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34 results for “Periodic wave”
SBC LTER: Daily averages of modeled significant wave height (Hs) and peak wave period (Tp) in the Santa Barbara Coastal area from the Coastal Data Information Program - Monitoring and Prediction System (CDIP MOP)
From http://cdip.ucsb.edu: The Coastal Data Information Program (CDIP) is a research group at Scripps Institution of Oceanography that monitors coastal waves and nearshore sand levels on regional scales. CDIP maintains a network of optimally-placed, directional wave buoys from San Diego to Eureka. The buoy measurements are used to initialize a high spatial resolution (100m x 100m) linear spectral wave propagation model. The resulting hourly hindcasts and nowcasts of CA coastal wave conditions have a level of accuracy that is not possible with more traditional wind-wave generation models that are initialized with modeled wind fields.
Statistical characterization of Andalusian wave climate for several combinations of Global Climate Models and Regional Climate Models and periods 2026 - 2045 and 2081 - 2100.
<p>The following text is an extract of the extended abstract entitled "<strong>Parametric Characterization of Wave Climate along the Andalusian Coast for Non-Stationary Stochastic Simulation</strong>" whose authors are Manuel Cobos, Pedro Magaña, Pedro Otiñar and Asunción Baquerizo, and that was included in proceedings of <em>39th IAHR World Congress</em> where this dataset is included.</p> <p><em>Processed data comes from PIMA Adapta Costas project (Ramírez et al., 2019), in particular, from projections of maritime climate for 2026-2045 and 2081-2100. Sea climate contains, among other information, time series of the significant wave height (H<sub>s</sub>) obtained for several combinations of GCM-RCM projections of EUR-11 for the RCP 8.5. GCM-RCM combinations ACCE, CMCC, CNRM, GFDL, HADG, IPSL, MIRO with a 0.1 degrees grid were used for the Atlantic facade while CNRM, HADG, IPSL, MIRO, MEDC, MPIE, ESM2, EART models with 1/11 degrees were used for the Mediterranean one. A total of 210 locations were analyzed, 54 at the Atlantic facade and 156 at the Mediterranean one (Figure 1). The data was bias adjusted using the Empirical Quantile Mapping (Déqué et al., 2007; Michelangeli et al., 2009). Information of the significant wave height and the dependence between the values at a given time with previous values with a VAR(q) model is already available. </em></p> <p><em>At each location, the methodology of Lira-Loarca et al. (2021) was applied, using the software described in Cobos et al. (2022a). More precisely, for every GCM-RCM (hereinafter, model n for n = 1, .., N where N = 7 for Atlantic data and N = 8 for the Mediterranean data), a non-stationary marginal distribution of H<sub>s</sub>, , assuming that the year was the largest periodicity of the climate, was fitted to data using a lognormal model for the central part and two generalized Pareto distribution for the lower and upper tails, as in Solari and Losada (2011). The non- stationarity is considered by assuming a decomposition of the parameters of the distribution and of the percentiles of the common end points of the interval into a trigonometric truncated expansion.</em></p> <p><em>In addition, the coefficients of the matrix, C<sub>n</sub>, of a VAR(q) model with q up to 92 hours were estimated. The ensemble multi-model characteristics of the data were obtained from the compound distributions and the weighted averaged matrix coefficients. </em></p> <p><em>Soon, the results of the peak period (T<sub>p</sub>) and mean incoming wave direction (ϑ<sub>m</sub>) and the coefficients of the multivariate VAR model will also be included.</em></p> <p> </p> <p> </p>
Data Set - Laboratory measurement of the wave–induced plastic particles motion: The influence of wave period, plastic size and plastic density
<p><strong>Data set - Laboratory measurement of the wave–induced plastic particles motion: The influence of wave period, plastic size and plastic density</strong></p> <p>This data set describes the wave flume experimental data on the wave-induced plastic particles motion induced by different wave conditions and different plastic particles density and size. A manuscript is currently under review describing the analysis of the data.</p> <p>The data set is divided in two parts:</p> <p>- <strong>Wave flume hydrodynamics</strong>. With measured water surface elevation at different locations within the wave flume. These data are stored in txt files with headings describing the type of measurement, i.e. wave paddle motion, water surface elevation at different sensors, synchronization signal for the video-cameras.</p> <p>- <strong>Lagrangian trajectories. </strong>hdf5 files with information of the particles position, velocity and time (with respect to the synchronization signal in the respective hydrodynamic file) for each experiment. Two tar.gx files have been uploaded with trajectories information:</p> <p> - TOPIOS_Trajectories_FloatingParticles.tar.xz, with information of floating plastic particles and,</p> <p> - TOPIOS_Trajectories_NonfloatingParticles.tar.xz, with information of non-floating plastc particles.</p> <p>An excel file with information of filenames, cross-shore locations of sensors, plastic particles and wave conditions is also uploaded (TOPIOS_Control_exp.xlsx).</p> <p>Any question regarding the data can be addressed at jose.alsina@upc.edu</p>
Train and test datasets used for the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods"
<p>This repository contains the datasets used for training and testing during the work discussed in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad40f0" target="_blank" rel="noopener">Neural network time-series classifiers for gravitational-wave searches in single-detector periods</a>". Please refer to this paper for more details on how the dataset was produced and cite it if you use these data:</p> <p><em>A. Trovato et al "Neural network time-series classifiers for gravitational-wave searches in single-detector periods", Class. Quant. Grav. 2024 DOI 10.1088/1361-6382/ad40f0.</em></p> <p>In this repository you will find six files in format npz, three of which refer to the test dataset and three to the train dataset. Each file name is of the type {label}_{train or test}.npz where "label" can be "glitch", "noise" or "signal", while the second part of the name indicates whether the file was used for training or testing.</p> <p>Each file is a collection of numpy arrays so it should be read with python. It contains 3 numpy arrays: 'X', 'Y' and 'metadata'. 'X' is a matrix containing 1-second segments of data sampled at 2048 Hz of the LIGO-Livingston detector, so it has shape: (number of samples, 2048). 'Y' contains the label for each segment, which is 0 for noise, 1 for signal and 2 for glitch, so it has shape: (number of samples,). In this case, the information on 'Y' is redundant since it's given directly by the filename. The 'metadata' matrix contains 17 metadata for each sample only for the case of signals, for glitch or noise it contains just 17 zeros for each sample. The shape of 'metadata' is thus: (number of samples, 17). For the signal files, for each sample the metadata is an array with these components:</p> <ol> <li>GPS start of the file from which this segment comes</li> <li>starting GPS time of this segment</li> <li>duration of the segment [s]</li> <li>mass1 [solar masses]</li> <li>mass2 [solar masses]</li> <li>spin1z</li> <li>spin2z</li> <li>inclination [radians]</li> <li>coalescence phase [radians]</li> <li>distance [Mpc]</li> <li>right_ascension [radians]</li> <li>declination [radians]</li> <li>polarization [radians]</li> <li>SNR (signal to noise ratio)</li> <li>shift of the signal w.r.t. the timeseries [s]</li> <li>length of the signal [s]</li> <li>fraction of the signal contained in the time window</li> </ol> <p>Number of samples:</p> <ul> <li>80000 for the file glitch_test.npz</li> <li>69998 for the file glitch_train.npz</li> <li>500000 for the file noise_test.npz</li> <li>250000 for the file noise_train.npz</li> <li>500000 for the file signal_test.npz</li> <li>250000 for the file signal_train.npz</li> </ul> <p>An example of few lines of python code to read each file is:</p> <pre><code>import numpy as np f = np.load("filename.npz") X = f['X'] Y = f['Y'] m = f['metadata'] </code></pre> <p>For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3]. </p> <p>This research has made use of data or software obtained from the Gravitational Wave Open Science Center (<a href="https://gwosc.org/" target="_blank" rel="noopener">gwosc.org</a>), a service of the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation, as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan.</p> <p>[1] https://gwpy.github.io<br>[2] https://pycbc.org<br>[3] https://lscsoft.docs.ligo.org/lalsuite</p>
Wave Parameters - North Atlantic Ocean - Period 2091-2100 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <p>ACCESS (Australian Community Climate and Earth System Simulator)</p> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <p>ACCESS (Australian Community Climate and Earth System Simulator)</p> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave Parameters - Azores - Period 1996-2005 - MODEL: Wavewatch III - Global Driver: HadGEM
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <p>HadGEM (Hadley Centre Global Environmental Model)</p> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.05° x 0.05° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 36.6°</li> <li>northernmost latitude = 39.5°</li> <li>westernmost longitude = 330.5°</li> <li>easternmost longitude = 335.8°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave Parameters - Azores - Period 1995-2004 - MODEL: Wavewatch III - Global Driver: ACCESS
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <p>ACCESS (Australian Community Climate and Earth System Simulator)</p> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.05° x 0.05° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 36.5°</li> <li>northernmost latitude = 39.5°</li> <li>westernmost longitude = 330.5°</li> <li>easternmost longitude = 335.8°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: HadGEM
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <ul> <li>HadGEM (Hadley Centre Global Environmental Model)</li> </ul> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10.</li> <li>northernmost latitude = 42.</li> <li>westernmost longitude = -70.</li> <li>easternmost longitude = -5.</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave Parameters - North Atlantic Ocean - Period 2081-2099 - RCP8.5 - MODEL: Wavewatch III - Global Driver: HadGEM
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <ul> <li>HadGEM (Hadley Centre Global Environmental Model)</li> </ul> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave height and period in South Bay, VA 2011-2013
RBR Wave gauges were deployed in South Bay behind Wreck Island on the Coast of Virginia. Measurements were recorded every 10 minutes, four times per year (typically, Jan, April, July and October) for 20-30 day deployments from 2011 to 2013. Units were mounted on cinderblocks and submerged.
Dataset and simulation files for article: "Designing of strongly confined short-wave Brillouin phonons in silicon waveguide periodic lattices"
<p>Dataset and simulation files for article: "Designing of strongly confined short-wave Brillouin phonons in silicon waveguide periodic lattices"</p>
Data set for "Chiral Magnonic Crystals: Unconventional Spin-Wave Phenomena Induced by a Periodic Dzyaloshinskii-Moriya Interaction"
<p>Scripts for the micromagnetic simulations of the publication "Chiral Magnonic Crystals: Unconventional Spin-Wave Phenomena Induced by a Periodic Dzyaloshinskii-Moriya Interaction", using the OOMMF software. These codes reproduce the result of magnonic waveguides with periodic Dzyaloshinskii-Moriya interactions. A Dockerfile and a Makefile are included for the reproducibility of the results.</p> <p>The repository containing these results, together with a explanatory README document can be found in:</p> <p>https://github.com/davidcortesortuno/paper-2018-chiral_magnonic_crystals</p> <p> </p> <p>The files included in this Zenodo release refer to the v1.0 version of the data set. For an updated version of the scripts refer to the Github repository.</p>
Bichromatic wave groups with DIFFerent REPetition periods over a 1:100 sloping bed (DIFFREP-ICL).
<p><strong>DATASET DESCRIPTION</strong></p> <p>DIFFREP-ICL is the dataset that gathers the measurements described in <em>Padilla and Alsina, 2018</em>: <em>Long Wave Generation Induced by Differences in the Wave-Group Structure</em>.</p> <p>For wave cases from MR-01 to MR-10, the provided variables are:</p> <p><strong>i.</strong> Spatial domain measured from the wave paddle (<em>X</em>)</p> <p><strong>ii.</strong> Spatial domain measured from the shoreline at still water conditions (<em>x</em>)</p> <p><strong>iii.</strong> Water depth (<em>d</em>)</p> <p><strong>iv.</strong> Temporal domain (<em>time</em>)</p> <p><strong>v.</strong> Surface elevation series (<em>eta</em>) </p> <p><strong>vi.</strong> Run-up series measured according to the x-coordinate (<em>runup</em>) </p>
Observation of very short period atmospheric gravity waves in the lower Ionosphere using Very Low Frequency waves
<p>This is the VLF frequency data recorded at PARI station owned by Prof Morris Cohen, at Georgia Institute of Technology, Atlanta, USA.</p> <p>Data is for six transmitter signals, DHO, NAA, NAU, NLK, NPM, NML with both amplitude and Phase</p>
Nonlinear periodic and supernonlinear periodic ion-acoustic wave solutions for the mKdV equation
<p>(a) - (b) Nonlinear periodic ion-acoustic wave solutions and (c) Supernonlinear periodic ion-acoustic wave solution of the mKdV equation.</p>
Supernonlinear periodic ion-acoustic wave solution for the modified Gardner equation
<p>Small-amplitude supernonlinear periodic ion-acoustic wave solution (SPIAWs) for the modified Gardner equation in nonextensive plasmas. Here, SPIAW corresponds to supernonlinear periodic trajectory which encloses three fixed points with one separatrix.</p>
Migration of mechanical perturbations estimated by seismic coda wave interferometry during the 2018 pre-eruptive period at Kīlauea volcano, Hawaii : Noise Cross-correlation Functions, Seismic catalog, and GNSS data
<p>ARCHIVE_NCFs_KILAUEA_2018.zip : Compress folder with (1) the daily noise cross-correlation functions (in MSEED format) of the station pairs used in the paper and (2) the one hour noise cross-correlation functions (in H5 format) of the station pairs used in the figure 9 of the paper.</p> <p>Code_Data_HVO.ipynb : Code to download the seismic data, available on IRIS, used in this paper.</p> <p>GPS_data_AHUP.zip : Compress folder with the daily GPS data of the station AHUP used in the paper [Year, Month, Day, Day_of_the_year, Second_of_the_day, East_comp(mm), North_comp(mm), Vertical_comp(mm), Sig_East_comp, Sig_North_comp, Sig_Vertical_comp].</p> <p>Radial_tilt_UWD.txt : Daily radial tilt measurement of the tiltmeter UWD [Year, Month, Day, Radial_tilt(µrad)].</p> <p>Seismic_stations_Kilauea.txt : Name code and location of the seismic stations used in the paper [Station_code, Longitude, Latitude].</p> <p>Seismicity_Catalog_Kilauea_2018_USGS.txt : Seismic catalog from USGS used in the paper [Date_Time, Latitude, Longitude, Depth, Magnitude].</p>
Collocated model and observation datasets for the validation of the Copernicus Mediterranean Sea Waves Analysis and Forecast for the period 2018-2020.
<p>The collocated values are used for skill evaluation of the Mediterranean Sea Waves Analysis and Forecast system for a three-year-long period (Korres et al., 2022). The list of datasets includes:</p> <ol> <li>The collocated model (analysis) – buoy values for significant wave height Hs (Insitu_Hs.mat)</li> <li>The collocated model (analysis) – buoy values for spectral moments (0,2) wave period Tm (Insitu_Tm.mat)</li> <li>The collocated model (first – guess) – satellite values for significant wave height Hs (Satellite_Hs.mat)</li> <li>The collocated model – satellite values for wind speed U10 (Satellite_U10.mat)</li> </ol> <p>Each .mat file contains a header for the variables included. Collocations can be used to estimate standard quality metrics (e.g. scatter index, bias, root-mean-squared-difference). Procedures to produce these model-observation collocated datasets and determine the overall skill assessment are described in detail in Ravdas et al. (2018) and Oikonomou et al. (2022). The buoy (in-situ) measurements are obtained from the product INSITU_GLO_WAV_DISCRETE_MY_013_045 (EU Copernicus Marine Service Product, 2022a), and associated variables contain a quality flag (“time_qc”, “position_qc”, “buoy_Hs_qc”, “buoy_Tm_qc”) (de Alfonso et al., 2022a,b). In addition, the model first-guess significant wave height and the wind speed forcing (Hersbach et al., 2023) are collocated with available satellite observations (EU Copernicus Marine Service Product, 2022b) over the entire model domain.</p> <p>References</p> <p>de Alfonso, M., Manzano, F., and Gallardo, A. (2022a): EU Copernicus Marine Service Quality Information Document for the In Situ TAC Product, INSITU_GLO_WAV_DISCRETE_MY_013_045, Issue 5.0, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-045.pdf">https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-045.pdf</a></p> <p>de Alfonso, M., Manzano, F., Gallardo, A., and In Situ TAC (2022b): EU Copernicus Marine Service Product User Manual for Multi-Year WAVE In Situ Product, INSITU_GLO_WAV_DISCRETE_MY_013_045, Issue 2.0, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-045.pdf">https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-045.pdf</a></p> <p>EU Copernicus Marine Service Product (2022a): Multi-Year WAVE In Situ Product, Mercator Ocean International, [dataset], <a href="https://doi.org/10.17882/70345">https://doi.org/10.17882/70345</a></p> <p>EU Copernicus Marine Service Product (2022b): Global Ocean L 3 Significant Wave Height From Reprocessed Satellite Measurements, Mercator Ocean International, [dataset], <a href="https://doi.org/10.48670/moi-00176">https://doi.org/10.48670/moi-00176</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed on 26-09-2023)</p> <p>Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2022): Mediterranean Sea Waves Analysis and Forecast (CMEMS MED-Waves, MEDWAΜ4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). <a href="https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4">https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4</a></p> <p>Oikonomou, C., Denaxa D., and Korres, G. (2022): EU Copernicus Marine Service Quality Information Document for the Mediterranean Sea Waves Reanalysis, MEDSEA_ANALYSISFORECAST_WAV_006_017, Issue 2.2, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-MED-QUID-006-017.pdf">https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-MED-QUID-006-017.pdf</a>.</p> <p>Ravdas, M., Zacharioudaki, A., and Korres, G. (2018): Implementation and validation of a new operational wave forecasting system of the Mediterranean Monitoring and Forecasting Centre in the framework of the Copernicus Marine Environment Monitoring Service, Nat. Hazards Earth Syst. Sci., 18, 2675–2695, <a href="https://doi.org/10.5194/nhess-18-2675-2018">https://doi.org/10.5194/nhess-18-2675-2018</a></p> <p> </p>
Data from: Claw-waving rate of male Austruca perplexa in peak and off-peak breeding periods
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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.