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1,066 results for “bayesian”
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>
Bayesian hierarchical model gridded solar-induced fluorescence (BHM gridded SIF) data product
<p>This archive provides the solar-induced fluorescence (SIF) data product documented in "Estimation of solar-induced chlorophyll fluorescence using Bayesian hierarchical regression". The archive includes daily NetCDF files with the global gridded SIF estimates and associated uncertainties.</p>
Bayesian Analysis of Paleotsunami Sources: Data and Stochastic Simulations
<p>This repository contains the datasets utilized in the research titled “Tracing the Sources of Paleotsunamis Using Bayesian Frameworks.” Each dataset is integral to the analysis and reconstruction efforts undertaken in the study.</p> <p> </p> <p><strong>File Descriptions</strong></p> <p> </p> <p><strong>1. CorrectedShoreline_jogan_deposit_data.csv</strong></p> <p> </p> <p>This file contains paleotsunami data collected by Sugawara et al. The data has been corrected to account for the shoreline position at the time of the paleotsunami event.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Sugawara, D., Goto, K., Imamura, F., Matsumoto, H., & Minoura, K. (2012). Assessing the magnitude of the 869 Jogan tsunami using sedimentary deposits: Prediction and consequence of the 2011 Tohoku-oki tsunami. <em>Sedimentary Geology, 282</em>, 14–26.</p> <p> </p> <p><strong>2. Stochastic_samples.zip</strong></p> <p> </p> <p>This archive contains the stochastic samples generated for the Japan Trench, utilizing the coupling distribution model from Loveless et al.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Loveless, J. P., & Meade, B. J. (2011). Spatial correlation of interseismic coupling and coseismic rupture extent of the 2011 Mw = 9.0 Tohoku-oki earthquake. <em>Geophysical Research Letters, 38</em>.</p> <p> </p> <p><strong>3. Selected_Stochastic_Samples.zip</strong></p> <p> </p> <p>This file includes a reduced sample space derived from the original stochastic samples, specifically selected for statistical analysis.</p> <p> </p> <p><strong>4. Sendai_1961.zip</strong></p> <p> </p> <p>This dataset contains the reconstructed morphology of the Sendai plain as it appeared in 1961. The reconstruction is based on aerial photographs provided by the Geospatial Information Authority of Japan (GSI).</p>
Experimental result data for primal-dual contextual Bayesian optimization
<p>This dataset is the result of the paper "Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints" published in IEEE Conference on Decision and Control 2023. </p>
Sequential Bayesian Inference of Finite-strain Visco-elastic Visco-plastic model parameters of 22-month aged PA12 bulk material printed along different directions
<p>These are the data related to aged PA12 (22 months) following the methodology described in the following publication in which non-aged PA12 has been tested:</p> <p>title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.",<br>journal = "International Journal of Solids and Structures",<br>year = "2023",<br>volume = "283",<br>pages = "112470",<br>doi = "10.1016/j.ijsolstr.2023.112470",<br>author = "Wu, Ling and Anglade, Cyrielle and Cobian, Lucia and Monclus, Miguel and Segurado, Javier and Karayagiz, Fatma and Freitas, Ubiratan and Noels Ludovic"</p> <p>Contrarily to the non-aged material, since high-strain-rate tests are not available, only 5 Maxwell's branches are considered herein.</p> <h1>Description</h1> <p>BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a></p> <p>The sequential BI is described in [WU23] The experimental protocol is reported in [COB22,COB22b] but is herein applied on aged PA12</p> <p>To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a></p> <p>If you use these data or model, we would be grateful if you could cite the related papers:</p> <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: <a href="https://doi.org/10.1016/j.ijsolstr.2023.112470" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.ijsolstr.2023.112470</a></li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: <a href="https://doi.org/10.1016/j.polymertesting.2022.107556" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.polymertesting.2022.107556</a> (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” <a href="http://dx.doi.org/10.5281/zenodo.6136935" target="_blank" rel="nofollow noreferrer noopener">http://dx.doi.org/10.5281/zenodo.6136935</a> (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; <a href="https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008" target="_blank" rel="nofollow noreferrer noopener">https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008</a>, Open access: <a href="https://orbi.uliege.be/handle/2268/197898" target="_blank" rel="nofollow noreferrer noopener">https://orbi.uliege.be/handle/2268/197898</a></li> </ul> <h1>Directories</h1> <p>All the codes and experimental results are in three directories:</p> <ol> <li>Experiment_PA12_AGED: experimental data of aged material, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters<br>2.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range<br>2.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI.<br>2.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py<br>2.1.3. Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI<br>2.2. VE_V and VE_H: BI for viscoelastic properties of "V" specimen (VE_V) and "H" specimen (VE_H)<br>2.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat<br>2.2.2. WarmStart = True is used to restart an inference<br>2.2.3. MCMC_VE_....dat in the VE_V and VE_H directories are the BI results<br>2.3. CheckBayRes: to visualize predictions of a BI sample and experimental curves 2.3.1. MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line 7)<br>2.3.2. ResKGEmu.py plots the evolution of elastic properties with time<br>2.3.3. uses as input VE_V/MCMC_VE_....dat or VE_H/MCMC_VE_....dat<br>2.3.4. uses local ViscoElasticTest.py, line.geo, line. msh as interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code<br>2.3.5. uses local functions plotExp.py<br>2.4. ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V and VE_H to call the VEVP model</li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters<br>3.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges<br>3.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage.<br>3.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py<br>3.1.3. Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI<br>3.2. VP_V and VP_H: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V) and "H" specimen (VP_H)<br>3.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat<br>3.2.2. WarmStart = True is used to restart an inference 3.2.3. It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3. CheckBayRes: visualize predictions of a BI sample and experimental curves<br>3.3.1. MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples (inclusing MAP, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3.2. plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3.3. Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?_1Step.dat (? being H or V) 3.3.4. ResKGEmu.py plots the evolution of elastic properties with time<br>3.4. VEVPTest.py: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> <h1>Figures (reference to the number in [WU23] but for aged PA12)</h1> <ul> <li>Fig. 5 (Selected observations): From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,14,18,22,23,24,25]</li> <li>Fig. 8 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 15B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 16B: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 19B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> </ul> <p> </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015.</p>
Characterizing the spatial correlation of coseismic slip distributions: A data driven Bayesian approach
<p>Slip models for the simulated case and the Illapel earthquake are provided. The zip file contains processed data, predictions, and uncertainty estimates for the Illapel event.</p>
Data from: Integrating Bayesian genomic cline analyses and association mapping of morphological and ecological traits to dissect reproductive isolation and introgression in a Louisiana Iris hybrid zone
Hybrid zones provide unique opportunities to examine reproductive isolation and introgression in nature. We utilized 45,384 Single Nucleotide Polymorphism (SNP) loci to perform association mapping of 14 floral, vegetative, and ecological traits that differ between Iris hexagona and Iris fulva, and to investigate, using a Bayesian Genomic Cline (BGC) framework, patterns of genomic introgression in a large and phenotypically diverse hybrid zone in southern Louisiana. Many loci of small effect-size were consistently found to be associated with phenotypic variation across all traits, and several individual loci were revealed to influence phenotypic variation across multiple traits. Patterns of genomic introgression were quite heterogeneous throughout the Louisiana Iris genome, with I. hexagona alleles tending to be favored over those of I. fulva. Loci that were found to have exceptional patterns of introgression were also found to be significantly associated with phenotypic variation in a small number of morphological traits. However, this was the exception rather than the rule, as most loci that were associated with morphological trait variation were not significantly associated with excess ancestry. These findings provide insights into the complexity of the genomic architecture of phenotypic differences and are a first step towards identifying loci that are associated with both trait variation and reproductive isolation in nature.
Results files for "End-to-end Bayesian analysis for summarizing sets of radiocarbon dates"
<p>These are the results files for the following peer-reviewed article:</p> <p>Price, M.H., J.M. Capriles, J. Hoggarth, R.K. Bocinsky, C.E. Ebert, and J.H. Jones, (2021). End-to-end Bayesian analysis for summarizing sets of radiocarbon dates. Journal of Archaeological Science.</p> <p>They were generated inside a Docker container as outlined in the README of this github repository:</p> <p>https://github.com/MichaelHoltonPrice/price_et_al_tikal_rc</p> <p>The analyses rely on an R package located in this github repository:</p> <p>https://github.com/eehh-stanford/baydem</p> <p>For the results archived here, the commits for each repository are:</p> <pre>price_et_al_tikal_rc 3ac1e35f4277ef878f8e3aac3d05159928a09a2b baydem 1220a60a860633b51f9f07cbff3eb78f458efc1a</pre>
Dataset and Code for "Bayesian spline method for assessing extreme loads on wind turbines"
<p>Here are two files. One file contains the datasets and the other contains the computer code used to generate the results in the paper, Lee, Byon, Ntaimo, and Ding, 2013, “Bayesian spline method for assessing extreme loads on wind turbines,” <em> Annals of Applied Statistics</em>, Vol. 7, pp. 2034-2061.</p>
Dataset for "Bayesian hierarchical models for combining misaligned two-resolution metrology data"
<p>This file contains the datasets used in the paper, Xia, Ding, and Mallick, 2011, “Bayesian hierarchical models for combining misaligned two-resolution metrology data,” <em>IIE Transactions</em>, Vol. 43, pp. 242 – 258.</p>
DNA alignment and resulting bayesian trees of Epithelantha and sister species
<p><span> The use of environmental variables to explain the evolution of lineages has gained relevance in recent studies. Additionally, it has allowed the recognition of species by adding more characters to morphological and molecular information. This study focuses on identifying environmental and landscape variables that have acted as barriers that could have influenced the evolution of <i>Epithelantha</i> species and its close genera.</span></p> <p><span>Our results show that soil pH, isothermality, temperature seasonality, and annual precipitation have a significant phylogenetic signal for <i>Epithelantha</i>. Soil type and landforms are also relevant as ecological barriers that maintain the identity of <i>Epithelantha</i> species.</span></p> <p><span>The variables associated with the soil (pH) have influenced the evolution of <i>Epithelantha</i> and probably in other genera of Cactaceae. Additionally, <i>Epithelantha</i> is frequent in the piedmont and haplic kastanozems. Bioclimatic variables reinforce the recognition of <i>E. micromeris</i> and <i>E. cryptica</i> as independent species. Therefore, ecology can be considered as a factor to explain the high level of endemism in Cactaceae.</span></p>
Data for: Evaluating the impact of anatomical partitioning on summary topologies obtained with Bayesian phylogenetic analyses of morphological data
<p>Morphological data are a fundamental source of evidence to reconstruct the Tree of Life, and Bayesian phylogenetic methods are increasingly being used for this task. Bayesian phylogenetic analyses require the use of evolutionary models, which have been intensively studied in the past few years, with significant improvements to our knowledge. Notwithstanding, a systematic evaluation of the performance of partitioned models for morphological data has never been performed. Here we evaluate the influence of partitioned models, defined by anatomical criteria, on the precision and accuracy of summary tree topologies considering the effects of model misspecification. We simulated datasets using partitioning schemes, trees, and other properties obtained from two empirical datasets, and conducted Bayesian phylogenetic analyses. Additionally, we reanalysed 32 empirical datasets for different groups of vertebrates, applying unpartitioned and partitioned models, and, as a focused study case, we reanalysed a dataset including living and fossil armadillos, testing alternative partitioning hypotheses based on functional and ontogenetic modules. We found that, in general, partitioning by anatomy has little influence on summary topologies analysed under alternative partitioning schemes with a varying number of partitions. Nevertheless, models with unlinked branch lengths, which account for heterotachy across partitions, improve topological precision at the cost of reducing accuracy. In some instances, more complex partitioning schemes, led to topological changes, as tested for armadillos, mostly associated with models with unlinked branch lengths. We compare our results with other empirical evaluations of morphological data and those from empirical and simulation studies of partitioning of molecular data, considering the adequacy of anatomical partitioning relative to alternative methods of partitioning morphological datasets.</p>
Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance
<p>Using geospatial data of wildlife presence to predict a species distribution across a geographic area is among the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have access to larger amounts of low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually scarcer and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive presence-only datasets. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. Here, we apply this innovative approach to fit ISDMs to empirical data, using presence-absence and presence-only data for the three prevalent deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km<sup>2</sup> resolution across the island. Models' predictions were associated to spatial estimates of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we checked the performance of the three species-specific models using two datasets, independent deer hunting returns and deer densities based on faecal pellet counts. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unexplored and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.</p>
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>This dataset contains waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo sampling algorithm and a 3-D Earth model of the Japanese islands. Specifically, it includes processed observed waveforms from the Full Range Seismograph Network of Japan (F-Net, http://www.fnet.bosai.go.jp) and synthetic waveforms for the maximum-likelihood solutions as well as Global Centroid Moment Tensor (GCMT) solutions for all study events inverted at different periods. Detailed description of the dataset is included in the README file. </p>
Inferring predator-prey interactions from camera traps: A Bayesian co-abundance modelling approach
<p><span>Predator-prey dynamics are a fundamental part of ecology, but directly studying interactions has proven difficult. The proliferation of camera trapping has enabled the collection of large datasets on wildlife, but researchers face hurdles inferring interactions from observational data. </span><span>Recent advances in </span><span>hierarchical c</span><span>o-abundance models infer species interactions while </span><span>accounting for two species' detection probabilities, shared responses to environmental covariates, and propagate uncertainty throughout the</span> <span>entire modelling process. However, current approaches remain </span><span>unsuitable for interacting species </span><span>whose natural densities differ by an order of magnitude and have contrasting detection probabilities, such as predator-prey interactions, which introduce zero-inflation and overdispersion in count histories. </span><span>Here we developed </span><span>a Bayesian hierarchical N-mixture co-abundance model that is </span><span>suitable for </span><span>inferring </span><span>predator-prey </span><span>interactions. We accounted for excessive zeros in count histories using an informed zero-inflated Poisson distribution in the abundance formula and accounted for overdispersion in count histories by including a random effect per sampling unit and sampling occasion in the detection probability formula. We demonstrate that models with these modifications outperform alternative approaches, improve model goodness-of-fit, and overcome parameter convergence failures. We highlight its utility using 20 camera trapping datasets </span><span>from 10 tropical forest landscapes in Southeast Asia and estimate four predator-prey relationships between tigers, clouded leopards, and muntjac and sambar deer. Tigers had a negative effect on muntjac abundance, providing support for top-down regulation, while clouded leopards had a positive effect on muntjac and sambar deer, likely driven by shared responses to unmodelled covariates like hunting. </span><span>This Bayesian co-abundance modelling approach to quantify predator-prey relationships </span><span>is widely applicable across species, ecosystems, and sampling approaches, and may be useful in forecasting cascading impacts following widespread predator declines. Taken together, this approach facilitates a nuanced and mechanistic understanding of food-web ecology.</span></p>
[Dataset] Lattice Metamaterials with Mesoscale Motifs: Exploration of Property Charts by Bayesian Optimisation
<p>[Dataset] Lattice Metamaterials with Mesoscale Motifs: Exploration of Property Charts by Bayesian Optimisation</p> <p>Roman Kulagin*, Patrick Reiser, Kyryl Truskovskyi, Arnd Koeppe, Yan Beygelzimer, Yuri Estrin, Pascal Friederich, Peter Gumbsch</p> <p>[*] Dr. R. Kulagin, Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany. E-Mail: roman.kulagin@kit.edu</p> <p>Dr. Patrick Reiser, Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany; Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131 Karlsruhe, Germany.</p> <p>Kyryl Truskovskyi, Georgian, Toronto, Canada</p> <p>Dr. Arnd Koeppe, Institute for Applied Materials (IAM-MMS), Karlsruhe Institute of Technology, Straße am Forum 7, 76131 Karlsruhe, Germany.</p> <p>Prof. Pascal Friederich, Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany; Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131 Karlsruhe, Germany.</p> <p>Prof. Y. Beygelzimer, Donetsk Institute for Physics and Engineering named after A.A. Galkin, National Academy of Sciences of Ukraine, Nauki ave., 46, 03028 Kyiv, Ukraine.</p> <p>Prof. Y. Estrin, Department of Materials Science and Engineering, Monash University, 22 Alliance Lane, Clayton 3800, Australia; Department of Mechanical Engineering, The University of Western Australia, Crawley 6009, Australia.</p> <p>Prof. P. Gumbsch, Institute for Applied Materials, Karlsruhe Institute of Technology, Straße am Forum 7, 76131, Karlsruhe, Germany; Fraunhofer Institute for Mechanics of Materials, Freiburg, Wöhlerstraße 11, 79108 Freiburg, Germany.</p> <p>Part of the work was supported by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) through the POLiS Cluster of Excellence (grant no. UP 33/1) under project ID 390874152 and by the Helmholtz association under the KNMFi program (grant no. 43.31.01).</p>
Constraining Bedrock Groundwater Residence Times in a Mountain System with Environmental Tracer Observations and Bayesian Uncertainty Quantification: Modeling and Data Package
<p>Here we present field observations of dissolved noble gases (He, Ne, Ar, Kr, and Xe), Chloroflourcarbons (CFCs), Sulfurhexaflouride (SF6), and tritium (3H) sampled from the PLM1, PLM6, and PLM7 wells in the East River Colorado (USA) sampled in May, 2021. This observation dataset, along with the presented python modeling scripts to interpret the data, can aide in quantifying groundwater residence times and recharge conditions. The README files describes the directories and scripts.</p>
Identifying the best approximating model in Bayesian phylogenetics: Bayes factors, cross-validation or wAIC?
<p>There is still no consensus as to how to select models in Bayesian phylogenetics, and more generally in applied Bayesian statistics. Bayes factors are often presented as the method of choice, yet other approaches have been proposed, such as cross-validation or information criteria. Each of these paradigms raises specific computational challenges, but they also differ in their statistical meaning, being motivated by different objectives: either testing hypotheses or finding the best-approximating model. These alternative goals entail different compromises, and as a result, Bayes factors, cross-validation and information criteria may be valid for addressing different questions. Here, the question of Bayesian model selection is revisited, with a focus on the problem of finding the best-approximating model. Several model selection approaches were re-implemented, numerically assessed and compared: Bayes factors, cross-validation (CV), in its different forms (k-fold or leave-one-out), and the widely applicable information criterion (wAIC), which is asymptotically equivalent to leave-one-out cross validation (LOO-CV). Using a combination of analytical results and empirical and simulation analyses, it is shown that Bayes factors are unduly conservative. In contrast, cross-validation represents a more adequate formalism for selecting the model returning the best approximation of the data-generating process and the most accurate estimates of the parameters of interest. Among alternative CV schemes, LOO-CV and its asymptotic equivalent represented by the wAIC, stand out as the best choices, conceptually and computationally, given that both can be simultaneously computed based on standard MCMC runs under the posterior distribution.</p>
Estimated parameters for Bayesian Multilevel Models of KM and kcat values
<p>RData (.rds) files containing brmsfit model objects estimated with the brms R package from KM and kcat values reported in BRENDA and SABIO-RK.</p> <p>These models are used by the ENKIE python package to predict kinetic parameter values and uncertainties.</p>
The optimal time to approach an unfamiliar object: A Bayesian model
<p>Many organisms take time before approaching unfamiliar objects. This caution forms the basis of some well-known assays in the fields of behavioral ecology, comparative psychology and animal welfare, including quantifying the personality traits of individuals and evaluating the extent of their neophobia. In this paper we present a mathematical model which identifies the optimal time an observer should wait before approaching an unfamiliar object. The model is Bayesian, and simply assumes that the longer the observer goes without being attacked by an unfamiliar object, the lower will be the observer's estimated probability that the object is dangerous. Given the information gained, a time is reached at which the expected benefits from approaching the object begin to exceed the costs. The model not only explains why latency to approach may be repeatable among individuals and vary with the object's appearance, but also why individuals habituate to the stimulus, approaching it more rapidly over repeated trials. We demonstrate the applicability of our model by fitting it to published data on the time taken by chicks to attack artificial caterpillars which share no, one, or two signaling traits with snakes (eyespots and posture). We use this example to show that while the optimal time to attack an unfamiliar object reflects the observer's expectation that the object is dangerous, the rate at which habituation arises is also a function of the observer's certainty in their belief. In so doing, we explain why observers become more rapidly habituated to "weaker" stimuli than "stronger" ones. </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.