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2,353 results for “channel”
Evaluation of a Novel 8-Channel RX Coil for Speech Production at 0.55 T DATASET.
<p>This dataset contains raw imaging MRI data used in SNR evaluation for 4 subjects. For all scans, volumetric data of the upper airway was obtained using a 3D spoiled gradient echo sequence with either a speech coil, a head coil, or an integrated body coil. Imaging parameters were: flip angle = 10, TE = 5 ms, TR = 10 ms, FOV = 32x32x16 cm^3, resolution = 2.5 x 2.5 x 5 mm^3. Pre-scan noise information for each scan is also included.</p> <p> </p> <p> </p>
Supplementary Data for "Interplay of river and tidal forcings promotes loops in coastal channel networks"
<p>This dataset contains supplemental data required to reproduce the results of the paper <em>Interplay of river and tidal forcings promotes loops in coastal channel networks</em> (in review at Geophysical Research Letters). We provide raw and extracted channel network data for 19 river deltas/coastal marsh sites. For each site, the following files are provided:</p> <p><strong>XXX_base.tif</strong> : the raw binary mask of the river channel network<br> <strong>XXX_clipper.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : polygon(s) used to clip the raw mask<br> <strong>XXX_clipped.tif</strong> : the binary mask of the river channel network after being clipped by XXX_clipper.shp<br> <strong>XXX_filled.tif</strong> : the binary mask after filling islands via the method specified in the paper<br> <strong>XXX_inlet_nodes.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : locations of the inlet nodes; used by RivGraph<br> <strong>XXX_shoreline.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : location of the shoreline; used by RivGraph<br> <strong>XXX_links.json</strong> : GeoJSON file containing the geometries, connectivities, and widths of each link in the network<br> <strong>XXX_nodes.json</strong> : GeoJSON file containing the locations of each node of the network<br> <strong>process_XXX.py</strong> : the python script used to generate the above files</p> <p>All files listed below (except .py files) are georeferenced (i.e. can be opened with QGIS, ArcGIS or another GIS). Exceptions to the provided files include:</p> <p><strong>Barnstable</strong>: no "base.tif" is provided. Use "filled.tif".<br> <strong>GBM</strong>: some hand-cleaning was performed on "filled.tif".<br> <strong>Mackenize</strong>: "clipper.shp" is not provided, but "clipped.tif" is.<br> <strong>Mississippi</strong>: "clipper.shp" is not provided as the mask was made from a shapefile.</p> <p>In order to run process_XXX.py, the RivGraph package will need to be installed. Instructions<br> can be found at https://github.com/jonschwenk/RivGraph.</p>
Demonstration of kilohertz operation of hydrodynamic optical-field-ionized plasma channels
<p>The compressed file contains the raw data used in the publication "Demonstration of kilohertz operation of Hydrodynamic Optical-Field-Ionized Plasma Channels," <em>Physical Review Accelerators and Beams </em><strong>25</strong>, 011301 (2022) DOI: 10.1103/PhysRevAccelBeams.25.011301.</p> <p>Further information on the organization of the data is provided in the README file included in the compressed file.</p> <p> </p>
Synthetic populations and trajectories for sdB stars ejected from the single degenerate helium donor channel for thermonuclear supernovae
<p>This repository is a supplement to a journal paper (Neunteufel+ 2022) and contains the synthetic populations of sdB stars and sdB remnants ejected from the single degenerate helium donor channel for thermonuclear supernovae. See Neunteufel+ 2021 and Neunteufel+ 2022 for simulation parameters. </p> <p>Synthetic populations, including initial and final positions, are contained in the MXX-out.tr (XX=10..15) files where XX is the mass of the WD companion divided by 0.1 solar masses. Each population is a snapshot of stars ejected at the end of a 300 Myr period. (Note that stellar lifetimes are not taken into account here. See Neunteufel+ 2022 on how stellar lifetimes should be truncated in order to produce realistic populations.)</p> <p>Columns:</p> <p>Zeroth column (ID) is the ID of the trajectory. These are assigned consecutively.</p> <p>First column (unnamed) indicates initial (0) and final (1) positions.</p> <p>Third column (time) is the time since ejection in Myrs. Note that stars further down in the list were ejected earlier.</p> <p>Fourth to Ninth columns (vx, y, vy etc..) are velocity (km/s) and position (kpc) in Gal. Carthesian coordinates (velocity first, position second)</p> <p>Tenth to Twelfth column are accelerations in Gal. carthesian coordinates </p> <p>Thirteenth column (v_space) is the total galactocentric space velocity (km/s) </p> <p>Fourteenth column (Phi) is the local Gal. potential according to Model 1 presented by Irrgang+2013</p> <p>Fifteenth column (E_kin/E_pot) is the local kinetic energy of the object divided by its potential energy with respect to the Gal. potential.</p> <p>Sixteenth column (rho) is the local Gal. baryon density according to Model 1 presented by Irrgang+2013</p> <p>Seventeenth column (label_c) is the mass of the ejected sdB star or sdB remnant in solar masses.</p>
Dataset: Effect of Macrorough Sidewalls on Flow Resistance in Steep Rough Channels
<p>The data set includes reach-averaged flow velocity measurements and bed and sidewall roughness parameters for flume experiments conducted at the Laboratory of Hydraulics, Hydrology, and Glaciology (VAW) at ETH Zurich.</p>
To what extent naringenin binding and membrane depolarization shape mitoBK channel gating - a machine learning approach (code and dataset)
<p>The dataset consists of dwell-time series (sampling frequency 100 kHz) of the mitoBK ion channel activation modulated by the naringenin binding and membrane<br> depolarization. It also contains the code written in Python, with the use of tslearn and scikit-learn packages, classifying the dwell-time subseries into right categories.</p> <p>The dataset is organized as follows. The mitoBK_ML.zip directory consists of two directories:</p> <ol> <li><strong>dwell times </strong>containing 5 subdirectories comprising groups of dwell-time subseries obtained at different pipette potentials and naringenin concentration. First number in the name od directory stands for the applied voltage in mV, whilst the second one denotes the naringenin concentration in µmol. For instance, directory named 20_3 means that the obtained dwell-times series were obtained at 20 mV (value of pipette potential) and 3 µmol (concentration of naringenin). These subdirectories are named as follows:</li> </ol> <ul> <li><strong>1group </strong>comprising dwell time series <strong>20_3, 40_1, 60_0</strong></li> <li><strong>2group</strong> comprising dwell-time series <strong>20_10, 60_1</strong></li> <li><strong>3group</strong> comprising dwell-time series <strong>40_10</strong>, <strong>60_3</strong></li> <li><strong>naringenina</strong> comprising dwell-time series <strong>60_0, 60_10</strong></li> <li><strong>voltage</strong> comprising dwell-time series <strong>20_10, 60_10</strong></li> </ul> <p><strong>1group, 2group and 3group</strong> contain the dwell-time series with approximately the same value of open-state probability of the ion channel.</p> <p>The <strong>naringenina</strong> contains the dwell-time series with the same value of potential (60 mV) and different values of naringenin concentration (0 µmol and 10 µmol). </p> <p>The <strong>voltage </strong>contains the dwell-time series with the same value of naringenin concentration (10 µmol) and different values of applied voltage (20 mV and 60 mV).</p> <p> 2. <strong>rslt </strong>is organized analogously to <strong>dwell times. </strong>The subdirectories are empty, but they will be filled with the results after launching the Python scripts placed in the <strong>knn_ion_channel.ipynb</strong> or <strong>shapelet_ion_channel.ipynb </strong>files.</p> <p>The Python code is placed in two files:</p> <ol> <li><strong>knn_ion_channel.ipynb </strong>containing kNN (<em>k-Nearest Neighbors</em>) algorithm classifying dwell-time series belonging to one of 5 different categories enumerated above: <strong>1group, 2group, 3group, naringenina, voltage</strong>. More detailed description of the code can be found inside uploaded Jupyter notebook.</li> <li><strong>shapelet_ion_channel.ipynb </strong>containing <em>shapelet-learning algorithm</em> classifying dwell-time series belonging to one of 5 different categories enumerated above. <strong>1group, 2group, 3group, naringenina, voltage. </strong>More detailed description of the code can be found inside uploaded Jupyter notebook.</li> </ol> <p> </p> <p> </p> <p> </p>
IODP Expedition 372A RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
IODP Expedition 374 RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
Isolated Ballistic Non-Abelian Interface Channel
<p>Dataset for B. Dutta et al., "Isolated Ballistic Non-Abelian Interface Channel".</p> <p>The following datafiles are used for the figures in main text and supplementary materials.</p>
Investigation on Dynamic Characteristics of Lightning Return Stroke Channel
<p>The dynamic characteristics in lightning discharge plasma channel are of great significance in studying the micro-physical mechanism of the discharge process. This paper established a simplified radiation-hydrodynamic model (RHM) of the return stroke channel radial expansion based on the fluid dynamics theory, and for the first time, considered the affect of the light radiation loss in the discharge process. The temporal evolutions of the characteristic parameters, such as temperature, pressure and expansion rate for return stroke channel were analyzed. The results indicated that the current peak value and the risetime are important factors in determining the channel dynamics characteristics. At the initial stage of return stroke, the light radiation loss has a distinct influence on channel temperature, which leads to a transitory drop in temperature forming a bimodal waveform structure. Following the peak current, the channel reaches peak pressure, which leads to a subsequent secondary temperature peak and accelerates the expansion of the channel. Peak current and the risetime are the indicator parameters of strong discharge and the main factors of lightning disaster. This work provides reference data for further research on the radial energy transport of lightning return stroke channel and the formation mechanism of shock waves.The dynamic characteristics in lightning discharge plasma channel are of great significance in studying the micro-physical mechanism of the discharge process. This paper established a simplified radiation-hydrodynamic model (RHM) of the return stroke channel radial expansion based on the fluid dynamics theory, and for the first time, considered the affect of the light radiation loss in the discharge process. The temporal evolutions of the characteristic parameters, such as temperature, pressure and expansion rate for return stroke channel were analyzed. The results indicated that the current peak value and the risetime are important factors in determining the channel dynamics characteristics. At the initial stage of return stroke, the light radiation loss has a distinct influence on channel temperature, which leads to a transitory drop in temperature forming a bimodal waveform structure. Following the peak current, the channel reaches peak pressure, which leads to a subsequent secondary temperature peak and accelerates the expansion of the channel. Peak current and the risetime are the indicator parameters of strong discharge and the main factors of lightning disaster. This work provides reference data for further research on the radial energy transport of lightning return stroke channel and the formation mechanism of shock waves.</p>
Influence of social networks as a distribution channel in volatile markets of non-fungible tokens..
<p>Dataset with tweets, prices (ETH & USD), volume (USD) and sentiment from BAYC, WOW and CoolCats NFTs. </p> <p>Obtained from Twitter and from the Ethereum blockchain using Dune Analytics (Dune.xyz)</p>
IODP Expedition 352 RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
IODP Expedition 351 RGB channels (calculated from core photos)
<p>Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.</p>
Composite geostationary weather satellite images (second time derivative of water vapor channel) for visualizing Lamb waves
<p>Second time derivative of water vapor channel (6.2 micrometer) brightness temperature from geostationary weather satellites (Units: K s<sup>-2</sup>)</p> <p>Himawari-8 (original data obtained from NICT Science Cloud)</p> <p>GOES-16/17 (original data obtained from Amazon AWS)</p> <p>Meteosat-8/9/10/11 (original data obtained from EUMETSAT)</p> <p> </p> <p>Time interval of the files: 5 minutes</p> <p> </p> <p>Time interval of each satellite, dt for time derivative:</p> <p>Himaawri-8, GOES-16/17: 10 minutes, 10 minutes</p> <p>Meteosat-8/9/11: 15 minutes, 15 minutes</p> <p>Meteosat-10: 5 minutes, 10 minutes</p> <p> </p> <p>Each file contains the latest images from those satellites at that time. The time stamp for each satellite represents the beginning of each full-disk scan.</p> <p> </p> <p>Bias correction:</p> <p>Himawari-8: bias removal for each swath</p> <p>GOES-16/17, Meteosat-11: bias removal for each east-west line</p> <p>Meteosat-8/9/10: bias removal for each east-west line (note: satellite attitude was not stable)</p> <p> </p> <p>Smoothing:</p> <p>Band-pass filter for each full-disk image separately: 2-40 degrees on lat-lon coordinate</p> <p>Stronger smoothing at latitudes higher than 60 degrees north/south</p> <p> </p> <p>Down-sampling:</p> <p>Full-disk images were mapped to a 0.04-degree lat-lon coordinate.</p> <p>Then, composite images were produced at the 0.2-degree resolution.</p> <p> </p> <p>Version 2:</p> <p>Improved interpolation algorithm</p> <p>Himawari-8: improved geolocation</p> <p>Meteosat-8/9: improved treatment of noise near the edge of full disk images</p>
Savi et al., 2020 -- tributary-main-channel interaction experiments -- Experiment No Change 2 subset as netCDF files
<p><strong>Overview</strong></p> <p>Zip file contains two netCDF files with a subset of data from the "No Change 2" (NC2) experiment conducted by Savi et al., 2020 and published in Earth Surface Dynamics (<a href="https://doi.org/10.5194/esurf-8-303-2020">https://doi.org/10.5194/esurf-8-303-2020</a>) with the original data available via the Sediment Experimentalists Network Project Space SEAD Internal Repository (<a href="https://doi.org/10.26009/s0ZOQ0S6">https://doi.org/10.26009/s0ZOQ0S6</a>). Topographic scan data were re-formatted into the netCDF file "T_NC2_scans.nc", and overhead imagery was extracted from the video of the experiment approximately once every minute of experimental time and RGB band data is provided in the formatted netCDF file "T_NC2_images.nc". These data were formatted into netCDF files for easy loading into the "deltametrics" analysis toolbox.</p> <p> </p> <p><strong>Additional Details</strong></p> <p>Re-packaging the scan data from the .tif files was straightforward. From the metadata spreadsheet, we know the times at which the scans were taken (and can eliminate the redundant scan). From the paper itself we know the resolution of the topographic scans is 1 mm in the horizontal and vertical. We also know the input discharges, both water and sediment, through both the main channel and tributary, from the paper. We provide these values as metadata in the netCDF files. The scans form the 'eta' field representing the topography in the file. The packaged up netCDF file is called 'T_NC2_scans.nc'.</p> <p>Overhead imagery from the T_NC2_Complete21fps.wmv video file was extracted using the following command:</p> <blockquote> <p>ffmpeg -i T_NC2_Complete21fps.wmv -r 21 T_NC2_frames/%04d.png</p> </blockquote> <p>This command utilizes the ffmpeg tool to extract the frames at a rate of 21 frames per second (-r 21) as the file name implies that is the rate at which the overhead photos were combined into a video. The NC designation indicates that this experiment was performed with no change in the input conditions in either the main or tributary channels.</p> <p>The experiment ran for a total of 480 minutes. A total of 1466 images were obtained from the ffmpeg extraction. This translates to an image approximately every 20 seconds of real time (480 minutes / 1466 frames * 60 seconds/minute = 19.6453 seconds / frame). We sample every 3rd frame, which gives us images roughly once a minute (489 frames in all), to create the subset of data re-packaged as a netCDF file for deltametrics. Dimensions for the pixels were approximated based on our knowledge of the topographic scan resolution. Assuming the extents of the scans and overhead images are the same (although they are not), we calculate the number of millimeters per pixel in the x and y directions for the overhead images. We assume the pixels are more likely to be square than rectangular, so we average these values and assign this as the distance per pixel in both the x and y dimensions for these data.</p> <p>Script used to re-package this dataset is available as a <a href="https://gist.github.com/elbeejay/f7603e712cf0ea30a8215b902715d222">GitHub Gist</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Savi, Sara, et al. "Interactions between main channels and tributary alluvial fans: channel adjustments and sediment-signal propagation." Earth Surface Dynamics 8.2 (2020): 303-322.</p> <p>Physical experiments on interactions between main-channels and tributary alluvial fans<br> S. Savi, Tofelde, A. Wickert, A. Bufe, T. Schildgen, and M. Strecker<br> https://doi.org/10.26009/s0ZOQ0S6</p>
PUMA IV: CO(2-1) channel maps
<p>CO(2-1) channel maps of the 25 ULIRGs systems (38 individual nuclei) of the <em>Physics of ULIRGs with MUSE and ALMA</em> (PUMA) sample (Perna et al., 2021; Pereira-Santaella et al., 2021). These figures are an extended appendix to the paper Lamperti et al (2022), published in A&A (arXiv:2209.03380).</p>
Dataset for: A hemispheric two-channel code accounts for binaural unmasking in humans
<p><strong>Dataset for the paper: A hemispheric two-channel code accounts for binaural unmasking in humans.</strong></p> <p>The model code to generate this data has been published here: <a href="https://doi.org/10.5281/zenodo.5643429">https://doi.org/10.5281/zenodo.5643429</a></p>
Code and data for: Decoupling channel count from field-of-view and spatial resolution in single-sensor imaging systems for fluorescence image-guided surgery
<p><em>Significance</em></p> <p>Near-infrared fluorescence image-guided surgery is often thought of as a spectral imaging problem where the channel count is the critical parameter, but it should also be thought of as a multiscale imaging problem where the field-of-view and spatial resolution are similarly important.</p> <p><em>Aim</em></p> <p>Conventional imaging systems based on division-of-focal-plane architectures suffer from a strict relationship between the channel count on one hand and the field-of-view and spatial resolution on the other, but bioinspired imaging systems that combine stacked photodiode image sensors and long-pass/short-pass filter arrays offer a weaker tradeoff.</p> <p><em>Approach</em></p> <p>In this paper, we explore how the relevant changes to the image sensor and associated image processing routines affect image fidelity during image-guided surgeries for tumor removal in an animal model of breast cancer and nodal mapping in women with breast cancer.</p> <p><em>Results</em></p> <p>We demonstrate that a transition from a conventional imaging system to a bioinspired one, along with optimization of the image processing routines, yields improvements in multiple measures of spectral and textural rendition relevant to surgical decision-making.</p> <p><em>Conclusions</em></p> <p>These results call for a critical examination of the devices and algorithms that underpin image-guided surgery to ensure that surgeons receive high-quality guidance and patients receive high-quality outcomes as these technologies enter clinical practice.</p>
Three-color whole cell LLSM imaging data of ER, H2B, and Lyso over 980 time points during mitosis (ER channel)
<p>This dataset includes the three-color whole cell lattice light-sheet microscopy (LLSM) data of ER, H2B, and Lysosomes over 980 time points at 6 sec intervals in a HeLa cell stably expressing calnexin-mEmerald, H2B-mCherry and Lamp1-Halo during mitosis (only ER channel here due to the file size restriction, other channels can be found in the same depository with different DOI), which was used to demonstrate SiS-rDL denoising algorithm in our Nature Biotechnology paper (DOI: 10.1038/s41587-022-01471-3). This dataset can be used for non-commercial purposes with proper citations of our NBT paper.</p>
Three-color whole cell LLSM imaging data of ER, H2B, and Lyso over 980 time points during mitosis (H2B channel)
<p>This dataset includes the three-color whole cell lattice light-sheet microscopy (LLSM) data of ER, H2B, and Lysosomes over 980 time points at 6 sec intervals in a HeLa cell stably expressing calnexin-mEmerald, H2B-mCherry and Lamp1-Halo during mitosis (only H2B channel here due to the file size restriction, other channels can be found in the same depository with different DOI), which was used to demonstrate SiS-rDL denoising algorithm in our Nature Biotechnology paper (DOI: 10.1038/s41587-022-01471-3). This dataset can be used for non-commercial purposes with proper citations of our NBT paper.</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.