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1,066 results for “bayesian”
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs - Dataset
<p>Dataset providing a set of measurement of performance and power consumpetion of a virtualized Base Station (srseNB).</p>
SEDflow: Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation
<p><a href="http://changhoonhahn.github.io/SEDflow">SEDflow</a> is an accelerated Bayesian SED modeling method that uses the <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220201809H/abstract">Hahn et al. (2022a)</a> PROVABGS SED model and Amortized Neural Posterior Estimation (ANPE) to derive posterior probability distributions of galaxy properties from optical photometry. SEDflow is<span class="math-tex">\(10^5\times\)</span> faster than conventional Markov Chain Monte Carlo sampling methods and takes ~1 second per galaxy to obtain posteriors. This repository includes all of the data used to train, validate, and test SEDflow.</p> <p>This repository also includes a value-added catalog with detailed physical properties of 33,884 galaxies in the NASA-Sloan Atlas (http://www.nsatlas.org/). The properties are inferred from optical photometry in the <em>u, g, r, i, z</em> bands using SEDflow. For more details on this catalog and SEDflow see the <a href="http://changhoonhahn.github.io/SEDflow">documentation</a> and Hahn & Melchior (2022). </p> <p>For each galaxy, the catalog provides posteriors of: </p> <ul> <li>log_mstar: log10 of stellar mass</li> <li>log_sfr_1gyr: log10 of average star formation rate over 1Gyr</li> <li>log_z_mw: log10 of mass-weighted metallicity</li> <li>beta1, beta2, beta3, beta4: coefficients of the non-negative matrix factorization (NMF) star formation history basis functions</li> <li>fburst: fraction of stellar mass formed by a starburst event</li> <li>tburst: time of the starburst event</li> <li>log_gamma1, log_gamma2: log10 of coefficients of the NMF metallicity history basis functions</li> <li>tau_bc: birth cloud optical depth</li> <li>tau_ism: diffuse dust optical depth</li> <li>n_dust: Calzetti (2001) dust index</li> </ul>
Catalogue of Bayesian SZNet's spectroscopic redshift predictions
<p>The "dr16q_superset_redshift.csv" file provides a catalogue of spectroscopic redshift predictions for spectra from the <a href="https://www.sdss.org/dr16/algorithms/qso_catalog/">16th data release of the Sloan Digital Sky Survey (SDSS) quasar superset catalogue</a> <a href="https://ui.adsabs.harvard.edu/abs/2020ApJS..250....8L/abstract">(Lyke et al., 2020)</a>. Redshifts are predicted by a Bayesian convolutional neural network named Bayesian SZNet with associated predictive uncertainties in the form of predictive variances. The catalogue is released in the CSV format with the following columns:</p> <ul> <li><em>plate</em>: spectroscopic plate number;</li> <li><em>mjd</em>: modified Julian day of the spectroscopic observation;</li> <li><em>fiberid</em>: fiber identification number;</li> <li><em>z_pred</em>: redshift from Bayesian SZNet;</li> <li><em>variance</em>: predictive variance associated with redshift from Bayesian SZNet;</li> <li><em>z</em>: primary redshift;</li> <li><em>source</em><em>_z</em>: origin of the reported redshift in <em>z;</em></li> <li><em>is_qso_final</em>: flag indicating quasars included in the DR16Q <a href="https://ui.adsabs.harvard.edu/abs/2020ApJS..250....8L/abstract">(Lyke et al., 2020)</a>;</li> <li><em>z_vi</em>: redshift from visual inspection;</li> <li><em>z_pipe</em>: redshift from the SDSS pipeline;</li> <li><em>zwarning</em>: quality flag on the redshift from the SDSS pipeline;</li> <li><em>z_dr12q</em>: redshift from the DR12Q catalogue <a href="http://ui.adsabs.harvard.edu/abs/2017A%26A...597A..79P/abstract">(Pâris et al., 2017)</a>;</li> <li><em>z_dr7q_sch</em>: redshift from the DR7Q catalogue <a href="https://ui.adsabs.harvard.edu/abs/2010AJ....139.2360S/abstract">(Schneider et al., 2010)</a>;</li> <li><em>z_dr6q_hw</em>: redshift from the DR6 catalogue <a href="https://ui.adsabs.harvard.edu/abs/2010MNRAS.405.2302H/abstract">(Hewett and Wild, 2010)</a>;</li> <li><em>z_10k</em>: redshift from the random visual inspection of 10000 spectra in the DR16Q superset;</li> <li><em>z_pca</em>: redshift from the <a href="https://ascl.net/2106.017">redvsblue algorithm</a>;</li> <li><em>z_qn</em>: redshift from QuasarNET <a href="https://arxiv.org/abs/1808.09955">(Busca and Balland, 2018)</a>;</li> <li><em>z_pred_1</em> to <em>z_pred_256</em>: sampled redshifts from Bayesian SZNet;</li> </ul> <p>where columns <em>z</em>, <em>source_z</em>, <em>is_qso_final</em>, <em>z_vi</em>, <em>z_pipe</em>, <em>zwarning</em>, <em>z_dr12q</em>, <em>z_dr7q_sch</em>, <em>z_dr6q_hw</em>, <em>z_10k</em>, <em>z_pca</em>, and <em>z_qn</em> are taken from the 16th data release of the SDSS quasar superset catalogue.</p>
"Chronomodel" Bayesian chronological models for East Borneo, based on data from the Liang Abu and Kimanis sites
<p>Bayesian chronological models generated using the <a href="https://chronomodel.com"><em>ChronoModel</em></a> software East Borneo (Indonesia), based on data from the Liang Abu and Kimanis (Arifin, 2017) archaeological sites.</p> <p>Two models were generated:</p> <ul> <li> a “<strong>conservative</strong>” model, observing the Bayesian approach and the distinction between<br> prior and posterior information;</li> <li>a “<strong>restricted</strong>” model: excluding possible outliers and without application of a “Fresh-<br> water reservoir effect” correction.</li> </ul> <p>Four files are provided:</p> <ul> <li>abu-kimanis-conservative-model.chr: model specification for the “conservative” model</li> <li>abu-kimanis-conservative-model_synthetic-stats-table.csv: results for the “conservative” model</li> <li>abu-kimanis-restricted-model.chr: model specification for the “restricted” model</li> <li>abu-kimanis_restricted-model_synthetic-stats-table.csv: results for the “restricted” model</li> </ul> <p>The .chr files can be open and edited using the <em>ChronoModel</em> software.</p>
Minimal dataset for "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models"
<p>This repository contains a minimal data set to reproduce all results that don't compromise the privacy concerns for the manuscript "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models".<br> <br> The repository contains the following data:</p> <ul> <li>adaptscore_acute.csv <ul> <li>A csv file that contains the estimated adaptation scores for the acute data set with HLA I model.</li> </ul> </li> <li>adaptscore_leftout.csv <ul> <li>A csv file that contains the estimated adaptation scores for the leftout data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training.csv <ul> <li>A csv file that contains the estimated adaptation scores for the traininig data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training_hla1_without_clin.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the HLA I model (via cross-validation)</li> </ul> </li> <li>adaptscore_training_seed2.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the joint HLA I and HLA II model via cross-validation with another seed</li> </ul> </li> </ul>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>The dataset includes waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo and a 3-D Earth model in the Japanese islands. The data are provided as Green's strains at the maximum-likelihood location (indicated in the title of each text file) for all study events inverted at different periods. Inversion period is also indicated in the title. All the data are filtered between 15 s and 80 s. Additionally we provide a Python code to obtain displacement from strains given a moment tensor.</p>
The molecular architecture of the yeast spindle pole body core determined by Bayesian integrative modeling
<p>This repository pertains to the molecular architecture of the yeast spindle pole body (SPB), the structural and functional equivalent of the metazoan centrosome. Data from in vivo FRET and yeast two-hybrid, along with SAXS, X-ray crystallography, and electron microscopy were integrated by a Bayesian structure modeling approach.</p> <p>For more information about how to reproduce this modeling, see the <a href="https://salilab.org/spb/">Sali lab website</a> or the README file.</p>
CLDF dataset derived from Lee and Hasegawa's "Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages" from 2011
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee, Sean and Hasegawa, Toshikazu (2011). Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages. Proceedings of the Royal Society B: Biological Sciences, 278(1725), 3662–3669. doi:10.1098/rspb.2011.0518.</p> </blockquote>
Research Data/Code for "Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification"
<p>This repository contains research data and code for supplementing the manuscript <br>"Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification" <br>by L. Gossel, E. Corbean, S. Dübal, P. Brand, M. Fricke, H. Nicolai, C. Hasse, S. Hartl, S. Ulbrich, and D. Bothe. </p> <p>There is a corresponding preprint available on Arxiv: https://doi.org/10.48550/arXiv.2404.13092</p> <p><br>Users are referred to the manuscript for background information. This repository shall enable reproduction of the reported results and does not stand alone. </p> <p>Please read important information in the README in the top-level directory. </p> <p>Funded by the Hessian Ministry of Higher Education, Research, Science and the Arts - cluster project Clean Circles. </p>
Data and codes for 'A Bayesian Approach to Blood Rheological Uncertainties in Aortic Hemodynamics'
<p>This submission is supplementary material in the form of data and codes used in and for the manuscript 'A Bayesian Approach to Blood Rheological Uncertainties in Aortic Hemodynamics' submitted to the International Journal of Numerical Methods in Biomedical Engineering (currently under review).</p>
Dataset for "A Bayesian neural network predicts the dissolution of compact planetary systems"
<p>The dataset used for training and evaluating the models in the paper "A Bayesian neural network predicts the dissolution of compact planetary systems": https://arxiv.org/abs/2101.04117. </p> <p>The code for working with this dataset, and other links, can be found at: https://github.com/MilesCranmer/bnn_chaos_model.</p>
Photometric Redshifts for Cosmology: Improving accuracy and uncertainty estimates using Bayesian Neural Networks
<p><strong>This data consists of 286,401 with broad-band g,r,i,z,y photometry from the HSC DR2 survey and spectroscopic redshifts. The majority of galaxies in our sample lies between redshift of 0.01 and 2.5</strong></p>
Extracting interpretable rules with Bayesian Networks. A case study of intrinsic human hazardous properties of silver nanoforms for the Safety Dimension of Safe and Sustainable by design paradigm.
<p>Three different datasets: toxicological attributes in i) lung and ii) intestinal cell line along with system dependent features and iii) system independent pchem properties) were merged. Each row represents one set of experimental testing conditions and related system dependent nanodescriptors based on the exposure dose and NFs pre-treatment (for intestinal assessments). The system independent inputs are NF specific and independent of experimental conditions. Data is captured via FAIR principles where the reader can find the origin (institution) of each data, the responsible data creators (experimentalists), the raw measurements, the protocols followed and the instrumentations used for each experiment. .</p>
Dataset for Bayesian parametric models for survival prediction in medical applications
<p><strong>Data Source</strong></p> <p>The data for these experiments were derived from these sources:</p> <p>* Hosmer Jr DW, Lemeshow S, May S. Applied Survival Analysis: Regression Modeling of Time-to-Event Data. 2nd ed: John Wiley & Sons; 2008.</p> <p>* Jd K, Prentice R. The statistical analysis of failure time data. New York: John Wiley and Sons; 1980.</p> <p>* Fleming T, Harrington D. Counting Processes and Survival Analysis: John Wiley & Sons; 1991.</p> <p> </p> <p>The raw data was downloaded from web archive.</p> <p>https://web.archive.org/web/20170114043458/http://www.umass.edu/statdata/statdata/data/</p> <p><strong>Contents</strong></p> <p>Each folder contains the original data, a textfile with a description of the data, and the pre-processed version with one-hot encoded variables. An additional YAML file is included with the list of included variables, name of the time and censor variable, name of continuous variables and splitting and partitioning information.</p>
Supplementary Materials to "Subgrouping in a `dialect continuum': A Bayesian phylogenetic analysis of the Mixtecan language family"
<p>SM0: metadata on the languages of the sample</p> <p>SM 1: custom word list</p> <p>SM2: prose explanation of cognate coding and IPA conversion</p> <p>SM3: annotated cognate sets</p> <p>SM4: nexus files of the broad and fine grained cognate coding</p> <p>SM5: NeighborNet visualization with coloring by Josserand (1983)'s groupings and by groupings from our analysis</p> <p>SM6: BEAST2 xml files</p> <p>SM7: MCC trees from BEAST2 analysis</p> <p>SM8: DensiTree visualization and visualization of full MCC tree of best performing model</p>
Simulation output for Improving the stability of bivariate correlations using informative Bayesian priors: A Monte Carlo simulation study
<p>This repository contains the (compressed) simulation output from <em>Improving the stability of bivariate correlations using informative Bayesian priors: A Monte Carlo simulation study</em>. On a Linux-based system, extract the contents with:</p> <pre><code class="language-bash">tar -xvzf raw_output_compressed.tar.gz </code></pre> <p>Please refer to the published article (https://doi.org/10.3389/fpsyg.2023.1253452) and the associated GitHub (<a href="https://github.com/carldelfin/stability-of-bivariate-correlations">github.com/carldelfin/stability-of-bivariate-correlations</a>) for additional information.</p>
Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O
<p>We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-16, and the abundance values range from log10(VMR) = -3.5 to -1.5 in steps of 0.5 and 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input H2O value (abundance case), the retrieved H2O value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for H2O. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input H2O': str})</p>
Phlorest phylogeny derived from Michael et al. 2015 'A Bayesian Phylogenetic Classification of Tupi-Guarani'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Michael L, Chousou-Polydouri N, Bartolomei K, Donnelly E, Wauters V, Meira S & O'Hagan Z. 2015. A Bayesian Phylogenetic Classification of Tupi-Guarani. LIAMES 15(2):1–36.</p> </blockquote>
Data related to the manuscript "Bayesian Calibration and Validation of a Large-scale and Time-demanding Sediment Transport Model"
<p>1) Riverbed_Elevation_Measurements.txt<br> Description: Measured riverbed geometry of available years<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2002 [m asl], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation <br> 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 2) Hydro_FT_2D_manual.txt<br> Description: Simulation results of the manually calibrated full model<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>3.1) Hydro_FT_2D_CollocationPointBase.txt<br> Description: Parameter combinations of the collocation point base for each of the 20 simulations conducted with the full model to <br> construct the surrogate<br> Rows: Critical Shields parameter, Grain Roughness, Grain Size distribution</p> <p>3.2) Hydro_FT_2D_CollocationResults.txt<br> Description: Simulation results of the 20 simulations conducted with the full model at the collocation points<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig<br> [m asl], Elevations 2010 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig [m asl], Elevations 2013 [m asl] of<br> simulation 1 through 20<br> ----------------------------------------------------------------------------------------------------------------------------<br> 4.1) aPC_MC_N_Combinations_Weights_prior.txt<br> Description: ID of prior MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.2) aPC_MC_2005_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of MC run 1 through 100,000<br> 4.3) aPC_MC_2010_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of MC run 1 through 100,000<br> 4.4) aPC_MC_2013_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2013 [m asl] of MC run 1 through 100,000<br> <br> 4.5) aPC_MC_N_Combinations_Weights_posterior.txt<br> Description: ID of accepted (posterior) MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of accepted MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.6) aPC_MC_2005_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of accepted MC run 1 through 857<br> 4.7) aPC_MC_2010_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of accepted MC run 1 through 857<br> 4.8) aPC_MC_2013_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2013 [m asl] of accepted MC run 1 through 857<br> ----------------------------------------------------------------------------------------------------------------------------<br> 5) aPC_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated aPC surrogate model using the MAP parameter <br> combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>6) Hydro_FT_2D_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated full model using the MAP parameter combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 7) dz.txt<br> Description: Riverbed Evolution for all nodes in the section of interest (n=1138) obtained with differently calibrated models for all <br> considered time periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</p> <p>8) dz_CalibrationNodes.txt<br> Description: Riverbed Evolution for calibration nodes (n=204) obtained with differently calibrated models for all considered time<br> periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</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.