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183 results for “Nucleation”
Data for "The role of H2SO4-NH3 anion clusters in ion-induced aerosol nucleation mechanisms in the boreal forest"
<p>This is the dataset that has been analyzed for "The role of H2SO4-NH3 anion clusters in ion-induced aerosol nucleation mechanisms in the boreal forest". Please contact the author (chao.yan@helsinki.fi) for more details. </p>
Earthquake Nucleation Size: Evidence of Loading Rate Dependence in Laboratory Faults
<p>The data presented here is complementary to the manuscript 'Earthquake Nucleation Size: Evidence of Loading Rate Dependence in Laboratory Faults'.</p> <p>It comprises selected movies of rupture propagation at different loading rates as well as strain gages time signals filtered at 500 kHz and 30 kHz.</p> <p>Useful information about the processed data can be found in excel spreadsheets and text files given in the folders. Some python scripts are also available to plot the data.</p>
Raw data of the manuscript "The Ice Nucleation Activity of Black and Brown Soot"
<p>Raw data of the manuscript "The Ice Nucleation Activity of Black and Brown Soot" to be published in the Journal of Geophysical Research 2018</p>
Supporting data: "How collective asperity detachments nucleate slip at frictional interfaces"
<p>This repository supports:</p> <p><strong>T.W.J. de Geus, M. Popović, W. Ji, A, Rosso, M. Wyart. How collective asperity detachments nucleate slip at frictional interfaces. Proc. Natl. Acad. Sci. U.S.A. 2019. <a href="https://dx.doi.org/10.1073/pnas.1906551116">doi: 10.1073/pnas.1906551116</a>, <a href="http://arxiv.org/abs/1904.07635">arXiv: 1904.07635</a></strong></p> <p>In particular, it provides all used data, all codes used to produce this data (including clones to all the used open-source libraries), and simple functions to plot the data. All data and code is free to use under the CC-BY-4 license, but: <em>Please cite the above research article</em> when using code or data (inspired) from this repository (or the open-source projects <a href="https://www.github.com/tdegeus/GooseFEM">GooseFEM</a> and <a href="https://www.github.com/tdegeus/GMatElastoPlasticQPot">GMatElastoPlasticQPot</a>), in addition to this dataset (<a href="https://dx.doi.org/10.5281/zenodo.3477938">doi: 10.5281/zenodo.3477938</a>).</p> <p>(c) T.W.J. de Geus | 2019 | contact: <a href="/Volumes/data/dataset/Geus_PNAS/tom%40gems.me">tom@geus.me</a>, <a href="http://www.geus.me">www.geus.me</a></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>Contents</strong></p> <ol> <li>In brief</li> <li>Data files</li> <li>Code</li> <li>Plots</li> </ol> <p><strong>1. In brief</strong></p> <p>All codes (<code>codes/</code>) are written in C++ using a number of open-source libraries (<code>libraries/</code>). All data (<code>data/</code>) is stored in the HDF5 format. All plots (<code>data/</code>) are generated using Python and a number of open-source libraries.</p> <p>All codes are developed and tested on macOS and Linux. The notation used here is consistent with these Unix-based platforms. Windows based compilation and use might differ from the description here.</p> <p><strong>2. Data files ("data/")</strong></p> <p>The different ensembles (datasets) are included in different directories in <code>data/</code>. They are distinguished through their directory name that comprises the system size (denoted <code>nx=...</code>) and the shape factor of the Weibull distribution from which the yield strains are drawn (denoted <code>weibull=...</code>).</p> <p>Each ensemble consists of a number of realisations of the random yield strains at the frictional interface. Each realisation is stored in a separate file (<code>id=xxx.hdf5</code>). This file serves as input for the event-driven code (<code>code/Run/main.cpp</code>). This code stores the displacement field at the end of each event-driven step (for which it may take significant time for energy to be minimised). With these displacement fields, all other quantities (stress, strain, plastic strain, ...) can be reconstructed. The relevant reconstructed data for the entire ensemble is collected in <code>EnsembleInfo.hdf5</code>.</p> <p>For the manually triggered avalanches at different stresses (and fixed relative strain increment w.r.t. the last system spanning event) only selected output is stored to limit storage usage (<code>code/AvalancheAfterPush...</code>). Please note that the simulations are stopped when an event becomes system spanning to save on computation time, for this case the output thus does not correspond to a state of mechanical equilibrium. By contrast, any simulation that was not system-spanning does correspond to a state of mechanical equilibrium.</p> <p><strong>2a. Realisation ("data/.../id=xxx.hdf5")</strong></p> <p>See code <code>code/Run/main.cpp</code> and generation <code>code/Generate/generate.py</code></p> <ul> <li>Mesh (input)<br> <ul> <li><code>/coor</code>: Nodal coordinates <code>[nnode, ndim]</code> (<code>ndim == 2</code>)</li> <li><code>/conn</code>: Connectivity <code>[nelem, nne]</code> (<code>nne = 4</code>)</li> <li><code>/dofs</code>: Degrees-of-freedom (DOF) per node <code>[nnode, ndim]</code></li> <li><code>/iip</code>: Prescribed DOFs <code>[n_iip]</code><br> </li> </ul> </li> <li>Material model (input)<br> <ul> <li><code>/elastic/elem</code>: Elastic elements <code>[n_elasic]</code></li> <li><code>/elastic/G</code>: Shear modulus <code>[n_elasic]</code></li> <li><code>/elastic/K</code>: Bulk modulus <code>[n_elasic]</code></li> <li><code>/cusp/elem</code>: Elasto-plastic elements <code>[n_cusp]</code></li> <li><code>/cusp/G</code>: Shear modulus <code>[n_cusp]</code></li> <li><code>/cusp/K</code>: Bulk modulus <code>[n_cusp]</code></li> <li><code>/cusp/epsy</code>: Yield strains <code>[n_cusp, n_potentials]</code></li> <li><code>/uuid</code>: Unique identifier for the realisation<br> <br> Note that <code>n_elasic + n_cusp == nelem</code><br> </li> </ul> </li> <li>Simulation (input)<br> <ul> <li><code>/alpha</code>: Background damping coefficient <code>[nelem]</code> (homogeneous)</li> <li><code>/rho</code>: Mass density <code>[nelem]</code> (homogeneous)</li> <li><code>/run/dt</code>: Time-step</li> <li><code>/run/epsd/kick</code>: Size of the strain kick</li> <li><code>/run/epsd/max</code>: Local strain at which to stop<br> </li> </ul> </li> <li>Output<br> <ul> <li><code>/completed</code>: Completion signal, emitted when <code>/run/epsd/max</code> was reached locally</li> <li><code>/stored</code>: Stored event-driven step numbers <code>[n_event]</code></li> <li><code>/t</code>: Time at the end of each event-driven step <code>[n_event]</code></li> <li><code>/kick</code>: Strain kick (yes/no) per event-driven step <code>[n_event]</code></li> <li><code>/disp/...</code>: Nodal displacements per event-driven step <code>[nnode, ndim]</code></li> </ul> </li> </ul> <p><strong>2b. Simulation output ("data/.../EnsembleInfo.hdf5")</strong></p> <p>See code and help <code>code/EnsembleInfo/main.cpp</code>.</p> <p><strong>2c. Distribution P(x) ("data/.../EnsembleYieldDistance*.hdf5")</strong></p> <p>See code and help <code>code/EnsembleYieldDistance_stressControl/main.cpp</code> and <code>EnsembleYieldDistance_strainControl/main.cpp</code>.</p> <p><strong>2d. Manual triggering of events ("data/.../AvalancheAfterPush*.hdf5")</strong></p> <p>See code and help <code>code/AvalancheAfterPush_stressControl/main.cpp</code> and <code>code/AvalancheAfterPush_strainControl/main.cpp</code>.</p> <p><strong>3. Code ("code/")</strong></p> <p>The relevant codes to generate the datasets are referenced above. All non-standard libraries have been cloned under <code>libraries/</code>. Please note that they are subject to evolution: their cloned versions allow one to rerun the code in this dataset, however, for further development one is strongly encouraged to use the latest version. Please check out the development of:</p> <ul> <li><a href="https://github.com/tdegeus/GooseFEM.git">GooseFEM (v0.2.3)</a></li> <li><a href="https://github.com/tdegeus/GMatElastoPlasticQPot.git">GMatElastoPlasticQPot (v0.2.1)</a></li> <li><a href="https://github.com/tdegeus/cpppath.git">cpppath (v0.0.7)</a></li> <li><a href="https://github.com/xtensor-stack/xtensor.git">xtensor (v0.20.8)</a></li> <li><a href="https://github.com/xtensor-stack/xtensor-blas.git">xtensor-blas (v0.16.1)</a></li> <li><a href="https://github.com/xtensor-stack/xtl.git">xtl (v0.6.5)</a></li> <li><a href="https://github.com/xtensor-stack/xsimd.git">xsimd (v7.2.5)</a></li> <li><a href="https://github.com/BlueBrain/HighFive.git">highfive (master)</a></li> <li><a href="https://github.com/docopt/docopt.git">docopt (master)</a></li> <li><a href="https://github.com/fmtlib/fmt.git">fmt (master)</a></li> <li><a href="https://github.com/tdegeus/pyxtensor.git">pyxtensor (v0.0.5)</a></li> <li><a href="https://github.com/tdegeus/GooseMPL.git">GooseMPL (v0.2.24)</a></li> <li><a href="https://github.com/tdegeus/GooseEYE.git">GooseEYE (v0.2.0)</a></li> <li><a href="https://github.com/h5py/h5py.git">h5py (master)</a></li> </ul> <p>To compile code, follow the following structure:</p> <pre>cd code/... mkdir build cmake .. make</pre> <p>Then to run use:</p> <pre>./Run ...</pre> <p>(use <code>./Run --help</code> for help, and/or read the code). For some codes a support function generates commands. They can be generated and run as follows:</p> <pre>python makeJob.py source commands.txt</pre> <p><strong>4. Plots ("data/.../*.py")</strong></p> <p>Basic plot functions are included with the datasets. Note that all scripts require <code>numpy</code>, <code>matplotlib</code>, <code>h5py</code>, and <code>GooseMPL</code> to be installed. The latter two are included here, the other two are considered standard.</p>
Dataset to: Vertical distribution of ice nucleating particles over the boreal forest of Hyytiälä, Finland
<p>This repository contains the datasets used in the study 'Vertical distribution of ice nucleating particles over the boreal forest of Hyytiälä, Finland'. Detailed information and technical aspects of the data can be found in the publication.</p>
Global atmospheric particle formation from CERN CLOUD measurements: nucleation rate dataset
<p>Data S1 for paper "Dunne et al, (the CLOUD collaboration), Global atmospheric particle formation from CERN CLOUD measurements, Science 354 6316 (2016). Data contains nucleation rates and chamber conditions (including precursor gas concentrations) for inorganic binary and ternary (H2SO4-H2O) and (H2SO4-NH3-H2O) neutral and ion-induced nucleation measurements presented in the paper. These data were originally attached as supplemental to the paper, but are not currently available on the Science website (as of October 2024). </p> <p>Relative humidity units are percent. Care is needed to interpret all the data, for example not all ammonia values quoted were measured directly, some are inferred. See the supplementary materials of the paper for more discussion, and users of data at relative humidity other than (38+/-5)% are advised to discuss with the contact author Hamish Gordon. Ion production rate and nucleation rate units are per cm3 per second. The sulfuric acid units can be interpreted by noting that the value of 632 in the first data entry is 6.32x10^8 cm-3. </p> <p>Please cite the original Science article if you use these data: https://www.science.org/doi/10.1126/science.aaf2649</p>
CESM2 simulation output used in the study "On the links between ice nucleation, cloud phase, and climate sensitivity in CESM2"
<p>Provided is all CESM2 model output used to generate figures in the study, for which a preprint is at 'https://doi.org/10.22541/essoar.167214452.25853014/v1'. File names indicate the experiment names used in the study. For each model experiment, there is one file containing variables in a present-day (PD) simulation, plus a second file containing cloud feedbacks calculated by the Zelinka et al 2012 kernel method (comparing PD to PD with 4K warming uniformly added to sea surface temperatures).</p>
Dataset for "Reactivation strategies for nucleation-inhibited catalyst beds in continuously operated gas-release reactions from liquids."
<p>This dataset contains the measurements that were used to create the diagrams in the manuscript "Reactivation strategies for nucleation-inhibited catalyst beds in continuously operated gas-release reactions from liquids."</p>
Strain localization in sandstone-derived fault gouges under conditions relevant to earthquake nucleation
<p>Constraining strain localization and the growth of shear fabrics within brittle fault zones at sub-seismic slip rates are important for understanding fault strength and frictional stability. We conducted direct shear experiments on simulated sandstone-derived fault gouges at an effective normal stress of 40 MPa, pore fluid pressure of 15 MPa, and temperature of 100°C. Using a passive strain marker and X-ray Computed Tomography (XCT), we analyzed the spatial deformation of the gouge samples obtained from the strain-hardening stage to strain-softening stage to steady-state at shearing velocities of 1, 30, and 1000 µm/s. We developed a machine-learning-based automatic boundary detection method to recognize the shear zone fabrics and quantify the slip partitioning between each fabric element. Our results show that R1 and Y (or boundary) shears are the two major shear zone fabrics. At velocities of 1 and 30 µm/s, the relative amount of slip on R1 shears is displacement dependent and increases to ~20% at the strain-softening stage and then decreases to ~10–18% at steady-state. This trend is absent at high velocity with an amount of ~18% through all investigated stages. At all velocities, the relative amount of slip on Y and boundary shears increases linearly with displacement to a total of more than 50% at steady-state. Our study provides constraints for the development of the active slip zone, which is an important input parameter for the heat budget for small-magnitude earthquakes with limited slip (mm-dm), such as those occurring in induced seismicity.</p>
Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations"
<p>Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations", accepted for publication in Atmospheric Chemistry and Physics, 2023, same authors.<br> The simulations were done using MIMICA version4 (Savre at el., 2014) and the data includes all model output presented in the publication.</p>
Study on the supercooling characteristics of freezing soil based on nucleation theory
<p>Abstract</p> <p>The clarification of freezing in a soil-water system is critical for assessing the formation of a freezing zone and liquid water flow. The supercooling phenomenon of soil pore solutions has been found during the freezing process, but the mechanism remains poorly understood. In this study, we propose a free energy function of soil-water systems based on the Classical Nucleation Theory (CNT). The analytical solution of the critical nucleation problem of saline soil-water system is obtained by combining the initial freezing temperature model and Pitzer activity coefficients model in electrolyte solutions. Then, the freezing-thawing experiments of saline soil with various salt contents were conducted for verifying the analytical solution. The derived boundary nucleation rate is the quantitative solution of the critical condition for the supercooling. The findings suggested that the theory results agreed well with the experiment results. For the salt-free soil-water system, the critical maximum radius of supercooling was 7.15 nm. We compared eight classical ice-water interfacial tension models, and the “Reinhardt & Doye” and “DeMott & Rogers” models showed excellent performance when using the new free energy theoretical framework to predict the crystallization nucleation rate of soil-water systems. A positive correlation between the boundary nucleation rate and soil-water potential is detected. According to the influencing factors, the boundary nucleation rate of soil-water system can be divided into three zones: salt nature control zone (R>100μm), salt-pore mixed control zone (100μm>R>100nm), and pore size control zone (R<100nm).</p> <p>Plain Language Summary</p> <p>Unfrozen water refers to the soil moisture that still exists in the form of liquid water in the soil at negative temperatures, which is critical for determining the material properties of frozen soil. The phenomenon of soil water not being able to freeze as soon as the temperature falls below the freezing temperature is referred to as “supercooling”. However, the critical condition of supercooling is still unclear. In this paper, we developed a nucleation model that can be applied to saline soils based on the thermodynamic approach. Subsequently, the critical conditions were verified by freezing-thawing tests of soil samples with different salt contents. We further optimized the model of this paper by selecting the best model for the intermediate variables. Quantitative relationships between the initiation condition of supercooling and soil properties were established. We also delineated pore size intervals for distinguishing differences in supercooling properties at different pore sizes. The proposed theoretical framework is developed from Classical Nucleation Theory, which may provide a theoretical reference for revealing the freezing-thawing mechanism of soil-water systems.</p>
Data for figures in "Next-generation ice nucleating particle sampling on aircraft: Characterization of the High-volume flow aERosol particle filter sAmpler (HERA)"
<p>Atmospheric ice nucleating particle (INP) concentration data from the free troposphere are sparse, but urgently needed to understand vertical transport processes of INPs and their influence on cloud formation and properties. Here, we introduce the new High-volume flow aERosol particle filter sAmpler (HERA) which was specially developed for installation on research aircraft and subsequent offline INP analysis. HERA is a modular system constisting of a sampling unit and a powerful pump unit and has several features which were integrated specifically for INP sampling. Firstly, the pump unit enables sampling at flow rates exceeding 100 L min<sup>−1</sup>, which is well above typical flow rates of aircraft INP sampling systems described in the literature (~10 L min<sup>−1</sup>). Consequently, required sampling times to capture rare, high-temperature INPs (≥-15 °C) are reduced in comparison to other systems and potential source regions of INPs can be confined more precisely. Secondly, the sampling unit is designed as a seven-way valve, enabling switching between six filter holders and a bypass with one filter being sampled at a time. In contrast to other aircraft INP sampling systems, the valve position is controlled remotely via software so that manual filter changes in-flight are eliminated and the potential for sample contamination is decreased. This design is compatible with a high degree of automation, i.e., triggering filter changes depending on parameters like flight altitude, geographical location, temperature, or time. In addition to the design and principle of operation of HERA, this paper presents laboratory characterization experiments with size-selected test substances, i.e., SNOMAX® and Arizona Test Dust. The particles were sampled on filters with HERA, varying either particle diameter (300 nm to 800 nm) or flow rate (10 L min<sup>−1</sup> to 100 L min<sup>−1</sup>) between experiments. The subsequent offline INP analysis showed good agreement with literature data and comparable sampling efficiencies for all investigated particle sizes and flow rates. Furthermore, the deposition efficiency of atmospheric INPs in HERA was compared to a straightforward filter sampler and good agreement was found. Finally, results from the first campaign of HERA on the High Altitude and LOng range research aircraft (HALO) demonstrate the functionality of the new system in the context of aircraft application.</p> <p>The given csv files contain the data for reproducing the figures in the publication. The data structure of the csv files is explained in the README file.</p>
Strain localization in sandstone-derived fault gouges under conditions relevant to earthquake nucleation
Open the record for dataset details and reuse information.
A conserved function of corepressors is to nucleate assembly of the preinitiation complex
Open the record for dataset details and reuse information.
Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
<p>The dynamics and organization of the actin cytoskeleton are crucial to many cellular events such as motility, polarization, cell shaping, and cell division. The intracellular and extracellular signaling associated with this cytoskeletal network is communicated through cell membranes. Hence the organization of membrane macromolecules and actin filament assembly are highly interdependent. Although the actin-membrane linkage is known to happen through many routes, the major class of interactions is through the direct interaction of actin-binding proteins with the lipid class containing poly-phosphatidylinositols (PPIs). Among the PPIs, phosphatidylinositol bisphosphate (PI(4,5)P<sub>2</sub>) acts as a significant factor controlling actin polymerization in the proximity of the membrane by binding to actin-associated proteins. The molecular interactions between these actin-binding proteins and the membrane lipids remain elusive. Here, using molecular modeling, analytical theory, and experimental methods, we investigate the binding of three different actin-binding proteins, mDia2, NWASP, and gelsolin, to membranes containing PI(4,5)P<sub>2</sub> lipids. We perform molecular dynamics simulations on the protein-bilayer system and analyze the membrane binding in the form of hydrogen bonds and salt bridges at various PI(4,5)P<sub>2</sub> and cholesterol concentrations. Our experimental study with PI(4,5)P<sub>2</sub>-containing large unilamellar vesicles mimics the computational experiments. Using the multivalencies of the proteins obtained in molecular simulations and the cooperative binding mechanisms of the proteins, we also propose a multivalent binding model that predicts the actin filament distributions at various PI(4,5)P<sub>2 </sub>and protein concentrations.</p>
Micromechanics of Void Nucleation and Early Growth at Incoherent Precipitates: Lattice-trapped and Dislocation-mediated Delamination Modes
<p>This repository contains raw data analyzed in the referenced manuscript published in Crystals (<a href="https://doi.org/10.3390/cryst11010045">10.3390/cryst11010045</a>). See the included README file for detailed information on the contents.</p>
Strain and slip data for Kinematic inversion of fault slip during the nucleation of laboratory earthquakes
<p>The file contains the timeseries of strain and average slip obatined from a 8 gauges array used to monitor an injection experiment on a saw-cut centimetric scale sample loaded in a triaxial cell, under 30 MPa, 60 MPa and 90 MPa of confining stress.</p>
Data for: Low temperature ice nucleation of sea spray and secondary marine aerosols under cirrus cloud conditions
<p>Sea spray aerosols (SSA) represent one of the most abundant aerosol types on a global scale and have been observed at all altitudes including the upper troposphere. SSA has been explored in recent years as a source of ice nucleating particles (INPs) in cirrus clouds due to the ubiquity of cirrus clouds and the uncertainties in their radiative forcing. This study expands upon previous works on low temperature ice nucleation of SSA by investigating the effects of atmospheric aging of SSA and the ice nucleating activity of newly formed secondary marine aerosols (SMA) using an oxidation flow reactor. Polydisperse aerosol distributions were generated from a Marine Aerosol Reference Tank (MART) filled with 120 L of real or artificial seawater and were dried to very low relative humidity to crystallize the salt constituents of SSA prior to their subsequent freezing, which was measured using a Continuous Flow Diffusion Chamber (CFDC). Results show that for both primary SSA (pSSA), and the aged SSA and SMA (aSSA+SMA) at temperatures > 220 K, homogeneous conditions (92–97 % relative humidity with respect to water (RHw)) were required to freeze 1 % of the particles. However, below 220 K, heterogeneous nucleation occurs for both pSSA and aSSA+SMA at much lower RHw, where up to 1 % of the aerosol population freezes between 75–80 % RHw. Similarities between freezing behaviors of the pSSA and aSSA+SMA at all temperatures suggest that the contributions of condensed organics onto the pSSA or alteration of functional groups in pSSA via atmospheric aging did not hinder the major heterogeneous ice nucleation process at these cirrus temperatures that has previously been shown to be dominated by the crystalline salts. Occurrence of 1% frozen fraction of SMA, generated in the absence of primary SSA, was observed at/near water saturation below 220 K, suggesting it is not an effective INP at cirrus temperatures, similar to findings in the literature of other organic aerosols. Thus, any SMA coatings on the pSSA would only decrease the ice nucleation behavior of pSSA if the organic components were able to significantly delay water uptake of the inorganic salts, and apparently, this was not the case. Results from this study demonstrate the ability of lofted primary sea spray particles to remain an effective ice nucleator at cirrus temperatures, even after atmospheric aging has occurred over a period of days in the marine boundary layer prior to lofting. We were not able to address aging processes under upper tropospheric conditions.</p>
Simulation data for paper "Evaluation of Fendiline Treatment in VP40 System with Nucleation-Elongation Process: A Computational Model of Ebola Virus Matrix Protein Assembly"
<p>This is the original simulation data sets for paper "Evaluation of Fendiline Treatment in VP40 System with Nucleation-Elongation Process: A Computational Model of Ebola Virus Matrix Protein Assembly".</p>
Data set for "Omphacite breakdown: nucleation and deformation of clinopyroxene-plagioclase symplectites"
<p>EBSD raw data used in the publication:</p> <p>Zertani, S., Morales, L.F.G., Menegon, L. (2024). Omphacite breakdown: nucleation and deformation of clinopyroxene-plagioclase symplectites. Contributions to Mineralogy and Petrology, 179:xx. https://doi.org/10.1007/s00410-024-02125-0</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.