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Dataset: A Labeled Dataset for Osteoporosis Screening Based on Electromagnetic Attenuation
<p><strong>README</strong></p> <p><strong>Dataset name:</strong> osseus_dataset.csv </p> <p><strong>Version:</strong> 1.0 </p> <p><strong>Dataset period:</strong> 07/01/2021 - 09/31/2023</p> <p><strong>Dataset Characteristics:</strong> Multivalued </p> <p><strong>Number of Instances:</strong> 669</p> <p><strong>Number of Attributes:</strong> 31</p> <p><strong>Missing Values:</strong> yes</p> <p><strong>Area(s):</strong> Health and technology </p> <p><strong>Sources:</strong> </p> <ul> <li> <p>Electronic Patient Record (EPR) - University Hospital Onofre Lopes of Federal University of Rio Grande do Norte (HUOL/UFRN), Brazil;</p> </li> <li> <p>OSSEUS (Osteoporosis screening based on electromagnetic waves); and,</p> </li> <li> <p>DXA (Dual-energy x-ray absorptiometry). </p> </li> </ul> <p> </p> <p><strong>Description</strong>: The dataset “osseus_dataset.csv” (Table 1) contains elementary data related to risk factors and examinations performed by individuals in Rio Grande do Norte, Brazil, to investigate bone mineral density. Data were collected using the EPR of HUOL/UFRN, DXA, and OSSEUS, a low-cost device based on electromagnetic waves, which measures the attenuation of the signal when crossing the medial phalanx of the middle finger (PINHEIRO et al., 2021, ALBUQUERQUE et al., 2022).</p> <p><strong>Descrição</strong>: O conjunto de dados “osseus_dataset.csv” (Tabela 1) contém dados elementares relacionados a fatores de risco e exames realizados por indivíduos no Estado do Rio Grande do Norte, Brasil, para a investigação da densidade mineral óssea. Os dados foram coletados por meio do EPR do HUOL/UFRN, DXA e OSSEUS, um dispositivo de baixo custo baseado em ondas eletromagnéticas, que mede a atenuação do sinal ao atravessar a falange medial do dedo médio (PINHEIRO et al., 2021, ALBUQUERQUE et al., 2022).</p> <p> </p> <p><strong><strong>Table 1: </strong></strong>Description of Dataset Features.</p> <div> <table> <tbody> <tr> <td> <p><strong>Attributes</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>Electronic Patient Record (EPR)</strong></p> </td> </tr> <tr> <td> <p><strong>id</strong></p> </td> <td> <p>Unique identifier for a person (anonymous).</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Person unique identifier.</p> </td> </tr> <tr> <td> <p><strong>gender</strong></p> </td> <td> <p>It informs the person's gender.</p> </td> <td> <p>Categorical.</p> </td> <td> <ul> <li> <p>female</p> </li> <li> <p>male</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>age</strong></p> </td> <td> <p>It informs the person's age.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Integer value for age</p> </td> </tr> <tr> <td> <p><strong>weight</strong></p> </td> <td> <p>Informs the value referring to the person's weight—the unit of mass in kilogram (kg).</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Integer value for weight</p> </td> </tr> <tr> <td> <p><strong>height</strong></p> </td> <td> <p>Informs the value relating to the person's height—the unit of measurement for size in centimeters (cm).</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Integer value for height</p> </td> </tr> <tr> <td> <p><strong>ethnicity</strong></p> </td> <td> <p>Informs the person's ethnicity.</p> </td> <td> <p>Categorical.</p> </td> <td> <ul> <li> <p>black</p> </li> <li> <p>brown</p> </li> <li> <p>white</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>target</strong></p> </td> <td> <p>Describe the person's diagnosis or medical report.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>normal</p> </li> <li> <p>osteoporosis</p> </li> <li> <p>low bone mineral density</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>alcohol</strong></p> </td> <td> <p>It informs whether the person consumes alcoholic beverages.</p> </td> <td> <p>Categorical.</p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>smoking</strong></p> </td> <td> <p>Informs whether the person is a smoker.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>activity</strong></p> </td> <td> <p>It informs whether the person practices physical activities.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>milk</strong></p> </td> <td> <p>It informs whether the person consumes dairy drinks.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>calcium</strong></p> </td> <td> <p>It informs whether the person uses calcium.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>vitamin_d</strong></p> </td> <td> <p>It informs whether the person uses Vitamin D.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>fall</strong></p> </td> <td> <p>It informs whether the person has a history of falling.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>parents_osteoporosis</strong></p> </td> <td> <p>It informs whether the person has a family history of osteoporosis.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>parents_curved</strong></p> </td> <td> <p>It informs whether the person has a family history of "parents curved."</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>corticosteroids</strong></p> </td> <td> <p>It informs whether the person uses corticosteroid-type medications for three months or longer.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>arthritis</strong></p> </td> <td> <p>Informs if the person has arthritis.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>diseases</strong></p> </td> <td> <p>Informs if the person has comorbidities.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>menopause</strong></p> </td> <td> <p>Informs if the person has menopause.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>testosterone</strong></p> </td> <td> <p>It informs whether the person uses testosterone.</p> </td> <td> <p>Categorical. </p> </td> <td> <ul> <li> <p>yes</p> </li> <li> <p>no</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>OSSEUS (Osteoporosis screening based on electromagnetic waves)</strong></p> </td> </tr> <tr> <td> <p><strong>medial_length</strong></p> </td> <td> <p>Length of the medial phalanx in mm.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Integer value for length.</p> </td> </tr> <tr> <td> <p><strong>medial_height</strong></p> </td> <td> <p>Height of the medial phalanx in mm.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Integer value for height.</p> </td> </tr> <tr> <td> <p><strong>medial_width</strong></p> </td> <td> <p>Width of the medial phalanx in mm.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Integer value for width.</p> </td> </tr> <tr> <td> <p><strong>calibration</strong></p> </td> <td> <p>Osseus signal strength with no obstacle between the antennas.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Float value for calibration.</p> </td> </tr> <tr> <td> <p><strong>attenuation</strong></p> </td> <td> <p>Osseus signal strength with obstacles between antennas.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Float value for attenuation.</p> </td> </tr> <tr> <td> <p><strong>DXA (Dual-energy x-ray absorptiometry)</strong></p> </td> </tr> <tr> <td> <p><strong>spine_deviation</strong></p> </td> <td> <p>Reports the spinal standard deviation score that represents the difference between bone density and the expected value.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Float value for deviation.</p> </td> </tr> <tr> <td> <p><strong>femur_deviation</strong></p> </td> <td> <p>Reports the femur standard deviation score that represents the difference between bone density and the expected value.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Float value for deviation.</p> </td> </tr> <tr> <td> <p><strong>body_deviation</strong></p> </td> <td> <p>Reports the full body standard deviation score that represents the difference between bone density and the expected value.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Float value for deviation.</p> </td> </tr> <tr> <td> <p><strong>forearm_deviation</strong></p> </td> <td> <p>Reports the forearm standard deviation score that represents the difference between bone density and the expected value.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Float value for deviation.</p> </td> </tr> <tr> <td> <p><strong>worst_deviation</strong></p> </td> <td> <p>Reports the worst standard deviation score among all deviations obtained from the record.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Float value for deviation.</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> </div> <p><strong>REFERENCES</strong><br>Albuquerque, G. et al. A method based on non-ionizing microwave radiation for ancillary diagnosis of osteoporosis: a pilot study. BioMedical Eng. OnLine 21, 70, https://doi.org/10.1186/s12938-022-01038-y (2022).</p> <p>Pinheiro, B. d. M. et al. The influence of antenna gain and beamwidth used in osseus in the screening process for osteoporosis. Sci. Reports 11, 19148, https://doi.org/10.1038/s41598-021-98204-4 (2021).</p> <div> <p> </p> </div>
Transient analysis of power loss density with time-harmonic electromagnetic waves in Debye media
<p>Due to the complex permittivity, it is difficult to directly clarify the transient mechanism between electromagnetic waves and Debye media. To overcome above problem, the temporal relationship between the electromagnetic waves and permittivity is explicitly derived by applying the Fourier inversion and introducing the remnant displacement. With the help of the Poynting theorem and energy conservation equation, the transient power loss density is derived to describe the transient dissipation of electromagnetic field and the mechanism on phase displacement has been explicitly revealed. Besides, the unique solution can be obtained by applying the time-domain analysis method rather than involving the frequency-domain characteristics. The effectiveness of transient analysis is demonstrated by giving a comparison simulation on one-dimensional example.</p>
High Granularity Electromagnetic Calorimeter Shower Images
<p>Each HDF5 file contains energy deposits (shower images) created by <strong>electrons</strong> in one of the two calorimeters, for a specific incident angle of particles. Each HDF5 file has a structure of datasets, where each dataset represents energy deposits for a specific particle energy (in GeV). Particle energies are ranging from <strong>1</strong> to <strong>1024 GeV </strong>in powers of 2 and particle angles are ranging from <strong>50</strong> to <strong>90 degrees</strong> in a step of 10 (angle of 90 degrees indicates a particle entering the detector perpendicularly). Each dataset has the following structure<strong> {number of events,18,50,45}</strong>, with <strong>18x50x45</strong> being the granularity of a shower image.</p> <p>The calorimeter used to produce those data is a setup of concentric cylinders (layers). Each layer consists of active and passive material. The <strong>SiW</strong> geometry has 90 layers of 1.4 mm of tungsten as passive absorber and 0.3 mm of silicon as active material. The <strong>SciPb</strong> geometry has 45 layers of 4.4 mm of lead and 1.2 mm of scintillator. The number of readout cells is <strong>RxPxZ=18x50x45=40500</strong>, representing the cylindrical segmentation (rho,phi,z). The size of a single cell has been chosen to correspond to (approximately) 0.25 Moliere radius along the R axis and 0.5 radiation length along Z axis.</p> <p><br> The samples were created with the <strong><a href="https://gitlab.cern.ch/geant4/geant4/-/tree/master/examples/extended/parameterisations/Par04">Par04</a> </strong>Geant4 example, which demonstrates how to use Machine Learning inference to create energy deposits as a fast simulation model using LWTNN and ONNX runtime.</p>
Influence of Titan's Variable Electromagnetic Environment on the Global Distribution of Energetic Neutral Atoms
<p>Data for the manuscript "Influence of Titan's Variable Electromagnetic Environment on the Global Distribution of Energetic Neutral Atoms" by Tippens et al., (2022). See README.txt for a description of the data files included here.</p>
[DATASET] Design and characterization of an RF applicator for in vitro tests of electromagnetic hyperthermia doi.org/10.3390/s22103610
<p>[DATASET] Design and characterization of an RF applicator for in vitro tests of electromagnetic hyperthermia <a href="https://doi.org/10.3390/s22103610">doi.org/10.3390/s22103610</a></p> <p>The data used in the paper are dived in separate folder for each published figure.</p>
Dataset for Effect of matrix solidification on the structure formation in electromagnetic suspensions
<p><strong>Introduction:</strong> Dataset from numerical simulations describing the structure formation in suspensions with electromagnetically active particles in an external field, as described in detail in the paper</p> <ul> <li>Konstantinos Manikas, Markus Hütter, Patrick D. Anderson: Effect of matrix solidification on the structure formation in electromagnetic suspensions. Appl. Phys. A, 128: 709 (11 pages), 2022. DOI: 10.1007/s00339-022-05844-y WWW: https://doi.org/10.1007/s00339-022-05844-y</li> </ul> <p>which should be cited whenever this dataset is used. The data compiled here is the basis for figures 4, 5, and 6 in that paper.</p> <p> </p> <p><strong>Format:</strong> The files are provided in plain-text format (ascii).</p> <p> </p> <p><strong>Filenames:</strong> The nomenclature for the filenames follows the following scheme:</p> <ul> <li>data_fig{number}_{param}_{value}.txt</li> </ul> <p>where {number} specifies the number of the figure in the paper, {param} is the parameter that is studied and {value} denotes its numerical value (see the paper for details).</p> <p> </p> <p><strong>File content:</strong> Each datafile contains 1 header line, in which the meaning of the data-columns are explained (time, S2, N*, lambda* -- see paper for details), and the studied parameter and its value are listed. After this header line, the data is presented in tab-delimited columns.</p> <p> </p> <p>For further details, the reader is referred to the paper mentioned above.</p>
Chemical, thermodynamic and electromagnetic parameters of electrochemically-stimulated flames (numerical data)
<p><span>Key component concentration fields in the stimulated flames</span></p> <p><span> </span><span>Main impact factors of the electrochemical stimulation and their influence on the NOx generation/suppression/destruction</span></p>
Data on thermodynamic, gas-dynamic and electromagnetic parameters of gas discharge in poly-phase medium
<p><span>Temperature, concentrations and electromagnetic parameters distribution in the model environment for main poly-phase medium types</span></p>
Chemical and electromagnetic parameters of NOx-rich gas flow under the influence of multi-frequency electromagnetic field (numerical data)
<p>Metastable and non-stable states lifetime dependence on the exciting electromagnetic field parameters for the quantum series of the main ground state – dissociation consequence.</p> <p>Overtones and main frequencies spectrum and probabilities for NO molecule resonance excitation.</p>
Fig. 1 in In vitro Trypanosoma cruzi Growth Inhibition by Extremely Low-frequency Electromagnetic Fields
Fig. 1. Effect of 60 Hz sinusoidal magnetic fields at 2.0 mT and 24 h exposure on cell growth of T. cruzi epimastigote cultures. Bars represent arithmetical grouped means ± standard deviations.
Figure 2 in Molecular transduction in receptor-ligand systems by planar electromagnetic fields
Figure 2. TCR(αβ) molecules in free (a, b, c) and coupled (d) states, showing the charged residues in red for negative charges and blue for positive charges. The fully conserved residues are highlighted in the boxes below each figure. Image records taken from Jmol.
Figure 1 in Molecular transduction in receptor-ligand systems by planar electromagnetic fields
Figure 1. Spatial positions of fully conserved, charged residues and peptide-recognition residues in TCR. These together form a PECC-i by which the signal reaches the ζζ transmembrane dimer domain. Image recordings taken from Jmol.
Figure 3 in Molecular transduction in receptor-ligand systems by planar electromagnetic fields
Figure 3. Some examples of PECC-i present in: calcium-ADP pump (a,b,c); haemoglobin-oxygen (d,e,f); and gp120-CD4 (g,h,i) receptorligand protein systems. Image records taken from Jmol.
The impact of pulsed Electromagnetic Fault Injection on true random number generators
<p>Random number acquisition from HECTOR daughter board and the clock signals of both decimator and Priority Encoder. The acquisition where done with a shift register between the output of Priority Encoder’s output and the scope (the same applies for the decimator’s output).</p> <p>Folder is class by type of faults:</p> <ol> <li>tmp_00_wb means two of Priority Encoder’s bits are stuck at 00.</li> <li>tmp_01_wb means two of Priority Encoder’s bits are stuck at 01.</li> <li>tmp_10_wb means two of Priority Encoder’s bits are stuck at 10.</li> <li>tmp_11_wb means two of Priority Encoder’s bits are stuck at 11.</li> <li>tmp_0_wb means one of Priority Encoder’s bit is stuck at 0.</li> <li>tmp_1_wb means one of Priority Encoder’s bit is stuck at 1.</li> <li>Decim_0_wb means one bit of decimator’s output stuck at 0.</li> <li>Decim_1_wb means one bit of decimator’s output stuck at 1.</li> </ol> <p>Curves are class as follows:</p> <p> (<em>Index</em> means the acquisition number)</p> <ol> <li>CIdecimIndex_0.trc files are the clock signal before decimation, i.e. the clock used to sample Priority encoder’s output.</li> <li>COdecimIndex_0.trc files are the clock signal after decimation, i.e. the clock used to sample decimator’s output.</li> <li>IrandDecim_Index_0.trc is the output of the Priority Encoder.</li> <li>OrandDecim_Index_0.trc is the output of the Priority Encoder.</li> </ol> <p>All trc files are binary files.</p> <p>The file conditions_wb recap all the injection parameter used to obtain the different fault.</p>
Occurrence characteristics of electromagnetic ion cyclotron waves at Maitri during solar cycle 24
<p>This data is of Induction Coil Magnetometer installed at the Indian Antarctic station- Maitri (geographic 70.7oS, 11.8oE, L = 5). It belongs to the Indian Institute of Geomagnetism, Navi Mumbai. The data is from January 2011 to December 2017. It contains the information about the EMIC wave ground observation at L~5.</p>
Realistic complex geoelectric model with topography, curved layers with the airborne electromagnetic (AEM) system positions and dBz/dt signals
<p>The uploaded files contain the description of the complex model that is used to provide some computational experiments.It is a realistic complex geoelectric model with topography, curved layers, 3-D objects of complex shape, and a fragment of a real observation system containing several thousand AEM system positions. The observation system file also includes dBz/dt values obtained in the measuring points.</p> <p>The model is described with several archieved text files which format is explained in the "readme.txt" file.</p>
Origin and Structure of Electromagnetic Generator Regions at the Edge of the Electron Diffusion Region
<p>The data included here is for an article submission to the Physics of Plasmas: MMS special Issue. There are electric field, magnetic field, and particle data files from MMS (cdf files). There are files from Particle-in-Cell simulations (xdmf files, h5 files, and a log file with run info). Included here is the python notebook (ipynb file) used to analyze the simulation data, and two IDL files (generator_fig.pro and Energy_Flux.pro) that were used to produce the panel plots of MMS data in the article.</p>
Dataset of the paper "Numerical Study of the Optical Response of ITO-In2O3 Core-Shell Nanocrystals for Multispectral Electromagnetic Shielding"
<p>This dataset provides the raw data of the paper "Numerical Study of the Optical Response of ITO-In2O3 Core-Shell Nanocrystals for Multispectral Electromagnetic Shielding"</p>
Strong enhancement of electromagnetic shower development induced by high-energy photons in a thick oriented tungsten crystal
<p>We have observed a significant enhancement in the energy deposition by 25-100 GeV photons in a 1 cm thick tungsten crystal oriented along its <111> lattice axes (data of Fig. 2). At 100 GeV, this enhancement, with respect to the value observed without axial alignment, is more than twofold. This effect, together with the measured huge increase in secondary particle generation (data of Fig. 3) is ascribed to the acceleration of the electromagnetic shower development by the strong axial electric field. The experimental results have been critically compared with a newly developed Monte Carlo adapted for use with crystals of multi-X_0 thickness (data of Fig.2, 3 and 4). These results may prove to be of significant interest for the development of high-performance photon absorbers and highly compact electromagnetic calorimeters and beam dumps for use at the energy and intensity frontiers.</p>
Dual-modal imaging of two-phase flows with electromagnetic flow tomography and electrical tomography -- experimental evaluation of the state estimation approach
<p>The supplementary files included are associated with our experimental research on two-phase flow estimation. This study experimentally investigates the feasibility of a state estimation approach for dynamic image reconstruction in dual-modal tomography of two-phase oil-water flows using electromagnetic flow tomography (EMFT) and electrical tomography (ET). By approximating the process with a convection-diffusion model, the extended Kalman filter and fixed-interval Kalman smoother are applied to reconstruct temporally evolving velocity and phase fraction distributions. The results demonstrate that the Kalman smoother-based reconstructions, along with uncertainty estimates, outperform conventional methods and provide feasible volumetric flow rate estimates for oil and water phases in a laboratory setup.</p>
ScienceDex guides
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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.