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.
77
datasets available to search
ShareScore release 0.9.0
Dataset results
77 results for “radiography”
X-ray radiography 4D particle tracking of heavy spheres suspended in a turbulent jet
<p>This database report 3d trajectories of heavy spheres suspended in a turbulent upward jet. A cylindrical tank is filled with water and the jet nozzle is placed on its axis on the bottom wall, and a constant flowrate (Q) of water is fed through the nozzle. Conditions at 1700 and 2200 mL/min are considered, and the number of spheres is varied between 1 and 12 (Nsphere). The spheres are glass and are detected using X-ray radiography at 60Hz. The 4d kinematics are obtained with this setup using radioSphere (E. Ando et<br> al., Measurement Science and Technology, 32(9), 095405, 2021). Each condition has a series of files named based on the number of spheres in the tank Nsphere and the flowrate Q, with each sphere of index isphere having its own file. Each file is 3 columns of doubles representing the 3d coordinates x, y, and z of the sphere, in mm, where z is the axis of the cylinder and the points up, against gravity.</p> <p>Results from this database are published here: https://doi.org/10.1016/j.ijmultiphaseflow.2023.104406<br> O. Stamati, B. Marks, E. Ando, S. Roux, N. Machicoane, X-ray radiography 4D particle tracking of heavy spheres suspended in a turbulent jet, <em>International Journal of Multiphase Flow</em> 162, 104406, 2023.</p>
Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes
<p><strong>Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes</strong><br>The associated publication can be found on https://iopscience.iop.org/article/10.1088/1361-6560/ad3326.<br>All graphs inside the publication can be recreated with this dataset. Similar to the publication, the data for the timewalk and offset correction are only given for one sensor and one channel as they only serve a representative purpose. The raw data for all other channels can be shared upon request. Furthermore, as in the publication, the data for the water-equivalent-thickness (WET) calibration and proton radiography (pRAD) creation are given by the median and the interquartile range of the measured quantities of the individual graphs. Those data are also calibrated. If required, the raw, unprocessed data of each measurement can be shared upon request.<br><br>In the following, a description of the individual files and corresponding figures in the publication is given. If not specified otherwise, the physical units are given in brackets next to the name of the corresponding physical quantity (usually first line in file):<br><br></p> <ul> <li><em><strong>Figure 6:</strong></em> <ul> <li> RawToTspectrumrescaledLGAD3.txt: <ul> <li>Describes the re-scaled time-over-threshold (ToT) spectrum measured inside the third LGAD of the time-of-flight-based ion computed tomography (TOF-iCT) demonstrator using 800 MeV protons (Figure 6a). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence (counts[#]).</li> </ul> </li> <li>ToTspectrumrescaledLocMaxLGAD3.txt <ul> <li>Describes the re-scaled ToT spectrum measured inside the third LGAD of the TOF-iCT demonstrator using only the local ToT maxima inside each 4D-cluster. The spectrum was obtained using 800 MeV protons (Figure 6b). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence (counts[#]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 7:</strong></em> <ul> <li>offsetpraecalib.txt: <ul> <li>Describes the raw, uncalibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7a). The first column represents the detector channel nr in LGAD3, the second column the raw, uncalibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li>offsetpraecalib.txt: <ul> <li>Describes the time walk and offset-calibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7b). The first column represents the detector channel nr in LGAD3, the second column the calibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li> praetwdata.txt: <ul> <li>Describes the ToT dependence of the measured time difference between LGAD1 and LGAD2 using the raw ToT of channel 31 in LGAD1 (figure 7c). The first column represents the raw, unscaled and uncalibrated ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column, the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> <li>posttwdata.txt <ul> <li>Describes the time walk-calibrated ToT vs TDiff spectrum using the measured time difference between LGAD1 and LGAD2 and the ToT of channel 31 in LGAD1 (figure 7d). The first column represents the ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> </ul> </li> <li><em><strong>Figure 8:</strong></em> <ul> <li>tofinaridata.txt: <ul> <li>Describes the measured TOF in air through the scanner w.r.t the TOF measured at 800MeV, i.e. the median TOF value at 800MeV was subtracted from all data points (Figure 8a). The first column describes the beam energy (beamenergy[MeV]), the second column the first quartile of the measured TOF per pixel (TOFperpixelQ1[ps]), the second column the median TOF per pixel (TOFperpixelQ2[ps]) and the last column the third quartile of the measured TOF per pixel (TOFperpixelQ3[ps]).</li> </ul> </li> <li>tofinairtheodata.txt: <ul> <li>Describes the theoretical TOF in air through the scanner w.r.t the theoretical TOF at 800MeV, i.e. the theoretical TOF value at 800MeV was subtracted from all data points (Figure 8a).</li> </ul> </li> <li>intrinsictimeresolution.txt: <ul> <li>Describes the energy dependence of the intrinsic time resolution per channel measured inside LGAD1 (figure 8b). The first column represents the primary beam energy (beamenergy[MeV), the second column the corresponding energy loss in MIPs (relativeenergylossi[MIP]), the third column the first quartile of the intrinsic time resolution per LGAD channel (timeresperpixelQ1[ps]), the fourth column the median of the intrinsic time resolution per LGAD channel and the last column the third quartile of the intrinsic time resolution per LGAD channel (timeresperpixelmedian[ps],timeresperpixelQ3[ps]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 9:</strong></em> <ul> <li>wetcalib.txt <ul> <li>Describes the measured TOF increase per pixel w.r.t to the TOF in air (i.e. without a phantom) for a given WET and primary beam energy. The first column represents the WET of the irradiated sample (WET[mm]), the second column the used beam energy (beamenergy[MeV]), the third column the first quartile of the measured TOF distribution (TOFperpixelQ1[ps]), the fourth column the median (TOFperpixelQ2[ps]) and the sixth column the third quartile (TOFperpixelQ3[ps]).</li> <li>For each energy, a fifth-order polynomial was used to fit the WET and the TOF increase (Delta TOF(E)~sum_i a_i*(WET_i )^i, with i in [0,5] ). The fit parameters are given in the following for each beam energy:<br> <ul> <li>83 MeV: a_i=[-4.70496227e-02,4.64323118e-01, -2.71391535e-02,4.23655842e-03, -1.13034255e-04,1.23725678e-06]</li> <li>100.4 MeV: a_i=[-3.28976022e-02,-3.68818468e-02,1.96339858e-02,7.31585040e-04, -4.38697681e-05 ,7.52163384e-07]</li> </ul> </li> </ul> </li> </ul> </li> <li><em><strong>Figure 10:</strong></em> <ul> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 83 MeV (Figure 10a). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 100.4 MeV (Figure 10b). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 11:</strong></em> <ul> <li>wetdistrdata83MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 83 MeV protons (Figure 11a). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> <li>wetdistrdata100MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 100.4 MeV protons (Figure 11b). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> </ul> </li> </ul>
Combined proton radiography and irradiation for high-precision preclinical studies in small animals
<p>Data used for the publication "Combined proton radiography and irradiation for high-precision preclinical studies in small animals".</p> <p>The repository contains all data that was used to generate the quantitative results and figures in the submitted manuscript.</p> <p>Further documentation for the provided code can be found here: https://github.com/jo-mueller/radiographic_workflow_evaluation</p>
Panoramic radiography database
<p>This database contains 598 panoramic radiographs, whose dimensions are 2041 x 1024 and are in a JPEG format. <br> These images were acquired with Owandy I-max Touch panoramic radiography equipment belonging to the <em>Departamento de Radiología de la Facultad de Odontología, Universidad Nacional de Asunción, </em>located in Asuncion, Paraguay<em>.</em></p> <p><em>If you use the dataset, please cite the paper:</em></p> <p>Román, J.C.M.; Fretes, V.R.; Adorno, C.G.; Silva, R.G.; Noguera, J.L.V.; Legal-Ayala, H.; Mello-Román, J.D.; Torres, R.D.E.; Facon, J. <strong>Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology</strong>. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 3110. https://doi.org/10.3390/s21093110</p>
Validating marker-less pose estimation with 3D x-ray radiography
<p class="MsoNormal"><span>These data were generated to evaluate the accuracy of DeepLabCut (DLC), a deep learning marker-less motion capture approach, by comparing it to a 3D x-ray video radiography system that tracks markers placed under the skin (XROMM). We recorded behavioral data simultaneously with XROMM and RGB video as marmosets foraged and reconstructed three-dimensional kinematics in a common coordinate system. We used XMALab to track 11 XROMM markers, and we used the toolkit Anipose to filter and triangulate DLC trajectories of 11 corresponding markers on the forelimb and torso. We performed a parameter sweep of relevant Anipose and post-processing parameters to characterize their effect on tracking quality. We compared the median error of DLC+Anipose to human labeling performance and placed this error in the context of the animal's range of motion. </span></p>
Supporting data: phase-contrast virtual chest radiography
<p>This dataset contains supporting data for the publication below:</p> <ul> <li>Ilian Häggmark, Kian Shaker, Sven Nyrén, Bariq Al-Amiry, Ehsan Abadi, William P. Segars, Ehsan Samei, and Hans M. Hertz, "Phase-contrast virtual chest radiography<em>"</em>, <em>Proceedings of the National Academy of Sciences </em><strong>120</strong><em> </em>(1), e2210214120 (2023). <a href="https://doi.org/10.1073/pnas.2210214120">https://doi.org/10.1073/pnas.2210214120</a></li> </ul> <p>If you use this dataset for your work, <strong>please cite this publication.</strong></p> <p>-------------------------------------</p> <p><strong>1. Virtual patient (2D) </strong></p> <p>An upsampled and projected virtual patient derived from the XCAT model (see paper for more details). The projected thickness (unit: [m]) of 28 separate materials are stored in the mat-file <em>'virtual_patient.mat'</em>. The accompanying text file <em>'virtual_patient_materials.txt' </em>lists all 28 materials (3rd dimension in the virtual patient .mat file) .</p> <p>Data information:</p> <ul> <li>File type: .mat</li> <li>Size: 39200x52200x28 (3D matrix, single, 32-bit)</li> <li>Pixel size: 7.69x7.69 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>2. Full-chest virtual radiographs</strong></p> <p>Three full-size virtual chest radiographs of the virtual patient (above) simulated with different settings:</p> <ul> <li>Conventional (<em>z</em> = 0 m, 120 kVp tungsten spectrum)</li> <li>Control (<em>z</em> = 0 m, 60 keV monochromatic)</li> <li>Phase contrast (<em>z</em> = 12 m, 60 keV monochromatic)</li> </ul> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 8000x6000 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>3. Zoom in on chest radiographs at different propagation distances</strong></p> <p>This is the underlying data for <strong>Figure 2</strong> in the paper.</p> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 600x600 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>4. Observing airway wall thickening</strong></p> <p>This is the underlying data for<strong> Figure 5</strong> in the paper.</p> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 750x750 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul>
Validating marker-less pose estimation with 3D x-ray radiography
Open the record for dataset details and reuse information.
Muon Scattering Radiography (MSR) measurements on blocks of ice in laboratory, and on simulated snowpack
<p>Experimental setup (scenario 5):</p> <p>Muon data used in this work has been collected with our muon detection system. This muon monitoring system is currently in use for both scientific and industrial purposes <a href="https://www.zotero.org/google-docs/?broken=RG5rWA">(Martínez-Ruiz del Árbol et al., 2022)</a>. The particle detectors are composed of four Multi-Wire Proportional Chambers (MWPC) and each chamber has two layers with 224 detection wires, all of them separated by 4 mm. The two layers form a two-dimensional grid of wires which covers an area of 89.6 x 89.6 cm and detects the positions where muons cross it.</p> <p>When a muon event is identified, our system detects four points located in the horizontal two-dimensional grids, two points before the particle goes through the target and another two points after the particle traverses it. With this data, way-in and way-out trajectories can be reconstructed, and muon deviations calculated. Specifically, in the numerical analysis of this work, we utilised the projection of muon deviations in two planes perpendicular to the detection wires.</p> <p>Simulation setup (scenarios 1 to 4):</p> <p>The snowpack was simulated using a one-dimensional snow model forced by surface meteorological data. We have used the SNOWPACK model <a href="https://www.zotero.org/google-docs/?EqYNAT">(Bartelt & Lehning, 2002</a><a href="https://www.zotero.org/google-docs/?LUKxAu">)</a> to realistically simulate the behaviour of the snowpack along two seasons, 2015/2016 (1_Modelling) and 2016/2017 (2_Testing). SNOWPACK was forced by the ERA5-Land surface reanalysis <a href="https://www.zotero.org/google-docs/?QCLBxK">(Muñoz-Sabater et al., 2021)</a>. The simulations were performed in the Pyrenees, using the ERA5-Land cell whose centroid falls closer to the Monte Perdido massif (42.7°N, -0.1°E), at an elevation of 2041m asl.</p> <p>We coupled the SNOWPACK simulations with a full MSR simulation setup that uses the Cosmic RaY generator <a href="https://www.zotero.org/google-docs/?oSIPTu">(Hagmann et al., 2012)</a> to reproduce the atmosphere muon flux and GEANT4 <a href="https://www.zotero.org/google-docs/?3ZDRDN">(Agostinelli et al., 2003)</a> to simulate the muon scattering caused by the snowpack. GEANT4 is a state-of-the-art software designed and maintained at CERN to simulate the interactions of particles and matter in high-energy and nuclear physics. Our simulation framework contains a model of our experimental setup including the muon detectors and their response. This framework has been successfully applied to multiple industrial problems, for instance, to steel-made pipe wear <a href="https://www.zotero.org/google-docs/?F8nYbS">(Martínez-Ruiz del Árbol et al., 2018)</a>. Similar simulation frameworks are typically used to research applications of muography <a href="https://www.zotero.org/google-docs/?115QeU">(Mori et al., 2017)</a>.</p> <p>We expanded the one-dimensional snowpack geometry to a 1m² snow column, assuming homogeneous snow layers in the longitude and latitude dimensions. Then, we propagated and measured muons penetrating the whole snow column, virtually reproducing the detection process using GEANT4. We collected muon deviations and their Root-mean-square (RMS) value for different accumulations of snow during the two simulated seasons.</p>
Post COVID-19 trends in simulation use within diagnostic radiography and radiation therapy education.
<p>The authors developed and deployed an online survey among diagnostic radiography and/or radiation therapy educators with experience in pre-registration training (academic, clinical or combined).The aim of this study was to capture global trends and activity on simulation-based education (SBE) in medical imaging and radiation therapy education. Simulation is a well-established component of medical radiation science training that embraces a wide range of activities and resources all aiming to provide a safe environment in which to practice clinical skills. The survey instrument design was informed by a prior review of the literature, a previous international survey conducted by members of the research team<sup>1</sup> and experience within the research team. It comprised two open and 27 closed questions designed to ascertain data across four domains: type of training environment of the respondent (n=13), access to and use of simulation within the participants’ institution (n=13) and possible future trends in simulation (n=3). Data captured in this study was compared with similar data from Bridge and colleagues previous survey in 2020.<sup>1 </sup>There is an opportunity for this dataset to add to the evidence base on the evolving landscape of the use of SBE in radiography/radiation therapy education. Such information has the potential to inform future practices and to create best-practice standards in the adoption of simulation-based educational resources and activities in diagnostic radiography and radiation therapy.</p> <p>Reference:</p> <ol> <li>Bridge P, Shiner N, Bolderston A, et al. International audit of simulation use in pre-registration medical radiation science training. <em>Radiography (Lond)</em>. Nov 2021;27(4):1172-1178. doi:10.1016/j.radi.2021.06.011</li> </ol>
Conventional Bite Wing Radiography Versus Stationary Intraoral Tomosynthesis, a Comparison Study
ClinicalTrials.gov study NCT02873585. IPD Sharing: NO. Countries: 1. Publications: 0.
Comparison of planar digital radiography and helical standing computed tomography for assessment of condylar stress fracture risk in Thoroughbred racehorses
<p class="MsoNormal"><strong>Background</strong>: Catastrophic injury has a low incidence but leads to the death of many Thoroughbred racehorses.</p> <p class="MsoNormal"><strong>Objectives</strong>: To determine sensitivity, specificity, and reliability for condylar stress fracture risk assessment from fetlock digital radiographs (DR) and standing computed tomography (sCT).</p> <p class="MsoNormal"><strong>Study design</strong>: Controlled <em>ex vivo</em> experiment.</p> <p class="MsoNormal"><strong>Methods</strong>: A blinded set of thoracic limb fetlock DR and sCT images were prepared from 31 Thoroughbreds. Four observers evaluated the condyles and parasagittal grooves (PSG) of the third metacarpal bone for the extent of dense bone and lucency/fissure and assigned a risk assessment grade for condylar stress fracture. Sensitivity and specificity for detection of subchondral structural changes in the condyles and PSG and for risk assessment for condylar stress fracture were determined by comparison with a reference. Agreement between observers and the reference assessment and reliability between observers were determined. Intra-observer repeatability was also assessed.</p> <p class="MsoNormal"><strong>Results</strong>: Sensitivity for detection of structural change was lower than specificity for both imaging methods and all observers. For horses with normal risk, observer assessment often agreed with the reference. Sensitivity for risk assessment was lower than specificity for all observers. For horses with a high risk of injury, observers generally underestimated risk. Diagnostic sensitivity of risk assessment was improved with sCT imaging, particularly for horses with elevated risk of injury. Assessment repeatability and reliability was better with sCT than DR.</p> <p class="MsoNormal"><strong>Main limitations</strong>: The <em>ex vivo</em> study design influenced DR image sets.</p> <p class="MsoNormal"><strong>Conclusions</strong>: Risk assessment through screening with diagnostic imaging is a promising approach to improve injury prevention in racing Thoroughbreds. Knowledge of sensitivity and specificity of fetlock lesion detection provides the critical guidance needed to improve screening programs for racehorses. We found improved detection of MC3 subchondral structural change and risk assessment for condylar stress fracture with sCT <em>ex vivo</em>.</p>
Data set from study: Identification of research priorities of radiography science – A modified Delphi study in Europe
<p>Data set consists of two round Delphi study conducted in Europe. The aim of the study was to identify research priorities in radiography science. The objective was to chart the opinions of radiography experts from different fields of radiography and different countries in Europe. </p>
A Study Comparing ex Vivo MRI Versus Radiography of Breast Specimens
ClinicalTrials.gov study NCT01869335. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Measurement of Lung Area at Chest Radiography to Define the Prognosis in Newborns With CDH
ClinicalTrials.gov study NCT04396028. IPD Sharing: Not stated. Countries: 1. Publications: 22.
The Use of MRI in the Assessment of Suspected Scaphoid Fracture With Negative Findings on the Initial Plain Radiography
ClinicalTrials.gov study NCT02801149. IPD Sharing: NO. Countries: 1. Publications: 1.
Early Prescription of Radiography Using the Ottawa Ankle Rules by a Nurse in the Management of Isolated Ankle Trauma
ClinicalTrials.gov study NCT04021511. IPD Sharing: YES. Countries: 1. Publications: 8.
Lung Ultrasound Versus Chest Radiography for Detection of Pneumothorax
ClinicalTrials.gov study NCT06022081. IPD Sharing: YES. Countries: 1. Publications: 10.
ANKLE TRAUMA Diagnostic Value of Ultrasound Compared to Standard Radiography in the Detection of Fractures
ClinicalTrials.gov study NCT05528432. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Performance of EOS Imaging System in the Assessment of Spondyloarthritis Structural Changes Compared With Standard Radiography
ClinicalTrials.gov study NCT03863756. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Digital Subtraction Radiography Evaluation of SMART Restoration With Partial Caries Removal in Primary Teeth
ClinicalTrials.gov study NCT01749267. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.