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96 results for “Free energy”
ΔG-RDKit: Solvation Free Energy Database
<p>We present the full database of the article "Explainable Supervised Machine Learning Model to Predict Solvation Free Energy".</p> <p>This is the database used for a ML model, containing a variety of solvent-solute pairs with known experimental solvation free energy Δ<em>G</em><sub>solv</sub> values. Data entries were collected from two separate databases. The <a href="https://link.springer.com/article/10.1007/s10822-014-9747-x">FreeSolv</a> library, with 642 experimental aqueous Δ<em>G</em><sub>solv </sub>determinations and the <a href="https://mediatum.ub.tum.de/1452571?v=1">Solv@TUM</a> database with 5597 entries for non-aqueous solvents. Both databases were selected given their wide-scale of solute/solvents pairs, amassing 6239 experimental values across light and heavy-atom solutes with a diverse solvent structure and with small value uncertainties.</p> <p>Experimental Δ<em>G</em><sub>solv</sub> values range from -14 to 4 kcal mol<sup>-1</sup> and each solute/solvent pair is represented by their chemical family, SMILES string and InChlKey. We generated 213 chemical descriptors for every solvent and solute in each entry using <a href="http://http://www.rdkit.org/">RDKit</a> software, version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the “MolFromSmiles” function in “RDKIT.Chem” as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and Δ<em>G</em><sub>solv</sub>.</p> <p>Through Machine Learning regression algorithms, our models were able to make Δ<em>G</em><sub>solv</sub> predictions with high accuracy, based on the information encoded in each chemical feature.</p>
Docked structures from "Optimizing active learning for free energy calculations"
<p>This archive contains the docked TYK2 structures used in the paper "Optimizing active learning for free energy calculations" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ailsci.2022.100050" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ailsci.2022.100050</span></span></a>). AM1-BCC charges are stored in the field "AM1Cache" in the SD file. The charges can be extracted using the code sample below. </p> <p> </p> <pre><code>from rdkit import Chem import base64 import pickle suppl = Chem.SDMolSupplier("10k_most_similar_tyk2_charged.sdf", removeHs=False) for mol in suppl: am1 = mol.GetProp("AM1Cache") am1_charges = pickle.loads(base64.b64decode(mol.GetProp("AM1Cache"))) assert len(am1_charges) == mol.GetNumAtoms(), "Charge cache has different number of charges than mol atoms"</code></pre>
Adsorption free energies and potentials of mean-force for interactions between amino acids, lipid fragments, and nanoparticles
<p>This dataset contains tabulated potentials of mean force (PMFs) and associated adsorption (binding) free energies for interactions of amino acids side chain analogues and lipid fragments (LF) with a range of materials: titanium dioxide, iron oxide, amorphous silica, quartz, and a range of carbon-based materials including amorphous carbon, graphene and carbon nanotubes both in a pristine form and functionalized by certain chemical groups. All data were computed from atomistic molecular dynamics simulations as a part of the SmartNanoTox project 2016-2020. Version 2 of the dataset includes additional materials: zink oxide, zink sulfate in pristine and PMMA-coated forms computed within NanoSolveIt project (2019-2023). The data are intended to be used in coarse-grained models describing interactions of nanomaterials with nanoparticles, for the prediction of the binding affinity of proteins and lipids to nanoparticles, and as biological "fingerprints" of nanomaterials characterizing behavior of the nanomaterials in biological environments. </p>
Minute-timescale free-energy calculations reveal a pseudo-active state in the adenosine A2A receptor activation mechanism
<p>Dataset of the paper "Minute-timescale free-energy calculations reveal a pseudo-active state in the adenosine A2A receptor activation mechanism" accepted for publication on ACS Chem journal.</p>
Associated Data: RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features
<p>Additional digital data to "RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features" (ChemRxiv preprint:<a href="https://doi.org/10.26434/chemrxiv.12636704.v1">https://doi.org/10.26434/chemrxiv.12636704</a>).</p> <p>Associated code can be found at: <a href="https://github.com/HITS-MCM/RASPDplus">https://github.com/HITS-MCM/RASPDplus</a></p> <p>Files:</p> <ul> <li>weights.tar.gz: contains the model weights of one random dataset split and its associated crossvalidation folds. Used for standard RASPD+ evaluation.</li> <li>additional_model_replicates.tar.gz: contains the remaining models trained on the full set of descriptors.</li> <li>external_test_sets.tar.gz: contains the descriptor tables for all external test sets used</li> <li>dude.tar.gz: contains the descriptor tables for and several identifier lists for evaluation on the Directory of Useful Decoys - Enhanced (DUD-E)</li> <li>run_outputs.tar.gz: Performance metric data and predicted values created during the model training and evaluation runs. Basis for the figures and metrics in the manuscript.</li> </ul> <p> </p>
Aldeghi et al. Files for absolute free energy calculations in gromacs.
<p>These are the files for performing absolute free energy calculations using gromacs as reported in "Accurate calculation of the absolute free energy of binding for drug molecules. Aldeghi M, Heifetz A, Bodkin MJ, Knapp S, Biggin PC.<br /> Chem Sci. 2016 Jan 14;7(1):207-218. DOI: 10.1039/C5SC02678D"</p> <p> </p> <p>The files should prove useful for anyone wishing to try out their own methodology for comparison purposes or even just to repeat the work on a known dataset. The data is presented as a zip archive that should unpack into a directory called "Aldeghi-et-al-chemical-science-2016". There are four sub-directories in there and a README.txt file which should explain the details of the data.</p> <p> </p>
IonSolv-Aq Dataset for: Experimental Compilation and Computation of Hydration Free Energies for Ionic Solutes
<p>This repository includes datasets and supplementary materials for the manuscript "Experimental Compilation and Computation of Hydration Free Energies for Ionic Solutes" by Jonathan W. Zheng and William H. Green. <strong>Citations should refer directly to the manuscript:</strong></p> <blockquote> <p>Zheng, J. W., & Green, W. H. (2023). Experimental Compilation and Computation of Hydration Free Energies for Ionic Solutes. <em>The Journal of Physical Chemistry A</em>, <em>127</em>(48), 10268-10281.</p> </blockquote> <p>This compilation includes experimental and computed solvation free energies for the compounds in the IonSolv-Aq dataset, as well as .xyz files for all conformers used in the corresponding work. The lower-quality set of data described in the manuscript is also available in the "extra-anion-data.zip" archive file.</p>
Binding Affinity Prediction Workflow - Simulation Input Files and Absolute Binding Free Energies
<p>The Binding Affinity Prediction (BAP) workflow calculates absolute binding free energies for protein-ligand complexes by taking their crystal structures, converting them into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks, and analysing the resulting trajectories with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the free-energy estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions.</p> <p>For the dataset published here, the BAP workflow was run on the PDBbind 2020 (http://www.pdbbind.org.cn/index.php) refined set. This entry contains the MD simulation input files (BAPSimulationInputFiles.tar.gz) and the ABFE estimates (BAPBindingFreeEnergyEstimates.csv) obtained from four 250 ns trajectories for each complex. The MD simulations for more than 4000 complexes were run on the Leonardo supercomputer while the implicit-solvent calculations were carried out on Galileo, both operated by Cineca (Italy). The MD trajectories will be stored at Cineca for approx. 1 year after publication of this entry; contact Cineca's user support if you are interested in the trajectories.</p> <p>The README file describes how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Binding-Affinity-Prediction-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).</p>
Absolute Binding Free Energies with OneOPES
<h1>Supporting Material: Absolute Binding Free Energies with OneOPES</h1> <p>Further information about the content of the directory can be found in the README file included</p> <p>The Plumed input files can also be found on Plumed-Nest https://www.plumed-nest.org/eggs/24/017/</p> <p> </p> <h3>Aknowledgements</h3> <p>The authors acknowledge PRACE and the Swiss National Supercomputing Centre (CSCS) for large supercomputer time allocations on Piz Daint, project IDs: pr126, s1107, s1169, s1228. FLG acknowledges the Swiss National Science Foundation and Bridge for financial support (projects number: 200021_204795, CRSII5_216587 and 40B2-0_203628). The authors are grateful to Nicola Piasentin for helping in devising the error-informed stopping strategy and for carefully reading the manuscript.</p> <p> </p> <h3>Reference</h3> <p>Absolute Binding Free Energies with OneOPES<br>Maurice Karrenbrock, Alberto Borsatto, Valerio Rizzi, Dominykas Lukauskis, Simone Aureli, and Francesco Luigi Gervasio<br>The Journal of Physical Chemistry Letters 0, <em> 15<br>DOI: 10.1021/acs.jpclett.4c02352 </em></p> <p> </p> <h3>Versions :</h3> <ul> <li>1.0.0 First version</li> <li>1.0.1 Bugfix: added the missing topology files (top.top)</li> <li>1.1.0 Bugfix: added missing index files (index.ndx) and missing Slurm files (run.slr). New: added a directory with what is needed to equilibrate the systems</li> </ul>
Free energy simulations of receptor-binding domain opening in the SARS-CoV-2 spike indicate a barrierless transition with slow conformational motions
<p>This online data set accompanies the manuscript entitled "Free energy<br> simulations of receptor-binding domain opening in the SARS-CoV-2 spike<br> indicate a barrierless transition with slow conformational motions."</p> <p>The dataset is composed of the following files:</p> <p>* pmf0-now.dcd -- pmf63-now.dcd : molecular dynamics trajectory frames in<br> each of the 64 umbrella sampling windows, from which water has been<br> removed to save space</p> <p>* s1am_0-now.pdb -- s1am_63-now.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, from which water has been removed,<br> corresponding to the trajectory data above</p> <p>* view -- Visual Molecular Dynamics command script to load a trajectory, <br> e.g., in Linux, use "vmd -e view"</p> <p>* s1am_0-cg.dcd -- s1am_63-cg.dcd : molecular dynamics<br> trajectory frames in each of the 64 umbrella sampling windows, coarse-grained to<br> 1 bead per residue.</p> <p>* s1am_0-cg.pdb -- s1am_63-cg.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, corresponding to the coarse-grained trajectory<br> data above.</p> <p>* viewcg -- Visual Molecular Dynamics command script to load a<br> coarse-grained trajectory, e.g., in Linux, use "vmd -e viewcg"</p> <p>* 0readme -- brief instructions on how to view the trajectories</p> <p>* colors.vmd -- utility script for VMD</p> <p>* covmacros.vmd -- VMD script to define coronavirus spike subdomains</p> <p>* fe.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the free energy profiles</p> <p>* diff.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the diffusion and mean first passage times calculations</p> <p>* pca-qha.zip -- ZIP archive that contains the data and Matlab analysis files<br> to compute the autocorrelation functions of trajectory displacements<br> along principal/quasiharmonic modes</p> <p>Each ZIP archive contains a "0readme" file with brief instructions, and also the <br> results of the calculations<br> </p>
Benchmark set for relative free energy calculations
<p>Created by Christina Schindler and Daniel Kuhn, Merck KGaA, Darmstadt, Germany.</p> <p>December 2018</p> <p>Manuscript in preparation.</p> <p>Previously presented at Alchemical Free Energy Workshop 2019 in Goettingen, Germany.</p> <p>DOI: 10.5281/zenodo.3258925</p> <p> </p> <p>References for datasets used in benchmark</p> <p>CDK8<br> Schiemann, Kai, et al. "Discovery of potent and selective CDK8 inhibitors from an HSP90 pharmacophore." Bioorganic & medicinal chemistry letters 26.5 (2016): 1443-1451.</p> <p>DOI: 10.1016/j.bmcl.2016.01.062</p> <p>c-Met</p> <p>Dorsch, Dieter, et al. "Identification and optimization of pyridazinones as potent and selective c-Met kinase inhibitors." Bioorganic & medicinal chemistry letters 25.7 (2015): 1597-1602.</p> <p>DOI: 10.1016/j.bmcl.2015.02.002<br> Eg5</p> <p>Schiemann, Kai, et al. "The discovery and optimization of hexahydro-2H-pyrano [3, 2-c] quinolines (HHPQs) as potent and selective inhibitors of the mitotic kinesin-5." Bioorganic & medicinal chemistry letters 20.5 (2010): 1491-1495.</p> <p>DOI: 10.1016/j.bmcl.2010.01.110<br> Hif2a</p> <p>Wallace, Eli M., et al. "A small-molecule antagonist of HIF2α is efficacious in preclinical models of renal cell carcinoma." Cancer research 76.18 (2016): 5491-5500.</p> <p>DOI: 10.1158/0008-5472.CAN-16-0473</p> <p>Dixon, Darryl David, et al. "Aryl ethers and uses thereof." U.S. Patent No. 9,908,845. 6 Mar. 2018.</p> <p>URL: Google Patents<br> PFKFB3</p> <p>Boutard, Nicolas, et al. "Discovery and Structure–Activity Relationships of N-Aryl 6-Aminoquinoxalines as Potent PFKFB3 Kinase Inhibitors." ChemMedChem 14.1 (2019): 169-181.</p> <p>DOI: 10.1002/cmdc.201800569<br> SHP2</p> <p>Chen, Ying-Nan P., et al. "Allosteric inhibition of SHP2 phosphatase inhibits cancers driven by receptor tyrosine kinases." Nature 535.7610 (2016): 148.</p> <p>DOI:10.1038/nature18621</p> <p>Garcia Fortanet, Jorge, et al. "Allosteric inhibition of SHP2: identification of a potent, selective, and orally efficacious phosphatase inhibitor." Journal of medicinal chemistry 59.17 (2016): 7773-7782.</p> <p>DOI: 10.1021/acs.jmedchem.6b00680</p> <p>Chen, Christine Hiu-tung, et al. "1-pyridazin-/triazin-3-yl-piper (-azine)/idine/pyrolidine derivatives and compositions thereof for inhibiting the activity of shp2." U.S. Patent Application No. 15/110,498.</p> <p>URL: Google Patents</p> <p> </p> <p>SYK<br> Currie, Kevin S., et al. "Discovery of GS-9973, a selective and orally efficacious inhibitor of spleen tyrosine kinase." Journal of medicinal chemistry 57.9 (2014): 3856-3873.</p> <p>DOI: 10.1021/jm500228a</p> <p>TNKS2<br> Buchstaller, Hans-Peter, et al. "Discovery and Optimization of 2-Arylquinazolin-4-ones into a Potent and Selective Tankyrase Inhibitor Modulating Wnt Pathway Activity." Journal of medicinal chemistry 62.17 (2019): 7897-7909.</p> <p>DOI: 10.1021/acs.jmedchem.9b00656</p>
Supplementary underlying data for "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"
<p>This dataset includes additional underlying data for the publication "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"</p> <p>Contents:</p> <p>Tukey Honest Significant Difference (HSD) results for solutes 1-47 across all seven parameter sets, as *.txt. These are pairwise comparisons of results between all possible parameter sets. Significant differences are treated as p < 0.05.</p>
Data for "Microscopic origin of the effect of substrate metallicity on interfacial free energies"
<p>Data related to the article "Microscopic origin of the effect of substrate metallicity on interfacial free energies"</p> <p>Laura Scalfi, Benjamin Rotenberg, arXiv:2105.06799 [physics.chem-ph]</p> <p> </p>
Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility - Dataset
<p>This zip contains all input structures for paper the: Alchemical Free<br> Energy Estimators and Molecular Dynamics<br> Engines: Accuracy, Precision and Reproducibility</p> <p>Authors: Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan, Peter V.Coveney</p> <p>The structures of the folders are protein/ligand_transformation/alchemical_leg/input/files</p> <p>The ligand transformation are derived from previous work by wang et al. (https://pubs.acs.org/doi/10.1021/ja512751q)</p> <p>There are two files for the solvent alchemical leg: complex.pdb and complex.prmtop</p> <p>complex.pdb is structure file that also denotes the alchemical atoms in the pdb beta column. complex.prmtop is an AMBER parameter/topology file</p> <p>For the complex alchemical leg there is an additional file constraints.pdb that contains the constraint information in the pdb beta column.</p> <p>These files can be used with TIES_MD (https://ucl-ccs.github.io/TIES_MD/) or other molecular dynamics engiens that take AMBER input.</p>
Data for FEgrow: An Open-Source Molecular Builder and Free Energy Preparation Workflow
<p>Data illustrating the use of de novo design in building and scoring protein-ligand complexes.</p> <p>This is relationship to the FEgrow publication with the intiial preprint here: <br> https://chemrxiv.org/engage/chemrxiv/article-details/6287bb98a42e9c78d34769f6<br> </p> <p>The FEgrow software snapshot used can be found here: https://zenodo.org/record/7105647#.YzFwINLMIUE</p>
Thermodynamic database and calculator of free energies and potentials for redox reactions involving iron minerals in aqueous media (IMTD)
<p>Database of free energies of formation for iron minerals and associated aqueous species, which are used in a tableu style spreadsheet to calculate free energies of redox reactions involving iron minerals, which in turn are used to calculate free energies and formal potentials for these reactions, under specified environmental conditions.</p> <p>The database and calculators were assembled by students and postdocs (Jeff Hudson, Ania Pavitt, Ying Lan, and Miranda Bradley) working under direction of Professor Paul G. Tratnyek at the Oregon Health & Science University, Portland, Oregon, USA. Drew Latta, Thomas Robinson, and Michelle Scherer contributed to the database and extended the calculations.</p> <p>Early versions of this tool were used in several publications, including (i) Fan, D., Y. Lan, P. G. Tratnyek, R. L. Johnson, J. Filip, D. M. O'Carroll, A. N. Garcia, and A. Agrawal. 2017. <em>Environ. Sci. Technol.</em> 51(22): 13070–13085. [DOI: 10.1021/acs.est.7b04177] and (ii) Bradley, M. J., and P. G. Tratnyek. 2019. <em>ACS Earth & Space Chemistry</em> 3(3): 688-699. [DOI: 10.1021/acsearthspacechem.8b00200].</p> <p>This tool is provided as a spreadsheet in .xlsx format. The file includes six sheets. The first contains background, constants, and calculations that apply throughout the remaining tabs. The second contains free energies of formation from various authoritative sources, and a mechanism for designating “recommend values”. The third contains a tableu that calculates free energies of redox reactions using the recommended free energy of formation and user-specified stoichiometries. The fourth calculates free energies and formal potentials of the redox reactions using the standard potentials, and specific solution conditions. The last tab summarizes previous published formal potentials from a variety of sources. </p> <p>While the database was checked thoroughly, it still is unlikely to be completely accurate. For critical applications, we recommend that you track-down the primary sources (listed on the first tab of the spreadsheet) and use them for data, conditions, and other caveats. Obviously, we do not accept any responsibility for what anyone does with information obtained from this document.</p> <p>In the future, if significantly corrections or additions are made to this document, we may publish it here as new versions. If the contributions of others result in major improvements, we are open to adding new authors to those versions. Feel free to contact us with corrections, suggests, or offers to help.</p> <p>The development of this version of the tool was funded through grants from the Strategic Environmental Research and Development Program (SERDP) and the U.S. Department of Energy.</p>
Pose Selector Workflow - Docking Poses, Absolute Binding Free Energy Estimates and Structure Input Files for Machine Learning
<p>The Pose Selector (PS) workflow calculates absolute binding free energies (ABFEs) for binding poses of protein-ligand complexes. First, it converts the binding poses (both docking poses as well as experimentally observed ligand binding poses), which are provided as a combination of protein PDB file and ligand MOL2 file, into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks and repair steps. Next, the PS workflow post-processes and analyses the last frame of the resulting eight 100 ps trajectories per binding pose with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the ABFE estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions.</p> <p>For the dataset published here, the PS workflow was run on docking poses generated for the PDBbind 2020 dataset (http://www.pdbbind.org.cn/index.php), shared in dockingPosesPDBBind2020.tar.gz. This entry and its partner entry 10.5281/zenodo.11397486 also share the intial coordinates used in the MD simulations of >800,000 docking poses of 4022 protein-ligand complexes (structureFiles_dockingPoses1.tar.gz in this entry and structureFiles_dockingPoses2.tar.gz in 10.5281/zenodo.11397486) and of the experimental ligand binding pose of 4549 complexes (structureFiles_experimentalStructures.tar.gz) as well as the corresponding ABFE estimates (absoluteBindingFreeEnergyEstimates.tar.gz). The MD simulations were run on the LUMI and MeluXina supercomputers while the implicit-solvent calculations were carried out on Galileo (Cineca).</p> <p>The README file describes the structure of the shared data in more detail and points out how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates as well as how to use the data provided in this entry to train a machine-learning model predicting the ABFE of binding poses of protein-ligand complexes. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Pose-Selector-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).</p>
Hit Expansion using Substructure Search, Virtual Screening & Free Energy Perturbation
<p>Identification of commercially available chemical analogs of primary hits previously crystallized in complex with the zinc finger ubiquitin binding domain (Zf-UBD) of USP5 and prioritization of chemical analogues by free energy perturbation (FEP). </p>
Raw data for High-speed shear mixing: a versatile energy-efficient ultra-fast strategy for solvent-free amine-functionalised solid CO2 adsorbents for direct air capture
<p><strong>Specification of affiliations:</strong></p> <ul> <li>Pavol Suly - Centre of Polymer Systems</li> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Abdulkadir Bozarslan - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> <li>Michal Urbanek - Centre of Polymer Systems</li> <li>Eva Domincova Bergerova - Centre of Polymer Systems</li> <li>Michal Machovsky - Centre of Polymer Systems</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript. </p>
Protein engineering using variational free energy approximation
<p>Data generated by PREVENT model and used in manuscript "Protein engineering using variational free energy approximation". Contains raw input data, R scripts and Jupyter Notebooks to process data and output figures used in the main text and supplementary materials of the manuscripts.</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.