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39
datasets available to search
ShareScore release 0.9.0
Dataset results
39 results for “Type defects”
P. falciparum heat shock response in a PfAP2-HS-defective population compared to its wild type control.
GEO Series GSE149392. Plasmodium falciparum. 21 samples. Type: Expression profiling by array.
Osteoblast-intrinsic defect in glucose metabolism impairs bone formation in type II diabetic male mice.
GEO Series GSE221936. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Dissection of the Gastrointestinal-mediated Glucose Disposal and Incretin Defect in Patients With Type 2 Diabetes
ClinicalTrials.gov study NCT02669524. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Aortic Regurgitation After Surgical Repair of Outlet-Type Ventricular Septal Defect
ClinicalTrials.gov study NCT00173186. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Clearance of defective muscle stem cells by senolytics reduces the expression of senescence-associated secretory phenotype and restores myogenesis in myotonic dystrophy type 1
GEO Series GSE208360. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Biallelic mutations in the 3’ exonuclease TOE1 cause Pontocerebellar Hypoplasia Type 7 and result in snRNA processing defects
GEO Series GSE71536. Homo sapiens. 154 samples. Type: Expression profiling by high throughput sequencing.
Comparison of Transcription Profiles of M. smegmatis overexpressing wild type and phopshorylation defective PknK
GEO Series GSE180348. Mycolicibacterium smegmatis. 8 samples. Type: Expression profiling by array.
Deregulation associated with cardiac conduction defects in Myotonic Dystrophy Type 1 using TU-Tagging
GEO Series GSE109370. Drosophila melanogaster. 10 samples. Type: Expression profiling by high throughput sequencing.
The IFITM5 mutation in osteogenesis imperfecta type V is associated with an ERK/SOX9-dependent osteoprogenitor differentiation defect
GEO Series GSE268601. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Prednisolone-induced differential gene expression in liver of mice carrying the wild type or a dimerization-defective glucocorticoid receptor
GEO Series GSE21048. Mus musculus. 24 samples. Type: Expression profiling by array.
Single-cell Transcriptomic Profiling Unveils Cardiac Cell-type Specific Response to Maternal Hyperglycemia Underlying the Risk of Congenital Heart Defects
GEO Series GSE193746. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Treatment of Gingival Recession-type Defects Using Different Restorative Materials
ClinicalTrials.gov study NCT02788266. IPD Sharing: NO. Countries: 0. Publications: 0.
CD4+ T cells gene-transduced with AML1, wild type Foxp3, and a Foxp3 mutant defective in binding to AML1
GEO Series GSE6939. Mus musculus. 4 samples. Type: Expression profiling by array.
Isogenic Wild Type and iPSC-Derived CTBP1 Mutant Neuronal Cells Exhibit Neurodevelopmental Defects
GEO Series GSE306099. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
The global fusarium graminearum gene expressional responses to different nitrogen source and the gene expression changes between histone methylation defective mutants and wild type strain
GEO Series GSE171288. Fusarium graminearum. 27 samples. Type: Expression profiling by high throughput sequencing.
Cranial pericytes derived from neural crest cells reveal an inherent cell type-specific defect in Alzheimer's Disease
GEO Series GSE104141. Homo sapiens. 11 samples. Type: Expression profiling by high throughput sequencing.
Empirical analysis of Type-Related Defects in Python projects
<p>This is the replication material related to the following paper submitted for TSE.</p> <ul> <li>Faizan Khan, Boqi Chen, Daniel Varro, and Shane McIntosh. An Empirical Study ofType-Related Defects in Python Projects.IEEE Transactions on Software Engineering,2021(under review)</li> </ul>
Empirical analysis of Type-Related Defects in Python projects
<p>This is the replication material related to the following paper submitted for TSE.</p> <ul> <li>Faizan Khan, Boqi Chen, Daniel Varro, and Shane McIntosh. An Empirical Study ofType-Related Defects in Python Projects.IEEE Transactions on Software Engineering,2021(under review)</li> </ul>
Dataset of Vibration, Temperature and Speed Measurements for Multiple Types of Localized Defects on Spherical Roller Bearings across Multiple Operating Conditions
<p><strong>Description:</strong></p> <p>This dataset has been created by the ISED group of Politecnico di Torino to address the lack of data available for developing fault detection models and/or predictive maintenance systems for medium/large-sized spherical roller bearings, commonly used in industrial settings.</p> <p>The data were collected using <a href="https://www.skf.com/it/products/rolling-bearings/roller-bearings/spherical-roller-bearings/productid-22240%20CCK%2FW33">SKF 22240 CCK/W33 spherical roller bearings</a> through an extensive experimental campaign on the medium/large-scale bearing test rig (capable of testing bearings with an outer diameter up to 420 mm), developed by the ISED research group at Politecnico di Torino (<a href="https://www.dimeas.polito.it/la_ricerca/gruppi/ised_industrial_systems_engineering_and_design">ISED Research Group</a>). The technical details of the test rig can be found in <a href="https://www.mdpi.com/2075-1702/10/1/54">this Paper</a>.</p> <p>The dataset contains data for individual localized defects applied to one of the four bearings tested simultaneously on the rig, which is capable of independently applying both axial and radial loads.</p> <p><strong>Dataset Structure:</strong></p> <p>The dataset is organized into four folders:</p> <ul> <li><code>Undamaged</code></li> <li><code>InnerRaceDamage</code></li> <li><code>OuterRaceDamage</code></li> <li><code>RollerDamage</code></li> </ul> <p>The <code>Undamaged</code> folder contains data for all bearings in healthy condition. The other folders contain data from tests where one of the bearings presents a localized defect. The defects, introduced by chip removal, have a diameter of 2 mm and a depth of 0.5 mm, affecting either the <strong>inner race</strong> (IR), <strong>outer race</strong> (OR), or <strong>roller elements</strong> (B). More detailed information about the defect geometry and location can be found in the following publications:</p> <ul> <li><a href="https://www.mdpi.com/1424-8220/23/1/211">Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification</a></li> <li><a href="https://www.mdpi.com/2076-3417/13/4/2038">Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring</a></li> <li><a href="https://www.mdpi.com/2076-3417/13/22/12458">Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks</a></li> </ul> <p>Each folder contains <code>.mat</code> files named according to the following format:</p> <p><code>(<em>Nominal_Rotation_Speed</em>)rpm_(<em>Radial_Force</em>)kN_(<em>Axial_Force</em>)kN.mat</code></p> <p>Where:</p> <ul> <li><strong>Nominal_Rotation_Speed</strong> is the machine's nominal rotational speed</li> <li><strong>Radial_Force</strong> is the radial force applied to the bearing</li> <li><strong>Axial_Force</strong> is the axial force applied to the bearing</li> </ul> <p>The dataset includes measurements from 10 different nominal rotation speeds and four load conditions, one of which includes an axial load. In some cases, "ramp" files are included, containing data where the rotational speed was linearly varied during the test.</p> <p>Each <code>.mat</code> file contains multiple structures, depending on the type of test. Each structure is labeled as <code>Signal_</code> followed by a number (0, 1, 2, 3, 4), where each represents a specific signal extracted during the test. There is no fixed correspondence between the signal number and the type of measurement. For example, <code>Signal_2</code> does not always represent the accelerometer signal. Users are encouraged to inspect the <code>y_values.quantity</code> field to identify the signal's unit and nature. For instance, if <code>y_values.quantity.label</code> shows "g", the signal corresponds to an accelerometric measurement.</p> <p>All signals have been exported using the <strong>MKS system</strong>, so <code>y_values.values</code> contains data in units of m/s² for acceleration signals. To convert the values to the unit indicated in <code>y_values.quantity.label</code>, users can apply the multiplication factor and offset provided in <code>y_values.quantity.unit_transformation</code>. In the case of accelerometric data, the multiplication factor is 0.1020, converting <code>y_values.values</code> from m/s² to g.</p> <p>A more detailed description of the data structure can be found in the Test.Lab documentation.</p> <p>In addition to acceleration data, the files include temperature signals, tacho sensor signals measuring shaft speed, and tacho impulse signals. In some cases, there is also a signal measured in "N", representing the frictional force generated by the bearings (as detailed in <a href="https://www.mdpi.com/2075-1702/10/1/54">this Paper</a>). This friction force signal is not always present, as the load cell could go into overload during certain tests.</p> <p><strong>Sensor Data Organization:</strong></p> <p>Acceleration and temperature data are presented in tables with four columns, each corresponding to one of the four bearings. In the damaged condition tests, the damaged bearing is always the one corresponding to sensor 4 (i.e., <code>y_values.values(:, 4)</code>).</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.