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490 results for “Propagation”
Wind Weimer Propagated 60 s Resolution Tri-axial Fluxgate Magnetometer in GSM Coordinates
Wind Weimer propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution tri-axial fluxgate magnetometer data in GSM coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405. There are now two version of this data set. An off set has been found in the Wind MFI Bz component that is present after November 2004. Version 2 has this offset removed. Prof. R.L. McPherron determined the correction to be Bz = Bz - (-0.000000130406219.*odoy.*odoy + 0.000576303146.*odoy + 0.679940509 + 0.3215*cos(2*pi*(doy-171)/366)) where doy is the day of the year in units of days and odoy is the days sinces Jan 1, 1999 00:00:00 UT in units of days.
Geotail Comprehensive Plasma Instrumentation (CPI) data Weimer Propagated 60 s Resolution in GSM Coordinates
Geotail Weimer propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution CPI data in GSM coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.
Geotail Weimer Propagated using CPI 60 s Resolution Tri-axial Fluxgate Magnetometer in GSM Coordinates
Geotail Weimer propagated solar wind data using CPI and linearly interpolated to have the measurements on the minute at 60 s resolution tri-axial fluxgate magnetometer data in GSM coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.
Wind 3DP Weimer Propagated 60 s Resolution in GSM Coordinates
Wind 3DP propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution data in GSM coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405. There are now two version of this data set. An off set has been found in the Wind MFI Bz component that is present after November 2004. Version 2 has this offset removed. Prof. R.L. McPherron determined the correction to be Bz = Bz - (-0.000000130406219.*odoy.*odoy + 0.000576303146.*odoy + 0.679940509 + 0.3215*cos(2*pi*(doy-171)/366)) where doy is the day of the year in units of days and odoy is the days sinces Jan 1, 1999 00:00:00 UT in units of days.
Geotail Comprehensive Plasma Instrumentation (CPI) data Weimer Propagated 60 s Resolution in GSE Coordinates
Geotail Weimer propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution CPI data in GSE coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.
ISEE-3 Weimer Propagated 60 s Resolution Tri-axial Fluxgate Magnetometer in GSE Coordinates
ISEE-3 Weimer propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution tri-axial fluxgate magnetometer data in GSE coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies. References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.
Modeling Propagation of Gas Path Damage
This paper describes how damage propagation can be tracked and modeled for a range of fault modes in some modules of commercial high bypass aircraft engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine (cycle deck) as a function of variations of flow and efficiency of the modules of interest. These surfaces are normalized and superimposed. Next, sensor readings are matched to those surfaces and – using an optimization approach – the corresponding flow and efficiency pair is found that best explains the sensor data. This flow and efficiency pair is then compared to previous pairs and the direction of the change as well as the rate of change is determined. The whole trajectory is then projected into the time domain. An extrapolation of the curve to the limit (which is established via operational margins) yields the remaining life. In a backward mode, the extrapolated curve is discretized and estimated future flow and efficiency pairs are retrieved. These pairs are then input to the cycle deck to produce future anticipated sensor readings as well as confirmatory trips of operational margins. Changes of the future sensor readings with real readings are used to adjust the remaining life calculations. The method is demonstrated on time series of historical engine faults.
Geotail Weimer Propagated using LEP 60 s Resolution Tri-axial Fluxgate Magnetometer in GSE Coordinates
Geotail Weimer propagated solar wind data using LEP and linearly interpolated to have the measurements on the minute at 60 s resolution tri-axial fluxgate magnetometer data in GSE coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies. References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.
Wind 3DP Weimer Propagated 60 s Resolution in GSE Coordinates
Wind 3DP propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution data in GSE coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405. There are now two version of this data set. An off set has been found in the Wind MFI Bz component that is present after November 2004. Version 2 has this offset removed. Prof. R.L. McPherron determined the correction to be Bz = Bz - (-0.000000130406219.*odoy.*odoy + 0.000576303146.*odoy + 0.679940509 + 0.3215*cos(2*pi*(doy-171)/366)) where doy is the day of the year in units of days and odoy is the days sinces Jan 1, 1999 00:00:00 UT in units of days.
Geotail Low Energy Particle (LEP) experiment data Weimer Propagated 60 s Resolution in GSM Coordinates
Geotail LEP Weimer propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution data in GSM coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.
ISEE-3 Weimer Propagated 60 s Resolution Tri-axial Fluxgate Magnetometer in GSM Coordinates
ISEE-3 Weimer propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution tri-axial fluxgate magnetometer data in GSM coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.
ACE SWEPAM Solar Wind Weimer Propagated 60 s Resolution Data in GSE Coordinates
ACE Weimer propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution SWEPAM data in GSE coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.
ARTs versus developmental plasticity: blastocyst abnormalities after ICSI and embryo culture do not propagate into the offspring in mice
GEO Series GSE50193. Mus musculus. 14 samples. Type: Expression profiling by array.
GBM cell lines derived and propagated according to Cambridge protocol
GEO Series GSE15846. Homo sapiens. 7 samples. Type: Expression profiling by array.
Small extracellular vesicles propagate the inflammatory response after trauma.
GEO Series GSE172212. Mus musculus; Homo sapiens. 45 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by array.
Pre-transplant conditioning propagates selective loss of host Helios+/Nrp1+ Treg in aHSCT
GEO Series GSE227929. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
In Vitro Propagation of XXY Human Klinefelter Spermatogonia Stem Cells; a step towards new fertility opportunities
GEO Series GSE208761. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
A self-propagating c-Met-SOX2 axis drives cancer-derived IgG signaling that promotes lung cancer cell stemness [RNA-seq]
GEO Series GSE222023. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Propagation of Human corneal Endothelial Cells – A Novel Dual Media Approach
GEO Series GSE50212. Homo sapiens. 12 samples. Type: Expression profiling by array.
Next Generation Sequencing: in vivo genome-wide CRISPR sgRNA screen in primary cancer-initiating and propagating bcCML stem cells
GEO Series GSE135300. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
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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.