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951 results for “Data release”
Dataset for "Initial insight of three modes of data sharing: Prevalence of primary reuse, data integration and dataset release in research articles"
<p>The dataset for "Initial insight of three modes of data sharing: Prevalence of primary reuse, data integration and dataset release in research articles" is coded as follows:</p> <p>01 DOI: DOI<br> 02 article number: the accession number in Web of Science<br> 03 article title: title of the articles<br> 04 exclude: if the article was excluded from the sample, assign 1.<br> 05 research_field: the categories of research fields are described in the Appendix (Table S1)<br> 06 target_of_study: the categories of the target of studies are described in the Appendix (Table S1)<br> 29 release_location_nameofpublicarchive: the names of the deposited public archives (comma separated)</p> <p>The following items, if they occur, are assigned a value of 1:<br> 07 No_datause: The article did not use data<br> 08 primary_reuse: primary reuse<br> 09 primary_data_specificresarchdata: primary reuse of specific research data<br> 10 primary_data_resource: primary reuse of resource<br> 11 primary_source_self: primary reuse from self-constructed data<br> 12 primary_source_citation: primary reuse from citation<br> 13 primary_source_archive: primary reuse from an archive<br> 14 primary_source_others: primary reuse from the other source<br> 15 primary_souce_na: primary reuse source is not available<br> 16 data_integration: data integration<br> 17 integration_type_empirical: data integration as empirical type<br> 18 integration_type_Introductionmaterialresearchmethod: data integration as introduction/material/research methods type<br> 19 integration_type_combinedanalysis: data integration as introduction/material/research methods type<br> 20 integration_source_self: data integration from self-constructed data<br> 21 integration_source_citation: data integration from citation<br> 22 integration_source_archive: data integration from an archive<br> 23 integration_source_others: data integration from the other source<br> 24 integration_source_na: data integration source is not available<br> 25 dataset_release: dataset release<br> 26 release_location_publicarchive: dataset deposit to a public archive <br> 27 release_location_supporting: dataset release in Supporting Information<br> 28 release_location_onrequest: dataset release through personal contacts </p> <p> </p> <p>The appendix includes following tables:<br> Table S1. Coding schema for analysis<br> Table S2. Primary reuse by research field and reused data<br> Table S3. Primary reuse by target of study and reused data<br> Table S4. Data integration by research field and reuse type<br> Table S5. Data integration by target of study and reuse type<br> Table S6. Dataset release by research field<br> Table S7. Dataset release by target of study and methods<br> Table S8. List of names of public data archives for dataset release</p>
Persistent and occasional: searching for the variable population of the ZTF/4MOST sky using ZTF data release 11.
<p>In this dataset we provide classifications of Zwicky Transient Facility (ZTF) Data Release 11 (DR11) light curves, from the work "Persistent and occasional: searching for the variable population of the ZTF/4MOST sky using ZTF data release 11", accepted for publication in the Astronomy and Astrophysics Journal (Sánchez-Sáez et al. 2023). Here we provide classifications for objects in the ZTF/4MOST sky, including 86,576,577 sources in the g band and 140,409,824 in the r band. The classifications are provided in parquet files, separated by class and ZTF band. We also provide the labeled sets used to train the models for each band, and the master catalog used to construct the labeled sets.</p> <p> </p> <p>File description:</p> <p>Classifications: files with names %class%_cand_%band%.parquet.gz</p> <p>Labeled set g band: LS_ZTFg.parquet.gz</p> <p>Labeled set r band: LS_ZTFr.parquet.gz</p> <p>Master catalog: mast_cat.parquet.gz</p>
WikiPathways April 2023 Release - RDF data
<p>Archive of the WikiPathways April 2022 RDF data for all species as GPMLRDF and WPRDF (.ttl format). The data is licensed under the <a href="https://creativecommons.org/share-your-work/public-domain/cc0/">CCZero waiver</a>.</p>
nychealth/food-pricing-survey-nyc-2019: Final data release
<p>This represents the only release for this data.</p>
Data release for "Updated T2K measurements of muon neutrino and antineutrino disappearance using 3.6E21 protons on target"
<p>This data release accompanies the results of T2K's analysis of muon neutrino and antineutrino oscillation data collected between 2010 and 2020. The file format is ROOT and contains the best-fit point and the 68% and 90% confidence level contours in the oscillation parameters space investigated by the analysis. The results for both mass ordering are included. Each entry in the file is a TGraph described in DataReleaseNuMuAntiNuMuDis.pdf.</p> <p>This is in <a href="https://doi.org/10.1103/PhysRevD.108.072011">Physical Review D </a>and available on the <a href="https://arxiv.org/abs/2305.09916">arXiv:2305.09916 [hep-ex]</a>.</p>
Constraining gravitational wave amplitude birefringence with GWTC-3: Data Release
<p>Dataset release accompanying Constraining gravitational wave amplitude birefringence with GWTC-3.</p>
Data release for "Identifying LISA verification binaries among the Galactic population of double white dwarfs"
<p>Posterior samples and SNR calculations associated with <em>Identifying LISA verification binaries among the Galactic population of double white dwarfs</em> (<a href="https://arxiv.org/abs/2210.10812">arxiv:2210.10812</a>, <a href="https://doi.org/10.1093/mnras/stad1288">MNRAS, 522, 5358 (2023)</a>).</p> <p>The <code>data</code> folder contains the following:</p> <ul> <li><code>vb_table</code>: Verification binary (VB) candidate properties obtained from electromagnetic (EM) observations. This data was compiled by Kupfer et al. and is available at <a href="https://gitlab.in2p3.fr/LISA/lisa-verification-binaries">https://gitlab.in2p3.fr/LISA/lisa-verification-binaries</a> (accessed on 2022 September 2). Used in Table 1.</li> <li><code>snr</code>: SNR calculations for each VB candidate as a function of mission duration. Used in Figure 2.</li> <li><code>basic</code>: Gravitational-wave (GW) parameter estimation study of each VB candidate. Used in Table 2 and Figure 3.</li> <li><code>detection_time</code>: VB SNR calculations with source property (and LISA launch time) uncertainty taken into account. Used in Figure 1 and Figure 4.</li> <li><code>em_priors</code>: GW parameter estimation study with EM information included in the priors. Used in Figure 5.</li> <li><code>unknown_noise</code>: GW parameter estimation study with the inclusion of noise parameters to vary the PSD. Used in Figure 6.</li> <li><code>filtered_populations</code>: Source properties of DWDs nearby in frequency to each VB candidate. See the <code>confused_sources.ipynb</code> notebook. Used in Figure 7.</li> <li><code>confusion_snr</code>: SNR calculations of the above confusion sources. Used in Figure 7.</li> <li><code>confusion</code>: A analysis of V803Cen with a realistic Galactic population of DWDs included in the data. Used in Figure 8.</li> </ul> <p>Code to reproduce every figure from the paper is available in <code>plots</code>.</p> <p>Supplementary notebooks associated with the generation of a realistic data instance (i.e. including a simulated Galactic population of DWDs nearby in frequency to V803Cen) can be found in <code>notebooks</code>.</p>
Harvey-Lab-UW/Buonanduci_etal_2023_EcolLett: Release of data and code for Buonanduci et al. 2023 Ecology Letters
<p>This is the latest release of data for reproducing the analyses in the manuscript 'Consistent spatial scaling of high-severity wildfire can inform expected future patterns of burn severity' by Buonanduci, Donato, Halofsky, Kennedy, and Harvey, accepted for publication in Ecology Letters. See the main text of the manuscript for complete descriptions of how data were processed and analyzed.</p> <p>This information is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. Any user of these data ("User" hereafter) is required to cite it appropriately in any publication that results from its use. These data may be actively used by others for ongoing research, so coordination may be necessary to prevent duplicate publication. The User is urged to contact the authors of these data for questions about methodology or results. The User is encouraged to consider collaboration or co-authorship with authors where appropriate. Misinterpretation of data may occur if used out of context of the original study. Substantial efforts are made to ensure accuracy of the data and documentation, however complete accuracy of data sets cannot be guaranteed. All data are made available as is. Data may be updated periodically and it is the responsibility of the User to check for new versions of the data. The authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data.</p>
Data Release: "A parameter-free tour of the binary black hole population"
<p>This dataset contains the results presented in "<strong>A parameter-free tour of the binary black hole population</strong>" (<a href="http://arxiv.org/abs/2302.07289">arXiv:2302.07289</a>).</p> <p>The code used to generate this data can be found in the repository <a href="https://github.com/tcallister/autoregressive-bbh-inference/">https://github.com/tcallister/autoregressive-bbh-inference/</a>. This repository includes <a href="https://github.com/tcallister/autoregressive-bbh-inference/tree/main/data">jupyter notebooks</a> that can be used to open, explore, and plot the files contained in this data set. Additional information about reproducing and/or using this dataset can be found in <a href="https://tcallister.github.io/autoregressive-bbh-inference/">our associated documentation</a>.</p> <p>Further notes:</p> <ul> <li>The files <em>sampleDict_FAR_1_in_1_yr.pickle</em> and <em>injectionDict_FAR_1_in_1.pickle</em>, used as inputs to our analyses, are created via code in the repository <a href="https://github.com/tcallister/get-lvk-data">https://github.com/tcallister/get-lvk-data</a> (see also <a href="https://zenodo.org/record/6505409">https://zenodo.org/record/6505409</a>).</li> <li>The files <em>posteriors_gaussian_spin_samples_FAR_1_in_1.json</em> and <em>o1o2o3_mass_c_iid_mag_iid_tilt_powerlaw_redshift_result.json</em>, used for figure generation, were published by the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration in support of the paper "<a href="https://arxiv.org/abs/2111.03634">The population of merging compact binaries inferred using gravitational waves through GWTC-3</a>" (see <a href="https://zenodo.org/record/5655785">https://zenodo.org/record/5655785</a>).</li> </ul> <p><em>New in this version: </em>This version (Version 3) contains the the newest results generated in the first round of journal referee review and reflected in v2 of the arXiv listing. Includes re-run of all analyses with updated priors, and new results from the injection study discussed in Appendix C.</p>
Symplectic lattice gauge theories on Grid: approaching the conformal window---data release
<p>This is the data release relative to the paper "Symplectic lattice gauge theories on Grid: approaching the conformal window" (arXiv:2306.11649).</p> <p>It contains pre-analysed data that can be plotted, and raw data that can be analysed and plotted through the analysis code in doi:10.5281/zenodo.8136514.</p>
First-order phase transitions in Yang-Mills theories and the density of state method---data and analysis code release
<p>Data release for paper: Lucini, B., Mason, D., Piai, M., Rinaldi, E., & Vadacchino, D. (2023). First-order phase transitions in Yang-Mills theories and the density of state method. arXiv preprint arXiv:2305.07463.</p> <p>This data release comprises of:</p> <p>Importance sampling results: Input and output files for PureGauge file of HiRep (https://github.com/claudiopica/HiRep) and csv files contains analysis of results.</p> <p>LLR results: Input files for LLR_HB for a modified version of HiRep for the heat bath LLR algorthim with umbrella sampling (https://github.com/dave452/Hirep-LLR-SU) and some csv files containing analysis of output.</p> <p>Analysis code within the LLRAnalysis.zip, it contains the code and the conda environment.</p>
Photometry of outer Solar System objects from the Dark Energy Survey I: photometric methods, light curve distributions and trans-Neptunian binaries - data release
<p>This repository contains the full data release for the 814 outer Solar System objects found in the Dark Energy Survey.</p> <p>A full description of the object search is described in <a href="http://(https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022)</a>, and a full description of the photometric processing is described in<a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230403017B/abstract"> Bernardinelli et al (2023)</a>. If you use these files, we ask you to cite the corresponding papers.</p> <p>The FITS table `y6_des_tnos_color.fits` contains both the orbital elements and the colors for each object. The full description of the orbital element information is given in Table 3 of <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022</a>). In addition to these, the table also includes the mean absolute magnitudes in each band, as well as mean <span class="math-tex">\((g-r, r-i, r-z)\)</span> colors and their corresponding covariance matrix, and the 68% confidence interval for the lightcurve amplitude.</p> <p>Inside the `fluxes` directory, the complete photometric record for each object is included in a `hdf5` file (for each object), and the MCMC chains for their fluxes and LCAs. The three Jupyter Notebooks included in the `notebooks` directory explains the columns and how to use these files to reproduce the results of the paper. Inside this directory there are also additional files needed to reproduce the code.</p> <p>The `binary` directory has the MCMC chains for their mutual orbits (in `.npy` files), as well as the astrometric and photometric record for the binary measurements. Another `README` file is included in that directory with a detailed explanation.</p>
The second data release from the European Pulsar Timing Array II. Customised pulsar noise models for spatially correlated gravitational waves
<p>Aims: The nanohertz gravitational wave background (GWB) is expected to be an aggregate signal of an ensemble of gravitational waves emitted predominantly by a large population of coalescing supermassive black hole binaries in the centres of merging galaxies. Pulsar tiNanohertz ming arrays (PTAs), which are ensembles of extremely stable pulsars at approximately kiloparsec distances precisely monitored for decades, are the most precise experiments capable of detecting this background. However, the subtle imprints that the GWB induces on pulsar timing data are obscured by many sources of noise that occur on various timescales. These must be carefully modelled and mitigated to increase the sensitivity to the background signal. Methods: In this paper, we present a novel technique to estimate the optimal number of frequency coefficients for modelling achromatic and chromatic noise, while selecting the preferred set of noise models to use for each pulsar. We also incorporated a new model to fit for scattering variations in the Bayesian pulsar timing package temponest. These customised noise models enable a more robust characterisation of single-pulsar noise. We developed a software package based on tempo2 to create realistic simulations of European Pulsar Timing Array (EPTA) datasets that allowed us to test the efficacy of our noise modelling algorithms. Results: Using these techniques, we present an in-depth analysis of the noise properties of 25 millisecond pulsars (MSPs) that form the second data release (DR2) of the EPTA and investigate the effect of incorporating low-frequency data from the Indian Pulsar Timing Array collaboration for a common sample of ten MSPs. We used two packages, enterprise and temponest, to estimate our noise models and compare them with those reported using EPTA DR1. We find that, while in some pulsars we can successfully disentangle chromatic from achromatic noise owing to the wider frequency coverage in DR2, in others the noise models evolve in a much more complicated way. We also find evidence of long-term scattering variations in PSR J1600-3053. Through our simulations, we identify intrinsic biases in our current noise analysis techniques and discuss their effect on GWB searches. The analysis and results discussed in this article directly help to improve the sensitivity to the GWB signal and they are already being used as part of global PTA efforts.</p>
Data release for "Measurements of the muon-neutrino and muon-antineutrino-induced coherent charged pion production cross sections on Carbon-12 by the T2K experiment"
<p>The T2K experiment reports the measurement of the flux averaged charged current coherent pion production cross section for neutrino and anti-neutrino scattering from a Carbon nucleus. These results are at a mean (anti)neutrino energy of 0.85~GeV in a restricted final state kinematic phase space. The neutrino measurement is an update to a previous result with systematic uncertainties reduced by a half. The antineutrino measurement is the first measurement of this cross section to be made at these energies. We find that the neutrino and antineutrino cross sections are consistent, as expected from theory, and that both agree with the current theoretical models, the Rein-Sehgal and Berger-Sehgal models.</p> <p>The data release contains a summary of these results as well as neutrino and antineutrino flux histograms with which the reader can make their own flux averaged cross section calculation.</p> <p>The paper is published in <a href="https://doi.org/10.1103/PhysRevD.108.092009">Physical Review D</a> and is available on the <a href="https://arxiv.org/abs/2308.16606">arXiv:2308.16606 [hep-ex]</a>.</p>
The second data release from the European Pulsar Timing Array I. The dataset and timing analysis
<p>Pulsar timing arrays offer a probe of the low-frequency gravitational wave spectrum (1−100 nanohertz), which is intimately connected to a number of markers that can uniquely trace the formation and evolution of the Universe. We present the dataset and the results of the timing analysis from the second data release of the European Pulsar Timing Array (EPTA). The dataset contains high-precision pulsar timing data from25 millisecond pulsars collected with the five largest radio telescopes in Europe, as well as the Large European Array for Pulsars. The dataset forms the foundation for the search for gravitational waves by the EPTA, presented in associated papers. We describe the dataset and present the results of the frequentist and Bayesian pulsar timing analysis for individual millisecond pulsars that have been observed over the last∼25 years.We discuss the improvements to the individual pulsar parameter estimates, as well as new measurements of the physical properties of these pulsars and their companions. This data release extends the dataset from EPTA Data Release 1 up to the beginning of 2021, with individual pulsar datasets with timespans ranging from 14 to 25 years. These lead to improved constraints on annual parallaxes, secular variation of the orbital period, and Shapiro delay for a number of sources. Based on these results, we derived astrophysical parameters that include distances, transverse velocities,binary pulsar masses, and annual orbital parallaxes.</p>
Keck Infrared Transient Survey Data Release 1
<p>We present the first data release from the Keck Infrared Transient Survey (KITS), a NASA Key Strategic Mission Support program to obtain near-infrared (NIR) spectra of astrophysical transients of all types. This data release consists of 105 NIR spectra of 50 transients. As we are entering a new era of infrared astronomy with the James Webb Space Telescope (JWST) and the upcoming Nancy Grace Roman Space Telescope (Roman), KITS provides a large, publicly available sample of IR spectroscopy for a wide range of transients. These data will be essential to search JWST images for stellar explosions of the first stars and to plan an effective Roman SN Ia cosmology survey, both key science objectives for mission success. The first data release represents the first semester, which is one third of the full survey. We systematically observed three samples: a flux-limited sample that includes all transients brighter than 17~mag in a red optical band (usually ZTF r or ATLAS o bands); a volume-limited sample including all transients within redshift z < 0.01; and an SN Ia sample targeting objects at phases and light-curve parameters that had scant existing NIR data in the literature. Please see the accompanying paper where we describe our observing procedures and data reduction using an automated pipeline pypeit with minimal human interaction to ensure reproducibility. In this dataset, we provide telluric-corrected spectra of the transient in CSV format. We also provide one-dimensional extracted spectra of transients and telluric standard stars in FITS format from pypeit. Users can use these intermediate data products to redo telluric correction if desired.</p>
juan9715/WaveVelocitiesSoftwareAndSpreadsheet: Wave Velocities Data Release
<p>Contains data for articles:</p> <p>Article 1: Wave Velocities and Poisson Ratio in Loose Sandy Martian Regolith Under Low Stresses. Part 1: Laboratory Investigation Article 2: Wave Velocities and Poisson Ratio in Loose Sandy Martian Regolith Under Low Stresses. Part 2: Theoretical Analysis</p>
Parameter estimation data release for paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method"
<p>This is a second data release for the paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method", which is available on <a href="https://arxiv.org/abs/2306.12908">https://arxiv.org/abs/2306.12908</a>.</p> <p>This data release contains posterior samples and configuration files for parameter estimation runs performed for the paper. For the lensing hypothesis tests results see the other <a href="https://doi.org/10.5281/zenodo.8409635">data release</a> for the same paper.</p> <p>For each event that we have run parameter estimation for, we provide a tar.gz file that includes the configurations, the priors and the results for each run, typically with several different waveforms. All runs were performed with <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">parallel bilby</a> with different versions as described in section 6.1 of the paper. The input data is available from <a href="https://gwosc.org/">GWOSC</a>.</p> <p>The results for IMRPhenom* waveforms (runs with parallel bilby version 1.0.1) are in json format, while for SEOBNRv5PHM and NRSur7dq4 (run with parallel bilby version 2.0.2) some of the results are in hdf5 and others in json, depending on whether multiple runs were merged together. These should be readable with bilby or <a href="https://lscsoft.docs.ligo.org/pesummary/">pesummary</a>.</p> <p>We provide the "complete" configuration files processed by bilby. These can be used for reproducing the runs, but the prior file information needs to be added with a syntax like the following `prior-file=ProdF4.prior`. Generally most runs use the same prior file, with the labels `Prod*.prior`, taken from the LVK analyses. The priors used for the aligned-spin waveforms have the label `_AS`. For the NRSur7dq4 waveform we use a restricted prior with the label `_NR_Sur_constrMtot`. For GW190527 we also use a different prior for IMRPhenomXP and IMRPhenomTPHM, which has the label `_restricted`.</p> <p>Also note that for the result file GW190527_NRSur7dq4_N4096_nact50_fmin0_nparallel3_merged_result.hdf5 this was merged from the results obtained with the config GW190527_NRSur7dq4_N4096_nact50_fmin0_nparallel3_config_complete.ini together with the fourth chain of identical configuration.</p>
Catalogue for NIRCAM ICEAGE data - public release to match spectra
<p>This is the latest catalogue for the NIRCAM team. V12. </p> <p>This should be used in conjunction with the spectral release </p> <p><a href="https://zenodo.org/records/15920870">https://zenodo.org/records/15920870</a></p> <p>to get photometry and source properties. RA DEC etc</p> <p> </p> <p>This is the initial catalogue delivery (V3_PUBLIC) for the ICEAGE NIRCAM WFSS observations of Cha I.</p> <p>This data was previously embargoed to ERS ICEAGE TEAM, but is now publically available.</p> <p> </p> <div> <p>Please START by reading the <strong>READ_ME_ICEAGE_catalogue_V12.pdf </strong>document.</p> <p>Please also read the READ on the spectral delivery page as this includes details wrt authorship and arising articles, as well as other very pertinent information that could answer A LOT of your questions and queries.<strong> </strong></p> <p>Until such a time, if there are any queries on teh catalogue - before using it for science <strong>PLEASE contact Helen Fraser and / or Zak Smith. We are very happy to help with identifying sutable spectra, understanding what you are looking at and matching the two filter datasets to get single spectra coverage from 2-5 mcirons. We are also happy to help with siftign catalogue sources to your needs. </strong>And don’t forget to check with the ICEAGE PIs and especially Adwin who is leading the NIRCAM team wrt. ongoing papers and work before initiating a project.</p> <p> </p> <p><em><strong>PLEASE do not use the data for publications or proposals without the express permission of the ICEAGE PIs Adwin Boogert and Melissa McClure : please also ensure that appropriate credit and authoriship is gifted to the individuals who did the hard work generating this community resource. </strong></em></p> <p><em><strong> </strong></em></p> <p>Enjoy doing new science!! We look forward to our data set beign well used.</p> </div> <p> </p> <p> </p>
Data from: To treat or Not to Treat? Experimental Pathogen Exposure, Treatment, and Release of a Threatened Amphibian
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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