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197 results for “time estimation”
Phylogeny and divergence time estimation of Io moths and relatives (Lepidoptera: Saturniidae: Automeris)
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Data from: Robustness of divergence time estimation despite gene tree error: A case study of fireflies (Coleoptera: Lampyridae)
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The implications of incongruence between gene tree and species tree topologies for divergence time estimation
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Insights from empirical analyses and simulations on using multiple fossil calibrations with relaxed clocks to estimate divergence times
<p>Relaxed clock methods account for among-branch-rate-variation when estimating divergence times by inferring different rates for individual branches. In order to infer different rates for individual branches, important assumptions are required. This is because molecular sequence data does not provide direct information about rates, but instead provides direct information about the total number of substitutions along any branch, which is a product of the rate and time for that branch. Often, the assumptions required for estimating rates for individual branches depend heavily on the implementation of multiple fossil calibrations in a single phylogeny. Here, we show that the basis of these assumptions is often critically undermined. First, we highlight that the temporal distribution of the fossil record often violates key assumptions of methods that use multiple fossil calibrations with relaxed clocks. With respect to "node calibration" methods, this conclusion is based on our inference that different fossil calibrations are unlikely to reflect the relative ages of different clades. With respect to the fossilised-birth-death-process, this conclusion is based on our inference that the fossil recovery rate is often highly heterogeneous. We then demonstrate that methods of divergence time estimation that use multiple fossil calibrations are highly sensitive to assumptions about the fossil record and among-branch-rate-variation. Given the problems associated with these assumptions, our results highlight that using multiple fossil calibrations with relaxed clocks often does little to improve the accuracy of divergence time estimates.</p> <p> </p>
Source data for graphs and charts used in the paper "A machine learning-based estimator for real-time earthquake ground-shaking predictions in Southern California"
<h1>DESCRIPTION:</h1> <h3>This repository contains the source data for graphs and charts used in the paper "A machine learning-based estimator for real-time earthquake ground-shaking predictions in Southern California" submitted and accepted at "Communications Earth & Environment journal" </h3> <h3>Marisol Monterrubio-Velasco, Scott Callaghan, David Modesto, Jose Carlos Carrasco, Rosa M. Badi , Pablo Pallares, Fernando Vázquez-Novoa, Enrique S. Quintana-Ortı́, Marta Pienkowska, and Josep de la Puente</h3> <h2><strong>DATA FOR FIGURES: </strong></h2> <h3><strong>Figure 1 : </strong></h3> <p>The data used in this figure comes from the CyberShake Study 15.4. Seismogram, intensity measure, and duration data from CyberShake Study 15.4 is available through the SCEC CyberShake Study 15.4 Globus Collection, served by the University of Southern California's Center for Advanced Research Computing. Direct link:<a href="https://g-46eaba.a78b8.36fe.data.globus.org/ACTN/3886/PeakVals_ACTN_10_0.bsa">https://g-46eaba.a78b8.36fe.data.globus.org</a>."</p> <h3><strong>Figure 2:</strong></h3> <p><strong>Model evaluation on the validation dataset for T = 2s</strong></p> <p>1. Random Forest predictions using the optimized hyperparameters depth=30, n_estimators=30 for the validation dataset at T=2s</p> <p><a href="../records/10640493/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10.dat?download=1&preview=1">y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10.dat</a></p> <p>2. Artificial Neural Network predictions using the optimized hyperparameters 9layer and 256 neurons for the validation dataset at T=2s</p> <p><a href="../api/records/10640493/draft/files/Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period2.0_ALL_Validation_log10.csv/content" target="_blank" rel="noopener noreferrer">Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period2.0_ALL_Validation_log10.csv</a></p> <p>3. True values for the validation dataset at T=2s</p> <p><a href="../records/10640493/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10.dat?download=1&preview=1">y_true_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10.dat</a></p> <h3><strong>Figure 3:</strong></h3> <p><strong>Error metrics obtained for each simulated scenario using:</strong></p> <p><strong>- Artificial Neural Networks</strong></p> <p><a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_2.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_3.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_5.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_10.0_NN.csv</a></p> <p><strong>- Random Forest regressor</strong></p> <p><a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_2.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_3.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_5.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_10.0_RF.csv</a></p> <p><strong>- ASK14 GMPE</strong></p> <p><a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_2.0_GMPE.csv</a>, <a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_3.0_GMPE.csv</a>, <a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_5.0_GMPE.csv, </a><a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_10.0_GMPE.csv</a></p> <h3>Figure 4:</h3> <p><strong>MLESmap RotD50 predictions on a validation event of magnitude 6.85</strong></p> <p><a href="../api/records/10640493/draft/files/RF_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">RF_predictions_T2s_map_2748.csv, </a><a href="../api/records/10640493/draft/files/ASK_14_prediction_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ASK_14_prediction_T2s_map_2748.csv </a>, <a href="../api/records/10640493/draft/files/ANN_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ANN_predictions_T2s_map_2748.csv</a></p> <p><a href="../api/records/10640493/draft/files/RF_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">RF_predictions_T3s_map_2748.csv, </a><a href="../api/records/10640493/draft/files/ASK_14_prediction_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ASK_14_prediction_T3s_map_2748.csv </a>, <a href="../api/records/10640493/draft/files/ANN_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ANN_predictions_T3s_map_2748.csv</a></p> <p><a href="../api/records/10640493/draft/files/RF_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">RF_predictions_T5s_map_2748.csv, </a><a href="../api/records/10640493/draft/files/ASK_14_prediction_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ASK_14_prediction_T5s_map_2748.csv </a>, <a href="../api/records/10640493/draft/files/ANN_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ANN_predictions_T5s_map_2748.csv</a></p> <p><a href="../api/records/10640493/draft/files/RF_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">RF_predictions_T10s_map_2748.csv, </a><a href="../api/records/10640493/draft/files/ASK_14_prediction_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ASK_14_prediction_T10s_map_2748.csv </a>, <a href="../api/records/10640493/draft/files/ANN_predictions_T10s_map_2748.csv/content" target="_blank" rel="noopener noreferrer">ANN_predictions_T10s_map_2748.csv</a></p> <h3>Figure 5:</h3> <p><strong>Spatial configuration of five historical earthquakes and BBP stations also including the coordinates of synthetic stations from the CS_15_4 study</strong></p> <p><a href="../api/records/10640493/draft/files/SyntheticStationsCoordinates_CS_15.4.csv/content" target="_blank" rel="noopener noreferrer">SyntheticStationsCoordinates_CS_15.4.csv, </a><a href="../api/records/10640493/draft/files/Whittier_BBP_sites.csv/content" target="_blank" rel="noopener noreferrer">Whittier_BBP_sites.csv</a>, <a href="../api/records/10640493/draft/files/Northridge_BBP_sites.csv/content" target="_blank" rel="noopener noreferrer">Northridge_BBP_sites.csv</a>, <a href="../api/records/10640493/draft/files/North_Palm_Springs_BBP_sites.csv/content" target="_blank" rel="noopener noreferrer">North_Palm_Springs_BBP_sites.csv</a>, <a href="../api/records/10640493/draft/files/Landers_BBP_sites.csv/content" target="_blank" rel="noopener noreferrer">Landers_BBP_sites.csv</a>, <a href="../api/records/10640493/draft/files/Hector_Mine_BBP_sites.csv/content" target="_blank" rel="noopener noreferrer">Hector_Mine_BBP_sites.csv</a></p> <h3><strong>Figure 6:</strong></h3> <p><strong>RotD50 predictions for real events for the ‘inside’ stations </strong></p> <p>NN_layers9North_Palm_Springs_IN_event_metrics-T_10.csv, NN_layers9Northridge_IN_event_metrics-T_10.csv, NN_layers9Landers_IN_event_metrics-T_10.csv, NN_layers9Hector_Mine_IN_event_metrics-T_10.csv, <a href="../api/records/10640493/draft/files/NN_layers9Whittier_IN_event_metrics-T_2.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Whittier_IN_event_metrics-T_10.csv</a></p> <p>NN_layers9North_Palm_Springs_IN_event_metrics-T_5.csv, NN_layers9Northridge_IN_event_metrics-T_5.csv, NN_layers9Landers_IN_event_metrics-T_5.csv, NN_layers9Hector_Mine_IN_event_metrics-T_5.csv, <a href="../api/records/10640493/draft/files/NN_layers9Whittier_IN_event_metrics-T_2.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Whittier_IN_event_metrics-T_5.csv</a></p> <p>NN_layers9North_Palm_Springs_IN_event_metrics-T_3.csv, NN_layers9Northridge_IN_event_metrics-T_3.csv, NN_layers9Landers_IN_event_metrics-T_3.csv, NN_layers9Hector_Mine_IN_event_metrics-T_3.csv, <a href="../api/records/10640493/draft/files/NN_layers9Whittier_IN_event_metrics-T_2.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Whittier_IN_event_metrics-T_3.csv</a></p> <p>NN_layers9North_Palm_Springs_IN_event_metrics-T_2.csv, NN_layers9Northridge_IN_event_metrics-T_2.csv, NN_layers9Landers_IN_event_metrics-T_2.csv, NN_layers9Hector_Mine_IN_event_metrics-T_2.csv, <a href="../api/records/10640493/draft/files/NN_layers9Whittier_IN_event_metrics-T_2.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Whittier_IN_event_metrics-T_2.csv</a></p> <h2>Supplementary material:</h2> <h3>Supplementary Fig 2</h3> <p><strong>Boxplots comparing ML models and "true" values</strong></p> <p><strong>- DNN</strong></p> <p><a href="10640493" target="_blank" rel="noopener noreferrer">Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period2.0_ALL_Validation_log10.csv</a></p> <p><a href="10640493" target="_blank" rel="noopener noreferrer">Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period3.0_ALL_Validation_log10.csv</a></p> <p><a href="10640493" target="_blank" rel="noopener noreferrer">Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period5.0_ALL_Validation_log10.csv</a></p> <p><a href="10640493" target="_blank" rel="noopener noreferrer">Prediction_NN_BS_256_9capas_DataSet_AllRuptureVariations_OneRuptureID_Period10.0_ALL_Validation_log10.csv</a></p> <p>- RF</p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_pred_dislib_T3s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_pred_dislib_T5s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y</a><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">_pred_dislib_T10s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p>- TRUE VALUES FROM CYBERSHAKE SIMULATIONS</p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_true_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_true_dislib_T3s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_true_dislib_T5s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p><a href="../api/records/10640493/draft/files/y_pred_dislib_T2s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat/content" target="_blank" rel="noopener noreferrer">y_true_dislib_T10s_depth-30_n_estimators-30_val_MODEL_ORIGINAL_Dataset_ORIGINAL_ALL_log10_8Feat_Plus_RealData.dat</a></p> <p> </p> <h3>Supplementary Fig 3</h3> <p><strong>Error metrics obtained for each simulated scenario:</strong></p> <p><strong>Artificial Neural Networks:</strong></p> <p><a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_2.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_3.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_5.0_NN.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_10.0_NN.csv</a></p> <p><strong> Random Forest regressor:</strong></p> <p><a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_2.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_3.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_5.0_RF.csv</a>, <a href="../api/records/10640493/draft/files/Test_Results_metrics_paper_5.0_NN.csv/content" target="_blank" rel="noopener noreferrer">Test_Results_metrics_paper_10.0_RF.csv</a></p> <p><strong>ASK14 GMPE</strong></p> <p><a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_2.0_GMPE.csv</a>, <a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_3.0_GMPE.csv</a>, <a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_5.0_GMPE.csv, </a><a href="../api/records/10640493/draft/files/Results_metrics_paper_3.0_GMPE.csv/content" target="_blank" rel="noopener noreferrer">Results_metrics_paper_10.0_GMPE.csv</a></p> <h3>Supplementary Fig 4</h3> <p><strong>Predictions for the Synthetic event of magnitude 7.45</strong></p> <p>RF_predictions_T2s_map_3.csv, ASK_14_prediction_T2s_map_3.csv , ANN_predictions_T2s_map_3.csv</p> <p>RF_predictions_T3s_map_3.csv, ASK_14_prediction_T3s_map_3.csv , ANN_predictions_T3s_map_3.csv</p> <p>RF_predictions_T5s_map_3.csv, ASK_14_prediction_T5s_map_3.csv , ANN_predictions_T5s_map_3.csv</p> <p>RF_predictions_T10s_map_3.csv, ASK_14_prediction_T10s_map_3.csv , ANN_predictions_T10s_map_3.csv</p> <h3>Supplementary Fig 5</h3> <p><strong>Predictions for the Synthetic event of magnitude 8.05</strong></p> <p>RF_predictions_T2s_map_1240.csv, ASK_14_prediction_T2s_map_1240.csv , ANN_predictions_T2s_map_1240.csv</p> <p>RF_predictions_T1240s_map_1240.csv, ASK_14_prediction_T1240s_map_1240.csv , ANN_predictions_T1240s_map_1240.csv</p> <p>RF_predictions_T5s_map_1240.csv, ASK_14_prediction_T5s_map_1240.csv , ANN_predictions_T5s_map_1240.csv</p> <p>RF_predictions_T10s_map_1240.csv, ASK_14_prediction_T10s_map_1240.csv , ANN_predictions_T10s_map_1240.csv</p> <h3>Supplementary Fig 6</h3> <p><a href="../api/records/10640493/draft/files/NN_layers9North_Palm_Springs_OUT_event_metrics-T_10.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9North_Palm_Springs_OUT_event_metrics-T_10.csv</a>, <a href="../api/records/10640493/draft/files/NN_layers9North_Palm_Springs_OUT_event_metrics-T_10.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Northridge_OUT_event_metrics-T_10.csv</a>, <a href="../api/records/10640493/draft/files/NN_layers9North_Palm_Springs_OUT_event_metrics-T_10.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Landers_OUT_event_metrics-T_10.csv</a>, <a href="../api/records/10640493/draft/files/NN_layers9North_Palm_Springs_OUT_event_metrics-T_10.csv/content" target="_blank" rel="noopener noreferrer">NN_layers9Hector_Mine_OUT_event_metrics-T_10.csv</a></p> <p>NN_layers9North_Palm_Springs_OUT_event_metrics-T_5.csv, NN_layers9Northridge_OUT_event_metrics-T_5.csv, NN_layers9Landers_OUT_event_metrics-T_5.csv, NN_layers9Hector_Mine_OUT_event_metrics-T_5.csv</p> <p>NN_layers9North_Palm_Springs_OUT_event_metrics-T_3.csv, NN_layers9Northridge_OUT_event_metrics-T_3.csv, NN_layers9Landers_OUT_event_metrics-T_3.csv, NN_layers9Hector_Mine_OUT_event_metrics-T_3.csv</p> <p>NN_layers9North_Palm_Springs_OUT_event_metrics-T_2.csv, NN_layers9Northridge_OUT_event_metrics-T_2.csv, NN_layers9Landers_OUT_event_metrics-T_2.csv, NN_layers9Hector_Mine_OUT_event_metrics-T_2.csv</p> <p> </p> <h3>Supplementary Fig 7</h3> <p>ASK-14 GMPE's</p> <p>df_InputDataPred_EQreal_Whittier_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Landers_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Northridge_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_North_Palm_Springs_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Hector_Mine_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p> </p> <p>RF and DNN</p> <p>Prediction_NN_BS_256_9capas_Northridge_3s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Landers_3s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Whittier_3s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_North_Palm_Springs_3s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Hector_Mine_3s_log10_Review_ALL.csv</p> <h3>Suplementary Fig 8</h3> <p>ASK-14 GMPE's</p> <p>df_InputDataPred_EQreal_Whittier_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Landers_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Northridge_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_North_Palm_Springs_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Hector_Mine_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p> </p> <p>RF and DNN</p> <p>Prediction_NN_BS_256_9capas_Northridge_5s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Landers_5s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Whittier_5s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_North_Palm_Springs_5s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Hector_Mine_5s_log10_Review_ALL.csv</p> <p> </p> <h3>Suplementary Fig 9</h3> <p>ASK-14 GMPE's</p> <p>df_InputDataPred_EQreal_Whittier_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Landers_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Northridge_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_North_Palm_Springs_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p>df_InputDataPred_EQreal_Hector_Mine_plus_CS_sites_Scott_REVIEW_ASK_2014.csv</p> <p> </p> <p>RF and DNN</p> <p>Prediction_NN_BS_256_9capas_Northridge_10s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Landers_10s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Whittier_10s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_North_Palm_Springs_10s_log10_Review_ALL.csv</p> <p>Prediction_NN_BS_256_9capas_Hector_Mine_10s_log10_Review_ALL.csv</p> <p> </p> <h3>Suplementary Fig 10</h3> <p><a href="../api/records/10640493/draft/files/HyperParameters_T2s_log10.csv/content" target="_blank" rel="noopener noreferrer">HyperParameters_T2s_log10.csv</a></p>
Fecal standing crop with real time correction using scat detection dogs to estimate population density
<p>Population density is fundamental information for assessing the conservation status of species and support management and conservation actions for in situ populations, but is unknown for many forest species due to their difficulty in detection. The Fecal Standing Crop (FSC) method using detection dogs is an alternative for cryptic or elusive species. An intrinsic difficulty of FSC is the ability to find fecal samples in the field and to estimate the probability of which feces detection is influenced by degradation due to climatic conditions. Our goal was to propose a concurrent FSC parameter estimation using a scat detection dog under different climatic conditions and apply those parameters in a wild deer population. Ten fecal samples of gray brocket deer (Subulo gouazoubira) were placed weekly in a transect (24 x 1200 m) in both dry and wet seasons (12 weeks each). A scat detection dog was then employed to find experimental fecal samples to determine the FSC parameters that were subsequently used with naturally occurring fecal samples (also dog-detected) to estimate population density. The oldest dog found samples were 21 (Dry) and seven (Wet) days after placement, resulting in dog efficiency of 23% (Dry) and 30% (Wet). Adjusting the model to account for efficiency and scat durability, we estimated similar, seasonal, densities of 4.54 individuals km-2 (SD = 2.21, Dry) and 5.52 indiv. km-2 (SD = 3.71, Wet).</p> <p><em>Synthesis and applications:</em> Our results demonstrate that our concurrent methodology corrected the effects of weather and habitat on FSC parameters thereby allowing for accurate population density estimation. Additionally, this method can provide reasonably precise density estimates with a logistically feasible sample size, as demonstrated by simulation. Following our recommendations, this method allows a reliable estimate of population density because it incorporates any influence of study area, dog ability, and climate in fecal sample detection, providing fundamental information for the conservation of many cryptic and elusive species.</p>
Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution
<p>Imperfect detection in field studies on animal abundance, including birds, is common and can be corrected for in various ways. The binomial N-mixture (hereafter binmix) model developed for this task is widely used in ecological studies owing to its simplicity: it requires replicated count results as the input. However, it may overestimate abundance and be sensitive to even small violations of its assumptions. We used a 33-year dataset on the Marsh Tit, Poecile palustris, a sedentary forest passerine, from Białowieża Forest, Poland to validate inference from binmix models by comparing model-estimated abundances to the true number of breeding pairs within the plots, determined by exhaustive population study. The abundance estimates, derived from six springtime (April-May) counts of males on each plot in each year, were highly reliable: 116 out of 132 year-plot estimates (88%) included the true number of pairs within the 95% confidence intervals. Over- and underestimations were thus rare and similarly frequent (9 and 12 cases, respectively), with a tendency to overestimate at low densities and underestimate at high densities. Marsh Tits sing rarely but the frequency of countersinging increases with abundance, leading to non-independence in detections. When accounted for in a submodel for detection, the per-survey number of countersinging events positively affected detection probability but only weakly affected abundance estimates. Simulations further demonstrate that this property, overestimation at low densities and underestimation at high densities, may be a systematic bias of binmix model even if density-dependent detection is absent. While the behaviour of binmix models in specific situations requires more study, we conclude that these models are a valid tool to estimate abundance reliably when intensive population monitoring is not feasible.</p>
Raw data and scripts for "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation"
<p>This package contains all required scripts to generate the simulations and figures from the study "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation" by Volker Hohmann, published in Acta Acustica:</p> <p>The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation<br>Volker Hohmann<br>Acta Acust. 5 56 (2021)<br>DOI: 10.1051/aacus/2021050</p> <p>When referring to this work, please cite the journal paper.</p> <p>Note that additive noise is generated at random, i.e., small differences in the estimation accuracy occur when repeating a simulation. For further details see the journal paper.</p> <p>Thank you for downloading the package. Your comments are very welcome!</p> <p>Method patented: DE Patent DE102021207339B3</p> <p>Author: Volker Hohmann, Carl von Ossietzky Universität Oldenburg, Germany</p>
Data from: How to date a crocodile – estimation of neosuchian clade ages and a comparison of four time-scaling methods
<p>Clade ages within the crocodylomorph clade Neosuchia have long been debated. Molecular and morphological studies have yielded remarkably divergent results. Despite recent advances, there has been no comprehensive relative comparison of the major time calibration methods available to estimate clade ages based on morphological data. We used four methods (cal3, Extended Hedman [EH], smoothed Ghost-Lineage-Analysis [sGLA] and the Fossilised Birth-Death model [FBD]) to date clade ages derived from a published crocodylomorph supertree and a new neosuchian phylogeny. All time-scaling methods applied here agree on the origination of Neosuchia during the Late Triassic/Early Jurassic, and the presence of the major extant eusuchian groups (Crocodyloidea, Gavialoidea, Alligatoroidea, and Caimaininae) by the end of the Late Cretaceous. The number of distinct lineages present before the K/Pg boundary is less certain, with support for two competing scenarios in which Crocodylinae, Tomistominae and Diplocynodontinae either: 1) diverged from other eusuchian lineages before the K/Pg boundary; or 2) evolved during a 'burst' of diversification after the K/Pg event. Cal3 and FBD are identified as the most suitable methods for time-scaling phylogenetic trees dominated by fossil taxa. Extended Hedman estimates are substantially older than the others, with larger standard deviations and a strong vulnerability to taxon sampling and topological changes. sGLA has similar problems and cannot be recommended either. We conclude that a detailed understanding of phylogenetic relationships, tree reconstruction methods, and good taxonomic coverage (in particular the inclusion of the oldest taxon in each clade) is essential when evaluating the results of such dating analyses.</p>
Influence of motor and cognitive tasks on time estimation
<p>The folder<strong> "DataTapisTime" </strong>contains <strong>thirty-two .MAT files.</strong> The .mat files’ name includes the number of subject (“SXX”, from S01 to S16) and the condition (Sitting or Walking). Each matrix contains 7 columns:</p> <ul> <li><strong>Column 1:</strong> Trial number</li> <li><strong>Column 2:</strong> Cognitive task code – 1) Look; 2) Read; 3) Solve Simple; 4) Solve Hard</li> <li><strong>Column 3:</strong> Time interval code – 1) 15 sec; 2) 30 sec; 3) 60 sec; 4) 90 sec; 5) 120 sec</li> <li><strong>Column 4:</strong> Tested duration (in seconds)</li> <li><strong>Column 5:</strong> Exact duration measured online (as a control)</li> <li><strong>Column 6:</strong> If the trial was “look” or “read” task the structure is composed only by “NaN”. In the two solve tasks the structure contains information about the operations to be solved. It contains 6 columns: 1) number1; 2) number2; 3) Participant solution; 4) Reaction time; 5) Correct solution; 6) Response correction (0: Wrong; 1: correct: NaN=not given).</li> <li><strong>Column 7:</strong> Temporal estimation (in seconds)</li> </ul>
Divergence time estimation using ddRAD data and an isolation-with-migration model applied to water vole populations of Arvicola
<p>Molecular dating methods of population splits are crucial in evolutionary biology, but they present important difficulties due to the complexity of the genealogical relationships of genes and past migrations between populations. Using the double digest restriction-site associated DNA (ddRAD) technique and an isolation-with-migration (IM) model, we studied the evolutionary history of water vole populations of the genus <em>Arvicola</em>, a group of complex evolution with fossorial and semi-aquatic ecotypes. To do this, we first estimated mutation rates of ddRAD loci using a phylogenetic approach. An IM model was then used to estimate split times and other relevant demographic parameters. A set of 300 ddRAD loci that included 85 calibrated loci resulted in good mixing and model convergence. The results showed that the two populations of <em>A. scherman</em> present in the Iberian Peninsula split 34 thousand years ago, during the last glaciation. In addition, the much greater divergence from its sister species, <em>A. amphibius</em>, may help to clarify the controversial taxonomy of the genus. We conclude that this approach, based on ddRAD data and an IM model, is highly useful for analyzing the origin of populations and species.</p>
Grape V1 Data: Frequency Estimation and Amplitudes of North American Time Standard Stations
<p>Frequency and amplitude estimation data collected by Grape V1 stations in the Personal Space Weather Station network. </p>
Supplementary material for: Impact of ghost introgression on coalescent-based species tree inference and estimation of divergence time
<p><span>The species studied in any evolutionary investigation generally constitute a small proportion of all the species currently existing or that have gone extinct. It is therefore likely that introgression, which is widespread across the tree of life, involves "ghosts," i.e., unsampled, unknown, or extinct lineages. However, the impact of ghost introgression on estimations of species trees has rarely been studied and is poorly understood. Here, we use mathematical analysis and simulations to examine the robustness of species tree methods based on the multispecies coalescent model to introgression from a ghost or extant lineage. We found that many results originally obtained for introgression between extant species can easily be extended to ghost introgression, such as the strongly interactive effects of incomplete lineage sorting (ILS) and introgression on the occurrence of anomalous gene trees (AGTs). The relative performance of the summary species tree method (ASTRAL) and the full-likelihood method (*BEAST) varies under different introgression scenarios, with the former being more robust to gene flow between non-sister species whereas the latter performing better under certain conditions of ghost introgression. When an outgroup ghost (defined as a lineage that diverged before the most basal species under investigation) acts as the donor of the introgressed genes, the time of root divergence among the investigated species generally was overestimated, whereas ingroup introgression, as commonly perceived, can only lead to underestimation. In many cases of ingroup introgression that may or may not involve ghost lineages, the stronger the ILS, the higher the accuracy achieved in estimating the time of root divergence, although the topology of the species tree is more prone to be biased by the effect of introgression.</span></p>
TrainRuns.jl: an Open-Source Tool for Running Time Estimation - Supplement Data
<p>This additional data contains the initial data and the calculated results for comparing FBS and TrainRuns.jl.</p> <p><strong>File description</strong></p> <ul> <li><em>local.yaml</em>: input parameters for the local train</li> <li><em>freight.yaml</em>: input parameters for the freight train</li> <li><em>running_path.yaml</em>: input parameters for the path</li> <li><em>freight_FBS.csv</em>: export of calculation from FBS for the freight train</li> <li><em>freight_TrainRuns.csv</em>: export of calculation from TrainRun.jl converted in FBS units for the freight train</li> <li><em>freight_diff.csv</em>: the calculated difference between FBS.csv and TrainRuns.csv for the freight train</li> <li><em>local_FBS.csv</em>: export of calculation from FBS for the local train</li> <li><em>local_TrainRuns.csv</em>: export of calculation from TrainRun.jl converted in FBS units for the local train</li> <li><em>local_diff.csv</em>: the calculated difference between FBS.csv and TrainRuns.csv for the local train</li> <li><em>running_path.csv</em>: converted running_path.yaml for displaying</li> <li><em>comparison.tex</em>: LaTeX code for the graph in comparison.pdf</li> </ul> <p><strong>Sources</strong></p> <p>The calculations in FBS were done with the file 'Ostsachsen_V220.railml'. FBS needs a commercial license, which can be purchased. License for 'Ostsachsen_V220.railml' is Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0).<br> The file 'Ostsachsen_V220.railml' can be found at:<br> https://www.railml.org/en/user/exampledata.html (last accessed 2022-06-06 with login) -> "Real world railway examples from professional tools" -> "East Saxony railway network by FBS" -> "Ostsachsen_V220.railml"</p> <p>Other sources are mentioned in the files.</p>
Time perception in human movement: Effects of Speed and Agency on Duration Estimation
<p>This dataset corresponds to the publication of the same title and the following DOI: <a href="https://doi.org/10.1177%2F1747021820979518">https://doi.org/10.1177/1747021820979518</a> </p> <p>The dataset contains 5 excel files whose titles correspond to the variables they contain (background variables, D-prime and C scores, duration estimation during action, duration estimation during perception, expressivity and quality ratings). In all files, descriptions of the variables can be found in the sheet titled "key", and the data can be found in the sheet titled "data".</p>
Data for "Estimation of Return Stroke Velocity by Time Reversal Reconstruction of Channel Feature Points"
<p>In the manuscript entitled “Estimation of Return Stroke Velocity by Time Reversal Reconstruction of Channel Feature Points”, station coordinates of the location system, simulation data, and experimental data can be obtained through the following attachment. These files can be opened by Matlab 2018(or later). The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citations.</p> <p> </p> <p>'IniationParameters_center.mat' is the coordinates of LFLLS stations and strike point (simulation).</p> <p>a) simulation_strike_point: coordinates of the return point (simulation).</p> <p>b) x0, y0, and z0: coordinates of LFLLS stations in x, y, and z directions.</p> <p> </p> <p>1. Simulation data (EE_zd: E-field waveforms (Unit V/m); Ee_zd: Electrostatic component of the E-field waveforms (Unit V/m); Ei_zd: Induction component of the E-field waveforms (Unit V/m); Er_zd: Radiation component of the E-field waveforms (Unit V/m); T: times corresponding to the E-field waveforms; vv: RS velocity condition; tort_x, tort_y, and tort_z: Coordinates of segmented channels).</p> <p>'Simulation_Vertical_channel_Ez_V1.mat' is the vertical channel E-field waveforms calculated under the RS velocity condition V1.</p> <p>'Simulation_Vertical_channel_Ez_V2.mat' is the vertical channel E-field waveforms calculated under the RS velocity condition V2.</p> <p>'Simulation_Vertical_channel_Ez_V3.mat' is the vertical channel E-field waveforms calculated under the RS velocity condition V3.</p> <p>'Simulation_Inclined_channel_Ez.mat' is the inclined channel E-field waveforms calculated under the RS velocity condition V1.</p> <p>'Simulation_Tortuous(randomly)_channel_Ez.mat' is the tortuous channel (randomly) E-field waveforms calculated under the condition of RS velocity constant.</p> <p>'Simulation_Tortuous_channel_Ez.mat' is the tortuous channel E-field waveforms calculated under the RS velocity condition V1.</p> <p> </p> <p>2. Experimental data</p> <p>'2020-08-09-002418-905.5ms(0.4)-siteidx(12346).mat' is the E-field original waveforms of -CG002418.RS2.</p> <p>a) wave: E-field original waveforms of -CG002418.RS2 (D.U.).</p> <p>b) time: times corresponding to the E-field waveforms.</p> <p> </p> <p>3. Figure data</p> <p>This folder contains the .fig format files of Figure 3 ~ 10 in the paper.</p>
Bayesian Estimation on Microbial Electrochemical Time Profiles obtained by a High-Throughput Bioelectrochemical Device
<p>Bioelectrochemical systems (BESs) are attracting much attention, but the mechanisms are not yet fully clarified. One of the issues for the BESs research is the lack of a high-quality database that is developed through well-controlled experiments with a high-throughput data-collecting system. Here, we developed a new high-throughput potentiostat with 96 well plates with silk-screen-printed electrodes. With this potentiostat, we obtained 576 time-profiles of microbial current production for viewing the complex landscape of the parameters, namely, the redox mediator concentration and the electrode potential. This dataset contains the following materials:</p> <ul> <li>Current production profiles for <em>Shewanella </em>(Data with average of four data sets for impact of mediators.zip)</li> <li>Summary of the calculated values (statistics summary.csv)</li> <li>Python code for calculating the maximum slope (Slope EF.py)</li> <li>Python codes for Bayesian estimation (2D Bayesian Optimization mapping.py and 2D Bayesian Optimization_slice.py)</li> </ul>
Microdata on vector abundance and IRS quality assurance (Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study)
<p>This repository contains the microdata on vector abundance and quality assurance of indoor residual spraying (IRS) that was used to estimate the impact of IRS on sandfly abundance and incidence of visceral leishmaniasis (VL) in India, as described in the paper "Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study" by Coffeng et al (<a href="https://doi.org/10.1016/S1473-3099(24)00420-1">https://doi.org/10.1016/S1473-3099(24)00420-1</a>). These data were collected as part of a BMGF-funded project led by dr. Michael Coleman at the Liverpool School for Tropical Medicine, as described in an earlier paper by Deb et al (<a href="https://doi.org/10.1371/journal.pntd.0009101">https://doi.org/10.1371/journal.pntd.0009101</a>).</p> <p>This repository does not include microdata on VL cases as these are owned by India's National Center for Vector Borne Disease Control (NCVBDC, <a href="https://ncvbdc.mohfw.gov.in/" target="_blank" rel="nofollow noreferrer noopener">https://ncvbdc.mohfw.gov.in/</a>).</p>
Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads
<p>This map contains the data used in the study 'Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads'. In this study a new method is presented to estimate average diffuse aquifer recharge of water table aquifers in temperate climates using time series analysis of water table level fluctuations. Recharge is estimated from time series models fitted to observed heads under the additional constraint that the seasonal harmonic of the observed head is reproduced as the sum of the transformed seasonal harmonics present in precipitation, evaporation, and pumping. The method is applied to measured heads obtained from piezometers situated on and around the ice-pushed sand ridge of Salland in the Netherlands. Results are compared with recharge estimates based on the saturated zone chloride mass balance.</p> <p>The data are ascii text files saved in the following compressed maps:<br> - Groundwater_chloride_concentration.zip<br> - Groundwater_head_time_series.zip<br> - Makkink_reference_evaporation_time_series.zip<br> - Measured_precipitation_time_series.zip<br> - Precipitation_chloride_concentrations.zip<br> - Pumping_time_series.zip</p> <p>For further details, please refer to the readme files contained in each submap.</p>
Dataset for: Estimating heating times in periodically driven quantum many-body systems via avoided crossing spectroscopy
<p>The dataset includes data and Python scripts for all figures in the corresponding article.</p> <p>The data is organized into self-contained folders for each individual figure (possibly consisting of multiple subplots) and should be accessible using standard libraries for scientific Python. The included scripts can serve as a guide for accessing and plotting the data.</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.