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1,870 results for “defect”
Fig. 1 in Options for managing Antestiopsis thunbergii (Hemiptera: Pentatomidae) and the relationship of bug density to the occurrence of potato taste defect in coffee
Fig. 1. Effects of pest management tactics on the occurrence of potato taste defect in coffee. No Prun(P) & No Pest (No Pruning and No Pesticide), P & No Pest (Pruning and No Pesticide), P & Fastac (Pruning and Fastac), P & Pyr 5EW (Pruning and Pyrethrum 5EW), P & Pyr EWC (Pruning & Pyrethrum EWC), P & Agroblast (Pruning and Agroblast), and P & Imida (Pruning and Imidacloprid). Fastac sprayed in pruned plots had the lowest levels of potato taste defect whereas the control had the highest. Bars represent the standard error of means. Means followed with the same letter are not statistically different (P ≤ 0.05, ANOVA and Tukey's test).
CSEM-MISD - CSEM's Multi-Illumination Surface Defect Detection Dataset
<p>In automated surface visual inspection, it is often necessary to capture the inspected part under many different illumination conditions to capture all the defects. To address this issue, at <a href="http://www.csem.ch/">CSEM</a> we have acquired a real-world multi-illumination defect segmentation dataset, called CSEM-MISD and we release it for research purposes to benefit the community.</p> <p>The dataset consists of three different types of metallic parts -- washers, screws, and gears. Parts were captured in a half-spherical <a href="https://register.epo.org/application?number=EP13197867">light-dome system</a> that filtered out all the ambient light and successively illuminated it from 108 distinct illumination angles. Each 12 illumination angles share the same elevation level and the relative azimuthal difference between the adjacent illumination angles on the same level is 30 degrees. For more details, please read Sections 3 and 4 of our paper.</p> <p>The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.</p> <p>We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.</p> <p>This dataset is challenging mainly because:</p> <ul> <li>each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space;</li> <li>the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors;</li> <li>the annotations are not very precise because the exact extent of defect contours is always subjective;</li> <li>the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. </li> </ul> <p>The dataset is organized as follows:</p> <ul> <li>each sample resides in the Test, Train, or Unannotated directory;</li> <li>each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks;</li> <li>each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels;</li> <li>Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12).</li> <li>Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.</li> </ul> <p>We provide data loaders implemented in python at the project's <a href="https://github.com/DawyD/illumination-preserving-rotations">repository</a>.</p> <p>If you find our dataset useful, please cite our paper:</p> <blockquote> <p>Honzátko, D., Türetken, E., Bigdeli, S. A., Dunbar, L. A., & Fua, P. (2021). Defect segmentation for multi-illumination quality control systems. <em>Machine vision and Applications</em>.</p> </blockquote>
Combined temperature and potential induced defect steering for local doping control in Cu2ZnSnSe4
<p>Raw experimental data of publication submitted at Materials Today: Physics ''Combined temperature and potential induced defect steering for local doping control in Cu2ZnSnSe4''</p>
US-XGB Models for Defect Prediction
<p>These files contain two pickle models featuring thousands of machine learning models drawn from the Bug Prediction and Jureczko datasets. These models were trained on extensive data and are suitable for use in bug prediction research data. The models are easy to employ and can be integrated into existing systems with minimal effort. We believe that these models will be a valuable resource for researchers and practitioners working in the field of machine learning and software engineering.</p>
Point defects in InGaN/GaN QWs CL dataset
<p>Dataset of hyperspectral cathodoluminescence (CL) maps accompanying the following publication:</p> <ul> <li><a href="https://doi.org/10.1021/acs.nanolett.1c01295">T. F. K. Weatherley, W. Liu, V. Osokin, D. T. L. Alexander, R. A. Taylor, J.-F. Carlin, R. Butté, and N. Grandjean, “Imaging nonradiative point defects buried in quantum wells using cathodoluminescence”, Nano Letters 21, 5217-5224 (2021).</a></li> </ul> <p>Hyperspectral data is stored in the <a href="http://hyperspy.org/hyperspy-doc/current/user_guide/io.html#hspy-format">"hspy"</a> HyperSpy HDF5 specification, and can be loaded and analysed using Python (see <a href="http://hyperspy.org/hyperspy-doc/current/index.html">HyperSpy documentation</a>). Each hspy file contains comprehensive measurement metadata accessible in the "original_metadata" attribute in Python. See the README file for more detail.</p>
Changing the reaction path of Al/Ni multilayers through planned growth defects
<p>To form line structures with 80 µm hills and valleys width into a Si <100> substrate, thermal oxidation, lithography, and wet chemical etching steps were necessary. During the procedure a inclination was formed in the transition zone between the hills and valleys, due to the KOH etching of the Si <100> substrate. A valley depth of 3.4 µm was measured after the KOH etching. To enable a self-propagating reaction on the structured Si surface, as seen in the video, a 1.2 µm thick layer of thermal SiO2 was produced. The 5 µm thick Al/Ni multilayers were then deposited with direct current magnetron sputtering in an atomic ratio of 1:1, while keeping a bilayer thickness of 50 nm. During the deposition, defects in the multilayers located at the inclined area between the hills and valleys were formed. As shown in the publication of Jaekel et al. (2022) the defects are gaps at the inclined transition zone [1]. During the ignition of the sample, these defects prevented a reaction of the multilayers deposited on the hills. Therefore, a new potential way to guide the reaction on a specific pathway could be established. The video displays an example of a self-propagating reaction, in which the bright reaction front propagates only in the valleys, with an average velocity of 7.4 m/s. The velocity is calculated with the pixels size of 34.4827 µm and the frame rate of 50000 per second. Highspeed-camera FASTCAM SA-X2 type 480K-M3 was used to obtain the video in a resolution of 512x408 pixels.</p>
Dopamine transporter and synaptic vesicle sorting defects underlie auxilin-associated Parkinson's disease
<p>Auxilin participates in clathrin uncoating to facilitate presynaptic endocytosis. Loss-of-function mutations of auxilin (<em>PARK19</em>) cause Parkinson’s disease. Using auxilin KO mice, Vidyadhara et al. (2023) show that synaptic vesicle sorting deficits, cytoplasmic dopamine accumulation, dopamine transporter mistrafficking, and synaptic autophagic overload may lead to pathogenesis of Parkinson’s disease in <em>PARK19</em> patients. This file contains the data set used to generate all the main figures.</p>
The cell-associated magnesium content is affected in Salmonella mutants defective for SigmaS/RpoS and O-antigen synthesis
<p>In many Gram-negative bacteria, the stress sigma factor of RNA polymerase, σS/RpoS, remodels global gene expression to reshape the physiology of quiescent cells and ensure their survival under non-optimal growth conditions. In the foodborne pathogen <i>Salmonella enterica</i> serovar Typhimurium, σS is also required for biofilm formation and virulence. </p><p>Our recent work has shown that a Δ<i>rpoS</i> mutation reduces the magnesium content of <i>Salmonella</i> (Metaane et al. 2022, PLoS ONE 17(3): e0265511). The O-antigen of LPS is a Mg2+ reservoir that can be used under magnesium deprivation. </p><p>We show here that eventhough the cell-associated magnesium content is affected when <i>Salmonella</i> lacks the entire O-antigen, an effect of the Δ<i>rpoS</i> mutation can be still observed. </p><p>We have also shown that the amount of intracellular free Mg2+ is slightly reduced in the Δ<i>rpoS</i> mutant compared to the wild-type strain (Metaane et al 2023, PLoS ONE 18(9): e0291736). </p><p><strong>This work was supported by the French National Research Agency (ANR-19-CE44-0005-01, PERIOMET project).</strong> </p>
Measurement Data for CT of Battery Pouch Cell with Defects
<p>These are the raw radiography images used to calculate the CT volume of a battery pouch cell with defects.</p> <p> </p> <p>Measured on a diondo d2 CT (using XWT-190 CT, Varex 4343DX-I) at TU Dresden.</p> <p>Tube: 150 KV, 100 µA</p> <p>Geometry: FOD 160 mm, FDD 1000 mm</p> <p>Detector: 3000 x 3000 px², 3000 Frames, 5x Framebinning, 3000 ms per Frame</p> <p>Data: 16bit unsigned integer, little endian</p> <p> </p> <p>The sample was prepared by Johannes Münch. For further details see the following paper:</p> <p><a href="https://doi.org/10.1002/ente.202300323">https://doi.org/10.1002/ente.202300323</a></p>
Datasets of paper "Towards Assessing the Real-World Impact of Defects in Blockchain-based Smart Contracts"
<p>Datasets of paper "Towards Assessing the Real-World Impact of Defects in Blockchain-based Smart Contracts" published on the 1st International Workshop on Software Defect Datasets (SDD 2023).</p> <p>The GitHub repository is available here: <a href="https://github.com/MichaelHettmer/sdd23">https://github.com/MichaelHettmer/sdd23</a></p>
Elastic Strain Associated with Irradiation-Induced Defects in Self-ion Irradiated Tungsten
<p>Elastic interactions play an important role in controlling irradiation damage evolution, but remain largely unexplored experimentally. Using transmission electron microscopy (TEM) and high-resolution on-axis transmission Kikuchi diffraction (HR-TKD), we correlate the evolution of irradiation-induced damage structures and the associated lattice strains in self-ion irradiated pure tungsten. TEM reveals different dislocation loop structures as a function of sample thickness, suggesting that free surfaces limit the formation of extended defect structures that are found in thicker samples. HR-TKD strain analysis shows the formation of crystallographically-orientated long-range strain fluctuation above 0.01 dpa and a decrease of total elastic energy above 0.1 dpa.</p>
Microscopic defect dynamics during a brittle-to-ductile transition
<p>Description of Data employed in “<em>Microscopic defect dynamics during a brittle-to-ductile transition</em>”-</p> <p>The data reported in Table S.1 of PNAS paper is as follows :</p> <ol> <li>All raw data [mechanical synchronized with ultrasonic probes ] are in .mat files.</li> <li>The data includes both strain, stress, P-velocityand waveforms of passive sensor in the point of strain-stress also are provided . All data are in 2ns time-resolution –</li> <li>AE_CAT_xxx includes all information recorded during test in Paterson rig. The data are synchronized with pulsing (active) and passive excitations based on simple off-line synchronized matching ;</li> <li>The index the AE_CAT_xxx table doe have a corresponding waveforms due to an AE in M_xxx</li> <li>UM_XXX are based on table S1 and does have full strain-stress data set.</li> <li>Dist_xx_yy is distance measurement calculated based on DTW algorithm between each pair of tests</li> </ol> <p>Feel free to request [ hoghaff@mit.edu or <a href="mailto:mpec@mit.edu">mpec@mit.edu</a>] further information regarding our methods of analysis the data or obtaining the waves in Paterson rig. </p> <p>**Apart of the presented data in the paper , we repeated each test at least twice to be confident about our results in particular ultra-high frequency AEs and the strain-stress history. Feel free to request data independently from corresponding authors.</p>
Defected phosphorene dataset and trained HNNP models
<p>The dataset contains pristine monolayer phosphorene as well as structures with monovacancies and divacancies. The data was obtained using Density Functional Theory (VASP). The Perdew-Burke-Ernzerhof PBE functional was employed with an energy cutoff of 600 eV. The Brillouin zone was sampled by using a 3 × 3 × 1 Gamma-centered mesh. The input.data file is suitable for packages such as n2p2 and Runner.</p> <p>Information about neural network potential are provided in the <a href="10.1021/acs.jpcc.3c05713" target="_blank" rel="noopener">publication</a> (10.1021/acs.jpcc.3c05713).</p>
Data from: Revisiting Volterra defects: Geometrical relation between edge dislocations and wedge disclinations
Open the record for dataset details and reuse information.
Data for: Dysregulation of mTOR signaling mediates common neurite and migration defects in both idiopathic and 16p11.2 deletion autism neural precursor cells
Open the record for dataset details and reuse information.
Rapid biphasic decay of intact and defective HIV DNA reservoir during acute treated HIV disease
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Paper Repository and References for "Early software defect prediction: A systematic map and review"
<p>Context: Software defect prediction is a trending research topic, and a wide variety of the published papers focus on coding phase or after. A limited number of papers, however, includes the prior (early) phases of the software development lifecycle (SDLC).<br> Objective: The goal of this study is to obtain a general view of the characteristics and usefulness of Early Software Defect Prediction (ESDP) models reported in scientific literature. <br> Method: A systematic mapping and systematic literature review study has been conducted. We searched for the studies reported between 2000 and 2016. We reviewed 52 studies and analyzed the trend and demographics, maturity of state-of-research, in-depth characteristics, success and benefits of ESDP models. <br> Results: We found that categorical models that rely on requirement and design phase metrics, and few continuous models including metrics from requirements phase are very successful. We also found that most studies reported qualitative benefits of using ESDP models.<br> Conclusion: We have highlighted the most preferred prediction methods, metrics, datasets and performance evaluation methods, as well as the addressed SDLC phases. We expect the results will be useful for software teams by guiding them to use early predictors effectively in practice, and for researchers in directing their future efforts.</p>
Dataset used to map a stump tail sperm defect of Swiss Large White boars
<p>This dataset contains genotypes for 106 Swiss Large White boars that were used to map a stump tail sperm defect and a VCF file that contains genotypes of candiate variants for 87 boars.</p> <p>The subdirectories contain plink binaries, corresponding phased haplotypes (in MaCH format), the results of a haplotype-based GWAS, and a VCF file that contains genotypes at candidate causal variants.</p> <p>The affection status (case=2, control=1) of 106 boars is indicated in the file "pheno".</p> <p>The R script "haplo_linear.R" performs the haplotype-based association study.</p> <p>The README file provides instructions on how to run the GWAS as well as on how to create the Manhattan plot.</p>
Data of publication: Defect engineering for Quantum Grade Rare-Earth Nanocrystals
<p>Data corresponding to the figures of the publication "Defect Engineering for Quantum Grade Rare-Earth Nanocrystals" by S. Liu et al. (hhttps://pubs.acs.org/doi/10.1021/acsnano.0c02971). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
Typical Sensor Defects Dataset
<p>Thirteen datasets of sensor values, with one dataset without sensor defects (data_standard.csv). All other datasets are based on the dataset without a defect, with the values of Temp_Sensor_2 modified to simulate different sensor defects:</p> <ul> <li>Sensor Drift: 1‰/hour (data_drift_0_001.csv), 2.5‰/hour (data_drift_0_0025.csv), 5‰/hour (data_drift_0_005.csv)</li> <li>Sensor Offset: 1°C Offset (data_offset_1.csv), 2°Offset (data_offset_2.csv), 5°Offset (data_offset_5.csv)</li> <li>Sensor Peaks: 1 Peak/Minute (data_peak_1.csv), 2 Peaks/Minute (data_peak_2.csv), 5 Peaks/Minute (data_peak_5.csv), 10 Peaks/Minute(data_peak_10.csv)</li> <li>Sensor Noise: 10 dB SNR (data_noise_10dB.csv), 0 dB SNR (data_noise_0dB.csv)</li> </ul> <p>The datasets are given as comma-separated values in text files. The first column in each file holds time stamps, while the following columns hold the sensor values. The first entry in every column gives the name of the sensor. All datasets are zipped into one file (data.zip).</p> <p>Additionally attached is configuration data (Configuration.pdf) for the sensor fusion approach that was used to classify the datasets.</p> <p>For more information please contact the uploader.</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.