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951 results for “Data release”
On Holographic Vacuum Misalignment—data release
<p>This dataset contains the data points in the plots of the preprint <a href="https://arxiv.org/abs/2405.08714">On Holographic Vacuum Misalignment</a>.</p> <p>If you use this data release in the context of your research, please cite the aforementioned paper.</p> <p>Further details are given in the file ReadMe.md.</p>
Fig. 1 in Selitrichodes neseri (Hymenoptera: Eulophidae) recovered from Leptocybe invasa (Hymenoptera: Eulophidae) galls afer initial release on Eucalyptus (Myrtaceae) in Brazil, and data on its biology
Fig. 1. Municipality of Rio Real, Bahia State, where Selitrichodes neseri (Hymenoptera: Eulophidae) adults were released for the first time in Brazil.
rchampieux/Biomedical_Journal_Data_Sharing_Policies: Data and Code Release for Publication
<p>Data and code for manuscript:</p> <p>David B Resnik, Melissa Morales, Rachel Landrum, Min Shi, Jessica Minnier, Nicole A. Vasilevsky & Robin E. Champieux (2019) <em>Effect of Impact Factor and Discipline on Journal Data Sharing Policies</em>, Accountability in Research, <a href="https://doi.org/10.1080/08989621.2019.1591277">DOI: 10.1080/08989621.2019.1591277</a></p> <p>Zenodo pre-print DOI: https://doi.org/10.5281/zenodo.2592682</p> <p>Data collection utilized three sources:</p> <ul> <li>2016 InCites Journal Citations Report</li> <li>Directory of Open Access Journal</li> <li>Journal websites and author guidelines</li> </ul> <p>The data was collected and analyzed between May 2018 and October 2018.</p> <p><strong>Data and Code</strong></p> <p>Data can be found in <a href="https://github.com/OHSU-Library/Effect-of-IF-and-Discipline-on-Journal-Data-Sharing-Policies/blob/master/data/if-discipline-datasharing-policy-rawdata-1.0.0.csv">data/if-discipline-datasharing-policy-rawdata-1.0.0.csv</a>.</p> <p>Analysis code for tables and figures can be seen in <a href="https://github.com/OHSU-Library/Effect-of-IF-and-Discipline-on-Journal-Data-Sharing-Policies/blob/master/code/analysis_report.md">code/analysis_report.md</a> (author of code: Jessica Minnier, OHSU, <a href="https://github.com/jminnier/">@jminnier</a>)</p>
Raw data: Supra-threshold perception and neural representation of tones presented in noise in conditions of masking release
<p>Raw data of three experiments:</p> <p>1) Exp1: Psychoacoustical masked thresholds of tone in noise masker (ASCII format)</p> <p>2) Exp2: 64ch EEG data (Biosemi data format .bdf)</p> <p>3) Exp3: Salience rating of a tone masked by various maskers and levels above masked threshold. (ASCII format)</p> <p>Preprint with details on experiments submitted to BioRxiv: https://doi.org/10.1101/575720</p>
gwastro/o2-bbh-pe: v2.2 data release of O2 Binary Black Hole posterior samples
<p>This is the v2.2 data release associated with the parameter estimation analysis of the binary black-hole signals from Advanced LIGO-Virgo's second observing run, using the PyCBC Inference toolkit : <a href="https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b">https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b</a> . A companion paper presenting our parameter estimation analysis and the data release is available here : <a href="https://arxiv.org/pdf/1811.09232.pdf">https://arxiv.org/pdf/1811.09232.pdf</a>.</p> <p>The analysis was performed using the PyCBC v1.12.3 code on the gravitational-wave data available at <a href="https://www.gw-openscience.org/catalog/GWTC-1-confident/html/">https://www.gw-openscience.org/catalog/GWTC-1-confident/html/</a> . Descriptions of the gravitational-wave data can be found in the paper <a href="https://arxiv.org/abs/1811.12907">https://arxiv.org/abs/1811.12907</a> .</p> <p>The changes in this release are</p> <ul> <li>An update to the plotting code in <code>data_release_o2_bbh_pe.ipynb</code> for generating Figs. 1, 2, and 3 in the companion paper to take into account cases where a boundary bias may be introduced for plotting probability contours.</li> <li>Addition of a plotting code in <code>data_release_o2_bbh_pe.ipynb</code> that generates a corner plot showing estimates (median and 90% credible interval) and posterior distributions for all the parameters presented in Table 1 of the companion paper.</li> <li>Addition of a notebook <code>o2_bbh_pe_skymaps.ipynb</code> that demonstrates the method for visualizing sky location posteriors as presented in Fig. 4 of the manuscript.</li> </ul> <p>The data and configuration files included remain the same as in the v2.1 release.</p> <p>This release includes :</p> <ul> <li>posterior and prior samples from parameter estimation analyses of the seven binary black-hole events---GW170104, GW170608, GW170729, GW170809, GW170814, GW170818, and GW170823.</li> <li>PSDs used in each of the analyses</li> <li>configuration files and run scripts for running the analyses and generating the data.</li> <li>tutorials for manipulating the data and reconstructing the figures in the companion paper.</li> </ul>
sirselim/immunecell_methylation_paper_data: First release of data for immune cell epigenetics (methylation) manuscript
<p>This is the first release of the data to be made public and accessible with the manuscript.</p>
Freischütz Digital: data-music, The Academy 2017 Release
<p>This is a copy of the <a href="https://github.com/Freischuetz-Digital/data-music" target="_blank" rel="noopener">Freischütz Digital/data-music</a> repository, which is meant for long-term preservation. A description of the dataset can be found in the <em>readme.md</em> file. </p>
Large mass hierarchies from strongly-coupled dynamics—Data release
<p>This release contains data associated with the publication <a href="https://arxiv.org/abs/1605.04258">Large mass hierarchies from strongly-coupled dynamics</a> (<a href="https://doi.org/10.1007/JHEP06(2016)114">JHEP 06 (2016) 114</a>)</p> <p>It comprises four files:</p> <ul> <li><code>README.md</code>: Containing this information, and a more detailed description of the dataset.</li> <li><code>Fig4.csv</code>: the data shown in Figure 4 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. The mass $M$ of composite spin-0 and spin-2 states, and their excitations, computed for $c_1 = 0 = A_0$, as a function of $\Delta$, for $r_{UV} = 25$ and $r_{IR} = 10^{-6}$, normalised to the mass $M_0$ of the lightest scalar.</li> <li><code>Fig5.csv</code>: the data shown in Figure 5 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. The mass $M$ of the composite spin-0 and spin-2 states, computed for $c_1 = 0 = A_0$, as a function of $-A(r_{IR}) = \log (\Lambda_0 / \Lambda_{IR})$, for $\Delta = 1.5$, normalised to the mass $M_T$ of the lightest tensor. $A(r_{UV}) - A(r_{IR})$ is fixed to 8.</li> <li><code>DataRelease.nb</code>: A Mathematica notebook that will take the above two files and generate plots similar to those shown in <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>.</li> </ul> <p> </p>
GBM Targeted Search Data Release for EP 240919a /GRB 240919A
<p>The following Files include:</p> <ul> <li>The summed GBM NaI detector lightcurve,</li> <li>The individual GBM NaI detector lightcurves,</li> <li>The BGO detector lightcurves, </li> <li>The GBM NaI detector lightcurves sliced by energy.</li> <li>The waterfall plot (all energy) showing the most significant timescale of the event,</li> <li>The waterfall plot with the three different spectral models (soft, normal and hard, see below).</li> <li>The localization of EP 240919a / GRB 240919A, with the green circle centered on the EP-WXT position with a 1 degree error radius.</li> <li>The healpix map for "Event 1", which is the event found by the Targeted Search and shown in these plots. </li> </ul> <p><br>The definitions for soft, normal and hard spectra are:<br><br>"soft" spectrum (Band function with Epeak = 70 keV, alpha = -1.9, beta = -3.7) for a GRB.<br>"normal" spectrum (Band function with Epeak = 230 keV, alpha = -1.0, beta = -2.3) for a GRB.<br>"hard" spectrum (Comptonized function with Epeak = 1500 keV, alpha = -0.5) for a GRB. </p> <p> </p>
All the Little Things (ALT) Data Release 1
<p>Catalog of redshifts measured by the JWST Cycle 2 program "All the Little Things" (GO-3516, PIs: Matthee & Naidu). Columns included are: the unique survey ID of the sources ("id"), their coordinates ("ra", "dec"), the measured grism redshift ("z_ALT"), and the number of emission lines detected with SNR>5 ("n_lines_detected"). The reported redshifts have a precision of ~60 km/s. Please refer to the accompanying release paper (Naidu & Matthee et al. 2024) for a detailed description of the survey and how this catalog was constructed.</p>
GBM Targeted Search Data Release for GRB 241002D
<p>The following Files include:</p> <ul> <li>The summed GBM NaI detector lightcurve,</li> <li>The individual GBM NaI detector lightcurves,</li> <li>The BGO detector lightcurves, </li> <li>The GBM NaI detector lightcurves sliced by energy.</li> <li>The waterfall plot (all energy) showing the most significant timescale of the event,</li> <li>The waterfall plot with the three different spectral models (soft, normal and hard, see below).</li> <li>The localization of GRB 241002D, showing the 3 and 1-sigma contours.</li> <li>The healpix map for "Event 1", which is the event found by the Targeted Search and shown in these plots. </li> </ul> <p><br>The definitions for soft, normal and hard spectra are:<br><br>"soft" spectrum (Band function with Epeak = 70 keV, alpha = -1.9, beta = -3.7) for a GRB.<br>"normal" spectrum (Band function with Epeak = 230 keV, alpha = -1.0, beta = -2.3) for a GRB.<br>"hard" spectrum (Comptonized function with Epeak = 1500 keV, alpha = -0.5) for a GRB. </p>
Towards the $\beta$ function of SU(2) with adjoint matter using Pauli–Villars fields—Data release
<p>This release contains all data generated in preparing the poster <a href="../records/13361520">Towards the function of SU(2) with adjoint matter using Pauli–Villars fields</a>, presented at <a href="https://conference.ippp.dur.ac.uk/event/1265/overview">Lattice 2024</a> in Liverpool, and its corresponding proceedings paper. It includes two classes of data, each provided both for the one- and two-flavour theories:</p> <ol> <li>Raw data, as generated from the HMC measurement code running on HPC, in their native formats.</li> <li>Metadata around the analysis of the ensembles, in YAML format.</li> </ol> <p>Due to their size, raw gauge configurations are not included in this release.</p> <p>Further documentation on the directory structure and file formats is provided in the file README.md</p>
Data release for paper "The Araucaria Project: Deep near-infrared photometric maps of Local and Sculptor Group galaxies. I. Carina, Fornax, Sculptor"
<p>Deep near-infrared J- and K-band photometry of three Local Group dwarf spheroidal galaxies: Fornax, Carina, and Sculptor, is made available for the community. Until now, these data have only been used by the Araucaria Project to determine distances using the tip of the red giant and RR Lyrae stars. Now, we present the entire data collection in a form of a database, consisting of accurate J- and K-band magnitudes, sky coordinates, ellipticity measurements, and timestamps of observations, complemented by stars' loci in their reference images. Depth of our photometry reaches about 22 mag at 5 sigma level, and is comparable to NIR surveys, like the UKIRT Infrared Deep Sky Survey (UKIDSS) or the VISTA Hemisphere Survey (VHS), and small overlap with VHS and no overlap with UKIDSS makes our database a unique source of quality photometry.</p> <p>Data release consists of:</p> <ul> <li>databases in a form of text files for Carina, Fornax and Sculptor galaxies<br> (db_Car.txt, db_For.txt , db_Scu.txt)</li> <li>completeness tables and plots for every field in Carina, Fornax and Sculptor galaxies<br> (compl_Car.pdf, compl_Car.txt, compl_Scu.pdf, compl_Scu.txt, compl_For.pdf, compl_For.txt)</li> <li>explanatory file for each galaxy<br> (info_Car.txt, info_Scu.txt, info_For.txt)</li> <li>FITS images of scientific quality of all analyzed fields in Carina, Fornax and Sculptor galaxies, archived in tar.gz files</li> </ul>
Data release: Whole-genome sequencing of Schistosoma mansoni reveals extensive diversity with limited selection despite mass drug administration
<p>Source data used in the publication: Berger et al. (2021) - Provisional title: 'Whole-genome sequencing of <em>Schistosoma mansoni</em> reveals extensive diversity with limited selection despite mass drug administration'. These data were used to generate all figures used in the publication and all files are organised and labelled specifically to run with the custom code that uses these data can be found at: http://doi.org/10.5281/zenodo.4975908. </p> <p><br> <strong>File descriptions:</strong></p> <p><strong>SOURCE DATA.zip - All source data for all figures. </strong></p> <p><strong>Figure 1b:</strong></p> <ul> <li>supplementary_data_9.txt - Metadata</li> </ul> <p><strong>Figure 2a&b:</strong></p> <ul> <li>207_PCA.eigenvec - PCA eigenvectors</li> <li>207_PCA.eigenval - PCA eigenvalues</li> </ul> <p><strong>Figure 2c:</strong></p> <ul> <li>autosomes.mdist - PLINK distance matrix used to build the neighbour joining phylogeny</li> </ul> <p><strong>Figure 2d:</strong></p> <ul> <li>all.pi.pixy.schools.txt - Nucleotide diversity results for each school subpopulation.</li> </ul> <p><strong>Figure 2e:</strong></p> <ul> <li>autosomes.dxy.5kb.schools.txt - Autosomal D<sub>XY</sub> results between school subpopulations. </li> <li>autosomes.fst.5kb.schools.txt - Autosomal F<sub>ST</sub> results between school subpopulations.</li> </ul> <p><strong>Figure 2f:</strong></p> <ul> <li>admixture_all.txt - ADMIXTURE results for each sample and population sizes, column 1 represents number of populations (K), columns 3-8 represent admixture values for each population. </li> </ul> <p><strong>Figure 3a, Supplementary figure 10a:</strong></p> <ul> <li>sfs.csv - Site frequency spectra (allelic proportions at each frequency bin) for each school. </li> </ul> <p><strong>Figure 3b:</strong></p> <ul> <li>TD.all.txt - Tajima's D values calculated in 5 kb windows for each school subpopulation. </li> </ul> <p><strong>Figure 4a, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.MAYUGE.IHS.ihs.out.100bins.norm.txt.zip - Normalised iHS scores for the Mayuge district parasite populations (Selscan output).</li> </ul> <p><strong>Figure 4b, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.TORORO.IHS.ihs.out.100bins.norm.txt.zip -<strong> - </strong>Normalised iHS scores for the Tororo district parasite populations (Selscan output).</li> </ul> <p><strong>Figure 4c, Supplementary figures 13-18: </strong></p> <ul> <li>ALL.MAYUGEvsTORORO.xpehh.xpehh.out.norm.txt.zip - - Normalised XP-EHH scores between Mayuge and Tororo parasite populations.</li> </ul> <p><strong>Figure 4d, Supplementary figures 13-18:</strong></p> <ul> <li>MAYUGE_TORORO_2000.windowed.weir.txt.zip - F<sub>ST</sub> values calculated between Mayuge and Tororo populations in 2kb windows. </li> </ul> <p><strong>Figure 4e, Supplementary figures 12a&c:</strong></p> <ul> <li>MAYUGE_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Mayuge populations. </li> <li>TORORO_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Kocoge populations (Tororo district).</li> </ul> <p><strong>Figure 5a:</strong></p> <ul> <li>all.pi.treat.fix.txt.zip - Nucleotide diversity results for each treatment subpopulation</li> </ul> <p><strong>Figure 5b</strong></p> <ul> <li>autosomes.dxy.5kb.treatment.txt - <strong> </strong>- Autosomal D<sub>XY</sub> results between clearance phenotype subpopulations. </li> <li>autosomes.fst.5kb.treatment.txt<strong> </strong>- Autosomal F<sub>ST</sub> results between clearance phenotype subpopulations. </li> </ul> <p><strong>Figure 5c:</strong></p> <ul> <li>fst.windows.2kb.treatment.txt.zip - F<sub>ST</sub> values for comparisons between different treatment groups (Pre-treatment, post-treatment (good clearers), post-treatment (poor clearers))</li> </ul> <p><strong>Figure 5d: </strong></p> <ul> <li>assoc_err_binary.txt.zip - Results of binary trait association between miracidia sampled from hosts with good clearance phenotypes (where treatment appeared to be highly effective) and miracidia isolated post-treatment from hosts with poor clearance phenotypes (where miracidia are potentially derived from parasites that survived treatment.</li> </ul> <p><strong>Figure 5e:</strong></p> <ul> <li>assoc_err_linear.txt.zip - - Results of linear regression genome-wide association study with the ERR estimates for all 198 samples, using the mean of the posterior ERR estimates from Crellen et al. (2016) as a quantitative trait.</li> </ul> <p><strong>Supplementary figure 1:</strong></p> <ul> <li>median.coverage.txt - Normalised depth of read coverage (column 4) calculated in 25 kb windows (columns 2&3) across all samples for all chromosomes (column 1).</li> </ul> <p><strong>Supplementary figure 2a-f: </strong></p> <ul> <li>cohort.genotyped.txt.zip - <strong> </strong>- Variant quality site values (used to inform variant site retention or removal). </li> </ul> <p><strong>Supplementary figure 2g:</strong></p> <ul> <li>hard_filtered.imiss.txt - Per sample variant missingness (used to inform quality control).</li> </ul> <p><strong>Supplementary figure 2h:</strong></p> <ul> <li>hard_filtered_filtindv.lmiss.txt.zip - Per site missingness (used to inform quality control).</li> </ul> <p><strong>Supplementary figure 3a, 4a, 4b:</strong></p> <ul> <li>prunedData.eigenvec - PCA eigenvectors</li> <li>prunedData.eigenval - PCA eigenvalues</li> </ul> <p><strong>Supplementary figure 3b:</strong></p> <ul> <li>pruned_data.mdist.csv - Distance matrix used as the basis for the neighbour joining phylogeny.</li> </ul> <p><strong>Supplementary figure 5:</strong></p> <ul> <li>cv_scores.txt - ADMIXTURE coefficient of variation scores (column 2) for each population size (1).</li> </ul> <p><strong>Supplementary figure 6:</strong></p> <ul> <li>*_SMC_SE.csv - SMC++ results (from 25 subsampled replicates) for each school subpopulation and outgroup samples. </li> </ul> <p><strong>Supplementary Figure 7:</strong></p> <ul> <li>smcpp.csv - SMC++ results for each school subpopulation and outgroup samples. </li> </ul> <p><strong>Supplementary Figure 8a-d</strong></p> <ul> <li>pi.per_host.txt.zip - Nucleotide diversity values for each host infrapopulation. </li> </ul> <p><strong>Supplementary Figure 9:</strong></p> <ul> <li>sexing.csv - inferred sex (based on differential read coverage over pseudoautosomal and Z-specific regions of the Z chromosome). </li> </ul> <p><strong>Supplementary Figure 10b:</strong></p> <ul> <li>sfs_res.csv - residuals for the SFS analysis in 3a/10a.</li> </ul> <p><strong>Supplementary Figure 11:</strong></p> <ul> <li>MAYUGE_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Mayuge population in 2kb windows. </li> <li>Tororo_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Tororo population in 2kb windows. </li> </ul> <p><strong>Supplementary Figures 13-18:</strong></p> <ul> <li>genes.bed - Coordinates of gene models (<em>S. mansoni </em>v7 annotation).</li> <li>KOCOGE_SITE_PI.sites.pi.txt.zip - Per site nucleotide diversity values</li> <li>MAYUGE_TORORO_sites.weir.fst.txt.zip - Per site F<sub>ST</sub> values between Mayuge and Tororo populations. </li> <li>coverage_5kb.windows.txt.zip - Per sample depth of read coverage in 5 kb windows. Columns 4,5,6 represent the median, mean and sstev of coverage for each 5kb window (columns 2&3) along each chromosome (column 1). </li> <li>median.sample.coverage.txt - Median chromosomal depth of read coverage for each sample. </li> </ul> <p><strong>Supplementary Figure 19:</strong></p> <ul> <li>kocoge_median.ld.txt.zip - <strong> </strong>- The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Kocoge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> <li>mayuge_median.ld.txt.zip - The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Mayuge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> </ul> <p><strong>Misc files:</strong></p> <p>schools.list - List of samples and schools where they were sampled. </p> <p> </p>
Urheberrechtsgesetz (UrhG): software and data related definitions depicted using UML class modeling — Release 10
<p>Diagram depicting definitions and conceptual relationships within the German Copyright and Related Rights Act or Urheberrechtsgesetz (UrhG) up to and including the amendments of 28 November 2018. The diagram focuses on software and data related definitions and presents these using Unified Modeling Language (UML) class modeling. Please contact the author is you require the underlying Inkscape SVG vector art.</p>
Observation of the gamma-ray binary HESS J0632+057 with the H.E.S.S., MAGIC, and VERITAS telescopes - data release
<p><strong>Observation of the gamma-ray binary HESS J0632+057 with the H.E.S.S., MAGIC, and VERITAS telescopes - data release</strong></p> <p>The results of gamma-ray observations of the binary system HESS J0632+057 collected during 450 hours over 15 years, between 2004 and 2019, with the H.E.S.S., MAGIC, and VERITAS telescopes are presented in <strong>Observation of the gamma-ray binary HESS J0632+057 with the H.E.S.S., MAGIC, and VERITAS telescopes</strong> (ApJ, to be published).<br> This repository provides access to all processed data presented in the publication in csv and ascii format.<br> For a detailed description of analysis and data processing, see the associated primary publication.</p> <p><strong>Please cite always the following primary reference when using these data: </strong></p> <ul> <li> <p><a href="https://doi.org/10.3847/1538-4357/ac29b7">The Astrophysical Journal, 923:241 (30pp), 2021 December 20</a></p> </li> <li> <p><a href="https://arxiv.org/abs/2109.11894">arXiv:2109.11894</a></p> </li> </ul> <p>Data Publication Year: 2021</p> <p>Citation: The VERITAS, MAGIC, and H.E.S.S. Collaborations (2021). Observation of the gamma-ray binary HESS J0632+057 with the HESS, MAGIC, and VERITAS telescopes - data release. DOI **[DOI to be added]**</p> <p>Additional information on the gamma-ray observatories:<br> - H.E.S.S. (<a href="https://www.mpi-hd.mpg.de/hfm/HESS/">https://www.mpi-hd.mpg.de/hfm/HESS/</a>) and H.E.S.S. Auxiliary Data Page (<a href="https://www.mpi-hd.mpg.de/hfm/HESS/pages/publications/auxiliary/auxinfo_hessj0632_HMVdata.html">https://www.mpi-hd.mpg.de/hfm/HESS/pages/publications/auxiliary/auxinfo_hessj0632_HMVdata.html</a>)<br> - MAGIC (<a href="https://magic.mpp.mpg.de/">https://magic.mpp.mpg.de/</a>) and MAGIC Data Page (<a href="http://vobs.magic.pic.es/fits/">http://vobs.magic.pic.es/fits/</a>)<br> - VERITAS (<a href="https://veritas.sao.arizona.edu/">https://veritas.sao.arizona.edu/</a>) and VERITAS Data Page (<a href="https://github.com/VERITAS-Observatory/VERITAS-VTSCat">https://github.com/VERITAS-Observatory/VERITAS-VTSCat</a>)</p> <p>This data repository is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://opendatacommons.org/licenses/pddl/1.0/</p> <p>## List of data:</p> <p>(best to view with a markdown reader)</p> <p>1. Gamma-ray and X-ray fluxes (Figures 2, 3, and 9):<br> - Gamma-ray integral flux (>350 GeV) from H.E.S.S. observations: [Fig02_03_09/LightCurve-HESS.ecsv](Fig02_03_09/LightCurve-HESS.ecsv)<br> - Gamma-ray integral flux (>350 GeV) from MAGIC observations: [Fig02_03_09/LightCurve-MAGIC.ecsv](Fig02_03_09/LightCurve-MAGIC.ecsv)<br> - Gamma-ray integral flux (>350 GeV) from VERITAS observations: [Fig02_03_09/LightCurve-VERITAS.ecsv](Fig02_03_09/LightCurve-VERITAS.ecsv)<br> - X-ray fluxes (0.3–10 keV) from Swift-XRT, Chandra, XMM, NuSTAR, Suzaku observations: [Fig02_03_09/LightCurve-XRay.ecsv](Fig02_03_09/LightCurve-XRay.ecsv)<br> 2. Halpha observations (Figure 4):<br> - Profile parameters of Halpha observations [Fig04/Halpha.ecsv](Fig04/Halpha.ecsv)<br> 3. Gamma-ray - X-ray correlation (Figure 5):<br> - Contemporaneous gamma-ray (>350 GeV) vs X-ray (0.3–10 keV) integral fluxes: [Fig05/LC-cross-Gamma-XRay.ecsv](Fig05/LC-cross-Gamma-XRay.ecsv)<br> - Discrete cross-correlation function (DCF) between gamma- ray and X-ray data: [Fig05/DCF-cross-Gamma-XRay-HESSJ0632p057.ecsv](Fig05/DCF-cross-Gamma-XRay-HESSJ0632p057.ecsv)<br> 4. Gamma-ray vs Optical and X-ray vs Optical correlations (Figure 6):<br> - Halpha vs gamma-ray observations: [Fig06/Gamma-ray-Optical-Correlation.ecsv](Fig06/Gamma-ray-Optical-Correlation.ecsv)<br> - Halpha vs X-ray observations: [Fig06/X-ray-Optical-Correlation.ecsv](Fig06/X-ray-Optical-Correlation.ecsv)<br> 5. Spectral energy distributions (phase averaged; Figure 7 and 8)<br> - SEDs from H.E.S.S. observations: [Fig07_08/HESS-phaserange04-spectrum.ecsv](Fig07_08/HESS-phaserange04-spectrum.ecsv), [Fig07_08/HESS-phaserange1-spectrum.ecsv](Fig07_08/HESS-phaserange1-spectrum.ecsv), [Fig07_08/HESS-phaserange2-spectrum.ecsv](Fig07_08/HESS-phaserange2-spectrum.ecsv), [Fig07_08/HESS-phaserange3-spectrum.ecsv](Fig07_08/HESS-phaserange3-spectrum.ecsv)<br> - SEDs from MAGIC observations: [Fig07_08/MAGIC-phaserange04-spectrum.ecsv](Fig07_08/MAGIC-phaserange04-spectrum.ecsv), [Fig07_08/MAGIC-phaserange1-spectrum.ecsv](Fig07_08/MAGIC-phaserange1-spectrum.ecsv), [Fig07_08/MAGIC-phaserange2-spectrum.ecsv](Fig07_08/MAGIC-phaserange2-spectrum.ecsv)<br> - SEDs from VERITAS observations: [Fig07_08/VERITAS-phaserange04-spectrum.ecsv](Fig07_08/VERITAS-phaserange04-spectrum.ecsv), [Fig07_08/VERITAS-phaserange1-spectrum.ecsv](Fig07_08/VERITAS-phaserange1-spectrum.ecsv), [Fig07_08/VERITAS-phaserange2-spectrum.ecsv](Fig07_08/VERITAS-phaserange2-spectrum.ecsv), [Fig07_08/VERITAS-phaserange3-spectrum.ecsv](Fig07_08/VERITAS-phaserange3-spectrum.ecsv)<br> - SEDs from Swift-XRT observations: [Fig07_08/XRT-phaserange04-spectrum.ecsv](Fig07_08/XRT-phaserange04-spectrum.ecsv), [Fig07_08/XRT-phaserange1-spectrum.ecsv](Fig07_08/XRT-phaserange1-spectrum.ecsv), [Fig07_08/XRT-phaserange2-spectrum.ecsv](Fig07_08/XRT-phaserange2-spectrum.ecsv), [Fig07_08/XRT-phaserange3-spectrum.ecsv](Fig07_08/XRT-phaserange3-spectrum.ecsv)<br> 6. Spectral energy distributions (orbit 9 and 17; Figure 10):<br> - SEDs from VERITAS observations: [Fig10/VERITAS-MJD55585-55600-spectrum.ecsv](Fig10/VERITAS-MJD55585-55600-spectrum.ecsv), [Fig10/VERITAS-MJD55600-55603-spectrum.ecsv](Fig10/VERITAS-MJD55600-55603-spectrum.ecsv), [Fig10/VERITAS-MJD55614-55623-spectrum.ecsv](Fig10/VERITAS-MJD55614-55623-spectrum.ecsv), [Fig10/VERITAS-MJD55624-55631-spectrum.ecsv](Fig10/VERITAS-MJD55624-55631-spectrum.ecsv), [Fig10/VERITAS-MJD58136-spectrum.ecsv](Fig10/VERITAS-MJD58136-spectrum.ecsv), [Fig10/VERITAS-MJD58141-spectrum.ecsv](Fig10/VERITAS-MJD58141-spectrum.ecsv), [Fig10/VERITAS-MJD58142-spectrum.ecsv](Fig10/VERITAS-MJD58142-spectrum.ecsv), [Fig10/VERITAS-MJD58143-spectrum.ecsv](Fig10/VERITAS-MJD58143-spectrum.ecsv), [Fig10/VERITAS-MJD58153-58154-spectrum.ecsv](Fig10/VERITAS-MJD58153-58154-spectrum.ecsv)<br> - SEDs from MAGIC observations: [Fig10/MAGIC-MJD55585-55600-spectrum.ecsv](Fig10/MAGIC-MJD55585-55600-spectrum.ecsv)<br> - SEDs from Swift-XRT observations: [Fig10/XRT-MJD55585-55600-spectrum.csv](Fig10/XRT-MJD55585-55600-spectrum.csv), [Fig10/XRT-MJD55600-55603-spectrum.csv](Fig10/XRT-MJD55600-55603-spectrum.csv), [Fig10/XRT-MJD55614-55623-spectrum.csv](Fig10/XRT-MJD55614-55623-spectrum.csv), [Fig10/XRT-MJD55624-55631-spectrum.csv](Fig10/XRT-MJD55624-55631-spectrum.csv), [Fig10/XRT-MJD58142-spectrum.csv](Fig10/XRT-MJD58142-spectrum.csv), [Fig10/XRT-MJD58143-spectrum.csv](Fig10/XRT-MJD58143-spectrum.csv), [Fig10/XRT-MJD58152-spectrum.csv](Fig10/XRT-MJD58152-spectrum.csv), [Fig10/XRT-MJD58153-spectrum.csv](Fig10/XRT-MJD58153-spectrum.csv)<br> 7. Contemporaneous X-ray and gamma-ray spectral energy distribution (Appendix D)<br> - SEDs from VERITAS observations: [Auxiliary/VERITAS*](Auxiliary/)</p>
The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document v1 Released Data Products
<p>This tarball includes software and data products associated with the DESC Science Requirements Document (SRD) v1. See the "Executive Summary and User Guide" in the enclosed PDF of the DESC SRD for instructions on how to use and cite those products. The DESC SRD is described on <a href="https://arxiv.org/abs/1809.01669">arXiv</a> as follows:</p> <p>The Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC) will use five cosmological probes: galaxy clusters, large scale structure, supernovae, strong lensing, and weak lensing. The Science Requirements Document (SRD) quantifies the expected dark energy constraining power of these probes individually and together, with conservative assumptions about analysis methodology and follow-up observational resources based on our current understanding and the expected evolution within the field in the coming years. We then define requirements on analysis pipelines that will enable us to achieve our goal of carrying out a dark energy analysis consistent with the Dark Energy Task Force definition of a Stage IV dark energy experiment.</p>
Data for Integrated Step Selection Analysis of translocated female greater sage-grouse in the 60 days post-release, North Dakota 2018-2020
<p>The data include used and random available steps at 11-hour resolution generated for 26 female greater sage-grouse in the 60 days post-translocation to North Dakota, with associated environmental predictors and individual information. The code fits individual habitat selection models in an Integrated Step Selection Analysis framework.</p> <p>Data used to fit the models described in:</p> <p>Picardi, S., Ranc, N., Smith, B.J., Coates, P.S., Mathews, S.R., Dahlgren, D.K. <i>Individual variation in temporal dynamics of post-release habitat selection</i>. Frontiers in Conservation Science (in review)</p> <p>Code used to implement the analysis is available on GitHub: https://github.com/picardis/picardi-et-al_2021_sage-grouse_frontiers-in-conservation</p>
Harvey-Lab-UW/Morris_etal_2021_EcolApps: Release of data for Morris et al. 2021 EcolApps
<p>This is the latest release of data for reproducing the analyses in the manuscript 'Does the legacy of historical thinning treatments foster resilience to bark beetle outbreaks in subalpine forests?' by Morris, Buonanduci, Agne, Battaglia, and Harvey published in Ecological Applications. <em>See the main text of the manuscript for complete descriptions of how data were collected, and greater specifics on values and classifications.</em></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>
Harvey-Lab-UW/Buonanduci_etal_2022_LandscEcol: Release of data for Buonanduci et al. 2022 LandscEcol
<p>This is the latest release of data for reproducing the analyses in the manuscript 'Fine-scale spatial heterogeneity shapes compensatory responses of a subalpine forest to severe bark beetle outbreak' by Buonanduci, Morris, Agne, Battaglia, and Harvey published in Landscape Ecology. See the main text of the manuscript for complete descriptions of how data were collected 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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.