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858 results for “monitoring data”
BeXRB Monitor Data API
A fully queryable REST API with JSON, XML, and CSV output as well as inline, runable examples. This is a monitor using data from Swift/BAT, MAXI and Fermi/GBM instruments.
ACE Electron Proton Alpha Monitor (EPAM) 5-min Count Rate Data
This descriptor identifies an ensemble of 8 data sets of EPAM count rates averaged over 5-min intervals in each of several energy bins. One data set (PG65) has spin-averaged rates while the other 7 have rates in each of 4 or 8 spin sectors. Each of the 7 sectored data sets is specific to a given one of the several EPAM instruments (apertures). Each of the 8 data sets is individually described in the VHO/VEPO interface at https://vho.gsfc.nasa.gov.
ACE Electron, Proton, and Alpha Monitor (EPAM) Solar Wind Plasma Energetic Particle Fluxes, Key Parameter (K1), 1 h Data
ACE Browse Data are designed for monitoring large-scale Particle and Field Behavior and for selecting interesting Time Periods. The Data are automatically generated from the Spacecraft Data Stream using simple Algorithms provided by the Instrument Teams. It is not routinely checked for Accuracy and is subject to Revision. Use this Data at your own Risk, and consult with the appropriate Instrument Teams about citing it. EPAM Browse Data are not validated by the Experimenters and should not be used except for Preliminary Examination prior to detailed Studies. ACE Electron, Proton, and Alpha Monitor (EPAM) References: http://www.srl.caltech.edu/ACE/.
ACE Electron Proton Alpha Monitor (EPAM) 12-sec Count Rate Data
This descriptor identifies an ensemble of 8 data sets of EPAM count rates averaged over 12-sec intervals in each of several energy bins. One data set (PG65) has spin-averaged rates while the other 7 have rates in each of 4 or 8 spin sectors. Each of the 7 sectored data sets is specific to a given one of the several EPAM instruments (apertures). Each of the 8 data sets is individually described in the VHO/VEPO interface at https://vho.gsfc.nasa.gov.
General Purpose Data-Driven System Monitoring for Space Operations
Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. Inductive Monitoring System uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing Inductive Monitoring System space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the Inductive Monitoring System data-driven technique as related to several Integrated Systems Health Management elements. Thematically, the projects listed will be used as case studies. The maturation of Inductive Monitoring System via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how Inductive Monitoring System can be used to complement a suite of other Integrated System Health Management tools, providing initial fault detection support for diagnosis and recovery.
ACE Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) Plasma Moments, Preliminary Values, Key Parameter (K0), 5 min Data
The ACE Browse Data are designed for monitoring large-scale Particle and Field Behavior and for selecting interesting Time Periods. The Data are automatically generated from the Spacecraft Data Stream using simple Algorithms provided by the Instrument Teams. It is not routinely checked for Accuracy and is subject to revision. Use this Data at your own Risk, and consult with the appropriate Instrument Teams about citing it. These are SWEPAM Browse Data. These Data have not validated by the Experimenters and should not be used except for preliminary Examination prior to detailed Studies. SWEPAM - Solar Wind Electron Proton Alpha Monitor References: https://izw1.caltech.edu/ACE/.
ACE Electron Proton Alpha Monitor (EPAM) Daily-Averaged Flux Data
This descriptor identifies an ensemble of 26 data sets of daily-averaged, background-corrected energetic particle fluxes. Various data sets have fluxes that are: spin-averaged or spin-sectored; if sectored then from one of four telescope apertures (LEMS30, LEFS60, LEMS120, LEFS150); of ~63-4430 keV protons or ~40-360 keV electrons or 100-6500 keV/n Z>1 ions where any one of these energy ranges is partitioned into 10-25 energy bins; in the spacecraft rest frame or solar wind rest frame. Each of the 26 data sets is individually described in the VHO/VEPO interface at https://vho.gsfc.nasa.gov.
ACE Electron Proton Alpha Monitor (EPAM) 1-Hour Level 2 Data
ACE Electron Proton Alpha Monitor (EPAM) Level 2 intensities at 1-hour time resolution. Calibrated science quality data. The data are accessible via ftp in HDF and CDF from the ACE Science Center and CDAWeb, respectively, and in ASCII format from the value-added interfaces at those sites. The "parameter keys" given below are as used by CDAWeb. Daily and 27-day averages are also available via the ASC paths given below.
ACE Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) Plasma Moments, Level 2 (H2), 1 h Data
This Data Product contains Measurements from the ACE Solar Wind Electron Proton Alpha Monitor (SWEPAM) Instrument. The Quality of the ACE Level 2 Data are such that it is suitable for serious Scientific Study. However, to avoid Confusion and Misunderstanding, it is recommended that Users consult with the appropriate ACE Team Members before publishing Work derived from the Data. The ACE Team has worked hard to ensure that the Level 2 Data are free from Errors, but the Team cannot accept Responsibility for erroneous Data, or for Misunderstandings about how the Data may be used. This is especially true if the appropriate ACE Team Members are not consulted before Publication. At the very least, Preprints should be forwarded to the ACE Team before Publication. SWEPAM References: https://izw1.caltech.edu/ACE/
SOHO Charge, Element, and Isotope Analysis System (CELIAS) Proton Monitor (PM) 5-minute Solar Wind Data in CDF Format
The data include 5-minute proton speed, density, most probable thermal speed, and arrival direction, as well as predicted alpha-particle speed. Also included are the SOHO spacecraft predicted positon in GSE coordinates, Heliocentric Range, Heliographic Latitude and Longitude, and Earth Carrington Rotation Number. This CDF dataset was converted from the original ASCII dataset at the SOHO Archive https://soho.nascom.nasa.gov/data/archive.html. Description of the CELIAS-PM instrument and scientific scope can be found on the CELIAS instrument home page http://www2.physik.uni-kiel.de/SOHO-CELIAS/ and on the SOHO mission home page https://sohowww.nascom.nasa.gov/. CELIAS Instrument Publication: Hovestadt, D., Hilchenbach, M., Bürgi, A. et al. CELIAS - Charge, Element and Isotope Analysis System for SOHO, Sol Phys 162, 441–481 (1995), https://doi.org/10.1007/BF00733436.
ACE Electron, Proton, and Alpha Monitor (EPAM) Solar Wind Plasma Energetic Particle Fluxes, Key Parameter (K0), 5 min Data
ACE Browse Data are designed for monitoring large-scale Particle and Field Behavior and for selecting interesting Time Periods. The Data are automatically generated from the Spacecraft Data Stream using simple Algorithms provided by the Instrument Teams. It is not routinely checked for Accuracy and is subject to Revision. Use this Data at your own Risk, and consult with the appropriate Instrument Teams about citing it. EPAM Browse Data are not validated by the Experimenters and should not be used except for Preliminary Examination prior to detailed Studies. ACE Electron, Proton, and Alpha Monitor (EPAM) References: http://www.srl.caltech.edu/ACE/.
ACE Electron Proton Alpha Monitor (EPAM) Daily Count Rate Data
This descriptor identifies an ensemble of 8 data sets of EPAM count rates averaged over 1-day intervals in each of several energy bins. One data set (PG65) has spin-averaged rates while the other 7 have rates in each of 4 or 8 spin sectors. Each of the 7 sectored data sets is specific to a given one of the several EPAM instruments (apertures). Each of the 8 data sets is individually described in the VHO/VEPO interface at https://vho.gsfc.nasa.gov.
SOHO Charge, Element, and Isotope Analysis System (CELIAS) Solar Extreme-Ultra-Violet Monitor (SEM) Daily Data in CDF Format
The data include daily average raw count rates in Channels 1 - 3 and First Order (26 - 34 nm, average of Ch1 and Ch3) and Central Order (0.1 - 50 nm, Ch2) photon fluxes, as well as SOHO Heliocentric location and distance. This CDF dataset was converted from the original ASCII dataset at the SOHO Archive https://soho.nascom.nasa.gov/data/archive.html. The original ASCII dataset Version 4 Science Product Release Notes are available at https://lasp.colorado.edu/eve/data_access/eve_data/lasp_soho_sem_data/long/LASP_SOHO_SEM_release_notes_v4.pdf. Description of the CELIAS-SEM instrument and scientific scope can befound on the CELIAS home page http://www2.physik.uni-kiel.de/SOHO-CELIAS/ and on the SOHO mission home page https://sohowww.nascom.nasa.gov/. CELIAS Instrument Publication: Hovestadt, D., Hilchenbach, M., Bürgi, A. et al. CELIAS - Charge, Element and Isotope Analysis System for SOHO, Sol Phys 162, 441–481 (1995), https://doi.org/10.1007/BF00733436.
SOHO Charge, Element, and Isotope Analysis System (CELIAS) Solar Extreme-Ultra-Violet Monitor (SEM) 15-second Data in CDF Format
The data include 15-second average raw count rates in Channels 1 - 3 and First Order (26 - 34 nm, average of Ch1 and Ch3) and Central Order (0.1 - 50 nm, Ch2) photon fluxes, as well as SOHO Heliocentric location and distance. This CDF dataset was converted from the original ASCII dataset at the SOHO Archive https://soho.nascom.nasa.gov/data/archive.html. The original ASCII dataset Version 4 science product release notes are available at https://lasp.colorado.edu/eve/data_access/eve_data/lasp_soho_sem_data/long/LASP_SOHO_SEM_release_notes_v4.pdf. Description of the CELIAS-SEM instrument and scientific scope can be found on the CELIAS home page http://www2.physik.uni-kiel.de/SOHO-CELIAS/ and on the SOHO home page https://sohowww.nascom.nasa.gov/. CELIAS Instrument Publication: Hovestadt, D., Hilchenbach, M., Bürgi, A. et al. CELIAS - Charge, Element and Isotope Analysis System for SOHO, Sol Phys 162, 441–481 (1995), https://doi.org/10.1007/BF00733436.
Scalable Distributed Change Detection from Astronomy Data Streams using Local, Asynchronous Eigen Monitoring Algorithms
This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeat images of the night sky every 20 seconds, thereby generating 30 terabytes of calibrated imagery every night that will need to be coanalyzed with other astronomical data stored at different locations around the world. Change point detection and event classification in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant variations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks is a challenging problem for such high-throughput distributed data streams. In this paper we propose a highly scalable and distributed asynchronous algorithm for monitoring the principal components (PC) of such dynamic data streams. We demonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as measuring variations in the fundamental plane of galaxy parameters. The proposed algorithm is provably correct (i.e. converges to the correct PCs without centralizing any data) and can seamlessly handle changes to the data or the network. Real experiments performed on Sloan Digital Sky Survey (SDSS) catalogue data show the effectiveness of the algorithm.
Rotor health monitoring combining spin tests and data-driven anomaly detection methods
Health monitoring is highly dependent on sensor systems that are capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system. Efforts are under way at NASA Glenn Research Center through support of the Intelligent Vehicle Health Management Project (IVHM) to develop and implement such sensor technology for a wide variety of applications. These efforts are focused on developing high temperature, wireless, low cost, and durable products. In an effort to address technical issues concerning health monitoring, this article considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip clearance and tip timing measurements in a rotating turbine engine-like-disk to detect the disk faults and assess its structural integrity. The experimental results composed at a range of rotational speeds from tests conducted at the NASA Glenn Research Center’s Rotordynamics Laboratory are evaluated and integrated into multiple data-driven anomaly detection techniques to identify faults and anomalies in the disk. In summary, this study presents a select evaluation of online health monitoring of a rotating disk using high caliber capacitive sensors and demonstrates the capability of the in-house spin system.
Propulsion Health Monitoring of a Turbine Engine Disk using Spin Test Data
On line detection techniques to monitor the health of rotating engine components are becoming increasingly attractive options to aircraft engine companies in order to increase safety of operation and lower maintenance costs. Health monitoring remains a challenging feature to easily implement, especially, in the presence of scattered loading conditions, crack size, component geometry and materials properties. The current trend, however, is to utilize noninvasive types of health monitoring or nondestructive techniques to detect hidden flaws and mini cracks before any catastrophic event occurs. These techniques go further to evaluate materials' discontinuities and other anomalies that have grown to the level of critical defects which can lead to failure. Generally, health monitoring is highly dependent on sensor systems that are capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system. Efforts are under way at NASA Glenn Research Center through support of the Intelligent Vehicle Health Management Project (IVHM) to develop and implement such sensor technology for a wide variety of applications [1-5]. These efforts are focused on developing high temperature, wireless, low cost and durable products.Therefore, in an effort to address the technical issues concerning health monitoring of a rotor disk, this paper considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip clearance and tip timing measurements in a rotating engine-like-disk-to predict the disk faults and assess its structural integrity. The experimental results collected at a range of rotational speeds from tests conducted at the NASA Glenn Research Center's Rotordynamics Laboratory will be evaluated using multiple data-driven anomaly detection techniques [6-9] to identify anomalies in the disk. This study is expected to present a select evaluation of online health monitoring of a rotating disk using these high caliber sensors and test the capability of the in-house spin system.
SOHO Charge, Element, and Isotope Analysis System (CELIAS) Proton Monitor (PM) 30-second Solar Wind Data in CDF Format
The data include 30-second proton speed, density, most probable thermal speed, and arrival direction, as well as predicted alpha-particle speed. Also included are the SOHO spacecraft predicted positon in GSE coordinates, Heliocentric Range, Heliographic Latitude and Longitude, and Earth Carrington Rotation Number. This CDF dataset was converted from the original ASCII dataset at the SOHO Archive https://soho.nascom.nasa.gov/data/archive.html. Description of the CELIAS-PM instrument and scientific scope can be found on the CELIAS instrument home page http://www2.physik.uni-kiel.de/SOHO-CELIAS/ and on the SOHO mission home page https://sohowww.nascom.nasa.gov/. CELIAS Instrument Publication: Hovestadt, D., Hilchenbach, M., Bürgi, A. et al. CELIAS - Charge, Element and Isotope Analysis System for SOHO, Sol Phys 162, 441–481 (1995), https://doi.org/10.1007/BF00733436.
Monitoring data_2021 (1)
<p>Monitoring data of the soil suction and volumetric water content measured at the standard vegetation area (A) and deep-rooting vegetation area (P), together with atmospheric data and rainfalls, for 2021.</p>
Towards Using Virtual Acoustics for Evaluating Spatial Ecoacoustic Monitoring Technologies - Data
<h1>About</h1> <p>This database contains the raw data and certain outputs used for the project 'Towards Using Virtual Acoustics for Evaluating Spatial Ecoacoustic Monitoring Technologies'. In this work, we developed an ambisonic Virtual Sound Environment (VSE) for simulating real natural soundscapes to evaluate spatial PAM technologies in a more controlled and repeatable manner.</p> <p>We set three objectives to validate this approach:</p> <ul> <li>(O1) to determine whether the VSE could replicate natural soundscapes well enough to be a test environment;</li> <li>(O2) to pilot the VSE as a test environment for Passive Acoustic Monitoring (PAM) hardware; and, </li> <li>(O3) to pilot the VSE as a test platform for PAM software.</li> </ul> <p>To meet these objectives, we used a recently-developed, open source six-microphone field recorder to capture recordings of six field sites and their VSE-based simulations. Sites were based at the Imperial College Silwood Park Campus (Ascot, UK). For O1, we compared field and VSE recordings using a typical suite of ecoacoustic metrics. For O2, we used the VSE to explore how orientation impacts the performance of the six-microphone array. We extended the suite of metrics from O1 to compare VSE recordings from this array at various pitch angles: vertical (as in the field), 45° pitch, and horizontal. For O3, we investigate how BirdNET and HARKBird, software for classifying and localising avian calls, respectively, perform on bird calls added to the VSE-replicated soundscapes. We compare adding calls by encoding to the ambisonics domain and by playback from individual loudspeakers. </p> <p> </p> <h3><strong>The data is organised as follows: </strong></h3> <p><strong>'6mic Audio O1O2': </strong>contains the six-channel field and VSE-based recordings of all six sites. Recordings are approximately 10 minutes long. Note that there are three VSE-based recording per site, for each recording orientation used in the VSE (vertical, 45°, and horizontal). Several of our analyses used low-passed versions of these recordings (4 kHz cutoff and 12 dB roll-off); note that the recordings provided here are raw and therefore not low-passed.</p> <ul> <li>File names indicate whether the recording was performed in the field or VSE ('Field' vs 'ReRec') and the orientation of the 6mic array for the latter ('V', '45', or 'H').</li> <li>The final number in each filename indicates the Site the recording corresponds to (1-6).</li> </ul> <p><strong>'6mic Audio O3':</strong> contains the six-channel VSE recordings of five of the original sites with additional avian calls located at certain moments in space and time in each recording. 10 bird calls were added to the soundscapes, each one in its own soundscape recording (each of the five sites' soundscapes was therefore used twice). Four methods of adding the bird calls were trialled, hence there are 40 files in this folder – 10 for each of: ambisonic encoding, playback from individual loudspeakers, playback from individual loudspeakers with no reverb, and playback from individual loudspeakers with no reverb or background soundscape (i.e., 'soloed'). Our analyses focussed on comparing the first two of these methods. The filenames are structured as follows:</p> <ul> <li> "sXsYbirdNameEmbeddingMethod', where X corresponds to the site the background soundscape was recorded at,</li> <li>Y indicates the position of the sinusoidal sweep used to create simulated reverb of the bird call (1-4 for 0°, 90°, 180°, or 270° around the device used to capture the ambisonic soundscape recordings),</li> <li>'birdName' is the common name of the added species, and</li> <li>'EmbeddingMethod' is either: <ul> <li>'Ambi' (ambisonic encoding),</li> <li>'LSPK' (playback from an individual loudspeaker),</li> <li>'LSPK-NR' (playback from an individual loudspeaker with no reverb), or</li> <li>'Solo' (soloed playback from an individual loudspeaker).</li> </ul> </li> <li>Note that due to issues of spatial aliasing, we low-passed the 'Ambi' and 'LSPK' recordings in our analyses (as the two main embedding methods compared) using a 4 kHz cutoff and 12 dB roll-off. However, again the raw (not low-passed) audio is shared here.</li> </ul> <p><strong>'Acoustic Indices':</strong> contains the CSV files with matrices of the Acoustic Indices extracted from the first channel of the low-passed '6mic Audio O1O2' recordings.</p> <ul> <li>Each column is for a different index, rows are the values over time (indices were extracted on 30 s windows).</li> <li>We extracted the following 7 common acoustic indices: <ul> <li>Acoustic Complexity Index (ACI; column 1),</li> <li>Acoustic Diversity Index (ADI; column 2),</li> <li>Acoustic Evenness (AEve; column 3),</li> <li>Bioacoustic Index (Bio; column 4),</li> <li>Normalised Difference Soundscape Index (NDSI; column 5)</li> <li>Acoustic Entropy (H; column 6), and</li> <li>Median of the Amplitude Envelope (M; column 7).</li> <li>Filenames indicate: <ul> <li>the field site,</li> <li>whether the indices are for a field or VSE ('Lab') recording, and</li> <li>the orientation of the recording for the latter ('Vert', '45', or 'H').</li> <li>The 'LP' suffix denotes that these indices were extracted from a low-passed version of the 6mic O1O2 recordings (4 kHz cutoff and 12 dB roll-off).</li> </ul> </li> </ul> </li> </ul> <p><strong>'BirdNET O1O2 Outputs':</strong> CSV files generated from avian call classifier BirdNET (using the Winows GUI version) using the first channel ('Mic 1') of the '6mic Audio O1O2' recordings.</p> <p><strong>'BirdNET O3 Outputs':</strong> outputs of BirdNET on the '6mic Audio O3' recordings.</p> <ul> <li>Here, rather than the raw CSV files generated by BirdNET, the BirdNET results have been filtered to just those during the added bird calls' start and end times, and have been compiled into two CSV files: <ul> <li>one for recordings of VSE-based soundscapes with bird calls added by <em>ambisonic encoding</em> and</li> <li>another for calls added by <em>individual loudspeaker playback</em>.</li> </ul> </li> <li>These CSV files also contain columns to specify added birds' site, sweep (used to generate reverberation, see '6mic Audio O3' above), azimuth and elevation.</li> </ul> <p><strong>'HARKBird O1O2 Outputs':</strong> outputs of avian call localisation tool HARKBird on the '6mic Audio O1O2' recordings. Note that HARKBird outputs a folder with additional results for each file, however, only the CSV files presented here. These contain the times and estimated azimuth angles of bird calls and were the only HARKBird ouput used for subsequent analyses.</p> <ul> <li>Filenames indicate whether recordings are from the field ('Field') or VSE ('ReRec'), and</li> <li>those for the latter also indicate the recording orientation ('Vert', 45' 'H').</li> <li>The final number in each filename indicates the site the recording corresponds to.</li> </ul> <p><strong>'HARKBird O3 Outputs':</strong> CSV files of HARKBird outputs (as described above) for the '6mic Audio O3' recordings.</p> <ul> <li>Filenames are based on those for this set of recordings (see '6mic Audio O3' above);</li> <li>names that contain 'LP' were low-passed (with a 4 kHz cutoff frequency and 12 dB roll-off) prior to passing through HARKBird.</li> </ul> <p><strong>'Manual Labels O2':</strong> manual labels of audible bird calls' species in the omnidirectional (first) channel of the Zylia ZM-1 ambisonic recordings (see 'Zylia Recordings' below) for all sites. This data was used to calculate the precision and recall of BirdNET's outputs on the 6mic O1O2 recordings for these sites.</p> <p><strong>'VGGish Features':</strong> contains the 128-dimension feature embedding of the pre-trained VGGish convolutional neural network extracted from the '6mic Audio O1O2' recordings.</p> <ul> <li>Filenames indicate the site,</li> <li>whether the recording was made in the field or VSE ('Lab'),</li> <li>and the recording's orientation.</li> <li>Note again that recordings were low-passed (4 kHz cutoff and 12 dB roll-off) prior to the feature extraction, hence the 'LP' suffix.</li> </ul> <p><strong>'Zylia Recordings':</strong> approximately 10 minute field recordings of the 6 study sites captured with the 19-microphone ZYLIA 'ZM-1' recorder. These recordings have been converted to third order ambisonic 'b-format' (16 channels) using Furse-Malham channel ordering and SN3D normalisation. This was achieved with the 'Zylia Ambisonics Converter' software. These third-order ambisonic recordings were used to replicate the six sites' soundscapes in the VSE.</p> <p>The accompanying code for this dataset has been submitted via ScholarOne with the manuscript for peer-review.</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.