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4,070 results for “plasma”
QuaLiKiz-v2.6.2 turbulent transport model evaluations based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/output", and "/label". The inputs to the QuaLiKiz evaluations are provided under "/input", representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. Selected relevant outputs of the QuaLiKiz evaluations are provided under "/output", namely the local turbulent transport coefficients after applying a semi-empirical turbulent fluctuation saturation rule. Some useful metadata is provided under "/label", giving some degree of provenance tracking back to the JET experimental database, as well as describing the applied parameter variations and explaining why certain output rows were removed from the output structure.</p>
Reprocessing of the dataset "Plasma Proteome Profiling Reveals the Effects of Weight Loss on the Apolipoprotein Family and Systemic Inflammation Status"
<p>Reprocessing of the MassIVE repository MSV000080596, originally generated to investigate the dynamic changes in the plasma proteomes of a cohort of individuals with obesity following weight loss and maintenance. The reprocessing included all samples from 52 individuals taken right after the weight-loss process and during the weight maintenance phase of the study (Weeks 0, 4, 13, 26, 39, and 52).</p> <p>We used the sequence database generated by ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) representing all populations from the 1000 Genomes Project (doi.org/10.5281/zenodo.10149277). For the search, SearchGUI version 4.3.1 and PeptideShaker version 3.0.0 were used with the X!Tandem and Tide search engines. The modification settings specified were carbamidomethylation of C as fixed and oxidation of M, deamidation of N and Q, Pyrrolidone of E and Q, and acetylation of protein N-terminus as variable modifications. The maximum peptide length was set to 40 amino acids and the precursor and fragment ion tolerances were set to 7 and 20 ppm, respectively. Resulting PSMs were processed as described in (doi.org/10.1021/acs.jproteome.3c00243) using Percolator version 3.5 provided with features based on peptide retention time (DeepLC version 1.1.2) and fragmentation predictors (MS2PIP version 3.9.0), and filtered at a 1% estimated FDR.</p> <p>The attached file contains all the peptide-spectrum matches identified at 1% FDR. The peptides have been annotated with transcripts, genes, and alleles using the ProHap Peptide Annotator v1.1 (<a href="https://github.com/ProGenNo/ProHap_PeptideAnnotator">https://github.com/ProGenNo/ProHap_PeptideAnnotator</a>).</p>
Genome-wide association summary statistics for human blood plasma glycome
<p>The dataset contains results of genome-wide association study of human blood plasma glycome. The 113 files contain association summary statistics for 113 glycome traits, of which 36 were directly measured by UPLC technology and 77 were derived glycome traits. Description of each glycome trait can be found in the <strong>Additional notes</strong> section. This dataset is also available for graphical exploration in the genomic context at <a href="http://gwasarchive.org">http://gwasarchive.org</a>. </p> <p>The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.</p> <p><strong>When using downloaded data, please cite corresponding paper and this repository:</strong></p> <ol> <li>Sharapov, S. Z., Tsepilov, Y. A., Klaric, L., Mangino, M., Thareja, G., Shadrina, A. S., … Aulchenko, Y. (2019). Defining the genetic control of human blood plasma N-glycome using genome-wide association study. <em>Human Molecular Genetics</em>. http://doi.org/10.1093/hmg/ddz054</li> <li>Sodbo Sharapov, Yakov Tsepilov, Lucija Klaric, Massimo Mangino, Gaurav Thareja, Mirna Simurina, Concetta Dagostino, Julia Dmitrieva, Marija Vilaj, FranoVuckovic, Tamara Pavic, Jerko Stambuk, Irena Trbojevic-Akmacic, Jasminka Kristic, Jelena Simunovic, Ana Momcilovic, Harry Campbell, Malcolm Dunlop, Susan Farrington, Maria Pucic-Bakovic, Christian Gieger, Massimo Allegri, Edouard Louis, Michel Georges, Karsten Suhre, Tim Spector, Frances MK Williams, Gordan Lauc, Yurii Aulchenko. (2018). Genome-wide association summary statistics for human blood plasma glycome (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1298406</li> </ol> <p><strong>Funding</strong></p> <p>This work was supported by the European Community’s Seventh Framework Programme funded project PainOmics (Grant agreement # 602736) and by the European Structural and Investments funding for the "Croatian National Centre of Research Excellence in Personalized Healthcare" (contract #KK.01.1.1.01.0010).</p> <p>The work of SSh was supported by the Russian Ministry of Science and Education under the 5-100 Excellence Programme.</p> <p>The work of YT was supported by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project #0324-2018-0017).</p> <p>Karsten Suhre and Gaurav Thareja are supported by ‘Biomedical Research Program’ funds at Weill Cornell Medicine - Qatar, a program funded by the Qatar Foundation. We thank all staff at Weill Cornell Medicine - Qatar and Hamad Medical Corporation, and especially all study participants who made the QMDiab study possible.</p> <p>The SOCCS study was supported by grants from Cancer Research UK (C348/A3758, C348/A8896, C348/ A18927); Scottish Government Chief Scientist Office (K/OPR/2/2/D333, CZB/4/94); Medical Research Council (G0000657-53203, MR/K018647/1); Centre Grant from CORE as part of the Digestive Cancer Campaign (<a href="http://www.corecharity.org.uk">http://www.corecharity.org.uk</a>).</p> <p>TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.</p> <p><strong>Column headers:</strong></p> <ol> <li>SNP: SNP rsID</li> <li>CHR: chromosome</li> <li>POS: position (GRCh37 build) </li> <li>OTHER_ALLELE: reference allele (coded as "0")</li> <li>EFFECT_ALLELE: effective allele (coded as "1")</li> <li>EAF: effective allele frequency </li> <li>N: sample size</li> <li>BETA: effect size of effective allele</li> <li>SE: standard error of effect size</li> <li>PVAL: P-value of association (without GC correction)</li> <li>IMPUTATION: imputation quality</li> </ol>
Plasma circulating microRNA-expression quantitative trait loci (eQTLs) data in the Rotterdam Study
<p>The dataset contains GWAS summary statistics for 2,083 plasma circulating microRNAs, obtained from nearly 2,178 participants of the Rotterdam Study. The dataset includes three files, as outlined below:</p> <p><strong>File1: SNP_reference_file_maf0.01_Rsq0.7.txt</strong></p> <p>A reference file for SNPs with good imputation quality (Rsq > 0.7) and minor allele frequency > 0.01 among participants included in our GWAS in the Rotterdam Study (N=2,178). The headers are:</p> <p>SNP: rsID</p> <p>chr: chromosome number according to GRCh37</p> <p>bp: basepair position according to GRCh37</p> <p>effect_allele: effect allele</p> <p>other_allele: other allele</p> <p>eaf: effect allele frequency</p> <p><strong>File2: miReQTLs_1e-5_maf0.01_Rsq0.7.txt</strong></p> <p>Summary statistics for all SNPs significantly associated with 2083 miRNAs (p-value < 1e-5), filtered by minor allele frequency > 0.01 and Rsq > 0.7. The headers are:</p> <p>SNP: rsID</p> <p>beta: effect estimate</p> <p>se: standard error</p> <p>pval: p-value</p> <p>miRNA: miRNA ID</p> <p><strong>File3: miReQTLs_nominal_sig.csv.gz</strong></p> <p>Summary statistics for all SNPs nominally associated with 2083 miRNAs (p-value < 0.05). The headers are:</p> <p>RSID: SNP ID</p> <p>p-value: p-value</p> <p>phenotype: miRNA</p> <p>SE: standard error</p> <p>BETA: effect estimate</p> <p> </p> <p>The SNP allelic information and frequency can be found in the reference file (<strong>File1</strong>). </p> <p><br>For more information, please contact: m.ghanbari@erasmusmc.nl</p>
Raw Data on Extracellular Particles in 613 Human and 163 Canine Diluted Plasma and Blood Samples Assessed by Interferometric Light Microscopy
<p><span>Extracellular nanoparticles (EPs) are cellular fragments. After being released in cell exterior, they become mediators of the cell-cell interaction. Their characterization in bodily fluids may reflect the clinical status of the organism. Here we present data on the number density <em>n</em> and hydrodynamic diameter <em>D</em><sub>h </sub>of EPs assessed directly in diluted plasma and blood by using a recently developed technique, Interferometric Light Microscopy (Romolo et al., 2022). The data are presented in the attached Table. </span></p> <p><span>We collected 613 blood and plasma samples from human patients with Inflammatory Bowel Disease (IBD) taken into tubes with trisodium citrate and ethylenediaminetetraacetic acid (EDTA) anticoagulants and 163 blood and plasma samples from canine patients with Brachycephalic Obstructive Airway Syndrome (BOAS). </span><span>The human study was conducted in accordance with the Declaration of Helsinki, and approved by the National Medical Ethics Committee of the Republic of Slovenia (0120-271/2022/4; KME 27 July 2022). All procedures in the animal study complied with the relevant Slovenian government regulations (Animal Protection Act, Official Gazette of the Republic of Slovenia, No. 43/2007). The animal study was approved by the Animals in Experiments Welfare Commission of the Veterinary Faculty, University of Ljubljana, approval number 18-3/2022-1. </span><span>Information regarding sample preparation is documented in the MIBlood-EV reports.</span></p> <div> <div> <div><span><a name="_msocom_1"></a></span></div> </div> </div>
QuaLiKiz-v2.6.2 linear instability spectra based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/spectrum", and "/wavenumber". The '/input' key contains the inputs used for the QuaLiKiz evaluations, representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. The "/spectrum" key contains the linear growth rate and frequency spectra corresponding to the 2 most dominant microinstabilities determined by the calculation (s0 = dominant, s1 = sub-dominant). The "/wavenumber" key contains an array representing the standard set of 18 wavenumbers (<span class="math-tex">\(k_y \rho_s\)</span>) was used to generate the spectra (k0 = lowest wavenumber, k17 = highest wavenumber).</p>
Influence of Atmospheric Air Plasma Pre-Treatment of Veneers on the Mechanical Properties and Stability of Beech Plywood
<p>Wood-based sheet materials such as plywood, fiberboard, particleboard, and oriented strain board find applications in civil engineering, building technology, furniture manufacturing and many more. All these materials rely strongly on an effective bond formation between the resin and the wood base material, which gives rise to their mechanical performance and stability, as well as their resistance to moisture and liquids. In our study, we present the use of a commercial atmospheric air plasma system, which we used for the pretreatment of veneers of common beech (<em>Fagus sylvatica</em> L.) wood before formation of plywood boards. Plasma treatment parameters were optimized following the change in water contact angle. Two different stacking patterns were used for plasma-treated veneers. The time stability of the plasma modification was investigated by forming a second set of plywood boards 70 hours after plasma treatment of the respective veneers. The influence of the plasma treatment on mechanical properties was studied via bending and shear strength of the four sets of plasma-treated boards in comparison to a plywood out of the same veneer without plasma treatment. Water and moisture resistance were tested through water immersion and surface water resistance tests. Further, confocal laser scanning microscopy was used to determine changes of the surfaces’ morphologies.</p>
Plasma-Prescribed Active Region Static Extrapolation Dataset
<p>A repository of extrapolations using RBF-FD Magnetohydrostatic techniques with imposed plasmas, based on the SHARP solar image photospheric magnetic field library.</p>
A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process, and Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform
<p>The data contained in this repository was used in the production of the publication "A laser-plasma platform for photon-photon physics: the two photon Breit-Wheeler process" (<a href="https://doi.org/10.1088/1367-2630/ac3048">https://doi.org/10.1088/1367-2630/ac3048</a>) and "Bounding elastic photon-photon scattering at $\sqrt s \approx 1$\,MeV using a laser-plasma platform" (<a href="https://doi.org/10.1016/j.physletb.2025.139247">https://doi.org/10.1016/j.physletb.2025.139247</a>).</p>
5D-NP-FABTECH_ALD - Open Dataset for: "Shedding light on the initial growth of ZnO during plasma-enhanced atomic layer deposition on vapor-deposited polymer thin films"
<p>This is the open dataset for the paper: "Demelius, L. <em>et al.</em> Shedding light on the initial growth of ZnO during plasma-enhanced atomic layer deposition on vapor-deposited polymer thin films. <em>Applied Surface Science</em> <strong>604</strong>, (2022)."</p> <p>This includes the supplementary information and all the source material that was used for the paper preparation.</p> <p>For each folder (sub-dataset), there exists a corresponding readme file describing the content and including material.</p>
8 years of dayside Magnetospheric Multiscale (MMS) unsupervised clustering plasma regions classifications
<p>These files contain the 1-minute resolution dataset (“labeled_sunside_data.csv”) and 15 minute or longer region list (“<region_name>_region_list.csv”) for Toy-Edens et al.'s Classifying 8 years of MMS Dayside Plasma Regions via Unsupervised Machine Learning. The 1-minute resolution file contains the rolled up 1-minute epoch, probe name (mms1, mms2, mms3, mms4), features that go into clustering and post-cleansing methods, spacecraft positions (in GSE, GSM, and magnetic latitude/local time), raw and cleansed clustering labels, and transition name. The 15+ minute region lists contain the name of the plasma region type, the probe name (mms1, mms2, mms3, mms4), and the start and stop epoch of >= 15 minute epoch where the probe is solidly within that region. NOTE: for the 15+ minute region lists we are only looking for changes in plasma regions, this means that missing data may artificially inflate the duration of the epoch, we suggest looking at the full 1-minute resolution dataset to confirm the region timing.</p> <p>We ask that if you use any parts of the dataset that you cite Toy-Edens et al.'s Classifying 8 years of MMS Dayside Plasma Regions via Unsupervised Machine Learning (DOI:10.1029/2024JA032431).</p> <p>This work was funded by grant 2225463 from the NSF GEM program.</p> <p> </p> <p>The following tables detail the contents of the described files:</p> <p><strong>labeled_sunside_data.csv description</strong></p> <table> <tbody> <tr> <td> <p><strong>Column Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Epoch</p> </td> <td> <p>Epoch in datetime</p> </td> </tr> <tr> <td> <p> probe</p> </td> <td> <p>MMS probe name</p> </td> </tr> <tr> <td> <p> ratio_max_width</p> </td> <td> <p>Ratio of the width of the most prominent ion spectra peak (in number of energy channels) to max number of energy channels. See paper for more information</p> </td> </tr> <tr> <td> <p> ratio_high_low</p> </td> <td> <p>Ratio of the mean of the log intensity of high energies in the ion spectra to the mean of the log intensity of low energies in the ion spectra. See paper for more information</p> </td> </tr> <tr> <td> <p> norm_Btot</p> </td> <td> <p>Magnitude of the total magnetic field normalized to 50nT. See paper for more information</p> </td> </tr> <tr> <td> <p> small_energy_mean</p> </td> <td> <p>The denominator in ratio_high_low</p> </td> </tr> <tr> <td> <p> large_energy_mean</p> </td> <td> <p>The numerator in ratio_high_low</p> </td> </tr> <tr> <td> <p> temp_total</p> </td> <td> <p>Total temperature from the DIS moments. See paper for more information</p> </td> </tr> <tr> <td> <p> r_gse_x</p> </td> <td> <p>x position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gse_y</p> </td> <td> <p>y position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gse_z</p> </td> <td> <p>z position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gsm_x</p> </td> <td> <p>x position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> r_gsm_y</p> </td> <td> <p>y position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> r_gsm_z</p> </td> <td> <p>z position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> mlat</p> </td> <td> <p>magnetic latitude of spacecraft</p> </td> </tr> <tr> <td> <p> mlt</p> </td> <td> <p>magnetic local time of spacecraft</p> </td> </tr> <tr> <td> <p> raw_named_label</p> </td> <td> <p>Raw cluster assigned plasma region label (allowed values: magnetosheath, magnetosphere, solar wind, ion foreshock)</p> </td> </tr> <tr> <td> <p> modified_named_label</p> </td> <td> <p>Cleansed cluster assigned plasma region label (use these unless have a specific reason to use raw labels). See paper for more information</p> </td> </tr> <tr> <td> <p> transition_name</p> </td> <td> <p>Transition names (e.g. quasi-perpendicular bow shock, magnetopause). See paper for more information</p> </td> </tr> </tbody> </table> <p> </p> <p><strong><region_name>_region_list.csv description</strong></p> <table> <tbody> <tr> <td> <p><strong>Column Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>start</p> </td> <td> <p>Starting Epoch in datetime</p> </td> </tr> <tr> <td> <p>stop</p> </td> <td> <p>Stopping Epoch in datetime</p> </td> </tr> <tr> <td> <p>probe</p> </td> <td> <p>MMS probe name</p> </td> </tr> <tr> <td> <p>region</p> </td> <td> <p>Cleansed cluster name associated with 1-minute resolution “modified_named_label”</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Dataset- Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)
<p>This repository collects all the data (Figures and Tables) presented in the review article "Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)"</p>
Influence of Anode Immersion Speed on Current and Power in Plasma Electrolytic Polishing
<p><span>Plasma electrolytic polishing (PeP) is mainly used to improve the surface quality and thus </span><span>the performance of electrically conductive parts. It is usually used as an anodic process, i.e., the </span><span>workpiece is positively charged. However, the process is susceptible to high current peaks during</span> <span>the formation of the vapour–gaseous envelope, especially when polishing workpieces with a large </span><span>surface area. In this study, the influence of the anode immersion speed on the current peaks and the</span> <span>average power during the initialisation of the PeP process is investigated for an anode the size of a </span><span>microreactor mould insert. Through systematic experimentation and analysis, this work provides</span> <span>insights into the control of the initialisation process by modulating the anode immersion speed. The </span><span>results clarify the relationship between immersion speed, peak current, and average power and</span> <span>provide a novel approach to improve process efficiency in PeP. The highest peak current and average </span><span>power occur when the electrolyte splashes over the top of the anode and not, as expected, when the </span><span>anode touches the electrolyte. By immersion of the anode while the voltage is applied to the anode</span> <span>and counterelectrode, the reduction of both parameters is over 80 %.</span></p>
Dataset for: Statistical properties of meso-scale plasma flows in the nightside high-latitude ionosphere
<p>This dataset is a compilation of statistical results from Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). If you would like to use the dataset, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com). Depending on how the results are used, the main authors request co-authorship on publications. </p> <p>The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt <br> Files named ***_FLOW-DATA-PCvsAO_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> FWHMavg_AO [degrees]<br> FWHMkmavg_AO=[km]<br> longtestranges=[ignore]<br> Velmaxavg_AO=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]<br> FWHMavg_PC=[degrees]<br> FWHMkmavg_PC=[km]<br> Velmaxavg_PC=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]<br> ;;For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not<br> ;;those used in the paper. They were not found with the strict selection criteria. Please do not use.<br> mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] <br> gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> ;;;;;;;;;;;;;;;<br> minlatAO=[degrees, min geographic latitude of the flow]<br> maxlatAO=[degrees, max geographic latitude of the flow]<br> minlatPC=[degrees, min geographic latitude of the flow]<br> maxlatPC=[degrees, max geographic latitude of the flow]<br> mltAO=[degrees (MLT)]<br> mltPC=[degrees (MLT)]<br> AE=[nT]<br> AL=[nT]<br> SYMH=[nT]<br> IMFBz=[nT]<br> IMFBy=[nT]<br> F107=[sfu]</p> <p>The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt <br> Files named ***_orientation_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. <br> https://doi.org/10.1029/2018JA025440 <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> mbearingAO [degrees clockwise from magnetic North]<br> gbearingAO [degrees clockwise from geographic North]<br> mbearingPC [degrees clockwise from magnetic North]<br> gbearingPC [degrees clockwise from geographic North]</p> <p>The following list describes the columns in each data file labeled, ***_SPEC_TEST_***_noRG1-2.txt</p> <p> time [YYYYMMDDhhmmss]<br> RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]</p>
Segmenting magnetized plasma turbulence with aweSOM
<p>This dataset contains a snapshot of a fully kinetic particle-in-cell simulation of freely evolving plasma turbulence, as described in <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ac1c76" target="_blank" rel="noopener">Nättilä & Beloborodov (2021)</a>.</p> <p>This dataset was used in the analysis of <a href="https://arxiv.org/abs/2410.01878" target="_blank" rel="noopener">Ha et al. (2024)</a> and partially to develop <a href="https://github.com/tvh0021/aweSOM"><strong>aweSOM</strong></a>.</p> <p>See the section: "Example : Intermittency detection in decaying plasma turbulence simulation" in the documentation of <strong>aweSOM</strong> for instructions on how to use these datasets.</p>
Simulation results: Radiative cooling induced coherent maser emission in relativistic plasmas
<p>This repository contains some of the simulation data presented in the recent article titled <em>"Radiative cooling induced coherent maser emission in relativistic plasmas"</em> (<a href="https://arxiv.org/abs/2409.18955" target="_new" rel="noopener">https://arxiv.org/abs/2409.18955</a>). The data available are from 2D particle-in-cell (PIC) simulations, which investigate the effects of radiative cooling in relativistic plasmas and its role in inducing coherent maser emission. The simulations were performed using OSIRIS, a massively parallel and fully-relativistic PIC code.</p> <p>The electric field data in the third direction (E3) included here has been spatially averaged by a factor of 8 in both directions, resulting in a dataset that reflects a resolution 64 times lower than the actual simulation. Additionally, the raw data includes only one two-thousandth of the simulated electron macro-particles. Also included is the phase space data in the x2, p2, and p3 dimensions.</p> <p>These datasets represent key aspects of the simulation results discussed in the paper, where the focus is on understanding the interplay between radiative losses and coherent emission mechanisms.</p> <p>More details on the simulations and the analysis of these results can be found in the corresponding article.</p>
Data from: Plasma acceleration in a magnetic arch
<p><strong>Data from: Plasma acceleration in a magnetic arch</strong></p> <p>- Authors: Mario Merino, Diego García, Eduardo Ahedo</p> <p>- Contact emails: mario.merino@uc3m.es, dieggarc@ing.uc3m.es</p> <p>- Date: 2023-06-08</p> <p>- Keywords: electric propulsion, electrodeless plasma thruster, magnetic arch, plasma expansion</p> <p>- Version: 1.0.4</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.7919577</p> <p>- License: This dataset is made available under the [Open Data Commons Attribution License](http://opendatacommons.org/licenses/by/1.0/)</p> <p> </p> <p><strong>Abstract</strong></p> <p>This dataset contains the data found in the plots of the paper:</p> <p>Mario Merino, Diego García, Eduardo Ahedo, "Plasma acceleration in a magnetic arch"</p> <p>Published in the journal Plasma Sources Science and Technology</p> <p> </p> <p><strong>Dataset description</strong></p> <p>The data in this repository has been extracted from the fluid simulations as described in the article. (https://iopscience.iop.org/article/10.1088/1361-6595/acd476).</p> <p>For further information on the setup for the simulation please refer to the article.</p> <p> </p> <p><strong>Data files</strong></p> <p>The data files are in .csv format. They were produced in numpy using the numpy.savetxt() function and can be easily read with numpy.loadtxt() or in any other language with the apropiate reader for .csv files.</p> <p>The files are organised following the order of the figures in the article. Therefore each file contains a different sized array. In the following one can find a description of all the data contained in each of the files:</p> <p>- fig2.csv</p> <p> - Applied magnetic field 'Ba/Ba0'</p> <p>- fig3.csv</p> <p> - Thermalised potential 'He'</p> <p> - Electron out of plane velocity 'uye'</p> <p>- fig4.csv</p> <p> - Plasma density 'n'</p> <p> - Electron temperature 'Te'</p> <p> - Electric potential 'phi'</p> <p> - Ion in-plane velocity 'uitilde'</p> <p> - Ion Mach number 'Mi'</p> <p>- fig5.csv</p> <p> - In-plane electric current density 'jitilde'</p> <p>- fig6.csv</p> <p> - Radial magnetic force density 'jyBz'</p> <p> - Axial magnetic force density '-jyBx'</p> <p>- fig7.csv</p> <p> - Thrust integral, beta = 0.00 case 'F_F0_beta_0.00'</p> <p> - Thrust integral, beta = 0.02 case 'F_F0_beta_0.02'</p> <p> - Thrust integral, beta = 0.04 case 'F_F0_beta_0.04'</p> <p> - Thrust integral, beta = 0.08 case 'F_F0_beta_0.08' </p> <p>- fig8.csv</p> <p> - Normalised induced magnetic field strength 'Bp_beta0_Ba0'</p> <p>- fig9.csv</p> <p> - Total magnetic field, beta = 0.00 case 'B_beta_0.00'</p> <p> - Total magnetic field, beta = 0.02 case 'B_beta_0.02'</p> <p> - Total magnetic field, beta = 0.04 case 'B_beta_0.04'</p> <p> - Total magnetic field, beta = 0.08 case 'B_beta_0.08'</p> <p>All files contain a matrix of comma separated values with 400 rows. The number of columns depends on the specific file, for the files corresponding to two dimensional maps (all files except fig7.csv) the number of columns is a multiple of 400, where the first 400 columns correspond to the Z positions values and the following 400 the X position values. These two 400 by 400 matrices correspond to a meshgrid common in Matlab and NumPy. The following columns correspond to the values of each quantity in the positions given by the grid. For example, files containing only one field such as 'fig2.csv' have 400 rows and 1200 columns with columns 801 to 1200 corresponding to the values of the given field. As an example for files containing multiple fields let us take 'fig3.csv', this file contains 400 rows and 1600 columns where columns 801 to 1200 contain the values for 'He' and columns 1201 to 1600 contain 'uye'.</p> <p>The file 'fig7.csv' contains the data for a 1D plot with multiple lines. In this case the data is matrix with 400 rows and 5 columns where column 1 contains the z axis positions column 2 contains the values for 'F_F0_beta_0.00' column 3 contains 'F_F0_beta_0.02' and so on.</p> <p>All values are normalised as explained in the article.</p> <p> </p> <p><strong>Citation</strong></p> <p>Any works using this dataset or any part of it in any form shall cite it as follows:</p> <p>The prefered means of citation is to reference the publication as soon as it is available.</p> <p>The BibTex is also provided for the sake of convinience:</p> <p>@article{Merino_2023,</p> <p>doi = {10.1088/1361-6595/acd476},</p> <p>url = {https://dx.doi.org/10.1088/1361-6595/acd476},</p> <p>year = {2023},</p> <p>month = {jun},</p> <p>publisher = {IOP Publishing},</p> <p>volume = {32},</p> <p>number = {6},</p> <p>pages = {065005},</p> <p>author = {Mario Merino and Diego García-Lahuerta and Eduardo Ahedo},</p> <p>title = {Plasma acceleration in a magnetic arch},</p> <p>journal = {Plasma Sources Science and Technology},</p> <p>abstract = {}</p> <p>}</p> <p>Optionally the dataset can be cited by referencing the DOI: 10.5281/zenodo.7919577</p> <p><strong>Acknowledgments</strong></p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466).</p>
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of tobacco seedlings
<p>Mass spectrometry imaging (MSI) data set in imzML format, of complete tobacco (<em>Nicotiana tabacum</em>) seedling using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to nicotine shows accumulation in the roots and at the borders of leaves.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p>
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of San Pedro cactus
<p>Mass spectrometry imaging (MSI) data set in imzML format, obtained from San Pedro cactus (<em>Echinopsis pachanoi</em>) cross-section using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to mescaline shows a star-like distribution of this interesting alkaloid.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p> <p>DOI: 10.1021/acs.analchem.8b04406</p> <p> </p>
A case study on the origin of near-Earth plasma
<p>Summary of data files.</p> <p>This is the dataset for a paper "A case study on the origin of near-Earth Plasma"<br> submitted to JGR-space by Glocer et al</p> <p>The paper conducts multifluid MHD simulations of the magnetosphere by the<br> BATSRUS code, with solar wind input, ionospheric outflow by the PWOM code,<br> and ionospheric potential solver, and inner magnetospehre by the CIMI code.<br> Solutions with and without plasmasphere are considered.</p> <p>All plots in the paper are made with spacepy (specific fork:<br> https://github.com/aglocer/spacepy) or with tecplot</p> <p>The data is organized as follows</p> <p>Top directory:<br> imf.dat: Has the solar wind input as a simple time series. Can be read and<br> plotted with the "ImfInput" tool in spacepy</p> <p>tared Directories with "PS" and "noPS" tags have simulation<br> output with and without plasmasphere. Each has three subdirectories described<br> as follows:</p> <p>GM: Has the "Global Magnetosphere" output from BATSRUS for the images shown.<br> y=0*out are cuts in the y=0 GSM plane and the *log file is the log output<br> containing Dst. Both can be plotted with spacepy pybats. The 3d files are<br> used in a few images and are tecplot binary files and read and plotted with<br> tecplot.</p> <p>PW: Has the "Polar Wind" output from the PWOM code for images shown. The<br> *out files are time dependent binary output for each field line. They can be<br> read and plotted with the pybats.pwom tool in spacepy. North and South<br> indicte northern and southern hemisphere respectively</p> <p>IE: Has the "Ionosphere Electrodynamics" output from the potential solver for<br> plots shown. the *log files have the CPCP data as a function of time. They<br> can be read and plotted with pybats in spacepy</p> <p>IM: Has the "Inner Magnetosphere" output from the CIMI code. The CIMIeq.out<br> file has time dependent snapshots of the solution on the min B surface for<br> plots shown. The *log files have the total energy as a function of time for<br> each species (among other variables). Both files are read and plotted with<br> pybats and pybats.cimi code in spacepy.</p> <p> </p>
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