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677 results for “time of use”
GNSS-IR data for "Real-time water levels using GNSS-IR: a potential tool for flood monitoring"
<p>Organised SNR data used for GNSS-IR analysis in the article "Real-time water levels using GNSS-IR: a potential tool for flood monitoring" by David Purnell, Natalya Gomez, William Minarik and Gregory Langston.</p><p> </p><p>The directories 'rv3s' and 'sjdlr' contain SNR data corresponding to sites Trois-Rivières and Saint-Joseph-de-la-Rive, respectively.</p><p> </p><p>Software for processing the data can be found at: https://github.com/purnelldj/gnssir_rt</p><p> </p><p>SNR data is given as text files in the format specified here except for columns 4+:</p><p>https://gnssrefl.readthedocs.io/en/latest/pages/file_structure.html#the-snr-data-format</p><p>columns:</p><p>1. sat PRN with an offset such that GLONASS satellites are between 100-200 and Galileo are between 200-300</p><p>2. satellite elevation (degrees)</p><p>3. azi is satellite azimuth (degrees)</p><p>4. GPS time (seconds since 1980)</p><p>5. L1 SNR</p>
Data for "Characterizing quantum gases in time-controlled disorder realizations using cross-correlations of density distributions"
<p>Here, data sets belonging to the preprint "Characterizing quantum gases in time-controlled disorder realizations using cross-correlations of density distributions" are uploaded.</p> <p>The file contains a folder for each figure in the manuscript.</p>
Spectroradiometry with sub-microsecond time resolution using multianode photomultiplier tube assemblies
<p>Raw data (e.g. MA-PMT timeseries), and processed data (e.g. temperature and emissivity fits) for the submitted manuscript. The python script shows a simple example of how to load and plot data from the "..._processed.json" files. The spreadsheet called "Descriptions of json file parameters" explains the meaning of parameters in all the json files for standard experiments. Parameters in "non-standard" json files (e.g. system response .json files; Fig18_accuracy-summary.json) are intended to be self-explanatory.</p>
Dataset of "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy"
<p>Dataset for the article entitled "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy" </p> <p> </p> <p>Abstract:</p> <p> </p> <p>Thermal Therapies treat tumors by means of heat, greatly reducing pain, post-operation complications, and cost as compared to traditional methods. Yet, effective tools to avoid under- or over-treatment are mostly needed, to guide surgeons in laparoscopic interventions.<br>In this work, we investigated the temperature-dependent optical signatures of ex-vivo calf brain, lung, and heart tissues, based on the reduced scattering and absorption coefficients in the near-infrared spectral range (657 to 1107 nm). These spectra were measured by time domain diffuse optics, applying a step-like spatially homogeneous thermal treatment at 43 °C, 60 °C, and 80 °C.<br>We found three main increases in scattering spectra, possibly due to the denaturation of collagen, myosin, and proteins secondary structure.<br>After 75 °C, we found the rise of two new peaks at 770 and 830 nm in the absorption spectra due to the formation of a new chromophore, possibly related to hemoglobin or myoglobin.<br>This research marks a significant step forward in controlling thermal therapies with diffuse optical techniques by identifying several key markers of thermal damage. This could enhance the ability to monitor and adjust treatment in real-time, promising improved outcomes in tumor therapy.</p> <p> </p> <p> </p> <p>Authors:</p> <p>ALESSANDRO BOSSI , LEONARDO BIANCHI , PAOLA SACCOMANDI , AND ANTONIO PIFFERI<br>Politecnico di Milano</p> <p> </p> <p> </p> <p><a href="https://opg.optica.org/boe/fulltext.cfm?uri=boe-15-4-2481&id=548108" target="_blank" rel="noopener">Link to the article</a></p>
The Effect of a New Generation of Ankle Foot Orthoses on Sloped Walking in Children with Hemiplegia Using the Gait Real Time Analysis Interactive Lab (GRAIL)
<div>The dataset includes the kinematics and kinetics of gait uphill (+10 deg), level ground (0 deg), and downhill (-5 deg) in children with unilateral cerebral palsy who underwent a single session of walking using immersive virtual reality (GRAIL system by Motek), while wearing traditional ankle-foot orthosis (oldAFOs) and a new generation AFO (newCAMOt1).</div> <div>In column A of the file, you will find the patient ID, the type of orthosis used, and the walking condition (e.g., P01_oldAFO_flat0001 indicates that patient 01 performed the walking trial on level ground with the traditional orthosis).</div> <div>In column B of the file, you will find the laterality (R = right, L = left), the name of the analyzed variable, and the stride number (e.g., L_Moment Ankle Flex_step1 means that we are considering the moment at the left ankle of the first left stride).</div> <div>From column C to column CY, you will find the values at the 101 temporal instants of the analyzed variable.</div>
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>
Exploring relationships between time of day and pollinator activity in the context of pesticide use - data
<p>Pesticide exposure can be harmful to insect pollinators and the ecosystem services they provide. As pesticide guidelines warn against applying such products when pollinators are active, it is important to determine how pollinator activity changes with the time of day and when the most appropriate time is to spray these chemicals. We walked transects from sunrise to sunset in oilseed rape (<em>Brassica napus</em> L.) fields in Ireland to capture the abundance of honeybees, bumblebees, solitary bees, and hoverflies across daylight hours. We also recorded the activity of representative species from the three bee groups at their nests across similar time periods to compare with field observations. Peak pollinator abundance was in the mid-afternoon with fewer individuals in the early morning and late evening for all groups. At the nest, we observed patterns of activity that broadly reflected field abundance but indicated that bees are active earlier and later than those observed on the crop. However, there were differences between pollinator groups. Overall, honeybee and solitary bee abundance and activity were found to peak in the middle of the day, while bumblebee abundance and activity were more consistent throughout daylight hours. Hoverflies were relatively abundant in the morning and increased in number towards the late afternoon and early evening. Our results confirm current recommendations that pesticide application should be avoided in the middle of the day when pollinators are most active. However, the diversity of responses within and between pollinator groups to time of day should be accounted for when shaping guidelines, and clearly defining optimal pesticide application timings for end users is difficult and needs further consideration as it will vary between regions and crops. Further research should also explore how time impacts pesticide efficacy and exposure of pollinators to residues post-application to allow full evaluation of how practical and beneficial timing of application may be when aiming to protect pollinators from pesticide exposure.</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>
Video Supplement for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive contains the video supplement for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques. The file contains an animation of the V2.1 and NRT average retrieved extinction coefficient between 19.5--21.5 km at 997 nm for the 2024 Ruang eruptions.</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>
Computation Time, Searched Nodes and Path Length for Navigation Using Improved A-Star, Directional ORCA and FLC-ORCA
<p>Naivation simulation using Improved A*, Directional ORCA and Novel FLC-ORCA method. The computational times, searched nodes and path length are recorded for comparison. A simulation study for Robotic Navigation Aid (RNA)</p>
Rethinking Sea-Level Projections using Families and Timing Differences
<p>Dataset for publication "Rethinking Sea-Level Projections using Families and Timing Differences"</p>
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>
Playing with time and holography by using two mobile whatsapp
<p>If one puts two mobiles near each other and connect them by whatsapp , many images of events between two mobiles could be formed. Any image corresponds to a time. Sometimes images from future are formed before images from past. For electrons within screen of mobile time states return. This could be a holography of time loop.</p>
Highly time-resolved chemical speciation and source apportionment of organic aerosol components in Delhi, India, using extractive electrospray ionization mass spectrometry
<p>In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution,<br> especially during winters. Comprehensive knowledge of the composition and sources of the organic aerosol<br> (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating<br> effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary<br> OA (POA) and showed that secondary OA (SOA) played a major role but were unable to resolve specific SOA<br> sources. We address the latter through the first field deployment of an extractive electrospray ionization timeof-<br> flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer<br> (AMS). Measurements were conducted during the winter of 2018/19, and positive matrix factorization (PMF)<br> was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded<br> three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning<br> OA (BBOA-1 and BBOA-2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LOOOA).<br> On average, 40%of the total OA mass was apportioned to the secondary factors. The SOA contribution to<br> total OA mass varied greatly between the daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %,<br> 21:00–04:00 LT). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA<br> sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary<br> biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin:<br> aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in<br> the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA-dominated factors were related to the<br> total AMS SOA (i.e. MO-OOA C LO-OOA) by multiple linear regression (MLR). Aromatic SOA was the major<br> SOA component during the daytime, with a 55.2% contribution to total SOA mass (42.4% contribution to total<br> OA). Its contribution to total SOA, however, decreased to 25.4% (7.9% of total OA) during the nighttime. This<br> factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA<br> accounted for 18.4% of total SOA mass (14.2% of total OA) during the daytime and 36.1% of total SOA mass<br> (11.2% of total OA) during the nighttime. Aged biomass burning and mixed urban SOA accounted for 15.2%<br> and 11.0% of total SOA mass (11.7% and 8.5% of total OA mass), respectively, during the daytime and 15.4%<br> and 22.9% of total SOA mass (4.8% and 7.1% of total OA mass), respectively, during the nighttime. A simple<br> dilution–partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed daytime<br> concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged<br> biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production,<br> likely from the oxidation of locally emitted volatile organic compounds (VOCs). In contrast, biogenic SOA was<br> related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study<br> show that in Delhi, the nighttime high concentrations are caused by POA emissions led by traffic and biomass<br> burning and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction.<br> Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the<br> daytime suggests an increased OA toxicity and health-related consequences for the general public.</p> <p>This repository has excel file corresponding to each figures presented in main text published version.</p>
The conductivity profile of Earth-ionosphere cavity used in the paper "Finite-difference time-domain analysis of ELF radio wave propagation in the spherical Earth-ionosphere waveguide and its validation based on analytical solutions" by Volodymyr Marchenko, Andrzej Kulak, Janusz Mlynarczyk
<p>The file "Marchenko_FDTD_Paper_Conductivity_Profile.dat" contains the conductivity profile of Earth-ionosphere cavity. The first column provides the altitude (in km) and the second column provides the conductivity (in S/m).</p>
Predicting past and future SARS-CoV-2-related sick leave using discrete time Markov modelling
<p><strong>Background: </strong>Prediction of SARS-CoV-2-induced sick leave among healthcare workers (HCWs) is essential for being able to plan the healthcare response to the epidemic.</p> <p><strong>Methods: </strong>During first wave of the SARS-Cov-2 epidemic (April 23<sup>rd </sup>to June 24<sup>th</sup>, 2020), the HCWs in the greater Stockholm region in Sweden were invited to a study of past or present SARS-CoV-2 infection. We develop a discrete time Markov model using a cohort of 9449 healthcare workers (HCWs) who had complete data on SARS-CoV-2 RNA and antibodies as well as sick leave data for the calendar year 2020. The one-week and standardized longer term transition probabilities of sick leave and the ratios of the standardized probabilities for the baseline covariate distribution were compared with the referent period (an independent period when there were no SARS-CoV-2 infections) in relation to PCR results, serology results and gender.</p> <p><strong>Results:</strong> The one-week probabilities of transitioning from healthy to partial sick leave or full sick leave during the outbreak as compared to after the outbreak were highest for healthy HCWs testing positive for large amounts of virus (ratio: 3.69, (95% confidence interval, CI: 2.44-5.59) and 6.67 (95% CI: 1.58-28.13), respectively). The proportion of all sick leaves attributed to COVID-19 during outbreak was at most 55% (95% CI: 50%-59%).</p> <p><strong>Conclusions: </strong>A robust Markov model enabled use of simple SARS-CoV-2 testing data for quantifying past and future COVID-related sick leave among HCWs, which can serve as a basis for planning of healthcare during outbreaks.</p>
Mitigating ecosystem service tradeoffs in rangelands by using grazing duration and timing to manage water quality
<p>1. Mitigating ecosystem service (ES) tradeoffs is a key management goal in locations where stakeholders value different and potentially conflicting ecosystem services (ESs). However, studies are not often designed to examine how local management actions address ES tradeoffs, and therefore do not provide options that can alleviate conflict.</p> <p>2. In semi-arid rangelands, we examined the potential for managers to mitigate tradeoffs between livestock production and water quality. To move away from solutions that offer cattle removal as a singular management strategy, we examined how cattle presence, plus two elements of rotational grazing - the length of time cattle spend on rangeland (i.e., duration), and the season grazed (i.e., timing), affected stream Escherichia coli (E. coli concentrations). We also modeled how grazing duration and timing affected the ability to meet regulatory benchmarks for water quality throughout a grazing season.</p> <p>3. Grazing duration controlled the length of time E. coli concentrations were high in streams. In short- and medium-duration systems, E. coli concentrations were high for shorter periods of time than in long-duration systems, resulting in fewer violations of national and state water quality standards.</p> <p>4. Stream E. coli concentrations showed a consistent seasonal pattern, starting low in spring, peaking in summer, and declining towards fall. Thus, grazing during spring or fall, rather than in summer, reduced the number of days that E. coli levels exceeded water quality standards.</p> <p>5. Our results suggest that reducing the grazing duration and shifting its timing are complementary strategies that can mitigate the tradeoffs between livestock grazing and water quality without fencing-off riparian areas or removing cattle from pastures with streams.</p> <p>6. Synthesis and applications. In this study, we found grazing duration and timing can be used as tools to mitigate ES tradeoffs between cattle production and water quality in rangeland streams. Shorter grazing durations reduced the number of days <i>E. coli</i> levels were above regulatory limits, as did grazing that occurred either early or late in the season. These results support the idea that rotational grazing can be an effective strategy to manage water quality in semi-arid rangelands. They also highlight the need for more grazing studies that incorporate gradients of duration and timing into study designs.</p>
Training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series
<p>This dataset contains training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series. The data have been derived from Sentinel-5P total column carbon monoxide observations, using the offline processing stream.</p> <p><strong>Preprocessing</strong></p> <p>The following operations have been applied on the original S5P imagery:</p> <ol> <li>Images have been resampled to 0.1 by 0.1 degree spatial resolution</li> <li>Pixels with quality assessment value less than or equal to 0.5 have been set to NA</li> <li>Images have been aggregated by day of observation</li> <li>Images have been cropped to -60 to 60 degrees latitude</li> <li>Images have been devided into spatiotemporal blocks of size 128 x 128 pixels and 16 days</li> </ol> <p>Imagery has been recorded between 2021-01-01 and 2021-11-25. Notice that both the training and the validation blocks have been randomly sampled from all available blocks.</p> <p><br> <strong>Data Format and Naming Conventions</strong></p> <p>Input and output data blocks are stored as GeoTIFF files, where bands represent time. Notice the following file naming conventions:</p> <ul> <li>Files starting with <em>X</em> represent input measurements for training, where artificial gaps have been added.</li> <li>Files starting with <em>Y</em> represent true measurements without artificially added gaps (but still containing gaps in many cases).</li> <li>Binary masks of input data where all pixels with valid measurements are 1 and others 0 are stored in files whose name starts with <em>MASK</em></li> <li>Files starting with <em>VALMASK</em> contain a binary mask where only pixels that are available in Y but not in X are 1. The latter is used for validation on artificially removed pixels only.</li> </ul> <p>Numbers in filenames encode spatial and temporal block indexes.</p> <p>In addition, the dataset contains prediction of the validation blocks from different models in the `predictions` directory. The subfolders contain output from different models:</p> <ul> <li>mean refers to simple block-wise mean predictions.</li> <li>timeseries refers to simple linear time series interpolation.</li> <li>gapfill refers to the method proposed in [1].</li> <li>stmra refers to the method proposed in [2].</li> <li>STpconv refers to predictions passed on an artificial neural netowork with three-dimensional partial convolutions.</li> </ul> <p><strong>References</strong></p> <p>[1] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., & Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2841-2853.</p> <p>[2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.</p>
SESMG scenario-files of the study "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution"
<p>This dataset contains model scenario-files belonging to the publication "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution".</p> <p>The individual scenarios can be executed and evaluated with the "Spreadsheet Energy System Model Generator" (<a href="https://github.com/chrklemm/SESMG">SESMG</a>) <a href="https://github.com/chrklemm/SESMG/tree/v0.4.0rc1">v0.4.0rc1</a></p> <p>The respective file names indicate to which model run mentioned in the main study the scenario-files belong. For model runs for which no sepparate scenario file exists, the scenario "reference.xlsx" with adjusted SESMG settings was used.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.