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374 results for “Power Data”
Data from: Time series dataset of fish assemblages near thermal discharges at nuclear power plants in northern Taiwan
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Data from: QTL detection power of multi-parental RIL populations in Arabidopsis thaliana
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Data from: Is your phylogeny informative? Measuring the power of comparative methods
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Data from: Long-term monitoring dataset of fish assemblages impinged at nuclear power plants in northern Taiwan
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Inflow and meteorological data of the planned Longyangxia hydro–PV-wind power plant
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Supplementary Data: The role of hydro power, storage and transmission in the decarbonization of the Chinese power system
<p>This is the supplementary data to the publication:</p> <p>The role of hydro power, storage and transmission in the decarbonization of the Chinese power system</p> <p><a href="https://doi.org/10.1016/j.apenergy.2019.02.009">https://doi.org/10.1016/j.apenergy.2019.02.009</a></p> <p>This dataset includes:</p> <ul> <li>Hourly wind, solar and electricity load time series for 31 Chinese provinces and their calculations</li> <li>Reservoir hydro stations' effective capacity and inflow time series (details are referred to <a href="https://zenodo.org/record/1471322">Daily hydro power time series (1979-2016) for 43 Chinese reservoir hydro stations</a>)</li> <li>Wind and solar power's geographical potential calculations</li> <li>Directory p_nom_min contains existing wind and solar power capacities up to end of 2014</li> <li>Long-range transmission topologies</li> </ul> <p>The bash command scripts initiate three sets of simulations respectively: CO2 emission reduction sweep; 38-year weather input sensitivity; constraining transmission volume.</p> <p>Dependencies:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">Pypsa</a></li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a></li> <li><a href="https://github.com/AUESG/REatlas-client">RE-atlas(optional)</a></li> </ul>
ONCOlogy-targeted NLP-powered Federated Hyper-archItecture and Data Sharing Framework for Health Data Reusability
ClinicalTrials.gov study NCT05060835. IPD Sharing: NO. Countries: 0. Publications: 0.
Prognostics Of Power Mosfets Under Thermal Stress Accelerated Aging Using Data-Driven And Model-Based Methodologies
An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Die-attach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.
ROSETTA SOLAR ARRAY AND POWER ENGINEERING DATA
This CODMAC level 3 data set contains the key parameters of the SA and Power Housekeeping. In particular, it provides information on the SA (mis)alignment, incidence angle & displacement errors as well as information on the Power subsystem units which include the Master bus voltage, currents etc. It covers the period from launch in 2004, through the 3 Earth and 1 Mars flyby, plus the hibernation phases, plus the asteroid flybys and finally covers the Prelanding, comet escort & Extension phases of the prime target of the mission. The prime target is comet 67P/Churyumov-Gerasimenko 1 (1969 R1). This version V1.0 is the first version of this dataset.
Prognostics of Power MOSFETs under Thermal Stress Accelerated Aging using Data-Driven and Model-Based Methodologies
An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Die- attach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.
IBEX High Energy Neutral Atom Imager (ENA-Hi) Data Release-14, Compton Getting corrected, not Survival Probability corrected, Ram direction, West Ecliptic Global Distributed Flux and Flux Power Law Slope Maps, Level H3 (H3), three year average Data
The Interstellar Boundary Explorer, IBEX, has operated in space since 2008 updating our knowledge of the outer heliosphere and its interaction with the local interstellar medium. Start-time: 2008-12-25. There are currently 15 releases of IBEX-HI and/or IBEX-LO data covering the years from 2009 to 2018. This data set is derived from the Release 14 three-year IBEX-Hi map data with two-year overlaps of adjacent maps, 2009-2011, 2010-2012, and so forth through 2015-2017 from ram-direction fluxes with corrections for spacecraft motion, cg: Compton-Getting, but with no corrections, sp, for Energetic Neutral Atom, ENA, survival probability between 1 and 100 AU. The data set parameters include line-of-sight, LOS, integrated pressures computed separately from the Global Distributed Flux, GDF, the Ribbon Flux, and the Total Flux from summing GDF and Ribbon LOS pressures. Additionally there are signal to noise ratios for the GDF, Ribbon, and Total LOS pressures. Finally, there are power law slope values for the GDF differential flux and signal to noise ratios of the slope. The IBEX Release 14 data are archived as fully citable data. Please consult IBEX team publications and personnel for further details on production, processing, and usage of these data. The data consist of ram-direction sky maps in Solar Ecliptic Longitude, east and west, and Latitude angles for the above parameters. Details of the data and enabled science from Release 14 are given in the following journal publication: Schwadron, N. A., et al. 2018, Time Dependence of the IBEX Ribbon and the Globally Distributed Energetic Neutral Atom Flux Using the First 9 Years of Observations, DOI: 10.3847/1538-4365/aae48e. The following codes are used to define data set types in the multiple IBEX data releases: +-----------------------------------------------------------------------------------------------------------------------------------------------------------------+ Code Code definition --------- ------------------------------------------------------------------------------------------------------------------------------------------------------- cg Compton-Getting corrections have been applied to the data to account for the speed of the spacecraft relative to the direction of arrival of the ENAs nocg no Compton-Getting corrections --------- ------------------------------------------------------------------------------------------------------------------------------------------------------- sp survival probability corrections have been applied to the data to account for the loss of ENAs due to radiation pressure, photoionization and ionization via charge exchange with solar wind protons as they stream through the heliosphere. This correction scales the data out from IBEX at 1 AU to approximately 100 AU. In the original data this mode is denoted as Tabular. noSP no survival probability corrections have been applied to the data --------- ------------------------------------------------------------------------------------------------------------------------------------------------------- omni data from all directions ram data was collected when the spacecraft was ramming into the incoming ENAs antiram data was collected when the spacecraft was moving away from the incoming ENAs +-----------------------------------------------------------------------------------------------------------------------------------------------------------------+ This particular data set denoted in the original ASCII files as: +------------------------------------------------------------------------------------------------------------------------------------------------------------+ Directory Name File Content Description +---------------- -------------------------------------------------------------------------------------------------------------------------------------------+ GDFPressure Globally Distributed Flux Line-of-Sight Integrated Pressure in pdyne-au/cm^2 GDFSlope Power Law Slope of the differential flux spectrum for the Globally Distributed Flux GDFSlopeSN Signal/Noise ratio of the GDF differential flux power law slope where noise represents uncertainty GDFSN Globally Distributed Flux Signal/Noise, where Noise is defined as the uncertainty and the Signal is GDF Line-of-Sight integrated pressure RibbonPressure Ribbon Line-of-Sight Integrated Pressure in pdyne-au/cm^2 RibbonSN Ribbon Signal/Noise, where Noise is defined as the uncertainty and the Signal is GDF Line-of-Sight integrated pressure TotPressure Total Pressure in ENA maps including both the GDF and Ribbon. Line-of-Sight Integrated Pressure in pdyne-au/cm^2 TotSN Total Pressure Signal-to-Noise where noise represents uncertainty and signal represents the Total LOS integrated pressure +------------------------------------------------------------------------------------------------------------------------------------------------------------+
IBEX High Energy Neutral Atom Imager (ENA-Hi) Data Release-13, Compton Getting, no Survival Probability, Antiram direction, West Ecliptic Pressure and Flux Power Law Slope Maps, Level H3 (H3), annually averaged Data
The IBEX ENA-Hi data sets are from Release 13 of all-sky map data for the first ten years, 2009-2018, in the form of ram direction Hydrogen, H, energetic neutral atom fluxes with Compton-Getting corrections for spacecraft motion and with no corrections for ENA survival probability between 1 and 100 AU. All-sky maps have been compiled for the whole 1 yr time interval. The Interstellar Boundary Explorer, IBEX, has operated in space since 2008 updating our knowledge of the outer heliosphere and its interaction with the local interstellar medium. Start-time: 2008-12-25. There are currently 14 releases of IBEX ENA-Hi and/or IBEX ENA-Lo data covering 2009-2018. The data consist of all-sky maps in Solar Ecliptic Longitude, east and west, and Latitude angles for Energetic Neutral Atom, ENA, Hydrogen fluxes from either IBEX ENA-Hi from energy band 2 through energy band 6, see the first table below, or from IBEX ENA-Lo from energy band 5 through energy band 8, see the second table below. Details of the data and enabled science from Release 13 are given in the following journal publications that describe the 1-yr data results and the IBEX-Hi and IBEX-Lo Instruments: McComas, D.J., et al. (2018), Heliosphere Responds to a Large Solar Wind Intensification: Decisive Observations from IBEX, Astrophys. J. Lett., 856(1), L10, (6 pp.), http://doi.org/10.3847/2041-8213/aab611 Funnsten, H.O., et al. (2009), The Interstellar Boundary Explorer High Energy (IBEX-Hi) Neutral Atom Imager, Space Sci. Rev., 146, 75-103, https://doi.org/10.1007/s11214-009-9504-y Fuselier, S.A., et al. (2009), The IBEX-Lo Sensor, Space Sci. Rev., 146, 117-147, https://doi.org/10.1007/s11214-009-9495-8 The IBEX ENA-Hi band/channel center energies and full width half maximum, FWHM, energy ranges are listed in a table below: +-----------------------------------------------------+ Energy Band Center Energy Energy Range ----------------------------------------------------- Channel 2 ~0.71 keV 0.52 keV to 0.95 keV Channel 3 ~1.11 keV 0.84 keV to 1.55 keV Channel 4 ~1.74 keV 1.36 keV to 2.50 keV Channel 5 ~2.73 keV 1.99 keV to 3.75 keV Channel 6 ~4.29 keV 3.13 keV to 6.00 keV +-----------------------------------------------------+ The IBEX ENA-Lo band/channel center energies are listed in a table below: +-----------------------------+ Energy Band Center Energy ----------------------------- Channel 1 0.015 keV Channel 2 0.029 keV Channel 3 0.055 keV Channel 4 0.110 keV Channel 5 0.209 keV Channel 6 0.439 keV Channel 7 0.872 keV Channel 8 1.821 keV +-----------------------------+ This particular IBEX-Hi CDF data product was constructed from the original ascii files named using the pattern hvset_noSP_antiram_cg_yearN for N=1,10, includes pixel map data from the antiram direction, with corrections, cg, for the Compton-Getting effect no corrections, nosp, for ENA survival probability between 1 AU and 100 AU, and a map compilation cadence equal to one year. In all, there are two IBEX ENA-Hi Release 13 CDF data products with one Compton-Getting correction setting, two survival probability settings, and one directional setting: ram. The table below defines how the file naming pattern is constructed for the two data products. Note that "ibex_h3_ena_hi_r13" is the file naming pattern root for these IBEX ENA-Hi CDF data products. The asterisk symbols in the last column of the table shows the line corresponding to this CDF data product within the expanded file naming pattern schema. +---------------------------------------------------------------------------------------------------+ C-G Corr. SP Corr. Dir. Acronym ENA Hi/Lo File Naming Pattern for 1 yr Skymaps --------------------------------------------------------------------------------------------------- cg nosp ram ENA Hi ibex_h3_ena_hi_r13_cg_nosp_ram_1yr cg nosp antiram ENA Hi ibex_h3_ena_hi_r13_cg_nosp_antiram_1yr *** +---------------------------------------------------------------------------------------------------+ The first column in the above table shows whether Compton-Getting, C-G, corrections have been applied to the data. C-G corrections account for how ENA measurements are affected by the the orientation of the IBEX spacecraft velocity vector relative to the arrival direction of the ENAs. * cg: Compton-Getting corrections applied * nocg: Compton-Getting corrections not applied The second column in the above table shows whether Survival Probability, SP, corrections have been applied to the data. SP corrections account for the loss of ENAs due to radiation pressure, photoionization and ionization via charge exchange with solar wind protons as they stream through the heliosphere. This correction scales the data out from IBEX at 1 AU to approximately 100 AU. In the original data this mode is denoted as Tabular. * sp: Survival Probability corrections applied *
Training data for the shared task Ideology and Power Identification in Parliamentary Debates - with errors please do not use!
<p><strong>This dataset is an early release with errors. Please do not use this data set. The official dataset for the shared task will be announced on <a href="https://touche.webis.de/clef24/touche24-web/ideology-and-power-identification-in-parliamentary-debates.html">the shared task webpage</a>.</strong></p>
Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify differences in DNA methylation [RRBS]
GEO Series GSE169234. Mus musculus. 125 samples. Type: Methylation profiling by high throughput sequencing.
Data for "A Fast-Charging Study on the Lifetime of Power-Optimized Lithium Titanite Batteries By Electrochemical Impedance Spectroscopy"
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Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify differences in DNA methylation
GEO Series GSE169235. Mus musculus; Homo sapiens; Rattus norvegicus. 205 samples. Type: Methylation profiling by genome tiling array; Methylation profiling by high throughput sequencing.
MMS 1 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data
The MMS magnetic field power spectral density (BPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Search Coil Magnetometer (SCM) in the SCM123 coordinate system, see SCM data product guide for details, https://lasp.colorado.edu/mms/sdc/public/datasets/fields/. This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 88 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The DSP and SCM instrument papers can be found at https://link.springer.com/article/10.1007/s11214-014-0115-x and https://link.springer.com/article/10.1007/s11214-014-0096-9, respectively. The DSP and SCM data product guides can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
MMS 4 Digital Signal Processor (DSP) Double Probe (ADP, SDP), Electric Field Power Spectral Density, Level 2 (L2), Slow Mode, 16 s Data
The MMS electric field power spectral density (EPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Axial Double Probe (ADP) and Spin-Plane Double Probe (SDP). This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 56 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The averaging process has 48-bit accuracy to maximize the dynamic range. The amplitudes undergo a pseudo-logarithmic compression to an 8-bit number representing over 120 dB of dynamic range at ~5% precision.
MMS 3 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Slow Mode, 16 s Data
The MMS magnetic field power spectral density (BPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Search Coil Magnetometer (SCM) in the SCM123 coordinate system (scm1 = - x sensor; scm2 = -z sensor; scm3 = -y sensor). This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 88 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The averaging process has 48-bit accuracy to maximize the dynamic range. The amplitudes undergo a pseudo-logarithmic compression to an 8-bit number representing over 120 dB of dynamic range at ~5% precision.
MMS 4 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Slow Mode, 16 s Data
The MMS magnetic field power spectral density (BPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Search Coil Magnetometer (SCM) in the SCM123 coordinate system (scm1 = - x sensor; scm2 = -z sensor; scm3 = -y sensor). This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 88 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The averaging process has 48-bit accuracy to maximize the dynamic range. The amplitudes undergo a pseudo-logarithmic compression to an 8-bit number representing over 120 dB of dynamic range at ~5% precision.
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.