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296 results for “fluctuation data”
Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem - Data
<p>Figures and corresponding data associated with the manuscript 'Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem' by Herbst et al.</p>
Data to support "Stochastic density effects on adult fish survival and implications for population fluctuations"
Data on stage-specific abundance of black surfperch (Embiotoca jacksoni), the amount of foraging habitat and the availability of surfperch prey (crustaceans) were collected at fixed sites on the north shore of Santa Cruz Island, California annually (autumn) from 1993-2009. Data are grouped into four regions. Counts of fish distinguished among young-of-year, juveniles (1 year old) and adults (>= 2 years old). These data have been presented in Okamoto, D. K., R. J. Schmitt and S. J. Holbrook. 2016. Sochastic density effects on adult fish survival and implications for population fluctuations. Ecology Letters, 19:153-162. doi: 10.1111/ele.12547.
Raw data of "Seasonal fluctuations of ichthyoplankton assemblage in the northeastern South China Sea influenced by the Kuroshio intrusion"
<p>The uploaded data here is the raw data of the manuscript "Seasonal fluctuations of ichthyoplankton assemblage in the northeastern South China Sea influenced by the Kuroshio intrusion" submitted to the Journal of Geophysical Research-Oceans. The CTD file (.cnv) is the data recorded by a Sea-Bird conductivity, temperature and depth (CTD) in the sampling stations. This data is used to analyze the water masses during the study period. It can be analyzed with free software of the Ocean Data View 4 (http://odv.awi.de/) or the MATLAB R2017 (http://www.mathworks.com/products/matlab/). The sequence data (.fasta) is used to evaluate the species composition. The data can be analyzed with free software of the BOLD Identification tool (http://www.boldsystems.org/), the basic local-alignment search tool (BLAST) (https://www.ncbi.nlm.nih.gov/), the Clustal X 2.1 (http://www.clustal.org/) and the MEGA 7 (http://www.megasoftware.net/). In additon, the .nc files are the data of surface temperature during the sampling periods. The data can be analyzed with the MATLAB R2017 (http://www.mathworks.com/products/matlab/).</p>
Sea ice proxy data from "Sea ice fluctuations in the Baffin Bay and the Labrador Sea during glacial abrupt climate changes"
<p>Dataset s1: Sub-decadal sodium, bromine, and bromine enrichment data from NEEM ice core between 34-42 ka b2k.</p> <p>Dataset s2: Magnetic susceptibility, total organic carbon (TOC) and biomarkers data (IP25, brassicasterol, HBI-III) from the Eirik Drift core GS16-204-23CC, covering 31-42 ka b2k.</p>
Code and data from "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations"
<p>Includes the microscopy images, cell tracking data and scripts used in the publication Huelsz-Prince, Guizela, et al. "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations." <em>eLife </em> 11:e80682 (2022). <a href="https://doi.org/10.7554/eLife.80682"> https://doi.org/10.7554/eLife.80682</a> .</p>
Data supporting 3D Super-resolution Optical Fluctuation Imaging with Temporal Focusing with two-photon excitation
<p>Data to support the publication combining temporal focusing two photon excitation with super-resolution optical fluctuation imaging.</p> <div>This research was funded by National Centre of Science, grant number: 2022/47/B/ST7/03465. For the purpose of Open Access, the author has applied a</div> <div>CC-BY public copyright licence to any author Accepted Manuscript (AAM) version arising from this submission</div>
Raw and post-processed data for the microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception
<p>The current dataset consists of three main folders:</p> <ul> <li><strong>01-Stimuli/</strong>: Contains the three sets of noises (white noise, bump noise, MPS noise) for the 12 study participants (S01 to S12).</li> <li><strong>02-Raw-data/fastACI/</strong>: Contains the raw data as obtained for each participant, which are also available within the GitHub repository of the fastACI toolbox, using the same directory tree. The results for each (anonymised) participant (under: <strong>publ_osses2022b/data_SXX/1-experimental_results/</strong>) include their audiometric thresholds (folder: <strong>audiometry</strong>), the results for the Intellitest speech test (folder: <strong>intellitest</strong>), and for the phoneme-in-noise test /aba/-/ada/ for the three noises (savegame files in MAT format).</li> <li><strong>02-Raw-data/ACI_sim/</strong>: Contains the raw data as obtained for the artificial listener, i.e., the model osses2022a.m (available within the fastACI toolbox). Twelve sets of simulations (using the waveforms of participants S01 to S12) were run for the three types of test noises. The results of the simulations of the phoneme-in-noise test are stored in the savegame MAT files. The template derived from 100 repetitions of /aba/ and /aba/ at an SNR=-6 dB in white noise is also included (template-osses2022a-speechACI_Logatome-abda-S43M-trial-1-v1-white-2022-7-15-N-0100.mat). The same template was used in all simulations.</li> <li><strong>03-Post-proc-data/ACI_exp/</strong>: Auditory classification images (ACIs) derived from the participants' data (folder: <strong>ACI_exp</strong>) and from the simulations (folder: <strong>ACI_sim</strong>). For each participant (or artificial listener) there are three ACIs (MAT files) for each of the corresponding noises. Cross predictions are also included with performance predictions across 'participants' (Crosspred.mat, 12 cross predictions for each noise) or across 'noises' (Crosspred-noise.mat, 3 cross predictions for each participant). The cross predictions all have the same names but are stored in dedicated directories.</li> </ul> <p><strong>Use these data:</strong></p> <ol> <li>Download all these data, place them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="http://github.com/aosses-tue/fastACI">GitHub</a>) you can recreate the figures of our paper.</li> <li>After initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB) and then type either of the following commands, to recreate the figure you want. To recreate the figures in the main text:</li> </ol> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1','zenodo'); publ_osses2022b_JASA_figs('fig2a','zenodo'); publ_osses2022b_JASA_figs('fig2b','zenodo'); publ_osses2022b_JASA_figs('fig3','zenodo'); publ_osses2022b_JASA_figs('fig4','zenodo'); publ_osses2022b_JASA_figs('fig5','zenodo'); publ_osses2022b_JASA_figs('fig6','zenodo'); publ_osses2022b_JASA_figs('fig7','zenodo'); publ_osses2022b_JASA_figs('fig8','zenodo'); publ_osses2022b_JASA_figs('fig8b','zenodo'); publ_osses2022b_JASA_figs('fig9','zenodo'); publ_osses2022b_JASA_figs('fig9b','zenodo'); publ_osses2022b_JASA_figs('fig10','zenodo');</code></pre> <p>To generate the figures of the supplementary materials (Appendix in the BioRxiv preprint):</p> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1_suppl','zenodo'); publ_osses2022b_JASA_figs('fig2_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5b_suppl','zenodo');</code></pre> <p><strong>References:</strong></p> <ul> <li><strong>Preprint</strong>: Alejandro Osses, Léo Varnet. "A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception." BioRxiv.</li> <li><strong>fastACI toolbox</strong>: Alejandro Osses, Léo Varnet. fastACI toolbox: the MATLAB toolbox for investigating auditory perception using reverse correlation (v1.2). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.7314014">10.5281/zenodo.7314014</a>. Supplement to: <a href="http://github.com/aosses-tue/fastACI/tree/v1.2">https://github.com/aosses-tue/fastACI/tree/v1.2</a></li> </ul>
Dataset for Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur)
<p>This file contains the R and C++ code used for simulating experiments, simulated fluctuation data, and the results of estimations used in the paper "Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur)".</p>
Data related to the article "Frequency-dependent impedance of nanocapacitors from electrode charge fluctuations as a probe of electrolyte dynamics"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Frequency-dependent impedance of nanocapacitors from electrode charge fluctuations as a probe of electrolyte dynamics<br> (Giovanni Pireddu and Benjamin Rotenberg)</p> <p>arXiv: https://arxiv.org/abs/2206.13322</p> <p>The folder EXAMPLE_INPUT_FILES contains typical <a href="https://doi.org/10.21105/joss.02373">MetalWalls</a> (<a href="https://gitlab.com/ampere2/metalwalls">repository</a>) input files used to perform the simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper (see below).<br> </p> <p>The 'd*' labels are used in the directory or file names to refer to the following interelectrode distances considered:<br> - 'd1' corresponds to 2.51 nm;<br> - 'd2' corresponds to 4.94 nm;<br> - 'd3' corresponds to 9.76 nm;<br> - 'd4' corresponds to 19.42 nm;</p> <p><br> Figure 1:<br> - 'Fig1_Continuum.dat': <br> - Capacitance calculated considering a continuum approximation<br> - 'Fig1_DDS.dat':<br> - Capacitance calculated considering the DDS model (three capacitors in series)<br> - 'Fig1_MD.dat':<br> - Capacitance calculated from MD simulations. Includes the capacitance of the empty capacitor.</p> <p>Figure 2:<br> Panel A<br> - 'Fig2_QACFd*.dat':<br> - Time autocorrelation function of the electrode charge fluctuations<br> Panel B<br> - 'Fig2_Qrampd*.dat':<br> - Charge profile upon a step in voltage (0 to 1 V)<br> - 'Fig2_Vramp.dat':<br> - Voltage ramp related to the charging profiles<br> Panel C<br> - 'Fig2_QACFNormd*.dat':<br> - Normalized charge autocorrelation function<br> -'Fig2_QrampNormd*.dat'<br> - Normalized charge profile</p> <p>Figure 3:<br> - 'Fig3_MDd*.dat':<br> - Real and imaginary parts of impedance as estimated from MD simulations<br> - 'Fig3_ECd*.dat'<br> - Real and imaginary parts of impedance as calculated from the equivalent circuit models</p> <p>Figure 4:<br> - 'Fig4_MDd*.dat':<br> - Magnitude of admittance as estimated from MD simulations<br> - 'Fig4_Debyed*.dat':<br> - Magnitude of admittance as calculated from the Debye relaxation model<br> Inset<br> - 'Fig4_MDTau.dat':<br> - Relaxation time estimated from MD simulations<br> - 'Fig4_TauFit.dat':<br> - Fit of the relaxation time</p>
Soil microbial community composition (16S) data from a laboratory redox fluctuation experiment conducted with an Oxisol and Mollisol
To test the response of microbial communities to periodic oxygen limitation, we conducted a laboratory experiment where two contrasting soils (a rainforest Oxisol from Puerto Rico, and an Iowa cropland Mollisol) were incubated under headspace treatments where oxygen availability varied cyclically over time. Treatments consisted of 0, 2, 4, 8, or 12 d of anoxic conditions (dinitrogen headspace) followed by 4 d of oxic conditions (i.e., ambient oxygen concentrations), and these treatments were repeated for a total of 384 d. At 0, 48, and 384 days, DNA was extracted from replicates from each treatment for sequencing of 16S rRNA amplicons. Companion biogeochemical measurements from this experiment were published previously by Huang et al. (2021a,b). These data support the Hall et al. (2022) manuscript published in Frontiers in Microbiology.
Tilted fluctuation electron microscopy data from simulated and deposited amorphous Ta
<p>These datasets were used to compare fluctuation electron microscopy analysis methods on simulated and sputter deposited amorphous tantalum. The Ta is 8 nm thick in both the simulated and deposited samples. The deposited Ta is sandwiched between two layers of amorphous 10 nm-thick SiN<sub>x</sub>. Data are also provided for SiN<sub>x</sub> deposited on SiN<sub>x</sub>.</p> <p>Deposited Ta FEM patterns were collected on a TitanX at 200 kV with a convergence angle of 0.51 mrad and a camera length of 300 mm. Simulated Ta FEM patterns were generated using the Prismatic STEM simulation software (see references).</p> <p>The samples were tilted between 0<sup>o</sup> and 45<sup>o</sup> in 15<sup>o</sup> increments. </p> <p><strong>Deposited Ta: </strong></p> <p>.dm4 (Gatan DigitalMicrograph) files are provided with the raw scanning nanodiffraction data for each tilt angle</p> <p>.png images of the mean CBED pattern for each tilt angle are provided</p> <p><strong>Simulated Ta:</strong></p> <p>.h5, .xyz (atomic coordinates), and .txt (Prismatic input parameters defined) files are provided with the raw scanning nanodiffraction data for each tilt angle</p> <p>.png images of the mean CBED pattern for each tilt angle are provided</p> <p> </p> <p>The atomic coordinates for the simulated Ta were provided by Jun Ding. Simulated FEM patterns were produced by Luis Rangel DaCosta using Prismatic STEM simulation software. Neal Reynolds grew the experimental Ta and SiN<sub>x</sub> thin films.</p>
Data from: Fluctuation of ecological niches and geographic range shifts along chile pepper's domestication gradient
<p>Domestication is an ongoing well-described process. However, while many have stud- ied the changes domestication causes in plant genetics, few have explored its impact on the portion of the geographic landscape in which the plants exist. Therefore, the goal of this study was to understand how the process of domestication changed the geographic space suitable for chile pepper (<em>Capsicum annuum</em>) in its center of origin (domestication). <em>C. annuum</em> is a major crop species globally whose center of domes- tication, Mexico, has been well-studied. It provides a unique opportunity to explore the degree to which ranges of different domestication classes diverged and how these ranges might be altered by climate change. To this end, we created ecological niche models for four domestication classes (wild, semiwild, landrace, modern cultivar) based on present climate and future climate scenarios for 2050, 2070, and 2090. Considering present environment, we found substantial overlap in the geographic niches of all the domestication classes. Yet, environmental and geographic aspects of the current ranges did vary among classes. Wild and commercial varieties could grow in desert conditions, while landraces could not. With projections into the future, habitat was lost asymmetrically, with wild, semiwild, and landraces at greater risk of territorial declines than modern cultivars. Further, we identified areas where future suitability overlap between landraces and wilds is expected to be lost. While range expansion is widely associated with domestication, we found little support of a con- stant niche expansion (either in environmental or geographical space) throughout the domestication gradient in chile peppers in Mexico. Instead, particular domestication transitions resulted in loss, followed by capturing or recapturing environmental or geographic space. The differences in environmental characterization among domes- tication gradient classes and their future potential range shifts increase the need for conservation efforts to preserve landraces and semiwild genotypes</p>
Data from: Early-life variation in migration is subject to strong fluctuating survival selection in a partially migratory bird
<p>Population dynamic and eco-evolutionary responses to environmental variation and change fundamentally depend on combinations of within- and among-cohort variation in phenotypic expression of key life-history traits, and on corresponding variation in selection on those traits. Specifically, in partially migratory populations, spatio-seasonal dynamics depend on the degree of adaptive phenotypic expression of seasonal migration versus residence, where more individuals migrate when selection favours migration.</p> <p>Opportunity for adaptive (or, conversely, maladaptive) expression could be particularly substantial in early life, through initial development of migration versus residence. However, within- and among-cohort dynamics of early-life migration, and of associated survival selection, have not been quantified in any system, preventing any inference on adaptive early-life expression. Such analyses have been precluded because data on seasonal movements and survival of sufficient young individuals, across multiple cohorts, have not been collected.</p> <p>We undertook extensive year-round field resightings of 9,359 colour-ringed juvenile European Shags (<em>Gulosus aristotelis</em>) from 11 successive cohorts in a partially-migratory population. We fitted advanced Bayesian multi-state capture-mark-recapture models to quantify early-life variation in migration versus residence and associated survival across short temporal occasions through each cohort's first year from fledging, thereby quantifying the degree of adaptive phenotypic expression of migration within and across years.</p> <p>All cohorts were highly partially migratory, but the degree and timing of migration varied considerably within and among cohorts. Episodes of strong survival selection on migration versus residence occurred both on short timeframes within years, and cumulatively across whole years, generating instances of instantaneous and cumulative net selection that would be obscured at coarser temporal resolutions. Further, the magnitude and direction of selection varied among years, generating strong fluctuating survival selection on early-life migration across cohorts, as rarely evidenced in nature. Yet, the degree of migration did not strongly covary with the direction of selection, indicating limited early-life adaptive phenotypic expression.</p> <p>These results reveal how dynamic early-life expression and selection on a key life-history trait, seasonal migration, can emerge across seasonal, annual, and multi-year timeframes, yet be substantially decoupled. This restricts the potential for adaptive phenotypic, micro-evolutionary, and population dynamic responses to changing seasonal environments.</p>
Patient-specific processed data, code and visualisations for "Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions"
<p>Processed data and code for reproducing the main results and figures of the paper "<strong>Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions</strong>".</p> <p>We analysed publicly available data from subjects with drug-resistant focal epilepsy. A total of 2656 hours of long-term intracranial electroencephalography (iEEG) from 18 subjects was obtained using the "The SWEC-ETHZ iEEG Database and Algorithms" (available at <a href="http://ieeg-swez.ethz.ch">http://ieeg-swez.ethz.ch</a>) (Burrello et al., 2019).</p> <p>Reference<br> A. Burrello, L. Cavigelli, K. Schindler, L. Benini, A. Rahimi, <strong>‘‘</strong>Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms<strong>’’</strong> <em>in proceedings of the</em> <em>ACM/IEEE Design, Automation, and Test in Europe Conference (DATE)</em>, Florence, Italy, March 25-29, 2019. </p>
Data for "How flexible electrification can integrate fluctuating renewables"
<p>This upload provides the files used to compile and compute the input data used in the aforementioned working paper.</p>
Data for the manuscript: Demographic basis of spatially structured fluctuations in a threespine stickleback metapopulation
<p>Uncovering the demographic basis of population fluctuations is a central goal of population biology. This is particularly challenging for spatially structured populations, which require disentangling synchrony in demographic rates from coupling via immigration. In this study, we fit a stage-structured metapopulation model to a 29-year times series of threespine stickleback abundance in the heterogeneous and productive Lake Myvatn, Iceland. The lake comprises two basins (North and South) connected by a channel through which the stickleback disperse. The model includes time-varying demographic rates, allowing us to assess the potential contributions of recruitment and survival, spatial coupling via immigration, and demographic transience to the population's large fluctuations in abundance. Our analyses indicate that recruitment was only modestly synchronized between the two basins, whereas survival probabilities of adults were more strongly synchronized, contributing to cyclic fluctuations in the lake-wide population size with a period of approximately six years. The analyses further show that the two basins are coupled through immigration, with the North Basin subsidizing the South Basin and playing a dominant role in driving the lake-wide dynamics. Our results show that cyclic fluctuations of a metapopulation can be explained in terms of the combined effects of synchronized demographic rates and spatial coupling.</p>
Data and code for Coherent Correlation Imaging: Resolving fluctuating states of matter
<p>Data and code to generate the figures in the main text and the Extended data figures.</p>
Data and Code for: Determination of the Signal Fluctuation Threshold of the Temperature Ion Composition Ambiguity Problem Using Monte Carlo Simulations
<p>This repository contains the datasets and scripts used to obtain the Figures of the paper "Determination of the Signal Fluctuation Threshold of the Temperature Ion Composition Ambiguity Problem Using Monte Carlo Simulations".</p> <p>The repository is organized as follows:</p> <p>- a) ISR model script</p> <p>- b) Script to visualize the two solutions of the TICA problem</p> <p>- c) Generator of uniformly selected parameters</p> <p>- d) Script to represent the space of parameters</p> <p>- Part 0) Monte Carlo simulation codes</p> <p>- Part I) Analysis of the efffect of the uncertainty on the initial guess</p> <p>- Part II) Analysis of the impact of the addition of parameters from the Plasma Line</p> <p>- Part III) Analysis of the information provided by the addition of each parameter</p> <p>- Part IV) Analysis of the effect of the uncertainty on the parameters added</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Gaussian16 data for "Dynamic electronic structure fluctuations in the de novo peptide ACC-dimer revealed by first-principles theory and machine learning"
<p>This is the Gaussian 16 input and corresponding output, which was used as input into the machine learning presented in the paper titled "Dynamic electronic structure fluctuations in the de novo peptide ACC-dimer revealed by first-principles theory and machine learning". This upload is required before submission of the paper.<br><br>The 1001 and 100 snapshots from different extractions are preserved in separated directories. Each snapshot directory <code>*_snapshot</code> has the initial GROMACS snapshot <code>test_*.pdb</code> , the geometry after truncating the solvation shell in various formats, the Gaussian16 input, qsub input and the output directory <code>*.1</code> with a JobID number assigned by qsub. The output directory has the standard output from Gaussian in a <code>.log</code> file and <code>grep</code>ed output from the <code>.fchk</code> file in <code>*.out</code> .</p>
Data from: Fertiliser application modulates the impact of interannual climate fluctuations and plant-to-plant interactions on the dynamics of annual species in a Mediterranean grassland
<p><span><strong><span>Background:</span></strong><span> Climate and land-use changes, which include the application of various types of organic and inorganic fertilisers, have been reducing the species diversity of Mediterranean grasslands and threatening their conservation. Annual plants are one of the most diverse functional groups of species in these grasslands, despite suffering competitive pressure from perennial herbaceous and woody species, and they are essential for ecosystem functioning and stability. </span></span></p> <p><span><strong><span>Aims:</span></strong><span> To quantify how fertilisation modulates the impact of plant-to-plant interactions and climate fluctuations on the dynamics of annuals in Mediterranean grasslands. We hypothesised that the application of sewage sludge would increase competition between functional groups, reducing the abundance of annuals in the long-term, but would buffer the negative impacts of drought on the year-to-year fluctuation of the diversity of annuals.</span></span></p> <p><span><strong><span>Methods:</span></strong><span> In a semi-natural species-rich Mediterranean grassland in northern Spain, we analysed the changes in the taxonomical and functional composition and diversity of annuals over 14 years in response to variations in the abundance of perennial herbaceous and woody species, climate fluctuations, and fertilisation with sewage sludge. We quantified separately the patterns of year-to-year fluctuations and long-term trends. </span></span></p> <p><span><strong><span>Results:</span></strong><span> The frequency and diversity of annuals decreased with a higher abundance of perennial herbaceous species, drought in June, and cold winters. The addition of sewage sludge decreased the abundance of annuals in the long-term, seemed to promote competition between annuals and other functional groups at an interannual scale, and mitigated the negative effects of drought and cold.</span></span></p> <p><span><span><strong>Conclusions:</strong> Fertilisation influences differently the temporal response of annuals to climate fluctuations and plant-to-plant interactions.</span></span></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.