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2,353 results for “channel”
Screaming Channels on Bluetooth Low Energy
<p>Publication of 2 datasets from the <a href="https://github.com/pierreay/screaming_channels_ble">Screaming Channels on Bluetooth Low Energy</a> project for the <a href="https://github.com/pierreay/screaming_channels_ble/blob/main/docs/demo_20240828_acsac/README.org">ACSAC24 Artifact Evaluation.</a></p>
Multisensory Number Channels Derived from Individual Differences
<p>This is the dataset related to the article: "Multisensory Number Channels Derived from Individual Differences"</p> <p>The file contains subjects data for the three experiments</p> <p>sub_ID is the participant's identification number<br><br>The subsequent columns indicate Wfs for each tested numerosity in each experiment.</p> <p>M = Digit to Actions Task<br>F = Digit to Visual Sequence Task<br>D = Visual Dot estimation Task<br><br>E.g. M10 indicates the Wf measured for that subjects with tested number 10 in the Motor condition </p>
Rapid Identification of Bacterial isolates Using Microfluidic Adaptive Channels and Multiplexed Fluorescence Microscopy
<p>Dataset for: Rapid Identification of Bacterial isolates Using Microfluidic Adaptive Channels and Multiplexed Fluorescence Microscopy</p> <p>doi: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4LC00325J">10.1039/D4LC00325J</a></p>
Rewarded task switching - 128 channel EEG data
<p>The zip-file contains the raw EEG-data used for our publication <em><span>Frontal midline theta power during the cue-target-interval reflects increased cognitive effort in rewarded task-switching </span></em><span>in</span><em> <span>Cortex</span><span>. </span></em></p>
Non-Newtonian Power-law fluid simulations in rectangular channel
<h1>Velocity distribution in rectangular channel for Power law fluid.</h1> <p>Using viscosity: eta(x)=K*(x/gamma0)^(n-1)/gamma0</p> <p>Solved the PDE equation: https://doc.comsol.com/5.5/doc/com.comsol.help.comsol/comsol_ref_equationbased.23.008.html<br>in rectangular channel in COMSOL with<br>c=eta(sqrt(d(u,x)^2+d(u,y)^2), f=0.013333333 [Pa*s]<br>e_a, d_a, alpha, beta, gamma=0<br>gamma0=1[1/s]<br>Boundary conditions on x=0, y=0, x=witdh, y=height is u=0</p> <p>Output file contains: Coordinates X, Y [mm] on triangular grid and Velocity u [m/s]</p> <p>File specific parameters:<br>"H2O_100x096.txt" - n=1, K=0.013333333 [Pa*s], height=1 [mm], width=0.96 [mm]<br>"10pFBS_37C_100x115.txt" - n=0.5, K=0.00541540444218501 [Pa*s], height=1 [mm], width=1.15 [mm]<br>"10pFBS_097x092.txt" - n=0.32, K=0.00541540444218501 [Pa*s], height=0.97 [mm], width=0.92 [mm]</p>
Bankfull and Mean-flow Channel Geometry Dataset across the CONtiguous United States (CONUS)
<p>This dataset includes estimated river channel geometry attributes, specifically width and depth, under bankfull and mean-flow conditions across the CONtiguous United States (CONUS). The method utilized for providing these estimations are based on eXtreme Gradient Boosting Regression (XGBR). This dataset can be linked to the National Hydrograohy Dataset Plus (NHDPlusV2.1) through Common identifier of the NHD feature, known as COMID.</p>
Export-Led Decay: The Trade Channel in the Gold Standard Era
<p>This package contains the data, programs and instructions to replicate manuscript "Export-Led Decay: The Trade Channel in the Gold Standard Era"by Bernardo Candia and Mathieu Pedemonte forthcoming at JEEA</p>
Experimental data for Dynamic cover effects in lateral bedrock channel bank abrasion: Experiment and model comparison
<p>Experimental data for bank erosion.xlsx contains the data used for the figures in the paper, and the distribution of bedrock bank erosion in the longitudinal direction in Run 1 - Run 18.</p>
Water in peripheral TM-interfaces of Orai1-channels triggers pore opening
Open the record for dataset details and reuse information.
Data for "Machine learning of reduced quantum channels on NISQ devices"
<p>This dataset contains the data, figures and code of the publication <a href="https://doi.org/10.48550/arXiv.2405.12598">"Machine learning of reduced quantum channels on NISQ devices"</a>. I.e., LeNoM (Learning Noise Models) represents the core implementation of our approach.</p>
Ethosuximide: subunit- and Gβγ-dependent blocker and reporter of allosteric changes in GIRK channels
<p><strong>Classical MD simulation of the GIRK2 channel (PDB: 3SYA) in a POPC membrane in presence of the inhibitor Ethosuximide. </strong></p> <p>The scope of the study was to find the ETX binding site. We conducted 5 (run1-5 ) runs each 1.5 μs long. The upload contains a .gro, .a tpr, and an .xtc file of each run. The .xtc files were processed before the upload and contain every 100th frame of the original data.</p> <p>The corresponding manuscript was uploaded on the bioRxiv (doi: https://doi.org/10.1101/2024.06.04.597296 ).</p> <p> </p> <p>Simulation paramters:<br>FFs: Amber99sb, Berger lipids, SPC/E water, GAFF2 (ETX), corrected monovalent Lennard–Jones parameters for ions<br>Software: Gromacs 5.1.2.</p> <p>Time step: 2fs<br>Lennard–Jones / electrostatic interactions cut-off: 1.0 nm<br>Long-range electrostatic interactions: Particle-Mesh Ewald algorithm <br>Bonds were constrained with the LINCS algorithm<br>Temperature: 310 K, V-rescale, τ = 0.1 ps<br>Pressure: 1 bar, Parirnello-Rahma, τ = 2 ps</p> <p> </p> <p>Composition of the system:<br>1 GIRK2 channel (PDB: 3SYA), consisting of 4 chains A, B, C, D<br>4 PIP2 bound to the channel, residue name MOL<br>588 POPC Berger lipids, residue name POPC<br>60897 SPC/E water, residue name SOL<br>322 K+, residue name K<br>274 Cl-, residue name CL<br>10 R-Ethosuximide, residue name ETR<br>10 S-Ethosuximide, residue name ETS</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Datatset: Machine-Learning Side-Channel Attacks on the GALACTICS Constant-Time Implementation of BLISS
<p>This dataset accompanies the paper "Machine-Learning Side-Channel Attacks on the GALACTICS Constant-Time Implementation of BLISS". It was used to experimentally prove the presented attack strategies on real hardware. The corresponding source code for all three attacks is also publicly available.</p> <p>A detailed description of how the data was obtained can be found in the paper. Section 4 precisely describes the experimental setup.</p> <p> </p> <p>Prerequisites:</p> <pre><code class="language-bash">sudo apt-get install p7zip</code></pre> <p> </p> <p>Extract the data:</p> <pre><code class="language-bash">7z x galactics_attack_data.7z</code></pre> <p> </p> <p>Running the attacks:</p> <p>The source code to run the three presented attacks can be found on Github. The instructions on how to use the python code can be obtained from the corresponding README.</p> <p> </p> <p>Re-using the dataset:</p> <p>The dataset consists of <em>.pickle</em> and <em>.bin</em> files. The <em>.pickle</em> files can be read using <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_pickle.html">Pythons Pandas library</a>. Python access functions for the <em>.bin</em> files are also provided.</p>
Clear optically matched panoramic access channel technique (COMPACT) for large-volume deep brain imaging
<p>Source data of paper "Clear optically matched panoramic access channel technique (COMPACT) for large-volume deep brain imaging" published on Nature Methods.</p>
Clear optically matched panoramic access channel technique (COMPACT) for large-volume deep brain imaging
<p>Source data of paper "Clear optically matched panoramic access channel technique (COMPACT) for large-volume deep brain imaging" published on Nature Methods.</p>
Inverted microscopy image dataset -- Carbon biomass of microplankton assemblages in southern Patagonian fjords and channels
<p>Images of main microplanktonic items (folders) obtained under inverted (mostly) and electronic microscope used to estimate biovolume and carbon biomass. Scale bar is shown on each picture and label of each image indicate the station ID (St.) and sampling depth (m). A table is provided with biovolume and equivalent spherical diameter calculations for each planktonic item. </p>
Stochastic Simulation of the Suspended Sediment Deposition in the Channel with Vegetation and Its Relevance to Turbulent Kinetic Energy
<p>This data deposit contains all the datasets needed to draw Figures 8 and 9 in the paper "Stochastic Simulation of the Suspended Sediment Deposition in the Channel with Vegetation and Its Relevance to Turbulent Kinetic Energy", which is now under review for potential publication in Water Resources Research. </p>
Building Object and Outdoor Scene Segmentation (BOOSS) - Multi-channel (RGB + Thermal) Aerial Imagery Datasets
<p>The dataset of <em>Building Object and Outdoor Scene Segmentation (BOOSS)</em> is based on multi-channel aerial imagery data. It covers </p> <p>- Ground Truth</p> <p>- RGB</p> <p>- Thermal</p> <p>The annotations in version 1.0 include roofs, facades, cars, roof equipment, and ground equipment</p> <p>Please cite as:</p> <p>Hou, Yu, Meida Chen, Rebekka Volk, and Lucio Soibelman. "An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets." <em>Remote Sensing</em> 13, no. 21 (2021): 4357.</p>
Fig. 2 in Ageneiosus uranophthalmus, a new species of auchenipterid catfish (Osteichthyes: Siluriformes) from river channels of the central Amazon basin, Brazil
Fig. 2. Ageneiosus uranophthalmus, paratype, INPA 22723, 192.5 mm SL. Ventral view of the head.
Fig. 3 in Ageneiosus uranophthalmus, a new species of auchenipterid catfish (Osteichthyes: Siluriformes) from river channels of the central Amazon basin, Brazil
Fig. 3. Ageneiosus uranophthalmus, paratype, INPA 22723, 192.5 mm SL, nuptial male in lateral view.
Electrophysiology Data for "Two functional epithelial sodium channel isoforms are present in rodents despite pronounced evolutionary pseudogenization and exon fusion"
<p>Here we provide the electrophysiology data for the manuscript "Two functional epithelial sodium channel isoforms are present in rodents despite pronounced evolutionary pseudogenization and exon fusion", published in Molecular Biology and Evolution (2021): msab271 (doi: 10.1093/molbev/msab271). Data are reported as current values in Excel format, sorted according to the appearance in Figures and supplemented by explanatory text on the procedures/data presentation.</p>
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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.