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Dataset results
490 results for “Propagation”
Gene expression array analysis of tumor-propagating cell populations in human osteosarcoma
GEO Series GSE63390. Homo sapiens. 12 samples. Type: Expression profiling by array.
Nucleation-dependent propagation of Polycomb modifications emerges during the Drosophila maternal to zygotic transition
GEO Series GSE299311. Drosophila melanogaster. 69 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Predicting the propagation track of internal solitary waves in the Sulu Sea
<div>The data and codes for manuscript submitted to Geophysical Research Letters.</div> <div> </div> <div>Manuscript title:"Predicting internal solitary waves propagation track in the Sulu Sea using deep learning and time-series imagery from tandem Geostationary Orbit Satellites"; by (Longyu Huang, Jingsong Yang, Lin Ren, Zetai Ma, Peng Chen, Shuangyan He, Bingqing Liu and Antony K. Liu) </div> <div> </div> <div>File:</div> <div> </div> <div>1. \ISW_4h_observation</div> <div> The 4h satellite observation data by FY-4A and GK-2A for ISWs in the Sulu Sea, with the format ".tiff";</div> <div> <p>2. \gebco_depth<br> The water depth from GEBCO_2023 Grid dataset, with the format ".nc";</p> <p>3. \woa_mlt<br> The mixed layer thickness from World Ocean Atlas 2018 (WOA18) dataset, with the format ".nc";</p> <p>4. \hycom_current<br> The current field from HYCOM's Global Ocean Forecasting System (GOFS) 3.1 dataset, with the format ".nc";</p> </div> <div>5. \short_long_predict</div> <div> \long</div> <div> The predicted locations of ISWs for long term, with the format '.npy';</div> <div> \short</div> <div> The predicted locations of ISWs for short term, with the format '.npy';</div> <div> \fig2.py</div> <div> The code for Figure 2 in the manuscript;</div> <div> \gebco_2023_n12.0_s4.0_w116.0_e124.0.nc</div> <div> The depth file for Figure 2 in the manuscript;</div> <div> </div> <div>6. \spatial_error_distribution</div> <div> \error</div> <div> The predicted error for distance, propagation speed and direction;</div> <div> \fig3.py</div> <div> The code for Figure 3 in the manuscript;</div> <div> \gebco_2023_n12.0_s4.0_w116.0_e124.0.nc</div> <div> The depth file for Figure 3 in the manuscript;</div>
Impact of storm propagation speed on coastal flood hazard induced by offshore storms in the North Sea
<p>This datasets include all numerical simulation results of the paper "Impact of storm propagation speed on coastal flood hazard induced by offshore storms in the North Sea".</p>
Neonatal monocytes in tracheal aspirates from infants at risk for Bronchopulmonary Dysplasia propagate IL-1 signaling in the first weeks of lung injury
GEO Series GSE127455. Homo sapiens. 72 samples. Type: Expression profiling by high throughput sequencing.
Chronic intermittent hypoxia enhances pathological tau seeding, propagation, and accumulation, and exacerbates Alzheimer’s-like memory and synaptic plasticity deficits and molecular signatures
GEO Series GSE169478. Mus musculus. 23 samples. Type: Expression profiling by high throughput sequencing.
CD9-dependent extracellular vesicles mediate the propagation of ferroptosis
GEO Series GSE251770. Homo sapiens. 3 samples. Type: Other.
Differential yeast gene transcription during brewery propagation
GEO Series GSE16376. Schizosaccharomyces pombe; Saccharomyces cerevisiae; Saccharomyces pastorianus. 12 samples. Type: Expression profiling by array.
The selfish yeast plasmid exploits a SWI/SNF-type chromatin remodeling complex for hitchhiking on chromosomes and ensuring high-fidelity propagation
GEO Series GSE225582. Saccharomyces cerevisiae. 47 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Clonal propagation of Moniliophthora roreri and the emergence of unique lineages with distinct genomes during range expansion
GEO Series GSE226817. Moniliophthora roreri; Moniliophthora perniciosa. 48 samples. Type: Expression profiling by high throughput sequencing.
TFAP2C and HNRNPK control cellular bioenergetic metabolism and prion propagation [RNA-seq]
GEO Series GSE279798. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Global gene expression changes during human Angiomyolipoma xenograft propagation
GEO Series GSE94114. Homo sapiens. 4 samples. Type: Expression profiling by array.
Yellow fever vaccine propagation in primary human hepatocytes triggers antiviral and cytolytic responses
GEO Series GSE306280. Homo sapiens. 36 samples. Type: Expression profiling by high throughput sequencing.
Propagation of Shoaling Internal Solitary Waves in the Northern South China Sea: Satellite Investigation and Theoretical Interpretation
<p>The dataset used to produce the figures in the manuscript. All the data are in MATLAB file format.</p> <p> </p> <p>Fig1.mat: time-distance plot of the red visible spectrum shown in Figure 1.</p> <p>Fig2.mat: wave speed as a function of distance along the distance.</p> <p>Fig3.mat: satellite images and wave speed curves shown in Figure 3.</p> <p> </p>
COMPARISON OF FATIGUE CRACK PROPAGATION RATES IN HIGH STRENGTH STEEL S460, S690 & S960 UNDER STRESS RATIO R = 0.1
Open the record for dataset details and reuse information.
Wind Solar Wind Weimer Propagation Details at 1 min Resolution
Wind Weimer propagated solar wind data and linearly interpolated time delay, cosine angle, and goodness information of propagated data at 1 min Resolution. 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 1 Solar Wind Weimer Propagation Details at 1 min Resolution
ISEE-1 Weimer propagated solar wind data and linearly interpolated time delay, cosine angle, and goodness information of propagated data at 1 min Resolution. 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 Solar Wind Weimer Propagation Details at 1 min Resolution
ISEE-3 Weimer propagated solar wind data and linearly interpolated time delay, cosine angle, and goodness information of propagated data at 1 min Resolution. 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.
IMP-8 PLS Solar Wind Weimer Propagated 60 s Resolution Data in GSE Coordinates
IMP-8 PLS 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.
Geotail Solar Wind Weimer Propagation Details at 1 min Resolution
Geotail Weimer propagated solar wind data using CPI and linearly interpolated time delay, cosine angle, and goodness information of propagated data at 1 min Resolution. 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.
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.