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1,890 results for “Defects”
Continuous generation of topological defects in a passively driven nematic liquid crystal DATA
<p>Data and code associated with the paper titled "Continuous generation of topological defects in a passively driven nematic liquid crystal".</p>
Simultaneous Control of Aluminum Atoms and Defects in MOR Zeolite Framework by Post-Synthetic Treatments
<p><span>Input and output files used to calculate 27Al NMR chemical shifts in Quantum Espresso 6.5 for modernities with various defects and Al substitutions.</span></p>
The energy bands of charged defect predicted by the HamGNN-Q model
<p>The dataset contains graph representations of GaAs defects for testing in the study that were not present in the training set, including single-point vacancies, interstitial atom defects, defect clusters, substitution defects, and large-sized polarons with varying background charges. charged_defect_hamiltoian.ckpt is the network weights for the HamGNN-Q model. config_charge.yaml is the input file of the model.</p>
HyperspectralBlueberries: a dataset of hyperspectral reflectance images of normal and defective blueberries
<p>The <strong>HyperspectralBluberries</strong> dataset consists of hyperspectral datacubes, which were acquired by an in-house assembled benchtop line scanning system, from 420 blueberries of two categories, including 210 sound fruit and 210 samples with various defects. The fruit samples were hand-picked from a commercial orchard. Each scanning event, which was done for an array of 42 samples, yields two files in image formats .bil (band-interleaved-by-line) and .hdr (header), which store the hyperspectral raw data and associated metadata, respectively, and are both necessary for loading hyperspectral data for processing. In addition to sample scanning, a white reference was also scanned, which can be used for standardizing spectral responses. As a result, there are 22 files in the dataset, totaling about 25 GB in file size. The sample file names are descriptive, indicating the blueberry category and number information. The dataset was used for developing machine learning models for differentiating between normal and defective blueberries, achieving an overall accuracy of 96.6%. Software programs for the modeling work are publicly available at: <a href="https://github.com/vicdxxx/Blueberry-Defect-Detection-by-Hyperspectral-Imaging">https://github.com/vicdxxx/Blueberry-Defect-Detection-by-Hyperspectral-Imaging.</a></p> <p>Details about the dataset curation and modeling experiments are described in the journal article: <a href="https://www.sciencedirect.com/science/article/pii/S2772375524000789">Deng, B., Lu, Y., Stafne, E. (2024). </a><a href="https://www.sciencedirect.com/science/article/pii/S2772375524000789">Fusing Spectral and Spatial Features of Hyperspectral Reflectance Imagery for Differentiating between Normal and Defective Blueberries. Smart Agricultural Technology</a>. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.atech.2024.100473" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.atech.2024.100473</a>. If you use the dataset in published research, please consider citing the dataset or the <a href="https://doi.org/10.1016/j.ecoinf.2024.102546">journal article</a>. Hopefully, you find the dataset useful. </p>
Dielectric Loss due to Charged-Defect Acoustic Phonon Emission
Open the record for dataset details and reuse information.
Tuning the electronic properties of Zr UiO-66 through defect-functionalised multivariate modulation
<p>Raw data supporting the article 'Tuning the electronic properties of Zr UiO-66 through defect-functionalised multivariate modulation'.</p>
Data from: Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study
<p>Data analyzed in "Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study". The data provided by the authors to benefit other researchers. The posted materials are not copyedited and are the sole responsibility of the authors, so questions should be addressed to the corresponding author.</p>
Data for "Identification of Killer Defects in Kesterite Thin-Film Solar Cells"
<p>**README**</p> <p>Data for "Identification of Killer Defects in Kesterite Thin-Film Solar Cells"</p> <p>DOI: 10.1021/acsenergylett.7b01313</p> <p><br> * File Tree <br> ---<br> * DFT_CALC // Row input file for DFT calculation (VASP)<br> * XX_DEFECT_CZTS(e) // Data for CZTS (or CZTSe)<br> * XX_PRIM(ORTHO/221) // Data for bulk (primitive, orthogonal or 2X2X1 supercell)<br> * XX_Defect // Data for defect (V_S, Sn_Cu, Sn_Zn, Cu_Sn)<br> * XX_q // SCF calculation with charge state q <br> * fig // data used to draw figures<br> * 00_atomic_structure <br> * 01_charge_transition_level<br> * 02_charge_density<br> * 03_configuration_coordinate<br> * 20_SI_PHASE_DIAGRAM</p>
Dataset for "Stability and electronic properties of planar defects in quaternary I2-II-IV-VI4 semiconductors"
<p>We are grateful to the UK Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC (EP/P020194/1). The research was supported by the Royal Society and the EU Horizon2020 Framework (STARCELL, Grant No. 720907). J.-S.P. thanks the Royal Society for Shooter International Fellowship.</p>
CODEBRIM: COncrete DEfect BRidge IMage Dataset
<p><strong>CODEBRIM: COncrete DEfect BRidge IMage Dataset</strong> for multi-target multi-class concrete defect classification in computer vision and machine learning.</p> <p>Dataset as presented and detailed in our CVPR 2019 publication: <a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Mundt_Meta-Learning_Convolutional_Neural_Architectures_for_Multi-Target_Concrete_Defect_Classification_With_CVPR_2019_paper.html">http://openaccess.thecvf.com/content_CVPR_2019/html/Mundt_Meta-Learning_Convolutional_Neural_Architectures_for_Multi-Target_Concrete_Defect_Classification_With_CVPR_2019_paper.html</a> or <a href="https://arxiv.org/abs/1904.08486">https://arxiv.org/abs/1904.08486</a> . If you make use of the dataset <strong>please cite it as follows</strong>:</p> <p><strong>"Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh. <em>Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset</em>. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019"</strong></p> <p>We offer a supplementary GitHub repository with code to reproduce the paper and data loaders: <a href="https://github.com/ccc-frankfurt/meta-learning-CODEBRIM">https://github.com/ccc-frankfurt/meta-learning-CODEBRIM</a></p> <p>For ease of use we provide the dataset in multiple different versions.</p> <p>Files contained:<br> * CODEBRIM_original_images: contains the original full-resolution images and bounding box annotations<br> * CODEBRIM_cropped_dataset: contains the extracted crops/patches with corresponding class labels from the bounding boxes <br> * CODEBRIM_classification_dataset: contains the cropped patches with corresponding class labels split into training, validation and test sets for machine learning<br> * CODEBRIM_classification_balanced_dataset: similar to "CODEBRIM_classification_dataset" but with the exact replication of training images to balance the dataset in order to reproduce results obtained in the paper. </p>
Data and Code for "Mesoscale modeling of deformations and defects in thin crystalline sheets"
<p>Research data and code supporting the paper ""Mesoscale modeling of deformations and defects in thin crystalline sheets"".</p> <p> </p> <p><strong>Code (apfc-python-surf.zip)</strong></p> <p>The implementation of the APFC model is performed in python by exploiting the pseudo-spectral Fourier method. Library pyfftw is adopted. However, standard fft libraries can be used as well by changing the corresponding module/functions. The code supports equations of the APFC model both coupling with the evolution of the surface considered in this work and on a simple flat domain. Updates can be found in the GitLab repository linked below.</p> <p> </p> <p><strong>GitLab repository for the code</strong></p> <p><a href="https://gitlab.com/3ms-group/apfc_python/">https://gitlab.com/3ms-group/apfc_python/</a></p> <p> </p> <p><strong>Data</strong></p> <p>The data.zip files contain the simulation results and auxiliary scripts used to produce the results illustrated in the paper's figures. The folder numbering refers to the one used for the figures in the final version.</p> <p> </p> <p>For further information please contact the authors.</p>
Hydrodynamic thinning of a coating film induced by a small solid defect: evidence of a time-minimum thickness
<p>groove_min.csv contains the data of the section on the minimum of the groove thickness.</p> <p>Newton.csv contains the data of the section on the Newton film.</p> <p>fig1.bil and fig8.bil are the files of the datacubes of the space-time diagrams of Fig.1 and 8 respectively, and the .bil.hdr file is the header corresponding to the .bil file.</p> <p>w header.csv is the header for the w files, which are all the remaining files; it contains the name of the files and the initial thickness e0 and fiber radius rf to make the correspondence with the remaining files.</p> <p>fig2.csv contains the thickness profiles (in nm): each row is the thickness profile at one time, and the last two rows are the time vector (in s) and the space vector (in m).</p> <p>fig3a.csv contains the data of the thickness and the position of the groove, and the time used in Fig.3.</p> <p> </p>
Characterization of extended defects in 2D materials using aperture-based dark-field STEM in SEM
<p>This is the raw data for the manuscript:</p> <p>Characterization of extended defects in 2D materials using aperture-based dark-field STEM in SEM</p> <p> </p> <p>A readme file containing all descriptions can be found in the main folder. </p> <p> </p> <p>Abstract:</p> <p>Quantitative diffraction contrast analysis with defined diffraction vectors is a wellestablished method in TEM for studying defects in crystalline materials. A comparable transmission techniques is however not available in the more widely used SEM platforms. In this work, we transfer the aperture-based dark-field imaging method from the TEM to the SEM, thus enabling quantitative diffraction contrast studies at lower voltages in SEM. This is achieved in STEM mode by inserting a custom-made aperture between the sample and the STEM detector and centering the hole on a desired reflection. To select individual reflections for dark-field imaging, we use our Low Energy Nanodiffraction (LEND) setup [Schweizer et al., Ultramicroscopy 213, 112956 (2020)], which captures transmission diffraction patterns from a fluorescent screen positioned below the sample. The aperture-based dark-field STEM method is particularly useful for studying extended defects in 2D materials, where (i) stronger diffraction at the lower voltages used in SEM is advantageous, but at the same time (ii) two-beam conditions cannot be established, making quantitative diffraction contrast analysis with standard bright-field and annular dark-field detectors impossible. We demonstrate the method by studying basal plane dislocations in bilayer graphene, which have attracted considerable research interest due to their exceptional structural and electronic properties. Direct comparison of results obtained on identical dislocations by the established TEM method and by the new aperture-based dark-field STEM method in SEM shows that a reliable Burgers vector analysis is possible by applying the wellknown g·b=0 invisibility criterion. We further use the LEND setup to acquire 4D-STEM data and show that the virtual dark-field images match well with those in aperturebased dark-field STEM images for reliable Burgers vector analysis.</p> <p> </p>
Data related to: Fast low-temperature irradiation creep driven by athermal defect dynamics
<h2>Data set related to article "Fast low-temperature irradiation creep driven by athermal defect dynamics"</h2> <h2>Simulation data</h2> <h3>Molecular dynamics data</h3> <p>The molecular dynamics data is the output of simulations of single-crystal tungsten with periodic boundary conditions evolving under irradiation up to high dose (0.5 dpa). The simulations are performed under a constant externally applied stress, uniaxial to z-direction, and zero stress conditions otherwise. Simulations were performed for stresses of -1.0 GPa, -0.5 GPa, 0 GPa, 0.5 GPa, 1.0 GPa, 1.5 GPa, and 2.0 GPa. Each stress condition was simulated five times independently. Simulations were performed in <a href="https://www.lammps.org/">LAMMPS</a> (see below for the simulation script). Only every 100th frame in LAMMPS Dump format is uploaded here. A frame corresponds to a dose increment of approximately 0.0002 dpa, i.e. frame 1000 corresponds to a dose of approximately 0.2 dpa. The files are zipped. A finer resolution can be supplied upon request (up to every 5th frame).</p> <p>The zip archive naming convention is as follows:</p> <blockquote> <p>For example, archive "srim_neg1p0.zip" contains data of the 5 simulations for -1.0 GPa (='negative 1 point 0'), where snapshot "srim_neg1p0_2/srim_neg1p0_2.1300.dump" refers to independent simulation ID 2, frame 1300, i.e. at a dose of 0.26 dpa. </p> <p>"srim_pos0p5.zip" contains the 5 simulations for 0.5 GPa (='positive 0 point 5'), and so on.</p> </blockquote> <p>Archive "logfiles.zip" contains the simulation output information, containing stresses and box dimensions for every cascade iteration of each simulation. These can be used to generate the box eigenstrains. The columns are defined as follows:</p> <blockquote> <p>iteration number, dose (dpa), total potential energy (eV), pressure xx (bar), pressure yy (bar), pressure zz (bar), pressure xy (bar), pressure xz (bar), pressure yz (bar), simulation box width x (Å), simulation box width y (Å), simulation box width z (Å)</p> </blockquote> <p>The files contain a few lines labelled with "# restart", which marks points at which the simulation was terminated and then continued.</p> <p>Archive "md_eigenstrains.zip" contains the eigenstrain tensor components parallel and perpendicular to the uniaxial stress direction obtained directly from the MD simulations. The format is as follows:</p> <blockquote> <p>For example, files "eigenpara_pos0p5_0.dat" and "eigenperp_pos0p5_0" contain the MD eigenstrains parallel and perpendicular to the uniaxial loading direction of simulation "eigenpara_pos0p5_0", respectively. The first column is the NRT dose (dpa), and the second column is the eigenstrain value at this dose. The perpendicular eigenstrain is the average of the eigenstrain components xx and yy.</p> </blockquote> <h3>Molecular dynamics script</h3> <p>The LAMMPS script for performing high-dose collision cascade simulations is available at:</p> <p> <a href="https://github.com/mb4512/ezcascades/">https://github.com/mb4512/ezcascades</a></p> <p>The simulation input files required to replicate the molecular dynamics simulations of this work are supplied here. Archive "md_input_files.zip" contains simulation input files, specifying box dimensions, stress constraints, the interatomic potential, paths to simulation and scratch directories, and so on. For example, "srim_neg1p0_0.json" is the input file for the "srim_neg1p0_0" simulation.</p> <p>The interatomic potential "W_MNB_JPCM17.eam.fs" used here is the embedded atom method potential for tungsten developed by Mason et al: <a href="https://doi.org/10.1088/1361-648X/aa9776">10.1088/1361-648X/aa9776</a>, available at the <a href="https://www.ctcms.nist.gov/potentials/">NIST Interatomic Potentials Repository</a>.</p> <h3>Surrogate model data</h3> <p>Archive "eigenstrain_models.zip" contains the MLE model parameters for the eigenstrain surrogate models. The format is as follows:</p> <blockquote> <p>For example, files "eigenpara_neg0p5.log" and "eigenperp_neg0p5.log" contain the cubic spline knot points for the MLE model of eigenstrains parallel and perpendicular to the uniaxial loading direction at -0.5 GPa, respectively. The file contains columns labelled as x, y, and sigma, which correspond to dose (dpa), eigenstrain mean, and eigenstrain standard deviation, respectively. The spline boundary conditions are f(x = 0) = 0, f''(x = 0) = 0, and f'(x_end) = 0, where x_end is the final dose value in the list. The model is extrapolated for higher doses: f(x > x_end) = f(x_end).</p> <p>The log files also contain the covariance matrix of the eigenstrain mean values, from which model uncertainties can be derived.</p> </blockquote> <p>Archive "doseprofile.zip" contains the irradiation dose profile as generated using <a href="https://www.srim.org/">SRIM</a> data following the procedure described in the Supplemental Information. The content is as follows:</p> <blockquote> <p>Files "vacgrid_x_8micro_HR.dat", "vacgrid_y_8micro_HR.dat" contain the x and y coordinates of the 2 dimensional grid, respectively. File "vacgrid_z_8micro_HR.dat" contains a list of dose values (in arbitrary units). The values are ordered consistently, that is, the n-th values of each file give the matching tuple (x, y, dose(x,y)).</p> </blockquote> <p> </p>
Determination of the geometric parameters of the defects based on the tomographically obtained data and their influence on the fatigue behavior of the S960 with laser cladded protective layers
<p>Original Figure 3: Geometric dimensions of the single-track deposition region of Aluminium-Bronze/S960</p>
BEIRUT: Repository Mining for Defect Prediction
<p>This artifact includes two CSV files.</p> <p>The sample_metrics.csv contains a sample of 153 metrics extracted from project <a href="https://ratis.apache.org/">ratis</a>.</p> <p>The sample_prediction.csv contains a sample of the prediction results from the application of defect prediction to the extracted metrics.</p>
Data from: Covalent disruptor of YAP-TEAD association suppresses defective Hippo signaling
<p>The transcription factor TEAD, together with its coactivator YAP/TAZ, is a key transcriptional modulator of the Hippo pathway. Activation of TEAD transcription by YAP has been implicated in a number of malignancies, and this complex represents a promising target for drug discovery. Here, we employed covalent fragment screening approach followed by structure-based design to develop an irreversible TEAD inhibitor MYF-03-69. Using a range of <em>in vitro</em> and cell-based assays we demonstrated that through a covalent binding with TEAD palmitate pocket, MYF-03-69 disrupts YAP-TEAD association, suppresses TEAD transcriptional activity and inhibits cell growth of Hippo signaling defective malignant pleural mesothelioma (MPM). Further, a cell viability screening with a panel of 903 cancer cell lines indicated a high correlation between TEAD-YAP dependency and the sensitivity to MYF-03-69.</p> <p>To validate MYF-03-69 as potent and selective pan-TEAD inhibitor, we interrogated the proteome-wide selectivity profile of MYF-03-69 on cysteine labeling using a streamlined cysteine activity-based protein profiling (SLC-ABPP) approach and generated the spreadsheet "Supplementary_Dataset_1._Proteome-wide_selectivity_profile_of_MYF-03-69_on_cysteines_labeling_using_SLC-ABPP_approach". We employed the cysteine reactive desthiobiotin iodoacetamide (DBIA) probe which was reported to map more than 8,000 cysteines and performed a competition study on NCI-H226 cells pretreated with 0.5, 2, 10 or 25 µM of MYF-03-69 for 3 hours in triplicate. The cysteines that were conjugated >50% (competition ratio CR>2) compared to DMSO control were analyzed and assigned to the protein targets. In the DMSO control group, although DBIA mapped 12,498 cysteines in total, the TEAD PBP cysteines were not detected. This might be due to low TEAD1-4 protein abundance and/or inability of the PBP cysteines to be labeled given that they are mostly modified by palmitate under physiological conditions. Among 12,498 mapped cysteines, only 7 cysteines were significantly labeled (i.e. exhibited >50% conjugation or CR>2) by 25 µM of MYF-03-69, and all of these sites exhibited dose-dependent engagement.</p> <p>To study the whole transcriptome perturbation by TEAD inhibitor MYF-03-69, mRNA sequencing was performed in NCI-H226 cells that were treated with 0.1 μM, 0.5 μM, and 2 μM of MYF-03-69 and generated the spreadsheet "Supplementary_Dataset_2._List_of_differentially_expressed_genes_under_MYF-03-69_treatments". The genes that were differentially expressed with statistical significance (Fold change > 1.5 and adjusted p value < 0.05) are listed in this dataset.</p> <p>To investigate whether TEAD inhibition by MYF-03-69 was selectively lethal to YAP/TEAD-dependent cancers, PRISM screening across a broad panel of cell lineages were performed and generated the spreadsheet "Supplementary_Dataset_3". 903 cancer cells were treated with TEAD inhibitor MYF-03-69 for 5 days. The viability values were measured at 8-point dose manner (3-fold dilution from 10 μM) and fitted a dose-response curve for each cell line. Area under the curve (AUC) was calculated as a measurement of compound effect on cell viability. CERES score of YAP1 or TEADs from CRISPR (Avana) Public 21Q1 dataset (DepMap) were listed in the spreadsheet and used to estimate gene-dependency. The CERES Score of most dependent TEAD isoform was used to represent TEAD dependency. With PRISM screen dataset of TEAD inhibitor MYF-03-69, we investigated whether TEAD inhibition recapulates genetically knockout outcome of YAP or TEADs and generated the spreadsheet "Supplementary_Dataset_4". Correlation analysis between compound PRISM sensitivity (log2.AUC of each cell line) and dependency of certain gene (CRISPR knockout score for each cell line, from DepMap Public 20Q4 Achilles_gene_effect.csv dataset) across the PRISM cell line panel. The Pearson correlation coefficients and associated p-values were computed. Positive correlations correspond to dependency correlating with increased sensitivity. The q-values (a corrected significance value accounting for false discovery rate) are computed from p-values using the Benjamini Hochberg algorithm. Associations with q-values above 0.1 are filtered out. This correlation analysis reveals that the dependency scores of TEAD1 and YAP1 according to genomic knockout dataset (DepMap portal) provided the highest correlation with the compound PRISM sensitivity profile. This is followed by TP53BP2, a gene that is also involved in Hippo pathway as activator of TAZ.</p>
dataset for T. Shimaya and K. A. Takeuchi, Tilt-induced polar order and topological defects in growing bacterial populations. PNAS Nexus 2022
<p>dataset for T. Shimaya and K. A. Takeuchi, Tilt-induced polar order and topological defects in growing bacterial populations. PNAS Nexus 2022. DOI: 10.1093/pnasnexus/pgac269</p>
Data set for "Electrical spectroscopy of defect states and their hybridization in monolayer MoS2"
<p>Data set for "Electrical spectroscopy of defect states and their hybridization in monolayer MoS2" doi:10.1038/s41467-022-35651-1</p>
Defectors: A Large Scale Python Dataset for Defect Prediction
<p>Defect prediction has been a major research problem in the software engineering domain for the last five decades.<br> In recent years, large deep-learning models have shifted the performance of software engineering tasks to new limits and are gaining usage in defect prediction.<br> However, these defect prediction models are often limited by the quality of their datasets, which are not large or diverse enough.<br> In this paper, we present Defectors, a large dataset for both line-level and just-in-time defect prediction.<br> Defectors consist of $\approx$ 213K source code files ($\approx$ 93K defective and $\approx$ 120K defect-free files) from 25 popular python projects from various domains and organizations.<br> These projects come from a diverse set of domains including machine learning, automation, and internet-of-things.<br> Such a scale and diversity make Defectors a suitable dataset for deep learning models, especially transformer models that require large and diverse datasets to effectively generalize defect-inducing patterns to predict future defects.</p> <table> <caption>Dataset Description</caption> <tbody> <tr> <td>File Name</td> <td>Description</td> </tr> <tr> <td>defectors.zip</td> <td>The original Dataset. Find its description in Section II of the paper.</td> </tr> <tr> <td>bug_inducing_commits.zip</td> <td>Each yaml file contains a map of bugfix commits to bug-inducing commits.</td> </tr> <tr> <td>filtered_bug_inducing_commits.yaml</td> <td>A map in structure {repo_name: {bug_inducing_commits: [list of python files in the commit]}}. This file only contains the bug-inducing commits that match the filtering criteria from Section III.D.</td> </tr> <tr> <td>repo_links.yaml</td> <td>Links to the repositories we used to construct the dataset.</td> </tr> </tbody> </table> <p> </p>
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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.