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17 results for “parameter inference”
1D cell trajectories as studied in "Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference"
<p>Trajectories of motile cells represent a rich source of data that provide insights into the mechanisms of cell migration via mathematical modeling and statistical analysis. Here, we present trajectories of MDA-MB-231 breast cancer cells and MCF-10A breast epithelial cells. Cells were confined to 1D using fibronectin lanes and exposed to three different treatments, namely the actin polymerisation inhibitor Latrunculin A (LatA), the ROCK inhibitor Y-27632 (Y27) and a control. Each csv file contains a number of 24h long trajectories of cells corresponding to the name of the file. The column names are:</p> <p>`traject_id`: The trajectories are numbered, starting from 0 in each file.</p> <p>`time (h)`: Time in h, starting at 0h for each trajctory and ending at 24h with a temporal resolution of 2min.</p> <p>`x_front`: Position of the cell's front.</p> <p>`x_nucleus`: Position of the cell's nucleus, where x_nucleus=0 for the first time point of the trajectory</p> <p>`x_rear`: Position of the cell's rear.</p> <p>The data was analysed in our study "Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference". Further information can be found there. </p>
Data of "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator"
<p><strong>General</strong></p> <p>Data of <a href="http://doi.org/10.1016/j.ijsolstr.2023.112470">https://doi.org/10.1016/j.ijsolstr.2023.112470</a> related to MOAMMM project.</p> <p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</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 = "Ling Wu, Cyrielle Anglade, Lucia Cobian, Miguel Monclus, Javier Segurado, Fatma Karayagiz, Ubiratan Freitas, and Ludovic Noels"</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <p><strong>Description</strong></p> <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>. The BI is described in [WU23] .The experimental results used in the BI are reported in [COB22,COB22b]. To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> If you use these data or model, we would be grateful if you could cite the related papers.</p> <p><strong>Bibliography</strong></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: https://doi.org/10.1016/j.ijsolstr.2023.112470</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: https://doi.org/10.1016/j.polymertesting.2022.107556 (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” http://dx.doi.org/10.5281/zenodo.6136935 (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; https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008, Open access: https://orbi.uliege.be/handle/2268/197898</li> </ul> <p><strong>Directories</strong></p> <p>All the codes and experimental results are in five directories:</p> <ol> <li>experimentalTests: experimental data, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range <ol> <li>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.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VE_V2Step and VE_H: BI for viscoelastic properties of "V" specimen (VE_V2Step) and "H" specimen (VE_H) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>MCMC_VE_....dat in the VE_V2Step and VE_H directories are the BI results</li> <li>When proceeding in two steps in VE_V2Step, a first step generates MCMC_VE_VN8_1st.dat whose posterior is used as prior in the second step to generate MCMC_VE_VN8_2nd.dat</li> </ol> </li> <li>CheckBayRes: to visualize predictions of a BI sample and experimental curves <ol> <li>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</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> <li>uses as input VE_V2Step/MCMC_VE_....dat or VE_H/MCMC_VE_....dat</li> <li>uses local ViscoElasticTest.py, line.geo, line. msh as interface with https://gitlab.onelab.info/cm3/cm3Libraries code</li> <li>uses local functions plotExpLoad_Unload.py, plotExp.py</li> </ol> </li> <li>ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V2Step and VE_H to call the VEVP model</li> </ol> </li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges <ol> <li>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.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VP_V2step and VP_H2step: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V2Step) and "H" specimen (VP_H2Step) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?_1of2Steps.dat (? being H or V)</li> <li>Then using MCMC_VP_?_1of2Steps.dat posterior to get a new prior, it generates MCMC_VP_?_2of2Steps.dat (? being H or V)</li> </ol> </li> <li>CheckBayRes: visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples ([28000, 45000,70000] by default, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> </ol> </li> <li>VEVPTest.py: interface with https://gitlab.onelab.info/cm3/cm3Libraries code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> </li> <li>RandomParametersGenerator: used to generate the parameters from the BI samples, with the same statistical content <ol> <li>Generator <ol> <li>DataProcess.py: creates normalized data for training from final inferred parameters in ../MCMC_ResData and creates ?_dirNormData (? being H or V)</li> <li>KmeanDataProcess.py: performs clustering for the data of H_dirNormDat and creates H_dirNormData_2cluster (no need for V direction because not bimodal)</li> <li>Gan_V.py and Gan_H.py are used to train the random material parameter generators and create the VDir_Gan or HDir_Gan200_0/HDir_Gan200_1</li> <li>GenerateParameters.py generates random parameters using the Gan files VDir_Gan or HDir_Gan200_0/HDir_Gan200_1 and checks the joint histograms of generated parameters, generated parameters are in V_GenData and H_GenData</li> <li>Ganlib.py is used by the generator</li> </ol> </li> <li>CheckRes <ol> <li>GenDataRes.py is used to check the numerical predictions with the generated parameter samples, see point 4) (using V_GenData and H_GenData).</li> <li>Plot_PropGen.py plots joints histograms of the generated parameters using the samples of V_GenData or H_GenData</li> </ol> </li> </ol> </li> <li>MCMC_ResData:All final data used in the paper (they can substitute the ones used here above) <ol> <li>H_direction and V_direction keep the MCMC random walk results of BI.</li> <li>RandomParameterGenerator keeps results of the generator Paper</li> </ol> </li> </ol> <p><strong>Figures of [WU23]</strong></p> <ul> <li>Fig. 5: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_V/plotExp_T.py or ./PrintDir_V/plotExp_C.py or ./PrintDir_V/plotExp_R.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 8: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires<a href="https://gitlab.onelab.info/cm3/cm3Libraries"> https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 11: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 12: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 13: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_H/plotExp_T.py or ./PrintDir_H/plotExp_C.py or ./PrintDir_H/plotExp_R.py</li> <li>Fig. 15C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 16C: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 19C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 21D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 22D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "H" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> </ul> <p> </p> <p> </p>
Data of Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network
<pre>Data from title = "Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network", journal = "Computer Methods in Applied Mechanics and Engineering", pages = "112693", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2019.112693", author = "Wu, Ling and Zulueta, Kepa and Major, Zoltan and Arriaga, Aitor and Noels, Ludovic" </pre>
Quantifying tensions in cosmological parameters: Interpreting the DES evidence ratio (supplementary inference products)
<p>These are the nested sampling inference products and input files that were used to compute results for <a href="https://arxiv.org/abs/1902.04029">arXiv:1902.04029</a> and <a href="https://arxiv.org/abs/1903.06682">arXiv:1903.06682</a></p> <p>An example plotting script and plots from the papers are included to demonstrate usage.</p> <p>Filename conventions:</p> <p>runs_default: default prior widths</p> <p>runs_narrow: narrowest prior widths</p> <p>runs_medium: intermediate prior widths</p> <p>planck: Planck 2018 baseline likelihoods</p> <p>SH0ES: Riess et al 2018 Hubble likelihood</p> <p>BAO: BOSS DR12 baryonic acoustic oscillations + redshift space distortion likelihood</p> <p>DES: Dark Energy Survey Y1 baseline</p> <p>Software used:</p> <p>CosmoChord <a href="https://github.com/williamjameshandley/CosmoChord/tree/e2f7020bd9d4254230097037fb6ebaee95a3d558">https://github.com/williamjameshandley/CosmoChord/tree/e2f7020bd9d4254230097037fb6ebaee95a3d558</a></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>
Estimating epidemiological parameters of highly pathogenic avian influenza in common terns using exact Bayesian inference
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Global Dataset of Ecohydrological Parameters Inferred from Satellite Observations
<p>This dataset contains global maps of</p> <p>(1) ecohydrological parameters for a theoretical model of the probability distribution of soil saturation;<br> (2) convergence, uncertainty and goodness-of-fit diagnostics; and<br> (3) soil water stress and uptake indexes,</p> <p>associated with analysis in: Bassiouni, M., S.P. Good, C.J. Still, and C.W. Higgins (2020), Plant water uptake thresholds inferred from satellite soil moisture. Geophysical Research Letters. <a href="https://doi.org/10.1029/2020GL087077">https://doi.org/10.1029/2020GL087077 </a></p> <p>All variable descriptions and units are included in the .nc metadata.</p> <p>Code associated with this dataset are publicly available:<br> Probabilistic Inference of Ecohydrological Parameters (PIEP): <a href="http://doi.org/10.5281/zenodo.1257718">http://doi.org/10.5281/zenodo.1257718</a>.<br> Data Management for Global PIEP: <a href="http://doi.org/10.5281/zenodo.3235820">http://doi.org/10.5281/zenodo.3235820</a></p> <p><strong>Abstract</strong><br> Empirical functions are widely used in hydrological, agricultural, and earth system models to parameterize plant water uptake. We infer soil water potentials at which uptake is downregulated from its maximum rate and at which uptake is zero, in biomes with < 60% woody vegetation at 36-km grid resolution. We estimate thresholds through Bayesian inference using a stochastic water balance framework to construct theoretical soil moisture probability distributions consistent with satellite surface soil moisture. The global median Nash–Sutcliffe efficiency between empirical soil moisture distributions derived from satellite soil moisture observations and best-fit theoretical distributions using inferred parameters is 0.8. Spatially variable thresholds capture location-specific vegetation and climate characteristics and can be connected to biome-level water uptake strategies.</p>
Mitigating flicker noise in high-precision photometry. I - Characterization of the noise structure, impact on the inferred transit parameters, and predictions for CHEOPS observations
<p>This repository contains the publicly available artificial exoplanet transit light curves that has been generated in HMI observations.</p> <p>Descriptions of the data are encapsulated in the READme.dat file. For further details, please report to Sec. 3 of the paper (arXiV link: <a href="https://arxiv.org/abs/2003.07707">https://arxiv.org/abs/2003.07707</a>).</p> <p>If you find these synthetic data useful in your own research please cite the Sulis. et al., (2020) paper.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates
We propose a two-step procedure for estimating multiple migration rates in an approximate Bayesian computation (ABC) framework, accounting for global nuisance parameters. The approach is not limited to migration, but generally of interest for inference problems with multiple parameters and a modular structure (e.g. independent sets of demes or loci). We condition on a known, but complex demographic model of a spatially subdivided population, motivated by the reintroduction of Alpine ibex (Capra ibex) into Switzerland. In the first step, the global parameters ancestral mutation rate and male mating skew have been estimated for the whole population in Aeschbacher et al. (Genetics 2012; 192: 1027). In the second step, we estimate in this study the migration rates independently for clusters of demes putatively connected by migration. For large clusters (many migration rates), ABC faces the problem of too many summary statistics. We therefore assess by simulation if estimation per pair of demes is a valid alternative. We find that the trade-off between reduced dimensionality for the pairwise estimation on the one hand and lower accuracy due to the assumption of pairwise independence on the other depends on the number of migration rates to be inferred: the accuracy of the pairwise approach increases with the number of parameters, relative to the joint estimation approach. To distinguish between low and zero migration, we perform ABC-type model comparison between a model with migration and one without. Applying the approach to microsatellite data from Alpine ibex, we find no evidence for substantial gene flow via migration, except for one pair of demes in one direction.
Data from: Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates
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Ambiguity coding allows accurate inference of evolutionary parameters from alignments in an aggregated state-space
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Inferring macroevolutionary parameters from on a model of adaptive radiation by Approximate Bayesian Computation - ABC Simulations
<p>Recent advances in DNA sequencing are providing increasingly accurate phylogenetic trees to study, but understanding the evolutionary forces at play in different contexts remains a huge challenge. To tackle this issue, we applied an Bayesian approach to an existing model of phenotypic and species diversification [1] in order to retrieve 11 underlying parameters (such as basal speciation and extinction rates, but also competition strength) from phylogenetic trees with known traits values at the tips.</p> <p>This dataset corresponds to the Approximate Bayesian Computation simulations realized for the inference.</p>
Data from: Model selection and parameter inference in phylogenetics using nested sampling
Bayesian inference methods rely on numerical algorithms for both model selection and parameter inference. In general, these algorithms require a high computational effort to yield reliable estimates. One of the major challenges in phylogenetics is the estimation of the marginal likelihood. This quantity is commonly used for comparing different evolutionary models, but its calculation, even for simple models, incurs high computational cost. Another interesting challenge relates to the estimation of the posterior distribution. Often, long Markov chains are required to get sufficient samples to carry out parameter inference, especially for tree distributions. In general, these problems are addressed separately by using different procedures. Nested sampling (NS) is a Bayesian computation algorithm which provides the means to estimate marginal likelihoods together with their uncertainties, and to sample from the posterior distribution at no extra cost. The methods currently used in phylogenetics for marginal likelihood estimation lack in practicality due to their dependence on many tuning parameters and their inability of most implementations to provide a direct way to calculate the uncertainties associated with the estimates, unlike NS. In this paper, we introduce NS to phylogenetics. Its performance is analysed under different scenarios and compared to established methods. We conclude that NS is a competitive and attractive algorithm for phylogenetic inference. An implementation is available as a package for BEAST 2 under the LGPL licence, accessible at https://github.com/BEAST2-Dev/nested-sampling.
Data from: Model selection and parameter inference in phylogenetics using nested sampling
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Data from: Low-parameter phylogenetic inference under the general Markov model
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Data from:Inferring flight parameters of Mesozoic avians through multivariate analyses of forelimb elements in their living relatives
Our knowledge of the diversity, ecology, and phylogeny of Mesozoic birds has increased significantly during recent decades, yet our understanding of their flight competence remains poor. Wing loading (WL) and aspect ratio (AR) are two aerodynamically relevant parameters, as they relate to energy costs of aerial locomotion and flight maneuverability. They can be calculated in living birds (i.e., Neornithes) from body mass (BM), wingspan (B) and lift surface (SL). However, the estimates for extinct birds can be subject to biases from statistical issues, phylogeny, locomotor adaptations, and diagenetic compaction. Here we develop a sequential approach for generating reliable multivariate models that allow estimating measurements necessary to determine WL and AR in the main clades of non-neornithine Mesozoic birds. The strength of our predictions is supported by the use of those variables that show similar scaling patterns in modern and stem taxa (i.e., non-neornithine birds), and the similarity of our predictions with measurements obtained from fossils preserving wing outlines. In addition, although our WL and AR values are based on estimates (BM, B, and SL) that have an associated error, there is no cumulative error in their calculation, and both parameters show low prediction errors. Therefore we present the first taxonomically broad, error-calibrated estimation of these two important aerodynamic parameters in non-neornithine birds. Such estimates show that the WL and AR of the non-neornithine birds here analyzed fall within the range of variation of modern birds (i.e., Neornithes). Our results indicate that most modern flight modes (e.g., continuous flapping, flap and gliding, flap and bounding, thermal soaring) were possible for the wide range of non-neornithine avian taxa; we found no evidence for the presence of dynamic soaring among these early birds.
Data from:Inferring flight parameters of Mesozoic avians through multivariate analyses of forelimb elements in their living relatives
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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.