Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “mcmc samples”
MCMC samples of the posterior distribution from the paper "TESS spots a mini-neptune interior to a hot saturn in the TOI-2000 system"
<p>This dataset contains the Hamiltonian Monte Carlo samples of the posterior distribution of the planetary and stellar parameters from the paper "TESS Spots a Mini-Neptune Interior to a Hot Saturn in the TOI-2000 System". The file format, NetCDF, is based on HDF5, and is meant to be read by the Python package <a href="https://python.arviz.org/en/latest/">ArviZ</a>.</p> <p>Hot jupiters (<em>P</em> < 10 d, <em>M</em> > 60 M<sub>⊕</sub>) are almost always found alone around their stars, but four out of hundreds known have inner companion planets. These rare companions allow us to constrain the hot jupiter's formation history by ruling out high-eccentricity tidal migration. Less is known about inner companions to hot Saturn-mass planets. We report here the discovery of the TOI-2000 system, which features a hot Saturn-mass planet with a smaller inner companion. The mini-neptune TOI-2000 b (2.70 ± 0.15 R<sub>⊕</sub>, 11.0 ± 2.4 M<sub>⊕</sub>) is in a 3.10-day orbit, and the hot saturn TOI-2000 c (<span class="math-tex">\(8.14^{+0.31}_{-0.30}\)</span> R<sub>⊕</sub>, <span class="math-tex">\(81.7^{+4.7}_{-4.6}\)</span> M<sub>⊕</sub>) is in a 9.13-day orbit. Both planets transit their host star TOI-2000 (TIC 371188886, <em>V</em> = 10.98, <em>TESS</em> magnitude = 10.36), a metal-rich ([Fe/H] = <span class="math-tex">\(0.439^{+0.041}_{-0.043}\)</span>) G dwarf 174 pc away. <em>TESS</em> observed the two planets in sectors 9–11 and 36–38, and we followed up with ground-based photometry, spectroscopy, and speckle imaging. Radial velocities from HARPS allowed us to confirm both planets by direct mass measurement. In addition, we demonstrate constraining planetary and stellar parameters with MIST stellar evolutionary tracks through Hamiltonian Monte Carlo under the PyMC framework, achieving higher sampling efficiency and shorter run time compared to traditional Markov chain Monte Carlo. Having the brightest host star in the <em>V</em> band among similar systems, TOI-2000 b and c are superb candidates for atmospheric characterization by the JWST, which can potentially distinguish whether they formed together or TOI-2000 c swept along material during migration to form TOI-2000 b.</p>
SCIAMACHY NO regression fit MCMC samples
<p><strong>SCIAMACHY mesosphere NO data and regression model samples</strong></p> <p>SCIAMACHY mesosphere daily zonal mean NO data and Markov-Chain Monte-Carlo samples from the regression coefficient distributions, derived from and for use with the <a href="https://github.com/st-bender/sciapy"><code>sciapy</code></a> regression module.</p> <p>This data set contains the following files:</p> <ul> <li><code>NO_regress_output_pGM_Lya_ltcs_exp1dscan60d_km32_float32.nc</code>, <code>NO_regress_output_pGM_Lya_ltcs_exp1dscan60d_km32_float64.nc</code> - samples from the regression coefficient distributions (single and double precision)</li> <li><code>NO_regress_quantiles_pGM_Lya_ltcs_exp1dscan60d_km32.nc</code> - the 0.1, 2.5, 16, 50, 84, 97.5, and 99.9 percentiles of the sampled distributions</li> <li><code>scia_nom_dzmNO_2002-2012_v6.2.1_2.2_akm0.002_geomag10_nw.nc</code> - the SCIAMACHY daily zonal mean NO data</li> <li><code>sciapy_regress_tutorial.ipynb</code> - example ipython notebook</li> </ul> <p><strong>MCMC Samples</strong></p> <p>The files <code>NO_regress_output..._float32.nc</code> and <code>NO_regress_output..._float64.nc</code> contain MCMC samples of the model as single and double precision floats. The file <code>NO_regress_quantiles....nc</code> contains the 0.1, 2.5, 16, 50, 84, 97.5, and 99.9 percentiles of the sampled distributions and is provided for convenience. The files contain the following parameters:</p> <ul> <li><code>kernel:log_sigma</code>, <code>kernel:log_rho</code> - the "strength" and "lengthscale" of the Matérn-3/2 Gaussian Process kernel</li> <li><code>mean:offset:value</code> - the constant offset of the NO model in [<span class="math-tex">\(10^6\)</span> cm<span class="math-tex">\(^{-3}\)</span>]</li> <li><code>mean:Lya:amp</code> - the Lyman-<span class="math-tex">\(\alpha\)</span> coefficient of the mean model in [<span class="math-tex">\(10^6\)</span> cm<span class="math-tex">\(^{-3}\)</span> / Lyman-<span class="math-tex">\(\alpha\)</span>]</li> <li><code>mean:GM:amp</code> - the geomagnetic coefficient (AE) in [<span class="math-tex">\(10^6\)</span> cm<span class="math-tex">\(^{-3}\)</span> / nT]</li> <li><code>mean:GM:tau0</code> - the constant lifetime of the geomagnetic lifetime in [d]</li> <li><code>mean:GM:taucos1</code>, <code>mean:GM:tausin1</code> - cosine and sine amplitudes of the yearly geomagnetic lifetime variation in [d]</li> </ul> <p><strong>Daily zonal mean NO data</strong></p> <p>The model was trained on the <a href="http://doi.org/10.5281/zenodo.1009078">SCIAMACHY mesosphere NO dataset</a>, binned into 10° geomagnetic latitude bins using the provided <code>gm_lat</code> variable and using the standard error of the mean as data uncertainties. The data are uploaded as <code>scia_nom_dzmNO_2002-2012_v6.2.1_2.2_akm0.002_geomag10_nw.nc</code> and were prepared by running (after installing <code><a href="https://github.com/st-bender/sciapy">sciapy</a>)</code>:</p> <pre><code class="language-bash">bash> scia_daily_zonal_mean.py -g -b'-90:90:10' -o <daily_zonal_mean_NO.nc> </path/to/SCIAMACHY_NO_NOM_orbits_20??_v6.2.1.nc></code></pre> <p><strong>Regression sampling</strong></p> <p>The samples were generated by running the following command:</p> <pre><code class="language-bash">bash> python -m sciapy.regress <daily_zonal_mean_NO.nc> --proxies Lya:<Lyman-alpha_file.dat>,GM:<AE_file.dat> -A <altitude> -L <geomag_latitude_bin> -w 14 -b 800 -p 1400 -F \"\" -I GM --fit_annlifetimes GM --positive_proxies GM --lifetime_scan=60 --lifetime_prior exp -k -K Mat32 -O0 -m "nom_pGM_Lya_ltcs_exp1dscan60d_km32" -P</code></pre> <p> </p>
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains
<p>We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (<a href="https://github.com/jiwoncpark/h0rton">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href="https://github.com/jiwoncpark/h0rton/issues">make an issue</a> for any questions.</p>
MCMC samples from an analysis reported in a paper titled "A Song of Neither Ice nor Fire: Temperature extremes had no impact on violent conflict among European societies during the 2nd millennium"
<p>These data are posterior samples from an MCMC used to estimate parameters for a Bayesian state-space time-series regression model. They are products of an analysis reported in "A Song of Neither Ice nor Fire: Temperature extremes had no impact on violent conflict among European societies during the 2nd millennium", an academic paper that has recently been submitted for peer review. We have archived the MCMC samples here in order to facilitate peer review, replication, and open science.</p> <p>The samples are stored in .RData format (matrices when loaded into an R environment) and the R code used to produce them is provided in a GitHub repo (https://github.com/wccarleton/extreme-conflict). Each .RData file contains a matrix called "samples". The columns of the matrix are each an MCMC chain for a given (column name) model parameter monitored during the MCMC simulation. See the aforementioned GitHub repo for more details.<br> <br> </p>
MCMC samples for X-ray spectra fits summarised in the paper "A strangely light neutron star"
<p>Posterior samples for NS mass and radius described based on fitting of XMM-Newton and Suzaku spectra of the CCO in HESS J1731-347 and described in the paper the paper "A strangely light neutron star" (DOI: <a href="https://doi.org/10.21203/rs.3.rs-1509469/v1">10.21203/rs.3.rs-1509469/v1</a>). Two files uploaded contain posterior samples based based </p> <p>a) on fitting X-ray data alone using single temperature carbon atmosphere model and Gaia parallax priors (xray_only_carbatm.txt)</p> <p>b) on fitting the same X-ray data but including also all additional priors described in the main text and full priors on distance rather than inverted parallax value (full_priors_carbatm.txt). Note that initial version contained wrong file uploaded by error, so the correct file to use in this case is full_priors_carbatm_corr.txt. We urge, however, to use a) as a baseline, i.e. as input for incorporating of other constraints for two reasons: first, the procedure of incorporating other constraints adopted by us and you may be different, and second, this file contains relatively small number of samples as it was mainly meant as illustration putting our results in context of other constraints and to give an idea of impact which our measurement has on EOS selection for a particular family of EOSs, so the result would be different if other set of EOSs is considered. </p> <p>in addition a file with updated weights for EOSs used by Dietrich et al 2021 (2020Sci...370.1450D, available on https://github.com/diettim/NMMA) which take into the account constrains from X-ray spectral fitting presented in our work for the CCO and constrains based on modeling of the 4U 1702-429 bursts reported by Nattila et al 2017 (2017A&A...608A..31N) are included in file chiralEFT_MTOV_NICER_GW170817_AT2017gfo_cco_weights.txt. This file is based on and has the same syntax as file chiralEFT_MTOV_NICER_GW170817_AT2017gfo_weighting.dat provided by Dietrich et al 2021 available on https://github.com/diettim/NMMA along with the corresponding tabulated EOS files.</p> <p> </p>
NICER PSR J0030+0451 Illinois-Maryland MCMC Samples
<p>Posterior samples from the Illinois-Maryland analysis of NICER data for PSR J0030+0451.</p>
CCFs and MCMC samples for Gullikson et al. 2016b
<p>This dataset holds all of the cross-correlation functions (CCFs) generated in Gullikson et al. (2016). (update link when I have it). The CCFs are stored as HDF5 files, and are split into 2 GB chunks such that they can go here on zenodo. You will likely want to combine them all together for each instrument. The filenames have the form 'INSTRUMENT_ccfs_n.h5' where n is an integer.</p> <p> </p> <p>This dataset also holds the MCMC samples for the primary star mass, companion mass, and system age for all of the stars listed in Table 1 of the paper. This is again stored as an HDF5 file. Finally, the dataset contains MCMC samples from the posterior distributions that result from fitting the data to a variety of parametric distributions. They files are generated by the pymultinest code, and the samples can be recovered with the same code (See RealData.ipynb notebook in the github repository for details: https://github.com/kgullikson88/BinaryInference). </p>
Updated NICER PSR J0740+6620 Illinois-Maryland MCMC Samples
<p>Posterior Samples from "A More Precise Measurement of the Radius of PSR J0740+6620 Using Updated NICER Data." <br><br>Our primary mass and radius samples can be found in "J0740_NICERXMM_full_mr.txt" </p> <p>Files ending in "all.txt" include values for all model parameters, while files ending in "mr.txt" contain only the mass and radius values for each sample. <br>Files named "NICERonly" include samples from analyses of only NICER data, while files named "NICERXMM" hold samples from joint analyses of NICER and XMM-Newton data. <br>"full", "partial" and "helium" indicate that those samples are from analyses using fully ionized hydrogen atmosphere models, atmosphere models allowing partially ionized hydrogen, and ionized helium atmospheres respectively. <br><br></p>
MCMC Samples for Gullikson et. al. 2016
<p>This includes a tarfile with MCMC samples and everything needed to run the 'RealData.ipynb' notebook in the github repository accompanying my paper: </p> <p>https://github.com/kgullikson88/BinaryInference</p>
Model fits (MCMC samples) for two papers on perceptual confidence
<p>These are model fits for two papers on perceptual confidence. See <a href="https://github.com/wtadler/confidence/">github.com/wtadler/confidence</a> for more info.</p>
A sample of Saturn interior density profiles derived with MCMC and gravity-based likelihood.
Open the record for dataset details and reuse information.
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.