Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
71
datasets available to search
ShareScore release 0.9.0
Dataset results
71 results for “Thermography”
Convolutional neural network for automated surface crack detection using inductive thermography
<p>Two phase images of the samples AIT_01 and AIT_08, analysed in the publication "Convolutional neural network for automated surface crack detection using inductive thermography", submitted to the Journal of Electronic Imaging.</p>
Infrared thermography of turbulence patterns of operational wind turbine rotor blades supported with high-resolution photography: KI-VISIR Dataset
<h2>Abstract</h2> <p><span>With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.</span></p>
Dataset for semantic segmentation in NDT with step-heating thermography for CFRP laminates
<p>Dataset composed of 36 images (640x480 pixels) of 30 bands or channels from step-heating for the same Carbon Fiber Reinforced Polymer (CFRP) laminate.</p> <p>In each image the specimen is rotated 10º to generate new data, with different illumination, background, and heating/cooling sequences.</p> <p>Images are generated from a video composed of the heating process, which takes 10 seconds, and the cooling process, which takes another 10 seconds, to a total of 1000 frames per video. This video is then reduced to 30 images using different processing methods explained below.</p> <p>The 30 bands or channels consist of the following bands repeated for the heating and cooling processes:</p> <ol> <li>First PCT component</li> <li>Second PCT component</li> <li>Third PCT component</li> <li>Fourth PCT component</li> <li>PPT</li> <li>Kurtosis</li> <li>Skewness</li> <li>TSR Coefficient 7</li> <li>TSR Coefficient 6</li> <li>TSR Coefficient 5</li> <li>TSR Coefficient 4</li> <li>TSR Coefficient 3</li> <li>TSR Coefficient 2</li> <li>TSR Coefficient 1</li> <li>TSR Coefficient 0</li> </ol> <p>Please cite the original paper:</p> <p>LINK: TODO</p> <p>BibTex:</p> <p>TODO</p> <p> </p> <p> </p>
Dataset of Thermography captures the differential sensitivity of dryland functional types to changes in rainfall event timing and magnitude
<p>The folder contains all data that have been used in the article "<strong>Thermography captures the differential sensitivity of dryland functional types to changes in rainfall event timing and magnitude" </strong>published in the "New Phytologist" journal.</p> <p>https://doi.org/10.1111/nph.19127</p>
Evaluating somatic cell count, the California mastitis test, and infrared thermography for subclinical mastitis detection in meat ewes
Open the record for dataset details and reuse information.
The dataset for an article - An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data
<p>Dataset used in the research presented in the article:</p> <p>Szymanik, Barbara. 2022. "An Evaluation of 3D-Printed Materials’ Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data" <em>Materials</em> 15, no. 10: 3727. https://doi.org/10.3390/ma15103727</p> <p>The database in the .mat (matlab) format contains arrays of double type related to: A - original thermograms obtained for the plate made with the 3D printing technique Ar - thermograms with ROI included FITorg - approximation of original thermograms ImDiff, ImInt, ImProp - data obtained after subtracting the approximation.</p>
Dataset for: Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning." Automation in Construction 143 (2022): 104575. https://doi.org/10.1016/j.autcon.2022.104575.</p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
Dataset for: Asphalt pavement crack detection based on convolutional neural network and infrared thermography
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement crack detection based on convolutional neural network and infrared thermography." IEEE Transactions on Intelligent Transportation Systems 23, no. 11 (2022): 22145-22155. https://doi.org/10.1109/TITS.2022.3142393. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (binary images)</li> </ul>
Dataset for: Deep learning and infrared thermography for asphalt pavement crack severity classification
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Deep learning and infrared thermography for asphalt pavement crack severity classification." Automation in Construction 140 (2022): 104383. https://doi.org/10.1016/j.autcon.2022.104383. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
Dataset for: Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, Linbing Wang, and Imad L. Al-Qadi. "Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning." Automation in Construction 161 (2024): 105355. https://doi.org/10.1016/j.autcon.2024.105355.</p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(25IRT) images: this folder includes fusion images (25% infrared + 75% visible)</li> <li>04-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>05-Fusion(75IRT) images: this folder includes fusion images (75% infrared + 25% visible)</li> <li>06-Annotations: this folder includes annotations (xml files) based on PASCAL VOC (PASCAL Visual Object Classes Challenge) styles.</li> </ul>
Active thermography to estimate leaf heat transfer - leaf level data to manuscript
<p>Active thermography is an informative methodology to measure that we adopted to plant sciences to measure leaf heat transfer and derive spatial maps of thermal responsiveness of leaves. This method and its usability is described in a publication in 'Frontiers of Plant Sciences'.</p> <p>Here we publish the data-set of active thermography measurements that were used in this publication. The Mat-lab code that was developed with this publication is also published at Zenodo under the doi 10.5281/zenodo.1195869</p>
Infrared Thermography-based Study of the Warming Effect Difference at Waist
ClinicalTrials.gov study NCT05665426. IPD Sharing: NO. Countries: 1. Publications: 2.
Infrared Thermography to Evaluate the Effect of Paravertebral Block
ClinicalTrials.gov study NCT04078347. IPD Sharing: NO. Countries: 1. Publications: 1.
FLYD-Movies : FLYing-spot thermography Dataset - thermal Movies
<p>This dataset contains several thermal recordings performed using flying-spot thermography. Thermal scans are subsampled from 100 Hz to 50 Hz. Then the scans are encoded using avi file format. Unzip the archive to access to the thermal scans. More details about acquisition settings in Quality Control by Artificial Vision 2023 conference proceeding, introducing the dataset: <i><strong>Laser flying-spot thermography: an open-access database for machine learning and deep learning [Helvig et al., 2023] </strong></i><a href="https://doi.org/10.1117/12.3000481">https://doi.org/10.1117/12.3000481 </a></p><p>Description of the repositories : </p><p>FLYD-Movies.zip : contains scans performed parallel to the defect, used for the proposed flying-spot thermography experiments, and for data annotation, trainings with deep learning ... etc ... </p><p>FLYD-perp.zip: contains several thermal recordings with the conventional experimental protocol, where the heat source passes across the defect (called perpendicular scan). </p><p>See the following github page for annotations and datasets (classification+localization tasks) on thermal maps reconstructed using these recordings: <a href="https://github.com/kevinhelvig/FLYD">https://github.com/kevinhelvig/FLYD</a> </p>
Modeling the 3D Breast Surface Using Thermography
<p>Breast thermographies can show temperature distribution and detect abnormalities in the body on examination. This work shows an implementation allowing their used for three-dimensional (3d) models of the breast surface from their acquisition in five different angles. Firstly, breast segmentation is performed using U-Nets trained for each angle. Then, the segmented edges are transformed into parametric curves. Geometric transformation converts these curves from two-dimensional (2d) to a three-dimensional after proper rotations and translation of them. These 3D curves are described by B-splines curves and used to build a Rational Non-Uniform B-splines Surface (NURBS) for the breast region. Texture mapping is applied to project the frontal thermal data onto the 3d surface. A Likert scale-based survey was conducted with healthcare experts, and experienced academics to evaluate the final results and compare them with examples of the same persons. The results received high grades of approval and agreements.</p>
Conduction thermography - experimental data and simulations - preliminary results
Open the record for dataset details and reuse information.
Infrared Thermography and Apgar Score
ClinicalTrials.gov study NCT04483869. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Evaluation of Peripheral Microcirculation Hemodynamics Following Various Changes Based on Noninvasive Thermography
ClinicalTrials.gov study NCT03357523. IPD Sharing: NO. Countries: 1. Publications: 1.
Dynamic Infrared Thermography as an Alternative to CT Angiography
ClinicalTrials.gov study NCT02806518. IPD Sharing: NO. Countries: 1. Publications: 6.
Early Warning of Diabetic Peripheral Neuropathy by Using Infrared Thermography and the Effectiveness of Electroacupuncture in Its Prevention
ClinicalTrials.gov study NCT06074913. IPD Sharing: Not stated. Countries: 1. Publications: 9.
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.