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1,870 results for “defect”
Dataset of "Electronic structure and defect states in bismuth and antimony sulphides identified by energy-resolved electrochemical impedance spectroscopy"
Understanding the nature of the defects in the absorber materials, namely point defects, their formation mechanism and the contribution to the properties is essential for the photovoltaic device performance improvement. They are one the reasons why chalcogenide-based solar cells do not yet meet expected high power conversion efficiencies. Here we identify and present energy distribution of defects in Bi2S3 and Sb2S3, and their (SbxBi(100-x))2S3 alloys (with x = 0, 10, 33, 50, 67, 90, 100 at% Sb content) chalcogenides, being explored for emerging photovoltaic applications as they are earth-abundant and highly absorbing in the visible light range. We show that their density of states (DOS) and related parameters can be obtained experimentally by energy-resolved electrochemical impedance spectroscopy (ER-EIS) in a technically simple and quick way, where ER-EIS data are well correlated with theoretical DFT calculations. ER-EIS reveals that in Bi2S3 there are only shallow defects at CBM. In Sb2S3, ER-EIS reveals also midgap states which can be the cause of low electrical conductivity of Sb2S3. We also explain the discrepancy in the reported values of ionisation potentials and the bandgaps of the Bi- and Sb-chalcogenides. Dominant sulphur vacancy defect was identified in Bi- and Sb-chalcogenides whereas in ternary (SbxBi(100-x))2S3 system, merely 10 at.% of Bi transforms the midgap sulphur defects to shallow ones. This provides novel strategy for healing the midgap defects in Sb2S3, which is crucial for boosting the PV performance and tuning the electrical conductivity in Sb2S3.
Defect Prediction Tool Validation Dataset 2
<p><strong>This dataset is used to address the Research Questions in the study at Transactions on Software Engineering</strong>: <strong>Within-Project</strong> <strong>Defect Prediction of Infrastructure-as-Code using Product and Process Metrics. </strong></p> <p><strong>See also: https://github.com/stefanodallapalma/TSE-2020-05-0217.</strong></p> <p>It provides</p> <p>* <strong>repositories.json</strong> - a list of repositories selected from open-source GitHub repositories based on the Ansible language.</p> <p>* <strong>fixing-commits.json</strong> - a list of defect-fixing commits extracted from those repositories.</p> <p>* <strong>fixed-files.json</strong> - a list of Ansible files fixed in those defect-fixing commits and respective bug-inducing commits.</p> <p>* <strong>failure-prone-files.json</strong> - a list of failure-prone files through the repository's commit history.</p> <p>* <strong>metrics.zip </strong>- csv files consisting of releases (set of files) and their IaC-oriented, delta and process metrics extracted from each analyzed repository</p> <p>* <strong>projects.zip </strong>- for each analyzed project, it contains the data (models, performance, and results of Recursive Feature Elimination) used to answer the Research Questions.</p> <p><strong>Context</strong></p> <p><em>Infrastructure-as-code (IaC)</em> is the DevOps strategy that allows management and provisioning of infrastructure through the definition of machine-readable files and automation around them, rather than physical hardware configuration or interactive configuration tools.</p> <p>On the one hand, although IaC represents an ever-increasing widely adopted practice nowadays, still little is known concerning how to best maintain, speedily evolve, and continuously improve the code behind the IaC strategy in a measurable fashion. <br> On the other hand, source code measurements are often computed and analyzed to evaluate the different quality aspects of the software developed.<br> In particular, Infrastructure-as-Code is simply "code", as such it is prone to defects as any other programming languages.</p> <p>This dataset targets the YAML-based Ansible language to devise <strong>within-project defects prediction</strong> approaches for IaC based on Machine-learning.</p> <p><strong>Content</strong></p> <p>The dataset contains metrics extracted from 85 open-source GitHub repositories based on the Ansible language that satisfied the following criteria:</p> <p>* The repository has at least one push event to its master branch in the last six months;<br> * The repository has at least 2 releases;<br> * At least 10% of the files in the repository are IaC scripts;<br> * The repository has at least 2 core contributors;<br> * The repository has evidence of continuous integration practice, such as the presence of a .travis.yaml file;<br> * The repository has a comments ratio of at least 0.1%;<br> * The repository has commit frequency of at least 2 per month on average;<br> * The repository has an issue frequency of at least 0.01 events per month on average;<br> * The repository has evidence of a license, such as the presence of a LICENSE.md file<br> * The repository has at least 100 source lines of code.</p> <p>Metrics are grouped into three categories:</p> <p>* <strong>IaC-Oriented:</strong> metrics of structural properties derived from the source code of infrastructure scripts. Click [here](https://www.sciencedirect.com/science/article/pii/S0164121220301618) for more info.</p> <p>* <strong>Delta</strong>: metrics that capture the amount of change in a file between two successive releases, collected for each IaC-oriented metric.</p> <p>* <strong>Process</strong>: metrics that capture aspects of the development process rather than aspects about the code itself. Description of the process metrics in this dataset can be found [here](https://pydriller.readthedocs.io/en/latest/processmetrics.html).</p> <p>In addition to the metrics, the dataset contains the pre-trained models (*.joblib) in the folders rq1 and rq2 of projects.zip.</p> <p>You can load the model in Python as follows:</p> <p>```<br> from joblib import load<br> model = load('projects/owner/repository/rq1/random_forest.joblib'), mmap_mode='r')</p> <p>best_estimator = model['estimator'] # The estimator that maximized the AUC-PR</p> <p>cv_results = model['cv_results'] # The results of each step of the validation procedure</p> <p>best_index = mode['best_index'] # The index to access the best cv_results<br> ```</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p> </p> <p>This work is supported by the European Commission grants no. 825040 (RADON H2020).</p> <p><br> <strong>Inspiration</strong></p> <p>What source code properties and properties about the development process are good predictors of defects in Infrastructure-as-Code scripts?</p>
Dataset for "Numerical methods for the detection of phase defect structures in excitable media"
<p>This archive contains the numerical methods presented in the publication "Numerical methods for the detection of phase defect structures in excitable media" as well as the data sets these methods have been applied on. The Python module for Ithildin (py_ithildin.zip) contains the actual Python source code of those methods. Additional Python scripts have been used to generate the figures in the paper (scripts-pdl-detection.zip). The optical voltage mapping data (optical_*) has been slightly pre-processed (noise reduction, re-scaling, etc). The second variable for the optical data (optical_20200204114234_v.npy) is a delayed version of the first variable. The other files contain simulation results from several finite differences simulations of the mono-domain model. For details, see our paper.</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271351"><strong>Numerical methods for the detection of phase defect structures in excitable media</strong></a><br> Kabus D, Arno L, Leenknegt L, Panfilov AV, Dierckx H (2022) Numerical methods for the detection of phase defect structures in excitable media. PLOS ONE 17(7): e0271351. <a href="https://doi.org/10.1371/journal.pone.0271351">https://doi.org/10.1371/journal.pone.0271351</a></p>
Deletion of porcine BOLL causes defective acrosomes and subfertility in Yorkshire boars
<p>This datset contains plink binaries (final_recode_ID.bed, final_recode_ID.bim, final_recode_ID.fam) that contain array-derived genotypes for 35 Yorkshire boars. 12 boars produced sperm with defective acrosomes. The case/control status (1-unaffected; 2-affected) of the 35 boars is indicated in the accompanying phenotype file (final_recode_ID.pheno).</p> <p>The corresponding pre-print is at the bioRxiv: https://biorxiv.org/cgi/content/short/2020.05.05.074724v1</p>
Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance
<p>Dataset of the results published in the <a href="https://zenodo.org/record/4742379#.YMzExOgzYmJ">J. Mater. Chem. A, 2021, 9, 10466</a>. The files represent: i) the measured compositional and optoelectronic data of each solar cell, as well as the data generated from the Raman spectra analysis; ii) Raman spectra of the representative cells; iii) Machine Learning discriminants.</p> <p>The elemental composition of the different cells of the combinatorial sample was determined by X-ray fluorescence (XRF) using a Fischerscope XDV system with a 1 mm spot diameter, a 50 kV acceleration voltage, a Ni10 lter and a 45 s acquisition time. Raman analysis with blue (442 nm) and green (532 nm) excitation wavelengths were performed on the bare absorber, while measurements with NIR (785 nm) were performed in complete devices using Horiba Jobin Yvon FHR640 and iHR320 monochromators coupled with CCD detectors. The first monochromator is optimized for the UV and visible spectral ranges and was used with 442 nm (He–Cd gas laser) and 532 nm (solid state laser) excitation wavelengths. The second monochromator is optimized for the NIR range and was used with a 785 nm (solid state laser) excitation wavelength. The power density of the lasers was kept below 150 W cm<sup>2</sup> and the spot size was ~70 <span class="math-tex">\(\mu\)</span>m. The measurements were performed in a backscattering configuration through a specific probe designed at IREC. The J–V characteristics of the devices were obtained under simulated AM1.5 illumination (1000 W m2 intensity at room temperature) using a pre-calibrated Class AAA solar simulator (Abet Technologies Sun 3000).</p>
Pinning and movement of individual nanoscale magnetic skyrmions via defects
<p>An understanding of the pinning of magnetic skyrmions to defects is crucial for the development of<br> future spintronic applications. While pinning is desirable for a precise positioning of magnetic<br> skyrmions it is detrimental when they are to be moved through a material.Weuse scanning tunneling<br> microscopy (STM) to study the interaction between atomic scale defects and magnetic skyrmions that<br> are only a few nanometers in diameter. The studied pinning centers range from single atom inlayer<br> defects and adatoms to clusters adsorbed on the surface of our model system.Wefind very different<br> pinning strengths and identify preferred positions of the skyrmion. The interaction between a cluster<br> and a skyrmion can be sufficiently strong for the skyrmion to follow when the cluster is moved across<br> the surface by lateral manipulation with the STMtip.</p>
Pushing the Study of Point Defects in Thin Film Ferrites to Low Temperatures Using In Situ Ellipsometry
<p>Dataset for article "Pushing the Study of Point Defects in Thin Film Ferrites to Low Temperatures Using In Situ Ellipsometry" published in <em>Adv. Mater. Interfaces</em> 2021, <strong>8</strong>, 2001881. </p> <p>The data includes:</p> <ul> <li>XRD data of La<sub>1-x</sub>Sr<sub>x</sub>FeO<sub>3</sub> thin films</li> <li>Optical conductivity of LSF films as a function of oxygen partial pressure and Sr content</li> <li>Concentration of electronic holes in LSF thin films as a function of oxygen partial pressure and temperature</li> <li>Defect chemistry models employed for describing the concentration of point defects in LSF thin films.</li> <li>Equilibrium constants for oxygen incorporation reactions in LSF thin films at different temperatures</li> <li>Ellipsometry raw data of LSF50 thin film as a function of equivalent oxygen pressure </li> <li>Electrochemical impedance spectra of the LSF50 thin film at 400 ºC</li> </ul>
Datasets for the paper "Microstates and defects of incoherent Σ3 [111] twin boundaries in aluminum"
<p>This repository contains the raw data of the experimental EBSD analysis and the STEM imaging of the grain boundary microstates of ORI and ORII of the paper "Microstates and defects of incoherent Σ3 [111] twin boundaries in aluminum". Simulation data is also provided.</p> <p>See the file README.md for a detailed description.</p>
(dataset) Interactions of irradiation defects with nitrogen in α-Fe: an integrated experimental and theoretical study
<p>This dataset contains data , simulations and plot scripts in support of the manuscript "<em>Interactions of irradiation defects with nitrogen in <span class="math-tex">\(\alpha\)</span>-Fe: an integrated experimental and theoretical study</em>"</p>
Nanoscale mapping of point defect concentrations with 4D-STEM
<p>The following 4D-STEM data sets were collected on the ThemIS and TitanX scanning transmission electron microscopes located at the National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. </p> <p>1• Au_thermal_beforeHT_17C.dm4, Au_thermal_HT_800C.dm4, Au_thermal_HT_1000C.dm4 and Au_thermal_afterHT_17C.dm4 are datasets from 4D-STEM measurements conducted <em>in situ</em> on an FEI ThemIS image corrected microscope at 300 kV during a thermal cycling experiment. These digital micrograph (.dm4) files were collected at 17 C before heat treatment, 800 C during heat treatment, 1000 C during heat treatment, and 17 C after heat treatment, respectively. Nano-diffraction data was collected using a Gatan K2-IS (2k x 2k) detector at 400 frames per second. Each dataset contains a set of electron diffraction patterns taken at each scan position with a ~ 1 nm probe step size. Approximately 80 x 80 scan positions were recorded from each region with a dwell time of 0.0025 seconds per frame. A custom 40µm patterned “bullseye” circular probe forming aperture was used to enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 3.20 milli-radians, spot size of 8, and diffraction pixel size of 0.16 Å<sup>-1</sup> was used in micro-probe lens configuration. The data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Data processing were performed using strain mapping scripts provided in the open source py4DSTEM software package. Au_thermal_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.</p> <p>2• Al_irradiated.dm4 is a dataset from 4D-STEM measurement conducted <em>in situ </em>on an FEI TitanX microscope equipped to do high-angle STEM tomography and operating at 300 kV. Nano-diffraction data was acquired using a Gatan Orius 830 (2k x 2k) detector capable of collecting 30 frames per second. Each dataset contains a stack of convergent beam electron diffraction (CBED) patterns taken at each scan position with maximum resolution equivalent to 1.6 nm probe size. Approximately 50 x 50 frame scan regions were recorded with a dwell time of 0.01 seconds per frame. A custom 70 µm patterned “bullseye” circular C2 aperture was used to greatly enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 2.7 milli-radians, spot size 10, and camera length 195 mm was used in micro-probe lens configuration. With a measured screen current of 300 pA in this configuration, the total sum of electrons incident in a region of the sample, commonly known as the fluence (total dose), was determined at 67,100 electronsÅ<sup>-2</sup> per 4D-STEM scan. The 4D-STEM data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Al_irradiated_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.</p>
Defective HNF4alpha-dependent gene expression as a driver of hepatocellular failure in alcoholic hepatitis [Suppl Data]
<p>Alcoholic hepatitis (AH) is a life-threatening condition characterized by profound hepatocellular dysfunction for which targeted treatments are urgently needed. Identification of molecular drivers is hampered by the lack of suitable animal models. By performing RNA sequencing in livers from patients with different phenotypes of alcohol-related liver disease (ALD), we show that the development of AH is characterized by the defective activity of liver-enriched transcription factors (LETFs). TGFb1is a key upstream transcriptome regulator in AH and induces the use of HNF4aP2 promoter in hepatocytes, which results in defective metabolic and synthetic functions. Gene polymorphisms in LETFs including HNF4aare not associated with the development of AH. In contrast, epigenetic studies show that AH livers have profound changes in DNA methylation state and chromatin remodeling, affecting HNF4a-dependent gene expression. We conclude that targeting TGFb1and epigenetic drivers that modulate HNF4a-dependent gene expression could be beneficial to improve hepatocellular function in patients with AH.</p>
Defective Drugs in Russian Federation
<p>Using this dataset it's possible to estimate control measures over quality control of medicines in Russian Federation. Datasets provide information about events of control and destruction of defective drugs.</p>
Extremely Imbalanced Smell-based Defect Prediction
<p><strong>Abstract: </strong>In continuous integration/continuous delivery, one of the main requirements for high-speed delivery of software is to find bugs efficiently. For this reason, multiple solutions were introduced in the literature. For instance, defect prediction approaches based on bad code smells detected in modules from each version of the software. Nevertheless, these approaches do not consider the problem where there may exist an extremely higher percentage of non-defective modules compared to defective modules. Given that, each version of the software may only have a small number of defects. As a result, in this thesis, we introduce a new model with an autoencoder algorithm that uses design and implementation smells to detect defective modules. Therefore, we trained five autoencoders with distinct architectures. Ad- ditionally, for evaluation, we compared each model against autoencoders with the same architecture, trained with traditional object-oriented metrics and the combination of both. Our analysis did not show promising results, as the use of only smells and the combination of features did not provide an improve- ment compared with the use of metrics. However, we introduce a starting point for smell-based defect prediction in the context of dataset imbalance. Furthermore, we introduce a baseline for future work.</p> <p> </p> <p><strong>Dataset Description:</strong></p> <p>We provide three datasets. The first results from the extraction of traditional object-oriented metrics (metric.csv). The second results from the extraction of design and implementation smells (smell.csv). The third is the combination of all the features (metricsmell.csv). Moreover, these features were extracted from Designite and Bugsdorjar software archives.</p>
The LAVA mutants defective in auxin-regulated primary or lateral root development.
<p><span>Regulation of PIN activity, polarity as well as auxin gradient generation and its canalisation remain crucial topics in plant developmental biology especially in the context of organogenesis like the formation new lateral roots. Here, we are presenting the LAVA (LR Alterations Visualised after Auxin) collection of 278 mutants, defective in auxin-induced lateral root (LR) morphogenesis. Those mutants were obtained from a forward genetic screen in which synthetic auxin 1-Naphtyl Acetic Acid (1-NAA) was used to induce LR formation in the mutagenized PIN3::PIN3-GFP population. Our database contains mutant root phenotyping and for a subset of the collection, we recorded PIN polarity and subcellular trafficking, cotyledon vasculature development, primary and LR gravitropism as well as aerial phenotypes with some reminiscent to auxin-regulated organogenesis aberrations. We are convinced that our dataset can serve as a unique tool to identify novel components of auxin signalling, transport, and cell polarity but also be of interest to the broader plant research community interested in the roots system architecture that is vital for plant survival, growth and adaptation to environmental conditions.</span></p> <p><span>The phenotype analysis of the mutant collection is summarized and organised in an Excel spreadsheet (2024-08-07_mutant_database_Table S1). The photographic material is organised in folder form (see Supporting Data S1 in this repository), where each folder number corresponds to a particular mutant and entry in the excel table.</span></p> <p><span> Mutant seeds will be available in the European Arabidopsis Stock Centre (NASC). The seed sending to the repository is in progress. </span></p> <p><span>The manuscript describing this work is currently being submitted to the research journal. Its version will be available on the open access Masaryk University repository.</span></p>
Light-Induced Metallic and Paramagnetic Defects in Halide Perovskites from Magnetic Resonance
<p>EPR and NMR data for the research article titled "Light-Induced Metallic and Paramagnetic Defects in Halide Perovskites from Magnetic Resonance". For further details see the readme.txt file. DOI: https://doi.org/10.1021/acsenergylett.4c02557</p>
Handbook on metallurgical defects and their genesis
<p>This dataset represents Deliverable D1.1 "Handbook on metallurgical defects and their genesis" published by PoliMi within Work Package 1 about Definition of thermal cycles to reduce defect formation in SLM/LBW.</p> <p>© COPYRIGHT 2019 The CUSTODIAN Consortium. All rights reserved.</p> <p><strong>Abstract:</strong></p> <p>Deliverable D1.1 of the Custodian Project gathers information on the features and mechanisms of generation of metallurgical defects that could be found after Laser powder bed fusion and Laser beam welding. Data are mainly obtained from a literature survey on available open publications. The reference materials for the project activities are the CM247LC and IN713LC alloys for LPBF and the AISI 304L austenitic stainless steel for LBW.</p>
Prenatal diet programs the transgenerational inheritance of brain macro and microstructure defects, coding for anxiety-like behavior in male rats.
<p>T1-w 3DFLASH Preprocessed MRI images used for wistar rat used for the deformation-based morphological (DBM) model are released.</p> <p>Nifti files are duplicated. DBM used only mnc format.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Retrieving Affected Versions by Leveraging the Life Cycle of Defects
<p>This is the online appendix for our paper submission entitled "Retrieving Affected Versions by Leveraging the Life Cycle of Defects"</p>
Defects in Power Distribution Components
<p>A set of 708 images and respective labels of defects on components from a electric distribution system. Each image contains one defect and the respective area is in the file "image-filename.txt".</p> <p>Each label contains the class of the defect, the coordinates <strong>x</strong> and <strong>y</strong> of the center in the image and the values of <strong>width </strong>and <strong>height </strong>of the defect area.</p> <p>The defects currently in the dataset are:</p> <p>0 - Cable out of spacer;</p> <p>1 - Cable out of insulator;</p> <p>2 - Insulator withour ring;</p>
Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects
<p>The attached folder contains the dataset for the manuscript entitled "Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects".</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)
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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.