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3,081 results for “array”
A temporary broadband seismic array in the largest desert of China: TASTE
<p>This includes the dataset of our manuscript submitted to Seismological Research Letters entitled with <em>A temporary broadband seismic array in the largest desert of China: TASTE</em></p>
Entropy driven order in an array of nanomagnets
<p>Long-range ordering, while typically understood as a decrease in entropy, can also be driven by increasing system entropy in certain special cases. We demonstrate that artificial spin ice arrays of single-domain nanomagnets can be designed to produce entropy-driven order. We probe thermally active tetris artificial spin ice, known to have a zero point Pauli entropy, both experimentally and through simulations. We find two-dimensional magnetic ordering in one subset of the nanomagnet moments, which we demonstrate to be induced by disorder (i.e., increased entropy) in another subset of moments. Contrasting with other entropy-driven systems, the degrees of freedom in artificial spin ice are both designable and directly observable at the microscale, and the entropy of the system is precisely calculable in simulations. This robust example, in which the system's interactions and ground state entropy are well-defined, significantly expands the experimental landscape for the study of entropy-driven ordering.</p>
Idealized Planar Array study for Quantifying Spatial heterogeneity (IPAQS) - Numerical Simulations
<p>The Idealized Planar Array study for Quantifying Spatial heterogeneity (IPAQS) is the result of a National Science Foundation (US) funded project, that aims at studying the effect of surface thermal heterogeneities of different length-scale on the atmospheric boundary layer. This project consisted of a computational effort (dataset here included), and an experimental effort (dataset being prepared for publication). </p> <p><strong>Overview of the numerical (Large Eddy) simulations:</strong></p> <p>The simulations are separated into two sets to study the differences between heterogeneous and homogeneous surfaces. In the first set, a total of seven configurations are considered, all with a homogeneous surface temperature fixed at a value of <span class="math-tex">\(T_s\)</span> = 290 K, and for which the geostrophic wind speed has been increased from 1 to 15 m s<sup>-1</sup> (i.e., U<sub>g</sub> = 1, 2, 3, 4, 6, 9, 15 m s<sup>−1</sup> ). These homogeneous cases are referred to as Homog-X, where X indicates the geostrophic wind speed corresponding case (see Margairaz et al. 2020a). In the second set, the surface temperature is distributed amongst square patches, where the temperature of each patch is determined by sampling a Gaussian distribution with a mean temperature of 290 K and a standard deviation of 5 K. In this case, three different patch sizes were considered (i.e., l<sub>h</sub> = 800, 400, and 200 m). The sizes of the heterogeneities were chosen to be of similar size (l<sub>h</sub> /l<sub>d</sub> ≈ 1), half the size (l<sub>h</sub> /l<sub>d</sub> ≈ 1/2), and about a quarter of the size (l<sub>h</sub> /l<sub>d</sub> ≈ 1/4) of the largest flow motions within the represented thermal boundary layer, assuming that this is of the order of the boundary-layer height (l<sub>d</sub> ∼ z <sub>i</sub> ). These heterogeneities are typically not resolved in NWP models. These cases have been studied for the same geostrophic wind speeds indicated above, and hereafter are referred to as PYYY-X-, where X indicates the corresponding geostrophic wind speed, and YYY refers to the size of the patches (e.g., P800_Ug1_ would be the heterogeneous case with patches of 800 m, and forced with Ug = 1 m s<sup>−1</sup> ). Additionally, for the case with larger patches, three different random distributions of the patches were considered to evaluate the potential effect of a given surface distribution for all geostrophic wind speeds. In this dataset we only include case v3. The LES imposed surface temperature distributions emulate the surface thermal conditions observed in Morrison et al. (2017 QJRMS, 2021 BLM, 2022 BLM), where measurements of the surface temperature were taken with a thermal camera at the SLTEST site of the US Army Dugway Proving Ground in Utah, USA. This is an ideal site with uniform roughness and a large unperturbed fetch, where surface thermal heterogeneities are naturally created by differences in surface salinity. In all studied cases, the surface roughness is assumed homogeneous, with z<sub>0</sub> = 0.1 m, and representative of a surface with sparse forest or farmland with many hedges (Brutsaert 1982; Stull 1988). The initial boundary-layer height is set to z<sub>i</sub> = 1000 m. The temperature profile is initialized with a mean air temperature of 285 K. At the top of the initial boundary layer, a capping inversion of 1000 m is used to limit its growth. The strength of this inversion is fixed at Γ = 0.012 K m<sup>−1</sup>. The atmospheric boundary layer (ABL) is considered dry and the latent heat flux is neglected in all cases. Further, in all simulations, the surface heat flux is computed using MOST, as explained in Margairaz et al. 2020a, where the surface temperature is kept constant in time throughout the simulations. Thus, there is no feedback from the atmosphere to the surface as the surface temperature does not cool down or warm up with local changes in velocities. As a consequence, the ABL gradually warms up as the simulations progress, and hence becomes less convective over time. However, the runs are not long enough for this to be significant. In addition, to ensure a degree of homogeneity within each patch and a certain degree of validity of MOST, note that even for the heterogeneous cases with the fewest amount of grid points per patch, a minimum of eight grid points is granted in each horizontal direction. The domain size is set to (L<sub>x</sub>, L<sub>y</sub>, L<sub>z</sub>) = (2π, 2π, 2) km at a grid size of (Nx , Ny , Nz ) = (256, 256, 256) resulting in a horizontal resolution of <span class="math-tex">\(\Delta\)</span>x = <span class="math-tex">\(\Delta\)</span>y = 24.5 m and a vertical grid spacing of <span class="math-tex">\(\Delta\)</span>z = 7.8 m. A timestep of <span class="math-tex">\(\Delta\)</span>t = 0.1 s is used to ensure the stability of the time integration. The two sets of simulations span a large range of geostrophic forcing conditions, allowing the study of the effect on the structure of the convective boundary layer (CBL) above a patchy surface compared to a homogeneous surface. The procedure used to spin up the simulations is the following: a spinup phase of four hours of real time is used to achieve converged turbulent statistics, which is then followed by an evaluation phase. During the latter, running averages are computed for the next hour of real time (dataset here published). Statistics have been computed for averaging times of 5 min to 1 h, showing statistical convergence at 30-min averages with negligible changes between the 30-min and the 60-min averages. The simulations cover a wide range of atmospheric stability regimes ranging from −z<sub>i</sub>/L < 5 to −z<sub>i</sub>/L > 700, and hence spanning from near neutral to highly convective scenarios.</p> <p><strong>Description of the Dataset as included in the NetCDF files:</strong></p> <p>Data for each study case is included in two files, one for momentum related variables, and one for temperature related variables. For example, the following files "P200_Ug1_Momentum.nc" and "P200_Ug1_Scalar.nc", include the 1h averaged variables for momentum and temperature for the case of 200 m surface patches with 1 m/s geostrophic winds. </p> <p>Each corresponding momentum file "PXXX_UgX_Momentum.nc" includes the following variables in a Python Xarray structure:</p> <ul> <li>'avgU' = mean streamwise wind speed; 'avgV' = mean spanwise wind speed; 'avgW' = mean vertical wind speed, 'avgP' = mean dynamic modified pressure field (<span class="math-tex">\(p^*\)</span>, see Margairaz et al 2020a),</li> <li>'avgU2', 'avgV2', 'avgW2' = correspond to <span class="math-tex">\(\overline{UU}\)</span>, <span class="math-tex">\(\overline{VV}\)</span>, and <span class="math-tex">\(\overline{WW}\)</span>, where the capital indicates the LES filtered variable.</li> <li>'avgUV', 'avgUW', 'avgVW' = correspond to <span class="math-tex">\(\overline{UV}\)</span>, <span class="math-tex">\(\overline{UW}\)</span>, and <span class="math-tex">\(\overline{VW}\)</span>. These variables together with the ones above are used to compute the Reynolds stress components (e.g. <span class="math-tex">\(R_{xz} = \overline{U}\overline{W} - \overline{UW}\)</span>).</li> <li>avgU3', 'avgV3', 'avgW3', 'avgU4', 'avgV4', 'avgW4' = correspond to the equivalent but instead of squared they are cubed and to the 4th power.</li> <li>'avgtxx','avgtyy','avgtzz','avgtxy','avgtxz','avgtyz' = These represent the corresponding averaged subgrid scale (SGS) stress.</li> <li>'avgdudz','avgdvdz','avgNut','avgCs' = Represent the averaged vertical derivatives, an averaged subgrid Nusselt number, and the Cs coefficient computed in the SGS model.</li> </ul> <p>Overall, there are a total of 26 variables related to the momentum field. Alternatively, the temperature fields are included in the "PXXX_UgX_Scalar.nc" files. These files include 10 variables, </p> <ul> <li>'avgT' = mean Temperature field, 'avgT2' = corresponds to <span class="math-tex">\(\overline{TT}\)</span>, 'avgUT' = correspond to <span class="math-tex">\(\overline{UT}\)</span>, 'avgVT' = correspond to <span class="math-tex">\(\overline{VT}\)</span>, 'avgWT' = correspond to <span class="math-tex">\(\overline{WT}\)</span>; one can use these terms to compute the corresponding Reynolds averaged turbulent fluxes as is the case for momentum. </li> <li>'avgUT_sgs','avgVT_sgs','avgWT_sgs' = These represent the corresponding subgrid scale fluxes.</li> <li>'avg_nus', avg_ds' = averaged subgrid Nusselt number, and the Ds coefficient computed in the scalar SGS model.</li> </ul> <p>All variables output from the LES are normalized by Tscale = 290 [K] when it includes dimensions of temperature, u_scale = 0.45 [m/s], when it relates to velocity fields, and z<sub>i</sub> = 1000 [m] for length scales.</p> <p>The only output variables that are expressed in dimensional form are those for the surface temperature included in the files "SurfTemp_DXXX.nc"</p> <p>Together with the data files we include a Python script that loads the data and includes it in two Xarray structures that one can then use to work with the datasets. </p>
Archive of the analysis results of the microtremor data obtained from a seismic array with a radius of 0.58 m distributed to the participants of the blind prediction experiments for the ESG6 symposium
<p>This is a supplemental material of the paper "Array-size dependency of the upper limit wavelength normalized by array radius for the standard spatial autocorrelation method" by Ikuo Cho, published in Earth, Planets and Space. It consists of the analysis results of the microtremor data observed using a seismic array with a radius of 0.58 m, which were distributed to the participants of the blind prediction experiments in ESG6. It involves all analysis results and script files to draw Figure 1 of the paper. See the "Availability of data and materials" section of the paper to download the original observed data and analysis code. See the main text of the paper for the details of the analysis.</p>
Enhancing bioreactor arrays for automated measurements and reactive control with ReacSight
<p>This dataset supports the paper <em>"</em>Enhancing bioreactor arrays for automated measurements and reactive control with ReacSight" (2021). ReacSight is a generic and flexible strategy to enhance bioreactor arrays for automated measurements and reactive experiment control. We used ReacSight to assemble a platform for cytometry-based characterization and reactive optogenetic control of parallel yeast continuous cultures. Using a dedicated bioreactor array, we showcased its capabilities on several applications. This dataset contains all the corresponding raw data. Together with code available in the <a href="https://gitlab.inria.fr/InBio/Public/reacsight">ReacSight git repository</a>, it allows to reproduce the analysis of raw data and the generation of the figures appearing in the paper.</p>
Development of a high-density 665 K SNP array for rainbow trout genome-wide genotyping. Supplemental VCF file
<p>Single nucleotide polymorphism (SNP) arrays, also named « SNP chips », enable very large numbers of individuals to be genotyped at a targeted set of thousands of genome-wide identified markers. We used preexisting variant datasets from USDA, a French commercial line and 30X-coverage whole genome sequencing of INRAE isogenic lines to develop an Affymetrix 665 K SNP array (HD chip) for rainbow trout. In total, we identified 32,372,492 SNPs that were polymorphic in the USDA or INRAE databases. A subset of identified SNPs were selected for inclusion on the chip, prioritizing SNPs whose flanking sequence uniquely aligned to the Swanson reference genome, with homogenous repartition over the genome and the highest Minimum Allele Frequency in both USDA and French databases. Of the 664,531 SNPs which passed the Affymetrix quality filters and were manufactured on the HD chip, 65.3% and 60.9% passed filtering metrics and were polymorphic in two other distinct French commercial populations in which, respectively, 288 and 175 sampled fish were genotyped. Only 576,118 SNPs mapped uniquely on both Swanson and Arlee reference genomes, and 12,071 SNPs did not map at all on the Arlee reference genome. Among those 576,118 SNPs, 38,948 SNPs were kept from the commercially available medium-density 57K SNP chip. We demonstrate the utility of the HD chip by describing the high rates of linkage disequilibrium at 2 kb to 10 kb in the rainbow trout genome in comparison to the linkage disequilibrium observed at 50 kb to 100 kb which are usual distances between markers of the medium-density chip.</p> <p> </p> <p>File submitted correspond to the supplementary data 1 of the publication (under submission) : INRAE_USDA_MAF1.vcf.gz</p>
Dataset of Violin Recordings with Spherical Microphone Arrays
<p>This data set contains simultaneous recordings of violin instrument sounds by multiple Spot microphones placed on a spherical skeleton.<br> There are 32 Spot microphones, recorded in 23 different ways of expression.</p> <p>Microphone ID</p> <p>Top Layer<br> 32<br> 27-28-29-30-31<br> 22-23-24-25-26<br> 17-18-19-20-21<br> 12-13-14-15-16<br> 7-8-9-10-11<br> 2-3-4-5-6<br> 1<br> Bottom Layer</p> <p> </p>
Localizing On-scalp MEG Sensors using an Array of Magnetic Dipole Coils
<p>Matlab scripts and data necessary to reproduce the results from the PLOS ONE paper. For more information see README.</p>
70K SNP array data for Lumpfish (Cyclopterus lumpus) across the trans-Atlantic
<p>In marine species with large populations and high dispersal potential, large-scale genetic differences and clinal trends in allele frequency can provide insight into the evolutionary processes that shape diversity. Lumpfish, <em>Cyclopterus lumpus</em>, is found throughout the North Atlantic and has traditionally been harvested for roe and more recently used as a cleaner fish in salmon aquaculture. We used a 70K SNP array to evaluate trans-Atlantic differentiation, genetic structuring, and clinal variation across the North Atlantic. Basin-scale structuring between the Northeast and Northwest Atlantic was significant, with enrichment for loci associated with developmental/mitochondrial function. We identified a putative structural variant on chromosome 2, likely contributing to differentiation between Northeast and Northwest Atlantic Lumpfish, and consistent with post-glacial trans-Atlantic secondary contact. Redundancy Analysis identified climate associations both in the Northeast (<em>N</em> = 1269 loci) and Northwest (<em>N</em> = 1637 loci), with 103 shared loci between them. Clinal patterns in allele frequencies were observed in some loci (15% - Northwest and 5% - Northeast) of which 708 loci were shared and involved with growth, developmental processes, and locomotion. The combined evidence of trans-Atlantic differentiation, environmental associations, and clinal loci, suggests that both regional and large-scale potentially-adaptive population structuring is present across the North Atlantic.</p>
Data from: Where's Whaledo: a software toolkit for array localization of animal vocalizations
<p><em>Where's Whaledo</em> is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to understand habitat use, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers. Where's Whaledo combines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness of Where's Whaledo in localizing groups of <em>Ziphius cavirostris</em>. We reconstruct the tracks of six individual animals vocalizing concurrently and identify <em>Ziphius cavirostris</em> tracks despite being obscured by a large pod of vocalizing dolphins.</p>
Attempted Speech on Single Words [intracortical array][T16][BG2]
<h2>Summary of the Data and Experiment</h2> <h3>General Overview</h3> <p>The data provided come from an attempted speech experiment involving a participant (T16) in the BrainGate2 clinical trial. The dataset includes neural recordings from four microelectrode arrays implanted in the left precentral gyrus. The primary focus is on the array located in the ventral precentral gyrus (6v), which is associated with speech-related activity.</p> <h3>Data Description</h3> <p>The neural data consists of two main streams:</p> <ol> <li>Log Local Field Potential (LFP) Power: <ul> <li>Description: Extracellular neural data from the lower frequency bands.</li> <li>Sampling Rate: Preprocessed to 2 kHz.</li> <li>Filtering: Bandpass filtered with a 150-450 Hz band.</li> <li>Scale: Log-scale</li> <li>Bin size: 20ms Summed</li> <li>Shape: 128457x64 (time by channels).</li> </ul> </li> <li>Binned Threshold Crossings: <ul> <li>Description: Extracellularly collected neural spiking activity related to attempted speech.</li> <li>Threshold: -4.5 RMS</li> <li>Bin size: 20ms Summed</li> <li>Shape: 128457x64 (time by channels)</li> </ul> </li> </ol> <p>The included data are within a single mat file with the keys `trial_info_df`, `lfp_matrix`, `spikes_matrix`, and `timevector`.</p> <h3>Trial Information</h3> <p>The `trial_info_df` key contains information about each trial in the form of a pandas DataFrame with the following columns:</p> <ul> <li>block_num</li> <li>cue</li> <li>start_time</li> <li>go_cue_time</li> <li>end_time</li> </ul> <h3>Participant and Experiment Information</h3> <ul> <li>Participant T16: Right-handed, 52-year-old woman with tetraplegia and dysarthria due to a pontine stroke.</li> <li>Implantation: Four 64-channel intracortical microelectrode arrays in the left precentral gyrus.</li> <li>Recording Day: Data collected 69 days post-implantation.</li> <li>Recording Platform: Backend for Realtime Asynchronous Neural Decoding (BRAND) platform.</li> <li>Spiking Data Extraction: Linear regression referencing, bandpass filtering (250-5000 Hz), and threshold crossings identification (-4.5 RMS threshold).</li> <li>LFP power Extraction: Band pass filtering (150 - 450 Hz), and notch filtering at harmonics of 60 Hz.</li> </ul> <h3>Experimental Task</h3> <ul> <li>Task: Cued speech task where Participant T16 vocalized words presented on a screen.</li> <li>Word Bank: Compiled from a 50-word vocabulary by Moses et al.</li> <li>Trial Structure:<br> <ul> <li>A red square appeared below a word.</li> <li>After 1500 ms, the square turned green, cueing the participant to vocalize the word.</li> <li>The trial ended after the participant finished speaking, with a 1000 ms interval before the next trial.</li> </ul> </li> </ul> <h2>Loading and Handling the Data</h2> <p>The data can be loaded into Python using `scipy.io.loadmat`:</p> <h3>Loading the Data</h3> <blockquote> <p>import scipy.io<br>import pandas as pd</p> <p># Load the .mat file<br>data = scipy.io.loadmat('path_to_mat_file.mat')</p> <p># Extracting the trial information<br>trial_info_df = pd.DataFrame(data['trial_info_df'])<br>trial_info_df.columns = ['block_num', 'cue', 'start_time', 'go_cue_time', 'end_time']</p> <p># Extracting neural data matrices<br>lfp_matrix = data['lfp_matrix']<br>spikes_matrix = data['spikes_matrix']</p> <p># Extracting time vector<br>timevector = data['timevector']</p> </blockquote> <h3>Wrapping Trial Information in a DataFrame</h3> <blockquote> <p>trial_info_df = pd.DataFrame(data['trial_info_df'], columns=['block_num', 'cue', 'start_time', 'go_cue_time', 'end_time'])</p> </blockquote> <h3>Exploring the Data</h3> <ul> <li>Log Local Field Potential (LFP) Power: `lfp_matrix`</li> <li>Threshold Crossings (Spikes): `spikes_matrix`</li> <li>Time Vector: `timevector`</li> </ul> <h2>Support</h2> <p><span>This work was supported by NIH-NINDS/OD DP2NS127291 (CP), NIH-NICHD F32HD112173 (SRN), NIH K08NS060223 (MS), R01NS112942 (MS), and RF1NS125026 (MS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, or the Department of Veterans Affairs, or the United States Government.</span></p> <p><span>*Caution: Investigational Device. Limited by federal law to investigational use. </span></p> <p> </p>
Data for "Controlled mechanochemical coupling of anti-junctions in DNA origami arrays"
<p>We provide the raw data for the paper "Controlled mechanochemical coupling of anti-junctions in DNA origami arrays". The files have .tif files-format.</p> <p>Additionally we provide the python scripts used to analyze these data sets.</p>
High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range - data
<div> <p>Experimental and simulation data from the paper "High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range".</p> <p> </p> </div>
Dataset: Array Technologies, Inc. (ARRY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Array Technologies, Inc. (ARRY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Lightning Observations with the1 Horus Polarimetric Phased Array Radar Supplemental Material
Open the record for dataset details and reuse information.
Figure 1 in Constellation array in scorpion genera Paruroctonus, Smeringurus, Vejovoidus, and Paravaejovis (Scorpiones: Vaejovidae)
Figure 1: Distal one-third of chelal fixed finger, external view, of Paruroctonus becki showing general layout of constellation array sensilla, setal rows, landmark setae, trichobothria, and finger denticles. In top figure the four setal rows are identified (setae of each row are connected with black lines) as well as trichobothria and finger denticles of importance. Constellation array and landmark setae region are indicated by a gray polygon. In bottom figure the constellation array sensilla and landmark setae region (with darkened areolae for emphasis) are shown in closeup and identified. s = sensilla; Ms = major seta; ms1–3 = minor setae 1–3; et = external terminal trichobothrium; OD–1 & 2 = outer (OD) denticles 1 & 2; MD = distal median (MD) denticles.
Electrophysiological recordings from human-derived cardiomyocytes in the form of MEA (microelectrode array) recordings
<p><span>The electrophysiological data were acquired using standard software for MEA acquisition, such as the programs provided with the used MEA acquisition system coupled with the SiMulTox prototype.</span></p>
Data supporting the publication "Multi-use of the sea: a wide array of opportunities from site-specific cases across Europe"
<p>Data supporting the publication "Multi-use of the sea: a wide array of opportunities from site-specific cases across Europe"</p>
Classification of small strength-2 covering arrays
<p>The files contain strength-2 covering arrays classified in the study "J. I. Kokkala, K. Meagher, R. Naserasr, K. J. Nurmela, P. R. J. Östergård, B. Stevens, On the Structure of Small Strength-2 Covering Arrays".</p> <p>Each line in a file corresponds to a <span class="math-tex">\(\mathrm{CA}(N;2,k,v)\)</span>, rows of the covering array are separated by space. The file name format is <em>ca_N_2_k_v.txt</em> if all covering arrays were classified. Whenever only uniform covering arrays are classified, the file name is <em>uca_N_2_k_v.txt</em>. An additional suffix <em>_delta_δ</em> in the file name implies that only covering arrays with that <span class="math-tex">\(\delta\)</span> parameter (see the publication) were classified. In some cases, the number of inequivalent covering arrays was too large for the arrays to be stored; those cases are omitted here.</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.