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978 results for “Instability”
Nebular spectra of pair-instability supernovae
<p>Spectral models of 70, 100, 130 Msun He cores at 400-1000d. Distance 10 Mpc assumed.</p>
Dataset for research paper "An Impact-Driven Approach to Predict User Stories Instability"
<p>Dump of all the data from an SQL server containing all the data used for the research in the research paper "An Impact-Driven Approach to Predict User Stories Instability", which is currently under review for Requirements Engineering journal. </p>
Light Curves and Event Rates of Axion Instability Supernovae
<p>These files are MESA (Modules for Experiments in Stellar Astrophysics) inlists that can reproduce results in Mori et al. (2022) (https://arxiv.org/abs/2209.03517). MESA version 12778 and MESA SDK version x86_64-linux-20.3.2 are used. Before running the code, the user should edit MESA following the instruction in Sakstein, Croon & McDermott (https://doi.org/10.5281/zenodo.6347632).</p> <p>PISN.tar.gz includes inlists for standard pair-instability supernovae while AISN_0.5.tar.gz and AISN_2.0.tar.gz are for axion instability supernovae with f=0.5 (i.e. ALP mass = 511 keV) and f=2.0 (i.e. ALP mass = 511*4 keV), respectively. Each directory consists of directories like "heavy_new_particle_100". The three digit number indicates the initial stellar mass.</p> <p>The users are encouraged to cite the following papers when they write a paper using these inlists.</p> <ul> <li>Mori et al. (2022), arXiv:2209.03517</li> <li>Sakstein, Croon & McDermott (2022) Phys. Rev. D <strong>105</strong>, 095038</li> <li>Croon, McDermott & Sakstein (2020) Phys. Rev. D <strong>102</strong>, 115024</li> <li>MESA instrumental papers</li> </ul>
Data for "Density staircases generated by symmetric instability in a cross-equatorial deep western boundary current"
<p>Data associated with the git repository <a href="https://GitHub.com/fraserwg/dwbc-proj">dwbc-proj</a>.</p>
Idealized simulations of marine ice sheet instability
<p>Ensemble of idealized simulations of the unstable retreat of an outlet glacier with the Parallel Ice Sheet Model (PISM). Varied parameters are the width, length and depth of the glacier, the softness of the ice and the basal friction.</p>
Gyrokinetic linear instabilities and quasilinear fluxes for variations of ITER tokamak baseline parameters
<p>Linear instability and quasilinear fluxes calculated with the <a href="https://genecode.org">GENE</a> plasma microturbulence code. The input parameters correspond to variations of ITER baseline scenario parameters calculated by integrated modelling using the <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/-/wikis/home">QuaLiKiz</a> transport model, as described in <a href="https://iopscience.iop.org/article/10.1088/1361-6587/ab5ae1">P. Mantica et al (2019) Plasma Physics and Controlled Fusion 62 014021</a>. <br> <br> The quasilinear fluxes were calculated with a bespoke saturation rule calibrated to dedicated GENE nonlinear simulations carried out in the same ITER regime. The quasilinear flux dataset was fit with a neural network (NN) regression model, which was then used for ITER baseline integrated modelling and performance projections. Alongside the linear stability dataset, the two separate quasilinear datasets correspond to an unfiltered dataset, and a filtered and data-augmented dataset used for the NN regression. For full details, please see reference [J. Citrin et al (2023) <em>submitted to Physics of Plasmas</em>]. <br> <br> The dimensionless input and output variables correspond to the IMAS gyrokinetic IDS standards. The major radius was taken as the reference length. A key and further details are found below.</p> <table> <caption><strong>Description of CSV file columns</strong></caption> <tbody> <tr> <td>rhoN</td> <td>Normalized toroidal flux coordinate</td> </tr> <tr> <td>ky</td> <td>Binormal wavenumber, normalized to the reference (ion scale) gyroradius</td> </tr> <tr> <td>omt_DT</td> <td>Normalized logarithmic main ion temperature gradient (<span class="math-tex">\(R/L_{Ti}\)</span>)</td> </tr> <tr> <td>omt_el</td> <td>Normalized logarithmic electron temperature gradient (<span class="math-tex">\(R/L_{Te}\)</span>)</td> </tr> <tr> <td>omn_el</td> <td>Normalized logarithmic electron density gradient (<span class="math-tex">\(R/L_{ne}\)</span>)</td> </tr> <tr> <td>s</td> <td>Magnetic shear</td> </tr> <tr> <td>q</td> <td>Safety factor (q-profile)</td> </tr> <tr> <td>gamma</td> <td>Instability growth rate (gyroBohm normalisation with IMAS standard)</td> </tr> <tr> <td>omega</td> <td>Instability frequency (gyroBohm normalisation). Positive frequencies correspond to the ion diamagnetic direction</td> </tr> <tr> <td>kperp2</td> <td>Square of perpendicular wavenumber weighted over poloidal mode structure <span class="math-tex">\(\langle{k_\perp^2}\rangle\)</span></td> </tr> <tr> <td>Q_DT</td> <td>ky-dependent ion heat flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>Q_el</td> <td>ky-dependent electron heat flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>G_el</td> <td>ky-dependent electron particle flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>QDT</td> <td>Quasilinear ion heat flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>Qe</td> <td>Quasilinear electron heat flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>Ge</td> <td>Quasilinear electron particle flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>QDT_ITG</td> <td>Quasilinear ion heat flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Qe_ITG</td> <td>Quasilinear electron heat flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Ge_ITG</td> <td>Quasilinear electron particle flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>QDT_TEM</td> <td>Quasilinear ion heat flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Qe_TEM</td> <td>Quasilinear electron heat flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Ge_TEM</td> <td>Quasilinear electron particle flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> </tbody> </table> <p> </p>
Pain et al. 2020 Behavioral and Physiological Responses to Instability in Group Membership in Wild Male Woolly Monkeys (Lagothrix lagotricha poeppigii)
<p>Data for Pain et al. 2020 Behavioral and Physiological Responses to Instability in Group Membership in Wild Male Woolly Monkeys (Lagothrix lagotricha poeppigii)</p>
Statistical evaluation of character support reveals the instability of higher-level dinosaur phylogeny
<p>The interrelationships of the three major dinosaur clades (Theropoda, Sauropodomorpha, and Ornithischia) have come under increased scrutiny following the recovery of conflicting phylogenies by a large new character matrix and its extensively modified revision. Here, we use tools derived from recent phylogenomic studies to investigate the strength and causes of this conflict. Using maximum likelihood as an overarching framework, we examine the global support for alternative hypotheses as well as the distribution of phylogenetic signal among individual characters in both the original and rescored dataset. We find the three possible ways of resolving the relationships among the main dinosaur lineages (Saurischia, Ornithischiformes, and Ornithoscelida) to be statistically indistinguishable and supported by nearly equal numbers of characters in both matrices. While the changes made to the revised matrix increased the mean phylogenetic signal of individual characters, this amplified rather than reduced their conflict, resulting in greater sensitivity to character removal or coding changes and little overall improvement in the ability to discriminate between alternative topologies. We conclude that early dinosaur relationships are unlikely to be resolved without fundamental changes to both the quality of available datasets and the techniques used to analyze them.</p>
Data and code for "Competitive hierarchies in bryozoan assemblages mitigate network instability by keeping short and long feedback loops weak"
<p>This repository contains all scripts and data files to reproduce the analysis of the manuscript "Competitive hierarchies in bryozoan assemblages mitigate network instability by keeping short and long feedback loops weak"</p> <p><strong>Abstract</strong></p> <p>Competitive hierarchies in diverse ecological communities have long been thought to lead to instability and prevent coexistence. However, system stability has never been tested and the relation between hierarchy and instability has never been explained in complex competition networks parameterised with data from direct observation. Here we test model stability of 30 multispecies bryozoan assemblages, using estimates of energy loss from observed interference competition to parameterise both the inter- and intraspecific interactions in the competition networks. We find that all competition networks are unstable. However, instability is mitigated considerably by asymmetries in the energy loss rates brought about by hierarchies of strong and weak competitors. This asymmetric organisation results in asymmetries in the interaction strengths, which reduces instability by keeping the weight of short (positive) and longer (positive and negative) feedback loops low. Our results support the idea that interference competition leads to instability and exclusion but demonstrate that this is not because of, but despite, competitive hierarchy.</p> <p><strong>Data</strong></p> <p>Our data set contains records of overgrowth competition in 30 high-latitude bryozoan assemblages. Rocks were collected by hand from shallow subtidal coastal locations at Rothera Island, West Antarctic Peninsula, Signy Island in the maritime Antarctic and Spitsbergen in the Arctic. For each assemblage, the data set contains one .csv file with abundance per species and one .csv file containing the species-contact-matrix. All bryozoans were identified to species and counted, giving abundance data in colonies per species. Then, all pairwise contests between colonies were classified as win, draw or loss and the results were compiled in the species-contact-matrices. For details, see the methods section of the paper.</p> <p><strong>Analysis </strong></p> <p>The analysis is subdivided into the following sections:</p> <ul> <li>0 <strong>Random matrices</strong>: Stability of random matrices with symmetric and asymmetric interactions.</li> <li>1 <strong>Preparation</strong>: Define functions to calculate asymmetry measures and set plotting parameters</li> <li>2 <strong>Read and process raw data</strong>: Converts raw data to Jacobian matrices</li> <li>3 <strong>Analysis of empirical matrices</strong>: Calculates stability, asymmetry measures, loop weights of empirical matrices.</li> <li>4 <strong>Analysis of randomised matrices:</strong> Randomises empirical matrices and analyses the effect on stability, asymmetry measures and loop weights.</li> <li>5<strong> Sensitivity</strong>: Effect of model assumptions (cost-values / replacement of missing values) on the results.</li> </ul> <p>Details on how to reproduce the full analysis, including all figures and tables in the manuscript can be found in the ReadMe file.</p>
processed single-cell data from "Cancer cell non-autonomous tumor progression from chromosomal instability"
<p>The h5ad files can be used as processed scRNA-seq input to the ContactTracing code (https://zenodo.org/badge/latestdoi/625036312).</p> <p>There is one file for the highCIN/lowCIN comparison, and another for the highCIN/noSTING comparison.</p>
The electromagnetic ion cyclotron instability affected by the temperature anisotropic electrons in the inner magnetosphere
<p>Data for the paper of "The electromagnetic ion cyclotron instability affected by the temperature anisotropic electrons in the inner magnetosphere".</p>
Data from: Barotropic instability during eyewall replacement
<div> <div>Prior to landfall in Puerto Rico, Hurricane Maria (2017) underwent an eyewall replacement cycle. The National Oceanic and Atmospheric Administration (NOAA) San Juan (TJUA) radar captured a robust outer convective ring with an inner ring first distorted into an ellipse and then disintegrated. To understand the dynamical processes during eyewall replacement, this work interprets the eyewall replacement event using the non-divergent barotopic model with a linear stability analysis and non-linear numerical simulations. For the linear stability analysis, the model's axisymmetric basic state vorticity distribution is piece-wise uniform in five regions: eye, inner eyewall, moat, outer eyewall, and far field. The stability of such structures is investigated by solving a simple eigenvalue/eigenvector problem. For the non-linear model, the evolution into a more stable structure is simulated using the non-linear barotropic model. Three types of instability and vorticity rearrangement are identified: (1) instability across the outer ring of enhanced vorticity, (2) instability across the low vorticity moat, and (3) instability across the inner ring of enhanced vorticity. This dataset includes (1) a loop of the NOAA TJUA radar during the landfall of Hurricane Maria (2017) in GIF format to show the convective evolution, (2) the output from the five-region linear stability analysis in NetCDF format, and (3) the output from the non-divergent barotropic model in NetCDF format and GIF format to show the vortex evolution. These data are provided without restrictions for further exploration into understanding barotropic instability during eyewall replacement.</div> </div>
Data for: Ecosystem connectivity and configuration can mediate instability at a distance in metaecosystems
<ol> <li><span>Ecosystems are connected by flows of nutrients and organisms. Changes to connectivity and nutrient enrichment may destabilise ecosystem dynamics far from the nutrient source.</span></li> <li><span>We used gradostats to examine the effects of trophic connectivity (movement of consumers and producers) versus nutrient-only connectivity on the dynamics of <em>Daphnia</em> <em>pulex</em> (consumers) and algae (resources) in two metaecosystem configurations (linear vs. dendritic). </span></li> <li><span>We found that <em>Daphnia</em> peak population size and instability (coefficient of variation; CV) increased as distance from the nutrient input increased, but these effects were lower in metaecosystems connected by all trophic levels compared to nutrient-only connected systems and/or in dendritic compared to linear systems. </span></li> <li><span>We examined the effects of trophic connectivity (i.e. both trophic levels move rather than one or the other) using a generic model to qualitatively assess whether the expectations align with the ecosystem dynamics we observed. </span></li> <li><span>Analysis of our model shows that increased <em>Daphnia</em> population sizes and fluctuations in consumer-resource dynamics are expected with nutrient connectivity, with this pattern being more pronounced in linear rather than dendritic systems. </span></li> <li><span>These results confirm that connectivity may propagate and even amplify instability over a metaecosystem to communities distant from the source disturbance, and suggest a direction for future experiments, that recreate conditions closer to those found in natural systems.</span></li> </ol>
Dataset for E. Grohs et al., Neutrino fast flavor instability in three dimensions for a neutron star merger, Physics Letters B, https://doi.org/10.1016/j.physletb.2023.138210
<p>.tgz file with hdf5 files for simulation data of a neutron star merger with neutrino flavor transformation. .h5 files are same information in plots 2 and 4 of https://doi.org/10.1016/j.physletb.2023.138210</p>
Submesoscaledynamics in the Bay of Bengal: Inversions and instabilities
<p>High-resolution observations reveal the complex processes controlling the evolution and subduction of a cold and salty, dense filament in the Bay of Bengal. The filament, likely formed through coastal upwelling, was advected offshore by a mesoscale strain field and brought adjacent to fresher water from runoff and rain. The front on an edge of the dense filament is observed to undergo restratification and steepening, responding to evolving mesoscale and submesoscale convergence and divergences. Measurements and analyses indicate the development of both small-scale instabilities (such as SI) and slightly larger-scale ageostrophic secondary circulation, acting in concert to subduct and stir surface heat into the interior. Our results highlight the importance of small-scale three-dimensional dynamics in setting upper ocean properties in the Bay of Bengal.</p>
Torsion of the Tibial Tuberosity, a New Factor of Patellar Instability?
ClinicalTrials.gov study NCT03304119. IPD Sharing: NO. Countries: 1. Publications: 6.
M7824 in Patients With Metastatic Colorectal Cancer or With Advanced Solid Tumors With Microsatellite Instability
ClinicalTrials.gov study NCT03436563. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of Pembrolizumab Following Surgery in Patients With Microsatellite Instability High (MSI-H) Solid Tumors
ClinicalTrials.gov study NCT03832569. IPD Sharing: YES. Countries: 1. Publications: 2.
Effect Of Thoracic Mobility Versus Lumbopelvic Stabilization Exercises On Patients With Chronic Ankle Instability
ClinicalTrials.gov study NCT06020131. IPD Sharing: YES. Countries: 1. Publications: 29.
The Impact of Real-World Vibration Feedback Gait Retraining on Gait Biomechanics in People With Chronic Ankle Instability
ClinicalTrials.gov study NCT05327244. IPD Sharing: YES. Countries: 1. Publications: 12.
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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.