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82 results for “Cosmology”
Early-Universe Simulations of the Cosmological Axion: Supplementary Data
<p>Public release of the axion field state obtained through simulations of the post-inflationary QCD axion as performed in "Early-Universe Simulations of the Cosmological Axion," from <a href="https://arxiv.org/abs/1906.00967">arXiv:1906.00967v1[astro-ph.CO]</a>.</p> <p>This dataset should enable researchers to construct density fields for gravitational simulations for axion structure formation. Alternatively, it provides initial conditions for the further evolution of a realistic axion field configuration to arbitrary late times under classical field dynamics. For detailed instructions regarding the data formatting, units, and density field construction, see README.pdf.</p>
Data for: Redshift Prediction with Images for Cosmology using a Bayesian Convolutional Neural Network with Conformal Predictions
<p>These files contain the predictions from the CNN and BCNN model from the paper titled: "Redshift Prediction with Images for Cosmology using a Bayesian Convolutional Neural Network with Conformal Predictions" (Jones et al. 2024). These files will allow reproduction of the performance metrics described in the paper.</p> <p> </p> <p>full_prediction_set_CNN.csv - predictions for the redshift using the CNN model of the entire dataset<br>cnn_evaluation.csv - predictions from just the evaluation dataset that was not used in training</p> <p>Columns are:</p> <p>photoz - predicted photoz from the model<br>specz - spectroscopic redshift<br>objectid - object ID from HSC PDR2 data release (Aihara et al. 2019)</p> <p><br>full_prediction_set_BCNN.csv - predictions for the redshift using the BCNN model of the entire dataset<br>bcnn_evaluation.csv - predictions from just the evaluation dataset that was not used in training</p> <p>Columns are:</p> <p>photoz - predicted photoz from the model<br>specz - spectroscopic redshift<br>objectid - object ID from HSC PDR2 data release (Aihara et al. 2019)<br>photoz_uncertainty - uncertainty in the predicted photoz</p>
Inference results from "No need to know: astrophysics-free gravitational-wave cosmology"
<p>Simulated GW data and inference results for all runs associated with the publication "No need to know: astrophysics-free gravitational-wave cosmology." This accompanies the code used to make the paper and run all analyses, hosted at: https://github.com/afarah18/spectral-sirens-with-GPs</p> <p> </p> <p>v4 and v5: updated after peer review changes</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.0eV-1024Mpc)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.0eV 1024Mpc simulation</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-fiducial)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the fiducial simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-ic-HR)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>initial condition data for the HR simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-ic-1024Mpc)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>initial condition data for the 1024Mpc simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-ic-fiducial)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>initial condition data for the fiducial simulations as well as the primordial phases used for all simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-HR-z0)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV HR simulation at z = 0</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.0eV-HR)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.0eV HR simulation</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-1024Mpc-z1)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV 1024Mpc simulation at z = 1</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-1024Mpc-z0)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV 1024Mpc simulation at z = 0</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-HR-z1)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV HR simulation at z = 1</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Wikidata Dump cosmology
<p>RDF dump of wikidata produced with <a href="//wdumps.toolforge.org/">wdumper</a>.</p><p><br><a href="//wdumps.toolforge.org/dump/2838">View on wdumper</a></p><p><b>entity count<b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0</b></b></p>
Data for: Reconstructing Cosmological Initial Conditions from Late-Time Structure with Convolutional Neural Networks
<p>Trained models and evaluation data for the revised submitted paper "Reconstructing Cosmological Initial Conditions from Late-Time Structure with Convolutional Neural Networks," Christopher J. Shallue & Daniel J. Eisenstein (2022)</p>
Cosmological Initial Conditions (3D magnetic fields for an alfa=0.0 magnetic spectrum) for 85Mpc^3
<p>Files representing the initial conditions at z=40 for ENZO-MHD cosmological simulation of a comoving 85Mpc^3 volume, for tangled magnetic fields from an alfaB=0.0 initial spectrum of magnetic fluctuations. The simulation has 1024^3 cells and 1024^3 DM particles. These data are in binary format and can be read by the ENZO code.</p> <p>More details of the simulations and on it cosmological parameter can be found at:</p> <ul> <li>https://ui.adsabs.harvard.edu/abs/2021Galax...9..109V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.5350V/abstract</li> <li>https://ui.adsabs.harvard.edu/abs/2017CQGra..34w4001V/abstract</li> </ul>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-figure)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>data needed for generating all figures</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Accurately Measuring Energy Consumption of Large Cosmological Simulations
<p><a href="https://event.pasc23-conference.org/session/sess138">https://event.pasc23-conference.org/session/sess138</a></p> <p>Minisymposium</p> <p>MS6G - Green Computing Architectures and Tools for Scientific Computing</p>
Cosmological constraints from the tomographic cross-correlation of DESI Luminous Red Galaxies and Planck CMB lensing
<p>Input maps and derived data for the DESI LRG samples, cross-correlated with the Planck CMB lensing maps, from</p> <p>Cosmological constraints from the tomographic cross-correlation of DESI Luminous Red Galaxies and Planck CMB lensing</p> <p>Martin White, et al.</p> <p>https://arxiv.org/abs/2111.09898</p> <p> </p>
Bayesian evidence-driven diagnosis of instrumental systematics for sky-averaged 21-cm cosmology experiments data
<p>Nested sampling posterior samples when a systematic structure is present inside the data.</p> <p>The data is organised in the following way:</p> <p>Folder structure: [likelihood used]/[systematic amplitude]/[type of systematic]/</p> <p>Result structure: [Noise Model used]_[likelihood used]_sys_[systematic amplitude]_[systematic period]_[systematic phase]_[G21 signal strength]</p> <p>If damped systematic, add _damp at result string.</p> <p>Within the results folder, the "enhance" folder contains the more accurate posterior samples of the enhanced PolyChord run.</p> <p>The third folder [systematicModelled] contains the results when the systematic structure is modelled with the same results string as above.</p> <p> </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.