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365 results for “neutron”
Raw neutron counts from a soil water content hydroprobe at 15 NPP study sites at the Jornada Basin LTER, 1989-2019
This data package contains raw neutron count data for monthly soil water probe measurements made at 15 net primary production (NPP) study locations on Jornada Experimental Range (JER) and Chihuahuan Desert Rangeland Research Center (CDRRC) lands. Once a month, soil water content measurements are made at 10 depths (where possible) at each of 10 access tubes at each of the 15 NPP sites using a neutron probe (CPN Model 503DR Hydroprobe). This dataset consists of the count of thermalized neutrons at 30 cm depth intervals to a maximum depth of 300 cm. These counts are subsequently converted to volumetric water content as provided in a separate EDI package (knb-lter-jrn.210013003). The NPP sites these measurements are made at represent the 5 dominant vegetation types of the Jornada Basin, which consist of 3 shrub (creosotebush, mesquite dune, and tarbush) and 2 grass (upland grassland and playa) types. Three NPP sites are located in each of the types. Collection of this dataset was discontinued when the neutron probe instrument was replaced in mid-2019. Neutron probe soil water content data are still collected from the same NPP locations using a new instrument, and the raw neutron counts and corrected VWC data are found in EDI dataset knb-lter-jrn.210013003. This dataset is complete.
Soil volumetric water content calculated from neutron hydroprobe data at 15 NPP study locations at the Jornada Basin LTER site, 1989-ongoing
This data package contains soil water content data calculated from monthly neutron hydroprobe count measurements made at 15 net primary production (NPP) study locations on Jornada Experimental Range (JER) and Chihuahuan Desert Rangeland Research Center (CDRRC) lands. Once a month, neutron probe measurements are made at 10 depths (where possible) at each of 10 access tubes at each of the 15 NPP sites using a neutron probe (CPN Model 503DR Hydroprobe and CPN Model 503 Elite Hydroprobe). The raw dataset, also on EDI (knb-lter-jrn.210013001), consists of the count of thermalized neutrons at 30 cm depth intervals to a maximum depth of 300 cm. In this data package, the raw neutron counts have been adjusted for radioactive decay of the neutron source, then converted to volumetric water content (VWC) to a maximum depth of 270 cm using calibration equations (deepest probe depths are excluded from VWC calculations). The NPP sites these measurements are made at represent the 5 dominant vegetation types of the Jornada Basin, which consist of 3 shrub (creosotebush, mesquite dune, and tarbush) and 2 grass (upland grassland and playa) types. Three NPP sites are located in each of the types. This data collection is ongoing with new data collected monthly (updates to the EDI package may occur less frequently). NOTE: This version of the dataset includes calibrated data from a new hydroprobe unit that has recently been put into service. Repair parts were no longer available for the older unit.
Dataset of "Neutron imaging and molecular simulation of systems from methane and p‑xylene"
<p>The dataset contains parameterizations, and input files for molecular dynamics simulations used in the study of methane dissolution in p-xylene. For selected conditions, full simulation data, i.e., trajectories and energetics are provided. All used simulation results data are provided in the table, along with the measured experimental data.</p>
Small Angle Neutron Scattering (SANS) virtual experiments at KWS-1
<p>Small Angle Neutron Scattering (SANS) virtual experiments at KWS-1, FRM-II dataset. Intended for Machine learning purposes. Data generated by performing simulations in <a href="https://www.mcstas.org/">McStas</a> with the <a href="https://www.sasview.org/docs/user/qtgui/Perspectives/Fitting/models/index.html">SasView small angle scattering form factor models</a> describing the sample interaction. Two parameter spaces are varied sistematically: form factor model parameters and instrument configuration parameters. For more detailed information, read the <code>README.md</code> file of this database.</p> <p>The database contains 46 SANS form factor models under different instrument configurations. All data is uploaded in <code>hdf5</code> files, and the corresponding metadata in <code>.csv</code> files. Description of what each instrument configuration means (sample-detector distance, collimation, incident wavelength) and which model is used is contained in the metadata file. </p> <p>Each array is the result of the position sensitive detector output in neutron intensity (float values). A Dataset loader for Pytorch may be found <a href="https://github.com/jorobledo/hdf_loader_pytorch" target="_blank" rel="noopener">in GitHub</a> and is intended for Machine Learning purposes.</p>
Relativistic description of dense matter equation of state and compatibility with neutron star observables: a Bayesian approach
<p>The general behavior of the nuclear equation of state (EOS), relevant for the description of neutron stars (NS), is studied within a Bayesian approach applied to a set of models based on a density-dependent relativistic mean-field description of nuclear matter <a href="https://arxiv.org/abs/2201.12552">Malik et al 2022</a>. The EOS is subjected to a minimal number of constraints based on nuclear saturation properties and the low-density pure neutron matter EOS obtained from a precise next-to-next-to-next-to-leading order (N$^{3}$LO) calculation in chiral effective field theory ($\chi$EFT). The number of final sample parameters corresponding to the posterior sets is around fourteen thousand. We present five EOSs among them, namely DDBl, DDBm, DDBu1, DDBu2, and DDBx. The DDBl, DDBm, DDBu2 were chosen so that the radius of the 1.4$M_\odot$ star has the lower limit, a medium value, and the upper limit of the 90% CI for the conditional probabilities $P(R|M)$. We have also included DDBu1 that has a slightly lower $R_{1.4}$ than the upper limit but lies completely inside the 90% CI for the conditional probabilities $P(R|M)$. The DDBx is the one that predicts a maximum mass of 2.5$M_\odot$ and has the following nuclear matter properties, $K_0=300$ MeV, $J_{sym,0}=30$ MeV and $L_{sym,0}=39$ MeV.</p> <p>We also release our entire sets of ~14K NS matter EOS. All the EOSs are for NS core and starting baryon density is 0.04 fm$^{-3}$. One needs to add their own choice of crust EOS for the star properties calculation. The uncertainty in star properties for the choice of the different crust has been discussed in Section 2.1 of the manuscript (arxiv: 2201.12552). </p> <pre> To extract the entire sets of ~14K NS matter EOS files, one needs to follow the steps, 1) unzip DDB_EOS_14K.zip ----------------------------Note------------------------------------- All the eos files have three columns baryon density (fm-3), energy density (MeV.fm-3), and pressure (MeV.fm-3). The starting density is 0.04 fm-3, as it is NS core eos. One needs to add their own choice of crust eos in order to calculate NS properties. ---------------------------------------------------------------</pre> <p> </p> <p> </p>
Data from calculated radial neutron flux distributions in a KBS-3 type geological repository
<p>Data from calculations of radial distribution of neutron flux per emitted neutron from rods of spent nuclear fuel in a KBS-3 type geological repository. Reference (<em>Jansson, 2022</em>) contain a summary of the calculations and a description of the structure of this data.</p> <p>This data was computed on resources provided by Swedish National Infrastructure for Computing (SNIC) at Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX), National Supercomputer Centre at Linköping University (NSC) and the SNIC Cloud, partially funded by the Swedish Research Council through grant agreement no. 2018-05973, under projects SNIC 2021/5-299 and SNIC 2021/18-12.</p>
Mars Odyssey Neutron Spectrometer Time Series of Corrected Cateogry 1 Counting Rates, 2002-2017
<p>Ubinned time series of derived neutron data from the Mars Odyssey Neutron Spectrometer (MONS) Category 1 data, from 200 - 2017.</p>
SEVN parameter file from the paper "Binary neutron star populations in the Milky Way" by Sgalletta et al., 2023
<p>The repository contains the runtime parameters used in the SEVN simulations analysed in the paper "Binary neutron star populations in the Milky Way" by Sgalletta et al., 2023.</p> <p><strong>Repository content: </strong></p> <p>- <em>used_params_Sgalletta2023.txt<br> </em>The file contains all the runtime parameters used in the SEVN simulations. The parameters that have been varied in different runs are indicated with **** and the explored values are reported in the comment. See the SEVN userguide (<a href="https://gitlab.com/sevncodes/sevn/-/blob/SEVN/resources/SEVN_userguide.pdf">https://gitlab.com/sevncodes/sevn/-/blob/SEVN/resources/SEVN_userguide.pdf</a>) for the description of each parameter </p> <p> </p>
Data release for "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors"
<p>We publish skymap files in fits format of the simulation in our work "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors". There are 68000 BNS events, and results of different negative latencies are zipped in different tar files. An example jupyter notebook for using the data is provided.</p> <p> </p> <p> </p>
Atmospheric Effects on Neutron Star Parameter Constraints with NICER
<p>Posterior sample files associated with the publication "Atmospheric Effects on Neutron Star Parameter Constraints with NICER" by Salmi et al. (2023; <a href="https://doi.org/10.48550/arXiv.2308.09319">arXiv:2308.09319</a>; <a href="https://doi.org/10.3847/1538-4357/acf49d">https://doi.org/10.3847/1538-4357/acf49d</a>).</p><p>Also included are: the data products; the numeric model files including the telescope calibration products; model modules in the Python language using the X-PSI framework; and Jupyter analysis notebooks.</p><p>Please refer to the README for detailed information.</p>
Raw neutron counts from a soil water content hydroprobe along the LTER-I transects (control and fertilized) at the Jornada Basin LTER, 1986-ongoing
This data package contains raw neutron count data from a series of soil neutron hydroprobe measurements along the permanent LTER-I transects located at Chihuahuan Desert Rangeland Research Center (CDRRC) in the Jornada Basin of southern New Mexico, USA. The control and treatment transects are parallel to each other and are 2.7 km in length extending from the middle of the College Playa to the foot of Mt. Summerford. The treatment transect was treated annually with ammonium nitrate fertilizer (NH4NO3 at 10g N/m2/yr) until 1987. Measurement stations are located at 30 meter intervals along each transect, and there are neutron probe access tubes located every station on the control transect (n=89) and at every fifth station at the treatment transect (n=19). Measurements were taken at 5 depths using a neutron probe (CPN Model 503DR Hydroprobe). Neutron probe readings in 3 non-weighing mini-lysimeters along each transect are also included. This dataset consists of the count of thermalized neutrons at 30 cm, 60 cm, 90 cm, 110 cm, and 130 cm depths. These counts are subsequently converted with transect site-specific calibration equations to the volumetric water content data provided in EDI packageID knb-lter-jrn.210012002. Measurements were taken at 2 week intervals from April 1982 to 1987 and monthly thereafter. Data collection for this study is ongoing.
ASM01 Soil water content measured by neutron probe at Konza Prairie
Data set contains measurements of soil moisture (%volume) at various depths (25-200 cm) in deep (lowland) soils collected on LTER grazed and ungrazed watersheds burned at 1-, 4-, and 20-year intervals. Soil moisture measured by the neutron probe method.
Dynamic Albedo of Neutrons (DAN) Simulated and Observed Die-Away Data
<p>These datasets provide the data associated with the article, "Analysis of active neutron measurements from the Mars Science Laboratory Dynamic Albedo of Neutrons instrument: Intrinsic variability, outliers, and implications for future investigations" by H. R. Kerner et al. (full citation below). The Dynamic Albedo of Neutrons (DAN) is a nuclear spectroscopy investigation onboard the Mars Science Laboratory (Curiosity) rover.</p> <p>If you use this dataset, please use the following citation: Kerner, H. R., Hardgrove, C. J., Czarnecki, S., Gabriel, T. S. J., Mitrofanov, I. G., Litvak, M. L., Sanin, A. B., and Lisov, D. I. Analysis of active neutron measurements from the Mars Science Laboratory Dynamic Albedo of Neutrons instrument: Intrinsic variability, outliers, and implications for future investigations. Under review. </p>
Optimal neutron-star mass ranges to constrain the equation of state of nuclear matter with electromagnetic and gravitational-wave observations: EOS library
<p>This repository includes a library of equations of state (EOS) and stellar models presented in the publications Weih et al. (2019) (see also the related identifier) and Most et al. (2018). The library includes ~ 3 Million physically plausible EOSs that fulfill a number of astrophysical and nuclear constraints. See the README for more information. </p>
Axisymmetric models for neutron star merger remnants with realistic thermal and rotational profiles: dataset
<p>Dataset containing the results of the parameter space exploration of binary neutron star merger remnants and 12 selected models:<br> * `search_results.dat` contains the parameters and properties of the successful results of the study.<br> * `model_*.log` are the logs with settings, parameters, and properties of the selected models.<br> * `model_*.out` are the profiles of the selected models in binary format.<br> * `XNS_reader.py` is a python script to read the binary format, convert it to text, and compute some derived and global quantities. EDIT 2022-05-30: the output file in binary format does contain the profiles of temperature and entropy per baryon, but those are not outputted in the converted text file. You can manually modify the python script in order to output these profiles too.<br> * `properties.csv` is a summary of the parameters and properties of the selected models.<br> <br> This dataset has been obtained with the stationary code XNS in General Relativity with the Conformal Flatness Approximation [Bucciantini and Del Zanna 2011; Pili et al. 2014; Camelio et al. 2018 and 2019].<br> The EOS is implemented as a cold piecewise polytrope [Read et al. 2009] plus a thermal gamma law.<br> The models have been selected between those obtained in the parameter space exploration.<br> For details see the companion paper [Camelio et al. 2021, PRD 103:063014].<br> <br> If you use this dataset, please cite its Zenodo DOI and the companion paper [Camelio et al. 2021, PRD 103:063014].</p> <p>EDIT 2022-05-30: an updated version of the code that has been used to produce this dataset is now on Zenodo (https://doi.org/10.5281/zenodo.6594069).<br> This updated version is called ASWNS code, and it does not contain the model of binary neutron star merger remnant used for this dataset, but an older version of the model of nonbarotropic neutron star (from Camelio et al. 2019).<br> You can implement any neutron star model on top of ASWNS, as shown in the examples provided with ASWNS.</p>
The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves
<p>We distribute complete gravitational-wave signals in the Advanced LIGO band (10 Hz - 8192 Hz) of the inspiral and merger of two neutron stars. These waveforms been constructed by hybridizing numerical-relativity data obtained with the WhiskyTHC code [1] with tidal effective-one-body waveforms [2,3]. More details on the procedure used to generate these waveforms are given in [4]. </p> <p>The waveforms are distributed as HDF5 files containing the amplitude and phase of the -2 spin-weighted spherical harmonics multipoles of the strain:</p> <p><span class="math-tex">\(( h_+ - \mathrm{i} h_\times )_{l,m} = \frac{A_{l,m}}{D_{\rm cm}} \exp(-\mathrm{i} \phi_{l,m} )\)</span></p> <p>where <span class="math-tex">\(D_{\rm cm}\)</span> is the distance in cm from the source.</p> <p>The data files include a machine readable "/metadata" group with:</p> <ul> <li>/metadata/EOS: name of the equation of state</li> <li>/metadata/M_{A|B}: mass in isolation of star A (or B) in grams</li> <li>/metadata/R_{A|B}: radius of star A (or B) in cm</li> <li>/metadata/k2T: tidal coupling constant of the binary (see [3])</li> <li>/metadata/kl_{A|B}: l=2,3,4 dimensionless Love numbers of star A (or B)</li> </ul> <p>We store amplitude and phase for multipoles modes up to l=4 as time series sampled at 16384 Hz.</p> <p>We make these waveforms freely available in the hope that they will be useful. We kindly ask you to cite [3] and [4] in any publication resulting from the use of these waveforms.</p> <p>---<br /> [1] http://www.tapir.caltech.edu/~david_e/whiskythc.html<br /> [2] https://eob.ihes.fr/<br /> [3] S. Bernuzzi, A. Nagar, T. Dietrich, T. Damour; Modeling the Dynamics of Tidally Interacting Binary Neutron Stars up to the Merger; Phys.Rev.Lett. 114 (2015) 16, 161103.<br /> [4] D. Radice, S. Bernuzzi, C. D. Ott; The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves; arXiv:1603.05726.</p>
Contour method and neutron diffraction dataset to determine the weld fusion zone shape on residual stress in submerged arc welding
<p>This is a dataset which formed the basis for "The effect of the weld fusion zone shape on residual stress in submerged arc welding" by A. Ishigami, M. J. Roy, J. N. Walsh and P. J. Withers appearing in the Journal of Advanced Manufacturing Technology.</p> <p>Two X-grade steel specimens with different high speed, submerged arc welds with very slight differences in fusion zone shape were compared with a novel contour method application as well as with neutron diffraction. Neutron diffraction was carried out with the SALSA instrument at the Institut Laue-Langevin in Grenoble, France with the assistance of T. Pirling. Data files with 441 in the descriptor refer to 'conventional' parameters (see publication), while 241 refers to 'new'.</p> <p>Provided in this dataset are four *.dat files, which contains data is in the form of a point cloud with one point per line, whitespace delimited in microns. Data was captured with a Nanofocus CF-4 laser profilometer sensor with point spacing 30 µm apart. Data with z coordinates below or above 500 µm are considered outside of the surface detection limits.</p> <p>Also included is an Excel worksheet, which contains the calculated residual stresses as found with LAMP (https://www.ill.eu/instruments-support/computing-for-science/cs-software/all-software/lamp/). Raw data is available here:</p> <p>P. J. Withers, A. Ishigami, T. Pirling, M. Roy, J. Walsh (2014). The effect of weld bead shape on residual stress in novel low heat input welding of steel [Data set]. ILL. http://doi.ill.fr/10.5291/ILL-DATA.1-02-145</p> <p>The authors would like to thank JFE Steel Corporation for both direct and in-direct support of this research. The authors would also like to thank the Institut Max von Laue-Paul Langevin for the allocation of beamtime at SALSA and gratefully acknowledge the help of Thilo Pirling for his assistance in performing the neutron diffraction experiments. A. Ishigami would like to thank Kenji Oi for his support of this research. M. J. Roy would like to thank Ian Winstanley for his assistance in performing the contour cuts. M. J. Roy acknowledges financial support from the EPSRC (EP/L01680X/1) through the Materials for Demanding Environments Centre for Doctoral Training.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
1.3A (Ge337) calibration data for new Ge115 monochromator installed on Echidna Neutron Powder Diffraction Instrument
<p>In early October 2024 the Echidna neutron powder instrument located at the OPAL reactor, ANSTO, installed a new monochromator with Ge115 cut. The present calibration data were collected shortly afterwards from a standard LaB6 sample in a 6mm diameter Vanadium can. The instrument was set to 140 degrees takeoff angle and monochromator angle 85.08 degrees, corresponding to the Ge337 reflection. Raw data in NeXus format are contained in <strong>ECH0034261.nx.hdf</strong>. These data were corrected for variable detector response using the information in <strong>eff_2024-10-06.cif</strong> and pixel vertical positions adjusted according to the table in <strong>vertical_offsets_2024-10-06.txt. </strong>Deviations from the ideal detector 1.25 degree angular spacing were applied using <strong>echidna-Apr2018.ang</strong>. The detector response was then recorrected based on overlapping measurements using the algorithm described in <a href="https://doi.org/10.1107/S1600576718014048">Avdeev and Hester (2018)</a> resulting in a 1D pattern suitable for fitting wavelength and peak shapes. This 1D pattern is provided here as a plain table (<strong>ECH0034261_LaB6.xyd</strong>) and as a pdCIF file (<strong>ECH0034261_LaB6.cif</strong>) including metadata on data collection and reduction. Details of data reduction are described in the above paper, and the data reduction routines used are included in the <a href="https://github.com/Gumtree/Echidna_scripts">Gumtree package as python code</a>.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.