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380 results for “Pressure Data”
Raw diffraction data for [NiFeSe] hydrogenase G491A variant pressurized with Kr gas - dataset G491A-Kr
<p>Diffraction data measured at ESRF beamline ID30A-3 on September 27, 2017.</p>
Data sets for "Chemical reaction between iron and a limited water supply under pressure: implications for water behavior at the core-mantle boundary" by Nishi et al.
<p>This is the XRD datasets for the article "Chemical reaction between iron and a limited water supply under pressure: implications for water behavior at the core-mantle boundary" by Nishi et al.</p>
Data for: "Random forest algorithms for recognizing daily life activities using plantar pressure information: A smart-shoe study"
<p>Data and software related to the manuscript "Random forest algorithms for recognizing daily life activities using plantar pressure information: A smart-shoe study" (2020). </p> <p>========</p> <p>Typo in README_DataRep.txt:</p> <p>' 2) "one_config" [...] Selected results requiring these data are shown in Fig. <strong>13</strong>'<strong> </strong>(not 11).</p>
Data from: First-principles calculations of structural, electronic, magnetic and elastic properties of Mo2FeB2 under high pressure
The structural and electronic density of states, magnetic, and elastic properties of Mo2FeB2 under high pressure have been investigated with first principles calculations. Furthermore, the thermal dynamic properties of Mo2FeB2 were also studied with the quasiharmonic Debye model. The volume of Mo2FeB2 decreases with the increasing pressure. Using the analysis of the density of the states, atom population and Mulliken overlap population, it is observed that as the pressure increases, the B-B bonds are strengthened, and the B-Mo covalency decreases. Moreover, for all pressures, Mo2FeB2 is detected in the antiferromagnetic phase and the magnetic moments decrease with the increasing pressure. The calculated bulk modulus, shear modulus, Young's modulus, Poisson's ratio and universal anisotropy index all increase with the increasing pressure. From thermal expansion coefficient analysis, it is found that Mo2FeB2 shows good volume invariance under high pressure and temperature. The examination of the dependence of heat capacity on the temperature and pressure shows that heat capacity is more sensitive to temperature than to pressure
Research data for "Hydrogen under Pressure as a Benchmark for Machine-Learning Potentials"
<p>This dataset supports the paper: "Hydrogen under Pressure as a Benchmark for Machine-Learning Potentials". The paper is online here: https://doi.org/XXXXXXXXXXXXXXXXX.</p>
Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas)
<p> </p> <p>Data accompanying the paper: Jeantet, L., Vigon, V., Geiger, S., & Chevallier, D. (2021). Fully convolutional neural network: A solution to infer animal behaviours from multi-sensor data. <em>Ecological Modelling</em>, <em>450</em>, 109555.. doi : <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ecolmodel.2021.109555" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ecolmodel.2021.109555</a></p> <p> </p> <p>In this paper we developped a fully convolutional network, the V-Net, to automatically identify the behaviors of green turtle from acceleration, gyroscope, depth sensor data. With minimal preprocessing, we obtained a F1-score of 81.1% and a Global accuracy of 97.2%. </p> <p> </p> <p><strong>Associated Github with the V-Net script : </strong><a href="https://github.com/jeantetlorene/Vnet_seaturtle_behavior">https://github.com/jeantetlorene/Vnet_seaturtle_behavior</a></p> <p> </p> <p>The dataset comprised the raw acceleration, gyroscope and depth sequence of 13 free-ranging green turtles associated with the behaviors. The indiviuals were equipped with a on-board video recorder combined with an accelerometer, gyroscope, magnetometer and luminosity, temperature and depth sensors using four suction cups and an automatic release system over a two-day periods (see Jeantet et al. 2020 for details and the associated article). The accelerometer, gyroscope, magnetometer recorded at 20 Hz and the pressure, temperature and luminosity sensors at 1 Hz. The cameras were programmed to record until nightfall (6 pm) and resume at daybreak (6 am). The magnetometer, luminosity and temperature data are not provided in this dataset. </p> <p>For each individual, the data collected by the devices was correlated with observed behaviors from video recordings. Unlabeled sequences, primarily night recordings, were excluded, resulting in the creation of one file per day of deployment for each individual. A total of 46 behaviors were observed and are described in detail in Jeantet et al. (2020). The labels for these behaviors are found in the column "beh." The behaviors were grouped into six main categories: Breathing, Feeding, Gliding, Resting, Scratching, and Swimming. Any other observed behavior was categorized as Other. The associated labels for the categories can be found in the column "beh_merge."</p> <p>To process the depth data and increase the sampling rate to 20 Hz, we used a linear interpolation technique. We called this new variable "Pressure_corr". Additionally, we calculated the pressure difference ("Pressure_diff") between each measuring point (originally at 1 Hz).</p> <p>"In total, the green turtle dataset contained 68.6 hours of labelled sequences from 13 individuals (approximately 5.29 hours per individual, max = 14.67 hours, min = 0.96 hours, standard deviation = 3.39 hours). The predominant behavior observed in the videos was Resting, totaling over 34.3 hours, followed by Swimming and Breathing, with 22.3 hours and 5.7 hours, respectively. The other behaviors were expressed in minority (Gliding: 2.3 hours, Feeding: 1.8 hours, Scratching: 1.2 hours and Other: 1 hour). "</p> <p> </p> <p>The folder contains 16 Python matrices, each with 11 columns (AccX, AccY, AccZ, GyrX, GyrY, GyrZ, Depth, beh, beh_merge, Pressure_corr, Pressur_diff) and a number of rows corresponding to the deployment duration. The title of each file indicates the camera number used (CC-07-XX) and the deployment day (DD-MM-YYYY), with an additional number if the file was split due to unlabeled sequences.</p> <p> </p> <p>The folder also contains two dictionaries (behInd_to_behName, behName_to_behInd) that specify the behaviors associated with each number used as a label in the "beh" column. Two dictionaries (behInd_to_behName_cat, behName_to_behInd_cat) that specify the behavioral categories associated with each number used as a label in the "beh_merge" column. Additionally, there is a dictionary (dico_info) that provides the names of the matrix columns and the frequence of recording.</p> <p> </p> <p><strong>Please cite this dataset as :</strong> </p> <p>Jeantet, L., Planas-Bielsa, V., Benhamou, S., Geiger, S., Martin, J., Siegwalt, F., Lelong, P., Gresser, J., Etienne, D., Hielard, G., Arque, A., Regis, S., Lecerf, N., Frouin, C., Benhalilou, A., Murgale, C., Maillet, T., Andreani, L., Campistron, G., … Chevallier, D. (2024). Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Chelonia mydas) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11643602</p> <p> </p>
SCB model data for article 'The effects of localized thermal pressure on equilibrium magnetic fields and particle drifts in the inner magnetosphere'
<p>The SCB model simulation data for the article 'The effects of localized thermal pressure on equilibrium magnetic fields and particle drifts in the inner magnetosphere'.</p> <p>The 'Rxx_A xx_Sxx_Lxxparameters' files are the data files, where Rxx is the beta_0 value, Axx is the A_e value, Sxx is the sigma_0 value and Lxx is the L_0 value (see the article for the definition of the values)</p> <p>The 'SCB_variables.xlsx' is a table to describe the variables in the data file.</p> <p>The 'read_scb.pro' is an IDL program to read the data files.</p>
Measurement Data of Characterisation of a self-sustained, water-based condensation particle counter for aircraft cruising pressure level operation
<p>Measurement Data of the MAGIC-CPC at low pressure condition in comparission with an electrometer and a butanol CPC.</p>
A REAL WORLD DATA COLLECTION OF HUMAN AMNIOTIC MEMBRANE(S) ON DIABETIC FOOT ULCERS, VENOUS LEG ULCERS, PRESSURE ULCERS AND POST OPERATIVE WOUNDS
ClinicalTrials.gov study NCT07078968. IPD Sharing: NO. Countries: 1. Publications: 0.
Anonymized Data Collection From the CS6BP and Other Modalities for the Purpose Measurement of Blood Pressure
ClinicalTrials.gov study NCT05370066. IPD Sharing: NO. Countries: 1. Publications: 0.
Morphometric Study of the Legs and Feet of Diabetic Patients in Order to Collect Data Intended to be Used to Measure by Dynamometry the Pressures Exerted by Several Medical Compression Socks at the Le
ClinicalTrials.gov study NCT05594446. IPD Sharing: NO. Countries: 1. Publications: 0.
Data Collection Study of Wedge Pressure Data in Patients With CRT-D Devices
ClinicalTrials.gov study NCT00811551. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prediction of Pressure Injury Risk in ICU Using Data Mining
ClinicalTrials.gov study NCT07306143. IPD Sharing: NO. Countries: 1. Publications: 0.
Continuous Non-Invasive Blood Pressure System Data Collection in Comparison to Invasive Radial Arterial Pressure
ClinicalTrials.gov study NCT04456179. IPD Sharing: NO. Countries: 1. Publications: 0.
EVD Drainage Data and Intracranial Pressure (ICP) Measurements
ClinicalTrials.gov study NCT05546996. IPD Sharing: NO. Countries: 1. Publications: 0.
Intraocular Pressure Data Collection With Tonometers
ClinicalTrials.gov study NCT06450587. IPD Sharing: NO. Countries: 1. Publications: 0.
Automated Dating Scan Probe Pressure Data Collection Plan
ClinicalTrials.gov study NCT05963243. IPD Sharing: NO. Countries: 1. Publications: 0.
Big Data Analysis of OSA Patients on Positive Airway Pressure (PAP) Treatment: Real-life Monitoring
ClinicalTrials.gov study NCT05495347. IPD Sharing: NO. Countries: 1. Publications: 0.
Data Collection of Heart Rate and Blood Pressure
ClinicalTrials.gov study NCT05095753. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Diastolic pressure ratio: new approach and validation vs. the instantaneous wave-free ratio
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