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951 results for “Data release”
RELEASE-2022 and provisional data for NEON DP1.20264.001 at BARC, SUGG, CRAM, LIRO, PRLA, and PRPO
<p>NEON DP1.20264.001: Temperature at specific depth in surface water for BARC, SUGG, CRAM, LIRO, PRLA, and PRPO accessed on January 29, 2022 using the neonstore package on January 29, 2022. The following code was use to export the data from neonstore</p> <pre><code>neonstore::neon_export(archive = "neonstore.zip", product = "DP1.20264.001", table = "TSD_30_min-basic")</code></pre> <p>More information about the data product can be found here:<a href="https://data.neonscience.org/data-products/DP1.20264.001"> https://data.neonscience.org/data-products/DP1.20264.001</a></p> <p>The data include data from RELEASE-2022 and provisional data. The use of provisional data necessates the generation of this Zenado object to ensure reproducibility. </p> <p>Citations for data:</p> <p>NEON (National Ecological Observatory Network). Temperature at specific depth in surface water (DP1.20264.001). https://data.neonscience.org (accessed January 29, 2022)</p> <p>NEON (National Ecological Observatory Network). Temperature at specific depth in surface water, RELEASE-2022 (DP1.20264.001). https://doi.org/10.48443/g7bs-7j57. Dataset accessed from https://data.neonscience.org on January 29, 2022</p>
Software and data release for Fermi Pulsar Timing Array
<p>The archive include photon data from the Fermi Large Area Telescope, processed to enable its use for pulsar timing. It includes Python code for performing pulsar timing using Poisson likelihood and for producing pulse time-of-arrival measurements. It further includes code and scripts for constraining noise processes in the data, including the a signal from the nanohertz gravitational wave background.</p> <p> </p> <p>See README for more information.</p>
Data from: Traits across trophic levels interact to influence parasitoid establishment in biological control releases
<p><span>A central goal in ecology is to </span><span>predict what governs a species' ability to establish in a new environment.</span><span> One mechanism driving </span><span>establishment success</span><span> is individual species' traits, but the role of trait combinations among interacting species across different trophic levels are less clear. Deliberate or accidental species additions to existing communities provide opportunities to study larger scale patterns of establishment success. Biological control introductions are especially valuable because they contain data on both the successfully established and unestablished species. </span></p> <p><span>Here, we supplemented a recent dataset of importation biological control introductions with life-history traits for the parasitoid species and the herbivorus hosts they were released to control </span><span>to explore how life-history traits of 132 parasitoid species and their herbivorous hosts interact to affect </span><span>parasitoid establishment. </span><span>We find that</span><span> of five parasitoid and herbivore traits investigated, one</span> <span>parasitoid trait—host range—weakly predicts parasitoid establishment; parasitoids with higher levels of phylogenetic specialization have higher establishment success, though the effect is marginal. In addition, parasitoids are more likely to establish when their herbivore host has had a shorter residence time. Interestingly, we do not corroborate earlier findings that gregarious parasitoids and endo-parasitoids are more likely to establish. Most importantly, we find that life-history traits of </span><span>the parasitoid species </span><span>and their hosts can interact</span><span> to influence establishment. Specifically, parasitoids with broader host ranges are more likely to establish when the herbivore they have been released to control is also </span><span>more of </span><span>a generalist. These results provide insight into how multiple </span><span>species'</span><span> traits and their interactions</span><span>,</span><span> both within and across trophic levels</span><span>,</span><span> can </span><span>influence establishment of species of higher trophic levels</span><span>.</span></p>
Data from: Spatial release from masking in crocodilians
<p>Dataset, codes, videos, and audio signals used in the study "Spatial release from masking in crocodilians". All supplementary figures are also available.</p>
Data release: Genomic evidence of contemporary hybridization between Schistosoma species
<p>This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see https://www.sanger.ac.uk/about/who-we-are/research-policies/open-access-science/</p> <p>Please contact Duncan Berger (db22@sanger.ac.uk) with questions regarding pre-publication use of this dataset. </p> <p>SchCurr1.primary.fa - <em>Schistosoma curassoni</em> primary genome assembly </p> <p>SchCurr1.haplotypes.fa - Haplotype variants (unphased from SchCurr1.primary.fa)</p> <p>SchCurr1.primary.fa.tbl - RepeatMasker2 output (run on the primary assembly).</p> <p>allchrs.vcf.gz - All variants called on chromosomes 1-7+Z (Post quality control, with the exception that variants found within repetitive regions are included)</p> <p>MITO.vcf.gz - All mitochondrial variants. </p> <p>SchCurr1.genomethreader.gff3 - Genomethreader based gene structure predictions (based on spliced alignments of <em>S. mansoni</em> (v9) transcript and protein sequences). </p>
Data release for the paper "Measurements of protons and charged pions emitted from the $\nu_{\mu}$ charged-current interactions on iron at a mean neutrino energy of 1.49 GeV using a nuclear emulsion detector"
<p>This data release is associated with the paper "Measurements of protons and charged pions emitted from the <span class="math-tex">\(\nu_{\mu}\)</span> charged-current interactions on iron at a mean neutrino energy of 1.49 GeV using a nuclear emulsion detector". It is currently available on <a href="http://arxiv.org/abs/2203.08367">arXiv:2203.08367</a> and to be submitted to Phys. Rev. D.</p> <p><strong>When citing this data release, please cite as well the paper.</strong></p> <p>The provided zip file contains the data as below.</p> <ol> <li>event.root: Event by event information of 183 iron-target interactions.</li> <li>plot.root: Plot information as shown in the paper.</li> <li>detector_efficiency.root: Detectrion efficiencies for muons, charged pions, and protons.</li> <li>momentum_resolution.root: Relation between true and reconstructed momentum for muons, charged pions, and protons.</li> <li>misPID.root: Mis-PID rates of protons and pions.</li> <li>syscov.root: Covariance matrices of systematic uncertainties.</li> <li>flux.root: The neutrino flux and the covariance matrix of the flux error.</li> </ol> <p>The zip file also contains a README.pdf file with detailed information on the included files. Please read it.</p>
Data release for "Scintillator ageing of the T2K near detectors from 2010 to 2021"
<p>The data release is associated with the paper "Scintillator ageing of the T2K near detectors from 2010 to 2021”, published in the <a href="https://iopscience.iop.org/article/10.1088/1748-0221/17/10/P10028">Journal of Instrumentation</a> and <a href="https://arxiv.org/abs/2207.12982">arXiv:2207.12982 [physics]</a>.</p> <p>The data release contains the data points and associated fits from the paper within two root files:</p> <ul> <li>The file T2KNearDetector_ScintAgeing_Standard_DataRelease.root contains the data points and fits shown in the standard ageing results described in Sections 4 and 5 of the paper.</li> <li>The file T2KNearDetector_ScintAgeing_ECalSupplemental_DataRelease.root contains the data points and fits shown in the supplemental ageing results described in Section 6 of the paper.</li> </ul> <p>All data points are provided as ROOT TGraphError objects and fit as ROOT TF1 objects. A full description of the contents of each root file is provided in the README.txt.</p>
MELLODDY TUNER release v3 public data
<p>A public dataset from ChEMBL (v25) for MELLODDY TUNER release v3.</p> <p>Data extracted from ChEMBL (LICENSE attached), and processed with https://github.com/melloddy/MELLODDY-TUNER release v3.</p> <p>Data can be used for technical tests, template for your own dataset and machine learning with SparseChem (https://github.com/melloddy/SparseChem).</p> <p> </p>
Data release for "Measurements of muon-antineutrino and muon-neutrino+muon-antineutrino charged-current cross-sections without detected pions nor protons on water and hydrocarbon at mean antineutrino energy of 0.86 GeV"
<p>This data release is associated with the publication "Measurement of charged-current cross-sections on water and hydrocarbon without detected pions nor protons using the T2K anti-neutrino beam at an off-axis angle 1.5 degrees". It is available in <a href="https://doi.org/10.1093/ptep/ptab014">Progress of Theoretical and Experimental Physics</a> and <a href="https://arxiv.org/abs/2004.13989">arXiv:2004.13989 [hep-ex]</a>.<br><br>The data release contains:</p> <ul> <li>The "histograms.root" file contains several histograms related to the cross-sections. <ul> <li>flux_numubar_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>flux_numu_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>Err_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numubar cross-sections and their uncertainties.</li> <li>Err_numu_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numu+numubar cross-sections and their uncertainties.</li> <li>xsec_numubar_* -> 1D histogram with the predicted flux-integrated numubar cross-sections by NEUT (5.3.3).</li> <li>xsec_numu_numubar_* -> 1D histogram with the predicted flux-integrated numu+numubar cross-sections by NEUT (5.3.3).</li> </ul> </li> <li>The "Covariance_Matrix_Numubar.root" file contains the covariance matrix for the flux-integrated numubar cross-sections, considering all the uncertainties.</li> <li>The "Covariance_Matrix_Numu+Numubar.root" file contains the covariance matrix for the flux-integrated numu+numubar cross-sections, considering all the uncertainties.</li> <li>The "flux" file contains the (anti-)muon neutrino flux prediction at the WAGASCI module or the Proton Module of the T2K experiment</li> </ul>
Data release for "Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries"
<p>Posterior samples and code to reproduce all figures associated with <em>Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries</em>.</p> <p>The <code>parameter_estimation</code> folder contains the following:</p> <ul> <li><code>campaigns</code>: Analyses of eccentric quasi-monochromatic binaries, gridding over gravitational-wave frequency, eccentricty, and SNR. See the <code>README</code> inside for more information. The resulting posteriors are used in Figure 3, and the fitting formula Eq. 21. </li> <li><code>fiducial_source_checks</code>: Analyses that vary parameters other than SNR and frequency to investigate the effect on the minimum eccentricity that can be recovered. Used in Figure A1. Posteriors used for Figure 4 are also found in the <code>golden_binary</code> folder. </li> <li><code>nhat_runs</code>: Various analyses used for Figures 5, 6, B1, and C1. See the <code>README</code> inside for more information. Also see the <code>README</code> in <code>eccentric_gb_scripts</code> and links therein.</li> </ul> <p>Within each parameter estimation output folder there are <code>.dat</code> files for quantities such as the source SNR, log evidence, and posterior. There are also configuration <code>.yaml</code> files which are used by the BALROG code. These contain:</p> <ul> <li><code>lisa_config</code>: Parameters describing the LISA mission, including the duration in seconds. </li> <li><code>nessai_opts</code>: Settings used by nessai (the sampler used in this work). </li> <li><code>priors</code>: Lower and upper limits used for each source parameter. </li> <li><code>sources</code>: Injected values for each source parameter.</li> </ul> <p>The <code>notebooks</code> folder contains code to produce Figures 2, 3, 4, 6, and A1. Also included are notebooks to produce the fitting formula Eq. 21 (<code>emin_grid.ipynb</code>), and to inspect analyses in the <code>campaigns</code> and <code>fiducial_source_checks</code> folders.</p> <p>The <code>eccentric_gb_scripts</code> folder contains code to produce Figures 1, 5, B1 and C1. See the <code>README</code> inside for more information.</p>
Data from: Mycorrhizal-herbivore interactions and the competitive release of subdominant tallgrass prairie species
<p>Plant-microbial-herbivore interactions play a crucial role in the structuring and maintenance of plant communities and biodiversity, yet these relationships are complex. In grassland ecosystems, herbivores have the potential to greatly influence the survival, growth, and reproduction of plants. However, few studies examine interactions of above- and belowground grazing and AM mycorrhizal symbiosis on plant community structure. We established experimental mesocosms containing an assemblage of eight tallgrass prairie grass and forb species in native prairie soil, maintained under mycorrhizal and nonmycorrhizal conditions, with and without native herbivorous soil nematodes, and with and without grasshopper herbivory. Using factorial analysis of variance and principal component analysis, we examined: a) the independent and interacting effects of above- and belowground herbivores on AM symbiosis in tallgrass prairie mesocosms, b) independent and interacting effects of above- and belowground herbivores and mycorrhizal fungi on plant community structure, and c) potential influences of mycorrhizal responsiveness of host plants on herbivory tolerance, and concomitant shifts in plant community composition. Treatment effects were characterized by interactions between AM fungi and both aboveground and belowground herbivores, while herbivore effects were additive. The dominance of mycorrhizal-dependent C<sub>4</sub> grasses in the presence of AMF symbiosis was increased (<em>p</em> < 0.0001) by grasshopper herbivory but reduced (<em>p</em> < 0.0001) by nematode herbivory. Cool-season C<sub>3</sub> grasses exhibited a competitive release in the absence of AMF symbiosis but this effect was largely reversed in the presence of grasshopper herbivory. Forbs showed species-specific responses to both AM fungal inoculation and the addition of herbivores. Biomass of the grazing-avoidant, facultatively mycotrophic forb <em>Brickellia eupatorioides</em> increased (<em>p</em> < 0.0001) in the absence of AMF symbiosis and with grasshopper herbivory, while AMF-related increases in the aboveground biomass of mycorrhizal-dependent forbs <em>Rudbeckia hirta</em> and <em>Salvia azurea</em> were eradicated (<em>p</em> < 0.0001) by grasshopper herbivory. In contrast, nematode herbivory enhanced (<em>p</em> = 0.001) the contribution of <em>Salvia azurea</em> to total biomass.</p> <p><em>Synthesis</em>: Our research indicates that AM symbiosis is the key driver of the dominance of C<sub>4</sub> grasses in the tallgrass prairie, with foliar and root herbivory being two mechanisms for the maintenance of plant diversity.</p>
Example Case Data of Forward Radar Operator ZJU-AERO Release V0.6.1
<p>The NWP model grid data and radar observation products for the two demonstration cases of ZJU-AERO V0.6.1. </p> <p>./case-01-Haishen-2020-09-05.zip is for a demonstration forward simulation case of the space-borne radar.</p> <p>./case-02-Henan-2021-07-20.zip is for a demonstration forward simulation case of ground-based radar.</p> <p>./case-03-MeltingLayer-2023-02-05.zip is for a demonstration forward simulation case of melting layer.</p> <p>./case-04-Doksuri-2023-07-28.zip is for a demonstration forward simulation case of typhoon observed by ground based radar (as required by one of the reviewers of the GMD paper, new).</p> <p>For more details, please view the user manual in the software release of ZJU-AERO.</p>
Data from: Ecological release and insular shifts in avian morphological traits in the Caribbean
<p>We compared support for 3 hypotheses that might explain observed morphological variation among islands of 4 1.70 species of Caribbean land birds: ecological release from competition and predation pressure, predation pressure from 1 novel predator species (small Indian mongoose, <em>Herpestes auropunctatus</em>), and climate. We measured wing chord, tarsus length, bill length, and mass of Bananaquits (<em>Coereba flaveola</em>), Black-faced Grassquits (<em>Tiaris bicolor</em>), Lesser Antillean Bullfinches (<em>Loxigilla noctis</em>), and Common Ground Doves (<em>Columbina passerina</em>) in Grenada, 2015–2017, and combined these measures with data from 23 other Caribbean islands collated from academic papers and researchers, for a total sample size of 6,518 individuals. We found the strongest support for the ecological release hypothesis, but each of our hypotheses received some support, suggesting that ecological release from competition, predation pressure from mongoose, and climate may all interact to influence morphological adaptations of birds to local conditions in the Caribbean.</p>
Visium HD Human Colorectal Cancer (FFPE) data release pathologist annotation
<p>10X Genomics released a spatial <a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc">transcriptomic dataset of human colorectal cancer collected on the Visium HD platform. </a></p> <p>The dataset was divided into different spatial domains based on the accompanying HE stain and the expression of characteristic marker genes.</p> <p>The pathologist's annotation was added with the help of Napari and the Spatialdata python package.</p> <p>Every .csv file contains the Visium HD bin barcode and annotation for the respective level of binning.</p> <p>In addition the HE image was segmented and bins were assigned to Nuclei for a pseudo single cell resolution, as described <a href="https://www.10xgenomics.com/analysis-guides/segmentation-visium-hd">here.</a></p> <p>This work was carried out for the <a href="https://github.com/SpatialHackathon/SpaceHack2023">SpaceHack2023 project </a>and the data shared here is licensed CC0. </p> <p> </p>
Data Release Scrutinising evidence for the triggering of Active Galactic Nuclei in the outskirts of massive galaxy clusters at z~1
<p>Dataset of the paper "Scrutinising evidence for the triggering of Active Galactic Nuclei in the outskirts of massive galaxy clusters at z~1".</p> <p> </p> <p>All the necessary code to deal with these data can be found at: https://github.com/IvanMuro/agn_frac_data_release</p>
Light holographic dilatons near critical points — data release
<p>This dataset consists of .csv files containing all of the data used to make the plots appearing in the article <a href="https://arxiv.org/abs/2406.04974" target="_blank" rel="noopener">Light holographic dilatons near critical points</a>. There are also Mathematica and Jupyter Notebooks which may be used to reproduce the plots.</p>
Legacy Survey Data Release 9 Blue Horizontal Star candidates
<p>We use objects from Legacy Survey Data Release 9 (Dey et. al 2019 to construct a catalogue with 95446 objects containing "release", "brickid" and "objid". These three parameters provide a unique identifier hash (check <a href="https://www.legacysurvey.org/dr9/catalogs/">here</a> for details ). The catalogue also includes "R.A.", "Dec.", the extinction corrected "g_0", "r_0", "z_0", photometric bands, the colour "grz", the absolute "M_g" magnitude, and the probability of a stellar object being a BHB star, "P_BHB". See the original paper for details: https://arxiv.org/abs/2404.09825</p>
The history of chromosomal instability in genome doubled tumors : Data release
<p>Released data for '<em>The history of chromosomal instability in genome doubled tumors</em>'.</p>
Data Release for "First joint oscillation analysis of Super-Kamiokande atmospheric and T2K accelerator neutrino data"
<p>This archive contains the electronic version in ROOT and pdf formats of the measurements of oscillation parameters obtained with the analyses from the paper “First joint oscillation analysis of Super-Kamiokande atmospheric and T2K accelerator neutrino data”.<br><br>It is published in <a href="https://doi.org/10.1103/PhysRevLett.134.011801">Physical Review Letters</a> and is available on the <a href="https://arxiv.org/abs/2405.12488">arXiv:2405.12488 [hep-ex]</a>. </p> <p>**************************************<br>***** Results included in this release<br>**************************************<br>This release includes the results of the measurements of the different oscillation parameters obtained with the four analyses appearing in the paper. This corresponds to various 1D and 2D DeltaChi^2 and posterior probability maps as well as 2D confidence/credible regions for the parameters sin^2(theta13), sin^2(theta23), dm^2_32/|dm^2_31|, delta_cp, J_cp.</p> <p>The results are separated into different files for the four analyses. Additional details on these analyses can be found below, but two of them use a Bayesian approach (producing posterior probabilities and credible intervals/regions) and two of them follow a frequentist approach (producing DeltaChi^2 maps and confidence intervals/regions). A tag in the TGraph and histogram names also allow to differentiate the different types of intervals/regions: "cred" for credible interval from the Bayesian analysis, "conf" for confidence interval from the frequentist analysis. The tag “posterior” indicates that the object corresponds to a posterior probability distribution, while “chi2” indicates a DeltaChi^2 one.</p> <p>Results for each mass ordering hypothesis are provided, denoted "NO" for normal ordering and "IO" for inverted ordering. The Bayesian files also include results marginalised over the mass ordering, denoted by the tag "both" in the object names. The frequentist files include results profiled over the mass ordering, indicated by a tag “profMO” in the object names.<br>The Bayesian and frequentist results use different conventions for the mass splitting in the inverted ordering: the Bayesian results are in term of #Deltam^{2}_{32} for both NO and IO, whereas the frequentist results are plotted versus #Deltam^{2}_{32} for the NO, and |#Deltam^{2}_{31}| for the IO. </p> <p>A constraint on theta13 from reactor experiment measurements is used for all results in this release. It corresponds to the value in the PDG 2019 review: sin^2(2theta_13)=(8.53+-0.27) x 10^{-2}. This is commonly referred to as "the reactor constraint", and a tag “wRC” is included in the name of the different objects as a reminder that it is used for these results.</p> <p>**************************************<br>***** Brief descriptions of the four analyses<br>**************************************<br>Results are provided for the four analyses mentioned in the “Oscillation analysis” part of the paper. They were given names (Bayesian1, Bayesian2, Frequentist1, Frequentist2) based on the statistical approach they follow.</p> <p>The Bayesian analyses are based on the two T2K analyses described in Eur. Phys. J. C 83, 782 (2023), extended to include the Super-Kamiokande atmospheric data, and with modifications to use the model described in the paper to which the present release is attached to. These analyses use Markov Chain Monte Carlo methods to compute marginal likelihoods for the parameter of interests. </p> <p>For the frequentist analyses, Frequentist1 is a modified version of Bayesian1, optimized for speed to be able to address the computational challenges of producing frequentist results from an ensemble of pseudo-experiments. Frequentist2 is based on the Super-Kamiokande atmospheric analysis described in PTEP 2019, 053F01 (2019), extended to include the T2K data, and also with modifications to follow the model described in the paper. These two analyses compute profile likelihood on a grid of oscillation parameters of interest to produce measurements of these parameters.</p> <p>In terms of the differences between analyses mentioned in the paper, Bayesian2 is the analysis that does a simultaneous fit of the T2K near detector data with the events observed at SK, and the one for which the momentum scale uncertainty is not correlated between the atmospheric and T2K events observed at SK. The three other analyses use a covariance matrix to propagate the constraint on systematic uncertainties from T2K near detector data to the analysis of the events observed at SK, and treat the momentum scale uncertainty as correlated between atmospheric and T2K far detector events.</p> <p>**************************************<br>***** Example codes<br>**************************************<br>Example codes are provided for each of the four analyses, showing how to produce the pdf file from this analysis from the corresponding ROOT file. How to run these example codes is indicated in the comments at the start of each of the example files.</p> <p>**************************************<br>***** Objects inside the ROOT files<br>**************************************<br>The ROOT objects contained inside the files are named first with an identifier of which parameter(s) are being shown, followed by the reactor constraint tag, followed by the mass ordering tag.</p> <p>For the Bayesian results, there is an additional tag to indicate if the results was obtained with a prior probability uniform in deltaCP (“flatdcp”) or uniform in sin(deltaCP) (“flatsindcp”)</p> <p>A glossary is provided at the end of this readme.</p> <p>**************************************<br>*** 2D regions<br>**************************************<br>Objects of the form:<br>gr2D_varX_varY_wRC_<NO,IO,both>_<conf,cred><68,90,955,997>(_N)<br>are TGraphs corresponding to the 2D confidence ("conf") or credible ("cred") regions for the 2 variables (varX, varY). <br>N is the iterator for different TGraphs corresponding to the same region; these occur when confidence regions are discontinuous (for example when deltaCP loops over from +pi to -pi).</p> <p>68, 90, 955, 997 are the percentage credible/confidence levels.</p> <p>Most of the 2D frequentist regions were computed using the standard DeltaChi^2 values (from the Gaussian case), and therefore have only approximate coverage. However, the {sin^2(theta_23), deltaCP} confidence regions of analysis Frequentist1 were built using critical DeltaChi^2 values computed with the Feldman-Cousins method. To distinguish them from other confidence regions, a tag "FC" is included in the name of the corresponding TGraph.</p> <p>The best fit markers are also provided for the 2D results:<br>gr2D_varX_varY_wRC_<NO,IO,both>_bestfit</p> <p>The best fit markers and contour lines are generally for each MO *separately*, i.e. assuming DeltaChi^2 is 0 at the minimum or that the total posterior probability integrates to 1 in the mass ordering considered. There are some exceptions, in particular some 2D regions for (sin^2(theta_23), dcp) are also provided using a best fit over both MO to allow for comparisons with other experiments using this convention. This special set of contours has an extra tag "globalMO" in its name to distinguish it from the others.</p> <p><br>**************************************<br>*** 1D and 2D histograms<br>**************************************<br>Objects of the form<br>h1D_var_<chi2,posterior>_wRC,_<NO,IO, both, profMO><br>h2D_var1_var2_<chi2,posterior>_wRC_<NO,IO, both><br>are respectively TH1D of the DeltaChi^2 ("chi2") or posterior probability ("posterior") for oscillation parameter "var" or TH2D for the couple of parameters (var1, var2)</p> <p>The Bayesian and frequentist results use different conventions with respect to the mass ordering:<br>- DeltaChi^2 plots use a global minimum over both hierarchies<br>- Posterior probability plots integrate to unity *individually*</p> <p>**************************************<br>***** Critical values for frequentist results<br>**************************************<br>For the 1D plots, critical delta chi2 values obtained with the Feldman-Cousins method are provided for theta23 and deltaCP <br>grCritical_{variable}_chi2_wRC_{NO,IO,profMO}_conf{68, 90, 955}<br>variable: th23, dCP</p> <p>The FC-corrected confidence intervals for these 2 variables can be obtained as the region for which the corresponding 1D DeltaChi^2 histogram is below the grCritical graph of a given level.</p> <p>**************************************<br>***** Additional notes for Bayesian results<br>**************************************<br>For plots involving the mass splitting, the mass ordering is given by the sign:<br> dm32>0 is normal hierarchy (Delta m^2_{32} > 0)<br> dm32<0 is inverted hierarchy (Delta m^2_{32} < 0)</p> <p>Note that the posteriors have not been smoothed, and may contain small discontinuities due to MCMC statistical uncertainties.</p> <p>Plots with "_bestfit" appended indicate the point in the 2D parameter space (marginalized over the other parameters) with the highest posterior density, and is not necessarily the global minimum of the likelihood.</p> <p>For the 1D posterior distributions, the user can freely calculate credible intervals from the distributions. It is recommended to start at the point of the highest posterior density, and moving down in posterior density to produce highest posterior credible intervals, which is the kind of credible intervals reported in the paper. </p> <p>**************************************<br>***** Glossary of tags used in objects names<br>**************************************</p> <p>"wRC" - Uses “reactor constraint” on theta13, sin^2(2theta_13)=(8.53+-0.27) x 10^{-2}<br>"FC" - Feldman-Cousins<br>"NO" - Normal mass Ordering<br>"IO" - Inverted mass Ordering<br>"both" - Marginalised over normal and inverted mass orderings<br>"profMO" - Profiled over normal and inverted mass orderings<br>"cred" - Credible interval<br>"conf" - Confidence interval<br>"68" - 68.3% (1 sigma)<br>"90" - 90%<br>"955" - 95.5% (2 sigma)<br>"997" - 99.7% (3 sigma)<br>"chi2" - DeltaChi^2 (-2lnL) for parameter<br>"Critical" - Critical DeltaChi^2 computed using Feldman-Cousins method<br>"th13" - sin^2(theta_13)<br>"th23" - sin^2(theta_23)<br>"dCP" - delta CP<br>"dm2" - Delta m^2_{23} (NO), |Delta m^2_{13} (IO)| for confidence intervals; used for frequentist analyses results.<br>"dm32" - Delta m^{2_{23} regardless of mass ordering; in the Bayesian analyses, Delta m^2_{23} is always the variable that is plotted.<br>"jarlskog" - Jarlskog invariant<br>"flatdcp" - Using prior probability uniform in deltaCP<br>"flatsindcp" - Using prior probability uniform in sin(deltaCP)</p>
juliacarbajal/bilingual_assimilation: First release of data & analysis scripts for bilingual assimilation project.
<p>This release contains all the app data and vocabulary questionnaires collected for Carbajal et al. (in preparation)'s project on bilingual assimilation (OSF project <a href="https://osf.io/52z9g/">https://osf.io/52z9g/</a>). It also includes analysis scripts as of the 22nd of March, 2018.</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.