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97 results for “helium”
Output files for Degassing of CO2 triggers large-scale loss of helium from magma oceans
<p>This upload includes output files for the paper Degassing of CO2 triggers large-scale loss of helium from magma oceans.</p>
Helium emission coefficients
<p>Table 3 in Morishita et al. 2024 (https://ui.adsabs.harvard.edu/abs/2024arXiv240410037M/abstract)</p>
Derivative products: "Very Local Interstellar Medium Revealed by Complete Solar Cycle of Interstellar Neutral Helium Observations with IBEX" by Swaczyna et al.
<p>Derivative products from: Swaczyna et al. 2022, "Very Local Interstellar Medium Revealed by Complete Solar Cycle of Interstellar Neutral Helium Observations with IBEX", ApJS, under review</p>
Spectrally resolved helium absorption from the extended atmosphere of a warm Neptune exoplanet
<p>Stellar heating causes atmospheres of close-in exoplanets to expand and escape. These extended atmospheres are difficult to observe because their main spectral signature – neutral hydrogen at ultraviolet wavelengths – is strongly absorbed by interstellar medium. We report the detection of the near-infrared triplet of neutral helium in the transiting warm Neptune-mass exoplanet HAT-P-11b using ground-based, high-resolution observations. The helium feature is repeatable over two independent transits, with an average absorption depth of 1.08±0.05%. Interpreting absorption spectra with 3D simulations of the planet’s upper atmosphere suggests it extends beyond 5 planetary radii, with a large scale height and a helium mass loss rate ≲ 3x10<sup>5</sup> g‧s<sup>−1</sup>. A net blueshift of the absorption might be explained by high-altitude winds flowing at 3 km‧s<sup>−1</sup>from day to night-side.</p>
Integrating Bayesian groundwater mixing modeling with on-site helium analysis to identify unknown water sources
<p>Analyzing groundwater mixing ratios is crucial for many groundwater management tasks such as assessing sources of groundwater recharge and flow paths. However, estimating groundwater mixing ratios is affected by various uncertainties, which are related to analytical and measurement errors of tracers, the selection of end-members and finding the most suitable set of tracers. Although these uncertainties are well recognized, it is still not common practice to account for them. We address this issue by using a new set of tracers in combination with a Bayesian modeling approach, which explicitly considers the possibility of unknown end-members while fully accounting for tracer uncertainties. We apply the Bayesian model we developed to a tracer set which includes helium-4 analyzed on-site to determine mixing ratios in groundwater. Thereby, we identify an unknown end-member, that contributes up to 84% to the water mixture observed at our study site. For the helium-4 analysis, we use a newly developed Gas Equilibrium Membrane Inlet Mass Spectrometer (GE-MIMS), operated in the field. To test the reliability of on-site helium-4 analysis, we compare results obtained with the GE-MIMS to the conventional lab-based method, which is comparatively expensive and labor intensive. Our work demonstrates that (i) tracer-aided Bayesian mixing modeling can detect unknown water sources, thereby revealing valuable insights into the conceptual understanding of the groundwater system studied and ii) on-site helium-4 analysis with the GE-MIMS system is an accurate and reliable alternative to the lab-based analysis.</p>
Low-level argon and air in helium for CNNs
<p>This repository contains datasets used for analyzing low-level argon and air impurity in helium. These datasets were utilized in the research paper titled "Impurity Gas Detection for SNF Canisters Using Probabilistic Deep Learning and Acoustic Sensing" available at (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4675917).</p> <p>Files</p> <p>1. Input_set.npy: This file contains the input dataset, consisting of excitation and gas concentration pairs.<br>2. Response_set.npy: This file contains the response dataset.<br>3. Concen_set.npy: This file contains the concentration pairs of Argon and Air used in the experiments.<br>4. Image_train.npy: This file contains the STFT spectrum data for the training set.<br>5. Image_test.npy:This file contains the STFT spectrum data for the testing set.</p> <p>Data Structure</p> <p>Image_train.npy<br>- Shape:(65,011, 38, 44, 2)<br>- Description: This array contains the STFT spectra for the training set.<br> - The first dimension (65,011) represents the number of training samples.<br> - The second and third dimensions (38, 44) represent the size of the STFT spectrum.<br> - The fourth dimension (2) represents the two channels:<br> - One channel contains the STFT of the excitation signal.<br> - The other channel contains the STFT of the response signal.</p> <p>Image_test.npy<br>- Shape: (27,862, 38, 44, 2)<br>- Description: This array contains the STFT spectra for the testing set.<br> - The first dimension (27,862) represents the number of testing samples.<br> - The second and third dimensions (38, 44) represent the size of the STFT spectrum.<br> - The fourth dimension (2) represents the two channels:<br> - One channel contains the STFT of the excitation signal.<br> - The other channel contains the STFT of the response signal.</p> <p>Input_set.npy<br>- Shape: (101,919, 253)<br>- Description: This array contains the input parameters for the ultrasonic experiments.<br> - The first 251 columns contain the excitation parameters.<br> - The last 2 columns represent the concentration pairs of Argon and Air.</p> <p>Response_set.npy<br>- Shape: (101,919, 426)<br>- Description: This array contains the ultrasonic response data corresponding to the input parameters in `Input_set.npy`.<br> - The 426 columns represent the time-series data points of the ultrasonic response.</p> <p>Concen_set.npy<br>- Shape: Corresponds to the concentration pairs of Argon and Air.<br>- Description: This array contains the concentration pairs used in the experiments.</p> <p>X_pure_new.npy<br>Shape: (4,718, 427)<br>Description: This array contains data representing pure helium samples.<br>The first 426 columns represent the response features of each sample.<br>The 427th column is the label for binary classification, and all values in this column are zeros, indicating that these are pure samples.</p> <p>X_with_impurity_new.npy<br>Shape: (101,700, 429)<br>Description: This array contains data representing samples with impurities.<br>The first 426 columns represent the response features of each sample.<br>The 427th column is the label for binary classification, and all values in this column are ones, indicating that these are impure samples.<br>The last two columns represent the concentrations of impurities in the samples.</p> <p>Citation<br>If you use this dataset in your research, please cite the above paper.</p> <p>Contact<br>For any questions or issues related to this dataset, please contact the repository owner via bozhouzh@usc.edu.</p>
Ultrasonic dataset with argon and air in helium
<p>This repository contains three datasets stored in NumPy array files (`.npy` format), which are used for analyzing the relationship between excitation and ultrasonic responses. These datasets were utilized in the research paper titled "Impurity Gas Monitoring Using Ultrasonic Sensing and Neural Networks: Forward and Inverse Problems" available at (https://doi.org/10.1016/j.measurement.2023.113822).</p> <p> Files</p> <p>1. Input_set.npy: This file contains the input dataset for the model.<br>2. Response_set.npy:This file contains the corresponding response dataset for the input data.<br>3. Concen_set.npy: This file contains the concentration pairs of Argon and Air used in the experiments.</p> <p>Data Structure</p> <p>Input_set.npy<br>- Shape:*The array has a shape of (121,498, 253).<br>- Description: This array represents the input excitation for the ultrasonic experiments.<br> - The first 251 columns contain the excitation used during the experiments. Each row corresponds to a excitation.<br> - The last 2 columns represent the concentration pairs of Argon and Air:<br> - Column 252 contains the Argon concentration.<br> - Column 253 contains the Air concentration.</p> <p>Response_set.npy<br>- Shape: The array has a shape of (121,498, 776).<br>- Description: This array contains the ultrasonic response data corresponding to the input parameters in `Input_set.npy`.<br> - The 776 columns represent a time-series data point of the ultrasonic response captured during the experiment. Each row corresponds to the ultrasonic response for a specific set of excitation parameters and gas concentration pairs.</p> <p>Concen_set.npy<br>- Shape: The array corresponds to the concentration pairs of Argon and Air.<br>- Description: This array contains the concentration pairs of Argon and Air used in the experiments.</p> <p>Citation<br>If you use this dataset in your research, please cite the above paper.</p> <p>Contact<br>For any questions or issues related to this dataset, please contact the repository owner via bozhouzh@usc.edu.</p>
Helium Evaporation
Open the record for dataset details and reuse information.
Heterodyne analysis of high-order partial waves in attosecond photoionization of Helium
<p>The data supports the mainfigures in the manuscript "Heterodyne analysis of high-order partial waves in attosecond photoinization of Helium".</p>
Seasonal and altitude dependence of thermospheric metastable helium densities measured by fluorescence lidar: lidar dataset
<p>These are NetCDF files containing lidar return data from the helium fluorescence lidar described in the referenced paper (doi to be added). The data contain the following variables:</p> <p>The data have one of two possible formats. For files beginning '2023', the instrument was operated in single frequency mode, and the files contain the following fields:</p> <p> 'photons_chX' with X between 0 and 11 - A Nx3000 array of detected photon counts binned into N 10-second intervals in time and 3000 2-km bins in altitude. Note that only the first 1000 bins in the second axis contain data, up to an altitude of 2000 km; the arrays are then padded with zeros.</p> <p> 'shots_chX' with X between 0 and 11 - An array of length N with the number of laser pulses fired during the corresponding interval.</p> <p> 'time_offset' - A single value, giving the unix timestamp relative to which the 'time' variable is measured.</p> <p>'time' - An array of length N with the midpoint time in milliseconds of the corresponding interval, measured relative to the 'time_offset'.</p> <p>'altitude' - An array of length 3000 with the lower edges of the corresponding altitude bins, in meters.</p> <p>For files beginning '2024', the instrument was operated in multi-frequency mode, and the files contain the following fields:</p> <p>'photons_chX' with X between 0 and 11 - A 41xNx3000 array of detected photon counts binned into 41 1-pm bins in wavelength, N 10-s intervals in time, and 3000 2-km bins in altitude. Note that only the first 600 bins in the third axis contain data, up to an altitude of 1200 km; the arrays are then padded with zeros.</p> <p> 'shots_chX' with X between 0 and 11 - A 41xN array with the number of laser pulses fired with the corresponding wavelength during the corresponding interval.</p> <p>'wavelength' - An array of length 41 giving the central wavelength of each wavelength bin.</p> <p> 'time_offset' - A single value, giving the unix timestamp relative to which the 'time' variable is measured.</p> <p>'time' - An array of length N with the midpoint time in milliseconds of the corresponding interval, measured relative to the 'time_offset'.</p> <p>'altitude' - An array of length 3000 with the lower edges of the corresponding altitude bins, in meters.</p> <p> </p> <p>Note that this is a standardized data format used by many instruments; it contains several other variable fields that are not used by the present instrument and are therefore omitted from this description.</p>
QMC Raw Data for Superfulid Helium Adsorbed on Strained Graphene
<p>Data Summary</p> <p>Raw data of path integral Monte Carlo simulation for phase diagram analysis of atomic helium adsorbed on isotropically strained graphene.<br> For more details, please check README.md on Github repository</p> <p>lindenset.zip</p> <ul> <li>Raw data for linear density plot</li> <li> Corresponds to lindenset.ipynb</li> </ul> <p>OUTPUT_largebox.zip</p> <ul> <li>Raw data for simulations with N_ad = 144</li> <li>Corresponds to LargeBoxSim.ipynb</li> </ul> <p>OUTPUT_phase.zip</p> <ul> <li>Raw data for analysis of adsorption phases</li> <li>Corresponds to AdsorptionPhaseAnalyzer.ipynb</li> </ul> <p>OUTPUT_T1set.zip</p> <ul> <li>Raw data for analysis of PIMC phase diagram</li> <li>Corresponds to T1comm_phases.ipynb</li> </ul> <p>OUTPUT_vacuum.zip</p> <ul> <li>Raw data for analysis of vacuum phase boundary</li> <li>Corresponds to VacuumFinder.ipynb</li> </ul>
Dataset for "Energization of Cold Protons and Helium Ions by EMIC Waves in the Inner Magnetosphere: Hybrid Simulations"
<p>* Reduced datasets for figures in the manuscript.</p> <p>* Code for producing the simulation data.</p>
Longitudinal Study of Helium-3 and Xenon-129 Magnetic Resonance Imaging
ClinicalTrials.gov study NCT02279329. IPD Sharing: Not stated. Countries: 1. Publications: 8.
The Effect of Breathing Helium-Hyperoxia During Pulmonary Rehabilitation in Patients With COPD
ClinicalTrials.gov study NCT00406705. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Lung HeXeRT: Helium, Xenon MRI for NSCLC Patients
ClinicalTrials.gov study NCT01859650. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Effects of Non-invasive Ventilation With Helium-oxygen Mixture in Premature Infants With Respiratory Distress Syndrome
ClinicalTrials.gov study NCT04404816. IPD Sharing: NO. Countries: 1. Publications: 13.
Proton and Helium Ion Acceleration at Magnetotail Plasma Jets
<p>Additional files</p>
3D lookup tables for helium-graphene interaction for isotropically strained graphene
<p>Data files containing the 3D lookup tables for the helium-graphene Lennard Jones interaction for isotropically strained graphene from 0$\AA$ to 10$\AA$ above the sheet. Path integral Monte Carlo simulations can be performed using these data files and our open source code located <a href="https://code.delmaestro.org/">here</a>.</p>
Supporting data: Nutrient vertical flux in Indonesian seas as constrained by non-atmospheric helium-3
Open the record for dataset details and reuse information.
Models of low-mass helium white dwarfs including gravitational settling, thermal and chemical diffusion, and rotational mixing⋆
<p>MESA inlists, data (from a single run: rotation + diffusion, M1=1.4, M2=1.2, Porb=3.4 days, Z=0.02) and run_star_extras associated with <a href="http://adsabs.harvard.edu/abs/2016A%26A...595A..35I">Istrate et. al 2016</a>. MESA version 7624. The grid of models produced in this paper can be found <a href="http://adsabs.harvard.edu/abs/2016yCat..35950035I">here</a>.</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.