and "fig8b.xlsx" contain the phase diagrams in Figure 8 of the main text. The columns in fig8a.xlsx and fig8b.xlsx represent the voltages of the RF-field, and the rows represent the detuning. The values in the table represent transimission.</div> <div> </div> <div>The files "fig9b.xlsx" and "fig9d.xlsx" contain the phase diagrams in Figure 9 of the main text. The columns in fig9b.xlsx represent the frequency of the RF-field, and the rows represent the frequencies of the Fourier spectrum. The columns in fig9d.xlsx represent the voltages of the RF-field, and the rows represent the frequencies of the Fourier spectrum. The values in the table represent transimission.</div>
DPA-2: A Large Atomic Model As a Multi-task Learner
<h2><strong>Data:</strong></h2> <div> <ul> <li>The complete collection of datasets employed in this research is encapsulated within the archive file <em>data-v1.3.tgz. </em>This encompasses both the upstream datasets for pre-training and downstream datasets for fine-tuning, all in <a href="https://github.com/deepmodeling/deepmd-kit/blob/master/doc/data/system.md" target="_blank" rel="noopener">DeePMD format</a>. We recommend creating a new directory and employing the command 'tar -xzvf data-v1.3.tgz' to extract the data files.</li> <li>Inside each dataset contained in subdirectories (e.g., Domains, Metals, H2O, and Others), one will find: <ul> <li>A README file</li> <li>A 'train' directory (included if utilized in upstream pre-training) <ul> <li>train.json -- A list of file paths for training systems</li> <li>test.json -- A list of file paths for testing systems</li> </ul> </li> <li>A 'downstream' directory (included if utilized in downstream fine-tuning) <ul> <li>train.json -- A list of file paths for training systems</li> <li>test.json -- A list of file paths for testing systems</li> </ul> </li> <li>*Main data files comprising various structures</li> <li>*Additional processing scripts</li> </ul> </li> <li> <div>The root directory contains train.json and downstream.json files that amalgamate the respective upstream and downstream splits mentioned above.</div> </li> <li> <div>The datasets used in this study are described in Section S1 of the Supplementary Materials and are readily accessible on <a href="https://www.aissquare.com/" target="_blank" rel="noopener">AIS Square</a>, which provides extensive details.</div> </li> </ul> </div> <p> </p> <h2><strong>Code:</strong></h2> <ul> <li> <div>The 'code' directory, extractable from the archive <em>Code_model_script.tgz</em>, includes the DeePMD-kit's source code, which is based on PyTorch (2.0) Version. Installation and usage instructions can be found within the README file located in deepmd-pytorch-devel.zip.</div> </li> <li>UPDATE: deepmd-pytorch-devel-0110.zip supports unsupervised learning through denoising, see its README for more details.</li> </ul> <p> </p> <h2><strong>Model:</strong></h2> <ul> <li>Within the 'model' directory, also found in the extracted <em>Code_model_script.tgz</em>, resides the multi-task pre-trained DPA-2 model utilized in this research. Accompanying the model is its configuration file, input.json, which details the simultaneous pre-training of this model across 18 upstream datasets with shared descriptor parameters for 1 million steps.</li> </ul> <p> </p> <h2><strong>Scripts:</strong></h2> <ul> <li>The 'scripts' directory, part of the uncompressed <em>Code_model_script.tgz</em>, comprises all the scripts used for training, fine-tuning (learning curve analysis), and distillation in this work: <ul> <li>1. Upstream_single_task_training: Contains individual training scripts for DPA-2, Gemnet-OC, Equiformer-V2, Nequip, and Allegro, corresponding to the 18 upstream datasets. The training script for MACE is also contained and is applicable across all datasets.</li> <li>2. Downstream_lcurve_workflow: Includes code and input files to evaluate the learning curves, including tests for DPA-2 fine-tuning transferability across 15 downstream datasets, as depicted in Figure 3 of the manuscript. </li> <li>3. Distillation_workflow: Provides input files for distilling the fine-tuned DPA-2 models in datasets such as H2O-PBE0TS-MD, SSE-PBE-D, and FerroEle-D, as illustrated in Figure 4 of the manuscript.</li> </ul> </li> <li>It is important to note that the scripts in 'Upstream_single_task_training' require the installation of deepmd-pytorch and other related models from their respective repositories (Gemnet-OC and Equiformer-V2: <a href="https://github.com/Open-Catalyst-Project/ocp" target="_blank" rel="noopener">here</a> [commit hash: 9bc9373], Nequip: <a href="https://github.com/mir-group/nequip" target="_blank" rel="noopener">here</a> [commit hash: dceaf49, tag: v0.5.6], Allegro: <a href="https://github.com/mir-group/allegro" target="_blank" rel="noopener">here</a> [commit hash: 22f673c]), MACE <a href="https://github.com/ACEsuit/mace">here</a> [commit hash: b76a2a9].</li> <li>The scripts in 'Downstream_lcurve_workflow' and 'Distillation_workflow' leverage <a href="https://github.com/deepmodeling/dflow" target="_blank" rel="noopener">Dflow</a>—a Python framework for constructing scientific computing workflow—and <a href="https://github.com/deepmodeling/dpgen2" target="_blank" rel="noopener">dpgen2</a>, the 2nd generation of the Deep Potential GENerator, both of which are repositories in the <a href="https://deepmodeling.com/" target="_blank" rel="noopener">Deep Modeling Community</a>.</li> </ul>
Rac1b and Rac1 all-atom simulations
<div> <div> <div> <div> <p><strong>File Extensions:</strong></p> <p>filename.parm7: Refers to topology files.<br>filename.rst7: Refers to Amber format coordinates.<br>filename.gro: Refers to Gromacs format coordinates.</p> <p><br><strong>File Naming Convention:</strong></p> <p>The term "monomer" in the file names refers to the ELMO1:DOCK5:Rac1/Rac1b-GDP complex. </p> <p>The files named "<a href="../api/records/13165257/draft/files/NCIs_ELMO1_DOCK5_Rac1_prolif.ipynb/content" target="_blank" rel="noopener noreferrer">NCIs_${complex_name}_prolif.ipynb"</a> refer to the scripts used to assess noncovalent interactions between proteins in each considered system, by using ProLIF (Bouysset, C.; Fiorucci, S., J Cheminform, 2021, 13, 72.) In our paper, we analyzed 500 frames for each system trajectory. However, to conserve storage space on Zenodo, we uploaded only every 10th frame from each trajectory. As a result, the trajectories were reduced from 500 frames to 50 frames.</p> </div> </div> </div> </div>
Data for "Fast delivery of heralded atom-photon quantum correlation over 12 km fiber through multiplexing enhancement"
<p><span>This dataset is for the research article "Fast delivery of heralded atom-photon quantum correlation over 12 km fiber through multiplexing enhancement".</span></p> <p><span> </span></p>
H atom representations
<p>This analysis examines if the use of energy operators is valid for the hydroen atom when in reality all states have spin</p>
Transferability of Atom-Based Neural Networks Datasets
<p>This repository contains the datasets used in our article "Transferability of Atom-Based Neural Networks".</p> <p>The datasets are in the extxyz format, which can be used to train NequIP models. </p> <p>The extended xyz files contain all necessary information for the training of the models. The first four columns contain the xyz representation (Ångstrom), while the last column contains the atomic energies (kcal/mol), computed using either the AO-based EDA or MO-based IBO/IAO decomposition scheme. The total atomization energy (kcal/mol) is in the line below the number of atoms.</p> <p>The files are named as {dataset}_{decomp}.xyz.</p>
Dataset containing over 7,400 activity cliffs produced by 10 widely used single-atom modifications.
<p>A dataset containing 7,407 activity cliffs generated by single-atom modifications is provided. For each compound, its ChEMBL ID and atom modifications are included, along with PDB IDs when available</p>
Atomically resolved imaging of the conformations and adsorption geometries of individual β-cyclodextrins with non-contact AFM
<p>Raw data for publication titled <em>Atomically resolved imaging of the conformations and adsorption geometries of individual β-cyclodextrins with non-contact AFM.</em></p>
Observation of collapse and revival in a superconducting atomic frequency comb
<p>This dataset comprises all data shown in the figures of the submitted article "Observation of collapse and revival in a superconducting atomic frequency comb" at arXiv:2310.04200. Additional raw data are available from the corresponding author upon reasonable request.</p>
Data to "Symmetry breaking and non-ergodicity in a driven-dissipative ensemble of multilevel atoms in a cavity"
<p>The zip files contains the tex file, figure, matlab files, and raw experimental and simulation data of the paper "Symmetry breaking and non-ergodicity in a driven-dissipative ensemble of multilevel atoms in a cavity"</p>
Data for manuscript: An all-atom view into the disordered interaction interface of the TRIM5alpha PRYSPRY domain and the HIV capsid
<p>The data is provided as a part of the manuscript "<strong>An all-atom view into the disordered interaction interface of the TRIM5alpha PRYSPRY domain and the HIV capsid</strong>". </p> <p>This repository includes an archive with folders:</p> <div> </div> <div>MDData</div> <div>-- Contains shortened versions of the MD trajectories, and initial structure files used to run simulations</div> <div> </div> <div>FigureData </div> <div>-- Contains comma separated value files for each figure</div>
Data for "In-situ Measurements of Light Diffusion in an Optically Dense Atomic Ensemble"
<p>The files uploaded here include the data shown in Figures 3.c), 4.a) and 4.b) of the article "In-situ Measurements of Light Diffusion in an Optically Dense Atomic Ensemble", that can be found in: arXiv:2409.11117 </p> <p>Four datasets are included: </p> <p>df_diffusion.csv --> Figure 3.c</p> <p>df_vtransport.csv --> Figure 4.a </p> <p>df_ttransport.csv --> Inset figure 4.a </p> <p>df_decay.csv --> Figure 4.b</p> <p> </p>
Molecular dynamics analysis of iPP-polymorphs; a dataset of alpha and beta atomic structures
<p>This is the dataset corresponding to the publication in the <em>Polymer </em>journal:</p> <p>"Molecular dynamics analysis of iPP-polymorphs; Investigating thermal expansion and elastic properties"</p> <p>Authors:<strong> H.N. Chávez Thielemann, J.A.W. van Dommelen, L.E. Govaert, M. Hütter</strong></p> <p>Year: 2024</p> <p> </p> <p>The dataset presented here provides the chemical structures of iPP crystals, including COMPASS forcefield parameters, as follows:</p> <p>α structures were obtained by repeating the crystalline unit cell 4 times in a, 2 times in b, and 4 times in c (comprising 32 chains and 3456 atoms)</p> <ul> <li><strong><a href="https://zenodo.org/records/14048060/files/alpha2.zip?download=1">alpha2.zip</a>: </strong>α2 is the unit cell with perfect up-down alternation.</li> <li><a href="https://zenodo.org/records/14048060/files/alpha1.zip?download=1"><strong>alpha1.zip</strong></a>: α1 is an α2 but with 50% random up-down alternation.</li> </ul> <p>A X% of regio defects means that X% of the monomers are incorporated with the inverse head-tail order than in a perfect α2 case.<br>Thus, number of atoms and chains remain unvaried.</p> <ul> <li><a href="https://zenodo.org/records/14048060/files/d2p.zip?download=1"><strong>d2p.zip:</strong></a> α2 containing 2% of regio defects.</li> <li><strong><a href="https://zenodo.org/records/14048060/files/d4p.zip?download=1">d4p.zip:</a> </strong>α2 containing 4% of regio defects.</li> </ul> <p>Vacancy, 31 chains, 3348 atoms:</p> <ul> <li><strong><a href="https://zenodo.org/records/14048060/files/v1.zip?download=1">v1.zip:</a> </strong>contains the same α2 but with a vacancy, i.e. a complete chain is missing.</li> </ul> <p>β structures were obtained by repeating the crystalline unit cell 3 times in a, 2 times in b, and 4 times in c (comprising 36 chains and 3888 atoms)</p> <ul> <li><strong><a href="https://zenodo.org/records/14048060/files/beta2.zip?download=1">beta2.zip:</a> </strong>β2 structure is a monochiral domain, with purely right-handed chains.</li> <li><a href="https://zenodo.org/records/14048060/files/beta1.zip?download=1"><strong>beta1.zip:</strong></a> β1 structure comprises twelve left- and twenty-four right-handed chains.</li> </ul> <p> </p> <p>File names ended with <strong>_img</strong> indicates that supplementary images are provided for that structure.</p> <p>In most of the cases, the LAMMPS data files are accompanied by a PDB file for completeness.</p> <p>To download them all including extra files at once, then download the archive file <a href="https://zenodo.org/api/records/14048060/files-archive"><strong>14048060.zip</strong>.</a></p>
All-atom molecular dynamics simulations for Targeting Human Prostaglandin Reductase 1 with Licochalcone A: Insights from Molecular Dynamics and Covalent Docking Studies
<p>The dataset comprises simulations of the PTGR1 protein under four different conditions: in its apo (unbound) form, bound to the cofactor NADH, and in complex with both covalently and non-covalently bound licochalcone A. Each simulation was conducted using the ff19SB force field and the OPC water model, with water molecules excluded from the trajectories.</p> <p>For the apo form, NADH-bound, and non-covalently bound licochalcone A conditions, each trajectory consists of 5000 snapshots, representing a total of 500 nanoseconds of simulation. However, the trajectory for the no covalently bound licochalcone A condition includes only 1000 frames, corresponding to 150 nanoseconds. This discrepancy in frame count and simulation length across conditions is important to consider when comparing dynamics and structural behavior within the dataset.</p> <p><strong>PTGR1-NADPH.tar.xz</strong> - PTGR1 dimer in complex with NADPH<br><strong>PTGR1-apo.tar.xz</strong> - PTGR1 apo dimer<br><strong>PTGR1-monomer_NADPH.tar.xz</strong> - PTGR1 monomer in complex with NADPH<br><strong>PTGR1-LicA_covalent.tar.xz </strong>- PTGR1 dimer with covalently bound licochalcone A<br><strong>PTGR1-LicA_NO_covalent.tar.xz</strong> - PTGR1 dimer with covalently bound licochalcone A</p> <p> </p> <p>All folders contain:</p> <p><em>*.parm7</em> - dry topology in amber format</p> <p><em>*.nc</em> - dry trajectories in netcdf format</p> <p> </p> <p><strong>Plain molecular dynamics simulations.</strong> Two structures of human PTGR1 have been deposited in the PDB: one bound to NADPH and the raloxifene inhibitor (PDB ID 2Y05, 2.2 Å resolution), and another in apo form (PDB ID 1ZSV, 2.3 Å resolution). In the first structure, it is reported as a monomer, whereas in the second as a dimer. However, the protomers exhibit highly similar conformations in both structures (backbone RMSD ~0.5 Å). Experimental evidence, akin to PTGR1 orthologs and many other MDR enzymes, indicates that the functional form of human PTGR1 is a homodimer (Mesa et al., 2015). Thus, to construct the dimeric form of the coenzyme complex, we duplicated the NADPH-bound protomer from 2Y05 and aligned the two subunits with the dimer from 1ZSV, deleting the raloxifene molecule. In the resulting structure, no steric clashes between the protomers were observed. This structure was used as the starting point for the simulations. Additionally, the apo dimer was generated by removing the NADPH from both subunits, and the monomeric form in complex with NADPH was derived from the initial structure. </p> <p>The pmemd.cuda module of AMBER 22 was used to perform the MD simulations, employing the force field FF19SB and the OPC water model (Case et al., 2022; Izadi, Anandakrishnan, & Onufriev, 2014; Salomon-Ferrer, Götz, Poole Duncan and Le Grand, & Walker, 2013; Tian et al., 2020). NADPH parameters were taken from (Cummins, Ramnarayan, Singh, & Gready, 1991). The system was protonated at pH 7.4 with PDBfixer (Eastman et al., 2017a) and placed in a truncated octahedral box, initially spanning 12 Å further from the solute in each direction using the AMBER tLeap module. The overall charge of the system was neutralized by the addition of four sodium ions. ParmEd (Eastman et al., 2017b) was used to implement the hydrogen mass repartitioning scheme (Hopkins, Le Grand, Walker, & Roitberg, 2015). Local clashes and solvent orientation were corrected using the steepest descent algorithm for 5,000 cycles. During the initial NVT equilibration, the velocities gradually increased through five steps of 200 ps each. The temperature progression started at 150 K and was raised to 200 K, 250 K, 300 K, and finally, 310 K. Position restraints were applied to heavy atoms of the protein, with the restraining forces progressively decreasing at each step. The spring constants were set at 4, 5, 3, and 1 kcal/mol Å2, respectively, to allow for the gradual relaxation of the protein. The system was further equilibrated for 1 ns in the NPT ensemble with no restraints. For treating long-range electrostatic interactions, periodic boundary conditions and Ewald sums were used with a 9 Å cutoff for direct interactions (Darden, York, & Pedersen, 1993; Simmonett & Brooks, 2021). The same cutoff was used for Lennard-Jones interactions. The Langevin thermostat (Sindhikara, Kim, Voter, & Roitberg, 2009) with a collision frequency of 4 ps-1 and the Monte Carlo barostat(Åqvist, Wennerström, Nervall, Bjelic, & Brandsdal, 2004) with a pressure relaxation time of 2 ps were used to control temperatures and pressures, respectively. The SHAKE algorithm was used to fix any bond involving hydrogen atoms (Ryckaert, Ciccotti, & Berendsen, 1977), and a 4-fs time step integration was used. This protocol was taken from (Cofas-Vargas et al., 2022; Medrano‐Cerano et al., 2024) Unless otherwise stated, no other constraints were used. Five replicas of 500 ns each per system were produced.</p> <p>The topology and parameter files for a LicA molecule and for this inhibitor covalently bound to the sulfur atom of a cysteine residue were generated with Antechamber suite (J. Wang, Wang, Kollman, & Case, 2006), using the general Amber force field (GAFF2) for organic molecules (He, Man, Yang, Lee, & Wang, 2020). Atomic charges were derived using the AM1-BB method (Jakalian, Jack, & Bayly, 2002). The parameters are documented in Supplementary Tables SI-1 and SI-2. Trajectories for PTGR1 covalently and noncovalently bound to LicA were run using the same conditions as described above. All molecular structure representations were created using UCSF ChimeraX v1.8 (Meng et al., 2023; Pettersen et al., 2021).</p> <p> </p> <p><strong>Solvent-site identification and guided docking.</strong> Determination of solvent sites (SS) for ethanol and water molecules was conducted by employing the MDmix method. After removing both NADPH molecules from the enzyme dimer, the system was protonated at pH 7.4 with PDBfixer (Eastman et al., 2017a) and placed in a truncated octahedral box of water/ethanol 80/20% v/v, extending12 Å beyond the solute in each direction using the AMBER tLeap module. Five 20 ns replicas were run, using the same conditions described above, but applying Cartesian restrictions of 0.01 kcal/mol A2 over all heavy atoms. After the alignment of trajectories, density maps for probe atoms were generated by constructing a static mesh with cubic grids (0.5 Å edge length) over the entire simulation box. The occurrence of probe atoms within each grid were tracked across the trajectories. These density distributions were then converted into binding free energy using the Boltzmann relationship, comparing observed probe atom distributions against the expected bulk solvent distribution at 1.0 M. Solvent sites were then filtered by applying an energy threshold of 1 kcal/mol, as previously described (Alvarez-Garcia & Barril, 2014; Avila-Barrientos et al., 2022).</p> <p><strong>LicA docking. </strong>For covalent docking, LicA, bound through its Cb atom to the sulfur atom of C239, was docked onto the NADPH-binding site of human PTGR1 employing the covalent docking module of AutoDock4 v4.2.6 (Bianco, Forli, Goodsell, & Olson, 2016; Morris et al., 2009). The flexible side-chain methodology was used. In a subsequent non-covalent docking, solvent sites previously identified for ethanol and water were used as pharmacophoric element for rDock (Ruiz-Carmona et al., 2014). This docking involved defining the receptor system and generating a binding cavity using the NADPH as a reference molecule. During the non-covalent docking, a penalty score proportional to the square of the distance from each ligand conformation to a solvent site (SS) was applied when the separation exceeded 2 Å. The docking run included 100 simulations, generating a set of potential binding modes for LicA within the NADPH site. </p>
High-Speed Atomic Force Microscopy Highlights New Molecular Mechanism of Daptomycin Action
<p>Data underlying the figures in the publication “High-speed atomic force microscopy highlights new molecular mechanism of daptomycin action”, published in <em>Nat Commun, </em><strong>2020</strong>, 11, 6312. <a href="https://doi.org/10.1038/s41467-020-19710-z">https://doi.org/10.1038/s41467-020-19710-z</a></p> <p>Table of contents:</p> <p><strong>1.</strong> <strong>Movie 1</strong>; HS-AFM movie of the first minutes after exposure to sub-MIC Dap on a POPG supported membrane. Guides to the eye highlights those oligomers identifiable. Movie parameters: frame rate 33 ms; full image of 90 nm x 65 nm and 256x180 pixels; colour depth 8bit (256 values); full colour scale 4 nm.</p> <p><strong>2. </strong><strong>Movie 2</strong>; HS-AFM movie after tens of minutes after exposure to sub-MIC Dap that shows diffusing dimples on a POPG supported membrane which interact by swinging trajectories. Movie parameters: frame rate 83 ms; full image of 150nm x 150nm and 256x256 pixels; colour depth 8bit (256 values); full colour scale 4 nm.</p> <p><strong>3. </strong><strong>Movie 3</strong>; HS-AFM movie of the first minutes after exposure to over-MIC of a POPG supported membrane. A flow of material is visualized thanks to the motion of the ripples, it starts at the lm3m cubic phase (left) and ends at a tubulation (right). Movie parameters: frame rate 456 ms; full image of 400nm x 400nm and 300x300 pixels; colour depth 8bit (256 values); full colour scale 16 nm.</p> <p><strong>4. </strong><strong>Movie 4</strong>; HS-AFM movie of the cyclic accumulation of material in the pores created on TOCL/POPG supported membranes under the exposure of the outer leaflet to supplementary quantities of Dap added to the imaging solution. The process seems to eject material out of the membrane; see the material that appears next to the pore at 1.30s. Movie parameters: frame rate 260 ms; zoom of a full image of 140nm x 100nm and 256x180 pixels; colour 29 depth 8bit (256 values); full colour scale 3 nm.</p>
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.