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5,462 results for “binary”
Dimensionality reduction of local structure in glassy binary mixtures
<p>This dataset is associated with "<em>Dimensionality reduction of local structure in glassy binary mixtures</em>", by D. Coslovich, R. L. Jack, J. Paret. It includes data and workflow to allow for the replication of the analysis and figures of the manuscript.</p> <p>To reproduce the workflow and the figures, download and extract the package sprouts.tar.gz, then execute</p> <pre><code class="language-bash">./make all</code></pre> <p>This will create the figures under plots/paper and recompile project.pdf. If the workflow fails because of missing dependencies, read the project.pdf file below and check the requirements, or download the <a href="http://www.docker.com">docker</a> image sprouts-docker.tar.gz, load the image and execute the same command within the container.</p> <p><em>Make sure you have at least 20 Gb of free space on your disk if you use the package, and 22 Gb if you use the docker image.</em></p> <p>To speed up the execution of the workflow, download the data cache (cache.tar) and extract it at the root of the project folder.</p> <p>See project.pdf below for full details about the workflow.</p> <p><strong>Changelog</strong>:</p> <ul> <li>1.0.2: use more portable she-bang in scripts</li> <li>1.0.1: fix typos, remove some dead code</li> <li>1.0.0: initial submission</li> </ul>
Datasets for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants"
<p>All data used for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants", Criswell, A.W., et al. (2022). The code used to create the paper results from this data can be found at <a href="https://github.com/criswellalexander/hbpm_paper">https://github.com/criswellalexander/hbpm_paper</a> and the underlying software package can be found at <a href="https://github.com/criswellalexander/bayestack">https://github.com/criswellalexander/bayestack</a>.</p>
Ups!... I did it again: unveiling the hidden companion in Upsilon Sagittarii, a unique binary system at a second mass transfer stage
<p>Input files and simulation results for stellar evolution tracks computed for the paper "Ups!... I did it again: unveiling the hidden companion in Upsilon Sagittarii, a unique binary system at a second mass transfer stage". Version 15140 of MESA was used for the simulations. More details in the README.txt file and in the paper.</p>
Binary LCD codes
<p>LCD</p>
Binary Stanford Sentiment Treebank 2 (SST-2)
<p>Binary Stanford Sentiment Treebank (SST2) is a binary version of SST and Movie Review dataset (the neutral class was removed), that is, the data was classified only into positive and negative classes.</p> <p>The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_<k>.pkl: pandas DataFrame with k-cross validation partition</p>
Cover Your Basis: Comprehensive Data-Driven Characterization of the Binary Black Hole Population
<p>The accompanying data and code release for the analyses presented in "Cover Your Basis: Comprehensive Data-Driven Characterization of the Binary Black Hole Population". See the github paper repository at https://github.com/bruce-edelman/CoveryingYourBasis and the arxiv release of the paper at: https://arxiv.org/abs/2210.12834</p>
Dataset for "Binary-coupling sparse Sachdev-Ye-Kitaev model: an improved model of quantum chaos and holography"
<p>Spectral data for binary, unary and Gaussian-coupling, sparse and dense Sachdev-Ye-Kitaev model used for the publication.</p> <p>Directories are named by the number of Majorana fermions, and the coupling type (binary, Gaussian, unary) and the number of non-zero couplings are indicated in the file name.</p> <p>In each file, the computed eigenenergies are given in little-endian double-precision floating numbers, eight bytes for each eigenstate, in ascending order for each parity sector (even followed by odd) for each generated sample.</p> <p>Except for N = 32 and 34 where a single sample is given, each file contains 2^{24-(N/2)} samples, 131 072 for N = 14, 65 536 for N = 16, ..., and 512 for N = 30.</p>
Reproduction package for the paper "Forming equal mass planetary binaries via pebble accretion"
<p>This is a basic reproduction package for the paper "Forming equal mass planetary binaries via pebble accretion" by T.J. Konijn et al.</p>
Precessing binary-black-hole numerical relativity catalogue (complete data release)
<p>This page contains the minimal data release associated with the catalogue presented in <a href="https://dcc.ligo.org/DocDB/0186/P2300054/001/catalogue.pdf">A catalogue of precessing black-hole-binary numerical-relativity simulations</a>. This catalogue contains 80 single-spin precessing black-hole-binary configurations. </p> <p>The content of the data release is described <a href="https://data.cardiffgravity.org/bam-catalogue/">here</a>, along with instructions on how to parse the data.</p>
Incentivized binary choice under risk - data from experiment
<p>The dataset contains lotteries (Distributions_stimuli) for the experiment and the subjects' choices. The computerized experiment was performed using an application created for this purpose. The experiment was run in 2018 in the laboratory of the University of Gdańsk, Poland. The subjects were undergraduate students who volunteered for the survey. An incentives system allowing for gains and losses was applied. </p>
Daset from the paper "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"
<p>This repository contains the dataset produced by the population-synthesis code SEVN for the paper:<br> "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"</p> <p>In this paper, we exploit the SEVN code (publicly available at <a href="https://gitlab.com/sevncodes/sevn">https://gitlab.com/sevncodes/sevn</a>) to analyse the formation and properties of binary compact objects.</p> <p>The repository also includes the initial condistions used as input and the SEVN version used to run the simulations.</p> <p>#Content</p> <p>The repository contains the following folders:</p> <p>- data_from_simulations: the folder contains all the data produced by the simulations and used in the Iorio+22 paper<br> - InitialConditions: the folder contains all the intial conditions (and the code to generate them) that have been used for the Iorio+22 paper<br> - SEVN_iorio22: this folder contains the version of the SEVN code that has been used to run the simulations in the Iorio+22 paper</p> <p>Each folder contains a specific README with additional information</p> <p> </p> <p>#Contatcts</p> <p>giuliano.iorio.astro@gmail.com</p> <p> </p> <p> </p>
SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation
<p>The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA.<br> <br> The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively.<br> <br> The structure of the data is as follows:<br> 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks.<br> 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels.<br> <br> Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type.<br> <br> 1-Panels-Ortho<br> |----Train<br> |----Test<br> -----Validation<br> <br> 1-Panels-Mask<br> |----Train<br> |----Test<br> -----Validation<br> <br> 2-NoPanels-Ortho<br> |----Train<br> |----Test<br> -----Validation</p>
BISCUIT: Causal Representation Learning from Binary Interactions
<p>This repository contains the datasets from the paper "BISCUIT: Causal Representation Learning from Binary Interactions" (<a href="https://phlippe.github.io/BISCUIT/">link</a>) by Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves. </p> <p><strong>iTHOR Embodied AI </strong>- The Embodied AI dataset is generated with the iTHOR simulator. We use the default kitchen environment, FloorPlan10, and position the robot in front of the kitchen counter. The robot interacts with different objects in the environment, including a Microwave, cabinet, stove, and an egg. For more details on the dataset, see <a href="https://github.com/phlippe/BISCUIT">our GitHub repository</a> and the appendix of our paper.</p> <p><strong>CausalWorld </strong>- The CausalWorld environment implements a tri-finger robot which can interact with a cube in the center of a stage. We additionally introduce causal variables for the friction parameters of the stage, floor and cube, as well as color changes. For more details on the dataset as well as the code to generate this dataset, see <a href="https://github.com/phlippe/BISCUIT">our GitHub repository</a> and the appendix of our paper.</p> <p><strong>Voronoi</strong> - The Voronoi benchmark allows for creating causal systems with arbitrary number of causal variables and causal graphs. We provide datasets with 6 and 9 variables, as well as systems with minimal number of interactions. For details, see <a href="https://github.com/phlippe/BISCUIT">our GitHub repository</a> and our paper.</p>
MultiScent-20 Data - Binary responses (correct vs. incorrect answers) data from full sample, for each odour
<p>Data used for IRT analysis in the article</p> <p>"The Multiscent-20: A Digital Odour Identification Test Developed with Item<br> Response Theory", with the following authors:</p> <p>Marcio Nakanishi 1, *, Pedro Renato de Paula Brandão 2, *, Gustavo Subtil Magalhães<br> Freire 1 , Luis Gustavo do Amaral Vinha 3 , Marco Aurélio Fornazieri 4 , Wilma Terezinha<br> Anselmo Lima 5 , Claudia Galvão 6 , Thomas Hummel 7</p> <p> </p> <p>1 Department of Otorhinolaryngology, University Hospital of Brasília, University of<br> Brasília, Brasília, DF, Brazil<br> 2 Neurology Department, Hospital Sírio-Libanês, Brasília, DF, Brazil<br> 3 Statistics Department, University of Brasília, DF, Brazil<br> 4 State University of Londrina, Pontifical Catholic University of Paraná<br> 5 Department of Ophthalmology and Otorhinolaryngology, Ribeirão Preto Medical<br> School-University of São Paulo, Brazil<br> 6 NOAR Brasil Ltda, São Paulo-SP, Brazil<br> 7 Smell and Taste Clinic, Department of Otorhinolaryngology, Technical University of<br> Dresden, Dresden, Germany</p> <p> </p> <p> </p> <p>MultiScent-20 descrition:</p> <p>The Multiscent-20 is a portable tablet computer with an integrated hardware<br> system consisting of a central processing unit, touchscreen, Wi-Fi antenna, USB dock,<br> power supply, and rechargeable battery. Additionally, it contains an odour system<br> composed of 20 microcartridges, an air filter, a system that generates a dry air stream,<br> and an odour-dispensing opening. The device is capable of presenting 20 different<br> odours from individual odour capsules, which store the olfactory stimuli by incorporating<br> the odours into an oil-resistant polymer. The capsules are loaded through an insertion<br> port on the back of the device.<br> A software-controlled processing unit governs the flow of dry air, generating a<br> constant air stream that passes through the capsule, releasing individual odours<br> through a small opening at the upper front of the device. In this study, each scent was<br> presented for 5 seconds, with an interval of at least 6 seconds between odour<br> presentations. Each capsule contained 35 µL of oil-based odour solution, allowing the<br> device to maintain consistent odour intensity and identifiability for up to 100 activations,<br> as per the manufacturer's instructions. The Givaudan Flavours and Fragrance<br> Corporation (São Paulo, Brazil) prepared the odours used in the capsules.<br> A digital application (software) was developed to present odours and record<br> olfactory function test results. The device delivers odours through a dry air system,<br> leaving no residue in the environment or on users. Users can access the test using an<br> application on the device (screen version) or mobile phone. The results are accessible<br> through the integrated software, allowing for storage and analysis of the data.</p>
The GALEX-Gaia-EDR3 Catalogue of Single and Binary White Dwarfs
<p>We present a catalogue of white dwarf candidates constructed from the GALEX and Gaia EDR3 catalogues.<br> The catalogue contains 332,111 candidate binary white dwarf systems and 111,996 candidate single white dwarfs. Where available, the catalogue is augmented with photometry from Pan-STARRS DR1, SDSS DR12 and classifications from StarHorse. We fit photometric data with modeled white dwarf cooling sequences to derive mass, age and effective temperature of the white dwarf as well as mass estimates for the companion. We test our classifications against StarHorse, the Gentile-Fusillo Gaia EDR3 catalogue, and white-dwarf-main-sequence binaries identified in SDSS DR12. This catalogue provides a unique probe of the binarity of white dwarfs as well as the abundance of white-dwarf giant binaries and large mass-ratio stellar binaries which are difficult to probe otherwise.</p>
Data for AJ paper: Mass ratio of single-line spectroscopic binaries with visual orbits using Bayesian inference and suitable priors
<p>Data and plots for paper "Mass ratio of single-line spectroscopic binaries with visual orbits using Bayesian inference and suitable priors" accepted for publication in The Astronomical Journal.</p>
Virtual benchmarking study training time series - binary set 1
<p>A dataset with time series for training virtual sensor models for wind turbine aeroelastic loads. Data are in zipped format, each zip file contains 1000 individual time series. For the purpose of space preservation, each time series is stored in parquet binary format with "snappy" encoding.</p> <p>Reading a parquet file in Python can be done with the pandas library, with the following command sequence:</p> <p>import pandas as pd<br> Data = pd.read_parquet(filename)</p> <p> </p>
A Binary Integer Programming-based Method for Qubit Mapping in Sparse Architectures
<p>Qubit Mapping Based on MIP</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Data behind the figures
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><br> <strong>Data behind the figures</strong></p> <p>This page contains the data behind various paper figures. The material for each figure is contained in a tar file. A short description can be found below. Figures not included here are associated with one of the other GWTC-3 data releases.</p> <p> </p> <p><strong>Figure 1</strong></p> <ul> <li>Figure01.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 1. This shows the number of candidates with probability of astrophysical origin > 50% as a function of surveyed time–volume.</p> <p>The dates for the first observing run (<a href="https://doi.org/10.1103/PhysRevX.6.041015">O1</a>) and second observing run (<a href="https://doi.org/10.1103/PhysRevX.9.031040">O2</a>) candidates are hard-coded into the script, and the dates for third observing run (<a href="https://arxiv.org/abs/2108.01045">O3a</a> and O3b) candidates are included in two text files. The effective binary neutron star time–volume (BNS-VT) for each observing run is stored in separate .csv files.</p> <p>Each .csv file contains two columns, the first is the GPS time and the second is the cumulative effective BNS VT in Mpc<sup>3</sup> kyr (this is converted to Gpc<sup>3</sup> yr in the included script).</p> <p>The included script reproduces Figure 1 from GWTC-3 using the supplied data.</p> <p> </p> <p><strong>Figure 2</strong></p> <ul> <li>Figure02.tar.gz</li> </ul> <p>Data and plotting scripts for GWTC-3: Figure 2. The figure shows sensitivity curves for LIGO Hanford, LIGO Livingston, and Virgo.</p> <p>The Python script reads the .txt files containing strain data for Hanford, Livingston, and Virgo and saves figures as PDF files.</p> <p>The sensitivity curves are representative of performance during O3b. Further examples of sensitivity curves across observing runs can be found from the <a href="https://www.gw-openscience.org/detector_status/">Gravitational Wave Open Science Center</a>.</p> <p> </p> <p><strong>Figure 3</strong></p> <ul> <li>Figure03.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 3. The left panel shows the <a href="https://doi.org/10.1088/1361-6382/abd594">binary neutron star inspiral range</a> of LIGO Hanford, LIGO Livingston, and Virgo versus time. The right panel shows histograms of the binary neutron star ranges for LIGO Hanford, LIGO Livingston, and Virgo.</p> <p>The Python script (figure_3.py) reads the range.txt files and the histogram.txt files to produce each panel and saves them as PDF files.</p> <p>Further summary information about the O3b run can be obtained from the <a href="https://www.gw-openscience.org/detector_status/O3b/">Gravitational Wave open Science Center</a>.</p> <p> </p> <p><strong>Figure 4</strong></p> <ul> <li>Figure04.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 4. This plot shows the rate of <a href="https://doi.org/10.1088/1361-6382/abfd85">non-Gaussian noise transients (glitches)</a> in the LIGO Hanford, LIGO Livingston and Virgo data across O3b. The recorded glitches are identified by the <a href="https://virgo.docs.ligo.org/virgoapp/Omicron/">Omicron</a> pipeline with signal-to-noise ratio of > 6.5. There is a reduction in the LIGO glitch rate after the introduction of <a href="https://doi.org/10.1088/1361-6382/abc906">reaction chain (RC) tracking</a>, which reduced the incidence of scattered light (slow scattering) glitches.</p> <p>The script glitch_rates_GWTC-3_Fig_4.py produces Figure 4 of GWTC-3 making use of the glitch rates stored in the glitch_rates_GWTC-3_Fig_4.h5 file. Run the script within an <a href="https://computing.docs.ligo.org/conda/environments/igwn-py37/">igwn-py37</a> or <a href="https://computing.docs.ligo.org/conda/environments/igwn-py38/">igwn-py38</a> Conda environment, paying attention to having the hdf5 file glitch_rates_GWTC-3_Fig_4.h5 in the same directory of the script. Pass the argument -v or --verbose for additional info about the rates.</p> <p> </p> <p><strong>Figure 5</strong></p> <ul> <li>Figure05.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 5. This figure illustrates the time–frequency structure of two common types of glitch seen in O3: <a href="https://doi.org/10.1088/1361-6382/abc906">slow scattering</a> and <a href="https://doi.org/10.1088/1361-6382/ac1ccb">fast scattering</a>. Both are caused by light scattering within the LIGO detectors.</p> <p>The Python script scattering_GWTC-3_Fig_5.py produces Figure 5 in the GWTC-3 Catalog paper using <a href="https://www.gw-openscience.org/O3/">open data</a>. The script saves the plot as a PDF file namely, scattering_GWTC-3_Fig_5.pdf and the data used to generate the plot in the files data_fast_scattering.txt and data_slow_scattering.txt.</p> <p>For further examples of the time–frequency structure of glitches, the community-science project <a href="https://gravityspy.org/">Gravity Spy</a> catalogs visualizations of glitches in gravitational-wave data.</p> <p> </p> <p><strong>Figure 12</strong></p> <ul> <li>Figure12.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 12. This plots results of the waveform consistency test (as does Figure 13), plotting the match between waveform templates and minimally modeled reconstructions. The on-source results are for the candidate signals, while the off-source results are for simulated signals with compatible properties.</p> <p>The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze.</p> <p>The Python script (figure_12.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB to produce the corresponding match–match plots (PDF files).</p> <p> </p> <p><strong>Figure 13</strong></p> <ul> <li>Figure13.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 13. This plots results of the waveform consistency test (as does Figure 12), plotting the p-values for the minimally modeled waveform reconstructions. The p-values are plotted in increasing order.</p> <p>The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze.</p> <p>The script (figure_13.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB (the same files used to produce Figure 12) to produce the corresponding p-value plots (PDF files).</p> <p> </p> <p><strong>Figure 14</strong></p> <ul> <li>Figure14.tar.gz</li> </ul> <p>Data and script to produce GWTC-3 Figure 14. This figure shows representative noise amplitude spectral densities for LIGO Hanford, LIGO Livingston, and Virgo during Observing Run 2 and Observing Run 3b.</p> <p>The script (figure_14.py) reads data from the .txt files included in the release to reproduce Figure 14 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 15</strong></p> <ul> <li>Figure15.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 15. This shows differences in the data used to analyze <a href="https://doi.org/10.7935/b024-1886">GW200115_042309</a>, with and without glitch subtraction. A low frequency cut (illustrated by the dotted white line) was used to remove the glitch in the <a href="https://doi.org/10.3847/2041-8213/ac082e">first analysis</a> of this candidate, whereas glitch subtraction is now used when performing parameter estimation. The curving orange line shows the approximate signal track.</p> <p>The script reads in the deglitched frame L-L1_HOFT_CLEAN_SUB60HZ_C01_T1700406_v4-1263095808-4096.gwf, downloaded from <a href="http://doi.org/10.5281/zenodo.5546679">an associated data release</a>, query raw <a href="https://www.gw-openscience.org/O3/">public LIGO Livingston data</a>, and reproduce Figure 15 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 16</strong></p> <ul> <li>Figure16.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 16. This figure illustrates the <a href="https://gwpy.github.io/docs/latest/examples/timeseries/qscan/">time–frequency structure</a> of data containing three O3 candidates identified only by <a href="https://gwburst.gitlab.io/">cWB</a> (the same as shown in Figure 17). Each shows evidence of instrumental origin. </p> <p>The script queries <a href="http://www.gw-openscience.org/O3/">public LIGO data</a> and reproduce Figure 16 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 17</strong></p> <ul> <li>Figure17.tar.gz</li> </ul> <p>Data and script for GWTC-3: Figure 17. This figure illustrates the <a href="https://doi.org/10.1088/1742-6596/363/1/012032">time–frequency structure</a> of candidate signals as reconstructed by <a href="https://gwburst.gitlab.io/">cWB</a> for three O3 candidates identified only by cWB (the same as shown in Figure 16). Each shows evidence of instrumental origin. For a compact binary coalescence signal, we would expect the signal to have a chirp structure, sweeping up from low to high frequencies.</p> <p>The script figs.py reads data (the reconstruction from cWB) from the .txt files to produce the corresponding time–frequency plots (PDF files). The script must be run three times to produce the panels of Figure 17: the event names are hardcoded into the script, which must be edited to produce the desired panel.</p> <p> </p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5571766 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data analysis, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p> <p> </p>
Habitats as predictors in species distribution models: Shall we use continuous or binary data?
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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.