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5,462 results for “binary”
Dataset for "DEM-CFD analysis of swelling behaviours of binary particle systems with a microscopic diffusion model"
<p>This dataset contains both numerical simulation data and experimental data describing the swelling behaviour of binary particle systems. Detailed study on the swelling behaviour is presented in a journal paper entitled "DEM-CFD analysis of swelling behaviours of binary particle systems with a microscopic diffusion mode". We provide all the essential data in a single excel file with the description below:</p> <p>Dataset 1- Evolution of the height of the particle bed obtained from experiments and simulations (used in Fig.6 of the associated paper): Experimental data for the measured hight and associated standard deviation at various time, as well as the numerical data.</p> <p>Dataset 2 - Evolutions of the normalised average radius r/r_0 (used in Fig.7a) and the coefficient of variation CV (used in Fig.7b) for SAP particles obtained from DEM-CFD simulations.</p> <p>Dataset 3 - Radial distribution Functions (RDFs) for (Fig.9a) SAP particles and (Fig.9b) POM particles at different time instants.</p> <p>Dataset 4 - Positions of POM particles projected into the x-y plane at different time instants (the red dots are particle centres), (a) t = 0 min; (b) t = 3 min; (c) t = 6 min; (d) t = 10 min.</p> <p>Dataset 5 - Evolution of Lacey mixing index for the binary particles during the swelling process.</p> <p>Dataset 6 - Average normal stress distribution of particles in x-y and x-z planes at t = 2 min.</p> <p>DataSet 7 - Velocity vectors of particles projected in x-z planes at different time instants: (a) t = 1 min; (b) t= 2 min (Data for Fig.13)</p> <p>Dataset 8 - Positions of POM particles projected into x-y plane at time t= 10 min (the red dots are particle centres) with different coefficient of frictions (a) μ=0.1; (b) μ=0.2; (c) μ=0.3; (d) μ=0.4; (e) μ=0.5.(Data for Fig.15)</p> <p>Dataset 9 - RDFs for POM particles at time t= 10 min with different coefficients of friction.(Data for Fig.16)</p>
Properties of binary systems in a one-dimensional approximation
<p>We present numerically obtained tables of properties of stars in a binary system as a function of the effective potential: volume-equivalent radii of the equipotential surfaces, effective accelerations averaged over the same equipotential surfaces and the properties of the L<sub>1</sub> plane cross-sections. The tables are obtained for binaries where the ratios of the primary star mass to the companion star mass are from 10<sup>-6</sup> to 10<sup>5</sup> and include equipotential surfaces up to the star's outer Lagrangian point. We supply a sample code showing how to use our tables to get the average effective accelerations in one-dimensional stellar codes.</p> <p>Updates will be available at <a href="https://github.com/AliPourmand/1D_binary_star_properties">https://github.com/AliPourmand/1D_binary_star_properties</a>. Paper describing the tables can be found on ApJ, <strong>DOI</strong> 10.3847/1538-4357/acd4c1, https://iopscience.iop.org/article/10.3847/1538-4357/acd4c1 If you make use of the provided tables or the code, we ask you to cite the paper (and zenodo deposit if such reference is allowed).</p>
Vulnerable and patched binaries
<p>This dataset contains a collection of vulnerable and patched binaries, each with specific function names.</p> <p>To access detailed information about the available vulnerable versions and their corresponding patched versions for a specific CVE (Common Vulnerabilities and Exposures), please refer to the 'cve_all' text file.</p> <p>For example, the first line of the file 'cve_all' is as follows: 'CVE-2018-20712,binaries/binutils/O0/addr2line-2.31,binaries/binutils/O0/addr2line-2.32,d_expression_1'. This line indicates that the vulnerability with the identifier CVE-2018-20712 has a vulnerable version binary located at 'binaries/binutils/O0/addr2line-2.31' and a patched version binary located at 'binaries/binutils/O0/addr2line-2.32'. The related function name associated with this vulnerability is 'd_expression_1'.</p>
Yields from paper: ALUMINIUM-26 FROM MASSIVE BINARY STARS III. BINARY STARS UP TO CORE-COLLAPSE AND THEIR IMPACT ON THE EARLY SOLAR SYSTEM
<p>================================================================================<br> Title: Aluminium-26 From Massive Binary Stars III: BINARY STARS UP TO CORE-COLLAPSE AND THEIR IMPACT ON THE EARLY SOLAR SYSTEM<br> Authors: Brinkman H.E., Doherty C.L., Pignatari M., Pols, O. R., Lugaro M.<br> ================================================================================<br> Description of contents: A .tar.gz package containing 12 files with the complete set<br> of yields from the models presented in this paper. Each file contains one primary mass,<br> e.g., Yields10Msun contains the yields for the binary systems with a 10Msun primary.<br> The second line in each file has the periods.<br> ================================================================================</p>
A Transformer-based Function Symbol Name Inference Model from an Assembly Language for Binary Reversing
<p>This is a dataset and pre-trained model for the official implementation of <a href="https://github.com/agwaBom/AsmDepictor"><strong>AsmDepictor</strong></a>, "A Transformer-based Function Symbol Name Inference Model from an Assembly Language for Binary Reversing", In the 18th ACM Asia Conference on Computer and Communications Security <a href="https://asiaccs2023.org/">AsiaCCS '2023</a></p> <p> </p>
Data release for "Identifying LISA verification binaries among the Galactic population of double white dwarfs"
<p>Posterior samples and SNR calculations associated with <em>Identifying LISA verification binaries among the Galactic population of double white dwarfs</em> (<a href="https://arxiv.org/abs/2210.10812">arxiv:2210.10812</a>, <a href="https://doi.org/10.1093/mnras/stad1288">MNRAS, 522, 5358 (2023)</a>).</p> <p>The <code>data</code> folder contains the following:</p> <ul> <li><code>vb_table</code>: Verification binary (VB) candidate properties obtained from electromagnetic (EM) observations. This data was compiled by Kupfer et al. and is available at <a href="https://gitlab.in2p3.fr/LISA/lisa-verification-binaries">https://gitlab.in2p3.fr/LISA/lisa-verification-binaries</a> (accessed on 2022 September 2). Used in Table 1.</li> <li><code>snr</code>: SNR calculations for each VB candidate as a function of mission duration. Used in Figure 2.</li> <li><code>basic</code>: Gravitational-wave (GW) parameter estimation study of each VB candidate. Used in Table 2 and Figure 3.</li> <li><code>detection_time</code>: VB SNR calculations with source property (and LISA launch time) uncertainty taken into account. Used in Figure 1 and Figure 4.</li> <li><code>em_priors</code>: GW parameter estimation study with EM information included in the priors. Used in Figure 5.</li> <li><code>unknown_noise</code>: GW parameter estimation study with the inclusion of noise parameters to vary the PSD. Used in Figure 6.</li> <li><code>filtered_populations</code>: Source properties of DWDs nearby in frequency to each VB candidate. See the <code>confused_sources.ipynb</code> notebook. Used in Figure 7.</li> <li><code>confusion_snr</code>: SNR calculations of the above confusion sources. Used in Figure 7.</li> <li><code>confusion</code>: A analysis of V803Cen with a realistic Galactic population of DWDs included in the data. Used in Figure 8.</li> </ul> <p>Code to reproduce every figure from the paper is available in <code>plots</code>.</p> <p>Supplementary notebooks associated with the generation of a realistic data instance (i.e. including a simulated Galactic population of DWDs nearby in frequency to V803Cen) can be found in <code>notebooks</code>.</p>
GWAS dataset with simulated binary phenotypes for 1000 Genome Project
<p>Phenotypes in this dataset are generated by simulating quantitative blood lipid level phenotype using the method introduced in by (https://app.terra.bio/#workspaces/amp-t2d-op/2019_ASHG_Reproducible_GWAS-V2), and binarized to be suitable as a test case for GWAS methods.</p> <p>ALL.shapeit2_integrated_v1a.GRCh38.20181129.phased.rsid files and allpopid.txt are the 1000 Genome Project reference data used for generating MDS plot in https://github.com/csbio/BridGE-Python</p> <p> </p>
Data Release: "A parameter-free tour of the binary black hole population"
<p>This dataset contains the results presented in "<strong>A parameter-free tour of the binary black hole population</strong>" (<a href="http://arxiv.org/abs/2302.07289">arXiv:2302.07289</a>).</p> <p>The code used to generate this data can be found in the repository <a href="https://github.com/tcallister/autoregressive-bbh-inference/">https://github.com/tcallister/autoregressive-bbh-inference/</a>. This repository includes <a href="https://github.com/tcallister/autoregressive-bbh-inference/tree/main/data">jupyter notebooks</a> that can be used to open, explore, and plot the files contained in this data set. Additional information about reproducing and/or using this dataset can be found in <a href="https://tcallister.github.io/autoregressive-bbh-inference/">our associated documentation</a>.</p> <p>Further notes:</p> <ul> <li>The files <em>sampleDict_FAR_1_in_1_yr.pickle</em> and <em>injectionDict_FAR_1_in_1.pickle</em>, used as inputs to our analyses, are created via code in the repository <a href="https://github.com/tcallister/get-lvk-data">https://github.com/tcallister/get-lvk-data</a> (see also <a href="https://zenodo.org/record/6505409">https://zenodo.org/record/6505409</a>).</li> <li>The files <em>posteriors_gaussian_spin_samples_FAR_1_in_1.json</em> and <em>o1o2o3_mass_c_iid_mag_iid_tilt_powerlaw_redshift_result.json</em>, used for figure generation, were published by the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration in support of the paper "<a href="https://arxiv.org/abs/2111.03634">The population of merging compact binaries inferred using gravitational waves through GWTC-3</a>" (see <a href="https://zenodo.org/record/5655785">https://zenodo.org/record/5655785</a>).</li> </ul> <p><em>New in this version: </em>This version (Version 3) contains the the newest results generated in the first round of journal referee review and reflected in v2 of the arXiv listing. Includes re-run of all analyses with updated priors, and new results from the injection study discussed in Appendix C.</p>
White Dwarfs Revealed in Gaia's Candidate Compact Object Binaries
<p>Discovery and characterisation of black holes (BHs), neutron stars (NSs), and white dwarfs (WDs) with detached luminous companions (LCs) in wide orbits are exciting because they are important test beds for dark remnant (DR) formation physics as well as binary stellar evolution models. Recently, 187 candidates have been identified from <em>Gaia</em>'s non-single star catalog as wide orbit (P<sub>orb</sub>/day>45), detached binaries hosting DRs. We identify UV counterparts for 49 of these sources in the archival GALEX data. Modeling the observed spectral energy distribution (SED) spanning FUV-NUV to IR for these sources and stellar evolution models, we constrain the LC properties including mass, bolometric luminosity, and effective temperature for these 49 sources. Using the LC masses, and the astrometric mass function constrained by <em>Gaia,</em> we constrain the DR masses for these sources. We find that 9 have masses clearly in the NS or BH mass range. Fifteen sources exhibit significant NUV excess and 4 show excess both in FUV and NUV. The simplest explanation for these excess UV fluxes is that the DRs in these sources are WDs. Using SED modeling we constrain the effective temperature and bolometric luminosity for these 15 sources. Our estimated DR masses for all of these 15 sources are lower than the Chandrasekhar mass limit for WDs. Interestingly, five of these sources had been wrongly identified as neutron stars in literature.</p> <p>MIST_OUTPUT.zip : MIST output file for mass estimations</p> <p>Readme : Description of MIST_output files.</p>
Dynamic Slicing of WebAssembly Binaries
<p>This is the replication package that accompanies the paper titled: "Dynamic Slicing of WebAssembly Binaries".</p> <p># Slices dataset<br> ## Generating the dataset<br> The dynamic slices have been generated with [P-ORBS](https://syed-islam.github.io/research/program-analysis/#observation-based-program-slicing-orbs).<br> The steps and scripts to generate the dynamic slices are included in the `slicing-steps/` directory.<br> These steps also describe how to generate the `stats.csv` file that is included in this dataset.</p> <p>The static slices have been generated with [wassail](https://github.com/acieroid/wassail).<br> The scripts to generate the static slices are included in the current directory (`generate-static-slices.sh` which relies on `run_wassail.sh`).<br> These steps generate the file `static-stats.csv` that is included in this dataset.</p> <p>The `original-size.csv` file, included in this dataset, can be generated as follows:<br> ```sh<br> find subjects-wasm-extract-slice -name \*.c.wat -exec c_count {} \; | grep subjects > counts.txt<br> echo 'slice,original fn slice' > evaluation/original-sizes.csv<br> sed -E 's|^(.*) subjects-wasm-extract-slice/[^/]*/([^/]*)/.*$|\2,\1|' counts.txt >> evaluation/original-sizes.csv<br> ```</p> <p>The numbers in Table 1 of the paper (the list of programs in the dataset along with their sizes) can be generated as follows.<br> For the WebAssembly files, we can count the function size:</p> <p>```<br> find subjects-wasm-extract-slice -name \*.c.wat -exec c_count {} \; | grep subjects > counts.txt<br> sed -E 's|^(.*) subjects-wasm-extract-slice/([^/]*)/.*$|\1 \2|' counts.txt | python evaluation/table1-wasm-mean.py<br> ```</p> <p>or the full program size:</p> <p>```<br> find subjects-wasm-extract-slice -name t.wat -exec c_count {} \; | grep subjects > counts.txt<br> sed -E 's|^(.*) subjects-wasm-extract-slice/([^/]*)/.*$|\1 \2|' counts.txt | python evaluation/table1-wasm-mean.py<br> ```</p> <p>## Structure of the dataset</p> <p>The dataset is structured as follows:</p> <p> - `subjects/` contains the instrumented `.c` source code, along with scripts to generate the dynamic slices.<br> The original source code can be obtained by removing the line `printf("\nORBS:%x\n....`.<br> - `subjects-wasm-extract-slice/` contains the original WebAssembly programs to slice. Each program has two files: `t.wat` is the full binary file, and `name.c.wat` is the binary code of the function containing the slicing criterion.<br> - `all_slices/` contains the slices. For example, program `adpcm_ah1_254_expr` has the following files<br> - `adpcm/adpcm_ah1_254_expr/EWS_adpcm.wat`: the EWS slice<br> - `adpcm/adpcm_ah1_254_expr/SEW_adpcm.wat`: the SEW slice<br> - `adpcm/adpcm_ah1_254_expr/ESW_adpcm.wat`: the ESW slice<br> - `adpcm/adpcm_ah1_254_expr/static_adpcm.wat.slice`: the SWS slice<br> The other files are produced by intermediary steps and can be ignored. They are:<br> - `adpcm/adpcm_ah1_254_expr/ESW_adpcm.wat.orig`: original (unsliced) binary *file* from which SW and ESW slices are computed<br> - `adpcm/adpcm_ah1_254_expr/SEW_adpcm.wat.orig`: original (unsliced) binary *function* from which SEW slice is computed<br> - `adpcm/adpcm_ah1_254_expr/SW_adpcm.wat`: slice of entire binary file from which ESW slice is extracted<br> - `adpcm/adpcm_ah1_254_expr/WS_adpcm.wat`: compiled (binary) version of dynamic C slice from which EWS slice is extracted<br> <br> # Research questions<br> ## RQ1</p> <p>The script `./RQ1.py` found in the `evaluation/` directory generates:</p> <p> - Figure 3 (time.pdf)<br> - The mean, min, max, and stddev of the times<br> - How many slices are computed below 10, 100, 1000, and 10000 seconds</p> <p>## RQ2</p> <p>The script `./RQ2.py` found in the `evaluation/` directory generates:</p> <p> - Figure 4 (loc.pdf)<br> - The mean, median, min, max, and stddev of the sizes<br> - The largest differences between the approaches<br> - The number of slices larger than the original program<br> <br> ## RQ3 and RQ4</p> <p>The process for these research questions is manual and requires comparing slices.<br> It cannot be automated. <br> We did make heavy use of `diff --side-by-side` in this analysis.<br> </p>
Investigating the Lower Mass Gap with Low Mass X-ray Binary Population Synthesis
<p>Title: Investigating the Lower Mass Gap with Low Mass X-ray Binary Population Synthesis </p> <p>Authors: Jared C. Siegel, Ilia Kiato, Vicky Kalogera, Christopher P. L. Berry,<br> Thomas J. Maccarone, Katelyn Breivik, Jeff J. Andrews, Simone S. Bavera,<br> Aaron Dotter, Tassos Fragos, Konstantinos Kovlakas, Devina Misra,<br> Kyle A. Rocha, Philipp M. Srivastava, Meng Sun, Zepei Xing,<br> and Emmanouil Zapartas<br> <br> Table: Summary of Population Synthesis Models</p> <p>Columns:<br> Population parameters:<br> CE: efficiency of common envelope unbinding, either:<br> * 1 or 5<br> CE merge: stars without a distinct core-envelope boundary that instigate a CE <br> event are either assumed to survive the CE (Optimistic) or are <br> assumed to merge (Pessimistic).<br> QMIN: minimum ZAMS ratio, either:<br> * 0.01<br> * Lifetime limited (pre-main-sequence lifetime of the secondary is shorter<br> than the main-sequence lifetime of the primary)<br> BRK: prescription for magnetic braking of a tidally coupled magnetic wind, either:<br> * H02: Hurley et al. 2002<br> * IT03: Ivanova & Taam 2003<br> ACC: amount of mass accreted during Roche-lobe overflow, either:<br> * 50% efficiency<br> * State dependent: (i) limited to ten-times the thermal rate if the accretor is a main-sequence, <br> Hertzsprung gap or core helium burning star, or (ii) unlimited if the accretor is a giant branch, <br> early asymptotic giant branch or asymptotic giant branch star. <br> Compact objects also experience conservative mass transfer, provided the <br> mass-transfer rate is sub-Eddington.<br> <br> Selection effect parameters:<br> weighting: logs what selection effects were considered for the detection weighted results, either:<br> *prob_occ: the probability that an outburst occurs and turns off during the survey<br> *prob_see: the probability that the outburst is bright enough to be detected<br> *prob: the product of prob_occ and prob_see<br> <br> RIA_nu: determines when the transition to radiatively inefficient accretion occurs, either:<br> *0 (no transition) or 0.05<br> <br> Results:<br> f_gap_N: fraction of LMXB systems with a mass gap BH (M<4.5Msol)<br> f_AIC_N: fraction of LMXB systems where the BH formed through accretion induced collapse of a NS<br> f_AIC_gap_N: fraction of LMXB systems where the BH formed through accretion induced collapse of a NS and<br> the BH is in the mass gap<br> fw_X_N: detection weighted fraction of X (e.g., gap, AIC, AIC_gap)<br> <br> Notes:<br> Each quantity is calculated for 5000 snapshots of the evolution tracks. <br> Here we report the Nth percentiles (e.g., 16, 50, 84) of each quantity.</p>
Photometry of outer Solar System objects from the Dark Energy Survey I: photometric methods, light curve distributions and trans-Neptunian binaries - data release
<p>This repository contains the full data release for the 814 outer Solar System objects found in the Dark Energy Survey.</p> <p>A full description of the object search is described in <a href="http://(https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022)</a>, and a full description of the photometric processing is described in<a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230403017B/abstract"> Bernardinelli et al (2023)</a>. If you use these files, we ask you to cite the corresponding papers.</p> <p>The FITS table `y6_des_tnos_color.fits` contains both the orbital elements and the colors for each object. The full description of the orbital element information is given in Table 3 of <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022</a>). In addition to these, the table also includes the mean absolute magnitudes in each band, as well as mean <span class="math-tex">\((g-r, r-i, r-z)\)</span> colors and their corresponding covariance matrix, and the 68% confidence interval for the lightcurve amplitude.</p> <p>Inside the `fluxes` directory, the complete photometric record for each object is included in a `hdf5` file (for each object), and the MCMC chains for their fluxes and LCAs. The three Jupyter Notebooks included in the `notebooks` directory explains the columns and how to use these files to reproduce the results of the paper. Inside this directory there are also additional files needed to reproduce the code.</p> <p>The `binary` directory has the MCMC chains for their mutual orbits (in `.npy` files), as well as the astrometric and photometric record for the binary measurements. Another `README` file is included in that directory with a detailed explanation.</p>
Dataset: "GW190521: tracing imprints of spin-precession on the most massive black hole binary"
<p>This dataset contains the results presented in "<em>GW190521: tracing imprints of spin-precession on the most massive black hole binary</em>": <a href="https://arxiv.org/abs/2310.01544">https://arxiv.org/abs/2310.01544</a>.</p> <p>The accompanying repository used to take this data and generate the figures in the paper can be found at <a href="https://github.com/simonajmiller/gw190521-timedomain-release/">https://github.com/simonajmiller/gw190521-timedomain-release/</a>.</p>
Dataset from: The Impact of Angular Momentum Loss on the Outcomes of Binary Mass Transfer
<p>Data for the paper: The Impact of Angular Momentum Loss on the Outcomes of Binary Mass Transfer (<a href="https://arxiv.org/abs/2308.06666">https://arxiv.org/abs/2308.06666</a>).</p> <p>Associated data processing scripts can be found on github, at the fixed version linked here: <a href="https://github.com/reinhold-willcox/arXiv.2308.06666_scripts/tree/09b8ece5306d108b5890b74b7890b6e6455fe485">arXiv.2308.06666_scripts</a></p> <p>Each dataset corresponds to a variation in angular momentum loss value or mass transfer efficiency (often both), or in the mass transfer stability prescription. These all include the following COMPAS hdf5 groups: 'BSE_System_Parameters', 'BSE_RLOF', and 'BSE_Supernovae'. They are designed to be used in conjunction with the data processing scripts above, though you are free to try to write your own independent post-processing scripts to test the validity of the presented results. For any questions, please contact the corresponding author. </p>
Data and code for "Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification"
<p>It contains the code and the data to reproduce the figures in the paper "Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification" published in arXiv: https://arxiv.org/abs/2309.09976</p>
Breaking Barriers: Investigating the Sense of Belonging Among Women and Non-Binary Students in Software Engineering
<p>This is a package published with the paper "Breaking Barriers: Investigating the Sense of Belonging Among Women and Non-Binary Students in Software Engineering" at the Software Engineering Education and Training track of the 2024 International Conference on Software Engineering (<a href="https://conf.researchr.org/track/icse-2024/icse-2024-software-engineering-education-and-training-track">ICSE-SEET 2024</a>).</p> <p>The data includes the data used in a study about Sense of Belonging among women and non-binary students in software engineering Education.</p>
Selected L-Galaxies and binary_c output data (Yates+23)
<p>Dataset to accompany the publication entitled <a href="https://arxiv.org/abs/2310.15218"><strong>"The impact of binary stars on the dust and metal evolution of galaxies"</strong></a> by R. M. Yates, D. Hendriks, A. P. Vijayan, R. G. Izzard, P. A. Thomas, and P. Das.</p><p> </p><p>The L-Galaxies files included in this dataset were generated from the MM+binaryC and MM+singleStars versions of L-Galaxies, run in snapshot mode on representative sub-volumes of the Millennium-I and Millennium-II simulations, with the LITE_OUTPUT switch turned on. In detail:</p><p> </p><p><i><strong>MM+binaryC version:</strong></i></p><p><strong>SA_MM+binaryC_MRI_z0.00_0-5: </strong>Zip file containing output files (in binary format) for the MM+binaryC version of L-Galaxies in snapshot mode, run on treefiles 0-5 of Millennium-I at redshift 0.00.</p><p> </p><p><strong>SA_MM+binaryC_MRI_z0.00-8.22_0-5: </strong>Zip file containing output files (in binary format) for the MM+binaryC version of L-Galaxies in snapshot mode, run on treefiles 0-5 of Millennium-I at nine selected redshifts from 0.00 to 8.22.</p><p> </p><p><strong>SA_MM+binaryC_MRII_z#.##_40-79: </strong>Zip files containing output files (in binary format) for the MM+binaryC version of L-Galaxies in snapshot mode, run on treefiles 40-79 of Millennium-II at redshift #.## = [0.00,1.04,2.07,3.11,3.95,5.03,5.92,6.97,8.22].</p><p> </p><p> </p><p><i><strong>MM+singleStars version:</strong></i></p><p><strong>SA_MM+singleStars_MRI_z0.00_0-5:</strong> Zip file containing output files (in binary format) for the MM+singleStars version of L-Galaxies in snapshot mode, run on treefiles 0-5 of Millennium-I at redshift 0.00.</p><p> </p><p><strong>SA_MM+singleStars_MRI_z0.00-8.22_0-5:</strong> Zip file containing output files (in binary format) for the MM+singleStars version of L-Galaxies in snapshot mode, run on treefiles 0-5 of Millennium-I at nine selected redshifts from 0.00 to 8.22.</p><p> </p><p><strong>SA_MM+singleStars_MRII_z#.##_40-79: </strong>Zip files containing output files (in binary format) for the MM+singleStars version of L-Galaxies in snapshot mode, run on treefiles 40-79 of Millennium-II at redshift #.## = [0.00,1.04,2.07,3.11,3.95,5.03,5.92,6.97,8.22].</p><p> </p><p> </p><p><i><strong>Other:</strong></i><br><strong>L-Galaxies_and_binaryC_processing:</strong></p><p><strong>a) </strong>A set of simple python scripts to read the L-Galaxies outputs, process them into samples (as python "dictionaries"), and make some plots (including those presented in Yates+23). To use, place the desired output files into the /output folder, and execute main_lgals.py<i>. </i>Switches are provided at the top of main_lgals.py to modify functionality. The relevant data structure for python is also provided (LGalaxy_snapshots_liteOutput.py), as are conversions between snapshot number, redshift, and other properties for Millennium-I and Millennium-II snapshots for reference (Millennium_Planck1_Snapshots_Redshifts.txt and MillenniumII_Planck1_Snapshots_Redshifts.txt). For catalogues containing star formation histories, the corresponding times and widths of each bin are read from Database_SFH_table.fits.</p><p><strong>b)</strong> The binaryC ensemble files used in Yates+23 are included, along with a script to read these ensemble files and generate L-Galaxies yield tables from them (process_binaryc_outputs.py). Switches are provided at the top of process_binaryc_outputs.py to modify functionality.</p><p> </p><p> </p>
Application of Ideal Binary Masking to Disordered Speech
ClinicalTrials.gov study NCT05244603. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: A pulsar-helium star compact binary system formed by common envelope evolution
Open the record for dataset details and reuse information.
Yellowstone Upper Basin Geyser binary eruption data (April 2007- October 2008; revised)
Open the record for dataset details and reuse information.
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.