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5,462 results for “Binaries”
Binary fractions for Galactic globular clusters
<p>Binary fractions for Galactic globular clusters</p>
A Catalogue of All Known Mass-Transferring Ultracompact Binary Systems
<p>Introduction: I present a catalogue that collects all known mass-transferring ultracompact binary systems in one place. This includes AM CVn-type binaries, helium-enriched CVs and a variety of other objects.</p> <p>The goal of this catalogue is to prevent the duplicated effort of multiple researchers searching for known systems through the literature. Where applicable, the catalogue includes orbital periods, measured masses, Gaia cross-matches, as well as important references for each system. The catalogue includes 'confirmed' systems (usually means an orbital period measurement and/or a spectrum) and 'candidates' (may be selected based on other properties such as outburst shape).</p> <p>For full details and references see the associated paper (accepted to A&A). Preprint at https://arxiv.org/abs/2505.10535</p> <p>See the catalogue itself in the file 'amcvn_catalogue.fits'.</p> <p>Up to date as of 2025-04-01.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Data Quality Products for GW Searches
<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>This release contains two data-quality products that are used by search analyses to help mitigate non-Gaussian noise in the detector data. Gating removes short-duration artifacts from the data by smoothly rolling the affected data to zero. The <a href="https://doi.org/10.1088/2632-2153/abab5f">iDQ glitch likelihood</a> uses machine learning to predict the probability that a non-Gaussian transient is present using information from auxiliary channels.</p> <p><br> <strong>Gating files used in analyses of O3 LIGO data</strong></p> <p>As a pre-processing step, the <a href="https://pycbc.org/">PyCBC</a> search pipeline uses an inverted-Tukey window to mitigate the effect of loud, non-Gaussian features in the data. This is further described in <a href="https://dx.doi.org/10.1088/0264-9381/33/21/215004">Usman <em>et al.</em> 2016</a>.</p> <p>A subset of these times are the times listed in the txt files</p> <ul> <li>H1-O3_GATES_1238166018-31197600.txt</li> <li>L1-O3_GATES_1238166018-31197600.txt</li> </ul> <p>These times in these files were chosen based on auxiliary monitors of overflows in the digital-to-analog converters used to control the positions of the test masses. The gated times (i.e. the time period where the data is zeroed) are time segments where these monitors recorded an overflow were. The central time and suggested half-width of zero time were chosen to fully cover these time seconds. The final gating parameter, the suggested taper time was chosen to be 0.5 to balance the cost of impacting more data with the window function versus introducing additional artifacts into the data.</p> <p>The syntax of the files themselves is</p> <p>{central time} {suggested half-width of zero time} {suggested taper time}</p> <p>with each row containing the parameters of a single gate.</p> <p>The included notebook provides an example of how to read in and apply one of the suggested gates.</p> <p><br> <strong>Renormalized iDQ timeseries</strong></p> <p>The renormalized iDQ timeseries data-quality product is used within the GstLAL search pipeline to generate results for GWTC-3. This data product was found to be statistically helpful in improving data quality within the <a href="https://lscsoft.docs.ligo.org/gstlal/">GstLAL</a> search pipeline. This is further described in <a href="https://arxiv.org/abs/2010.15282">Godwin <em>et al</em>. 2020</a>.</p> <p>This file contains a time series for each LIGO detector related to the probability of a glitch in the strain data given the behavior in analyzed auxiliary channels monitoring the behavior of the detectors and their environment.</p> <ul> <li>H1L1-IDQ_TIMESERIES-1256655642-12905976.h5</li> </ul> <p>The HDF5-formatted file contains two groups, H1 and L1, corresponding to LIGO Hanford and LIGO Livingston, respectively. Each group contains several datasets; the data dataset corresponds to the renormalized iDQ log-likelihoods, as described in <a href="http://doi.org/10.1088/2632-2153/abab5f">Godwin <em>et al</em>. 2020</a>, and the time dataset corresponds to the times associated with the renormalized iDQ log-likelihoods in the data dataset.</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 5636795 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 quality, 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>
Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455
<p>This is a basic reproduction package for the paper "Constraining the properties of dense neutron star cores: The case of the transient low-mass X-ray binary HETE J1900.1-2455" by <a href="https://doi.org/10.1093/mnras/stab2202">N. Degenaar et al. (2021)</a>. It provides reduced data products, simulated data and scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data.</p>
Supplementary data release for "Cosmology and modified gravitational wave propagation from binary black hole population models"
<p>We release the data products associated to the paper <a href="https://arxiv.org/abs/2112.05728">"Cosmology and modified gravitational wave propagation from binary black hole population models", </a><a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.064030"><em>Phys.Rev.D</em> 105 (2022) 6 </a>.</p> <p>The data can be used in conjunction with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> to reproduce the results of the paper. </p> <p>The data product contains the following folders:</p> <p>* injections_GWTC3: injections used to analyze the GWTC3 catalog, generated with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> . Injections are available separately for O1-O2, O3a, O3b for minimum SNR of 10, 11, 12 (folder names are self-explicative). Each folder contains a file named selected.h5 with the injections. For loading them, refer to the tutorial of the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> .</p> <p>* mock_BPL_5yr_GR : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to General Relativity (see the paper for details)</p> <p>* mock_BPL_5yr_MG : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to a modified gravity model with modified gravitational-wave propagation (see the paper for details)</p> <p>* injections_mock : injections for analyzing the mock datasets above</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Data Quality Products for GW Searches
<p>This material is part of several data products associated with GWTC-2.1, the deep extended catalog of compact binary coalescences observed by the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration and the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration during the first half of the third observing run. For further information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">www.gw-openscience.org/GWTC-2.1/</a>).</p> <p>This release contains data quality products that are used by search analyses to help mitigate non-Gaussian noise in the detector data.</p> <p><strong>Renormalized iDQ timeseries</strong></p> <p>This release contains the renormalized iDQ timeseries data quality product used within the GstLAL search to generate results for GWTC-2.1 as described in <a href="https://arxiv.org/abs/2010.15282">Goodwin <em>et al</em>. 2020</a>. This data product was found to be statistically helpful in improving data quality within the GstLAL search. For further information about iDQ see <a href="https://iopscience.iop.org/article/10.1088/2632-2153/abab5f">Essick <em>et al</em>. 2020</a>.</p> <p>The file </p> <ul> <li>H1L1-IDQ_TIMESERIES-1238166018-15843600.h5</li> </ul> <p>contains a time series for each LIGO detector related to the probability of a glitch in the strain data given the behavior in the analyzed auxiliary channels which monitor the behavior of the detectors and their environment.</p> <p>The HDF5-formatted file contains two groups, H1 and L1, corresponding to LIGO Hanford and LIGO Livingston, respectively. Each group contains several datasets; the data dataset corresponds to the renormalized iDQ log-likelihoods, as described in Godwin <em>et al</em>. 2020, and the time dataset corresponds to the times associated with the renormalized iDQ log-likelihoods in the data dataset.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the guide to <a href="https://doi.org/10.1088/1361-6382/ab685e">LIGO-Virgo data analysis</a>.</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Parameter Estimation Data Release
<p>This material is part of several data products associated with GWTC-2.1, an update to the second 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-P2100063/public">https://dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">https://www.gw-openscience.org/GWTC-2.1/</a>).</p> <p><strong>Parameter estimation data release</strong></p> <p>This data release contains posterior samples (*.h5) for gravitational-wave candidates through the first part of the third observing run (O3a). We provide results for the 44 candidates that have a probability of astrophysical origin of over 0.5 from O3 as well as the 10 previously-reported binary-black-hole candidates from GWTC-1 (this excludes GW170817). There are two .h5 files per event</p> <ul> <li> <p>Cosmologically reweighted (*cosmo.h5)</p> </li> <li> <p>Not cosmologically reweighted (*nocosmo.h5)</p> </li> </ul> <p>The cosmologically reweighted posteriors are reweighted to have a luminosity-distance prior that has a uniform merger rate in the source's comoving frame. Each .h5 file contains samples for multiple runs with keys C01:RUN_NAME, where RUN_NAME is the waveform used for the run (and additional prior-choice information if necessary) or Mixed, indicating an equal mixture of samples from runs with similar physics if they exist. In cases where only one waveform was used, the Mixed dataset is simply a resampling of those results . GW190425 does not have Mixed samples. See the <a href="https://dcc.ligo.org/LIGO-P2100063/public">paper</a> appendices for further information. In addition to containing the posterior samples, the .h5 files also contain metadata about the analyses including the configuration files (which specify details such as the detector data analyzed), noise power spectral densities (potentially for a superset of the detectors used in the analysis) and calibration uncertainty envelopes.</p> <p>The python notebook explains how to use the posterior samples. This data release also contains .FITS skymap files, which can be read with <a href="https://lscsoft.docs.ligo.org/ligo.skymap/#">ligo.skymap</a>, and skymap statistics in *.txt files.</p> <p>The inference of the source parameters were performed with <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby</a>, <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">Parallel Bilby</a> and <a href="https://git.ligo.org/richard-oshaughnessy/research-projects-RIT/tree/temp-RIT-Tides">RIFT</a>. The results are formatted using <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a>.</p> <p><a href="https://zenodo.org/record/5546663#.YnAAcvPMKqC">A similar release has been made to accompany GWTC-3</a> for results from the second part of the third observing run.</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 5117702 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p>For more general background on gravitational-wave parameter estimation, 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>
Data Release: "No evidence that the majority of black holes in binaries have zero spin"
<p>This dataset contains the results presented in "<em>No evidence that the majority of black holes in binaries have zero spin</em>".</p> <p>In this paper, we systematically explored the effective and component spin distributions of binary black holes among the LIGO/Virgo GWTC-3 catalog. In particular, we tried to answer the following core questions, which have been the subject of active exploration and some debate in the literature:</p> <p><em>1. Is there an excess of binary black holes with vanishing spin, as predicted by some theories of angular momentum transport in stellar cores?</em></p> <p><strong>We find no evidence for an excess of vanishing spin systems.</strong> This finding is confirmed by three complementary analyses: one relying only on the Bayes factors between spinning and non-spinning priors for each BBH observation, one that seeks to model the distribution of effective aligned spins, and one modeling the distribution of component spin magnitudes and misalignment angles. Instead, we find BBH spin magnitudes to be consistent with a single, continuous distribution that remains finite at magnitude zero.</p> <p><em>2. Do there exist binaries with component spins misaligned by more than 90 degrees relative to their orbits?</em></p> <p><strong>We find a strong preference for the existence of such strongly misaligned spins.</strong> Our analysis of the BBH component spin distribution indicates that at least some component spins are misaligned from their orbits by more than 90 degrees. This result is robust under a variety of modeling choices regarding both the distribution of component spin magnitudes and tilts.</p> <p>The code used to generate this data can be found in the repository <a href="https://github.com/tcallister/gwtc3-spin-studies/">https://github.com/tcallister/gwtc3-spin-studies/</a>. This repository includes <a href="https://github.com/tcallister/gwtc3-spin-studies/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/gwtc3-spin-studies/build/html/index.html">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 file <em>posteriors_gaussian_spin_samples_FAR_1_in_1.json</em>, used for figure generation, was 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>
Raw data for the article entitled "Facile Solution Synthesis, Processing and Characterization of n- and p-Type Binary and Ternary Bi–Sb Tellurides"
<p>Raw data for the plots in the open access article "Facile Solution Synthesis, Processing and Characterization of n- and p-Type Binary and Ternary Bi–Sb Tellurides"</p>
Optical polarimetric observations of the black hole binary star Cyg X-1 with RoboPol
<p>The dataset contains raw FITS images of the black hole X-ray binary star <a href="https://simbad.cds.unistra.fr/simbad/sim-id?Ident=%402905066&Name=HD%20226868&submit=submit">Cyg X-1</a>, raw FITS images of a nearby field star used for the interstellar polarization correction and processed measurements of polarimetric standards used for the instrumental polarization correction. The dataset was obtained with the <a href="http://robopol.org">RoboPol</a> optical polarimeter in the R-band mounted at the 1.3 m telescope of the Skinakas Observatory, Greece. The data were collected between 13 May and 1 June 2022.<br> </p>
Jacobaea vulgaris and meadow image classification dataset (binary)
<h3>General Information</h3> <p>Instances in the Jacobaea vulgaris class: 895<br>Instances in the Meadow class: 9141<br>Image sizes from 77x77 to 817x817 pixels on three color channels (RGB)</p> <p> </p> <h3>Data Generation and Source</h3> <p>The images in this dataset were taken as part of the project “UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene” (engl. “UAV-based Grassland Monitoring at Population and Individual Plant Level”), financed by the Authority for Economy, Transport, and Innovation of Hamburg. <br>In September 2018, flights with an octocopter were conducted over two extensively used grassland areas in the urban area of Hamburg. The multicopter flew in a height of circa 11 meters and took pictures with a ground resolution of approximately 3,18 mm/pixel. Additional information about the process of image generation for this dataset are to be found in the relevant papers written by P. Zacharias: 1) <a href="https://archiv.geomv.de/geoforum/2019/doc/Tagungsband_GeoForum-MV-2019_eBook.pdf" target="_blank" rel="noopener">UAV-basiertes Grünland-Monitoring und Schadpflanzenkartierung mit offenen Geodaten</a> [p. 45–53] and 2) <a href="https://www.auf.uni-rostock.de/storages/uni-rostock/Alle_AUF/AUF/GG/PDF/gruenlandmonitoring/2019-12-12-FHH-Workshop_Vortrag_Zacharias.pdf" target="_blank" rel="noopener">UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene</a>.</p> <p>Additionally, to the images of Jacobaea vulgaris taken by the UAV, the dataset includes images of Jacobaea vulgaris plants from the internet (included in the total 895 images; e.g. images 'jkk0523.jpg', 'jkk0527.jpg'). Furthermore, some of the images of the Jacobaea vulgaris plants have been rotated, further cropped or a filter has been applied. The exact number of augmentations made is unknown. As there are augmented images included in the datasets -which makes the dataset useful for training and validation- a use of the dataset for testing purposes is not recommended due to the risk of data leakage.</p> <h3>Data License</h3> <p>The dataset is licensed under the license CC BY 4.0. The attributor of the data is the Chair of Geodesy and Geoinformatics at the University of Rostock. The data was created within the scope of the project 'UAV-based Grassland Monitoring at Population and Individual Plant Level', financed by the Authority for Economy, Transport, and Innovation of Hamburg.</p> <p> </p>
Jacobaea vulgaris and meadow Augmented image classification dataset (binary)
<h3>General Information</h3> <p>Total instances: 117008<br>Instances in the Jacobaea vulgaris class: 58504 <br>Instances in the Meadow class: 58504<br>Image sizes from 224x224 pixels on three color channels (RGB)</p> <p><br>Performance increase training a ResNet50 on the base dataset versus the same architecture on the augmented data set shared here: +3,79 percent points in ROC AUC on an independent test set with 240 instances.<br><br></p> <h3>Data Generation and Source</h3> <p>The initial images in this dataset were taken as part of the project “UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene” (engl. “UAV-based Grassland Monitoring at Population and Individual Plant Level”), financed by the Authority for Economy, Transport, and Innovation of Hamburg. <br>In September 2018, flights with an octocopter were conducted over two extensively used grassland areas in the urban area of Hamburg.</p> <p>In my master's thesis at <a href="https://www.tu.berlin/dams">DAMS Lab</a> at TU Berlin, I evaluated the effect of different augmentation strategies for Jacobaea vulgaris image classification on the several performance metrics (most importantly the ROC AUC score). The identified augmentation strategies are -besides to performance based selection- also selected based on domain knowledge, which I acquired during the research for my master thesis. </p> <p>Additional information about the initial image generation process is to be found <a href="https://archiv.geomv.de/geoforum/2019/doc/Tagungsband_GeoForum-MV-2019_eBook.pdf">here </a> [p. 45–53] and <a href="https://www.auf.uni-rostock.de/storages/uni-rostock/Alle_AUF/AUF/GG/PDF/gruenlandmonitoring/2019-12-12-FHH-Workshop_Vortrag_Zacharias.pdf">here</a>. </p> <h3> </h3> <h3>Augmentations applied</h3> <ul> <li>Gaussian Noise: For the Gaussian noise augmentation, the mean of the added noise is set to zero. The lower and upper bounds for the random variance of the noise are 20.4663 and 54.0395 respectively. The bounds were identified by hyperparameter tuning. The search space for the lower bound was set from 5 to 30 and for the upper bound from 31 to 100. Those two search spaces were defined by visual inspection of the effects of applying Gaussian noise with different variance values to images of both classes. The Gaussian noise is sampled for each color channel individually. </li> <li>Random Brightness and Contrast: The brightness will randomly be increased or decreased by a factor ranging from 0.7010 to 1.2990. The The contrast will also be randomly increased by a factor ranging from 0.5775 to 1.4225. Those two ranges were identified using hyperparameter tuning. The search space for the maximal percentual increase or decrease of brightness and contrast was individually set from 1% to maximally 50% increase or decrease.</li> <li>Cutout Dropout: In this augmentation method a certain percentage of the input image is getting covered by black patches. The patches have a certain size in pixels, the implementation of this technique in this thesis uses square patches. The black patches are then randomly introduced into the image, by randomly alloacting the<br>patches across the image and then setting the corresponding pixel values to zero. The iamge is getting covered with patches until the cover percentage is reached. We<br>set percentage of the image to be randomly covered by black patches to 56.76%. The size of the patches, which randomly cover the image, is set to 4 pixels. A<br>good illustration of this is found in figure 4.2. The augmentation technique is inspired by the research proposed by Devries et al.[8]. Both values were identified by hyperparameter tuning. The search space for the patch size in pixels is categorical and includes the values [1, 2, 4, 7, 8, 14, 16, 28]. Those values all are multiples of 224, which is the image width and height in pixels. The patch size needs to be a multiple of the width and height in order to be suitable for the algorithm implementation. The search space for the cover percentage of the image had been set from 1% to 60%. This search space limits narrows the search down to a space where still a big part of the image is uncovered. The algorithm rearranges the image into a two dimensional grid and randomly masks rows of this grid by setting the pixel values in this row to zero. Then, the image gets rearranged, now with the randomly generated patches included.</li> <li>Random Saturation: The saturation of each pixel is randomly getting shifted. The upper bound for randomly shifting<span> </span>the saturation value of each pixel is set to 231.689%. This value was identified using hyperparameter tuning. An upper limit of the maximal saturation shift had been set to 40% shift in either direction for hyperparameter tuning.</li> <li>Horizontal Flip: The image gets flipped along the horizontal axis. </li> <li>Vertical Flip: The image gets flipped along the vertical axis.</li> <li>Random Rotation 90 degrees: Randomly rotates the image by a k-fold of 90 degrees, whereby k = {0, 1, 2, 3}.</li> </ul> <p> </p> <p>All augmentation methods and with their tuned augmentation hyperparameters (if existent) are applied to an image from the test set in figure 4.2. With the seven identified<br>augmentation techniques a dataset of 800% the size of the original dataset is created. The Augment model is trained on exactly this dataset. Of course next to the augmented images, the dataset still includes the original, unaugmented images. TensorFlow, along with additional libraries including Optuna for hyperparameter optimization and Albumentations for image augmentation, were used in for the implementation of this project.</p> <p> </p> <h3>Rational behind the augmentations applied</h3> <ul> <li>Random Rotation, Vertical and Horizontal Flip: These three augmentation strategies were chosen to make the classifier less sensitive to the orientation of the plant. The goal is to train a model that can classify plants regardless of their orientation. In order to achieve this effectively across different orientations, vertical flips, horizontal flips, and random 90-degree rotations are chosen for evaluation.</li> <li>Random Saturation: The varying saturation of the images simulates different levels of chlorophyll in the leaves, which is responsible for the green color of the<br>leaves and the intensity of this color. The color of the plant parts (leaves, stems, and flowers) is also influenced by factors such as soil, sun, weed density and pressure, location, and water availability. Varying the saturation of the images simulates changes in these factors.</li> <li>Gaussian Noise: By adding noise, in this case Gaussian noise, different lighting conditions are simulated when capturing the images. We specifically chose Gaussian<br>noise because it is common in many real-world scenarios and is based on the Central Limit Theorem, which states that the sum of many independent random variables.<br>tends to be normally distributed. This makes Gaussian noise a logical choice for simulating real-world random noise.</li> <li>Random Brightness Contrast: The Random Brightness and Random Contrast Augmentation uses brightness to mimic varying lighting conditions and contrast to highlight differences between plants by contrasting them more strongly, thereby highlighting their edges. This approach for highlighting edges is of course much more subtle than the canny edge detection augmentation. This augmentation method combines a weak focus on edges with variations in lighting conditions in one approach. The random contrast is a much softer approach for highlighting edges of plants, compared to the Canny edge detection augmentation. The other features in the images do not get changed that much, compared to the changes from edge detection augmentation.</li> <li>Cutout Dropout: The cutout augmentation simulates random occlusion by other plants. These occlusions are common and expected. Jacobaea vulgaris plants may be partially or completely obscured by other plants during image capturing. This augmentation technique makes the models more robust to random occlusion.</li> </ul> <h3> </h3> <h3>Data License</h3> <p>The dataset is licensed under the license CC BY 4.0. The attributor of the data is the Chair of Geodesy and Geoinformatics at the University of Rostock. The data was created within the scope of the project 'UAV-based Grassland Monitoring at Population and Individual Plant Level', financed by the Authority for Economy, Transport, and Innovation of Hamburg.</p>
Lookup tables for H2SO4-H2O binary and H2SO4-H2O-NH3 ternary homogeneous and ion-mediated nucleation
<p>2019/10/10</p> <p>This directory contains fortran code and lookup tables for calculating<br> nucleation rate for the following four nucleation mechanims:<br> 1. H2SO4-H2O binary homogenous nucleation (BHN),<br> 2. H2SO4-H2O-NH3 ternary homogeneous nucleation (THN),<br> 3. H2SO4-H2O-Ions binary ion-mediated nucleation (BIMN), and<br> 4. H2SO4-H2O-NH3-Ions ternary ion-mediated nucleation (TIMN)</p> <p>References:</p> <p>1. Yu, F., Nadykto, A. B., Herb, J., Luo, G., Nazarenko, K. M., and<br> Uvarova, L. A.: H2SO4-H2O-NH3 ternary ion-mediated nucleation (BHN):<br> Kinetic-based model and comparison with CLOUD measurements, Atmos.<br> Chem. Phys.,18, 17451-17474,<br> https://doi.org/10.5194/acp-18-17451-2018, 2018.<br> 2. Yu, F., Nadykto, A. B., Herb, and J., Luo, H2SO4-H2O binary and<br> H2SO4-H2O-NH3 ternary homogeneous and ion-mediated nucleation: Lookup tables,<br> in review, GMD, 2019.</p> <p><br> Files included in the package:</p> <p>YBHN/: BHN code and lookup table<br> YTHN/: THN code and lookup table<br> YBIMN/: BIMN code and lookup table<br> YTIMN/: TIMN code and lookup table</p> <p>nucl_XXX_mod.f (XXX = BHN, THN, BIMN, TIMN): Stand alone module for XXX rate calculation.</p> <p>mainXXX.f: Test program to demonstrate how the lookup table is used.</p> <p>inputXXX.dat: Example input data for testing the code and lookup table.<br> outputXXX.dat: Example output after running the test code</p> <p>Use "f95 *.f" to compile and get executable program a.out</p> <p>To run the test program:</p> <p>>a.out</p> <p>For your reference, output of running the program on my workstation has been saved to outputXXX.dat.</p> <p>I hope that the comments provided in the subroutines make<br> the code self-explanatory. Contact me if you have questions.</p> <p>Enjoy and have fun!</p> <p>Fangqun Yu<br> SUNY-Albany<br> (fyu@albany.edu)</p>
Gran Paradiso - Nivolet: binary map vegetated/not vegetated (2016)
<p>A vegetated/not vegetated binary map for the Nivolet area in Gran Paradiso (Italy) PA, detected by a thresholding on NDVI spectral index extracted from a Sentinel-2A image dated 13 August 2016, at 10 meters spatial resolution, projected in WGS84/UTM32N. <br> The map has binary values where value 1 indicates pixels of vegetation whereas value 0 indicates No vegetation pixels.<br> </p>
Dataset of "Photodetector Based on the Non-Centrosymmetric 2D Pseudo-Binary Chalcogenide MnIn2Se4"
<p>Due to their attractive band gap properties and Van de Waals structure 2D binary chalcogenides materials have been widely investigated in the last decade, finding applications in several fields such as catalysis, spintronic, and optoelectronic. Ternary 2D chalcogenide materials are a subject of growing interest in material science due to their superior chemical tunability which endows tailored properties to the devices prepared thereof. In the family of AIIBIII2XVI4, ordered ZnIn2S4-like based photocatalytic systems have been studied meticulously. In contrast, reports on disordered phases appear to a minor extent. Herein, a photo-electrochemical (PEC) detector based on the pseudo-binary MnIn2Se4 system is presented. A combination of optical measurements and DFT calculations confirmed that the nature of the bandgap in MnIn2Se4 is indirect. Its performances outclass parent compounds, reaching responsivity values in the order of 8.41 mA W-1. The role of the non-centrosymmetric crystal structure is briefly discussed as a possible cause of the improved charge separation of the photogenerated charge carriers. </p>
Experimental measurements of the H2-H2 and H2-He binary absorption coefficients in the [2500 ,5900] cm^-1 spectral range from 120 to 500 K
<p>Binary absorption coefficients (BAC) of the Collision-Induced Absorption (CIA) fundamental band of H2 are given in tabular form for both the H2-H2 and H2-He contributions for seven temperatures in the [120, 500] K range and [2500, 5900] cm^-1 spectral range.</p> <p>Tables are composed of three columns. The first column represents the wavenumber in cm^-1, in the second column the binary absorption coefficients in cm^5 molecule^-2 are collected, and the third column contains the accuracy of the binary coefficients in cm^5 molecule^-2. Every column is delimited with a comma. </p> <p>The accuracy can be supposed to be of three significant digits, even if the trailing zeros are not displayed. </p> <p> </p>
Galactic Populations of LISA DWD Binaries
<p>These COSMIC simulation outputs and assembled galactic populations of<br> Double White Dwarf (DWD) binaries<br> represent the work published by V. Delfavero et al,<br> in https://arxiv.org/abs/2409.15230</p> <p>The COSMIC version used in these simulations is 3.4.10</p> <p>The git repository for the accompanying code which built this dataset<br> will be available at https://gitlab.com/xevra/basil-cosmic<br> upon the publication of our article.</p> <p>Also useful is the fork of ldasoft used in this pipeline:<br> https://github.com/xevra/ldasoft</p> <p>Please cite this work if you use the included data in a publication.</p> <p>-------------------------------------------------------------------------------<br>Summary<br>-------------------------------------------------------------------------------<br>Params_[MODEL].ini<br> This initialization file was used for COSMIC, <br> for the runs tagged with [MODEL].</p> <p>[MODEL]_[KSTAR1]_[KSTAR2]_[METALLICITY_BIN].tar.gz<br> This tarball contains the outputs from one run of COSMIC,<br> using Params_[MODEL].ini, for a type of binary indicated by<br> [KSTAR1] and [KSTAR2] according to COSMIC syntax,<br> and for the [METALLICITY_BIN]'th metallicity bin.</p> <p>[MODEL]_COSMIC.hdf5<br> This hdf5 database holds the DWD population outputs for all 60<br> COSMIC simulations in a compressed format.<br> It only holds information about binary systems at the end<br> of evolution (conv, according to COSMIC syntax).<br> It does not contain the evolutionary history of individual binaries<br> (the bpp information, according to COSMIC syntax).</p> <p>[MODEL]_m12i-000_LISA.nal.hdf5<br> This hdf5 database holds the LISA "resolved" population of DWD binaries<br> for a single realization of a Milky-Way-like galaxy.<br> It also stores many attributes indicating assumptions and various<br> population statistics.<br> The "nal" extension indicates that this database contains<br> truncated Gaussian likelihood models for the resolved population<br> of LISA DWDs,<br> which can be loaded by the gwalk code (https://gitlab.com/xevra/gwalk).<br> This database can also be explored by any hdf5 library (such as h5py).</p> <p>[MODEL]_m12i-000_LISA.hdf5<br> This hdf5 database holds all of the LISA-band binaries (f_GW > 10^-4 Hz)<br> for a single galaxy realization.</p> <p>[MODEL]_m12i-000_LISA.txt.gz<br> This text file stores the inputs for ldasoft gbfisher analysis,<br> for a specific galaxy realization under assumptions<br> broadly described by [MODEL]<br><br>See README.txt for full description<br><br>-------------------------------------------------------------------------------<br>Funding<br>-------------------------------------------------------------------------------<br>VD is supported by an appointment to the NASA Postdoctoral Program at the NASA Goddard Space Flight Center administered by Oak Ridge Associated Universities under contract NPP-GSFC-NOV21-0031. ROS gratefully acknowledges support from NSF awards NSF PHY-1912632, PHY-2012057, PHY-2309172, AST-2206321, and the Simons Foundation. JB is supported by the NASA LISA Project Office.<br><br>We acknowledge software packages used in this publication, including NUMPY (Harris et al. 2020), SCIPY (Virtanen et al. 2020), MATPLOTLIB (Hunter 2007), ASTROPY (Astropy Collaboration et al. 2013, 2018), H5PY (Collette 2013), LEGWORK (Wagg et al. 2022), and ldasoft Littenberg et al. (2020). This research was done using resources provided by the Open Science Grid (Pordes et al. 2007; Sfiligoi et al. 2009), which is supported by the National Science Foundation awards #2030508 and #1836650, and the U.S. Department of Energy’s Office of Science</p>
GWTC-2.1: Deep extended-catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Sensitivity of search pipelines to simulated signals
<p>Results of search pipelines (GSTLAL, MBTA, PYCBC, PYCBC BBH) used to identify candidates in <a href="https://dcc.ligo.org/LIGO-P2100063/public">GWTC-2.1</a> on a set of simulated signals corresponding to binary neutron star (BNS), neutron star black holes (NSBH), and binary black holes (BBH) signals. Additionally, we include a README file which provides information on how to read these files.</p>
All centralising monoids with binary operations as witnesses on the set {0,1,2,3}.
<p>This dataset contains all 33684 centralising monoids on the set <span>\(\{0,1,2,3\}\)</span> that can be witnessed by sets of binary operations. Among them the inclusion maximal proper monoids are shown. Classifications of various subsets of these monoids up to conjugacy are presented, as well. A detailed description of the files in the dataset can be obtained from the file binary_centralising_monoids.pdf (the code source for this file is given in binary_centralising_monoids.tex).</p>
Data for `Identifying New Pulsating Variables and Eclipsing Binaries Using TESS Data'
<p>This study presents a series of surveys using TESS data to identify new δ Scuti and γ Doradus stars, as well as eclipsing binaries with pulsating components. Preliminary catalogs of newly discovered variables are being made publicly available to encourage community use as the project progresses. Please visit this website for updates. </p> <p> 1. New δ Scuti stars, γ Doradus stars, and eclipsing binaries from a subset of 709,000 selected AF-type stars observed by TESS. Version 4.5 is an update to Version 4.0 (the first release for Part IV), while Part III was provided in Version 3.0.</p> <p> The attached CSV file <strong>NewVar_AF50w_R2.csv,</strong> contains the catalog of newly identified variables from Phases I and II of the survey (i.e. R2 includes R1 from Version 3.0). The accompanying file <strong>ms2RNAAS_2025AF50w_IV.pdf</strong> is the initial draft describing the Phase-II identifications. The published paper can be found in 2025 <em>Research Notes of the AAS</em>, <strong>Vol. 9, No. 1, 23</strong> (Zhou 2025, https://iopscience.iop.org/article/10.3847/2515-5172/adaf8c (ADS code: 2025RNAAS...9...23Z). These unpublished discoveries have been compiled into catalogs of δ Scuti and γ Doradus stars available at Zenodo https://zenodo.org/records/17096360 (DOI: <a href="https://doi.org/10.5281/zenodo.17096360">10.5281/zenodo.17096360</a>) -- which currently include <strong>118,410</strong> and <strong>41,622</strong> entries, respectively, as of the release date.</p> <p> 2. New Pulsating Variable Stars and Eclipsing Binaries around BL Cam (added in Version 2.0)</p> <p> The attached CSV file NewVar_BLCam_R2D.csv provides a catalog of the new variables with the following columns: TIC ID, Simbad main ID, Gaia DR3 ID, RA_deg Dec_deg(J2000), Tmag, Teff, SpType, Lum, logg, mass, VarType_Notes</p> <p> 3. New Pulsating Variable Stars and Eclipsing Binaries near NGC 6302 (Version 1.0)</p> <p> Check corresponding files containing the string "NGC 6302".</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.