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.
47
datasets available to search
ShareScore release 0.9.0
Dataset results
47 results for “mcmc”
Data from: Quantifying MCMC exploration of phylogenetic tree space
Open the record for dataset details and reuse information.
A sample of Saturn interior density profiles derived with MCMC and gravity-based likelihood.
Open the record for dataset details and reuse information.
NESOSIM-MCMC Multi-Reanalysis-Average Product With Uncertainty Estimates
<p><strong>Updates:</strong></p> <p>12 Feb 2025: extending output up to April 2023, adding ERA5 snowfall as ERA5.zip</p> <p><strong>Overview:</strong></p> <p>This repository contains output from the NASA Eulerian Snow On Sea Ice Model (NESOSIM; Petty et al., 2018; available at <a href="../doi/10.5281/zenodo.4448355">https://zenodo.org/doi/10.5281/zenodo.4448355</a> ) calibrated to snow depth and density observations using a Markov chain Monte Carlo (MCMC) approach (Cabaj et al. 2023; available at <a href="../doi/10.5281/zenodo.7644947">https://zenodo.org/doi/10.5281/zenodo.7644947</a> ) with ECMWF ERA5 (Hersbach et al., 2020), NASA GMAO MERRA-2 (Gelaro et al., 2017), and JMA JRA-55 (Kobayashi et al., 2015) reanalysis snowfall inputs, for the 1980-2020 time period. Reanalysis snowfall input used with NESOSIM is scaled to scaling factors calculated from CloudSat-derived monthly snowfall climatologies, interpolated over the NESOSIM model domain (Cabaj et al. 2020, 2023). The ERA5, MERRA-2 and JRA-55 reanalysis inputs and CloudSat scaling factors are included in this repository. Other forcing data used as inputs to NESOSIM to generate this output are provided in <a href="../records/7051062">https://zenodo.org/records/7051062</a> .</p> <p>The NESOSIM-MCMC-Average product is a snow-on-sea-ice product constructed from the average of MCMC-calibrated NESOSIM outputs when the model is run with CloudSat-scaled snowfall from ERA5, MERRA-2, and JRA55. This product also includes an estimate of uncertainty due to model parameter uncertainties (derived from the MCMC calibration process run for each reanalysis product) and due to differences between reanalysis snowfall products providing snowfall input to NESOSIM. Snow depth and bulk snow density, with associated uncertainty estimates, are provided.</p> <p><strong>Repository Structure</strong></p> <p>ERA5.zip, MERRA2.zip and JRA55.zip: Contain ERA5, MERRA-2, and JRA-55 (respectively) reanalysis snowfall data regridded for use as forcing input to NESOSIM, from 1980-2023. This data is stored as daily binary NumPy files (the default NESOSIM input format) and does not have CloudSat scaling applied. Corresponding ERA5 snowfall for NESOSIM input is available at <a href="../records/7051062">https://zenodo.org/records/7051062</a>.</p> <p>CloudSat_Scaling_Factors.zip: Contains netCDF files with monthly scaling factors generated from the monthly climatology of the CloudSat 2C-SNOW-PROFILE product, version P1_R05 (Wood et al., 2013, 2014; scaling method cf. Cabaj et al., 2020) to be applied to ERA5, MERRA-2, and JRA-55 snowfall in NESOSIM. To be placed in the anc_data folder when running the model.</p> <p>NESOSIM_MCMC-*.zip: NESOSIM output data from 1980-2020 for the model when MCMC-calibrated (following the approach in Cabaj et al., 2023) with snowfall input from ERA5, MERRA-2, and JRA-55, respectively. The NESOSIM-MCMC-Average product (calculated as the average of the outputs) is also included. Within each zip file, model output is included in the 'Output' subdirectory, and snow depth and density uncertainties estimated from the ensemble-propagated spread of the posterior MCMC distributions (cf. Cabaj et al., 2023) are included in the 'Uncertainty' subdirectory. For the multi-product average, uncertainties are calculated as the combined standard deviation of the three separate output ensembles, and are provided in separate files for snow depth and snow density. All output is stored in netCDF format.</p> <p><strong>References:</strong></p> <p>Cabaj, A., P. J. Kushner, C. G. Fletcher, S. Howell, A. Petty (2020), Constraining reanalysis snowfall over the Arctic Ocean using CloudSat observations, Geophysical Research Letters, 47, doi:10.1029/2019GL086426.</p> <p>Cabaj, A., P. J. Kushner, A. A. Petty (2023), Automated calibration of a snow-on-sea-ice model. Earth and Space Science, 10, doi:10.1029/2022EA002655.</p> <p>Gelaro, R. et al. (2017), The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Journal of Climate, 30, 5419–5454, doi:10.1175/JCLI-D-16-0758.1.</p> <p>Hersbach, H. et al. (2020), The ERA5 Global Reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, doi:10.1002/qj.3803.</p> <p>Kobayashi, S. et al. (2015), The JRA-55 Reanalysis: General Specifications and Basic Characteristics, Journal of the Meteorological Society of Japan, 93, 5–48, doi:10.2151/jmsj.2015-001.</p> <p>Petty, A. A., M. Webster, L. N. Boisvert, T. Markus (2018), The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: Initial model development and analysis, Geosci. Model Dev., doi: 10.5194/gmd-11-4577-2018.</p> <p>Wood, N. B., T. S. L'Ecuyer, A. J. Heymsfield, G. L. Stephens, D. R. Hudak, P. Rodrigues (2014), Estimating snow microphysical properties using collocated multisensor observations. J. Geophys. Res. Atmos., 119, 8941-8961, doi:10.1002/2013JD021303.</p> <p>Wood, N. B., T. S. L'Ecuyer, F. L. Bliven, and G. L. Stephens (2013), Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity, Atmos. Meas. Tech., 6, 3635-3648, doi:10.5194/amt-6-3635-2013.</p>
MCMC running scripts (polychord)
<p>Running scripts generated to produce MCMC. The jupyter notebook to generate them can be found at <a href="https://gitlab.euclid-sgs.uk/pf-ist-likelihood/likelihood-mcmc-generator">IST Euclid Gitlab (likelihood-mcmc-generator)</a></p> <p>Updated 19th April 2024: fixed bug in the definition of derived bgamma3 parameters for GCsp.</p> <p>Updated 2nd April 2024: updated polychord set up (higher number of nlives), added scripts for LCDM_gamma case, updated set-up for GCsp cases (purity parameter priors with 1% uncertainty, fixed counter terms, higher k_max) </p>
v2.0.2 data for MCMC runs
<p>Input files, data vectors and covariance matrices for the MCMC runs with CLOE v2.0.2 <br><br>- <em>nzTabSPV3.dat:</em> n(z) for the 13 equipopulated redshift bins case<br>- <em>nuiTabSPV3.dat:</em> fiducial values of the nuisance parameters (#bin centre, source number density, galaxy bias, logaritmic slope of the luminosity function, shift in bin centre, variance of the zp - z relation)<br> </p> <p>- <em>Cls_zNLA_PosPos_C00.dat:</em> GCph data vector<br>- <em>Cls_zNLA_PosShear_C00.dat:</em> GGL data vector (in the order GL)<br>- <em>Cls_zNLA_ShearShear_C00.dat:</em> WL data vector<br> </p> <p>- <em>CovMat-3x2pt-Gauss-32Bins.npy:</em> Gauss only 3x2pt covariance matrix</p> <p>- <em>CovMat-3x2pt-GaussSSC-32Bins.npy:</em> Gauss + SSC 3x2pt covariance matrix</p> <p>- <em>CovMat-3x2pt-BNT-Gauss-32Bins.npy:</em> Gauss only 3x2pt covariance matrix, BNT-transformed</p> <p>- <em>CovMat-3x2pt-BNT-GaussSSC-32Bins.npy:</em> Gauss + SSC 3x2pt covariance matrix, BNT-transformed</p> <p>- <em>BNT-matrix.npy</em>: BNT matrix used for transforming the covariance matrices</p> <p><br>- <em>power_galaxies_EFT_benchmark_z1..fits:</em> GCsp data vector for bin centred on z = 1.0</p> <p>- <em>power_galaxies_EFT_benchmark_z1.2.fits:</em> GCsp data vector for bin centred on z = 1.2</p> <p>- <em>power_galaxies_EFT_benchmark_z1.4.fits:</em> GCsp data vector for bin centred on z = 1.4</p> <p>- <em>power_galaxies_EFT_benchmark_z1.65.fits:</em> GCsp data vector for bin centred on z = 1.65</p> <p> </p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1..fits:</em> GCsp Gauss only covariance for bin centred on z = 1.0</p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1.2.fits:</em> GCsp Gauss only covariance for bin centred on z = 1.2</p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1.4.fits:</em> GCsp Gauss only covariance for bin centred on z = 1.4</p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1.65.fits:</em> GCsp Gauss only covariance for bin centred on z = 1.65</p>
mcmc running scripts (Nautilus)
<p>New set of running scripts for MCMCing using Nautilus as a sampler. </p>
CLOE MCMC mock data and covariance
<p>Input files (data, Gaussian and SSC covariance) for the upcoming CLOE MCMC runs</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.