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704 results for “Interference”
Abrupt hippocampal remapping signals resolution of memory interference.
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Programmable multi-photon quantum interference in a single spatial mode -- Data and code for simulations
<h2>Description of the data and file structure</h2> <p>This Dataset contains data files with experimental results for the manuscript "<strong>Programmable multi-photon quantum interference in a single spatial mode</strong>" (pre-print version at <a href="https://arxiv.org/abs/2305.11157">https://arxiv.org/abs/2305.11157</a>).</p> <p>The CSV files contain the measured output distributions of our time-bin interferometer, for the various experiments we run. In the first column is the number of counts detected and in the following columns the corresponding output modes. The counts were detected by post-processing the time-tags of the recorded single photon events (a detailed explanation can be found in the Supplementary Informations of the paper).The number of counts is reported for all possible combinations of output modes in order to reconstruct the entire output distribution of collisionless events.</p> <p>The text file contains the data points of the time-bin HOM histogram shown in the paper.</p> <p> </p> <h2>Code/Software</h2> <p>We also provide the Jupyter Notebook (LoopExperiment.ipynb) we used to simulate the experiments, developed by Dr. Tobias Guggemos.</p> <p>The Loop-based architecture is a photonic experiment, that allows scalable implementation of Boson Sampling and arbitrary unitaries on a photonic platform. It can be implemented as a single, sequenced or nested architecture.</p> <p>We use the python framework Perceval to simulate our experiments. We simulate the conversion of the time-bin encoded setup as path encoded photonic qubits.</p> <p>More details can be found in the Notebook.</p>
Efficient Detection of Test Interference in C Projects (Artifact)
<p>This record provides research artifacts for the article "Efficient Detection of Test Interference in C Projects", accepted and to be presented at <a href="https://conf.researchr.org/home/ase-2024">ASE 2024</a>. Please refer to the README.md in the tgz file for details about the artifact and how it relates to the manuscript describing our study. Please also see our related Zenodo record with the container images used in the study: <a href="https://doi.org/10.5281/zenodo.7935821" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7935821</a>.</p>
Fano Interference in Microwave Resonator Measurements
<p>Data, Python notebooks and figures associated with the paper "Fano Interference in Microwave Resonator Measurements" by D. Rieger & S. Günzler et al. at Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.</p> <p>The data is saved in NumPy binary compressed .npz format. The notebooks and data reproduce the figures of the manuscript. Moreover, we provide an example implementation and measurement analysis notebook for the circle fit discussed in the manuscript.</p> <p>For any additional information please contact: dennis.rieger@kit.edu, simon.guenzler@kit.edu or ioan.pop@kit.edu</p>
InSecTT TDMA Interference Dataset
<p>This dataset provides interference measurements with a wireless sensor network (WSN) based on a time division multiple access (TDMA) protocol running on low-cost hardware. The WSN consists of a network coordinator and several measurement nodes, where each device is allowed to transmit at a certain timeslot. For the interference measurements, special measurement nodes are introduced, that measure the average signal level of all possible timeslots and send the data to the network coordinator. This provides a somehow continuous measurement of the signal level in the channel. Since only the average signal level is considered, the channel access of different communication protocols can be evaluated. Furthermore, if a device shows deterministic access to the channel, e.g. periodic access, certain patterns can be observed in the measurements. These patterns can be used to identify the source and apply countermeasures for the WSN.</p> <p>This dataset is a work from <a href="https://silicon-austria-labs.com/">Silicon Austria Labs GmbH</a> (SAL) and the <a href="https://www.jku.at/en/institute-for-communications-engineering-and-rf-systems/">Institute for Communications Engineering and RF-Systems</a> (NTHFS) of the Johannes Kepler University (JKU) in Linz for the <a href="https://www.insectt.eu/">InSecTT project</a>.</p>
Model results and observation data in Zhang et al. modeling of wave interference at Ocean Beach, CA
<p>The dataset contains modeling results and observation data supporting the manuscript of Phase-resolved modeling of wave interference and its effects on nearshore circulation in a large ebb shoal-beach system by Yu Zhang, Fengyan Shi, Jim Kirby, Xi Feng.</p>
PhasAGE Training School 2 - Phase separations and transitions by viral proteins: from viral factories to interference with host cell functions- LECTURE
<p>The Training School 2 “Biomolecular condensates in cell function, aging and disease” is the <strong>second</strong> edition of a series of PhasAGE training activities.</p> <p> </p> <p>The main goal of this training school is to raise awareness and provide expertise on fundamental aspects of phase separation and formation of <strong>biomolecular condensates</strong>, specifically covering the importance of this process to cellular biology and its contribution to the aging process and age-related diseases.</p>
Raw data for the manuscript "Experimental Higher-Order Interference in a Nonlinear Triple Slit"
<p>This folder contains the raw data and analysis coded need to reproduce all of the major results in the manuscript "Experimental Higher-Order Interference in a Nonlinear Triple Slit". </p> <p>The folder "matlab" contains the data and code used to produce all of the main data figures in the paper. The various script load the data, and plots the final results used in the paper. </p> <p>The folder "mathematica" contains the scripts to implement our 4-th order perturbative quantum solution. There is one script to fit to experimental data to find the effective nonlinearity, and another to generate the dependence of the different terms if kappa as a function of Z. The result is plotted in the matlab script found in the directory "matlab\quantum"</p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
InSAR interferences and slip model related to the 2021 Maduo earthquake in Qinghai province, China
<p>The dataset includes the SAR unwrapped interferograms, and the slip model related to the 2021 Maduo earthquake on western Maduo County, Qinghai Province of China.</p> <p>SAR images:</p> <p>Sensor: Sentinel-1 A/B ascending and descending tracks interferograms, including the T099A, T026A, T172A, T106D, T004D and T033D.</p> <p>Time: 2021.05.13 - 2019.05.27, 6 radar phases images and 2 range offset images.</p> <p>Processing software: GAMMA</p> <p>Topographic data from the Shuttle Radar Topography Mission (SRTM) with a resolution of 1 arcsec were used to align the images and remove the topographic phase.</p> <p>First‐order tropospheric delays were mitigated by using the Generic Atmospheric Correction Online Service (GACOS)</p> <p>Silp Models:</p> <p>The model is generated through triangular dislocation inversion.</p> <p>The slip models is composed of two files:</p> <p> Main rupture: Slip_Maduo_Main.gmt</p> <p> Tip rupture: Slip_Maduo_Tip.gmt</p> <p>Format: GMT</p>
Radio Frequency Interference (RFI)
<p>In this dataset, Signal of Interest (SoI) is a real-time video stream that is transmitted using DVB-S2 standards in four modulation types including (QPSK, 8/16/32 APSK). Further, this SoI combined by three well-known jamming signals namely, Continuous Wave Interference (CWI), Multiple CWI (MCWI), and Chirp Interference (CI). This dataset includes 300 samples per modulation type for each type of signal. Therefore, totally there are 4800 samples in the dataset and each sample is a vector of size 1 by 32488 (8ms) at sample frequency 40 Hz. Also, AWGN power is -140 dBm which is approximately equal to SNR=9 dB.</p> <p>More importantly, SoI is modulated and processed by GNU radio and transmitted using a Universal Software Radio Peripheral (USRP-N210). In GNU radio, the modulation type and amplitude of the transmitted signal can be easily adjusted. A SatCom Emulator (RTLogic T400) is used for modeling a real-time communication channel. The programmatic control of the channel simulator is facilitated over an Ethernet connection using a control protocol or optional plugin to STK software. The Channel Simulator produces IF/RF signals with extracting signal characteristics for any scenario. The Kratos STK plugin provides real-time, phase-continuous control of the channel simulator when playing STK scenarios. Further, the generated jamming signals (CWI, MCWI, and CI) are transmitted using a NanoBee modem and combined to SoI by a combiner. Finally, the combined signal is received by a MegaBee modem. </p> <p>Notably, in this dataset, each jammer indicates a combination of SoI with that jammer, as an instance "CWI_16APSK " refers to SoI (16APSK)+CWI.</p> <p> </p>
Prescheduled Interleaving of Processing Reduces Interference in Motor-Cognitive Dual Tasks
<p>All performance-data sets are provided in three seperate .txt-files for each subject.</p> <p>Subject_##_CognitiveTask.txt consists of a chronology table of 8 columns and subject depent number of rows for the 2-back task.</p> <p>col. 1: "Stimulus number" = running number of n stimuli ("0001,0002, ..., n").<br> col. 2: "Day" = Number of the testing day, which is:<br> "1" for day 1, <br> "2" for day 2, or<br> "3" for day 3.<br> col. 3: "Walking Speed [km/h]" = Speed condition of the testing day, which is:<br> "3" for 3 km/h,<br> "4" for 4 km/h, or<br> "5" for 5 km/h.<br> col. 4: "Test block" = Each day includes seven test blocks, where:<br> "1" is the Sitting condition conducted twice, in the beginning and the end of each testing day.<br> "2" is the 0% condition,<br> "3" is the 50% condition,<br> "4" is the 75% condition,<br> "5" is the Earlier condition, and<br> "6" is the Later condition.<br> col. 5: "Trial" = "1" is the first trial,<br> "2" is the second trial, and<br> "3" is the third trial.<br> col. 6: "Reaction time [ms]" = Reaction time data in milliseconds as floating-point numbers. Missing values are substituted by a "NaN" signature.<br> col. 7: "Required decision" = This column shows, whether there was a matching ("1") or a non-matching ("0") required in the 2-back task. Missing values are substituted by a "NaN" signature.<br> col. 8: "Performed decision" = The column shows, whether the subject responded indicating a matching ("1") or a non-matching ("0") in the 2-back task. Missing values are substituted by a "NaN" signature.</p> <p><br> Subject_##_MotorTask_StrideDuration.txt consists of a chronology table of 7 columns and subject depent number of rows for walking strides under single-task and dual-task condition.</p> <p>col. 1: Stride number" = running number of n strides ("0001,0002, ..., n").<br> col. 2: "Day" = Number of the testing day, which is:<br> "1" for day 1, <br> "2" for day 2, or<br> "3" for day 3.<br> col. 3: "Walking Speed [km/h]" = Speed condition of the testing day, which is:<br> "3" for 3 km/h,<br> "4" for 4 km/h, or<br> "5" for 5 km/h.<br> col. 4: "Test block" = Each day includes seven test blocks, where:<br> "2" is the 0% condition,<br> "3" is the 50% condition,<br> "4" is the 75% condition,<br> "5" is the Earlier condition, and<br> "6" is the Later condition.<br> col. 5: "Trial" = Each block consisted of six trials ("1, 2,..., or 6").<br> col. 6: "Task condition" = shows whether the trial was instructed as a single-task ("1") or as a motor-cognitive dualtask ("2").<br> col. 7: "Stride duration [s]" = The duration of each detected stride is written in seconds. Missing values are substituted by a "NaN" signature.</p> <p><br> Subject_##_MotorTask_StrideLength.txt consists of a chronology table of 7 columns and subject depent number of rows for walking strides under single-task and dual-task condition.</p> <p>col. 1: Stride number" = running number of n strides ("0001,0002, ..., n").<br> col. 2: "Day" = Number of the testing day, which is:<br> "1" for day 1, <br> "2" for day 2, or<br> "3" for day 3.<br> col. 3: "Walking Speed [km/h]" = Speed condition of the testing day, which is:<br> "3" for 3 km/h,<br> "4" for 4 km/h, or<br> "5" for 5 km/h.<br> col. 4: "Test block" = Each day includes seven test blocks, where:<br> "2" is the 0% condition,<br> "3" is the 50% condition,<br> "4" is the 75% condition,<br> "5" is the Earlier condition, and<br> "6" is the Later condition.<br> col. 5: "Trial" = Each block consisted of six trials ("1, 2,..., or 6").<br> col. 6: "Task condition" = shows whether the trial was instructed as a single-task ("1") or as a motor-cognitive dual task ("2").<br> col. 7: "Stride duration [mm]" = The length of each detected stride is written in millimeter. Missing values are substituted by a "NaN" signature.</p>
Dataset for Dual Fraunhofer interference and charge fluctuations in long quantum phase slip wires, Phys. Rev. B 102, 144509 (2020)
<p>Dataset for manuscript 'Dual Fraunhofer interference and charge fluctuations in long quantum phase slip wires', Phys. Rev. B 102, 144509 (2020). Dataset contains data from numerical simulations presented in the manuscript and generating scripts. The data describes the quantum phase slip rate and its suppression due to charge and width disorder in superconducting nanowires in the phase slip regime. This disorder leads to temporal fluctuations which affects the current-voltage characteristics of nanowires. </p>
Tutorial Photonics Explorer Module 7: Interference and Diffraction
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes about Photonics - the Photonics Explorer - in order to promote the potential of photonics to enliven physics lessons. This video shows several experiments on the subject of interference and diffraction.</p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 4 relative to Figure 7 – ArhGEF11 CRISPR interference
<p><span>Raw image files (TIFF format), corresponding 2D-cartographies (_2Dmap.tiff files) and metadata files for 2D-cartographies (.xml files, readable with the opensource software Icy), relative to <strong>Figure 7B </strong>and<strong> Figure 7 - Figure Supplement 6</strong> (see <strong>Materials and Methods — Morphological and morphometric analysis of aortic and hemogenic cells</strong>).</span></p> <p><span>The source data comprises for each 48 - 55 hpf <em>Tg(kdrl:eGFP-JAM3b; kdrl:nls-mKate2)</em> zebrafish embryo 3 z-stack and 2D cartographies (segments 1 to 3) encompassing the whole length of the aorta, for control condition (n = 2 individuals) and morpholino splicing interference condition (n = 2 individuals). For z-stacks of both control and morphant conditions, two fluorescence channels were acquired, corresponding to the nuclear mKate2 expressed in endothelial cells and the eGFP-JAMs signal localized at the intercellular junctions of endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm. 2D-cartographies were obtained using the Icy plugin “TubeSkinner”, and the semi-manual segmentation of all aortic cells can be uploaded from the corresponding metadata file on the 2D-cartographies using the load ROI function of Icy.</span></p>
Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 3 relative to Figure 7 – ArhGEF11 morpholino splicing interference
<p><span>Raw image files (TIFF format), corresponding 2D-cartographies (_2Dmap.tiff files) and metadata files for 2D-cartographies (.xml files, readable with the opensource software Icy), relative to <strong>Figure 7A </strong>and<strong> Figure 7 - Figure Supplement 5</strong> (see <strong>Materials and Methods — Morphological and morphometric analysis of aortic and hemogenic cells</strong>).</span></p> <p><span>The source data comprises for each 48 - 55 hpf <em>Tg(kdrl:eGFP-JAM2a; kdrl:nls-mKate2)</em> zebrafish embryo 3 z-stack and 2D cartographies (segments 1 to 3) encompassing the whole length of the aorta, for control condition (n = 2 individuals) and morpholino splicing interference condition (n = 3 individuals). For z-stacks of both control and morphant conditions, two fluorescence channels were acquired, corresponding to the nuclear mKate2 expressed in endothelial cells and the eGFP-JAMs signal localized at the intercellular junctions of endothelial cells. Z-stack were acquired using a confocal spinning disk microscope. Voxel size: x: 0.1635, y: 0.1635, z:0.3 µm. 2D-cartographies were obtained using the Icy plugin “TubeSkinner”, and the semi-manual segmentation of all aortic cells can be uploaded from the corresponding metadata file on the 2D-cartographies using the load ROI function of Icy.</span></p>
Dataset for Evaluating habitat-specific interference in automated radio telemetry systems: implications for animal movement studies
<h1>Abstract </h1> <p>Automated radio telemetry systems have become a popular and invaluable tool in tracking the activity and movement of wild animals. However, many environmental conditions can hinder accuracy when tracking with this technology. For instance, study sites may contain multiple habitat types, each habitat uniquely affecting the signal strength received from tagged species. To investigate the influence of a structurally diverse study site on an automated radio telemetry system, we conducted this project at a restored and managed pine barren habitat that consisted of a mix of mature pitch pine, treated pitch pine, scrub oak, and hardwood forests. This site, Montague Plains Wildlife Management Area, Montague, Massachusetts, is also a known breeding ground for Eastern whip-poor-will (Antrostomus vociferus). To measure the relationship of radio signal strength with distance across each habitat, we used radio telemetry equipment manufactured by Cellular Tracking Technologies. We produced negative exponential decay functions measuring radio signal strength over distance and tested for differences among habitat types on radio signal strength (RSS). We found that decay function parameters significantly differed by habitat type, prompting us to investigate if accounting for these differences improved location estimate accuracy. To test this, we estimated known locations using trilateration methods with and without habitat calibration. Comparing these tests indicates that habitat-specific adjustments significantly improved location accuracy. Lastly, we visualized estimated RSS-based locations of one week of whip-poor-will data and compared them to GPS data generated from the same individual. Previous studies have accounted for types of environmental interference (like elevation) in the field but have avoided incorporating habitat-specific factors by working with node networks covering a relatively small area, but in this study, we examined the potential to scale up for larger areas and in more complex habitats.</p> <p> </p>
Data for Falgenhauer, et al., "Transcriptional interference in toehold switch-based RNA circuits"
<p>Contains DNA sequences of the gene specific primers for RT-qPCR, the RT-qPCR raw data and output files used in "Transcriptional interference in toehold switch-based RNA circuits" by Falgenhauer et al.</p>
Data and Rscripts from: An integrated experimental and mathematical approach to inferring the role of food exploitation and interference interactions in shaping life history
<p><span>Intraspecific interactions can occur through many ways but the mechanisms can be broadly categorized as food exploitation and interference interactions. Identifying how intraspecific interactions impact life history is crucial to accurately predict how population density and structure influence dynamics. However, disentangling the effects of interference interactions from exploitation using experiments, is challenging for most biological systems.</span></p> <p><span>Here we propose an approach that combines experiments with modeling to infer the pathways of intraspecific interactions in a system. First, a consumer-resource model is built without intraspecific interactions. Then, the model is parameterized by fitting it to life-history data from a first experiment in which food abundance was varied. Next, hypothesized scenarios of intraspecific interactions are incorporated into the model which is then used to predict life histories with increasing competitor density. Lastly, model predictions are compared against data from a second experiment which raised groups of competitors of different densities. This comparison allows us to infer the role of interference and exploitation in shaping life history.</span></p> <p><span>We demonstrated the approach using the smaller tea tortrix <em>Adoxophyes honmai</em> across a range of temperature. We investigated five scenarios of interactions that included exploitation and three pathways for interference through some effects either on energetics to represent changes in ingestion or activity, or on mortality to model deadly interactions, or on mortality and ingestion to model cannibalism.</span></p> <p><span>Overall, intraspecific interactions in tea tortrix are best explained by a high level of deadly interactions along with some level of interference that acts on energy such as escaping and blocking access to food. Deadly interactions increase with temperature while interference that acts on energy is strongest close to the optimal temperature for reproduction. Interestingly, exploitation is more important than interference at low competitor density.</span></p> <p><span>The combination of mathematical modeling and experimentation allowed us to mechanistically characterize the intraspecific interactions in tea tortrix in a way that is readily incorporated into population-level mathematical models. The primary value of this approach, however, is that it can be applied to a much wider range of taxa than is possible with pure experimental approaches. </span></p>
Raw data: quantum interference of identical photons from remote GaAs quantum dots
<p>This is the raw data supporting the findings in the letter titled "<em>Quantum Interference of Identical Photons from Remote GaAs Quantum Dots</em>" that is published in <em>Nature Nanotechnology.</em></p>
ScienceDex guides
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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.