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490
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Dataset results
490 results for “Propagation”
Data for Cell Reports paper "Cortex-wide spontaneous activity non-linearly steers propagating sensory-evoked activity in awake mice"
<p>Datasets for Cell Reports paper "Cortex-wide spontaneous activity non-linearly steers propagating sensory-evoked activity in awake mice", including mouse cortical neural activity recording (preprocessed), behavioral recording and other analysis results during intermedia computation steps.</p>
Validation of an Uncertainty Propagation Method for Moving-Boat ADCP Discharge Measurements
<p>ADCP intercomparisons data from Génissiat (2010) and Chauvan (2016). Data used in the article <strong>Validation of an Uncertainty Propagation Method for Moving-Boat ADCP Discharge Measurements</strong>, <em>Water Resources Research</em>, Despax et al..</p>
Observations of natural positive leader featuring stepwise propagation in low frequency magnetic field
<p>The high-speed video, B-field and E-field data of two cases in the manuscript is presented here.</p>
Dataset: Architecture-based Attack Propagation and Variation Analysis for Identifying Confidentiality Issues in Industry 4.0
<p>Dataset for the publication <em>Architecture-based Attack Propagation and Variation Analysis for Identifying Confidentiality Issues in Industry 4.0</em></p> <p>More Information can be found in the zipped Readme</p>
Ion friction and quantification of the geomagnetic influence on gravity wave propagation and dissipation in the thermosphere-ionosphere
<p>Data supporting figures 2, 3 and 4 of the manuscript doi:10.1002/2017JA024785<br> </p> <p> </p>
Simulation data used in "Modeling the Inception and Stepped Propagation of Positive Lightning Leaders"
<p>This dataset includes all simulation data used in "Modeling the Inception and Stepped Propagation of Positive Lightning Leaders." These datasets are output from an upward leader model, described in the paper, under a variety of different conditions and settings. Included also are several charts and animations for select datasets.</p>
Supplementary material for 3D wave propagation and earthquake dynamic rupture simulations in complex poroelastic media
<p>This repository contains all files to reproduce the SeisSol simulations for the article "3D wave propagation and earthquake dynamic rupture<br>simulations in complex poroelastic media" submitted to GJI.</p>
Human-driven land surface transformations are major influences in global drought propagation
<p>This upload contains two data sets </p> <p>1) oldata.zip : This dataset contains time series for soil moisture, groundwater, precipitation and evapotranspiration within Open loop simulations </p> <p>2) dadata.zip : This dataset contains time series for soil moisture, groundwater, precipitation and evapotranspiration within Data assimilation simulations </p>
Wave propagation on a tabla membrane
<p><span>The video shows the propagation of the depression in the membrane after </span><span><span>striking a /ɽ/ <em>bol</em></span></span><span>.</span><span> <span>For such a depression to travel across the <em>syahi</em>, the black central patch 8 cm in diameter, it took a time duration corresponding to ten frames. As the images were captured at 4000 frames per second, every frame corresponds to 0.25 ms. <span> </span>Thus, 10 frames took 2.5 ms (10*0.25). Hence, the wave velocity on the membrane equals 32 m/s (8 cm/2.5 ms). </span></span></p>
Event Abstraction on Partially Ordered Event Data using Label Propagation
Open the record for dataset details and reuse information.
Supplementary videos for "Identification of continental mantle earthquakes using regional waves propagating into a thinned crust"
<p>These are supplementary videos S1 and S2 described in the supplementary materials to "Identification of continental mantle earthquakes using regional waves propagating into a thinned crust". </p>
Dataset for paper "Equatorial propagation of the magnetosonic mode across the plasmapause: 2-D PIC simulations"
<p>Dataset used to produce figures in the paper "Equatorial propagation of the magnetosonic mode across the plasmapause: 2-D PIC simulations"</p>
High clonal propagation and low population connectivity in the holothurian Stichopus chloronotus from the Indo-Pacific (Dataset)
<p>Genetic dataset associated to "High clonal propagation and low population connectivity in the holothurian <em>Stichopus chloronotus</em> from the Indo-Pacific" article published in Marine Biology (2019)</p>
Plane-wave propagation path data from wideband MIMO channel sounding in an urban microcellular scenario
<p>We provide plane-wave propagation path data from a wideband MIMO radio channel sounding measurement in an urban microcell scenario. The binary Matlab file includes: direction of departure (DOD: variables "par.PhiTx" and "par.ThetaTx" in [rad]), direction of arrival (DOA: "par.PhiRx" and "par.ThetaRx" in [rad]), delay ("par.Tau" to be multiplied with 8.3ns, the tab length), and complex polarimetric path gain ("par.Alpha" is a 2x2 matrix, where element [1,1]=TXtheta ->RXtheta, [1,2]=TXphi ->RXtheta, [2,1]=TXtheta->RXphi, and [2,2]=TXphi->RXphi), for the 30 strongest signal paths (from TX to RX) at each of the 4574 RX locations along the route described below. In the element names above, the term "theta" refers to the vertically polarised component, and accordingly the term "phi" referes to the horizontally polarised component.<br> Note #1: The exact RX location for each individual measured radio channel was NOT recorded (see route description below). <br> Note #2: The complex path gain (par.Alpha) is NOT calibrated, but depends on the initially fixed AGC level in the receiver, which was chosen to provide the best dynamic range for the given mobile (RX) route.<br> Both these limitations are seen reasonable since this dataset is meant for the realistic *statistical comparison* of the performance of different RX antennas in a microcell environment (and not to determine the actual received power at each exact location of the measured route).<br> Background information: The provided dataset is processed and is based on a radio channel sounder measurement at 5.3 GHz, carried out in downtown Helsinki, Finland, in April 2004. The uniform rectangular transmit (TX) array was placed at 10 m height in Aleksanterinkatu-street (an approx. 15-m wide street canyon), in front of the Nordea building, broadside pointing westwards (towards Stockmann building). The semishperical receive (RX) array was moved at 1.6-m height and for about 50 m along Aleksanterinkatu-street in line-of-sight (LOS), i.e. from in front of Kluuvi shopping centre westwards just across the crossing of Kluuvikatu-street. The TX and RX arrays cover the relevant azimuth and elevation ranges, so that this plane wave propagation path data can directly be combined with the polarimetric directional radiation pattern(s) of an antenna (array).</p>
supplementary expressions of "On the derivation of an admissibility condition for phase boundary propagation in an SMA bar based on a 3-D fo
<p>supplementary expressions of "On the derivation of an admissibility condition for phase boundary propagation in an SMA bar based on a 3-D fo-0</p>
One-Way Wave Propagator driven by pure visual neural network
<p>this repository is used to reproduce the key Figures of our manuscript titled "One-Way Wave Propagator driven by pure visual neural network". </p> <p>The code is running using MATLAB, Recommend using a GPU device.</p>
Citrus Tree Root System Image Dataset: Effects of Propagation Methods on Root Architecture
<p>This dataset is composed of images from two different citrus rootstock trials designated "Field Trial 1" and "Field Trial 2". Each field trial was planted with trees of <em>Citrus sinensis</em> cv. 'Valencia' grafted onto 4 different, commercially available USDA citrus rootstocks: US-812, US-897, US-942, US-1516. The trees were excavated two years after planting, cleaned, dried, and imaged in two ways. First, the root systems were imaged radially as though you are looking down through the trunk of the tree with the roots radiating outward in all direction. Secondly, the root systems were imaged vertically in six different positions such that each image was a side view of the root system from a different angle. Additionally, the binary masks of the vertical root systems were included in each .tar file.</p> <p>All images were acquired with a Canon EOS Rebel T6 against a professional photography blue screen. A mapping of the image names to the experimental trial information is included in each .tar file, and a series of scaled images were taken before and after root system imaging to calculate the pixel to centimeter conversion for absolute measurements.</p> <p>This dataset is currently in beta as it may change at some point in the future, therefore until that time, this record will be set to restricted.</p>
Pan-Cancer T cell atlas from "The combined use of scRNA-seq and network propagation highlights key features of pan-cancer Tumor-Infiltrating T cells" (https://doi.org/10.1371/journal.pone.0315980)
<p>The scRNA-seq data were collected from previously published datasets (GSE140228, GSE139555, GSE155698, GSE121636, and GSE139324), adhering to the following selection criteria: 1) presence of T cells, 2) treatment-naïve patients, 3) solid tumors, and 4) inclusion of at least tumor and blood samples.<br>Each scRNA-seq dataset underwent separate preprocessing in R (v4.0.2). We filtered out cells from the original count matrices that had fewer than 200 genes detected or more than 10% mitochondrial UMI counts and we only kept genes detected in at least 3 cells. Then, we applied Seurat (v4.0.5) with default parameters for count data normalization and scaling. Each cell was assigned a cell cycle score using the CellCycleScoring function and we computed the difference between the G2M and S phase scores. This approach allows for the separation of non-cycling from cycling cells while minimizing the differences in cell cycle phase among proliferating cells. The SelectIntegrationFeatures function was ran with the nfeatures parameter set to 3,000 before merging all samples from each dataset. These integration features were then used for Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). Clustering was performed using the Louvain algorithm with the resolution parameter set to 2.0 for all datasets. Finally, T cells were isolated based on CD3D and CD3G genes expression (CD3D or CD3G expression level > 0).</p> <p>To integrate heterogeneous data from different sources, a two-step procedure was applied. We first concatenated all datasets together and ran the scaling and PCA steps based on the top 3,000 highly variable genes identified by the FindVariableFeatures function with the “vst” method. Harmony was applied for batch effect correction then UMAP and clustering using the Louvain algorithm with the resolution parameter set to 2.0 were performed on the harmony reduction. Examining the result from the first clustering run, we identified contamination clusters and clusters that arose from unwanted factors: we removed the contamination clusters including low quality cells highly expressing marker genes associated with apoptosis and tissue dissociation operation, pancreatic acinar cells (expressing PRSS1, CLPS, PNLIP and CTRB1 among others), myeloid cells (expressing CD68) and B cells (expressing CD79A). Then, we performed the second run of integration and clustering excluding immunoglobulin, ribosome-protein-coding, and T cell receptor (TCR) genes (gene symbol with string pattern "^IGK|^IGH|^IGL|^IGJ|^IGS|^IGD|IGFN1", "^RP([0–9]+-|[LS])", and "^TRA|^TRB|^TRG" respectively) from the top 3,000 highly variable genes and regressing out the cell cycle difference effect as well as the percentage of mitochondrial UMI counts. Harmony (v0.1.0) was applied again for batch effect correction and UMAP was performed on the harmony reduction.<br>T cell subtypes identification and annotation was performed by clustering cells using the Louvain algorithm with the resolution parameter set to 4.1 after iterative testing from 3.5 to 5.0 by 0.1 (more granular than default), computing clusters signatures based on differential gene expression using the FindAllMarkers function with the “MAST” method and interrogating known gene markers expression. A resolution value of 4.1 was notably found to be the lowest resolution value enabling the correct separation of proliferating CD4+ T cells from proliferating CD8+ T cells.</p>
Fig. 2 in Ultrastructure of somatic embryo development and plant propagation for Lachenalia montana
Fig. 2. The effect of MS medium salt concentrations, sucrose and PG on the germination of somatic embryos of L. montana. The results are expressed as the means with SE from five replicates per treatment. The data were recorded after 8 weeks of culture. Means ± SE followed by the same letter are not significantly different at the 5% level as determined by Duncan's multiple range test.
Fig. 4 in Ultrastructure of somatic embryo development and plant propagation for Lachenalia montana
Fig. 4. TEM analyses of different developmental stages of somatic embryos of L. montana. Formation of cell with large vacuole (V), mitochondria (M), nucleus (N) and nucleolus (NU) in globular embryos (Bar, 0.5 μm) (A). More vacuolated with cytoplasmic organelles in SM regions of pear-shaped (Bar, 0.5 μm) (B) and early torpedo-shaped (Bar, 0.5 μm) (C) embryos. Development of cytoplasmic components with nucleus (N), nucleolus (NU), Golgi apparatuses (GA), mitochondria (M) and chloroplasts (CP) in SM regions of torpedo (Bar, 0.5 μm and 0.2 μm) (D) and cotyledonary embryos (Bar, 0.5 μm) (E). Large intercellular spaces (IS) and thick cell wall (CW) in connective regions of SM and RM of cotyledonary embryos (Bar, 0.5 μm and 0.2 μm) (F). Development of thick IS and CW with cytoplasmic components of mitochondria, starch grains (SG) and plastids (PS) in RMs of torpedo (Bar, 0.2 μm) (G) and cotyledonary (Bar, 0.5 μm and 0.2 μm) (H) embryos.
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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.