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203 results for “Seismic data”
Code for noise-based seismic velocity changes estimation with the Bezymianny volcano data set. Journal of Volcanology and Geothermal Research.
<p>This file contains all the data and the python scripts used to estimate seismic velocity changes for the Bezymianny volcano (Klyuchevskoy volcano group). It also includes a guideline README.pdf with the description how to reproduce all the results presented in the paper <strong>Berezhnev Y., Belovezhets N., Shapiro N., Koulakov I. (2022), Temporal changes of seismic velocities below Bezymianny volcano prior to its explosive eruption on 20.12.2017, Journal of Volcanology and Geothermal Research</strong></p>
Low-fold seismic reflection data acquired across the Northern Hikurangi subduction margin and incoming Hikurangi Plateau, New Zealand
<p>Two-dimensional seismic reflection data were acquired on two surveys of the Northern Hikurangi subduction margin, New Zealand in 2011 and 2015. The survey data were collected to support research of tectonic structure, slow slip processes, stratigraphic architecture, and thermal state of the subduction margin and incoming plate, as well as to support ocean-floor drilling associated with IODP Expeditions 372 and 375. The surveys include (1) R/V <em>Tangaroa</em> NIWA voyage TAN1114 undertaken in 2011 by the National Institute of Water and Atmospheric Research (NIWA) and GNS Science, as part of the <em>OS2020 Northern Hikurangi Margin Geohazards</em> survey; and (2) R/V <em>Rodger Revelle</em> cruise RR1508 undertaken in 2015 by Oregon State University as part of the <em>Subduction Thrust Investigation of New Zealand using Geothermics and Seismics (STINGS)</em> project (see Figure 1). TAN1114 voyage was funded by the New Zealand Government Oceans 2020 Programme, and core research programme funding by NIWA and GNS Science. Cruise RR1508 was funded by NSF grants OCE-1355878 and OCE-1355870.</p> <p> </p> <p><strong>R/V <em>Tangaroa</em> TAN1114 Seismic Data</strong></p> <p><strong>Data Acquisition:</strong> The seismic system used on R/V <em>Tangaroa</em> during the 2011 National Institute of Water and Atmospheric Research (NIWA) survey TAN1114 included a source comprising two Sodera 45/105 GI guns operated in true GI mode. The guns were deployed 35 m behind the vessel RV <em>Tangaroa</em> at 5 m water depth. Lines TAN1114-01 to -13, and part of line 14 were acquired with a shot interval of 10.8 seconds (~25 m sailing at 4.5 knots), providing a nominal coverage of 12-fold data. Part of line TAN1114-14 and lines 15-23 were acquired with a shot interval of 21.6 seconds (~50 m sailing at 4.5 knots), providing a nominal 6-fold coverage. Data were recorded on a Geometrics GeoEel 48-channel seismic streamer with 6 X 100 m active sections, and a group interval of 12.5 m. The streamer was deployed at a depth of 7.5 m, apart from line TAN1114-01 where it was towed at 5 m depth. Depth control was maintained with a CSMX depth control system including three DigiCourse 5011 compass birds. The record length was 8 s and the sample rate 2 ms. Differential GPS was used for positioning. Table 1 summarises TAN1114 recording parameters and Table 2 lists TAN1114 lines acquired and processed. TAN1114 line coordinates are detailed in Table 3.</p> <p><strong>Data Processing:</strong> A total of 29 seismic lines were processed providing 1350 km of multichannel seismic reflection data. The lines were processed to post-stack time-migrated SEGY sections, using GNS Science GLOBE CLARITAS. With allowance for overlap of line segments the data were grouped into 51167 shot-point locations. Raw data were written to disk as IBM standard SEG-Y files. IBM Claritas Extended SEG-Y data were written to disk after geometry was added, after stack, and after migration. Shots were CDP sorted from disk during the stacking process to avoid creating large and unnecessary separate CDP sorted files. </p> <p>Post-stack migration (finite difference migration) has been applied to the stacked sections to produce a dip-true image, this results in clearer resolution of structural features such as faults and folds, and of detailed sedimentary features such as on-lapping and truncated reflections. Sea-floor multiple reflections disturb structural imaging especially in water depths less than 500 m. All seismic data are written to disk as processed sections in SEG-Y format. Line TAN1114-A is a composite splice including parts of lines TAN1114-4A, -6A and 7A. Details of the TAN1114 processing parameters are given in Table 4 and SEG-Y trace headers in Table 5.</p> <p> </p> <p><strong>R/V <em>Rodger Revelle</em> RR1508 Seismic Data</strong></p> <p><strong>Data Acquisition:</strong> The 2015 R/V <em>Rodger Revelle</em> survey RR1508 used a seismic system operated by Scripps Institute of Oceanography. Of two sub-regions surveyed during this cruise, only data from the northern Hikurangi margin are presented here. The seismic system used was similar to that on <em>Tangaroa</em> TAN1114, including a source comprising two Sodera 45/105 GI guns operated in true GI mode. The guns were deployed at a depth of 3.5 m and the shot spacing was 25 m. Data were recorded on a Geometrics GeoEel 48-channel seismic streamer with 6 X 100 m active sections, and a group interval of 12.5 m. The streamer was deployed at a depth of 3.5 m. During acquisition of the HKS01 lines, only the nearest 40 data channels were recorded. The record length was 8 s and the sample rate 1 ms. Differential GPS was used for positioning. Table 6 summarises RR1508 recording parameters and Table 7 lists RR1508 lines acquired and processed.</p> <p><strong>Data Processing: </strong>A total of 13 HKS01 seismic lines were processed to post-stack time-migrated SEGY sections, using GNS Science GLOBE CLARITAS. Data processing included application of geometry, sorting, trace editing, normal moveout correction, stack, filtering and finite difference migration. All seismic data are written to disk as processed sections in SEG-Y format. Details of the RR1508 processing parameters are given in Table 8 and SEG-Y trace headers in Table 9.</p> <p> </p> <p><strong>List of files</strong></p> <p>Figure 1. TAN1114 and RR1508 seismic line locations on the northern Hikurangi margin.</p> <p>Table 1. Summary of TAN1114 recording parameters.</p> <p>Table 2. Summary of TAN1114 lines acquired and processed.</p> <p>Table 3. Summary of TAN1114 line coordinates.</p> <p>Table 4. Summary of TAN1114 seismic processing sequence.</p> <p>Table 5. Summary of TAN1114 SEG-Y trace headers.</p> <p>Table 6. Summary of RR1508 recording parameters.</p> <p>Table 7. Summary of RR1508 lines acquired and processed.</p> <p>Table 8. Summary of RR1508 seismic processing sequence.</p> <p>Table 9. Summary of RR1508 SEG-Y trace headers.</p> <p> </p> <p>Processed SEGY seismic data</p> <p>TAN1114-01.sgy</p> <p>TAN1114-02.sgy</p> <p>TAN1114-03.sgy</p> <p>TAN1114-04.sgy</p> <p>TAN1114-04A.sgy</p> <p>TAN1114-05.sgy</p> <p>TAN1114-06.sgy</p> <p>TAN1114-06A.sgy</p> <p>TAN1114-07.sgy</p> <p>TAN1114-07A.sgy</p> <p>TAN1114-08.sgy</p> <p>TAN1114-09.sgy</p> <p>TAN1114-10.sgy</p> <p>TAN1114-10B.sgy</p> <p>TAN1114-11.sgy</p> <p>TAN1114-12.sgy</p> <p>TAN1114-12T.sgy</p> <p>TAN1114-13.sgy</p> <p>TAN1114-14.sgy</p> <p>TAN1114-15.sgy</p> <p>TAN1114-16.sgy</p> <p>TAN1114-17.sgy</p> <p>TAN1114-18.sgy</p> <p>TAN1114-19.sgy</p> <p>TAN1114-20.sgy</p> <p>TAN1114-21.sgy</p> <p>TAN1114-22.sgy</p> <p>TAN1114-23.sgy</p> <p>TAN1114-A.sgy</p> <p>RR1508-HKS01_01.sgy</p> <p>RR1508-HKS01_02.sgy</p> <p>RR1508-HKS01_02A.sgy</p> <p>RR1508-HKS01_03.sgy</p> <p>RR1508-HKS01_04.sgy</p> <p>RR1508-HKS01_05.sgy</p> <p>RR1508-HKS01_05A.sgy</p> <p>RR1508-HKS01_06.sgy</p> <p>RR1508-HKS01_07.sgy</p> <p>RR1508-HKS01_08.sgy</p> <p>RR1508-HKS01_09.sgy</p> <p>RR1508-HKS01_09A.sgy</p> <p>RR1508-HKS01_10.sgy</p>
Data for Estimation of 3D Moho depths beneath Southern Indian Shield by inverting seismic constraint gravity anomalies
<p>This is a help file for a description of all Data used for the implementation of our present paper<br> 'Estimation of 3D Moho depths beneath Southern Indian Shield by inverting seismic constraint gravity anomalies.' </p> <p> </p>
Data and program codes to reproduce the results of local earthquake seismic tomography for Tenerife Island
<p>This file contains the files to reproduce the results presented in the article: <strong>Local earthquake seismic tomography reveals the link between the crustal structure and volcanism in Tenerife (Canary Islands) </strong>by Ivan Koulakov, Luca D'Auria, Janire Prudencio, Iván Cabrera-Pérez, Nemesio M. Pérez, Jesús M. Ibáñez, <em>Journal of Geophysical Research, Solid Earth</em>.</p> <p>This file includes:</p> <p>1. The full folder with the LOTOS code for the passive-source seismic tomography (Koulakov, 2009, BSSA). </p> <p>2. Folder with the dataset including arrival times of the P and S waves from local seismicity in the area of the Tenerife Island, Canary Archipelago.</p> <p>3. README_TENERIFE.PDF file with the description of the workflow on how to reproduce the tomography models based on experimental and synthetic data presented in the article. </p> <p>Koulakov, I., 2009, LOTOS code for local earthquake tomographic inversion: Benchmarks for testing tomographic algorithms: Bulletin of the Seismological Society of America, v. 99, p. 194–214, https://doi.org/10.1785/0120080013.</p>
Data and Code: Detecting Blue Whale Calls in the Northeast Pacific Using Seismic Systems (Undergraduate Thesis)
<p><strong>SeismoData.ipynb</strong>: This Jupyter notebook is adapted from seismosocialdistancing.ipynb created by Thomas Lecocq, Fred Massin and Claudio Satriano. SeismoData.ipynb was used to retrieve seismic waveform data from the Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC) (https://ds.iris.edu/ds/ nodes/dmc/) and convert files from miniSEED to SAC format. It was also used to preview waveform and spectrogram plots.</p> <p><strong>BlueWhaleDetectionResults_J53A_Dec132011.mat</strong>: This .mat file summarizes whale detection results from OBS J53A on December 13th 2011. The objective was to calibrate a detection algoritm created for Northwest Atlantic blue whale A calls by Plourde and Nedimovic (2022), so that it can target Northeast Pacific blue whale B calls using seismometers off Washington and California. Three tests were performed to find optimal parameters. <em>BlueWhaleDetections_J53A_Test1</em><strong> </strong>are the detection results of a control that uses Northwest Atlantic blue whale parameters (16.25-18Hz frequency and 68-78s period ranges). <em>BlueWhaleDetections_J53A_Test2</em> <strong> </strong>are the detection results using Northeast Pacific blue whale B call parameters (14-17Hz frequency and 45-55s period ranges). <em>BlueWhaleDetections_J53A_Test2 </em>are the detection results using Northeast Pacific blue whale B call and C call parameters (10.5-12Hz and 14-17Hz frequency and 45-55s period ranges). <em>BlueWhaleDetections_J53A_SCC</em> are the 95% probability detection results from the Wilcock and Hilmo (2021) blue whale catalogue created using spectrogram cross-correlation. </p> <p><strong>NEPBlueWhaleMATLABcodes.zip</strong>: Contains scripts to run the recurrence interval power ratio method created by Plourde and Nedimovic (2022), adjusted to detect Northeast Pacific blue whale B calls and plot waveforms/spectrograms. First run <em>DetectBlueWhales.m </em>to calculate the recurrence power ratio every 12 minutes, then run C<em>reateBlueWhaleDetectionList.m </em>to classify detections with high power ratios and likely blue whale call detections.</p> <p><strong>BlueWhaleDetectionResults_CapeMendocino_Dec15to292014.mat</strong>: This .mat file summarizes the blue whale detection results from 2 OBS (FS02D and FS07D) and 1 land seismometer (CM09A) in close proximity, using Test 2 parameters. Note if there are less than 3 BWD in a given day, these are likely false detections.</p>
Marine seismic multichannel data collected in the Pomeranian Bay and around Rügen (southern Baltic Sea) by University of Hamburg
<p>The dataset includes multi-channel seismic data collected during various marine student training cruises. The cruises were organized and led by the Institute of Geophysics at the University of Hamburg.</p> <p>The seismic sources were GI-Guns or Mini-GI-Guns from the company SODERA. The data was recorded with various analog streamer systems. All data are poststack time-migrated with suppressed seafloor multiples.</p> <p>All data are in SEG-Y format with CDP-coordinates at standard byte positions (UTM33)</p> <table> <tbody> <tr> <td> <p>Vessel</p> </td> <td> <p>Cruise-ID</p> </td> <td> <p>Year</p> </td> </tr> <tr> <td> <p>RV ALKOR</p> </td> <td> <p>AL225</p> </td> <td> <p>2003</p> </td> </tr> <tr> <td> <p>RV HEINCKE</p> </td> <td> <p>HE217</p> </td> <td> <p>2004</p> </td> </tr> <tr> <td> <p>RV ALKOR</p> </td> <td> <p>AL263</p> </td> <td> <p>2005</p> </td> </tr> <tr> <td> <p>RV ALKOR</p> </td> <td> <p>AL562</p> </td> <td> <p>2021</p> </td> </tr> <tr> <td> <p>RV ALKOR</p> </td> <td> <p>AL582</p> </td> <td> <p>2022</p> </td> </tr> <tr> <td> <p>RV ALKOR</p> </td> <td> <p>AL605</p> </td> <td> <p>2023</p> </td> </tr> </tbody> </table>
Phase picker models and training data for paper "Deep learning models for regional phase detection on seismic stations in Northern Europe and the European Arctic"
<p>This ZIP file includes tensorflow models for seismic phase detection. Please see how to use these models here: https://github.com/NorwegianSeismicArray/tphasenet</p> <p>The HDF5 files includes waveforms and labels which are part of the training data set (only NORSAR event catalogue and station ARA0).</p>
Fiber-optic seismic sensing of vadose zone soil moisture dynamics data sets
<p>CC_daily.h5: Daily cross-correlation functions for common-offset DAS channels.</p> <p>RCC_dv_v_full.csv: Summary of all the measured dv/v from the ballistic surface waves in daily cross-correlation functions.</p>
Codes, Catalogues and Data for "Deep Learning Phase Pickers: How Well Can Existing Models Detect Hydraulic-Fracturing Induced Seismicity from a Downhole Array"
<p><strong>Codes, Catalogues and Data available for:</strong> <br>"Deep Learning Phase Pickers: How Well Can Existing Models Detect Hydraulic-Fracturing Induced Seismicity from a Downhole Array"</p> <p><strong>Catalog</strong> folder: Contains the CMM (beam-forming based) event catalogue as well as event and station information for the PNR-1z site.</p> <p><strong>Classification Test</strong> folder: Jupyter notebooks that run the classification tests and mseed input data of isolated phases (P, S, Noise).</p> <p><strong>DL_model_catalogues</strong> folder: Contains full catalogues for each DL phase picker (GPD, U-GPD, EQT and PhaseNet) and the LinMEF-filtered catalogues.</p> <p><strong>Model_run_docs</strong> folder: Util/core files for PhaseNet and EQTransformer to read data with different sampling frequencies (i.e., not 100 Hz)</p> <p><strong>Data</strong> folder: Contains one hour of continuous downhole data (11th December 2018, 9am-10am) from the PNR-1z dataset.</p>
GPS, InSAR, and seismic waveform data for study of 2014 South Napa, California, earthquake
<p>GPS_Brocher_et_al2015.txt : Observed static offsets at CGPS and SGPS sites, respectively, presented by Brocher et al. (2015) determined using GPS time series up to several days after the event</p> <p>napa_CSK_20140619_20140903_asc.grd : Observed unwrapped COSMO-SkyMed ascending interferogram spanning June 19 - September 3, 2014</p> <p>napa_CSK_20140726_20140827_desc.grd : Observed unwrapped COSMO-SkyMed descending interferogram spanning July 26 - August 27, 2014</p> <p>napa_sentinel_20140807_20140831_desc.grd : Observed unwrapped Sentinel descending interferogram spanning August 7 - August 31, 2014</p> <p>seismic_waveforms.tar.gz : Three-component seismic waveforms in (time (s after origin time), velocity (m/s)) format for 16 stations bandpass filtered between 0.067 and 1.5 Hz. Filenames indicate which velocity component (East, North, or Up=Z) and station name.</p> <p>Study: "Coseismic slip and early after slip of the M6.0 August 24, 2014 South Napa, California, earthquake" by Fred F. Pollitz, Jessica R. Murray, Sarah E. Minson, Charles W. Wicks, and Jerry L. Svarc. Journal of Geophysical Research, <em>in press</em></p>
Lithospheric structure and strength variations in Antarctica from joint modeling of elevation, geoid and seismic data
<p>These models include Moho depth, LAB depth and integrated lithospheric strength based on a 1D approach involving thermal analysis under local isostasy and a rheological method.</p>
Kawah Ijen volcano seismic data 2012
<p>Seismic data (vertical channel) from Kawah Ijen volcano at station IJEN. Information regarding the station location and sensor can be found in <a href="https://doi.org/10.1002/2014JB011590">https://doi.org/10.1002/2014JB011590</a>. Data were collected and archived thanks to Devy Syahbana, Suparjan and Bambang Heri Purwanto from CVGHM (Center for Volcanology and Geological Hazard Mitigation).</p>
Data for Urban Seismic Source Mapping
<p>In this study, we explore the innovative use of existing and ubiquitous urban infrastructure--telecommunication optical fibers--to map urban seismic sources. We integrate seismic interferometry and beamforming algorithms to overcome the proximity limitations of previous urban seismology approaches. <br>Our method models the propagation of seismic surface waves to estimate the spatio-temporal distribution of seismic source power (SSP; average seismic energy per unit of time), enabling the detection and localization of urban seismic sources occurring remotely from optical fibers.</p> <p><code>Put these pickle files into '/urban_das/data/das_data/' and run the scripts at https://github.com/jingxiaoliu/urban_das</code></p> <p>If you use this implementation, please cite our papers:</p> <blockquote> <p>[1] Liu, J., Li, H., Noh, H. Y., Santi, P., Biondi, B., & Ratti, C. (2025). Urban sensing using existing fiber-optic networks. <em>Nature Communications</em>, <em>16</em>(1), 3091.</p> <p>[2] Yuan, S., Liu, J., Noh, H. Y., Clapp, R., & Biondi, B. (2024). Using vehicle‐induced DAS signals for near‐surface characterization with high spatiotemporal resolution. <em>Journal of Geophysical Research: Solid Earth</em>, <em>129</em>(4), e2023JB028033.</p> <p>[3] Liu, J., Yuan, S., Dong, Y., Biondi, B., & Noh, H. Y. (2023). TelecomTM: A fine-grained and ubiquitous traffic monitoring system using pre-existing telecommunication fiber-optic cables as sensors. <em>Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies</em>, <em>7</em>(2), 1-24.</p> </blockquote>
Geodetic model of the March 2021 Thessaly seismic sequence inferred from seismological and InSAR data
<p>A selection of Sentinel-1 (S1) wrapped and unwrapped measurements used in this study (from "a" to "u" files in tiff format as indicated in the word file attached). S1 data were processed by using our own internally developed InSAR processing chain.<br> <br> Earthquakes data locations.</p> <p><br> </p> <p> </p>
Dataset - seismic data from central-western Italy used in the paper on rapid prediction of ground motion using a Convolutional Neural Network
<p>The dataset published here is the central-western Italy dataset used in the paper "<em>Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data"</em> (<a href="https://arxiv.org/abs/2105.05075">https://arxiv.org/abs/2105.05075</a>). The code for the paper is available at <a href="https://github.com/djozinovi/TLpredIM">https://github.com/djozinovi/TLpredIM</a>. The abstract of the paper:</p> <blockquote> <p>In a recent study (Jozinović et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs from the standard procedure adopted by earthquake early warning systems (EEWSs) that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation waveforms for the 2016 earthquake sequence in central Italy for 915 events (CI dataset). The CI dataset has a large number of spatially concentrated earthquakes and a dense station network. In this work, we applied the CNN model to an area around area near Pisa, Italy. In our initial application of the technique, we used a dataset consisting of 266 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller dataset performed worse compared to the results presented in the original study by Jozinović et al. (2020). To counter the lack of data, we adopted transfer learning (TL) using two approaches: first, by using a pre-trained model built on the CI dataset and, next, by using a pre-trained model built on a different (seismological) problem that has a larger dataset available for training. We show that the use of TL improves the results in terms of outliers, bias, and variability of the residuals between predicted and true IMs values. We also demonstrate that adding knowledge of station positions as an additional layer in the neural network improves the results. The possible use for EEW is demonstrated by the times for the warnings that would be received at the station PII.</p> </blockquote>
Saskatchewan seismic data set 2
<p>Seismic data from Saskatchewan glacier. Includes the waveform data, the log files, and the instrument response file.</p>
Woerthersee sediment core data for the publication "Validation of seismic hazard curves using a calibrated 14 ka lacustrine record in the Eastern Alps, Austria"
<p>This dataset comprises sediment core data of Wörthersee, a lake in the Eastern European Alps, Austria. Together with a dataset comprising the seismic data (10.5281/zenodo.6479186), this is the basis for the publication Daxer et al. "Validation of seismic hazard curves using a calibrated 14 ka lacustrine record in the Eastern Alps, Austria".</p> <p>The files contain the following data:</p> <ul> <li>Core images Long Cores.zip: Core images of the Wörthersee Kullenberg-type long cores acquired with an ITRAX core scanner</li> <li>Core images Short Cores.zip: Core images of the Wörthersee gravity short cores (hammer-coring or trigger cores of the Kullenberg system) acquired with an ITRAX core scanner; provided as .tif files</li> <li>CT data WOER18-L5-X-Dicom.zip: X-ray computed tomography data acquired with a Siemens SOMATOM Definition AS (voxel size 0.2 x 0.2 x 0.3 mm); provided in DICOM format</li> <li>MSCL data.zip: Data acquired with a Geotek Multi-sensor core logger (e.g. magnetic susceptibility and gamma density); provided as Excel spreadsheets</li> <li>XRF data.zip: X-ray fluorescence data acquired with a ITRAX core scanner; provided in .csv format</li> </ul>
Seismic Magnitude Clustering is Prevalent in Field and Laboratory Catalogs [DATA]
<p>Catalogs for Nature Communications article: Seismic Magnitude Clustering is Prevalent in Field and Laboratory Catalogs.</p> <p> </p> <p>Update: In DataVariableExplanation, two catalogs from University of Minnesota</p> <p>Mixed mode and mode I bending data description needs to show that the third column is in seconds.</p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>This dataset contains waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo sampling algorithm and a 3-D Earth model of the Japanese islands. Specifically, it includes processed observed waveforms from the Full Range Seismograph Network of Japan (F-Net, http://www.fnet.bosai.go.jp) and synthetic waveforms for the maximum-likelihood solutions as well as Global Centroid Moment Tensor (GCMT) solutions for all study events inverted at different periods. Detailed description of the dataset is included in the README file. </p>
cross-correlations of seismic data nodes Lipari 2018
<p>Stacked cross-correlation functions of the continuous seismic data recorded at the nodes installed at Lipari in 2018. Data in SAC format. All details about stations (name, latitude longitude, elevation) and data ( sampling rate, starting and ending time, etc) are in the HEADER of each file.</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.