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978 results for “Instability”
Somatic copy number and structural variation in RPE-1 cells with induced chromosomal instability
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<i>MSH3</i> is a genetic modifier of somatic repeat instability in X-linked dystonia parkinsonism
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Temporal instability of lake charr phenotypes: Synchronicity of growth rates and morphology linked to environmental variables?
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Cosmic Rates of Black Hole Mergers and Pair-Instability Supernovae from Chemically Homogeneous Binary Evolution
<p>Data products and files to reproduce the results from du Buisson et al. (2020).</p> <p><strong>MESA Calculations</strong></p> <p>This work included the calculation of large grids of binary models with the MESA code version 11701. The template used for these calculations is included (template.tar.xz), to model any specific system the files inlist_extra and inlist_extra_sj must be updated with the necessary initial parameters.</p> <p>The resulting data from all simulations is also included in the files named Z*.tar.xz, where the "*" should be replaced for the specific value of log10(Z) for that set of simulations. Data is included in folders with a naming format of logM1_massratio_periodindays, so for instance the folder named 2.100_1.000_2.800 contains a simulation performed for a primary of mass 10^(2.1) Msun, a mass ratio of unity, and an initial orbital period of 2.8 days (note that all simulations are done for a mass ratio of unity). Each folder is a full MESA work directory, including the necessary input files and source code to rerun the simulation. The data files included in each folder are:</p> <ol> <li>LOGS1/history.data.s: A MESA history file containing information of the primary every 5 steps of the simulation. This file has been processed from the original MESA output to reduce its size.</li> <li>binary_history.data.s: Similar to the previous one but containing information of the binary system itself</li> <li>last_profile_1.data.s: MESA profile file containing the interior structure of the star at the end of the simulation. These files are only stored for systems that deplete central carbon, or become pair unstable. This file has also been post-processed to reduce its size, mostly by removing redundant columns and reducing the reported precision.</li> <li>out.txt.s: The last 100 lines of the terminal output from the simulation, useful to quickly glance the outcome.</li> </ol> <p><strong>Summary data tables for the MESA simulations</strong></p> <p>Data tables summarizing the outcome of all simulations are included in the file data_tables.tar.xz. Each of the files contained in that archive have the results from one metallicity, with the name indicating the value of log10(Z). Each row in the files represents one simulation, and the data provided in the different columns is:</p> <ol> <li>"log10(M_1i)(Msun)", "qratio(M_2i/M_1i)", "P_i(days)", "metallicity": The initial parameters of the simulation.</li> <li>"result": A string indicating the outcome of the system (see the header of the data files for a list of outcomes). In particular when the outcome is "double_BH", "PISN", "PPISN", the following columns indicate information at the terminal point of the simulation. For all other outcomes, most of the following columns are empty.</li> <li>had_contact: Specifies whether the system undergoes a contact phase (see header in the data files for allowed values).</li> <li>"M_1f(Msun)", "M_2f(Msun)", "P_f(days)": Final orbital parameters.</li> <li>"merge_time(Gyr)": Time to merge from the system assuming the system forms a binary black hole with masses and period given by the M_1f, M_2f and P_f values.</li> <li>"Kerr_param_1", "Kerr_param_2": Black hole spin assuming all angular momentum and mass is conserved at collapse.</li> <li>For the values listed in 4,5 and 6, we also provide values with an added "wpi" description, these indicate the expected results when including pair and pulsational pair instability supernovae, as described in the paper.</li> <li>"he_core_mass_1": Mass coordinate in Msun of the uppermost layer of the star with a mass fraction of hydrogen < 0.01. Any other arbitrary cut can be computed using the profile data.</li> <li>"c_core_mass_1": Mass coordinate in Msun of the uppermost layer of the star with a mass fraction of helium < 0.01.</li> <li>"total_mass_h1_1", "total_mass_he4_1": Total mass of hydrogen and helium at the end of the simulation</li> <li>The remaining columns contain the same information as points 8,9,10 for the secondary. Since all our simulations have q=1 they're actually redundant.</li> </ol> <p><strong>Montecarlo Simulations</strong></p> <p>Using the results of the MESA calculations, Montecarlo simulations were performed that sample the relevant distribution functions and the cosmic star formation history. The code used to perform these calculations is included in the archive montecarlo_code.tar.xz, and it includes a readme.txt file with instructions on how to use it. The outcome of these Montecarlo simulations is included in the files:</p> <ol> <li>fiducial.tar.xz</li> <li>sfr_c1.tar.xz</li> <li>sfr_c2.tar.xz</li> <li>sfr_c3.tar.xz</li> <li>sfr_c4.tar.xz</li> </ol> <p>The first file contains the information on our standard choice of SFR history, including simulations with no kicks, and with and without PPISN. The other four files correspond to each of the cases of SFR variations that we considered in the paper. Each of these archives contain three files:</p> <ol> <li>direct.txt: Information for systems that form BHs through direct collapse</li> <li>PISN.txt: Sampled systems which undergo PISN.</li> <li>PPISN.txt: Systems that fall in the range where we expect PPISN to occur. Information is provided assuming both direct collapse and PPISN mass loss, so the effect of PPISNe can be distinguished.</li> <li>volume.txt: Comoving value for the simulation. All the formed BHs and PISN listed can be assumed to be a complete sample of a box of this size through cosmic time (formed by the CHE channel of course, we don't include our evolutionary channels here).</li> </ol> <p>The data contained for these systems is</p> <ol> <li>"M1i[Msun]", "Pi[days]", "Z", "z_b": Initial primary mass (same as secondary), orbital period in days, metallicity and birth redshift. We assume the time between birth and formation of the binary black hole or PISN event is negligible.</li> <li>"M1f[Msun]", "Pf[days]": For the case of BH formation, these indicate the mass of the black hole formed by the primary (which is equal to the secondary) and the orbital period at BBH formation. For the case of systems undergoing PISN, they represent instead the properties at the onset of the PISN.</li> <li>"spin": Spin of each BH. We assume the spin is aligned with the orbit. Not included for PISN systems.</li> <li>"z_m": Redshift at which the BBH would merge from GW emission. Not included for PISN systems.</li> <li>"t_d[Gyr]": The delay time between BBH formation and merger due to GW emission. Not included for PISN systems.</li> <li>"p_*": Detection probability for the source assuming a random orientation of the source in the sky with respect to the detector. This includes "O1", "O2" and "O3" for LIGO's first observing runs, as well as "F" for LIGO's design sensitivity. "ET" is used for the detection probability for the Einstein telescope. Not included for PISN systems. Also, for the different studies of SFR variations we only considered LIGO at full design sensitivity and ET.</li> <li>All quantities denoted with "_pp" indicate variations to the previous values in the case we consider pulsational pair instability supernovae, as described in the paper.</li> </ol> <p>The Montecarlo simulations with kicks are not included, but can be recomputed using the source code provided.</p>
Pseudo-Soundings for Publication "Changes in the simulation of instability indices over the Iberian Peninsula due to the use of 3DVAR data assimilation"
<p>These data are made available as part of paper: S. J. González-Rojí, S. Carreno-Madinabeitia, J. Sáenz, and G. Ibarra-Berastegi (2021) "Changes in the simulation of atmospheric instability over the Iberian Peninsula due to the use of 3DVAR data assimilation", published in <em>Hydrology and Earth System Sciences (HESS) (</em><a href="https://doi.org/10.5194/hess-25-3471-2021">https://doi.org/10.5194/hess-25-3471-2021</a><em>)</em>. The dataset holds selected postprocessed files that allow to reproduce all the results in the paper.</p> <p>Two WRF experiments nested in ERA-Interim were prepared. The first one (N) was configured as in standard numerical downscaling experiments. The second one (D), with the same parameterizations, included a step of 3DVAR data assimilation every 6 hours. The original experiments covered period 2010-2014 after a year of spin-up (2019). </p> <p>The following files are included:</p> <p>- PseudoSoundings_EXP_YYYY: Contains the Pressure, Temperature and Mixing Ratio on each point included in the mask defined for the Iberian Peninsula. The values of each model level are included. The values at 00 and 12 UTC are included. </p> <p>- ListPointsWRF.txt: Provides the order followed in the nPoints dimension of the nc files. The longitud and latitude of each point is also provided in that list. </p> <p>The reference data of the soundings was download from the University of Wyoming, and they are freely available in this website: <a href="http://weather.uwyo.edu/upperair/sounding.html">http://weather.uwyo.edu/upperair/sounding.html</a></p> <p> </p> <p> </p>
Data from: Host-induced genome instability rapidly generates phenotypic variation across Candida albicans strains and ploidy states
<p>Candida albicans is an opportunistic fungal pathogen of humans that is typically diploid yet has a highly labile genome tolerant of large-scale perturbations including chromosomal aneuploidy and loss-of-heterozygosity events. The ability to rapidly generate genetic variation is crucial for C. albicans to adapt to changing or stressful environments, like those encountered in the host. Genetic variation occurs via stress-induced mutagenesis or can be generated through its parasexual cycle, in which tetraploids arise via diploid mating or stress-induced mitotic defects and undergo nonmeiotic ploidy reduction. However, it remains largely unknown how genetic background contributes to C. albicans genome instability in vitro or in the host environment. Here, we tested how genetic background, ploidy, and the host environment impacts C. albicans genome stability. We found that host association induced both loss-of-heterozygosity events and genome size changes, regardless of genetic background or ploidy. However, the magnitude and types of genome changes varied across C. albicans strain background and ploidy state. We then assessed if host-induced genomic changes resulted in fitness consequences on growth rate and nonlethal virulence phenotypes and found that many host-derived isolates significantly changed relative to their parental strain. Interestingly, diploid host-associated C. albicans predominantly decreased host reproductive fitness, whereas tetraploid host-associated C. albicans increased host reproductive fitness. Together, these results are important for understanding how host-induced genomic changes in C. albicans alter its relationship with the host.</p> <p>IMPORTANCE Candida albicans is an opportunistic fungal pathogen of humans. The ability to generate genetic variation is essential for adaptation and is a strategy that C. albicans and other fungal pathogens use to change their genome size. Stressful environments, including the host, induce C. albicans genome instability. Here, we investigated how C. albicans genetic background and ploidy state impact genome instability, both in vitro and in a host environment. We show that the host environment induces genome instability, but the magnitude depends on C. albicans genetic background. Furthermore, we show that tetraploid C. albicans is highly unstable in host environments and rapidly reduces in genome size. These reductions in genome size often resulted in reduced virulence. In contrast, diploid C. albicans displayed modest host-induced genome size changes, yet these frequently resulted in increased virulence. Such studies are essential for understanding how opportunistic pathogens respond and potentially adapt to the host environment.</p> <p> </p>
The impact of stellar rotation on the black hole mass-gap from pair-instability supernovae
<p>Necessary files to reproduce the simulations of the paper "The impact of stellar rotation on the black hole mass-gap from pair-instability supernovae", as well as simulation output.</p> <p>These simulations were performed using MESA version 13311, with a small bugfix that can be applied with the provided <a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/hydro_riemann.f90?versionId=36bcfc7a-4535-4494-ba2b-c7ea69b9b362">hydro_riemann.f90</a> file.</p> <p>A description of the included files is as follows:</p> <ul> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/final_profiles.tar.gz?versionId=b85755ce-cd64-4e6b-ac14-d9e5364ee86b">final_profiles.tar.gz</a>: Individual profiles for all models at iron-core collapse (or last profile for PISN models). Files are separated into three folders, 'non_rot', 'rot_ST', 'rot_no_ST' corresponding to non-rotating models, rotating models with ST and rotating models without ST. Names of individual files contain the initial mass and the initial ratio between surface omega and its critical value. So for example, a file named "50.00_0.90_profile.data" represents an initial mass of 50 Msun and an initial rotation rate of 0.9 times critical. This work only contains non-rotating models and models at 0.9 critical though.</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/histories.tar.gz?versionId=d3876d5e-c95e-4945-8715-9da428837afa">histories.tar.gz</a>: MESA history files containing various stellar properties describable by a single number at each timestep of the simulation (for instance, mass, effective temperature, etc...). Files follow the same naming scheme as those in final_profiles.tar.gz. For each simulation there is an additional file with a name ending in "pulse_indexes" that contains various lines with two integers per line. These specify for PPISN models the simulation step (or model_number in MESA-speak) at which a pulsation begins, and the step at which it ends.</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/tables.tar.gz?versionId=69974ece-20e5-4509-a1a4-7dad7a92f4b6">tables.tar.gz</a>: Machine readable tables summarizing our simulations, these correspond to the data shown in the tables in the paper. The names of the tables "non_rot.txt", "rot_ST.txt" and "rot_no_ST.txt" correspond to non-rotating models, rotating models with ST and rotation models without ST respectively. The meaning of each column is: <ul> <li>M_i[Msun]: Initial mass of the helium star</li> <li>(om/omc)_i: Ratio between omega and its critical value at the star of core helium burning.</li> <li>M_Hedep[Msun]: Mass of the star at core helium depletion</li> <li>M_CO[Msun]: Carbon oxygen core mass at core helium depletion. Defined as the innermost mass coordinate at which Y<0.01.</li> <li>M_preSN[Msun]: Mass at the onset of PPISN/PISN. For models that do not undergo pair-instability, it corresponds to the mass at iron-core collapse.</li> <li>M_He,preSN[Msun]: Total mass in helium of the star at the onset of PPISN/PISN, or at iron-core collapse for non pair-unstable models. This does not represent the mass of the helium-rich envelope, but the total baryonic mass in helium of the star.</li> <li>M_ejecta[Msun]: Mass ejected by pulsations.</li> <li>M_final[Msun]: Final mass of the star at iron-core collapse.</li> <li>number_of_pulses: Number of pulsations.</li> <li>time_to_coll[yrs]: Time between the first pulse and iron-core collapse.</li> <li>max_KE[foe]: Maximum kinetic energy achieved by any pulse, measured in 10^51 erg/s=1 foe.</li> <li>a_i: Dimensionless spin of the star (a=jc/Gm) at the beginning of core helium burning.</li> <li>a_he_dep: Dimensionless spin at core helium depletion</li> <li>a_preSN: Dimensionless spin at the onset of PPISN/PISN.</li> <li>a_f: Final dimensionless spin from our simulations at iron-core collapse.</li> <li>BH_mass[Msun]: Final BH mass predicted from the properties of our model at iron-core collapse following Batta & Ramirez-Ruiz (2019).</li> <li>BH_spin: Final BH spin predicted from the properties of our model at iron-core collapse following Batta & Ramirez-Ruiz (2019).</li> </ul> </li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/pulses.tar.gz?versionId=5afe548e-3f3f-429a-98f6-b3cb14c77cc6">pulses.tar.gz</a>: Information on individual pulses for each PPISN simulation in our grid. Files are contained in three separate folders "non_rot", "rot_ST" and "rot_no_ST" for models that are non-rotating, rotating with ST and rotating without ST respectively. Naming of individual files is the same as those in final_profiles.tar.gz. Information contained in these tables is: <ul> <li>pulse_num: Integer identifying the pulse.</li> <li>ejecta[Msun]: Total mass ejected by the pulse.</li> <li>time_to_coll[yrs]: Time between the onset of this pulse and iron-core collapse.</li> <li>KE[foe]: Maximum kinetic energy reached during the pulse.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/template_ST.tar.gz?versionId=ac1563c9-5b0c-422a-8739-2fbc82ff5788">template_ST.tar.gz</a>: Template used for simulations. This template is setup for the simulations that include the ST dynamo and have an initial rotation rate of 90% critical. To modify the rotation rate and the initial mass, modify new_omega_div_omega_crit and initial_mass in the inlist_extra file. To turn of ST, set am_nu_ST_factor=0 in inlist_ppisn. Only one model required additional tuning to comple, the 60 Msun rotating model with ST. For this one, the model was restarted at step 98000 with the value of the option x_ctrl(18) switched from 0.025d0 to 0.25d0. This particular simulation did not reach the automated terminating condition and was stopped manually, but at the end it does have a collapsing iron-core with an infall velocity ~5000 km/s.</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/notebook.tar.gz?versionId=d59d2404-1517-4c0a-910e-4adbc14511f8">notebook.tar.gz</a>: jupyter notebook used to process the data from our simulations and produce tables and figures (except for the information on the spin of the primary BH of GW170729).</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/GW170729_spin.tar.gz?versionId=47f46740-3c0d-4355-b2a2-9d7a454f388b">GW170729_spin.tar.gz</a>: jupyter notebook used to compute the spin posterior of GW170729 using the data from the first catalogue of gravitational wave transients.</li> </ul>
Symmetric instability model data
<p>The data include CROCO output netcdf file for symmetric instability analysis and also the grid, initial and forcing files to set up the CROCO simulations. The configuration files are available at https://github.com/jhdong2016/SI_model. Files with names including "si" are from SI550, otherwise from KPP550.</p>
Data from: Historical climatic instability predicts the inverse latitudinal pattern in speciation rate of modern mammalian biota
<p>Evolutionary rate explanations for latitudinal diversity gradients predict faster speciation and diversification rates in richer, older, and more stable tropical regions (climatic stability hypothesis). Numerous modern lineages have emerged in high latitudes, however, suggesting that climatic oscillations can drive population divergence, at least among extratropical species (glacial refugia hypothesis). This conflicting evidence suggests that geographical patterns of evolutionary rates are more complicated than previously thought.</p> <p>Here, we reconstructed the complex evolutionary dynamics of a comprehensive dataset of modern mammals, both terrestrial and marine. We performed global and regional regression analyses to investigate how climatic instability could have indirectly influenced contemporary diversity gradients through its effects on evolutionary rates. In particular, we explored global and regional patterns of the relationships between species richness and assemblage-level evolutionary rates and between evolutionary rates and climatic instability.</p> <p>We found an inverse relationship between evolutionary rates and species richness, especially in the terrestrial domain. Additionally, climatic instability was strongly associated with the highest evolutionary rates at high terrestrial latitudes, supporting the glacial refugia hypothesis there. At low latitudes, evolutionary rates were unrelated to climatic stability.</p> <p>The inverse relationship between evolutionary rates and the modern latitudinal diversity gradient casts doubt on the idea that higher evolutionary rates in the tropics underlie the current diversity patterns of modern mammals. Alternatively, the longer time spans for diversity to accumulate in the older and more stable tropics (and not high diversification rates) may explain the latitudinal diversity gradient.</p>
Surface-wave instability without inertia in shear-thickening suspensions
<p>All data plotted in the figures of the article "Surface-wave instability without inertia in shear-thickening suspensions".</p> <p> </p>
cGAS suppresses genomic instability as a decelerator of replication forks
The cyclic GMP-AMP synthase (cGAS), a sensor of cytosolic DNA, is critical for the innate immune response. Here, we show that loss of cGAS in untransformed and cancer cells results in uncontrolled DNA replication, hyperproliferation, and genomic instability. While the majority of cGAS is cytoplasmic, a fraction of cGAS associates with chromatin. cGAS interacts with replication fork proteins in a DNA binding–dependent manner, suggesting that cGAS encounters replication forks in DNA. Independent of cGAMP and STING, cGAS slows replication forks by binding to DNA in the nucleus. In the absence of cGAS, replication forks are accelerated, but fork stability is compromised. Consequently, cGAS-deficient cells are exposed to replication stress and become increasingly sensitive to radiation and chemotherapy. Thus, by acting as a decelerator of DNA replication forks, cGAS controls replication dynamics and suppresses replication-associated DNA damage, suggesting that cGAS is an attractive target for exploiting the genomic instability of cancer cells.
Data from: Range instability leads to cytonuclear discordance in a morphologically cryptic ground squirrel species complex
The processes responsible for cytonuclear discordance frequently remain unclear. Here, we employed an exon capture dataset and demographic methods to test hypotheses generated by species distribution models to examine how contrasting histories of range stability vs. fluctuation have caused cytonuclear concordance and discordance in ground squirrel lineages from the Otospermophilus beecheyi species complex. Previous studies in O. beecheyi revealed three morphologically cryptic and highly divergent mitochondrial DNA lineages (named the Northern, Central, and Southern lineages based on geography) with only the Northern lineage exhibiting concordant divergence for nuclear genes. Here, we showed that these mtDNA lineages likely formed in allopatry during the Pleistocene, but responded differentially to climatic changes that occurred since the last interglacial (~120,000 years ago). We find that the Northern lineage maintained a stable range throughout this period, correlating with genetic distinctiveness among all genetic markers and low migration rates with the other lineages. In contrast, our results suggested that the Southern lineage expanded from Baja California Sur during the Late Pleistocene to overlap and potentially swamp a contracting Central lineage. High rates of intraspecific gene flow between Southern lineage individuals among expansion origin and expansion edge populations largely eroded Central ancestry from autosomal markers. However, male-biased dispersal in this system preserved signals of this past hybridization and introgression event in matrilineal-biased X-chromosome and mtDNA markers. Our results highlight the importance of range stability in maintaining the persistence of phylogeographic lineages, whereas unstable range dynamics can increase the tendency for lineages to merge upon secondary contact.
Data from: Developmental instability is genetically correlated with phenotypic plasticity, constraining heritability, and fitness
Although adaptive plasticity would seem always to be favored by selection, it occurs less often than expected. This lack of ubiquity suggests that there must be trade-offs, costs, or limitations associated with plasticity. Yet, few costs have been found. We explore one type of limitation, a correlation between plasticity and developmental instability, and use quantitative genetic theory to show why one should expect a genetic correlation. We test that hypothesis using the Landsberg erecta × Cape Verde Islands recombinant inbred lines (RILs) of Arabidopsis thaliana. RILs were grown at four different nitrogen (N) supply levels that span the range of N availabilities previously documented in North American field populations. We found a significant multivariate relationship between the cross-environment trait plasticity and the within-environment, within-RIL developmental instability across 13 traits. This genetic covariation between plasticity and developmental instability has two costs. First, theory predicts diminished fitness for highly plastic lines under stabilizing selection, because their developmental instability and variance around the optimum phenotype will be greater compared to nonplastic genotypes. Second, empirically the most plastic traits exhibited heritabilities reduced by 57% on average compared to nonplastic traits. This demonstration of potential costs in inclusive fitness and heritability provoke a rethinking of the evolutionary role of plasticity.
Data from: Temporal population genetic instability in range edge Western Toads, Anaxyrus boreas
In this article, we address the temporal stability of population genetic structure in a range-edge population that is undergoing continual, short-distance colonization events. We sampled western toad, Anaxyrus boreas, breeding populations over 2 seasons near their northern range limit in southeast Alaska. We sampled 20 ponds each during the summers of 2008 and 2009, with 14 ponds sampled in both summers. We found considerable turnover in the population genetic relationships among ponds in those 2 seasons, as well as biologically meaningful genetic differentiation between years within some ponds. We found relatively consistent relationships between major population centers, whereas the relationships between the central ponds and smaller, outlying populations differed year to year. This finding indicates that multiple years of genetic sampling may be important for understanding the genetic landscape of some populations.
Data from "Short-Lived Gravitational Instability in Isolated Irradiated Discs"
<p>The paper by Rowther et al. (2024) simulates the evolution of irradiated gravitationally unstable protoplanetary discs with live radiative transfer using Phantom (Price et al. 2018) coupled with MCFOST (Pinte et al. 2006, 2009). The codes used to perform the simulations are available at </p> <ul> <li>Phantom -- <a href="https://github.com/danieljprice/phantom" target="_blank" rel="noopener">https://github.com/danieljprice/phantom</a></li> <li>MCFOST -- <a href="https://github.com/cpinte/mcfost" target="_blank" rel="noopener">https://github.com/cpinte/mcfost</a></li> </ul> <p>The dataset contains the following data to recreate any of the figures:</p> <ol> <li>Selected snapshots of the 5 simulations shown in the paper. </li> <li>Post-processed data to recreate the line-plots shown in the paper.</li> <li>.fits files of the synthetic continuum images created by post-processing the simulation snapshot with MCFOST at 1.3mm.</li> </ol> <p>To recreate any of the simulations in the paper, all relevant files required are in <strong>Simulations.zip</strong>.</p> <ul> <li><strong>.setup: </strong>The file used by phantomsetup to create the initial conditions of the disc.</li> <li><strong>.in: </strong>The file used by phantom to perform the simulation.</li> <li><strong>.para:</strong> A file containing the parameters used by MCFOST to perform the Radiative Transfer calculations.</li> <li>The files for the 0.1 Solar mass disc can be identified by the prefix <strong>Md0p1</strong>. Similarly, the prefix <strong>Md0p25 </strong>identifies the files for 0.25 Solar mass disc.</li> </ul> <p>The data to recreate any of the figures in the paper are found in <strong>Figure_*.zip</strong>.</p> <p>The binary code snapshots (<strong>Md0p1_01400, Md0p25_00300</strong> etc.) containing the raw data (particle positions, velocities, thermal energy etc.) can be visualised by <a href="https://github.com/danieljprice/splash" target="_blank" rel="noopener">Splash</a> (Price 2007) or <a href="https://github.com/ttricco/sarracen/" target="_blank" rel="noopener">Sarracen</a> (Harris & Tricco 2023). The latter was used to create the figures in the paper.</p> <p> </p>
Data associated with the study, "Non-Invasive Biomarkers for Detecting Progression Toward Hypovolemic Cardiovascular Instability In A Lower Body Negative Pressure Model".
<p>Raw data associated with the study entitled "Non-Invasive Biomarkers for Detecting Progression Toward Hypovolemic Cardiovascular Instability In A Lower Body Negative Pressure Model". There were 16 subjects with 1. electrocardiogram (ECG), 2. mean arterial pressure (MAP), 3) photoplethysmography (pleth), 4) Bioimpedance Cardiography measured via a Cheetah (Startling/Medtronic) system referred to as cheetah, 5) electrical impedance from a Sentec electrical impedance tomography system, referred to as ST, and electrical impedance from a sciospec impedance analyzer, referred to as SS. Each subject underwent a lower body negative pressure (LBNP) procedure, where the LBNP was increased modeling a small hemorrhage by drawing blood to their lower extremeties. The dataset contains 5 mat (MATLAB data files), with time reported in minutes on the day of the study, i.e.10 am = 600 minutes. The file details are as follows:</p><ul><li><strong>LBNP data</strong>: LBNP_level_times.mat. The data contains 1 structure (LBNPinf) of length 16 (corresponding to each subject) with the following fields<ul><li>ts: time vector in minutes</li><li>lbnp: LBNP value at each noted time</li></ul></li><li><strong>Vital Sign data</strong>: raw_labchart_data.mat. The data contains 1 structure array (Labchart) of length 16 (corresponding to each subject) with the following fields<ul><li>ts: time vector in minutes</li><li>ecgs: ECG data</li><li>MAP: MAP data</li><li>pleth: pleth data recorded from a single channel (V)</li></ul></li><li><strong>Sentec EIT data</strong>: raw_av_ST_impedance_data.mat. The data contains 1 structure array (STout) of length 16 (corresponding to each subject) with the following fields<ul><li>t_thx: time vector in minutes corresponding to thorax data</li><li>Z_thx: average impedance data at each time over the thorax</li><li>t_spl: time vector in minutes corresponding to abdomen data</li><li>Z_spl: average impedance data at each time over the abdomen</li></ul></li><li><strong>Sciospec EIS data</strong>: raw_sciospec_dat.mat. The data contains 1 cell array & 1 structure array (sciodat) of length 16 (corresponding to each subject).<ul><li>Cell array: Locations of the Sciospec measurements: 'Thoracic', 'Abdominal', 'Arm'</li><li>Sciodat fields:<ul><li>sciodat structure array of length 3 corresponding to the 'Thoracic', 'Abdominal', 'Arm' locations, respectively. Each component has the following fields<ul><li>tvec: time vector in minutes</li><li>fs: frequencies that the impedance is recorded over (Hz)</li><li>Zmat: matrix of impedance data size time versus frequency</li><li>erflg: not used</li></ul></li></ul></li></ul></li><li><strong>Bioimpedance cardiography data</strong>: raw_cheetah_bioimpedance.mat. The data contains a cell array of column labels (col_labs, 1x11) and a matrix (cheetah_db) of the BC data.</li></ul><p> </p><p> </p><p> </p>
The data and notebook for "Predicting the Slowing of Stellar Differential Rotation by Instability-Driven Turbulence"
<p>The data and Jupyter notebook for "Predicting the Slowing of Stellar Differential Rotation by Instability-Driven Turbulence."</p> <p><br><strong>Fig_6_code_implementation.ipynb</strong></p> <p>This notebook presents implementation of our closure model in python to predict turbulent transport for all $(r, \mathrm{Pr})$ in Fig. 6(a).</p> <p><br><strong>GSF_r_Pr_scan__Shear_eq_3.h5</strong></p> <p>This file contains data output from the $(r, \mathrm{Pr})$-scan of the closure model, obtained using "Fig_6_code_implementation.ipynb". The h5 data file can be simply read using the following lines of code:</p> <p><br><code>import h5py</code><br><code>hf=h5py.File('~/GSF_r_Pr_scan__Shear_eq_3.h5', 'r')</code><br><code>ux_uy = hf['ux_uy/ux_uy/ux_uy'][()]</code><br><code>ux_th = hf['ux_th/ux_th/ux_th'][()]</code></p> <p><code>Pr_exp = np.linspace(0.02, 7, 28)</code><br><code>Pr_array = 10**(-Pr_exp) #These are the values of Pr for which the transport is computed.</code></p> <p><code>r_exp = np.linspace(0.02, 5, 20)</code><br><code>r_array = 10**(-r_exp) #These are the values of r for which the transport is computed.</code><br><br></p> <p> </p> <blockquote> <p>Authors:</p> <p><strong>B. Tripathi<br></strong>Department of Physics, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA<br>ORCID : 0000-0002-4723-2170<br>Email : btripathi@wisc.edu</p> <p><strong>A.J. Barker</strong><br>Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds LS2 9JT, UK<strong><br></strong>ORCID : 0000-0003-4397-7332<br>Email : A.J.Barker@leeds.ac.uk</p> <p><strong>A.E. Fraser<br></strong>Department of Applied Mathematics, University of Colorado, Boulder, Colorado 80309, USA<br>Department of Astrophysical and Planetary Sciences, University of Colorado, Boulder, Colorado 80309, USA<br>Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, Colorado 80309, USA<br>ORCID : 0000-0003-4323-2082</p> <p><strong>P.W. Terry<br></strong>Department of Physics, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA<br>ORCID : 0000-0002-4981-9637</p> <p><strong>E.G. Zweibel<br></strong>Department of Physics, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA<br>Department of Astronomy, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA<br>ORCID : 0000-0003-4821-713X</p> </blockquote>
Lidar Data for "Kelvin_Helmholtz_Instabilities_Within_a_Semi_diurnal_Tide"
<p>These files contains the lidar observations of the KHI and generated waves observed by the sodium resonance wind temperature lidar and the Rayleigh density temperature lidar</p>
Self-oscillations of the tube-like astrosphere: new type of instability
<p>Video files complement the main paper, which discusses the instability of astrospheric jets around stars with strong intrinsic magnetic fields.</p> <p><br>"Video-1.avi" corresponds to the 2D flow shown in Figure 2 (Panel A), where the interstellar medium is at rest, $M_A$ = 8, $\chi$ = 4.</p> <p><br>"Video-2.avi" corresponds to the 3D flow shown in Figure 4, with the interstellar medium also at rest and $M_A$ = 3, $\chi$ = 4.</p>
Supporting Information for: Characterization of fingers resulting from settling-driven gravitational instabilities through simultaneous PIV and PLIF imaging of laboratory experiments
<p>This repositery contains supporting information accompanying the submission of the manuscript entitled: "Characterization of fingers resulting from settling-driven gravitational instabilities through simultaneous PIV and PLIF imaging of laboratory experiments" by Fries, Lemus, Jarvis, Clarke, Phillips, Manzella and Bonadonna. The manuscript has been submitted on 02 December 2024 to the Journal of Geophysical Research - Solid Earth. All the files present in this repositery are described in the file "Supporting_Information_Friesetal.pdf". Please refer to that file for additional information on the data present in the repositery.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.