Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
144
datasets available to search
ShareScore release 0.9.0
Dataset results
144 results for “graph data”
Knowledge-Graphs-for-Seismic-Data-and-Metadata: Data
<p><strong>Abstract</strong></p> <p>The increasing scale and diversity of seismic data, and the growing role of big data in seismology, has raised interest in methods to make data exploration more accessible. This paper presents the use of knowledge graphs (KGs) for representing seismic data and metadata to improve data exploration and analysis, focusing on usability, flexibility, and extensibility. Using constraints derived from domain knowledge in seismology, we define semantic models of seismic station and event information used to construct the KGs. Our approach utilizes the capability of KGs to integrate data across many sources and diverse schema formats. We use schema-diverse, real-world seismic data to construct KGs with millions of nodes, and illustrate potential applications with three big-data examples. Our findings demonstrate the potential of KGs to enhance the efficiency and efficacy of seismological workflows in research and beyond, indicating a promising interdisciplinary future for this technology.</p> <p><strong>Methods</strong></p> <p>The data here consists of, and was collected from:</p> <ul> <li>Station metadata, in StationXML format, acquired from IRIS DMC using the fdsnws-station webservice (https://service.iris.edu/fdsnws/station/1/).</li> <li>Earthquake event data, in NDK format, acquired from the Global Centroid-Moment Tensor (GCMT) catalog webservice (https://www.globalcmt.org) [1,2]. </li> <li>Earthquake event data, in CSV format, acquired from the Northern California Seismic Network (NCSN) catalog using the NCEDC's Northern California Earthquake Catalog Search webservice (doi.org/10.7932/NCEDC) [3].</li> <li>Earthquake event data, in CSV format, acquired from the USGS earthquake catalog webservice (doi.org/10.5066/F7MS3QZH) [4].</li> </ul> <p>A complete description of the StationXML file format can be found at https://www.fdsn.org/xml/station/.</p> <p>A complete description of the NDK file format can be found at https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/allorder.ndk_explained. </p> <p>A complete description of the NCEDC file format can be found at https://ncedc.org/pub/doc/cat1/catlist.txt.</p> <p>A complete description of the USGS file format can be found at https://earthquake.usgs.gov/data/comcat/#event-terms.</p> <p>Also provided are conversions from NDK and StationXML file formats into JSON format. </p> <p><strong>Usage Notes</strong></p> <p>No special programs or software is reqired to open the data files included here.</p> <p><strong>References</strong></p> <p>[1] Dziewonski, A. M., Chou, T. A., & Woodhouse, J. H. (1981). Determination of earthquake source parameters from waveform data for studies of global and regional seismicity. <em>Journal of Geophysical Research: Solid Earth</em>, <em>86</em>(B4), 2825-2852.</p> <p>[2] Ekström, G., Nettles, M., & Dziewoński, A. M. (2012). The global CMT project 2004–2010: Centroid-moment tensors for 13,017 earthquakes. <em>Physics of the Earth and Planetary Interiors</em>, <em>200</em>, 1-9.</p> <p>[3] NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC.</p> <p>[4] U.S. Geological Survey, Earthquake Hazards Program, 2017, Advanced National Seismic System (ANSS) Comprehensive Catalog of Earthquake Events and Products: Various, https://doi.org/10.5066/F7MS3QZH.</p> <p> </p>
Data from: Predicting primate-parasite associations with exponential random graph models
Open the record for dataset details and reuse information.
Data from: Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations
Open the record for dataset details and reuse information.
Data from: Improving quartet graph construction for scalable and accurate species tree estimation from gene trees
Open the record for dataset details and reuse information.
Sequence data from viral assembly graph analysis of the SERC virome
<p>Various sequence data and details that were used in publishing the manuscript describing assembly graph binning in the SERC viral metagenome. Including: assembled contigs, FASTG, predicted ORFs, two tables describing the assembled contigs and graphs.</p> <p>Also PolA and RNR sequences that were mined from the SERC assembly.</p>
Semantic links between selected CSV datasets harvested by the European Data Portal and the DBpedia knowledge graph
<p>These dataset contains the results of the interlinking process between selected csv datasets harvested by the European DAta Portal and the DBpedia knowledge graph. </p> <p>We aim at answering the following questions:<br> What are the more popular column types? This will provide hindsight about what the datasets hold and how they can be joined. It will also provide hindsight on what specific linking schemes could be applied in future elements.<br> What datasets have columns of the same type? This will suggest datasets that may be similar or related.<br> What entities appear in most datasets (co-referent entities)? This will suggest entities for which more data is published.<br> What datasets share a particular entity? This will suggest datasets that may be joined, or are related through that particular entity</p> <p>Results are provided as augmented tables, that contain the columns of the original csv, plus a metadata file in JSON-LD format. The metadata files can be loaded in an RDF-store and queried.</p> <p>Refer to the accompanying report of activities for more details on the methodolog and how to query the dataset.</p> <p><br> </p>
Dataset: Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data
<p>This dataset is the dataset used to train and test the object condensation particle flow approach described in <a href="https://arxiv.org/abs/2002.03605">arxiv:2002.03605</a>.</p> <p>The data can be read with DeepJetCore 3.1 (https://github.com/DL4Jets/DeepJetCore)<br> The entries in the truth array are of dimension (batch, 200, N_truth). The truth inputs are:</p> <p>isElectron,<br> isGamma,<br> isPositron,<br> true_energy,<br> true_x,<br> true_y</p> <p>The entries in the feature array are of dimension (batch, 200, N_features), with the features being:</p> <p>rechit_energy,<br> rechit_x,<br> rechit_y,<br> rechit_z,<br> rechit_layer,<br> rechit_detid</p> <p>The "train.zip" file contains the training sample<br> The "test.zip" file the test sample</p> <p>The main test sample is identical to the training sample in composition, but statistically independent.<br> Other samples can be found in subfolders:</p> <p>test/flatNpart: sample with flat distribution of additional particles in the event w.r.t. each individual particle<br> Test/hiNPart: sample with up to 15 particles per event</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>
Data Graphs | Public Engagement with Science: A Survey for India Alliance Grantees
<p>Lately, the Indian research ecosystem has seen an upward trend in scientists showing interest in communicating their science and engaging with non-scientific audiences; however, the number and variety of science communication or public engagement activities undertaken formally by scientists remains low in the country. There could be many contributing factors for this trend. To explore this further, the science funding public charity in India, DBT/Wellcome Trust India Alliance (India Alliance), in a first of its kind of study by a funding agency in India, surveyed its 243 research grantees in November 2020 requesting their views on public engagement with science in India through an online survey. The survey included both quantitative as well as open-ended questions to assess the understanding of, participation in, and attitude of India Alliance Fellows/Grantees towards public engagement with research, identify the enablers, challenges, and barriers to public engagement for India Alliance Fellows/Grantees, understand the specific needs (training/capacity-building, funding, etc.) and develop recommendations for India Alliance as well as for the larger scientific ecosystem in the country. The survey showed that India Alliance grantees are largely motivated to engage with the public about science or their research but lack professional recognition and incentives, training and structural support to undertake public engagement activities.</p> <p>A shareable survey was designed using Microsoft Forms. The survey employed a mixed methods strategy and included 24 closed-ended questions (multiple choice and Likert scale) and nine qualitative questions, similar to open-ended questions. The questions were modelled on a similar survey carried out by Wellcome in 2016 to gather views of researchers on public engagement in Asia and Africa.</p> <p>The survey was shared by email with 243 Indian researchers in receipt of India Alliance fellowships or grants, out of which 137 (male – 78; female – 59) responded to the survey. Of these, 90 respondents were basic science researchers, 22 were clinical researchers, and 25 were public health researchers. A majority, i.e. 81% of the respondents indicated working as an independent researcher for more than 4 years (time post-PhD). Furthermore, 58% of the respondents were based at research institutions, 18% at higher education institutions, and 17% at central, state and private universities; these organisations geographically represent around 31 cities of India (9 respondents chose to not reveal their host institution in the survey). Periodic reminders were sent by email to the fellows to complete the survey. Three fellows who received funding from India Alliance for their public engagement projects were contacted via email after the survey for their views (in 50-150 words) on the public engagement funding programme of India Alliance. They were informed that their input is intended to be included in a report of the survey results, and they can choose to either stay anonymous or be credited. The data was collected from 27 November 2020 until 28 April 2021.</p> <p>Following the end of the survey, the data automatically mapped on Microsoft Excel was cleaned for duplication and errors. Microsoft Excel tools were used to analyse and visualise the data.</p> <p>The study did not undergo a formal ethical clearance process as the primary objective of the study was to gather insights of grantees to inform India Alliance’s public engagement support mechanisms. At the start of the survey, the objectives of the survey were clearly stated and the respondents were informed that the anonymized data of the survey may be communicated in the future. Participation in the survey was entirely voluntary.</p>
Genome graphs detect human polymorphisms in active epigenomic states during influenza infection: code and processed data
<p>Manuscript, figure, and analysis code and processed data for the Groza et al (2022) preprint.</p>
Cell Graph data for predicting MSI vs. MSS from histological images
<p><strong>Abstract: </strong>The cell graph data extracted from histological images for predicting microstatellite status. This is a patch-level (patch refers to the sub-image tessellated from the whole-slide image) binary classification task: the two datasets classify images patches to either MSS (microsatellite stable) or MSIMUT (microsatellite instable or highly mutated). The original histology patches are available from [1]. </p> <p><strong>Cell Graph Contraction:</strong> In this dataset, we establish a cell graph for each patch. The cell graph can represent the cell-cell interaction and the collection of cell graphs for all patches provide a precise characterization of tumor microenvironment. With only the availability of raw image patches, we leverage the nuclei regions segmented by a well-tuned CA<sup>2.5</sup>-Net [2] to extract the node features of each single nuclei node. And then we extract a total number of 94 pre-defined pathomics features for each nuclei region as the corresponding graph node feature. As the morphological signals are believed relative to cell-cell interplay, the cell-specific features which include the nuclei coordination, optical, and representations, then characterize the cell-level morphological behavior. We then calculate the pair-wise Euclidean distance between nuclei centroids to establish edges of a cell graph</p> <p><strong>Reference</strong></p> <p>For more details about the construction of cell-graph, please visit our paper: https://arxiv.org/abs/2206.07599.</p> <p>The associated codes and usage for the datasets are available at: https://github.com/yiqings/HEGnnEnhanceCnn.</p> <p>[1] Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples. https://doi.org/10.5281/zenodo.2530835</p> <p>[2] Huang, J., Shen, Y., Shen, D., & Ke, J. (2021, September). CA 2.5-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection. In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em> (pp. 445-454). Springer, Cham.</p>
Wikidata Dump A graph data group thing
<p>RDF dump of wikidata produced with <a href="//wdumps.toolforge.org/">wdumper</a>.</p><p>Made for a group graph project. Only includes labels and sitselinks<br><a href="//wdumps.toolforge.org/dump/2647">View on wdumper</a></p><p><b>entity count<b>: 98491518, <b>statement count</b>: 1399484145, <b>triple count</b>: 2497451237</b></b></p>
Data for "Haplotype-aware pantranscriptome analyses using spliced pangenome graphs"
<p>This archive contains mapping benchmark tables and haplotype-specific expression estimates from the paper "Haplotype-aware pantranscriptome analyses using spliced pangenome graphs".</p> <p> </p>
Census-based mobility graph data across 12 U.S. metro regions
<p>This dataset contains mobility information among census tracts for 12 U.S. cities in 2021. Mobility is defined to be the daily commute flow of residents, the data for which is retrieved from the Longitudinal Employer-Household Dynamics (LEHD), a U.S. Census Bureau program. </p> <p>In each of the individual city's directory, the file 'city_network_edges.csv', the 'S000' column the commute flow number between 'origin' and 'destination' census tracts of the city. The geographical boundaries of census tracts are also present in each directory, with 'city_network_nodes' representing shapefiles.</p>
Data for benchmarking graph-based read mappers
<p>Contains all data required for reproducing the experiments in Grytten et. al <strong><em>"Assessing graph-based read mappers against a novel baseline approach highlights strengths and weaknesses of the current generation of methods", </em></strong>including a human genome graphs and one smaller graph for the MCH region.</p>
Dataset for: A graph-based algorithm for RNA-seq data normalization
<p>mRNA-seq assays on mouse tissues were downloaded from the ENCODE project and consolidated into matrices of expression</p>
Semantic Triples from "A Collaborative, Realism-Based, Electronic Healthcare Graph: Public Data, Common Data Models, and Practical Instantiation"
<p>These RDF triples (<a href="https://zenodo.org/api/files/3d3308cb-8221-4a17-abc5-0ae32bb33f26/synthea_graph_exportable.nq.zip?versionId=7aff7c4a-bb0c-46ae-b006-7a80bcec0925">synthea_graph_exportable.nq.zip</a>) are the result of modeling electronic health records (<a href="https://zenodo.org/api/files/3d3308cb-8221-4a17-abc5-0ae32bb33f26/synthea_csv_output_turbo_cannonical.zip">synthea_csv_output_turbo_cannonical.zip), </a>that were synthesized with the Synthea software (https://github.com/synthetichealth/synthea). Anyone who loads them into a triplestore database is encouraged to provide feedback at https://github.com/PennTURBO/EhrGraphCollab/issues. The following abstract comes from a paper, describing the semantic instantiation process, and presented to the ICBO 2019 conference (https://drive.google.com/file/d/1eYXTBl75Wx3XPMmCIOZba-8Cv0DIhlRq/view).</p> <p>ABSTRACT: There is ample literature on the semantic modeling of biomedical data in general, but less has been published on realism-based, semantic instantiation of electronic health records (EHR). Reasons include difficult design decisions and issues of data governance. A collaborative approach can address design and technology utilization issues, but is especially constrained by limited access to the data at hand: protected health information.</p> <p>Effective collaboration can be facilitated by public EHR-like data sets, which would ideally include a large variety of datatypes mirroring actual EHRs and enough records to drive a performance assessment. An investment into reading public EHR-like data from a popular common data model (CDM) is preferable over reading each public data set’s native format.</p> <p>In addition to identifying suitable public EHR-like data sets and CDMs, this paper addresses instantiation via relational-to-RDF mapping. The completed instantiation is available for download, and a competency question demonstrates fidelity across all discussed formats.</p>
Numerical data achieved to plot the graphs
Open the record for dataset details and reuse information.
IsoQuant graphs from PacBio and ONT Mouse sequencing data
<p>Graph files and simulated ONT BAM files.</p>
Object Detection meets Knowledge Graphs Raw Data
<p>Raw data used to reproduce the results of 'Object Detection meets Knowledge Graphs'. Combination of VOC 2007 images, MS COCO 2014 images and ConceptNet knowledge graph. Also the checkpoints of the two trained models for the VOC dataset and COCO dataset.</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.