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5,462 results for “binary”
Replication package for "An Empirical Evaluation of Static, Dynamic, and Hybrid Slicing of WebAssembly Binaries"
<p>This is the replication package that accompanies the paper "An Empirical Evaluation of Static, Dynamic, and Hybrid Slicing of WebAssembly Binaries".</p> <p>It is structured as follows.</p> <p> - RQs.py is the script that generates the data that is included in the paper. For each research question, it generates statistics and plots.<br> - The .csv files and nok.txt are used by RQs.py. They contain the raw size and timing data for each slice. The headers of the .csv files indicate what each column represents.<br> - `sqlite-slices.csv` contains the raw data for RQ8, which is contained in the paper<br> - The .tar.gz files contain the source and the slices generated by each slicer, namely:<br> - `static_slices.tar.gz` contains the slices generated by CsE, the static slicer. For example, the file `static_slices/adpcm/adpcm_ah1_254_expr/static_adpcm.wat.slice` is the slice of the adpcm program taken with respect to the variable `ah1` at line `254` of the original `.c` program.<br> - `slice.tar.gz` contains slices from the dynamic slicers:<br> - slice-sce - the SCE slices<br> - slice-ces - the CES slices<br> - slice-cse - the CSE slices<br> - slice-sces - the SCES slices<br> - slice-cses - the CsES slices<br> - `src.tar.gz` includes the unsliced `C` and `wasm` source<br> - `bc.support.tar.gz` includes the complete `bc` source code <br> - `sqlite.tar.gz` contains the slices of RQ8, each in a directory named after the slicer used. For example, the file `sqlite/CES/avg.wat` contains the CES slice for the `avg` slicing criterion.<br> <br>In order to regenerate the data and plots that are in the paper, you should simply run:</p> <p>```<br>python RQs.py<br>```</p>
Data release: Understanding binary neutron star collisions with hypermodels
<pre># Data release for "Understanding binary neutron star collisions with hypermodels" ## Summary For each event, we include sub-directories of the studies performed. In each directory, we provide the `bilby_pipe` configuration (`.ini`) files, `bilby` result file (`.json`). We also provide figures, samples (in the form of `.csv` files), and summary statistics where they are relevant. ## Hypermodel script All results obtained using the *hypermodel* technique use the script `multiwaveform.py`. We include this script at the top-level of this directory. To reproduce results, in each configuration file, replace the `analysis-executable` with the path to this script. ## Software versions All results obtained using * bilby_pipe=1.0.3: (CLEAN) 1220709 2021-05-14 06:28:48 -0700 * bilby=1.1.2: (CLEAN) e5481028 2021-07-05 16:45:01 +0100</pre>
Homogenized anisotropic thermal conductivity on microstructure of binary composite with thermally imperfect diffuse interface
<p>This dataset contains supplementary data and utilities of the publication "A diffuse-interface model of anisotropic interface thermal conductivity and its application in thermal homogenization of composites" (<a href="https://doi.org/10.1016/j.scriptamat.2022.114537">Yang, 2022</a>).</p> <p>We performed thermal homogenization on microstructures with three types of inclusion geometries, in which ones with oval-shaped and fiber-shaped inclusions were read from characterized/generated digital microstructures, and ones with irregular-shaped inclusions were imported from the phase-field additive manufacturing simulations <a href="https://doi.org/10.1002/gamm.202100017">(Zhou, 2021)</a>. Si-Hf-N was chosen as the composite material system with β-Si<sub>3</sub>N<sub>4</sub> as the sole matrix phase and HfN as the sole inclusion phase. Direct homogenization method with the linear temperature BCs (see Supplementary Note 3 of the publication) was adopted.</p> <p>This dataset documents homogenized anisotropic thermal conductivity of corresponding microstructure as a tensor with normalized interface thermal resistance varying from 10<sup>-8</sup> to 10<sup>12</sup>. Voxelized digital microstructures and utilities for visualizing the overall thermal anisotropy are also attached.</p> <table> <tbody> <tr> <td>Properties</td> <td>Value</td> <td>Dimension</td> <td>Description</td> </tr> <tr> <td><span class="math-tex">\(k_\mathrm{(i)}\)</span></td> <td>90</td> <td><span class="math-tex">\(\mathrm{W~m^{-1}~K^{-1}}\)</span></td> <td>Thermal conductivity of HfN inclusion (<a href="https://10.1016/j.mtphys.2020.100256">Li, 2020</a>)</td> </tr> <tr> <td><span class="math-tex">\(k_\mathrm{(m)}\)</span></td> <td>180</td> <td><span class="math-tex">\(\mathrm{W~m^{-1}~K^{-1}}\)</span></td> <td>Thermal conductivity of β-Si<sub>3</sub>N<sub>4</sub> matrix (<a href="https://10.1016/s0955-2219(98)00258-1">Li, 1999</a>)</td> </tr> <tr> <td><span class="math-tex">\(\ell\)</span></td> <td>5</td> <td><span class="math-tex">\(\mathrm{nm}\)</span></td> <td>Diffuse interface width</td> </tr> <tr> <td><span class="math-tex">\((X,Y,Z)\)</span></td> <td>(500,500,500)</td> <td><span class="math-tex">\(\mathrm{nm}\)</span></td> <td>Simulation domain size</td> </tr> </tbody> </table> <p><strong>Notice:</strong> The digital microstructure has been voxelized, which can be loaded by default as a 200x200x200 numpy array (see utilities.ipynb). In order to perform the homogenization, interface smoothening is required, i.e., to generate diffuse interfaces. In this work, we smoothened the interface by operating transient Allen-Cahn calculation with finite timesteps. See Supplementary Note 4 of the publication for more information.</p>
Dataset: High-accuracy simulations of highly spinning binary neutron star systems
<p><strong>Dataset for Gravitational Waveforms for the work of Dudi et al., "High-accuracy simulations of highly spinning binary neutron star systems"; arXiv: 2108.10429 </strong></p> <p> </p> <p><strong>The naming of the files: </strong>EOS_SpinSetup_NumberOfPoints_Mode.dat<br> EOS: SLy<br> SpinSetup: dd - down down; ud - up, down; uu037 -- up, up with dimensionless spin of 0.37; uu057 -- up, up with dimensionless spin of 0.57<br> NumberOfPoints: Points inside the finest level: 96, 144, 192, 240<br> Mode: Only 2,2-mode available <br> </p>
Ground truth of binary disassembly
<p>Dataset of binary disassembly, including x86/x64, arm32/aarch64, and mips32/mips64 binaries. Ground truth of binary disassembly includes instruction starts, function entries and jump tables.</p>
Binary (presence/absence) maps of phototrophic biofilms - Otemma floodplain, June 2020 - November 2020
<p>Maps of presence/absence of phototrophic biofilms - Otemma floodplain, June 2020 - November 2020</p> <p>Presence/Absence is binary, where 0 is "not biofilm" and 1 is "biofilm". <strong>Binary maps generated from probability maps using date-by-date logistic models of Roncoroni et al. (2022)</strong>.</p> <p>For further information see Roncoroni et al.(2022), <a href="http://dx.doi.org/10.1080/01431161.2022.2079963">http://dx.doi.org/10.1080/01431161.2022.2079963</a></p> <p>Details:</p> <ul> <li>Format: .ascii </li> <li>Name format: mmdd_Binary (where mm is the month, and dd the day)</li> <li>Coordinate system: CH1903+ LV95 (EPSG:2056)</li> <li>Spatial resolution: 0.05 m</li> </ul>
Chiral flow in a binary mixture of two-dimensional active disks - Supplementary Data
<p>Supplementary data for the paper: "Chiral flow in a binary mixture of two-dimensional active disks"</p> <p> </p> <ol> <li>*_raw_trajectories.pkl.xz files contain tracking data for each experiment as a pickled Pandas DataFrame object (xz compression) in pixel units (particle diameter=77px)</li> <li>*_w.pkl.xz files contain self-rotation velocity for each experiment as a pickled Pandas DataFrame object (xz compression) in rad/s</li> <li>experiments_properties.dat is a summary table containing the average fields (including vorticity and kinetic energy) for all experiments. Also subdivided by species.</li> </ol>
Dataset for paper "1-to-1 or 1-to-n? Investigating the effect of function inlining on binary similarity analysis"
<p>This is the dataset constructed and used in the paper "1-to-1 or 1-to-n? Investigating the effect of function inlining on binary similarity analysis". </p> <p>We release it with its ground truth labels to facilitate further research in binary similarity analysis under function inlining. Besides, we also welcome others to find miss-labeling in this dataset and fix them. We hope our dataset could help researchers conduct their studies and improve the binary similarity analysis techniques.</p> <p>Binaries of dataset-I can also be accessed from https://github.com/SoftSec-KAIST/BinKit. It is named the "normal dataset".</p> <p>Any miss-labeling can be reported at https://github.com/island255/TOSEM2022.</p> <p>Using this dataset, please cite our paper, "1-to-1 or 1-to-n? Investigating the effect of function inlining on binary similarity analysis".</p>
Curated data-set of crystal-structure prototypes of binary and ternary sp-d valent compounds
<p>Curated collection of binary and ternary compounds of sp-valent elements and d-valent elements and their crystal-structure prototype. The data set includes binary and pseudo-binary compounds in binary prototypes (BinaryPrototype-BinaryCompound.csv), offstoichoimetric and ternary compounds in binary prototypes (BinaryPrototype-BinaryOffstoichiometricAndTernaryCompound.csv) and ternary compounds in ternary prototypes (TernaryPrototype-TernaryCompound.csv). The first column in each data set corresponds to the crystal-structure prototype, the second column to the chemical composition. Ternary compositions labelled A-B+C indicate that element B and element C occupy the same sublattice of the crystal structure. A+B-C correspondingly indicates that element A and element B occupy the same sublattice. These data sets were used to construct structure maps for predicting the crystal structure of a compound from only its chemical composition. For details on curation, further discussions and structure maps see original publications (Chem. Mater. 28, 2550−2556, 2016 and Modelling Simul. Mater. Sci. Eng. 25, 074002, 2017).</p>
Artifacts for the 2022 ATVA Paper: SISL: Concolic Testing of Structured Binary Input Formats via Partial Specification
<p>Artifacts for the evaluation of the publication <em>SISL: Concolic Testing of Structured Binary Input Formats via Partial Specification,</em> which will be published as part of the 2022 <em>International Symposium on Automated Technology for Verification and Analysis </em>(<a href="https://atva-conference.org/2022/">ATVA</a>).</p> <p>Usage of the provided artifacts requires installing the SISL tooling first. In this regard, please refer to either the <a href="https://agra-uni-bremen.github.io/sisl/">SISL user manual</a> or the documentation in the <em><a href="https://github.com/agra-uni-bremen/sisl">sisl</a> </em>and <a href="https://github.com/agra-uni-bremen/sisl-vp"><em>sisl-vp</em></a> GitHub repositories. Both <em>sisl</em> and <em>sisl-vp</em> need to be installed to system-wide locations in order to be able to use the scripts provided as part of these artifacts.</p> <p>More information on using the artifacts is available in the README.md file.</p>
The cosmic carbon footprint of massive stars stripped in binary systems
<p># Structure</p> <p>## Overview</p> <p>The folder data/ contains the inlists and mod files used in this work. Intermediate data (like history or profile) files must be regenerated from the provided files.</p> <p>The folder plots/ contains a jupyter notebook set-up to remake all plots (assuming the data is saved in the data/ folder). There are also additional scripts and files needed to reproduce this work.</p> <p>The griffith.txt file is the data from https://ui.adsabs.harvard.edu/abs/2021arXiv210309837G/abstract and was accessed from https://github.com/giganano/VICE/blob/master/vice/yields/ccsne/S16/W18F/FeH0/v0/explosive/c.dat</p> <p><br> ## Data folders</p> <p>corehedep - Evolution from ZAMS to end of core helium burning<br> coreodep - Evolution from end of core helium burning to end of core oxygen burning<br> cc - Evolution from end of core oxygen burning up to core collapse<br> ccsn - Evolution from core collapse to shock breakout</p> <p>engmc - Tests variations in the injection energy and mass cut of ccsn explosions<br> spacetime - Test variations in space/time resolution of ccsn explosions<br> ccsn_t_m - Test variations in injection time and injection mass of ccsn explosions</p> <p>## Sub-folders</p> <p>corehedep/base - Base folder with inlists for this set of models<br> corehedep/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> corehedep/single - Contains a folder for each mass for the single stars (11-45)<br> corehedep/net/23 - A single star 23msun model ran with a larger nuclear network</p> <p>coreodep/base - Base folder with inlists for this set of models<br> coreodep/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> coreodep/single - Contains a folder for each mass for the single stars (11-45)</p> <p>coreodep/mesh - Test variations with respect to space and time during carbon burning<br> coreodep/overshoot - Test variations with respect to overshoot during carbon burning<br> coreodep/net - A single star 23msun model ran with a larger nuclear network</p> <p>cc/base - Base folder with inlists for this set of models<br> cc/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> cc/single - Contains a folder for each mass for the single stars (11-45)</p> <p><br> ccsn/base - Base folder with inlists for this set of models<br> ccsn/binary - Contains a folder for each mass for the binary-stripped stars (11-45)<br> ccsn/single - Contains a folder for each mass for the single stars (11-45)<br> ccsn/laplace_binary - Contains core collapse explosions of the binary-stripped models from Laplace et al 2021<br> ccsn/laplace_single - Contains core collapse explosions of the single star models from Laplace et al 2021</p> <p>spacetime/base - Base folder with inlists for this set of models<br> spacetime/binary - Test variations in space/time resolution of ccsn explosions</p> <p>ccsn_t_m/base - Base folder with inlists for this set of models<br> ccsn_t_m/binary - Test variations in injection time and injection mass of ccsn explosions</p> <p>engmc/base - Base folder with inlists for this set of models<br> engmc/binary - Tests variations in the injection energy and mass cut of ccsn explosions</p> <p><br> ## Notes</p> <p>Folders with the name '_k' have enhanced profile output for use in Kippenhan plots.</p> <p>Folders with the name '_v' have enhanced profile output for use in the video of the shock explosion.</p> <p>Each numbered folder contains a set of inlists used (which usually only vary one or two parameters), the rest of the inlists are stored in the base/ folders (See the submit.sh files for how to get MESA to read these files). They also contain a initial.mod file (which is the starting point for this phase of evolution), this is a softlink to the final.mod file from the previous phase (thus coreodep soft links to files in corehedep, corehdep uses MESA's built in ZAMS models to start).</p> <p>The folders that handle the core collapse explosions have additional .mod files that handle each phase of the explosion. See the base/ folders for details on the order.</p> <p>## Files</p> <p>cacheHist.py - Runs mesaplot code to turn history files into a python binary file for faster reading.<br> plotKip.py - Does a quick kippenhan plot for diagnostics</p> <p> </p>
Revisiting the Milky Way — White Hole Binary with Energy Backlash and White Hole Seed Signature
<p>The observation made on Sep 25, 2022 on Harvard-Smithsonian MicroObservatory detected a white hole binary with white hole seed signatures in the collision momentum. A wide area of tree-shaped energy backlash were detected due to the high energy phenomenon with the angular momentum of the observation. The scattering of miniature black holes and miniature white holes was the main composition of the observation data, and contributed to the overall interference patterns in the observational result. The cluster formation in the observation mainly originated from between 948 and 999 Kelvins, and the energy backlash to the lower thermodynamic regime. The resonance signatures on the star systems above ~1360 Kelvins were observed, with lower surface threshold located between 1143 and 1263 Kelvins. The observation is consistent with the observation made on the Ring Nebula M57 on Jun 12, 2022, with silhouettes of the white hole seed and black hole seed collision momentum between 0 and 73.7 Kelvins. The observations confirmed the geodesics of ring singularity of black hole studies to be the collision momenta of black hole and white hole seeds.</p>
Applying the metallicity-dependent binary fraction to double white dwarf formation: Implications for LISA -- COSMIC + Ananke data
<p>This dataset contains all data required to run the pipeline which produces the results and figures in Thiele+2022 including:</p> <p>- results of all COSMIC simulations for the fiducial, alpha25, alpha5, and q3 models for each double white dwarf type and binary fraction assumption</p> <p>- metallicities, ages, positions and star particle kernel lengths from the Ananke framework of galaxy m12i in the Latte suite of the FIRE-2 simulations.</p>
CAPYBARA: Decompiled Binary Functions and Related Summaries
<p><strong>CAPYBARA</strong></p> <p>This dataset is published as part of the paper: "Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries". It includes both the training/evaluation data as well as the raw data.</p> <ul> <li>The data_split folder contains .pickle files with the test and validation repos, the train repos are all the remaining repos. It also contains a .pickle file with a dictionary that specifies the optimization level for each repository.</li> <li>In the processed_data folder, the processed datasets can be found in .csv format. The columns of the CSV are the `summaries`, the `original documentation`, the `repo`, the `source` and `decompiled` code, the `function name` and a unique `identifier`. We also include the deduplicated samples in separate CSVs.</li> <li>The processed training files can be found in the training_data folder. `Source C`, `decompiled`, `demiStripped`, and `stripped` can each be found in their corresponding folders and are split into deduplicated and regular datasets. The data is further split into .jsonl files, for the train, test, and validation sets. These .jsonl files can be loaded into CodeT5 and CodeXGlue as is. </li> <li>The raw_data folder contains all the stripped and decompiled functions without any pre-processing applied. The columns of the CSV are `repo`, the `location`, the `original` code, the corresponding `decompiled` code, the `function name`, a unique `identifier` key, and the corresponding `documentation` for both the decompiled and stripped functions.</li> </ul> <p><strong>License</strong></p> <p>Copyright 2022 ##########</p> <p>Licensed under the Apache License, Version 2.0 (the "License");</p> <p>you may not use this file except in compliance with the License.</p> <p>You may obtain a copy of the License at:</p> <p> </p> <p>http://www.apache.org/licenses/LICENSE-2.0</p> <p>Unless required by applicable law or agreed to in writing, software</p> <p>distributed under the License is distributed on an "AS IS" BASIS,</p> <p>WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</p> <p>See the License for the specific language governing permissions and</p> <p>limitations under the License.</p>
Properties of Galactic B[e] supergiants. X. Refined orbit and fundamental parameters of the HD 327083 binary system.
<p>This dataset developed with MESA version r23.05.1 (May 30, 2023) and SDK version 23.7.3 (July 24, 2023), presents a comprehensive simulation of the binary system HD 327083, focusing on its evolution under specific initial conditions. </p>
Data release for "Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries"
<p>Posterior samples and code to reproduce all figures associated with <em>Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries</em>.</p> <p>The <code>parameter_estimation</code> folder contains the following:</p> <ul> <li><code>campaigns</code>: Analyses of eccentric quasi-monochromatic binaries, gridding over gravitational-wave frequency, eccentricty, and SNR. See the <code>README</code> inside for more information. The resulting posteriors are used in Figure 3, and the fitting formula Eq. 21. </li> <li><code>fiducial_source_checks</code>: Analyses that vary parameters other than SNR and frequency to investigate the effect on the minimum eccentricity that can be recovered. Used in Figure A1. Posteriors used for Figure 4 are also found in the <code>golden_binary</code> folder. </li> <li><code>nhat_runs</code>: Various analyses used for Figures 5, 6, B1, and C1. See the <code>README</code> inside for more information. Also see the <code>README</code> in <code>eccentric_gb_scripts</code> and links therein.</li> </ul> <p>Within each parameter estimation output folder there are <code>.dat</code> files for quantities such as the source SNR, log evidence, and posterior. There are also configuration <code>.yaml</code> files which are used by the BALROG code. These contain:</p> <ul> <li><code>lisa_config</code>: Parameters describing the LISA mission, including the duration in seconds. </li> <li><code>nessai_opts</code>: Settings used by nessai (the sampler used in this work). </li> <li><code>priors</code>: Lower and upper limits used for each source parameter. </li> <li><code>sources</code>: Injected values for each source parameter.</li> </ul> <p>The <code>notebooks</code> folder contains code to produce Figures 2, 3, 4, 6, and A1. Also included are notebooks to produce the fitting formula Eq. 21 (<code>emin_grid.ipynb</code>), and to inspect analyses in the <code>campaigns</code> and <code>fiducial_source_checks</code> folders.</p> <p>The <code>eccentric_gb_scripts</code> folder contains code to produce Figures 1, 5, B1 and C1. See the <code>README</code> inside for more information.</p>
Mass-redshift dependency of Supermassive Black Hole Binaries for the Gravitational Wave Background
<p>These show the posterior distributions as supplementary material for arXiv:2305.18293 and doi:10.1093/mnras/stae1219</p> <p>The corner plots for the complete 20 parameters with amplitudes hc = 0.5e-15, 1e-15, 2e-15, 3e-15, and 4e-15 for both circular and eccentric population of SMBHBs are presented in the 'free_parameters' folder.</p> <p>The corner plots for the 16 parameters with amplitudes hc = 0.5e-15, 2e-15, and 4e-15 for both circular and eccentric population of SMBHBs using the fitted BH-bulge mass parameters from the simulations can be found in the 'simulation_parameters' folder.</p> <p>The posterior distributions are shown as black contours, while the prior distributions are denoted by light green lines. The top right inlay figure shows the median and central 2sigma range of the recovered characteristic spectrum, where the 5 points denote the frequency bins of 1/(25years), 2/(25years), 3/(25years), 4/(25years) and 5/(25years), which are used as the input data for the Bayesian analysis. To guide the eye the analytic sensitivity curve from the IPTA DR2 is also plotted.</p>
Wide post-common envelope binaries from Gaia: orbit validation and formation models
<p>Inlists and run_star_extras used in the models of Yamaguchi et al. (2024, submitted to PASP, arXiv:2405.06020<span>) ("Wide post-common envelope binaries from Gaia: orbit validation and formation models"). See Rees et al. (2024) for details about TP-AGB routines.</span></p> <p>MESA r22.05.1, measdk 22.6.1</p>
Dataset related to the article "Binary classification of copy number alteration profiles in liquid biopsy with potential clinical impact in advanced NSCLC"
<p>This record contains original data used in the article "Binary classification of copy number alteration profiles in liquid biopsy with potential clinical impact in advanced NSCLC" to develop a linear support vector machine (SVM) classifier to predict chromosomal instability. </p> <p>We retrospectively evaluated the results of plasma NGS analysis performed at our Institution by using the AVENIO ctDNA Expanded Kit, a panel of 77 genes, which detects the major classes of genetic alterations. Binary classification, into “stable” (SCP) or “unstable” (UCP) chromosomal profiles, was initially performed by visual inspection of individual CNV alteration profiles by two independent professionals of our group. Then we decided to implement a support vector machine (SVM) classifier to automatically classify CNV profiles as SCP or UCP, beyond operators’ experience. We considered the segmented log2 ratios (.cns) files provided by the CNV kit software and computed three features (Segments, Size, Chromosomes). An alteration (“occurrence of instability”) in the CNV profile was defined each time we found a DNA segment of any size with absolute value of the log2 copy ratio exceeding a fixed cut-off. Two different cut-off values on log2 copy ratio were examined: 0.1 and 0.2. Once the cut-off was defined, three features were considered as covariates in the SVM classifier: 1) number of altered segments (Segments), 2) total length of altered regions (Size) and 3) number of affected chromosomes (Chromosomes).</p> <p>The “dataset_0.1.txt” and “dataset_0.2.txt” files are the original data matrices obtained by considering a cut-off of 0.1 and 0.2, respectively, on the absolute value of the log2 copy ratio.</p> <p>Rows represent available samples in our study (n=177). Columns contain the following variables: anonymized sample IDs (Sample), the class, “stable” or “unstable”, as assigned by two independent professionals of our group (Class), the corresponding binary label (Label: 0 for “stable”, 1 for “unstable”), the three features used as covariates in the SVM classifier and computed as described above (Segments, Size, Chromosomes).</p> <p><br>For the detailed results of our work, please refer to the full article.</p>
Inlists for "Rethinking Thorne-Żytkow Object Formation: Assembly via Common Envelope in Field Binaries"
<p>Inlists used in the models of Everson et al. (2023, submitted to ApJ, arXiv:2310.08658) ("Rethinking Thorne-Żytkow Object Formation: Assembly via Common Envelope in Field Binaries").</p> <p> </p> <p>Version: MESA r22.05.1, mesasdk 22.6.1</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.