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978 results for “Instability”
Observations and Modeling of a Buoyant Plume Exiting into a Tidal Cross-flow and Exhibiting Along-front Instabilities
<p>This post-processed dataset contains the sUAS imagery and numerical modeling output used in the paper "Observations and Modeling of a Buoyant Plume Exiting into a Tidal Cross-flow and Exhibiting Along-front Instabilities". The provided code details the frontal processing routine used for both the observations and modeling results. </p>
Seagrass deformation affects fluid instability and tracer exchange in canopy flow
<p>Data and code used for the preparation of the manuscript "Seagrass deformation affects fluid instability and tracer exchange in canopy flow" (Vieira, Allshouse & Mahadevan 2022).</p> <p><em>Data and Code Repository Organization</em></p> <ul> <li><strong>data/ </strong>: contains the data presented in the manuscript (in .cdf and .mat format);</li> <li><strong>code/ </strong>: contains the code used for the numerical simulations (PSOM) and in processing the data and generating figures (MATLAB)</li> </ul> <p><em>Manuscript Abstract:</em></p> <p>Monami is the synchronous waving of a submerged seagrass bed in response to unidirectional fluid flow. Here we develop a multiphase model for the dynamical instabilities and flow-driven collective motions of buoyant, deformable seagrass. We show that the impedance to flow due to the seagrass results in an unstable velocity shear layer at the canopy interface, leading to a periodic array of vortices that propagate downstream. Each passing vortex locally weakens the along-stream velocity at the canopy top, reducing the drag and allowing the deformed grass to straighten up just beneath it. This causes the grass to oscillate periodically. Crucially, the maximal grass deflection is out of phase with the vortices. A phase diagram for the onset of instability shows its dependence on the fluid Reynolds number and an effective buoyancy parameter. Less buoyant grass is more easily deformed by the flow and forms a weaker shear layer, with smaller vortices and less material exchange across the canopy top. While higher Reynolds number leads to stronger vortices and larger waving amplitudes of the seagrass, waving is maximized at intermediate grass buoyancy. All together, our theory and computations correct some misconceptions in interpretation of the mechanism and provide a robust explanation consistent with a number of experimental observations.</p>
Network structural origin of instabilities in large complex systems
<p>Raw data used to generate figures in the following publication:</p> <p>Title: "Network structural origin of instabilities in large complex systems"<br> Authors: Chao Duan, Takashi Nishikawa, Deniz Eroglu, Adilson E. Motter<br> Journal: <a href="https://doi.org/10.1126/sciadv.abm8310">Science Advances 8, eabm8310 (2022)</a></p> <p>The CSV files are named by the corresponding figure numbers and the quantities (e.g., "Fig1A_data.csv" for data for Fig. 1A and "FigS1A_data_adj_mat.csv" for the adjacency matrix data for Fig. S1A).<br> </p>
Dataset: Code Comparison for Fast Flavor Instability Simulation
<p>This is the data provided in the article "Code Comparison for Fast Flavor Instability Simulation". The python scripts produce all of the plots present in the publication from the *.txt data files. The filenames of the *.txt files indicate the author of the dataset and the figure that the data/script produces. The meaning of each column of data is described in README.md. README.md also contains the git repository locations and commit hashes for the open-source codes used to produce this data.</p> <p>Figure 1: Psur-t</p> <p>Figure 2: P-u-t5000</p> <p>Figure 3: Sfft-k-t0 and Sfft-k-t5000</p> <p>Figure 4: deltaELN-t</p> <p>Figure 5: deltaP-t</p> <p> </p>
Raw data for: Dynamic instability of dendrite tips generates the highly branched morphologies of sensory neurons
<p>The highly ramified arbors of neuronal dendrites provide the substrate for the high connectivity and computational power of the brain. Altered dendritic morphology is associated with neuronal diseases. Many molecules have been shown to play crucial roles in shaping and maintaining dendrite morphology. Yet, the underlying principles by which molecular interactions generate branched morphologies are not understood. To elucidate these principles, we visualized the growth of dendrites throughout larval development of Drosophila sensory neurons and discovered that the tips of dendrites undergo dynamic instability, transitioning rapidly and stochastically between growing, shrinking, and paused states. By incorporating these measured dynamics into a novel, agent-based computational model, we showed that the complex and highly variable dendritic morphologies of these cells are a consequence of the stochastic dynamics of their dendrite tips. These principles may generalize to branching of other neuronal cell types, as well as to branching at the subcellular and tissue levels.</p>
Ocean-forced instability of the West Antarctic Ice Sheet since the mid-Pleistocene
<p>This data file contains the IRD abundance, clay mineralogy, water content in Excel file 'Wang et al.xlsx (sheet: sedimentology)', and Sr-Nd isotopes in Excel file 'Wang et al.xlsx (sheet: Sr-Nd isotope)' in gravity core ANT34/A2-10 (LATITUDE: -67.036111 and LONGITUDE: -125.591944) from the Amundsen abyssal plain since 770 ka.<br>Supplement to Jiakai Wang et al., Ocean-forced instability of the West Antarctic Ice Sheet since the mid-Pleistocene. Geochemistry Geophysics Geosystems (in review).<br> </p>
Climate velocity and land-use instability
<p>Climate and land-use dataset to support "Climate and land-use risk assessment for Earth's remaining wilderness". Dataset are in Mollweide equal area projections and 24-km resolution, for baseline (1971-2005) and projected (2016-2050) periods. Temperature and precipitation velocities is based on CORDEX climate data averaged across three GCMs (including, MOHC-HadGEM2-ES, MPI-M-MPI-ESM-LR and NCC-NorESM1-M). Land-use velocity is based on land-use harmonisation dataset (LUH2 v2h and LUH2 v2f).</p>
Supporting Data for Electrolyte-Induced Instability of Colloidal Dispersions in Nonpolar Solvents (J. Phys. Chem. Lett., doi:10.1021/acs.jpclett.7b01685)
<p>Raw data: interaction force curves (separation [m], force [N], error force [N]) and small-angle neutron scattering curves (Q [1/Å], I(Q) [1/cm], error I(Q) [1/cm]).</p>
The history of chromosomal instability in genome doubled tumors : Data release
<p>Released data for '<em>The history of chromosomal instability in genome doubled tumors</em>'.</p>
Dataset for "Drift instabilities in thin current sheets using a two fluid model with pressure tensor effects"
<p>Data for publication of the same title submitted to JGR space physics. Quantities and plotting scripts for the eigenmode figures are in the zip file. Simulation data for the time slice used in the figure are in the lhdi.tar and lhdi-fluid-2x2v.h5 files. "lhdi-fluid-2x2v.h5" contains electric field data for the five- and local ten-moment fluid simulations in 2x2v. The tar file contains kinetic simulation data, nonlocal ten-moment data, and the five- and ten-moment simulations using 2x3v. </p> <p>This is an update to the old dataset with the additional 2x2v simulation data.</p>
Effects of Rotation on the Minimum Mass of Primordial Progenitors of Pair-instability Supernovae
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2012ApJ...748...42C/abstract">Chatzopoulos & Wheeler (2012)</a>. MESA version 3647.</p> <p>Publication DOI: <a href="https://doi.org/10.1088/0004-637X/748/1/42">10.1088/0004-637X/748/1/42</a></p>
Episodic accretion: the interplay of infall and disc instabilities
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.475.2642K/abstract">Kuffmeier et al. (2018)</a>. MESA version 8845.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/sty024">10.1093/mnras/sty024</a></p>
Inlists for paper: Massive Stellar Mergers as Precursors of Hydrogen-rich Pulsational Pair Instability Supernovae
<p>MESA (v10108) inlists for the models shown the "Massive Stellar Mergers as Precursors of Hydrogen-rich Pulsational Pair Instability Supernovae" Letter (1903.02135). The inlists reproduce both the single and merger model.</p> <p>Contents:</p> <p>FiducialMergersPPISN.tar.gz</p> <ul> <li>doubleMass</li> <li>evolveMerged</li> <li>evolveSingle</li> <li>preMSmodel</li> <li>README</li> </ul> <p>We also attach the single and merger MESA models at the moment where the central temperature is close to 10^9 K. These models where used to estimate the properties of the star, particularly core and envelope masses, as well as element abundances.</p> <ul> <li>mergerAtLogTc9.data</li> <li>singleAtLogTc9.data</li> </ul>
Fundamental Limits From Chaos On Instability Time Predictions In Compact Planetary Systems
<p>REBOUND SimulationArchives analysed in Hussain & Tamayo (2019) Fundamental Limits From Chaos On Instability Time Predictions In Compact Planetary Systems. Dataset consists of a set of compact planetary configurations, and large ensembles of shadow trajectories from N-body simulations with initial conditions perturbed near machine precision to investigate the spread in instability times. Instructions and scripts for extracting the data and generating the plots in the paper can be found at https://github.com/Naireen/StabilitySetImage</p>
Mind the gap: The location of the lower edge of the pair instability supernovae black hole mass gap
<p>Inlists and run_star_extras.f90 used for the paper:</p> <p>"Mind the gap: The location of the pair instability supernovae black hole mass gap"</p> <p>datafile1.txt contains a machine readable table for all parameters varied in the paper.</p> <p>This work was done with MESA version 11123 and with the MESASDK version 20180822.</p> <p> </p>
BPASS pair-instability supernova tagging
<p>These data contain the tagging of individual models from the BPASS population for different pair-instability supernova prescriptions. The data is stored as a pandas DataFrame with the identifier: `rates`.</p> <pre><code>df = pd.read_hdf(f'22_3_2024_{MET}.h5', 'rates')</code></pre> <p>The following data columns are available:</p> <ul> <li>filenames: the BPASS model name. Note: some merger models in the v2.2 release failed before reaching/avoiding the PISN regime. These have been rerun and often do not lead to a PISN.</li> <li>model_imf: the weight of the model; directly from BPASS</li> <li>types: the type of model (merger, primary, secondary, single, effectively single)</li> <li>mixed_imf: the weight of the model for secondaries; directly from BPASS</li> <li>mixed_age: the start age of a secondary model due to rejuvenation; directly from BPASS</li> <li>total_mass: the total mass of the PISN progenitor model at the end of the model</li> <li>helium_mass: the total helium core mass of the PISN progenitor model at the end of the model</li> <li>co_mass: the total carbon-oxygen core mass of the PISN progenitor model at the end of the model</li> </ul> <p>The remaining columns are "booleans" for the different PISN prescriptions:</p> <ul> <li>standard: The fiducial model used in Briel et al. (2023)</li> <li>old_bpass: The original BPASS tagging used in Briel et al. (2022)</li> <li>CO_only: Tagging with only the CO core >= 60 Msun as a limit</li> <li>merchant: PISN limits from Marchant et al. (2019)</li> <li>noH: standard tagging but no hydrogen present.</li> <li> ['0', '1', '2', '3', '4', '5']: different formation channels, shifted one up from the BPASS model identification</li> <li>['R150', 'R175', 'R200', 'R225', 'R250', 'He70', 'He80', 'He90', 'He100', 'He110', 'He120', 'He130']: tagging based on each light curve.</li> </ul> <p>This can be used to get the metallicity bias functions (number of events per Msun over metallicity) or to create new taggings.</p> <p>For example, below we use both the existing tagging with `model_imf` weights to get the rate of PISN at each metallicity and we create a new tagging `shifted_up`, which is a tagging where the PISN limit is shifted upwards.</p> <p> </p> <pre><code>import pandas as pd import numpy as np from hoki.constants import BPASS_METALLICITIES # from Kaasen et al. 2008 paper envelopes = np.array([70.9, 79.4, 84.4, 96.8, 112.3, 0, 0, 0, 0, 0, 0, 0]) helium_cores = np.array([72.0, 84.4, 96.7, 103.5, 124.0, 70, 80, 90, 100, 110, 120, 130]) curve_models = [ 'R150', 'R175', 'R200', 'R225', 'R250', 'He70', 'He80', 'He90', 'He100', 'He110', 'He120', 'He130'] bias_functions = pd.DataFrame({'standard':np.zeros(13), 'standard_old':np.zeros(13), 'CO_only':np.zeros(13), 'marchant':np.zeros(13), 'shift_up':np.zeros(13), 'old_BPASS':np.zeros(13), 'He_only':np.zeros(13), 'primary':np.zeros(13), 'secondary':np.zeros(13), 'QHE':np.zeros(13), 'merger':np.zeros(13), 'single':np.zeros(13), 'R150':np.zeros(13), 'R175':np.zeros(13), 'R200':np.zeros(13), 'R225':np.zeros(13), 'R250':np.zeros(13), 'He70':np.zeros(13), 'He80':np.zeros(13), 'He90':np.zeros(13), 'He100':np.zeros(13), 'He110':np.zeros(13), 'He120':np.zeros(13), 'He130':np.zeros(13)}) bias_functions.index = BPASS_METALLICITIES for MET in BPASS_METALLICITIES[:-4]: print(MET) df = pd.read_hdf(f'PISN_data_{MET}.h5', 'rates') for i in curve_models: mask = (df['co_mass'] >=60) & (df['helium_mass'] < 133) & (df[i] == 1) bias_functions.loc[MET, i] = np.sum(df[mask]['model_imf'])/1e6 mask = (df['co_mass'] >=60) & (df['helium_mass'] < 133) bias_functions.loc[MET, 'standard'] = np.sum(df[mask]['model_imf'])/1e6 bias_functions.loc[MET, 'standard_old'] = np.sum(df[df['standard'] == 1]['model_imf'])/1e6 mask = (df['helium_mass'] >=64) & (df['helium_mass'] < 133) bias_functions.loc[MET, 'old_BPASS'] = np.sum(df[mask]['model_imf'][mask])/1e6 mask = (df['co_mass'] >= 60) bias_functions.loc[MET, 'CO_only'] = np.sum(df[df['CO_only'] == 1]['model_imf'][mask])/1e6 mask = (df['helium_mass'] >=60.8) & (df['helium_mass'] < 124) bias_functions.loc[MET, 'marchant'] = np.sum(df[df['marchant'] == 1]['model_imf'][mask])/1e6 bias_functions.loc[MET, 'noH'] = np.sum(df[df['noH'] == 1]['model_imf'])/1e6 mask = (df['helium_mass'] >=90) & (df['helium_mass'] < 180) bias_functions.loc[MET, 'shift_up'] = np.sum(df[mask]['model_imf'])/1e6 mask = np.isclose(df['total_mass'] - df['helium_mass'], 0, atol=0.1) & (df['helium_mass'] < 133) & (df['co_mass'] >=60) bias_functions.loc[MET, 'He_only'] = np.sum(df[mask]['model_imf'])/1e6 mask = (df['co_mass'] >=60) & (df['helium_mass'] < 133) bias_functions.loc[MET, 'merger'] = np.sum(df[(df['types'] == 0) & mask]['model_imf'])/1e6 bias_functions.loc[MET, 'single'] = np.sum(df[((df['types'] == -1) | (df['types'] == 3)) & mask]['model_imf'])/1e6 bias_functions.loc[MET, 'primary'] = np.sum(df[(df['types'] == 1) & mask]['model_imf'])/1e6 bias_functions.loc[MET, 'secondary'] = np.sum(df[(df['types'] == 2) & mask]['model_imf'])/1e6 bias_functions.loc[MET, 'QHE'] = np.sum(df[(df['types'] == 4) & mask]['model_imf'])/1e6</code></pre>
Sway frequencies may predict postural instability in Parkinson's disease: Data
<p>Dataset with raw Center of Pressure (COP) and Center of Mass (COM) time series along with respective wavelet spectrograms.</p> <p>Recorded during 30 seconds of quiet stance. 10 trials per participant. Sampled at 50 Hz.</p> <p>18 individuals with Parkinson's disease, 15 healthy controls.</p> <p>Detailed data description can be found in the word file.</p> <p> </p>
Air–Sea Coupling Feedbacks over Tropical Instability Waves
<p>This dataset contains a selection of the CROCO-WRF regional coupled model simulation output created and published in the article:</p> <p>Holmes, R., Renault, L., Maillard, L. and Boucharel, J. (2024) Air-sea coupling feedbacks over Tropical Instability Waves, J. Phys. Oceanogr., https://doi.org/10.1175/JPO-D-24-0010.1</p> <p>This dataset contains monthly-averaged output of several key surface variables analysed in the above article. These variables are provided for each of the 5 ensemble members of each of the 4 experiments: 1) the Control, 2) the NoMesoTFB experiment, 3) the NoMesoCFB experiment and 4) the NoCFB experiment. Each of the included tar archives contains all the variables for that experiment. For each experiment, this consists of the following files:</p> <p>croco_out_mon_exp**.ncrcat.nc: 5 files, one for each ensemble member, containing monthly mean SST (temp), zonal velocity (u), meridional velocity (v) and sea level (zeta).</p> <p>croco_out_mon_hp_exp**.ncrcat.nc: 5 files, one for each ensemble member, containing monthly mean high-pass (using the longitude filter described in the article) variances of SST (SST_hp_var), zonal velocity (U_hp_var), meridional velocity (V_hp_var), eddy wind work zonal component (high-pass(u)*high-pass(tau_x), EWWU) and eddy wind work meridional component (high-pass(v)*high-pass(tau_y), EWWV). </p> <p>wrf3d_1M_hp_exp**.ncrcat.nc: 5 files, one for each ensemble member, containing monthly mean high-pass (net surface heat flux) * high-pass (SST), computed on the WRF grid using the longitude filter described in the article [QofSST_hp]. This can be used to compute the APE production by surface heat flux anomalies.</p> <p>This is only a subset of the data used in the article, the largest subset of useful variables that fits within the 50GB Zenodo limit. If other variables are of interest, please contact the lead author.</p> <p>An extensive set of analysis scripts is available on github at https://github.com/rmholmes/PAC12_75_cpl-analysis.</p>
Video from: Two-dimensional hybrid model of gradient drift instability and enhanced electron transport in a Hall thruster
<p>The video data for Fig. 3 in "Two-dimensional hybrid model of gradient drift instability and enhanced electron transport in a Hall thruster."</p>
Data for Seasonal Modulation of Dissolved Oxygen in the Equatorial Pacific by Tropical Instability Vortices
<p>This repository contains data used for the Analysis of "Seasonal Modulation of Dissolved Oxygen in the Equatorial Pacific by Tropical Instability Vortices" By Eddebbar et al. Submitted to JGR Oceans.</p> <p>Code used for the analysis of CESM-HR and CESM-LR model outputs and running and analyzing the particle tracking simulations are available at Zenodo on https://zenodo.org/record/5266337</p> <p>This repository contains the following items: </p> <ol> <li> Observational datasets used for model validation Figure 1 and 2 (obs.tar.gz) </li> <li>Low Resolution CESM outputs used for model resolution comparison Figure 1-3 (CESM_LR.tar.gz)</li> <li>High resolution CESM climatological output (CESM_HR_CLM.tar.gz and CESM_HR_CLM_all.tar.gz)</li> <li>Physical and BGC outputs from high resolution CESM from snapshot for Oct, 03, 0005 (CESM-HR_BGC.0005-10-03.tar.gz and CESM_HR_0005_10_03.tar.gz) used in Fig 3, 4, 5, 6, and 9 and Supp Fig 3.</li> <li>Outputs for year 5 of simulation for select variables from CESM-HR (CESM_HR_0005.tar.gz)</li> <li>Model Grid file: CESM_HR_grid.tar.gz</li> <li>Output for month 8, 9, 10, and 11 of year 5 of CESM-HR simulation used in figure 10, 11, and 12 for select variables including: CESM_HR_HMXL_08_09.tar.gz, CESM_HR_HMXL_10_11.tar.gz, CESM_HR_O2_08_09.tar.gz, CESM_HR_O2_10_11.tar.gz, CESM_HR_PD_08_09.tar.gz, CESM_HR_PD_10_11.tar.gz, CESM_HR_SSH_08_09.tar.gz, CESM_HR_SSH_10_11.tar.gz, CESM_HR_STF_O2_10_11.tar.gz, CESM_HR_TEMP_08_09.tar.gz, CESM_HR_TEMP_10_11.tar.gz, CESM_HR_UVEL_08_09.tar.gz)</li> <li>Near surface (15m) meridional and zonal velocity for the full CESM-HR simulation (CESM_HR_UV.tar.gz) for Fig 7</li> <li>Integrated O2 budget terms for CESM-HR (CESM_HR_O2_budget.tar.gz) used in figure 8</li> <li>Monthly mean O2 and PD (CESM_HR_O2_mon.tar.gz and CESM_HR_PD_mon.tar.gz) for Fig 7</li> <li>Particle trajectories for forward and backward parcels simulations used in fig 10-12 (particle_tracking_bwd_.tar.gz and particle_tracking_fwd_.tar.gz)</li> </ol>
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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.