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67 results for “damage assessment”
Damage assessment of a physical beam reinforced with masses - dataset
<p>The dataset beam-signal contains the spectrum vibration signals in the frequency domain measured from a beam reinforced with masses under healthy and faulty conditions. This data is for a commonly used system in various industrial applications. The data can be used for online condition process monitoring to detect and diagnose any anomaly or faulty condition in the system. Hence, the datasets provide the geometric and experimental measurements performed on the beam reinforced with masses for various mass losses considered structural damage. The collected data included the following datasets:</p> <ul> <li>Dataset Mass-position contains 70 sampling positions for the six masses attached to the beam. (<a href="../api/records/8081690/draft/files/Mass%20position.xlsx/content">Mass position</a>)</li> <li>Dataset DI contains 280 damage indexes calculated using the FRAC method. (<a href="../api/records/8081690/draft/files/DI_FRAC_Exp-estimation.xlsx/content">DI_FRAC_Exp-estimation</a>)</li> <li>Dataset beam-signal includes 280 inertances responses magnitudes and respective phases considering 70 samples of healthy and 210 sampled of damaged conditions ( <a href="../api/records/8081690/draft/files/Dataset%20Beam-signal_Healthy.zip/content">Dataset Beam-signal_Healthy, </a><a href="../api/records/8081690/draft/files/Dateset%20Beam-signal_Damaged-2.96.zip/content">Dateset Beam-signal_Damaged-2.96, </a></li> </ul> <p><a href="../api/records/8081690/draft/files/Dataset%20Beam-signal_Damaged-5.92.zip/content"> Dataset Beam-signal_Damaged-5.92, </a><a href="../api/records/8081690/draft/files/Dataset%20Beam-signal_Damaged-8.87.zip/content">Dataset Beam-signal_Damaged-8.87) .</a></p> <p>The dataset beam-signal can be used to develop structural health monitoring techniques for detecting damage and anomalies in the structure. The dataset's Mass-position and DIs can impose parametric uncertainty in the experiment. Stochastic and damage identification metrics can be used for further insights on new monitoring and control techniques. Since the tests include paramedic uncertainty, they can also be employed in uncertainty quantification, stochastic modelling and supervised and unsupervised machine learning techniques. </p> <p>Therefore, the datasets are intended to benefit the scientific community investigating the dynamics of structures and readers interested in experimental practices applied to systems and modelling. These datasets can be used for numerical model validation, identification techniques, uncertainty quantification, machine learning, and structural integrity monitoring algorithms based on experimental measurement samples on the beam reinforced with mass.</p> <p>A detailed description of the experiment can be found in </p> <p>[1] Sousa, A.A.S.R., da Silva Coelho, J., Machado, M.R. et al. Multiclass Supervised Machine Learning Algorithms Applied to Damage and Assessment Using Beam Dynamic Response. J. Vib. Eng. Technol. (2023). https://doi.org/10.1007/s42417-023-01072-7</p> <p>[2] Monitoramento da Integridade Estrutural de Vigas utilizando Técnicas de Aprendizado de Máquina, 2023. Mestrado em Integridade de Materiais da Engenharia - Universidade de Brasília (In Portuguese)</p> <p>[3] Amanda A.S.R. de Sousa, Marcela R. Machado, Experimental vibration dataset collected of a beam reinforced with masses under different health conditions, Data in Brief, 2024, 110043, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110043.</p>
Crowd4SDG - Crowdsourced image classification and damage assessment
<p>This data set contains crowdsourced classification and damage assessment of images of an earthquake extracted from social media. <br> <br> A data set of 907 images posted on Twitter related to the 2019 Albanian Earthquake, that are filtered and pre-classified using an automated technique is cross-validated for accuracy by two different crowds. One, digital humanitarian volunteers using the crowdsourcing platform <a href="http://www.crowd4ems.org">CROWD4EMS</a> and another, paid micro-taskers of the Amazon Mechanical Turk. In order to compare and evaluate the efficiency and accuracy of the volunteers and the paid micro taskers, ground truth is established with the help of a team of experts, who validated the same set of data. <br> <br> <strong>Parameters considered for volunteer contributions:</strong> The dataset was imported to the Crowd4EMS platform for Crowd contribution. In the forum, each volunteer will see the image to be validated along with the tweet text and the link to the original tweet. The user has to validate whether the given image is <em>relevant or</em> <em>irrelevant</em> to the disaster. In case of doubt, the user can refer to the tutorial explaining the relevance or skip the task. Once the image's relevance is validated, the user will be asked to label the <em>severity</em> of the impact, as seen in the image.</p> <p>The Automated algorithm has pre-classified the images as <em>severe </em>and <em>minimal </em>damage. The Crowd4EMS platform lets the volunteer label them as '<em>severe damage</em>,' <em>moderate damage'</em>,' <em>minimal damage', </em>and' <em>no damage'.</em> Each task has to be answered <em>at least three times</em>, and the final consensus is taken as per the<em> inter-rater agreement. </em><br> <br> <strong>Parameters considered for micro-taskers contribution:</strong> The dataset was imported to the <em>Amazon Mechanical Turk</em> platform for Crowd contribution. In the platform, each worker will see only the image that is to be categorised as follows: The user has to validate whether the given image depicts <em>severe damage, moderate damage, minimal damage, no damage </em>or <em>irrelevant</em> to the disaster. Each task has to be answered <em>at least ten times</em>, and the final consensus is taken as per the<em> inter-rater agreement. </em><br> <br> <strong>Acknowledgements:</strong> We want to thank Muhammad Imran of Qatar Computing Research Institute for sharing their pre-filtered social media imagery dataset on the Albanian earthquake from the Artificial Intelligence for Disaster Response (AIDR) Platform. We would also like to extend our gratitude to the volunteers for their contribution on the Crowd4EMS Platform.<br> </p>
Orthophotos, DSMs and interpretation files of the remote sensing assessment of archaeological damage and destruction at Nineveh, Iraq, during the ISIS occupation
<p>Archaeological heritage has long been threatened by damage or destruction during armed conflicts. Recently, however, deliberate destruction has increasingly become a major part of daily threats in some areas. In that context these datasets describe the results of a programme of remote sensing of damage at Nineveh, within a wider research initiative involving six years of monitoring in northern Iraq. Analysis of satellite imagery, low-and level airphotography observation were combined in a comprehensive assessment of the damage. These datasets present an updated topographic map of Nineveh and its city walls, with a summary of the damage encountered.</p>
Data from: Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study
<p>This dataset accompanies the following article: "Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study," in <em>IEEE Transactions on Biomedical Engineering</em>, doi: 10.1109/TBME.2023.3263388.</p> <p>Knee acoustic emissions (AE) recorded in the 100-450 kHz and 15-200kHz frequency ranges from a cadaver specimen knee in flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. </p> <p><em><strong>Stages of artificially inflicted cartilage damage:</strong></em> the cartilage surface damage on the medial compartment, KL III; the cartilage surface damage on the medial compartment plus patellofemoral surface, KL III; the cartilage surface damage on the medial compartment plus on the patellofemoral surface KL IV; the cartilage surface damage on the medial compartment plus on the patellofemoral surface and lateral compartment.</p> <p><strong><em>Sensor positions</em></strong>: medial and lateral knee</p>
Analyzing the sensitivity of a flood risk assessment model towards its input data, twelve damage scenarios
<p>This dataset contains the output shapefiles of twelve different risk assessment scenarios for the case study of Annotto Bay, Jamaica. These assessments were performed in the context of the research 'Analyzing the sensitivity of a flood risk assessment model towards its input data', published in the journal Natural Hazards and Earth System Sciences. More information on the input data and methodology can be found in this paper.</p>
Experimental Assessment of Laser Scarecrows for Reducing Avian Damage to Sweet Corn
<p>Datasets and metadata for analyses presented in an article "Experimental Assessment of Laser Scarecrows for Reducing Avian Damage to Sweet Corn" submitted to Pest Management Science, June 2023.</p>
Data Sets: Experimental Assessment of Laser Scarecrows for Reducing Avian Damage to Sweet Corn
<p>This archive contains 2 data sets and a word doc with metadata and code for statistical analyses used within the manuscript entitled</p> <p><strong>Experimental Assessment of Laser Scarecrows for Reducing Avian Damage to Sweet Corn</strong></p> <p>by</p> <p>Sean T. Manz, Kathryn E. Sieving, Rebecca N. Brown, Page E. Klug, Bryan M. Kluever</p> <p>in press at Pest Management Science as of September 2023.</p>
Building damage assessment of Adassil, Morocco after 2023 Marrakesh–Safi earthquake
<p>The dataset contains shapefile of damaged and non damaged buildings of Adassil town, Chichaoua Province of the Marrakesh-Safi region of Morocco. The dataset has been created using openly available google buildings dataset, humanitarian open street data, MAXAR satellite data and segment-geospatial package. </p>
Datasets for assessment of damage in flat panel and final demonstrator with UoI's sensor and PPI-LT approach
<p>Datasets acquired during experimental assessment of damage in the composite flat panel and final demonstrator by the team of the University of Ioannina, in the context of project CompInnova: An Advanced Methodology for the Inspection and Quantification of Damage on Aerospace Composites and Metals using an Innovative Approach (H2020 FETOPEN, Grant Agreement No. 665238). The PPI-LT approach and dedicated IRT sensor developed within CompInnova were used for recording the data. Data are thermograms in image format (jpg, png).<br> <br> The data were used in deliverables D7.2 & D.7.3.</p> <p> </p>
Dataset for assessment of damage in composites and laminates with UoI's sensor and PPI-LT approach
<p>Datasets acquired during experimental assessment of damage in composites and laminates by the team of the University of Ioannina, in the context of project CompInnova: An Advanced Methodology for the Inspection and Quantification of Damage on Aerospace Composites and Metals using an Innovative Approach (H2020 FETOPEN, Grant Agreement No. 665238). The PPI-LT approach and dedicated IRT sensor developed within CompInnova were used for recording the data. Data are thermograms in image format (jpg, png).</p> <p>The data were used in deliverables D3.3 & D.7.1</p> <p> </p> <p> </p>
Considerations for high-resolution regional meteorological wind modelling over complex terrain: a typhoon case study for assessing forestry damage (data)
<p>This is the experiment data.</p> <p>The Weather Research and Forecasting (WRF) model is a popular and easily used as a numerical weather prediction (NWP) model, but configuring WRF to produce accurate results can be time-consuming. This is especially so when simulating extreme events, over complex terrain, or at high resolutions. In this study, a strong wind event from Tropical Cyclone (TC) Thad in year 1981 was simulated at 200 m resolution over an experiment forest in a mountainous region of Hokkaido island, Japan. The simulation configuration is challenging, in order to cover a larger area to produce a TC with appropriate track and intensity, and at the same time to resolve the smallest domain of sub-km grid spacing with computational stability. A mixed nesting method was applied with two-way nesting up for the first three domains, followed a separate simulation over the smallest domain. The mixed method could produce 10 min wind speed distributions similar to that of the full simulation with two-way nesting of all four domains, if a 30-minute boundary update interval was used for the separate simulation. Mixed nesting improves the efficiency of the simulation process, since the larger phenomenon scale and smaller human impact scale can be tuned separately. </p>
To Assess Safety and Efficacy of Agents Targeting DNA Damage Repair With Olaparib Versus Olaparib Monotherapy.
ClinicalTrials.gov study NCT03330847. IPD Sharing: YES. Countries: 15. Publications: 1.
A Study to Assess the Effect of Tocilizumab + Methotrexate on Prevention of Structural Joint Damage in Patients With Moderate to Severe Active Rheumatoid Arthritis (RA)
ClinicalTrials.gov study NCT00106535. IPD Sharing: Not stated. Countries: 16. Publications: 8.
Data for assessment of damage to residential dwellings using artificial neural networks
<p>The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture damage to residential home subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building's resulting damage state from an extreme wind event. </p>
Automated analysis of scanning electron microscopic images for assessment of hair surface damage
<p>Mechanical damage of hair can serve as an indicator of health status and its assessment relies on the measurement of morphological features via microscopic analysis, yet few studies have categorized the extent of damage sustained, and instead, have depended on qualitative profiling based on the presence or absence of specific features. We describe the development and application of a novel quantitative measure for scoring hair surface damage in scanning electron microscopic (SEM) images without predefined features, and automation of image analysis for characterization of morphological hair damage after exposure to an explosive blast. Application of an automated normalization procedure for SEM images revealed features indicative of contact with materials in an explosive device and characteristic of heat damage, though many were similar to features from physical and chemical weathering. Assessment of hair damage with tailing factor, a measure of asymmetry in pixel brightness histograms and proxy for surface roughness, yielded 81% classification accuracy to an existing damage classification system, indicating good agreement between the two metrics. Further ability of tailing factor to score features of hair damage reflecting explosion conditions demonstrates the broad applicability of the metric to assess damage to hairs containing a diverse set of morphological features. </p>
Histopathological, Oxidative Stress, and DNA Damage Assessment in the Vas Deferens Tissue of the Freshwater Leech Erpobdella johanssoni (Johansson, 1927) Following BTEX Exposure
Open the record for dataset details and reuse information.
Dataset to support "Explore the potential of using social media crowdsourcing for earthquake damage assessment"
<p>This dataset is used to support findings in the paper "<strong>Explore the potential of using social media crowdsourcing for earthquake damage assessment.</strong>"</p> <p>In this paper, we investigated six earthquake events to explore the potential of using social media crowdsourcing for earthquake damage assessment. These cases include the 2014 Iquique earthquake, the 2015 Napa earthquake, the 2015 Nepal earthquake, the 2019 Ridgecrest earthquake, the 2021 Fukushima earthquake, and the 2021 Haiti earthquake. For the first three cases, tweet ID information was collected from the publicly available website CrisisNLP (https://crisisnlp.qcri.org/). For the last three cases, we collected “near-real-time” tweets with the key search term “earthquake.” For the 2019 Ridgecrest earthquake, the search date range was from July 4 to 10, 2019. For the 2021 Fukushima earthquake, the search date range was February 13 to 17, 2021. For the 2021 Haiti earthquake, the search date range was August 14 to 19, 2021. It should be noted that restrictions apply to the availability of tweet data due to the policy from <em>Twitter</em> Inc. Tweet data showing user profiles or text content is not allowed to be shared publicly, but tweet IDs are available to download from this dataset. </p> <p>In addition, we attached our processed results to this dataset, including,</p> <ol> <li>Damage estimation. The damage estimation for each event is based on the average estimated level of all damage-related tweets identified by text classification models, as documented in our paper.</li> <li>Temporal dynamics of damage estimation. We conducted the temporal analysis to explore the convergence of damage estimates over time, in which data were first binned based on the number of damage-related tweets in each 2-hour interval.</li> </ol> <p>If you need the training and testing datasets or have any further questions regarding the dataset or paper, please do not hesitate to reach us. </p>
Contrast-inDuced nephRotoxicity as Assessed by the KIdney Load-to-DAmage RElationship
ClinicalTrials.gov study NCT01908309. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Assessment of the Safety of Adalimumab in Rheumatoid Arthritis Patients Showing Rapid Progression of Structural Damage of the Joints, Who Have no Prior History of Treatment With Disease-modifying Anti
ClinicalTrials.gov study NCT01783730. IPD Sharing: NO. Countries: 0. Publications: 1.
Prevention of Skin Damage in the Patient in Prone Position: Development of Education Interventions Based on a Training Needs Assessment Study.
ClinicalTrials.gov study NCT05238870. IPD Sharing: NO. Countries: 1. Publications: 1.
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International Brain Laboratory public data
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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.