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195 results for “equilibrium”
Equilibrium ellipsoids
<p>Equilibrium rubble pile ellipsoids. The models are obtained through numerical N-body simulations, using the GRAINS software. Simulations are performed using non-spherical particles, mutually interacting under contact/collisions and self-gravity. See Ferrari & Tanga 2020 (doi: 10.1016/j.icarus.2020.113871) for analysis and discussion of results. See Ferrari et al 2020 (doi: 10.1093/mnras/stz3458), Ferrari et al 2016 (doi: 10.1007/s11044-016-9547-2) for further details on the numerical model.</p>
Data for paper "Magnetohydrodynamic Equilibrium Reconstruction with Consistent Uncertainties"
<p>Data and scripts for the conference paper "Magnetohydrodynamic Equilibrium Reconstruction with Consistent Uncertainties" for the 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering.</p> <p><strong>Abstract</strong>: We report on progress towards a probabilistic framework for consistent uncertainty quantification and propagation in analysis and numerical modeling of physics in magnetically confined plasmas in the stellarator configuration. A frequent starting point in this process is the calculation of a magnetohydrodynamic equilibrium from plasma profiles. Profiles and therefore the equilibrium are typically reconstructed from experimental data. What sets equilibrium reconstruction apart from usual inverse problems is that profiles are given as functions over a magnetic flux derived from the magnetic field, rather than spatial coordinates. This makes it a fixed-point problem that is traditionally left inconsistent or solved iteratively in a least-squares sense[1–3]. The aim here is towards a straightforward and transparent process to quantify and propagate uncertainties and their correlations for function-valued fields and profiles in this setting. We propose a framework that utilizes a low dimensional prior distribution of equilibria, constructed with principal component analysis. A surrogate of the forward model[4] is trained to enable faster sampling.</p> <p><strong>Funding</strong>: The present contribution is supported by the Helmholtz Association of German Research Centers under the joint research school HIDSS-0006 'Munich School for Data Science - MUDS'. This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No 101052200 - EUROfusion). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.</p>
Greed is good? Of equilibrium impacts in environmental regulation - Dataset
<p>This dataset contains data and codes required to replicate the results in the article "Greed is good? Of equilibrium impacts in environmental regulation" to be published in Journal of Environmental Economics and Management. See the enclosed Readme for further instructions.</p>
Data for "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns"
<p>This dataset contains the raw data used for the publication "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns".</p>
Paths to equilibrium in non-conformal collisions
<p>Non-conformal planar shockwave collisions with the typical dataset from the study <strong>arXiv:1703.09681 </strong>published in JHEP.</p>
Out-of-equilibrium phonons in gated superconducting switches
<p>Raw data and processed data for the paper with the same title. Data is grouped in relation to figures.</p> <p> </p>
Data for "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning"
<p>Datasets and material for replicating plots and results from the paper "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning" <a href="https://scipost.org/SciPostPhys.15.1.018">SciPost Phys. 15, 018 (2023)</a>.</p> <p>You will find three data files and a ReadMe.txt:</p> <ul> <li><strong>couplings.tar.gz </strong>contains the random couplings of the system's Hamiltonian <span class="math-tex">\(H = \sum_{\langle ij \rangle}{J_{ij} \sigma_i \sigma_j}\)</span>;</li> <li><strong>datasets.tar.gz </strong>contains all the datasets generated by the <a href="https://www.dwavesys.com/">D-Wave</a> quantum computer. They are already split into train and validation and divided for the type of model and annealing time;</li> <li><strong>data_for_fig.tar.gz </strong>contains files for reproducing the plots of the article, almost all of them are saved in double format, .csv and .npy or .npz.</li> </ul> <p>We encourage you to download the GitHub code linked below to open all the listed data.</p> <p>All the data are zip, so to unzip them using</p> <pre><code class="language-bash">tar -xvf datasets.tar.gz</code></pre> <p>The code for training the Neural Networks and reproducing all the results is open access at <a href="https://doi.org/10.5281/zenodo.7118502">zenodo.7118502</a>.</p>
Data for: Klein et al., Viscosity of aqueous ammonium nitrate--organic particles: Equilibrium partitioning may be a reasonable assumption for most tropospheric conditions, egusphere-2024-1459
<p><strong>Experimental data </strong></p> <p>This folder contains the experimental and modelled data to the figures shown in the main manuscript and Appendix.</p> <p>Figure 3B AIOMFAC-VISC (AIOMFAC-VISC modelling of sucrose)</p> <p>Figure 3B Experimental (Viscosity measurements of sucrose)</p> <p>Figure 4 (Viscosity measurements of ammonium nitrate - sucrose - water mixtures)</p> <p>Figure 5 A and C (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using mixing rules)</p> <p>Figure 5 B and D (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using AIOMFAC-VISC)</p> <p>Figure 6 (Viscosity estimations of inorganic - sucrose - water mixtures using mixing rules)</p> <p>Figure 7 (Mixing times for ammonium nitrate - sucrose - water and Toluene SOA - sucrose - water aerosol particles for varies cities) </p> <p>Figure A2 (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using a mass fraction based mixing rules)</p>
Data for "Diversity of Non-Equilibrium Patterns and Emergence of Activity in Confined Electrohydrodynamically Driven Liquids"
<p>Raw data (microscopy videos and image sequences) and scripts used for the analysis for the publication "Diversity of Non-Equilibrium Patterns and Emergence of Activity in Confined Electrohydrodynamically Driven Liquids", Science Advances 7 (38), eabh1642</p>
Arbitrage equilibrium, invariance and the emergence of spontaneous order in the dynamics of birds flocking
<p>This is the dataset used to report the findings in the paper "Arbitrage equilibrium, invariance and the emergence of spontaneous order in the dynamics of birds flocking". </p>
Synthetic Colors for Brown Dwarfs using ATMO Non-Equilibrium Non-Adiabatic Atmospheres
<p>The Tables in the spreadsheets give magnitudes in the Vega system, calculated from synthetic spectra generated by ATMO non-equilibrium non-adiabatic atmospheres (Tremblin et al. 2015, Phillips et al. 2020, Leggett et al. 2021). Each file has three tabs corresponding to three metallicities: [m/H] = 0, -0.5 and -1.0. The file 2023_ATMO_MKO_WISE_Spitzer_phot gives MKO Y, J, H, K, Ks, and L'; WISE W1, W2, W3, and W4; and Spitzer [3.6] and [4.5] (columns 5 - 16 in the Tables). The other three files give colors for JWST NIRCam, NIRISS and MIRI filters, as indicated by the file name.</p> <p>The photometry is given for an observer at the Earth, for a brown dwarf at 10 pc with a radius of 0.1 Rsun. Column 3 in the Tables give the radius determined by evolutionary models for different metallicities by Marley et al. 2021 (and https://zenodo.org/record/5063476), for the specified temperature and gravity (columns 1 and 2). Column 4 gives the correction to the magnitudes for the correct (theoretical) radius. NOTE THAT THE CORRECTION MUST BE ADDED TO THE MAGNITUDES GIVEN IN COLUMNS 5 - 16 TO OBTAIN THE ABSOLUTE MAGNITUDE. For an analysis of the model color trends, using a comparison to observations, see Meisner et al. 2023.</p> <p>The photometry covers the following atmospheric parameters: effective temperatures between 1200 and 250 K (step size 100 K between 1200 and 400 K, with the step size decreasing for lower effective temperatures); log(g) with three values 4.0, 4.5 and 5.0; effective adiabatic index of 1.25; metallicity with three values -1.0, -0.5, and 0. A grid of synthetic spectra was computed at medium resolution (R ~3000) for wavelengths of 0.2 to 30 microns. All the models, for a wider range of parameters, are available at https://opendata.erc-atmo.eu. </p> <p>The models include rainout of condensates which depletes refractory species, but they do not include clouds. Tremblin et al. 2016 shows that diabatic convective processes (Tremblin et al. 2019) can reduce the temperature gradient in the atmosphere and reproduce the spectral reddening previously explained by clouds. Adjustments to the atmospheric temperature gradient have also been shown to be necessary to reproduce the energy distributions of the coldest brown dwarfs (Leggett et al. 2021). The grids used here modify the temperature gradient by adopting an effective adiabatic index. The levels modified are in between 0.15 and 15 bars at log g = 4.5 and are scaled by ×10<sup>(log(g)−4.5)</sup> at other surface gravities. Out-of-equilibrium chemistry is used with Kzz = 10<sup>5 </sup>cm<sup>2</sup>/s at log(g) = 5.0 and is scaled by ×10<sup>(2(5−log(g)))</sup> at other surface gravities. The mixing length is assumed to be 2 scale heights at 1.5 bars and higher pressures at log(g) = 4.5 and is scaled down by the ratio between the local pressure and the pressure at 1.5 bars for lower pressures. The 1.5 bars limit is scaled by ×10<sup>(log(g)−5) </sup>at other surface gravities. The chemistry includes 277 species and out-of-equilibrium chemistry has been performed using the model of Tsai et al. 2017. Opacity sources include H<sub>2</sub>-H<sub>2</sub>, H<sub>2</sub>-He, H<sub>2</sub>O, CO<sub>2</sub>, CO, CH<sub>4</sub>, NH<sub>3</sub>, Na, K, Li, Rb, Cs, TiO, VO, FeH, PH<sub>3</sub>, H<sub>2</sub>S, HCN, C<sub>2</sub>H<sub>2</sub>, SO<sub>2</sub>, Fe, H<sup>-</sup>, and the Rayleigh scattering opacities for H<sub>2</sub>, He, CO, N<sub>2</sub>, CH<sub>4</sub>, NH<sub>3</sub>, H<sub>2</sub>O, CO<sub>2</sub>, H<sub>2</sub>S, SO<sub>2</sub>.</p> <p> </p> <p>REFERENCES</p> <p>Leggett et al 2021 ApJ 918, 11</p> <p>Marley et al. 2021 ApJ 920, 85 (and https://zenodo.org/record/5063476)</p> <p>Meisner et al. 2023, ApJ in press </p> <p>Phillips et al. 2020 A & Ap 637, 38</p> <p>Tremblin et al. 2015 ApJ 804, L17</p> <p>Tremblin et al. 2016 ApJ 817, L19</p> <p>Tremblin et al. 2019 ApJ 876, 144</p>
Out-of-equilibrium charge redistribution data in a copper-oxide based superconductor by time-resolved X-ray photoelectron spectroscopy
<p>This dataset was measured using a momentum microscope by time-resolved X-ray photoelectron spectroscopy (XPS) on the prototypical high-temperature superconductor: optimally doped BSCCO at FEL FLASH, DESY in Hamburg. With time-resolved XPS, unique access to the dynamics of individual atoms in the unit cell is granted by means of chemical shifts of the core levels. Though the induced changes are small, with a rigorous fitting procedure, it is possible to extract significant changes observed mainly at the oxygen atoms in the copper oxide planes, while other oxygen atoms as well as strontium remain largely unaffected. Although it was acquired not in the superconducting phase, the observed dynamics point to a significant coupling of energy scales involving charge-transfer processes and optical excitations. Such findings can thus provide another puzzle piece for a better understanding of high-temperature superconductivity.</p>
Equilibrium Dynamics Shape Diversity Patterns Across Terrestrial Tetrapod Clades
<p>This repository contains all scripts, data, and documentation supporting the analyses in our study. The materials are organized into folders corresponding to specific steps of the workflow. This README provides a detailed guide to the structure, contents, and usage of each folder.</p> <h2>Folder Structure and Contents</h2> <h3>1. Environmental Variables (<code>Grid_level_environment</code>)</h3> <ul> <li> <p>Contains grid-cell level environmental variables in <code>environmental_data.rds</code>.</p> <ul> <li> <p>Includes <strong>temperature</strong> (°C), <strong>precipitation</strong> (mm), and <strong>net primary productivity (NPP)</strong>.</p> </li> <li> <p>Includes <strong>grid cell IDs</strong> and <strong>latitude/longitude coordinates</strong>.</p> </li> </ul> </li> </ul> <h3>2. Evolutionary Rates Across Species and Grid Cells (<code>Grid_level_speciation</code>)</h3> <ul> <li> <p>Contains present-day <strong>speciation rate estimates</strong> for each species.</p> <ul> <li> <p>Includes <strong>DR</strong>, <strong>BAMM</strong>, and <strong>ClaDS</strong> estimates.</p> </li> <li> <p>Maps each species’ speciation rate to its corresponding grid cells.</p> </li> </ul> </li> </ul> <h3>3. Evolutionary Time Across Grid Cells (<code>Grid_level_assemblage_age</code>)</h3> <ul> <li> <p>Contains files used for <strong>BioGeoBEARS DEC model integration</strong> at the grid-cell level for each tetrapod clade (amphibians, reptiles, birds, mammals).</p> </li> <li> <p>Each clade is organized in a separate folder with the following files:</p> <ul> <li> <p><strong>Assemblage age:</strong> <code>arrival_time_clade*.csv</code></p> </li> <li> <p><strong>Most likely biogeographic areas per grid cell:</strong> <code>clade*_biogeo_area.csv</code></p> </li> <li> <p><strong>Presence/absence matrix:</strong> <code>clade*_PAM.csv</code></p> </li> <li> <p><strong>Clade phylogenetic tree:</strong> <code>clade*_tree.tre</code></p> </li> <li> <p><strong>DEC area file:</strong> <code>geo_area_clade*.data</code></p> </li> <li> <p><strong>DEC outputs:</strong> <code>results_DEC_clade*.Rdata</code></p> </li> <li> <p><strong>Estimated geographic area plots across the phylogeny:</strong> <code>DEC_plot_clade*.pdf</code></p> </li> </ul> </li> </ul> <h3>4. Path Analysis Example (<code>Path_model</code>)</h3> <ul> <li> <p>Contains an example R script: <code>path_analysis_clades.R</code>.</p> <ul> <li> <p>Illustrates <strong>path models</strong> applied to tetrapod clades.</p> </li> <li> <p>Example uses <strong>mammalian clades</strong>; replace the dataset to run on other clades.</p> </li> </ul> </li> </ul> <h3>5. Path Analysis Outputs (<code>Path_outputs_&_clade_traits</code>)</h3> <ul> <li> <p>Contains outputs from <strong>path analyses</strong> for each tetrapod clade.</p> <ul> <li> <p><code>Path_all_effects.csv</code> consolidates all path outputs and includes <strong>clade-level traits</strong> (see Methods in the main paper).</p> </li> <li> <p>Other files include:</p> <ul> <li> <p><code>Path_direct_effects.csv</code> – direct effects across clades</p> </li> <li> <p><code>Path_indirect_via_productivity.csv</code> – indirect effects via productivity</p> </li> <li> <p><code>Path_indirect_via_speciation.csv</code> – indirect effects via speciation</p> </li> <li> <p><code>Path_indirect_via_time.csv</code> – indirect effects via evolutionary time</p> </li> </ul> </li> </ul> </li> </ul> <h3>6. Path Output Figures (<code>Path_outputs_figures</code>)</h3> <ul> <li> <p>Contains R scripts to <strong>visualize path model outputs</strong> across tetrapod clades:</p> <ul> <li> <p>Direct effects: <code>Path_direct_effects.R</code></p> </li> <li> <p>Indirect effects: <code>Path_indirect_via_productivity.R</code>, <code>Path_indirect_via_speciation.R</code>, <code>Path_indirect_via_time.R</code></p> </li> <li> <p>Total effects: <code>Path_total_effects.R</code></p> </li> </ul> </li> </ul> <h3>7. Clade-Level Trait Effects on Richness (<code>Clade_level_effects.R</code>)</h3> <ul> <li> <p>Contains the R script <code>Clade_level_effects.R</code>.</p> </li> <li> <p>Explores whether <strong>the effects of the tested predictors on species richness depend on clade-level traits</strong>, including:</p> <ul> <li> <p><strong>Physiological traits:</strong> endothermy vs ectothermy</p> </li> <li> <p><strong>Spatial–historical traits:</strong> climate origin, range size, centroid displacement, displacement rate</p> </li> <li> <p><strong>Temporal/size-related traits:</strong> clade age, species richness</p> </li> </ul> </li> </ul>
Data from: Ancient and modern genomes reveal microsatellites maintain a dynamic equilibrium through deep time
<p>Microsatellites are widely used in population genetics, but their evolutionary dynamics remain poorly understood. It is unclear whether microsatellite loci drift in length over time. This is important because the mutation processes that underlie these important genetic markers are central to the evolutionary models that employ microsatellites. We identify more than 27 million microsatellites using a novel and unique dataset of modern and ancient Adélie penguin genomes along with data from 63 published chordate genomes. We investigate microsatellite evolutionary dynamics over two time scales: one based on Adélie penguin samples dating to approximately 46.5 kya, the other dating to the diversification of chordates more than 500 Mya. We show that the process of microsatellite allele length evolution is at dynamic equilibrium; while there is length polymorphism among individuals, the length distribution for a given locus remains stable. Many microsatellites persist over very long time scales, particularly in exons and regulatory sequences. These often retain length variability, suggesting that they may play a role in maintaining phenotypic variation within populations.</p>
Performance of wave function and Green's function methods for non-equilibrium many-body dynamics
<p>In this repository we have compiled 1-RDMs on a time grid obtained from various methods, namely, time-dependent full configuration interaction (TD-FCI), time-dependent coupled cluster (TD-CC), time-dpendent Hartree-Fock (TD-HF), Kadanoff-Baym Equations, and generalized Kadanoff-Baym approximation (GKBA). We have evaluated the 1-RDMs from Hubbard model in presence of an external drive. We have included a PySCF script to generate the integrals with a specific choice for various parameters. One can reproduce the HF results from that script. The Python script to evaluate various observables that we have analyzed in our article, namely, time-dependent dipole moment, Von-Neumann entropy are also added. </p>
Cloud Feedbacks and Organized Convection in Radiative-Convective Equilibrium
<p>Derived data and scripts used in Stauffer and Wing (2024).</p> <p>The standardized RCEMIP output, including time and domain mean profiles, is hosted by the German Climate Computing Center (DKRZ) and is publicly available at <a href="http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e">http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e</a>.</p> <p>Descriptions of each of the data files and the variables contained within them are discussed in a README file. The jupyter notebook contains the scripts used to plot the figures used in Stauffer and Wing (2024).</p> <p>Updated 01 April 2025 to include corrected Index of Organization values.</p>
Life tables and graphs for Bahry (2022) - Equilibrium conditions in the evolution of senescence [MSc thesis, Carleton Univeristy]
<p>Life table data, and derived quantities, for <em>Equilibrium Conditions in the Evolution of Senescence</em> (Bahry, 2022, MSc thesis); adapted from the supplementary data of (Jones et al., 2014). Life table data for human (Japan 2009), human (Aché hunter-gatherer), fruit fly, Soay sheep, freshwater hydra, and desert tortoise.</p> <p>Basic life table quantities: age interval <span class="math-tex">\((X)\)</span>; survival function <span class="math-tex">\((l_X)\)</span>; and age-specific interval fecundity <span class="math-tex">\((m_X)\)</span>. Derived quantities include interval average force of mortality; reproductive value; residual reproductive value; Hamilton's indicators of the age-specific forces of selection; and actual age-specific mortality vs. predicted age-specific mortality based on models treated in (Bahry, 2022).</p> <p>In the original life tables of Jones et al. (2014), desert tortoises negatively senesce over the range of observed ages, but had a final observed cut-off age of 74; this causes reproductive value to artifactually fall to 0 as age-approached the cutoff. To get around this, I also used an extrapolated desert tortoise life table, assuming the age-74 mortality and fecundity rates remained constant until age 1000, then using the extrapolated life table to calculate reproductive value (and Hamilton's indicators) up to the cutoff age 74.</p> <p><strong>References</strong></p> <p>Bahry, D. (2022). <em>Equilibrium Conditions in the Evolution of Senescence</em> [Master's thesis, Carleton University].</p> <p>Jones, O. R. et al. (2014). Diversity of ageing across the tree of life. <em>Nature</em> 505: 169–174. https://doi.org/10.1038/nature12789</p>
Stability of patch-turnover relationships under equilibrium and nonequilibrium metapopulation dynamics driven by biogeography
<p>Two controversial tenets of metapopulation biology are whether patch quality and the surrounding matrix are more important to turnover (colonization and extinction) than biogeography (patch area and isolation) and whether factors governing turnover during equilibrium also dominate nonequilibrium dynamics. We tested both tenets using 18 years of surveys for two secretive wetland birds, black and Virginia rails, during (1) a period of equilibrium with stable occupancy and (2) after drought and arrival of West Nile Virus (WNV), which resulted in WNV infections in rails, increased extinction and decreased colonization probabilities modified by WNV, nonequilibrium dynamics for both species, and occupancy decline for black rails. Area (primarily) and isolation (secondarily) drove turnover during both stable and unstable metapopulation dynamics, greatly exceeding the effects of patch quality and matrix conditions. Moreover, slopes between turnover and patch characteristics changed little between equilibrium and nonequilibrium, confirming the overriding influences of biogeographic factors on turnover.</p>
Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery
<blockquote> <p>Version 2: Added missing files to <code>sniffing_data.zip</code></p> </blockquote> <p>See GitHub repository for data processing functions and examples: <a href="https://github.com/soerenbrandt/sniffing-sensor">https://github.com/soerenbrandt/sniffing-sensor</a></p> <p><strong>Abstract</strong>:<br>Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.</p>
Fig. 5 in New insight in the determination of thermodynamic equilibrium thickness using heat budget over Barents Sea
Fig. 5 — Retrieved Thermodynamic Equilibrium Thickness (TET) over Barents Sea during (a) November, (b) December, (c) January, (d) February, (e) March, and (f) April for the span of 2002 – 2020
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.