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3,688 results for “Computer”
Workshop Material - 3D-e-Chem Structural Cheminformatics Workflows for Computer-Aided Drug Discovery
<p>The workshop at the KNIME user meeting (Berlin 9th of March 2018) is set up to stimulate participants with varying degrees of experience in cheminformatics to learn and apply the different structural cheminformatics tools and workflows developed within the context of the 3D-e-Chem project. You will learn how to construct and apply integrated cheminformatics workflows using the 3D-e-Chem KNIME nodes for the exploitation of G protein-coupled receptor and kinase data (two important pharmaceutical target classes) to obtain useful information for drug discovery.</p> <p>Information on the 3D-e-Chem KNIME nodes and workflows can be found online:</p> <p>3D-e-Chem GitHub website: <a href="http://3d-e-chem.github.io/">http://3d-e-chem.github.io/</a></p>
The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility
<p>Contains all the data:</p> <p>Bentsen, T., T.May, A. A. Kresnner, and T. Dau. The benefit of combining<br> a deep neural network architecture with ideal ratio mask estimation<br> in computational speech segregation to improve speech intelligibility.<br> PLOS ONE., in review.</p> <p>There are two folders:</p> <ol> <li><strong>WRSs:</strong> the Word Recognition Scores (WRSs) from the listener study. The matrix has dimensions 9 conditions x 20 subjects. Data is ordered corresponding to the following condition order:<br> 'UP', 'GMM', 'GMM (3 subbands)', 'GMM (7 subbands)', 'GMM (11 subbands)', 'DNN (IBM)'; 'DNN (IBM, 40 ms)'; 'DNN (IRM)'; 'DNN (IRM, 40 ms)'</li> <li><strong>Masks:</strong> <ul> <li><strong>GMM-IBMs: </strong>IBMs and estimated IBMs for the models 'GMM', 'GMM (3 subbands)', 'GMM (7 subbands)', 'GMM (11 subbands)'</li> <li><strong>DNN-IBMs:</strong> IBMs and estimated IBMs for the models 'DNN (IBM)'; 'DNN (IBM, 40 ms)'</li> <li><strong>DNN-IRMs</strong>: IRMs and estimated IRMs for the models 'DNN (IRM)'; 'DNN (IRM, 40 ms)'</li> </ul> </li> </ol>
Ex-situ X-ray computed tomography data from two regions of non-crimp fabric based fibre composite under fatigue loading
<p>Ex-situ X-ray CT fatigue testing data published with data in brief: </p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). <em>Ex-situ X-ray computed tomography, tension clamp and in-situ transilluminated white light imaging data of non-crimp fabric based fibre composite under fatigue loading. Data in Brief.</em></p> <p>as a part of the below journal paper.</p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). Uncovering the fatigue damage initiation and progression in uni-directional non-crimp fabric reinforced polyester composite. Composites Part A.</p> <p>If using the data, please refer to one of the two.</p>
Beamforming in Noninvasive Brain–Computer Interfaces Dataset
<p>This is the dataset of 10 motor imagery subjects upon which the paper cited here is written. It is saved in the EEGLAB .set format with the digitized electrode positions included. The only issue is that the names for channels 65-128 are missing, and the head model that corresponds to these electrode locations is also poorly specified (see field Headmodel of the EEG struct). When using this data please cite:</p> <p>Grosse-Wentrup, Moritz, et al. "Beamforming in noninvasive brain–computer interfaces." <em>IEEE Transactions on Biomedical Engineering</em> 56.4 (2009): 1209-1219.</p> <p>DOI: <a href="https://doi.org/10.1109/TBME.2008.2009768">10.1109/TBME.2008.2009768</a></p>
Adaptive Computer Vision-Based 2D Tracking of Workers in Complex Environments
<p>Data set used for testing the efficiency of a vision-based tracking method on tracking construction workers. </p>
3D Dataset "Computation of Exact g-Factor Maps in 3D GRAPPA Reconstructions"
<p>Datasets used in the paper entitled "", containing the following acquisitions:</p> <p> <strong>1) Simulated abdomen data set</strong>: we have synthetized a 3D volume using the simulation environment XCAT based on the extended cardio-torso phantom. We simulated a T1-weighted acquisition using the following acquisition parameters: TE/TR=1.5/3ms, flip angle=60º, acquisition matrix size=60x60x32. A 32-coil acquisition was simulated by modulating the image using artificial sensitivity maps coded for each coil. The noise-free coil images were transformed into the \bk--space and corrupted with synthetic Gaussian noise characterized by the matrices <span class="math-tex">\(\Gamma_k\)</span>and <span class="math-tex">\(C_k\)</span> with SNR=25 for each coil, and the correlation coefficient between coils was set to <span class="math-tex">\(\rho\)</span>=0.1$. For statistical purposes, 4000 realizations of each image were used.</p> <p><br> <strong>2) Water phantom acquisition</strong>: A MR phantom sphere with solution (GE Medical Systems, Milwaukee, WI) was scanned in a 32-channel head coil on a 3.0T scanner (MR750, GE Healthcare, Waukesha, WI). A spoiled gradient-echo acquisition with 100 realizations of the same fully-encoded k-space sampling was used. Acquisition parameters included: coronal view, TE/TR=0.96/3.69ms, flip angle=12º, field of view=22x22$x30.7<span class="math-tex">\(cm³\)</span>, acquisition matrix size=60x60x32, bandwidth=62.5KHz. We corrected for <span class="math-tex">\(B_0\)</span> field drift related phase variations and magnitude decay by a pre-processing step. First we estimated the phase-shift between realizations from the center of the k-space as a cubic function of time and removed it afterwards. And, second, we estimated the magnitude-decay in the k-space as a linear function and substracted it in order not to affect the noise.</p> <p><br> <strong>3) In vivo acquisition</strong>: in order to assess the feasibility of the proposed method, after obtaining the approval fo the local institutional review board (IRB), a volunteer was scanned in a 32-channel head coil on a 3.0T scanner (MR750, GE Healthcare, Waukesha, WI). A spoiled gradient-echo acquisition of a fully-encoded \bk--space sampling was used. Acquisition parameters included: coronal view, TE/TR=2.2/5.7ms, flip angle=12º,field of view=22x22x22<span class="math-tex">\(cm³\)</span>, matrix size=220x220x220, bandwidth=62.5$KHz.</p>
Dataset: Methods for computing the maximum performance of computational models of fMRI responses.
<p>Accompanying data for manuscript: Methods for computing the maximum performance of computational models of fMRI responses. written by Agustin Lage-Castellanos, Giancarlo Valente, Elia Formisano, Federico De Martino, submitted for publication in Plos Computational Biology, July 2018.</p> <p>This dataset provide the Betas for subcortical and a subset of the cortical voxels for three subjects in matlab format.</p> <p>The field bTest refers to the Beta coefficients for every voxel in the test data. The fields beta1 and beta2 refer to the split-half partitions of the bTest coefficients. The field varBparam refers to the parametric variances of the Beta coefficients and the field varBBootstrap refers to the variances of the Betas computed with bootstrap. </p>
Videos for the paper "Towards fungal computer" (Royal Society Focus, 2018)
<p>RefDel3_k10_T4_RightFruitExcited: Video of excitation dynamics in two-fruit fungal system when right fruit is stimulated.</p> <p>RefDel3_k10_T4_LefttFruitExcited: Video of excitation dynamics in two-fruit fungal system when left fruit is stimulated.</p> <p>RefDel10_k10_T4_Input_0101: Video of excitation dynamics in four-fruit fungal system when upper and lower fruits are stimulated.</p> <p>RefDel10_k10_T4_Input_1010: Video of excitation dynamics in four-fruit fungal system when right and left fruits are stimulated.</p>
Simulation dataset for "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters"
<p>Simulated Illumina reads for SNP distance method evaluation and comparison used in the article "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters".</p> <p>Details for simulation can be found at https://gitlab.com/rki_bioinformatics/panpasco/tree/master/simulation_dataset.</p>
qplib-web: Mathematical Programming Computations
<p>Release to go along with the publication of the QPLIB paper on MPC.</p>
Supplementary materials for the paper Adamatzky A. 2018 Towards fungal computer. Interface Focus 8: 20180029. http://dx.doi.org/10.1098/rsfs.2018.0029
<p>Supplementary materials for the paper Adamatzky A. 2018 Towards fungal computer. Interface Focus 8: 20180029. http://dx.doi.org/10.1098/rsfs.2018.0029</p>
The OpenEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing
<p>The OpenEar Dataset provides a library consisting of eight three-dimensional models of the human temporal bone to enable surgical training including color data. Each dataset is based on a combination of multimodal imaging including Cone Beam Computed Tomography (CBCT) and micro-slicing. 3D reconstruction of micro-slicing images and subsequent registration to CBCT images allowed for relatively efficient multimodal segmentation of inner ear compartments, middle ear bones, tympanic membrane, relevant nerve structures, blood vessels and the temporal bone. Raw data from the experiment as well as voxel data and triangulated models from the segmentation are provided in full for use in surgical simulators or any other application which relies on high quality models of the human temporal bone.</p>
Raw Experimental Data for work presented in 'Leveraging Chaos for Wave-Based Analog Computation: Demonstration with Indoor Wireless Communication Signals'
<p>This is the raw experimental data for the work presented in 'Leveraging Chaos for Wave-Based Analog Computation: Demonstration with Indoor Wireless Communication Signals', to be published in Physical Review X.</p> <p> </p> <p>https://journals.aps.org/prx/accepted/dc07aKdcFa91ea06d2949139dac733fa62ce1c02c</p> <p> </p> <p>See the README files and sample pieces of codes for an explanation of the data.</p>
Computational biology and how this field supports new drug discovery
<p>PechaKucha 20x20 is a simple presentation format where you show 20 images, each for 20 seconds. The images advance automatically and this presentation will explore the use of computational modeling supports the early stage of drug discovery. https://www.pechakucha.org/presentations/drug-discovery-and-computational-biology</p>
Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization
<p>Data resulting from the experiments described in "Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization" (https://doi.org/10.1016/j.swevo.2018.12.010). The contents of the files is the following:</p> <ul> <li>CAMO-AIS_SWEVO.zip: All of the data below in a single file</li> <li>CAMO_Iowa.zip: Audio and Data for the orchestrations with the Iowa sound database found at http://theremin.music.uiowa.edu/MIS.html</li> <li>CAMO_Phil.zip: Audio and Data for the orchestrations with the Philharmonia sound database found at https://www.philharmonia.co.uk/explore/sound_samples</li> <li>CAMO_RWC.zip: Audio and Data for the orchestrations with the RWC sound database found at https://staff.aist.go.jp/m.goto/RWC-MDB/rwc-mdb-i.html</li> <li>CAMO_SOL.zip: Audio and Data for the orchestrations with the Studio Online sound database available with Orchids http://forumnet.ircam.fr/product/orchids-en/</li> <li>Listening_Test.zip: Raw data (i.e., perceptual similarity ratings) from the listening test found at http://http://camo.inesctec.pt/. This data is anonymous (each participant is assigned a reference number) so the participants cannot be identified.</li> </ul> <p>See the README.txt file for a detailed description of the contents of each file.</p>
Data: Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy
<p>We provide the generalised fractional anisotropy connectometry database used in our study titled: <em>Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy.</em></p>
Ptychographic X-ray computed tomography data for three Portland cement pastes
<p>Mortars and concretes are ubiquitous materials with very complex hierarchical microstructures. To fully understand their main properties and to decrease their CO<sub>2</sub> footprints, a sound description of their (spatially-resolved) mineralogy is compulsory. Developing this knowledge is very challenging as about half of the volume of hydrated cement is a nanocrystalline component, calcium-silicate-hydrate (C-S-H gel). Furthermore, other poorly crystalline phases (e.g. iron-siliceous hydrogarnet or silica oxide) may coexist which are even more difficult to characterise. Traditional spatially-resolved techniques like electron microscopies involve complex sample preparation steps that often lead to artefacts (e.g. dehydration and microstructural changes). Here, we have used synchrotron ptychographic tomography for obtaining spatially-resolved information on three unaltered representative samples: neat Portland paste, Portland-calcite and Portland-fly ash blend pastes with spatial resolution below 100 nm in samples of up to 5×10<sup>4</sup> mm<sup>3</sup> of volume. For the neat Portland paste, the ptychotomographic study gave densities of 2.11 and 2.52 gcm<sup>-3</sup> and contents of 41.1 and 6.4 vol% for nanocrystalline C-S-H gel and poorly crystalline iron-siliceous hydrogarnet, respectively. Furthermore, the spatially-resolved volumetric mass density information has allowed to characterise inner product and outer product C-S-H gels. The average density of inner product C-S-H is smaller than that of outer product and its variability larger. Full characterisation of the pastes, including segmentation of the different components, is reported and the contents are compared with the results obtained by thermodynamical modelling.</p> <p> </p> <p> </p> <p> </p> <p>Ptychographic X-ray computed tomography provides 3D electron mass density and attenuation coefficient distributions of unaltered cement pastes with an isotropic resolution below 100 nm. This imaging technique allows quantitatively distinguishing between different components with very similar absorption contrast.</p> <p>Samples were measured at the cSAXS beamline: i) a neat Portland Cement (PC); ii) a PC-CC blend: 80 wt% of PC and 20 wt% of CaCO<sub>3</sub>, and iii) a PC-FA blend: 70 wt% of PC and 30 wt% of fly ash. The main aim of this study is to have a better insight of the microstructure of the amorphous/nanocrystalline gels with submicrometer spatial resolution. It is worth noting that it is possible to determine the gel mass density and water content within the attained 3D resolution (about 100 nm).</p> <p>Here, we focused on the spatial distribution of the different components and in the variation of the electron density values which are very related to the mass density values. Special attention is paid to the density values of the amorphous (or nanocrystalline) components. The electron and mass density values of the C-S-H gel for three pastes are thoroughly analyzed. The density values range from 2.05-2.10 g·cm<sup>-3</sup> for high density C-S-H gel for neat PC and PC-CC pastes to 1.80 g<sup>.</sup>cm<sup>-3</sup> for low density C-S-H gel in PC-FA paste. The density value of poorly crystalline iron-siliceous hydrogarnet component, r=2.52 g·cm<sup>-3</sup>, has also been determined.</p> <p>A summary of our ongoing research focused on the analyses of cement pastes by synchrotron PXCT is reported and discussed.</p> <p> </p> <p> </p> <p> </p> <p><strong>PC sample:</strong></p> <p>tomo_beta_S02536_to_S03341_Hann_freqscl_0.35_0xxx</p> <p>tomo_delta_S02536_to_S03341_Hann_freqscl_1.00_0xxx</p> <p> </p> <p><strong>PC-CC sample:</strong></p> <p>tomo_beta_S04692_to_S06001_Hann_freqscl_0.35_0xxx</p> <p>tomo_delta_S04692_to_S06001_Hann_freqscl_1.00_0xxx</p> <p> </p> <p><strong>PC-FA sample:</strong></p> <p>tomo_beta_S03351_to_S04661_Hann_freqscl_0.35_0xxx</p> <p>tomo_delta_S03351_to_S04661_Hann_freqscl_1.00_0xxx</p> <p> </p> <p> </p>
Supplementary materials for the paper "Computing with liquid crystal fingers: Models of geometric and logical computation." Physical Review E 84.6 (2011): 061702.
<p>When a voltage is applied across a thin layer of cholesteric liquid crystal, fingers of cholesteric alignment can form and propagate in the layer. In computer simulation, based on experimental laboratory results, we demonstrate that these cholesteric fingers can solve selected problems of computational geometry, logic, and arithmetics. We show that branching fingers approximate a planar Voronoi diagram, and nonbranching fingers produce a convex subdivision of concave polygons. We also provide a detailed blueprint and simulation of a one-bit half-adder functioning on the principles of collision-based computing, where the implementation is via collision of liquid crystal fingers with obstacles and other fingers.</p>
Dataset for the article "MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry"
<p>This dataset contains additional material related to the article: "MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry". The preprint is available at <a href="https://doi.org/10.26434/chemrxiv.7635986">https://doi.org/10.26434/chemrxiv.7635986</a>. Final article is available at <a href="https://doi.org/10.1021/acs.jctc.9b00093">https://doi.org/10.1021/acs.jctc.9b00093</a>.</p>
Experimental–Computational Analysis of Nucleation Sites for Primary Static Recrystallization
<p>This repository contains supplementary material to our paper. Specifically, the Matlab, Python,a and Shell scripts and cellular automaton source code we used to run and post-process the simulations as well as the simulation results:</p> <p><strong>MTEXEBSDMappingStructureInitialization.zip</strong><br> Specifies, using MTex v5.0.3, how we converted the measured SEM/EBSD mapping to a synthetic 2d microstructure.</p> <p><strong>SCORESourceCode.zip</strong><br> Specifies the source code of SCORE. Version 1.2.1. Demands a local HDF5 installation. MPI/OpenMP parallelized.<br> Inspect www.github.com/mkuehbach/SCORE for further details on how to compile and background to the model<br> an implementation.</p> <p><strong>ExecuteSimulations.zip</strong><br> Specifies shell scripts and UDS input files to execute the simulations. Details via these UDS files also all parameter<br> settings we used to reproduce the runs.</p> <p><strong>ComparisonXaXv.tar.gz</strong><br> Compares in summarized form, and extracted from the RXAreaFractionDepthProfile folder files, the area vs<br> volume fraction at specified time snapshots for the z= [0.0, 0.5, 1.0] RDTD section.<br> <br> <strong>Inherited_GrainSizeMicrostructure.zip</strong><br> ANG-like serial sectioning snapshot results and IPF visualization of microstructure evolution for those<br> simulation cases in which the nuclei inherited the orientation from their site.</p> <p><strong>Random_GrainSizeMicrostructure.zip</strong><br> ANG-like serial sectioning snapshot results and IPF visualization of microstructure evolution for those<br> simulation cases in which the nuclei had random orientations form the SO3.</p> <p><strong>RXAreaFractionDepthProfile.zip</strong><br> Specifies the evolution of the area fraction recrystallized with grains in cross-sectional area >=13px<br> for every RDTD layer.</p> <p>The corresponding parameterization is detailed in the *.uds input file which specifies all constitutive parameter<br> and log settings of the automaton. The simulation is executed by compiling the program and linking to<br> HDF5. The OMP_NUM_THREADS environment variable should be set to not more than 10.<br> The SCORE is executed as follows:<br> mpirun -np 1 ./score <simid> <udsfile> <KAM Ang EBSD file> 1>STDOUT.txt 2>STDERR.txt<br> <br> <strong>Profiling.zip</strong><br> Details the execution log of the automaton ie runtime individual composition of nuclei volume transformation<br> progression, interfacial area evolution, etc.</p> <p><strong>SingleGrainData.zip</strong><br> Details the volume consumption / volume gain kinetics of every single deformed / recrystallized grain.</p> <p><strong>TemperatureTimeProfile.zip</strong><br> Details the time/temperature and step profile of the numerical integration.<br> This allows to map integration time steps to simulated microstructural states.</p> <p><strong>ThreadProfilingGrowth.zip</strong><br> Details the evolution of the recrystallized volume versus time and number of active cells per thread sub-domain.</p> <p><strong>MartinPostprocessingScripts.zip</strong><br> Is a collection of Python and MTex scripts to compile the area size distribution and compute ODFs.</p>
ScienceDex guides
Understand access before you commit
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.