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5,462 results for “binary”
binary_c V2.2.4 with binary_c-python V1.0.0 on Kubuntu 24.04 (Linux) desktop
<p>The <strong><em>binary_c</em></strong> software framework models the evolution of single, binary and multiple stars, including stellar evolution and nucleosynthesis. It is desgined for stellar population calculations, so is lightweight and versatile, and its support software contains tools for development and data analysis. <strong><em>binary_c</em></strong> is happy in standalone, virtual and HPC environments. Builds on <a href="https://en.wikipedia.org/wiki/Linux">Linux</a>, <a href="https://en.wikipedia.org/wiki/MacOS">OSX</a> and <a href="https://en.wikipedia.org/wiki/Windows_Subsystem_for_Linux">WSL</a>.</p> <p>This is version 2.2.4 of binary_c, with binary_c-python 1.0.0, installed on Kubuntu 24.04 desktop running a Linux kernel. Many useful tools are pre-installed, such as GCC, Clang, meson, ninja, valgrind, emacs, GSL, kcachegrind, gdb, gnuplot, and Jupyter so you can run binary_c-python's notebooks. Also included are commands to run either the latest stable versions or master versions straight from the gitlab repository.</p> <p>Homepage: <a href="https://binary_c.gitlab.io/">https://binary_c.gitlab.io/</a></p> <p>Login details:</p> <ul> <li>User: binary_c</li> <li>Password: binary_c</li> </ul> <p>The virtual machine is compressed with xzip, so you will need to decompress it before use.</p>
Dataset of Binary Cations Minimize Energy Loss in the Wide Bandgap Perovskite towards Efficient All-perovskite Tandem Solar Cells
Open the record for dataset details and reuse information.
Binary Approach to Ternary Cluster Expansions: NO–O–Vacancy System on Pt(111)
<p>Cluster expansions (CEs) provide an exact framework for representing the configurational energy of interacting adsorbates at a surface. Coupled with Monte Carlo methods, they can be used to predict both equilibrium and dynamic processes at surfaces. In this work, we propose a three-binary-to-single-ternary (TBST) fitting procedure, in which a ternary CE is approximated as a linear combination of the three binary CEs (O–vac, NO–vac, and NO–O) obtained by fitting to the three binary legs. We first construct a full ternary CE by fitting to a database of density functional theory (DFT) computed energies of configurations across a full range of adsorbate configurations and then construct a second ternary using the TBST approach. We compare two approaches for the NO–O–vacancy system on the (111) surface of Pt, a system of relevance to the catalytic oxidation of NO. We find that the TBST model matches the ternary CE to within 0.018 eV/site across a wide range of configurations. Further, surface coverages and NO oxidation rates extracted from Monte Carlo simulations show that the two models are qualitatively consistent over the range of conditions of practical interest.</p>
Effects of Rotationally Induced Mixing in Compact Binary Systems with Low-mass Secondaries and in Single Solar-type Stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2012ApJ...755...95C/abstract">Chatzopoulos et al. (2012)</a>. MESA version 3647.</p> <p>Publication DOI: <a href="https://www.doi.org/10.1088/0004-637X/755/2/95">10.1088/0004-637X/755/2/95</a></p>
A strange dwarf scenario for the formation of the peculiar double white dwarf binary SDSS J125733.63+542850.5
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.476..109J/abstract">Jiang et al. (2018)</a>. MESA version 8845.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/sty238">10.1093/mnras/sty238</a></p>
Binary black-hole simulation SXS:BBH:0125
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:0140
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:0119
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:0085
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:0131
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:0106
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:0111
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Binary black-hole simulation SXS:BBH:0108
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Data from: Identifying hidden rate changes in the evolution of a binary morphological character: the evolution of plant habit in Campanulid angiosperms
The growth of phylogenetic trees in scope and in size is promising from the standpoint of understanding a wide variety of evolutionary patterns and processes. With trees comprised of larger, older, and globally distributed clades, it is likely that the lability of a binary character will differ significantly among lineages, which could lead to errors in estimating transition rates and the associated inference of ancestral states. Here we develop and implement a new method for identifying different rates of evolution in a binary character along different branches of a phylogeny. We illustrate this approach by exploring the evolution of growth habit in Campanulidae, a flowering plant clade containing some 35,000 species. The distribution of woody versus herbaceous species calls into question the use of traditional models of binary character evolution. The recognition and accommodation of changes in the rate of growth form evolution in different lineages demonstrates, for the first time, a robust picture of growth form evolution across a very large, very old, and very widespread flowering plant clade.
Association between beliefs about mediations and adherence to medications: a stepwise binary logistic regression model
<p>Dataset of research article.</p>
Data for 'SPH modelling of wind-companion interactions in eccentric AGB binary systems'
<p>Additional material to "Malfait et al. 2021, SPH modelling of wind-companion interactions in eccentric AGB binary systems": <a href="https://ui.adsabs.harvard.edu/link_gateway/2021A&A...652A..51M/arxiv:2107.01074" target="_blank" rel="noreferrer noopener">arXiv:2107.01074</a></p> <p>This contains input files and final output dumps of the Phantom simulations of this paper, and additional movies of 4 of the simulations.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p>
Binary segmentation images for 3D segmentation model training
<p>Binary segmentation images for 3D segmentation model training</p>
Tests of General Relativity with Binary Black Holes from the second LIGO–Virgo Gravitational-Wave Transient Catalog - Full Posterior Sample Data Release
<p>Data release containing full posterior samples of the following analyses reported in the paper "Tests of General Relativity with Binary Black Holes from the second LIGO–Virgo Gravitational-Wave Transient Catalog" from the LIGO Scientific Collaboration and Virgo Collaboration (<a href="https://doi.org/10.1103/PhysRevD.103.122002">Phys. Rev. D 103, 122002</a>, also available at <a href="https://arxiv.org/abs/2010.14529">arxiv.org:2010.14529</a> and <a href="https://dcc.ligo.org/LIGO-P2000091/public">https://dcc.ligo.org/LIGO-P2000091/public</a>):</p> <ul> <li>Echoes (Sec VII B): ech.zip</li> <li>Inspiral-merger-ringdown consistency test (Sec IV B): imr.zip</li> <li>Lorentz invariance violation test (Sec V I): liv.zip</li> <li>Parametrized tests of general relativity (Sec V A): par.zip</li> <li>Ringdown test (Sec VII A): rin.zip</li> <li>Spin-induced quadrupole moment test (Sec V B): sim.zip</li> </ul> <p>Each zip file contains HDF5 files that can either be read directly with standard HDF5 tools, or using PESummary (<a href="https://docs.ligo.org/lscsoft/pesummary/">https://docs.ligo.org/lscsoft/pesummary/</a>)</p> <p> </p>
The Binary Fraction of Red Supergiants
<p>The percentage of massive main sequence OB stars in binary systems is thought to be as high as 100%. However, very few Galactic binary red supergiants (RSGs) have been identified, despite the fact that these stars are the evolved descendants of OB stars. As shown in our recent paper, binary RSGs will likely have B-type companions, as dictated by stellar evolution. Such a system will have a very unique photometric signature due to the shape of the spectral energy distribution. After observing candidate RSG+B star binaries in the Local Group Galaxies of M31, M33, and the Magellanic Clouds we discovered over 250 new systems. We are now in the process of determining the binary frequency of RSGs as a function of metallicity.</p>
Data and code for "Terrestrial water cycle acceleration-deceleration: Non-binary and space-time divergent"
<p>This dataset provides all data compiled and generated for the manuscript entitled "Terrestrial water cycle acceleration-deceleration: Non-binary and space-time divergent" (manuscript under consideration). This includes the boundaries for 3614 hydrological catchments, the curated data used for analysis and modelling, the developed machine learning model, shapley values and area of applicability results, and data for global extrapolation</p> <p>It also contains the code in a Quarto file ('Code.qmd') and a markdown file ('Code.html') which shows how to access and use the data, and generic sample codes used to generate these results.</p> <p> </p> <p>Code - R session info:</p> <p>R version 4.2.1 (2022-06-23 ucrt)</p> <p>Platform: x86_64-w64-mingw32/x64 (64-bit)</p> <p>Running under: Windows 10 x64 (build 22621)</p> <p>Matrix products: default locale: [1] LC_COLLATE=Portuguese_Brazil.utf8 LC_CTYPE=Portuguese_Brazil.utf8 [3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C [5] LC_TIME=Portuguese_Brazil.utf8</p> <p>attached base packages: [1] stats [2] graphics [3] grDevices [4] utils [5] datasets [6] methods [7] base</p> <p>other attached packages: [1] bundle_0.0.0.9200 [2] stacks_1.0.0 [3] bonsai_0.1.0 [4] rules_1.0.0 [5] finetune_0.2.0 [6] yardstick_1.0.0 [7] workflowsets_1.0.0 [8] workflows_1.0.0 [9] tune_1.0.0 [10] tibble_3.1.7 [11] rsample_1.0.0 [12] recipes_1.0.1 [13] purrr_0.3.4 [14] parsnip_1.0.0 [15] modeldata_1.0.0 [16] infer_1.0.2 [17] dials_1.0.0 [18] scales_1.2.0 [19] broom_1.0.0 [20] tidymodels_1.0.0 [21] ggplot2_3.3.6 [22] rnaturalearth_0.1.0 [23] sf_1.0-8 [24] terra_1.6-3 [25] lubridate_1.8.0 [26] stringr_1.4.0 [27] tidyr_1.2.0 [28] dplyr_1.0.9 [29] pacman_0.5.1</p>
ScienceDex guides
Understand access before you commit
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.