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Source data for the publication "Longitudinal coupling between a Si/SiGe double quantum dot and an off-chip TiN resonator"
<p>This repository contains data reported in the publication "Longitudinal coupling between a Si/SiGe double quantum dot and an off-chip TiN resonator."</p>
Aneuploid embryonic stem cells drive teratoma metastasis: source data
<h3>The dataset of "<strong>Aneuploid embryonic stem cells drive teratoma metastasis</strong>"</h3><p>1. Pathway enrichment of scRNA-seq.csv: a table containing the pathway enrichment results. (<strong>Supplementary fig10 d</strong>)</p><p>2. Bulk_RNA_processed.RDS: a Seurat object containing the results of bulk RNA-seq. (<strong>Supplementary fig11 h-l</strong>)</p><p>3. Relative cell abundance of ES captured by scRNA-seq.csv: A table containing the relative abundance of ES sub-populations in all ES cells for each sample. (<strong>fig5 c</strong>)</p><p>4. WES_out.RDS: an R object containing the results of WES processed by maftools. (<strong>fig2 b-g</strong>)</p><p>5. bulk_RNA_raw_matrix.RDS: an R object containing the raw count matrix of bulk RNA-seq samples. (<strong>Supplementary fig11 h-l</strong>)</p><p>6. scRNA-seq DEGs -- ES_stem vs. ES_Ori(WT).csv: a table containing the results of DEG analysis. (<strong>fig5 f</strong>)</p><p>7. scRNA-seq DEGs -- ES_stem vs. ES_Ori(AC).csv: a table containing the results of DEG analysis. (<strong>fig5 f</strong>)</p><p>8. pseudo time DEGs of scRNA-seq.csv: DEGs that change as ES differentiation. (<strong>fig5 e</strong>)</p><p>9. scRNA-seq_meta_info.tsv.gz: the meta information of scRNA-seq data. (<strong>fig5 and Supplementary fig10</strong>)</p><p>10. scRNA-seq_raw_matrix.mtx.gz: the raw count matrix of scRNA-seq data. (<strong>fig5 and Supplementary fig10</strong>)</p>
Source data for: All figures and tables included in NCOMMS-23-03997A
<p>Tools enable animals to exploit and command new resources. However, the neural circuits underpinning tool use and how neural activity varies with an animal's tool proficiency, are only known for humans and some other primates. We use 18F-fluorodeoxyglucose positron emission tomography to image the brain activity of naïve vs trained American crows (<em>Corvus brachyrhynchos</em>) when presented with a task requiring the use of stone tools. As in humans, talent affects the neural circuits activated by crows as they prepare to execute the task. Naïve and less proficient crows use neural circuits associated with sensory- and higher-order processing centers (the mesopallium and nidopallium), while highly proficient individuals increase activity in circuits associated with motor learning and tactile control (hippocampus, tegmentum, nucleus basorostralis, and cerebellum). Greater proficiency is found primarily in adult female crows and may reflect their need to use more cognitively complex strategies, like tool use, to obtain food.</p>
Data for: Automotive braking is a source of highly charged aerosol particles
<p>Although the last several decades have seen a dramatic reduction in emissions from vehicular exhaust, non-exhaust emissions (e.g., brake and tire wear) represent an increasingly significant class of traffic-related particulate pollution. Aerosol particles emitted from the wear of automotive brake pads contribute roughly half of the particle mass attributed to non-exhaust sources, while their relative contribution to urban air pollution overall will almost certainly grow, coinciding with vehicle fleet electrification and the transition to alternative fuels. To better understand the implications of this growing prominence, a more thorough understanding of the physicochemical properties of brake wear particles (BWPs) is needed. Here we investigate the electrical properties of BWPs as emitted from ceramic and semi-metallic brake pads. We show that up to 80% of BWPs emitted are electrically charged, and demonstrate a dependence of charge state on particle size and charge polarity. We find that brake wear produces both positive and negative charged particles that can hold in excess of 30 elementary charges, and show evidence that more negative charges are produced than positive. Our results will provide insights into the currently limited understanding of how BWPs behave in the atmosphere, including future investigations into their atmospheric lifetimes and potential climatic relevance. In addition, our study will inform future efforts to remove BWP emissions before entering the atmosphere by taking advantage of their electric charge.</p>
Data from: Geographic source of bats killed at wind-energy facilities in the eastern United States
<p>Bats subject to high rates of fatalities at wind-energy facilities are of conservation concern, but the impact on broader bat populations is difficult to assess. One reason is the poor understanding of the geographic source of individual fatalities and whether they constitute local resident individuals or migrants. Here, we used stable hydrogen isotopes, trace elements and species distribution models to determine the summer geographic origins of three different bat species (<em>Lasiurus borealis</em>, <em>L. cinereus</em>, and <em>Lasionycteris noctivagans</em>) killed at wind-energy facilities in Ohio and Maryland in the eastern United States. In Ohio, 58.4%, 78.7%, and 97.8% of all individuals of <em>L. borealis</em>, <em>L. cinereus</em>, and <em>L. noctivagans</em>, respectively, lacked evidence of movement and were likely residents. In contrast, in Maryland 22.7%, 62.9% and 72.7% of these same species were classified as residents. Our results suggest that a substantial portion of bats killed at a given wind facility are likely derived from resident populations. Finally, there is variation in the proportion of residents killed between seasons for some species and evidence of philopatry to summer roosts. Overall, these results indicate that impact of wind-energy facilities on resident bat populations may be greater than previously appreciated, but this impact is likely to vary across species and sites. Similar studies should be conducted across a boarder geographic scale to understand the impacts on bat populations from wind-energy facilities.</p>
Replication Data for: Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020
<p>Model and observation data, used to prepare the figures in the main text, are submitted at this repository.</p>
Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy"
<p>Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy", <em>Nat Methods</em> (2024), https://doi.org/10.1038/s41592-024-02275-w.</p>
Analysis of gene expression in the postmortem brain of neurotypical Black Americans reveals contributions of genetic ancestry: Source and Supplementary Data
<p><em><strong>Source and Supplementary data for AANRI manuscript</strong></em></p>
Data from: Odor source distance is predictable from time-histories of odor statistics for large scale outdoor plumes
<p>Odor plumes in turbulent environments are intermittent and sparse. Lab-scaled experiments suggest that information about the source distance may be encoded in odor signal statistics, yet it is unclear whether useful and continuous distance estimates can be made under real-world flow conditions. Here we analyze odor signals from outdoor experiments with a sensor moving across large spatial scales in desert and forest environments to show that odor signal statistics can yield useful estimates of distance. We show that achieving accurate estimates of distance requires integrating statistics from 5-10 seconds, with a high temporal encoding of the olfactory signal of at least 20 Hz. By combining distance estimates from a linear model with wind-relative motion dynamics, we achieved source distance estimates in a 60x60 m<sup>2</sup> search area with median errors of 3-8 meters, a distance at which point odor sources are often within visual range for animals such as mosquitoes.</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>
Extraembryonic gut endoderm cells undergo programmed cell death during development (source data and custom code)
<p>Despite a distinct developmental origin, extraembryonic cells in mice contribute to gut endoderm and converge to transcriptionally resemble their embryonic counterparts. Notably, extraembryonic progenitors share a non-canonical epigenome, raising several pertinent questions, including whether this landscape is reset to match the embryonic regulation and if these cells persist into later development. Here, we developed a two-color lineage tracing strategy to track and isolate extraembryonic cells over time. We find that extraembryonic gut cells display substantial memory of their developmental origin including retention of their original DNA methylation landscape and resulting transcriptional signatures. Furthermore, we show that extraembryonic gut cells undergo programmed cell death and neighboring embryonic cells clear their remnants via non-professional phagocytosis. By midgestation, we no longer detect extraembryonic cells in the wild type gut while they persist and differentiate further in p53 mutant embryos. Our study provides key insights into the molecular and developmental fate of extraembryonic cells inside the embryo.</p>
Source data for: Electrochemically controlled blinking of fluorophores for quantitative STORM imaging
<p>Stochastic optical reconstruction microscopy (STORM) allows widefield imaging with single-molecule resolution by calculating the coordinates of individual fluorophores from the separation of the fluorophore emission in both time and space. Such separation is achieved by photoswitching the fluorophores between a long-lived OFF state and an emissive ON state. While STORM can image single molecules, molecular counting remains challenging due to undercounting errors from photobleached or overlapping dyes and overcounting artifacts from the repetitive random blinking of the dyes. Here, we show that fluorophores can be switched electrochemically for STORM imaging (EC-STORM), with excellent control over the switching kinetics, duty cycle, and recovery yield. Using EC-STORM, we demonstrate molecular counting by using electrochemical potential to control the photophysics of dyes. The random blinking of dyes is suppressed by a negative potential but the switching ON event can be activated by a short pulsed positive potential, such that the frequency of ON events scales linearly with the number of underlying dyes. We also demonstrate the EC-STORM of tubulins in fixed cells with a spatial resolution as low as ~28 nm and counting of single Alexa 647 fluorophores on various DNA nanoruler structures. This control over fluorophore switching will enable EC-STORM to be broadly applicable in super-resolution imaging and molecular counting.</p>
Source Data File – Dual role of the peptide loading complex as proofreader and limiter of MHC-I presentation – PNAS 2023-21600
<p>Source Data Files</p>
Source Data for all Figures
<p>Underlying data of Figures in paper "Efficient Scaling of Large Language Models with Mixture of Experts and 3D Analog In-Memory Computing" authored by Büchel et al.</p>
Data from: Global Fjords Are Minor Sources Of Nitrous Oxide To The Atmosphere
<ol> <li>The study sites included six different fjords located in an area spanning from 56.6˚N to 66.6˚N and from −39.0˚W to 13.7˚E.</li> <li>All data were collected throughout five different cruises from April to July 2023, with instruments installed on R/V Skagerak (University of Gothenburg). N2O dissolved in the surface water was measured continuously via Cavity Enhanced Absorption Spectroscopy using a LI-7820 N2O/H2O trace gas analyzer (LI-COR Biosciences). The LI-7820 produced high-precision N2O (ppb) measurement data every second, response time of 0 to 330 ppb ≤ 2 seconds and maximum drift of < 1 ppb per 24-hour period.</li> <li> Nitrous oxide measurements (>1800 in each fjord) were integrated over 30 min and adjusted to account for gas exchange equilibration over the closed loop and for the ~10 min time lag (delayed response time of ~5 min from the exchanger to the gas detector and additional lag of ~5 min from sea to exchanger).The ship was equipped with a -4H-FerryBox (JENA Engeneering GmbH, Germany), an automatic flow-through system with various sensors measuring hydrographic and biochemical parameters such as <u>s</u>alinity, temperature, dissolved oxygen (O2), pH, chlorophyll, and turbidity (-4H-JENA engineering GmbH, n.d.). All potentiometric FerryBox pH (EGA150, Meinsberg) measurements on the NBS scale were corrected by a constant offset (ΔpH = -0.451 ±0.016) based on the concurrent spectrophotometric pH measurements (Müller et al., 2018) during the Greenlandic and Icelandic cruises. Water was pumped through a subsurface-inlet and circulates with a speed of 1 m<u> </u>s−1 in the system. Bubbles and particles are removed by a debubbling unit (Ferry- Box Task Team, n.d.). Windspeed (m s−1) measurements were recorded with an onboard sonic anemometer (Airmar PB200) mounted approximately 19 meters above the water line and then logarithmically corrected to 10 meters above water line.</li> <li>Additionally, water samples for dissolved nitrates and nitrites (NOx-) and ammonium (NH4+) concentrations were collected along the survey transects using Niskin-rosette bottles from a depth of ~3 m. Dissolved nutrient samples were collected by filtering sample water through cellulose acetate filters (0.45 μm) into pre-rinsed 12 mL polypropylene vials before immediate freezing until laboratory analysis. Nutrient samples for dissolved NH4+, NO3− and NO2−<u> </u>were filtered through pre-rinsed cellulose acetate filters (0.45 μm, Sartorius) and frozen at −20 °C until segmented flow analysis (QuAAtro, XY-3 Sampler, Seal Analytical 2015; detection limits and precisions 0.2 μM and 7% for NH4+, 0.05 μM and 7% for NO3− and 0.02 μM and 7% for NO2−).</li> <li>N2O saturation values were calculated as the ratio between concentrations of dissolved N2O in seawater and the corresponding computed concentrations in the atmosphere (Walter et al., 2004). Diffusive sea-air fluxes (f) were measured according to the formula: " f=(<em>p_wate</em>r- <em>p_air</em> ) α k " where <em>p_water</em> is the partial pressure of N2O in the surface water layers calculated after correcting for the water vapor partial pressure in the equilibrated headspace and local atmospheric pressure. <em>p_air</em> is the partial pressure of N2O in air. For pair the global values from NOAA were used (Lan, 2024). Both partial pressures are expressed in natm. α is the solubility coefficient that was calculated from temperature and salinity using equations of Weiss and Price (1980). <em>k</em> is the gas transfer velocity calculated from the wind-based empirical model by (Wanninkhof, 2014). The k values were calculated according to the formula: "k = 0.251 <em>U</em>^2 〖(<em>Sc</em>/660)〗^(-0.5)" where <em>U </em>is the wind speed. We used in situ daily averaged wind speed measurements. <em>Sc</em> is the Schmidt number, which is water kinematic viscosity divided by the molecular diffusion coefficient of N2O (Wanninkhof, 2014). We used 4H–FerryBox temperature observations from the same water line as for the continuous N2O measurements. N2O fluxes at the sea-air interface are expressed in µg N2O m−2 day−1.</li> </ol> <p> </p> <p>Refeferences:</p> <p>Lan, X., Thoning, K.W., Dlugokencky, E.J.:. (2024). Trends in globally-averaged CH4, N2O, and SF6 determined from NOAA Global Monitoring Laboratory measurements. Version 2024-10 <u><a>https://doi.org/</a></u> <u><a>https://doi.org/10.15138/P8XG-AA10</a></u></p> <p>Müller, J. D., Schneider, B., Aßmann, S., & Rehder, G. (2018). Spectrophotometric pH measurements in the presence of dissolved organic matter and hydrogen sulfide. Limnology and Oceanography: Methods, 16(2), 68-82.</p> <p>Wanninkhof, R. (2014). Relationship between wind speed and gas exchange over the ocean revisited. Limnology and Oceanography: Methods, 12(6), 351-362.</p> <p>Walter, S., Bange, H. W., & Wallace, D. W. (2004). Nitrous oxide in the surface layer of the tropical North Atlantic Ocean along a west to east transect. Geophysical Research Letters, 31(23).</p> <p>Weiss, R., & Price, B. (1980). Nitrous oxide solubility in water and seawater. Marine chemistry, 8(4), 347-359.</p> <p> </p>
An enhanced single Gaussian point continuum finite element formulation using automatic differentiation: Source code and data
<p>This dataset contains the source code and the data with an example of uniaxial strain of an enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</p> <p> </p> <p>This contribution presents a low-order 3D finite element formulation with hourglass stabilization using automatic differentiation. Here, the former Q1STc element formulation is enhanced by an approximation-free computation of the inverse of the Jacobian. The improved version is termed "Q1STc+."</p> <p> </p> <p>The corresponding publication is:</p> <p><br>Pacolli, N., Awad, A., Kehls, J., Sauren, B., Klinkel, S., Reese, S., Holthusen, H.<br><em>An enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</em></p> <p>Standalone_Elementroutine: <em>Q1STc+_Codes</em> contains:</p> <ul> <li><strong>main.f90</strong>: Standalone routine for local uniaxial strain test</li> <li><strong>Makefile</strong>: Makefile to create executable "Q1STc+"</li> <li><strong>elem40.f90</strong>: Element routine "Q1STc+" with elem_sub.f90 as the subroutine written in AceGen</li> <li><strong>mat52.f90</strong>: Elasto-plastic material routine with all subroutines written in AceGen</li> <li><strong>elem_mat_select.f90</strong>: The selected material routine (Here: mat52)</li> <li><strong>elem_subs.f90</strong>: Subroutines for elem40.f90</li> <li><strong>mat_subs.f90</strong>: Subroutines for mat52.f90</li> </ul>
Source Data: Visualization of chromosomal reorganization induced by heterologous fusions in the mammalian nucleus
<p>Source data from Visualization of chromosomal reorganization induced by heterologous fusions in the mammalian nucleus</p>
GloHydroRes - a global dataset combining open-source hydropower plant and reservoir data
<div> <div> <div> <div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>Analyzing the impacts of drought and climate change on hydropower requires detailed data not only on hydropower attributes such as plant type, head, and installed capacity, but also on reservoir characteristics like area, depth, and volume. Current open-source hydropower datasets typically lack information on reservoirs, while reservoir datasets often omit hydropower details. GloHydroRes is a global dataset that integrates open-source hydropower and reservoir data, offering 29 attributes, including key information such as installed capacity, plant type, dam height, reservoir depth, area, volume, and river name. Overall, GloHydroRes provides data on 7,775 hydropower plants across 128 countries.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div> </div> </div>
Source data for the publication "Ultra-dispersive resonator readout of a quantum-dot qubit using longitudinal coupling"
<div> <p>This repository contains data and source code for the publication "Ultra-dispersive resonator readout of a quantum-dot qubit using longitudinal coupling."</p> </div>
Source data for Chen et al (2024) entitled "Motor Cortical Neuronal Hyperexcitability Associated with α-Synuclein Aggregation"
<div> </div> <div>--------------------</div> <div>GENERAL INFORMATION </div> <div>--------------------</div> <div>This readme file was generated on [2024-01-15] by [Liqiang Chen].</div> <div> </div> <div>Title of Dataset:</div> <div>Description of Dataset: </div> <div>Principal Investigator: Hong-Yuan Chu, hc948@georgetown.edu, ORCID: 0000-0003-0923-683X. </div> <div>Date of Data Collection: 2023-04-01 to 2024-11-10 </div> <div>Software Dependencies: Excel and Image J.</div> <div> </div> <div>-------------</div> <div>FILE OVERVIEW </div> <div>-------------</div> <div>Directory of Files: Source data, Electrophysiology trace data, and Microscopy images.</div> <div>Relationship Between Files: Source data is used to make figures in GraphPad. Electrophysiology trace data is used to plot electrophysiology traces. Microscopy images are used for representative images. </div> <div>File Formats: Microsoft Excel Worksheet (.xlsx) and confocal images (.nd2)</div> <div>File Naming Convention: Based on file formats.</div> <div> </div> <div>----------------------------------------</div> <div>DATA SPECIFIC INFORMATION FOR [Source data]</div> <div>Date of Creation: 2024-08-13</div> <div>Description of Data: Source data is used to make figures in GraphPad.</div> <div>A. Missing data are represented n/a.</div> <div>B. Abbreviations (Primary cortex: M1; Secondary cortex: M2; α-Synulein: αSyn; intratelencephalic neurons: ITNs; corticospinal neurons: CSNs).</div> <div>C. Figure 5B data (ITN-Sholl analysis-Intersections-5 µm Radius) can not organized as tidy format because of too many data points in each group. </div> <div> </div> <div>DATA SPECIFIC INFORMATION FOR [Electrophysiology trace data]</div> <div>Date of Creation: 2024-08-13</div> <div>Description of Data: Electrophysiology trace data is used to plot electrophysiology traces.</div> <div>A. Electrophysiology traces can be plotted using Excel.</div> <div>B. Traces are plotted in Electrophysiology trace data file.</div> <div> </div> <div>DATA SPECIFIC INFORMATION FOR [Microscopy images]</div> <div>Date of Creation: 2024-08-13</div> <div>Description of Data: Microscopy images are used to make representative images. </div> <div>A. Microscopy images can be opened using Image J. </div> <div>B. Microscopy images are named based on experimental group, animal ID, and figure number in the manuscript. </div> <div> </div> <div> </div> <div>-----------</div> <div>METHODOLOGY</div> <div>-----------</div> <div>Description of methods used for data collection: Electrophysiology data is collected using MultiClamp 700B amplifier and Digidata 1550B. pClamp 11 software is used. Microscopy images are collected using an confocal microscope. </div> <div> </div> <div>Description of methods used for data processing: </div> <div>A. Electrophysiology data is processed using clampfit software, including measure the peak of EPSC, count the number of action potentials, measure the width/rise time/decay time of action potential.</div> <div>B. Microscopy images are processed using Image J software, including measure the α-Synulein pathologic area in motor cortex, quantify the TH staining, and verify the co-localization of pS129 and biocytin. </div> <div>C. All data after processed through clampfit and Image J is put into GraphPad to make figures.</div> <div> </div> <div>-----------------------</div> <div>DATA ACCESS AND SHARING</div> <div>-----------------------</div> <div>This research was funded in part by </div> <div>1. Aligning Science Across Parkinson’s (ASAP-020572) through the Michael J. Fox Foundation for Parkinson’s Research (MJFF).</div> <div>2. National Institute of Neurological Disorders and Stroke (R01NS121374).</div> <div>3. Congressionally Directed Medical Research Programs (W81XWH-21-1-0943).</div> <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.