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326 results for “Southern California”
Data from: Long-term monitoring of <em>Ziphius cavirostris</em> behavior using 3D tracking from fixed hydrophone arrays off Southern California
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Microsatellite data for Aedes aegypti populations in Florida and southern California
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SNP Data for Aedes aegypti populations in Florida and southern California
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SWIFT data collected in the Southern California Bight by SWIFT drifters as part of the ONR Langmuir Circulation Departmental Research Initiative (LC-DRI)
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Data from: Tall, heterogenous forests improve prey capture, delivery to nestlings, and reproductive success for Spotted Owls in southern California
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Pairwise FST values for Aedes aegypti populations in Florida and southern California
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Annual biomass data (2001-2023) for southern California: above- and below-ground, standing dead, and litter
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The influence of varying temperature across southern California Ecosystems, 2022-2023
Temperature across the dryland ecosystems of southern Califronia extensively vary. These temperatures change across seasons, however there is a significant difference in mean and daily maximum temperatures. Areas that are more arid tend to have significantly harsher temperatures at peak seasons. Here, we collected temperature data, through the utilization of OMEGA temperature pendants, during peak seasons in Summer 2022 and 2023.
SBC LTER and MBON: Sea urchin microbiomes in Southern California
Sea urchins are key grazers in coastal seas, where they can survive a variety of conditions and diets, enhancing their ecological impact on coral reefs, kelp forests, and other ecosystems. These data describe a comparative study of the microbiomes of the two dominant sea urchin species in southern California, the red urchin Mesocentrotus franciscanus, and the purple urchin Strongylocentrotus purpuratus across three rocky reef habitats that differed in the types and availability of food resources: kelp forests, urchin barrens and a hydrocarbon seep.
SBC LTER: Sargassum horneri life history in southern California
These data describe the results of surveys and experiments performed to characterize the life history of the invasive seaweed Sargassum horneri and quantify traits that promote its spread and persistence, including seasonality in abundance and reproduction, habitat affinity, and capacity for local dispersal. This data package includes five data tables and they are used to support a manuscript: Marks LM, Reed DC, Holbrook SJ (2018) Life history traits of the invasive seaweed Sargassum horneri at Santa Catalina Island, California. Aquatic Invasions 13 (3): 339–350. doi: https://doi.org/10.3391/ai.2018.13.3.03
SBC LTER: OCEAN: Temperature to Nitrate Lookup Tables for the Southern California Bight
This dataset is the temperature and nitrate lookup table in the southern California Bight. The temperature and nitrate measurements were compiled from three campaigns in the Southern California Bight. The temperature and nitrate data were binned in different regions (onshore, offshore, northern, central, southern, and all regions) and seasons (summer, winter, all season) for generating the lookup table. See method for details. Please cite the following publication: Snyder, J. N., Bell, T. W., Siegel, D. A., Nidzieko, N. J., & Cavanaugh, K. C. (2020). Sea Surface Temperature Imagery Elucidates Spatiotemporal Nutrient Patterns for Offshore Kelp Aquaculture Siting in the Southern California Bight, 7(22), 1–14. https://doi.org/10.3389/fmars.2020.00022
SBC LTER: REEF: Macrocystis pyrifera biomass and environmental drivers in southern and central California
These data describe 1987-2019 time series of giant kelp (Macrocystis pyrifera) biomass and associated environmental variables (wave height, nitrate concentration, climate indices) at quarterly and annual time intervals. Data for spatially resolvable variables (giant kelp biomass, wave height, nitrate concentration) pertain to 361 coastline segments (500 m length) in southern and central California where giant kelp was persistent over the sampling period. Data are contained in 5 tables: 1) quarterly time series of giant kelp biomass, wave height, and nitrate concentrations for 361 coastline segments; 2) quarterly time series of aspatial climate indices (NPGO, MEI, PDO); 3) annual time series of giant kelp biomass, wave height, and nitrate concentrations for 361 coastline segments; 4) annual time series of aspatial climate indices (NPGO, MEI, PDO); 5) locations (latitude and longitude of center) of coastline segments. Kelp data are derived from satellite imagery using empirical relationships. Wave data are derived from an empirically validated swell propagation model. Nitrate data are derived from empirical relationships with remotely-sensed sea surface temperature.
SBC LTER: Reef: Net primary production, growth and standing crop of Macrocystis pyrifera in Southern California
This data package has been deprecated because the models/methods have been changed. Please see the new data package using updated models: Updated Kelp NPP packages. These data are a time series of net primary production (NPP), growth and standing crop for the giant kelp, Macrocystis pyrifera, that is appropriate for examining seasonal and inter-annual patterns across multiple sites. The standing crop and loss rates of M. pyrifera are measured monthly in permanent plots at three sites in the Santa Barbara Channel, USA. Collection of these data began in May 2002 and is ongoing. Seasonal estimates of NPP and growth rate are made by combining the field data with a model of kelp dynamics. The dataset includes plant density in each plot and censuses of fronds on tagged plants at each site. NPP, mass specific growth rate and standing crop are presented in four metrics (wet mass, dry mass, carbon mass and nitrogen mass) to facilitate comparisons with previous studies of M. pyrifera and with NPP measured in other ecosystems. A subset of these data covering the time period 2002-05-01 to 2007-12-31 were contributed to Ecological Archives as Data Paper E089-119-D1 in 2008 (citation: Andrew Rassweiler, Katie K. Arkema, Daniel C. Reed, Richard C. Zimmerman, and Mark A. Brzezinski. 2008. Net primary production, growth, and standing crop of Macrocystis pyrifera in southern California. Ecology 89:2068).
A Baseline of Terrestrial Freshwater and Nitrogen Fluxes to the Southern California Bight, USA
<p>Terrestrial freshwater discharges into the Southern California Bight (SCB) contains nutrient and carbonate system constituents that make the region susceptible to eutrophication. This respository contains comprehensive time series discharge and constituent data of point and non-point sources, which consist of ocean outfalls, inland wastewater treatment plants, and rivers, to the SCB from the past twenty years and more. Constituents include nitrogen (N), phosphorus (P), carbon (C), iron (Fe), silica (Si) and carbonate system parameters. Many local and state government and non-government organization databases were accessed to construct this data set. In-house data that was previously not publicly available are published here as well. Predictive models and expert analysis addressed unmonitored sources and data gaps. </p> <p>The spreadsheet 'rivers_1997_2017_daily.xlsx' contains the data from 1997 to 2017 for 75 rivers alphabetically. River constituents include discharge volume, ammonium, nitrate, dissolved oxygen, temperature, pH, total N, total P, phosphate, organic P, total organic C, organic N, total Fe, alkalinity, salinity, dissolved Fe, total inorganic C, and silicate.<br> File 'major_potw_1971_2017.xlsx' contains data from 1971 to 2017 for the four large POTWs in the SCB, of which data before 2007 was not publicly available until now. Constituents in this file are discharge volume, ammonium, nitrate, nitrite, dissolved oxygen, temperature, biological oxygen demand, pH, TP, phosphate, OP, TOC, ON, TN, total Fe, silicate, alkalinity, salinity, and dissolved Fe. <br> The 'minor_potw_1997_2017.xlsx' contains data from 1997 to 2017 for the 19 minor POTWs alphabetically. Data from year 2000 was not publicly available until now. Constituents in this file are discharge volume, ammonium, nitrate, nitrite, dissolved oxygen, temperature, biological oxygen demand, pH, TP, phosphate, OP, TOC, ON, TN, total Fe, silicate, alkalinity, salinity, and dissolved Fe. <br> The 'inland_POTW.xlsx' file contains averaged data from the year 2009, unless otherwise noted, for the 18 inland POTWs. The region and city given for each plant. The discharge volume, TN, TP, dissolved inorganic N, and dissolved inorganic P are listed for each plant. <br> The 'natural_rivers.xlsx' file contains summarized annually averaged input of natural riverine sources for each region of the SCB. The approximate watershed area of each region is given and constituents in this file are TN, TP, DIN, and DIP.</p>
Data from: Lichen biodiversity and ecology in the San Bernardino and San Jacinto Mountains in southern California (U.S.A.)
San Bernardino National Forest in southern California encompasses two major mountain ranges, the San Bernardino Mountains and the San Jacinto Mountains. Here 414 taxa of lichenized fungi are reported from San Bernardino National Forest as a whole; 327 from the San Jacinto Mountains (including the Santa Rosa Mountains), and 289 from the San Bernardino Mountains. Two species new to science are described: Lecanora remota and Lecidea stratura. Two undescribed taxa of Bellemerea and Scytinium are reported, both currently under study. Five species are reported new for North America and California: Gloeoheppia rugosa, Lecanora formosa, Peccania cernohorskyi, P. corallina and Psorotichia vermiculata. Peccania cernohorskyi is also reported new for Canada (British Columbia). Eight species are reported new for California: Caloplaca diphasia, C. isidiigera, Peltigera extenuata, Rhizocarpon simillimum, Rinodina lobulata, R. terrestris, Sarcogyne squamosa, and Xylographa difformis. Lecidea xanthococcoides is recognized as a synonym of Lecanora cadubriae. The California endemic Lecidea kingmanii is reported as producing 4-0-demethylplanaic acid. Polysporina simplex is treated as Acarospora simplex and P. urceolata as A. urceolata. The new combination Acarospora gyrocarpa is proposed for Polysporina gyrocarpa.
California Sea Lion Stranding Records from Quantifying the linkages between California sea lion (Zalophus californianus) strandings and particulate domoic acid concentrations at piers across Southern California
<p>Pacific Marine Mammal Center (PMMC) is a member of the West Coast Marine Mammal Stranding Network, which was established under the United States Marine Mammal Protection Act by the National Oceanic and Atmospheric Administration (NOAA) to respond to stranded marine mammals. PMMC rescues, rehabilitates, and releases sick and injured marine mammals along the Orange County coastline, which is approximately 68 km of the Southern California coastline (<strong>Figure 1</strong>). All marine mammal rescue and rehabilitation activities are conducted by PMMC under a Stranding Agreement with National Marine Fisheries Service/NOAA.</p> <p>Patient case records from 2015-2019 for California sea lions (hereafter, sea lions) were reviewed and collated for each animal’s case presentation including sex, age class, seizure activity, postictal signs, comatose, abortion, and whether the sea lion was diagnosed by the attending veterinarian with domoic acid intoxication following the behavioral diagnostic criteria outlined by Gulland et al., <a href="https://www.zotero.org/google-docs/?XUrLiy">(2002)</a>. Multiple veterinarians made diagnoses during the time series based on these criteria which may have introduced some variation in the diagnosis of domoic acid intoxication during the study period. Age classes were defined following Greig et al. <a href="https://www.zotero.org/google-docs/?DGqEGG">(2005)</a> using dentition, straight length, pregnancy status, and sexual dimorphism to classify a sea lion’s age class. The following demographic categories are included in this study: juvenile/subadult male, subadult female, adult male, and adult female. Younger sea lions in the pup and yearling age classes were excluded as sea lions within these age classes are generally the most common patients at the center and often strand due to malnutrition <a href="https://www.zotero.org/google-docs/?eTzjbp">(Bejarano et al., 2008a)</a>, and rarely present with acute or chronic domoic acid intoxication during blooms. Adult and subadult age classes are the most commonly affected by domoic acid intoxication <a href="https://www.zotero.org/google-docs/?6xP9cn">(Bejarano et al., 2008a)</a>. Only live sea lions that were rescued on the Orange County coastline, brought to PMMC's facility, and assessed by the attending veterinarian were included. Adult and subadult sea lions that died on the beach were excluded from the study cohort because an attending veterinarian could not assess clinical signs to diagnosis domoic acid intoxication prior to death.</p>
Data related to "Free infragravity waves on the inner shelf: Observations and Parameterizations at two Southern California beaches"
<p><strong>Abstract: </strong></p> <p>Co-located pressure and velocity observations in 10-15m depth are used to estimate the relative contribution of bound and free infragravity (IG) wave energy to the IG wave field. Shoreward and seaward going IG waves are analyzed separately. At the Southern California sites, shoreward propagating IG waves are dominated by free waves, with the bound wave energy fraction <30% for moderate energy incident sea-swell and <10% for low energy incident sea-swell. Only the 5% of records with energetic long swell show primarily bound waves. Consistent with bound IG wave theory, the energy scales as the square (frequency integrated) sea-swell energy, with a higher correlation with swell than sea energy. Seaward and shoreward free IG energy is strongly tidally modulated. The ratio of free seaward to shoreward propagating IG energy suggests between 50-100% of the energy radiated offshore is trapped on the shelf seaward of 10-15m and redirected shoreward. Remote sources of IG energy are small.<br><br>The observed linear dependency of free seaward and shoreward IG energy on local sea-swell wave energy and tide are parameterized with good skill (R2 ~ 0.90). Free (random phase) and bound (phase-coupled) IG waves are included in numerically simulated timeseries for shoreward IG waves that are used to initialize (~ 10m depth) the numerical nonlinear wave transformation SWASH. On the low slope study beach, wave runup is only weakly influenced by free shoreward propagating waves observed at the offshore boundary (foreshore slope = 0.02). <br> </p> <p><strong>Plain Language Summary:</strong></p> <p>Infragravity (IG) waves are long-period (every 25 sec to 2.5 min) waves that contribute to coastal flooding and beach erosion. IG waves, generated near the shoreline by short-period sea-swell (SS) wave groups (known by surfers as "sets"), have long wavelengths (100s of m) and do not curl and break like ordinary sea and swell waves. Instead, they can bounce off the beach face and propagate seaward. Our study concerns IG waves on the inner shelf (10-15m depth, ~ 500-700m offshore), seaward of the region of IG generation. Similar to previous observations in Hawai'i and North Carolina, we find most of the bounced, seaward going IG energy cannot reach deep water and is trapped on the continental shelf. We develop an observation-based estimate IG wave energy on the inner shelf as a function of SS wave energy and tide level. Finally. we show with a numerical model that IG wave runup at the shoreline is influenced only weakly by IG waves on the inner shelf.<br> </p> <p><strong>About the data: </strong></p> <p>The PUV MATLAB data structure is saved in the .mat file provided. Data is aggregated from numerous deployments of Nortek Vectors (PUV) in San Diego County between 2019 and 2022, at Torrey Pines State Beach and Cardiff State Beach. This data was collected by the <a href="https://siocpg.ucsd.edu/">Coastal Processes Group</a> at the Scripps Institution of Oceanography. </p> <p>2488 3 hour at 2Hz timeseries of pressure, cross-shore and alongshore velocity are provided. </p> <ul> <li><em>time</em> is given in UTC time as a MATLAB datetime.</li> <li><em>MOP</em> is the location of the instrument for a specific timeseries given as a MOP number (corresponding to the MOP lines given in O'Reilly et al. (2016) and used by the Coastal Data Information Program (CDIP). The MOP number can be used to obtain shorenormal angle and location of the sensor from <a href="https://cdip.ucsd.edu/mops/">https://cdip.ucsd.edu/mops/</a>.</li> <li><em>depth</em> is given as the mean depth over the 3h pressure records in meters and includes the mean tidal elevation.</li> <li><em>P</em> is the detrended pressure data in meters, detided and the mean depth removed.</li> <li><em>U</em> is the detrended cross-shore velocity data in meters/second, with the main tidal constituents M2, S2 and K1 and the mean depth removed, and rotated to be shorenormal according the the MOP angle (+x is onshore).</li> <li><em>V </em>is the detrended alongshore velocity data in meters/second, with the main tidal constituents M2, S2 and K1 and the mean depth removed, and rotated to be shorenormal according the the MOP angle (+y is north).</li> <li><em>sensor_offsets</em> includes both the sensor offset from the sea floor in meters of the pressure sensor and the current meter. These can be used to surface correct the timeseries.</li> </ul> <p>The timeseries are grouped by instrument.</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>
Predicting peak daily maximum 8-hour ozone, and linkages to emissions and meteorology, in Southern California using machine learning methods
<p>crestlinetop30ozone19902019.csv includes the data used to build the modes for the annual 30 highest MDA8 concentrations at the Crestline site.</p>
Ziphius cavirostris presence relative to vertical and temporal variability of oceanographic conditions in the southern california bight
<p>The oceanographic conditions of the Southern California Bight (SCB) dictate the distribution and abundance of prey resources and therefore the presence of mobile predators, such as goose-beaked whales (<em>Ziphius cavirostris</em>). Goose-beaked whales are deep-diving odontocetes that spend a majority of their time foraging at depth. Due to their cryptic behavior, little is known about how they respond to seasonal and interannual changes in their environment. This study utilizes passive acoustic data recorded from two sites within the SCB to explore the oceanographic conditions that goose-beaked whales appear to favor. Utilizing optimum multiparameter analysis, modeled temperature and salinity data are used to identify and quantify these source waters: Pacific Subarctic Upper Water (PSUW), Pacific Equatorial Water (PEW), and Eastern North Pacific Central Water (ENPCW). The interannual and seasonal variability in goose-beaked whale presence was related to the variability in El Niño Southern Oscillation events and the fraction and vertical distribution of the three source waters. Goose-beaked whale acoustic presence was highest during the winter and spring and decreased during the late summer and early fall. These seasonal increases occurred at times of increased fractions of PEW in the California Undercurrent and decreased fractions of ENPCW in surface waters. Interannual increases in goose-beaked whale presence occurred during El Niño events. These results establish a baseline understanding of the oceanographic characteristics that correlate with goose-beaked whale presence in the SCB. Furthering our knowledge of this elusive species is key to understanding how anthropogenic activities impact goose-beaked whales.</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.