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394 results for “hazard”
Data/ codes used in the the Natural Hazards and Earth System Sciences (NHESS) publication titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast" by Pranavam Ayyappan Pillai et al. (2022)
<p>The archive contains datasets and codes used in the manuscript titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast", and published in the journal <em>Natural Hazards and Earth System Sciences</em> (<em>NHESS</em>) by Pranavam Ayyappan Pillai et al., 2022.</p> <p>Pranavam Ayyappan Pillai, U., Pinardi, N., Federico, I., Causio, S., Trotta, F., Unguendoli, S., and Valentini, A.: Wind-Wave Characteristics and extremes along the Emilia-Romagna coast, Nat. Hazards Earth Syst. Sci. Discuss. https://doi.org/10.5194/nhess-2022-103, 2022.</p>
Streamflow drought hazard indicators for monitoring drought risk for human water supply and river ecosystems at the global scale
<p><strong>1) Indicators of streamflow drought hazard (SDHI) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SPI12, SPEI12, SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong>CDQI1-Q50, CDQI1-Q80, CDQI1-Q80-HS, CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-Q50_f, CDQI1-Q80_f, CDQI1-WUs_f, CDQI1-WUs-EFR_f, CEP1(20%)_f, CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude </strong>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations and SDHI (based on observations) for two GRDC gauging stations: </strong>Input_Figure_2_Little_Colorado_River.txt (station near Cameron) and Input_Figure_2_Danube_River.txt (station at Hofkirchen)</p> <p><strong>4) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p><strong>5) Input data for computing Pearson correlation between SSI1 based on observations and each of the five indicators SSI1 (simulated), SPI3, SPI6, SPI9, and SPI12.</strong> Indicators computed for 218 out of 220 GRDC gauging stations with continuous streamflow observations between 1986 and 2015. The folder also contains a list of the 220 GRDC station numbers and the related WaterGAP grid cell ID ("arcid").</p> <p> </p>
Data repository for the publication "Economic Interests Cloud Hazard Reductions in the European Regulation of Substances of Very High Concern"
<p>This repository contains the data and scripts associated with the article “Economic Interests Cloud Hazard Reductions in the European Regulation of Substances of Very High Concern“, written by Jessica Coria, Erik Kristiansson and Mikael Gustavsson.</p>
S108 | SINLIST | SIN (Substitute It Now) list of hazardous chemicals by ChemSec
<p>This is the collection associated with list S108 SINLIST SIN (Substitute It Now) list of hazardous chemicals by ChemSec on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>SIN (Substitute It Now) list of hazardous chemicals used in a wide variety of articles, products and manufacturing processes around the globe.The SIN List consists of chemicals that have been identified by the non-profit <a href="https://sinlist.chemsec.org/">ChemSec</a> as being Substances of Very High Concern, based on the criteria defined within REACH, the EU chemicals legislation.The SIN abbreviation – Substitute It Now – implies that these chemicals should be removed as soon as possible as they pose a threat to human health and the environment.<br>The list was kindly provided by Anna Lennquist (ChemSec, Sweden) and Hans Peter Arp (NGI,NTNU, Norway).</p> <p>List updated with new substances on 08 May 2024</p>
Minimal data set for: Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend
<p>This minimal data set presents the values behind the means and standard deviation for the publication entitled: "Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend"</p>
Dataset: Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assessments
<p>The Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assements is a database that contains fragility and vulnerability curves that can be used to evaluate the expected or potential damages to infrastructure assets due to flooding, earthquakes, windstorms and landslides. The database consists of three Excel-spreadsheets:</p> <ul> <li><em>Table_D1_Summary_CI_Vulnerability_Data:</em> summary table with information on hazard, exposure, and vulnerability characteristics as well as a number of details regarding reliability and reference purposes.</li> <li><em>Table_D2_Hazard_Fragility_and_Vulnerability Curves:</em> collection of fragility and vulnerability curves</li> <li><em>Table_D3_Costs:</em> cost values that can be used in combination with the curves for the estimation of asset damages</li> </ul> <p>Please consult the following publication for detailed information: Nirandjan, S., Koks, E. E., Ye, M., Pant, R., van Ginkel, K. C. H., Aerts, J. C. J. H., and Ward, P. J.: Review article: Physical Vulnerability Database for Critical Infrastructure Multi-Hazard Risk Assessments – A systematic review and data collection, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2023-208, in review, 2024.</p>
Data from: A robust model for the assessment of oil spill hazards over land and water bodies
<p>This repository contains all the data required to generate the results and figures reported in the article:</p> <p><strong>A robust model for the assessment of oil spill hazards over land and water bodies. </strong><br>Pablo Vallés, Sergio Martínez-Aranda, Reinaldo García & Pilar García-Navarro <br>Fluid Dynamic Technologies TFD-I3A, Universidad de Zaragoza, Spain, 2024</p> <p><strong>Author:</strong> Sergio Martínez Aranda<br><strong>Email: </strong>sermar@unizar.es</p> <p><strong>Summary of the content:</strong></p> <p>*FILE* BSLmodel_code.c : Implementation of the BSL model in the software OILFlow2D (Hydronia LLC)</p> <p>*ZIP-FOLDER* testOilChannel : Synthetic test 1: Oil spill over water channel with parabolic velocity profile<br> Contains:<br> *FILE* plotter2D.m : Matlab file for plotting the article figures<br> *FILE* readVTK_hu.m : Ad-hoc Matlab function for reading VTK files and extract arrays of x, y, h, modU variables at cells<br> *FOLDER* graphics : Contains the output figures for the article<br> *FILE* free_surface_profiles_impCent.mat : Matlab structure containing the water level results along the longitudinal center profile for all the cases tested<br> *FILE* vel_profiles_impCent.mat : Matlab structure containing the velocity results along the cross-section x=900m for all the cases tested<br> *FOLDER* hydro_shear_layer : Folder with the 2D hydrodynamics fields for the Bottom Shear Layer used in the simulations<br> *FOLDER* BSL_disabled : Folders containing the raw simulation results with the BSL model disabled <br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges <br> *FOLDERS* BSL_impCent_CdXpXXXX : Folders containing the raw simulation results with the BSL model enabled for different drag coefficients Cd<br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges<br> </p> <p>*ZIP-FOLDER* testOilBay : Synthetic test 2: Oil spill from land to a rotating water bay <br> Contains:<br> *FILE* plotter2D.m : Matlab file for plotting the article figures<br> *FILE* readVTK_zhvel.m : Ad-hoc Matlab function for reading VTK files and extract arrays of x, y, z, h, u, v variables at cells<br> *FOLDER* graphics : Contains the output figures for the article.<br> *FOLDER* hydro_shear_layer : Folder with the 2D hydrodynamics rotating fields, including VTK files, for the Bottom Shear Layer used in the simulations<br> *FOLDER* BSL_disabled : Folders containing the raw simulation results with the BSL model disabled <br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges <br> *FOLDERS* BSL_impCent_CdXpXXXX : Folders containing the raw simulation results with the BSL model enabled for different drag coefficients Cd<br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges<br> <br> <br>*ZIP-FOLDER* caseSpillTilenga : Realistic case: Oil spill hazard assessment in the White Nile - Tilenga Project <br> Contains:<br> *FILE* Qgis_project.qgz : Portable QGIS project for plotting the article figures<br> *FOLDER* geoData : Contains the georeferenced data used for the simulation setup<br> *FOLDER* images : Contains the output figures for the article<br> *FOLDER* hydro_shear_layer : Folder with the 2D hydrodynamics fields for the Bottom Shear Layer used in the simulations<br> *FOLDER* spills : Folders containing the OilFlow2D project files to perform the simulation of the six spill scenarios reported in the article <br> *FOLDERS* spill_XXX_XX : Folders containing raster files with the oil spreading results at different times for the six spill scenarios reported in the article </p>
WP5.1 Stochastic and Empirical Multi-Hazard Event Sets for Europe: Winterstorm Stochastic Event Set for Europe (ISO3166 code)
<p>Stochastic event set of winter storms based on PRIMAVERA European winter windstorm event set: https://zenodo.org/record/6492182<br> Footprints for each model provided as *.csv file in xyz-format ("_footprints" suffix) and resampled as annualy maximum windspeeds for a 10.000-year stochastic run ("_10kyears" suffix). Winterstorm windspeed return period maps provided as *.tif at 5, 10, 20, 50 and 100-year periods. Windspeeds given as maximum surface windspeeds per event in "km/h".</p>
Extracting interpretable rules with Bayesian Networks. A case study of intrinsic human hazardous properties of silver nanoforms for the Safety Dimension of Safe and Sustainable by design paradigm.
<p>Three different datasets: toxicological attributes in i) lung and ii) intestinal cell line along with system dependent features and iii) system independent pchem properties) were merged. Each row represents one set of experimental testing conditions and related system dependent nanodescriptors based on the exposure dose and NFs pre-treatment (for intestinal assessments). The system independent inputs are NF specific and independent of experimental conditions. Data is captured via FAIR principles where the reader can find the origin (institution) of each data, the responsible data creators (experimentalists), the raw measurements, the protocols followed and the instrumentations used for each experiment. .</p>
Data used in the paper: Heatwaves, droughts, and fires: Exploring compound and cascading dry T hazards at the pan-European scale
<p>These datasets were used to analyze European compound and cascading dry hazards. The scripts are publicly available on GitHub: https://github.com/sjsutanto/Dryhazards.git.</p> <p>File Daily_SM_drought_WB_Converted.nc is for soil moisture drought, fwi_1990_2016_binary_95th_lowThreshold.nc is for wildfires, and datacube_2mtpp_19902016_HW.nc is for heatwaves.</p> <p> </p>
Data archive for Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning
<p>This dataset contains the machine learning training data files, pretrained model weights and results for the paper Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning, submitted to Natural Hazards and Earth System Sciences, 2023.</p> <p>The radar dataset can be found at the following Zenodo repository: <a href="https://doi.org/10.5281/zenodo.6325370">https://doi.org/10.5281/zenodo.6325370</a></p> <p>For instructions for using the data, please see the GitHub code repository at <a href="http://github.com/meteoswiss/c4dl-polar">https://github.com/meteoswiss/c4dl-polar</a>. Download all the files here and extract the contents to the following subdirectories in the ML code directory:</p> <ul> <li>Training data (patches_quality-index_2020.zip or patches_*_2020.nc) -> data/2020/</li> <li>Results: (results.zip) -> runs/run*/results/</li> <li>Pretrained models (models_run*) -> runs/run*/</li> </ul>
Hazards&Robots: A Dataset for Visual Anomaly Detection in Robotics
<p>This is the final version of our dataset; we further expand the Corridor scenario.</p> <p>This new version of Corridor includes 20 anomalies and the total frames are 324,408.</p> <p>In this version, we release feature embeddings extracted using a CLIP ViT-B/32 model.</p> <p>This dataset is part of a Data in Brief paper submission.</p> <p>For more information check https://github.com/idsia-robotics/hazard-detection</p> <p> </p>
New Zealand Seismic Hazard Z Factors
<p>This dataset presents our interpretation of the <em>Z</em> factor as a continuous surface across New Zealand. The GeoTiFF has been derived through a range of publicly available online resources including the MBIE website, reports, journal publications, and the <a href="https://gazetteer.linz.govt.nz/">New Zealand Gazetter</a> for matching placenames to locations, amongst others. The coordinate system is EPSG:2193 with ~5 km resolution. The raster has a single band and values are rounded to two decimal places.</p> <p>The <em>Z</em> factor is used to scale the 5% damped design seismic response spectrum based on the magnitude of the expected seismic hazard in different regions in New Zealand, as demonstrated through <a href="https://www.standards.govt.nz/shop/nzs-1170-52004/">NZS 1170.5:2004</a> and referred to in the seismic assessment of potentially earthquake prone buildings (EPB). It is underpinned by the 2001 National Seismic Hazard Model and is influenced by a wide range of factors such as proximity to faults and fault rupture mechanisms, geological and soil characteristics, and topography, amongst others. <em>Z</em> ranges from 0.10 (Northland Region) to 0.60 (Otira/Arthur’s Pass surrounds). Generally speaking, low seismic risk is where <em>Z</em> < 0.15; medium seismic risk where 0.15 ≤ <em>Z</em> < 0.30; and high seismic risk where <em>Z</em> ≥ 0.30.</p> <p>This GIS dataset is intended for educational purposes where students can download the dataset, create their own contours, or directly sample the raster. For more information see the numerous online resources and the official standard <a href="https://www.standards.govt.nz/shop/nzs-1170-52004/">NZS 1170.5:2004</a> where it is available for purchase from Standards NZ.</p>
Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"
<p>These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets:</p> <ol> <li><strong>"Sampling location names and coordinates.csv"</strong>: locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work.<strong> [NOTE: </strong>latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li><strong>"Dissemination Block Populations.csv"</strong>: These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li><strong>"Daily average concentrations by site and pollutant.csv"</strong>: This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to "Sampling location names and coordinates.csv" to parse the labels in this data set.</li> </ol> <p>There is also a sample code in Python to construct the kriging maps provided in 2 formats. <strong>[NOTE: </strong>we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.]</p> <ol> <li><strong>"Jain et al - GeoHealth - Kriging Script.ipynb"</strong>: A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li><strong>" Jain et al - GeoHealth - Kriging Script.pdf"</strong>: A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>
OpenFoodTox: EFSA's chemical hazards database
<p><strong>Background: EFSA's remit and chemical risk assessment of regulated products and contaminants</strong></p> <p>The European Food Safety Authority (EFSA) has the remit to provide scientific advice to risk managers and decision makers through risk assessment and risk communication on issues related to “food and feed safety, animal health and welfare, plant health, nutrition, and environmental issues”. Risk assessment has been defined as "a scientifically based process consisting of four steps: hazard identification, hazard characterisation, exposure assessment and risk characterisation" (EC, 2002). </p> <p>In the food and feed safety area, hazard identification and hazard characterisation aim to determine safe levels of exposure for regulated products or contaminants as “reference values<sup>1</sup>” to protect human health, animal health, environmental-relevant species or the whole ecosystem. Such reference values for a given species are most often derived by using a “reference point<sup>2</sup>" determined from the critical toxicological study on which an uncertainty factor<sup>3</sup> is applied.</p> <p>Since its creation in 2002, the European Food safety Authority (EFSA) has produced risk assessments for <strong>more than 5,700 unique substances</strong> in <strong>over 2,400 Scientific Opinions</strong><strong>, Statements and Conclusions</strong> through the work of its <a href="http://www.efsa.europa.eu/en/science/scientific-committee-and-panels">scientific Panels</a>, Units and Scientific Committee. </p> <p> </p> <p><strong>EFSA's Chemical Hazards Database: OpenFoodTox</strong></p> <p>OpenFoodTox is a structured database summarising the outcomes of hazard identification and characterisation for the human health (all regulated products and contaminants), the animal health (feed additives, pesticides and contaminants) and the environment (feed additives and pesticides). It provides open-source data for the substance characterisation, the links to EFSA’s related outputs, background European legislation, and a summary of the critical toxicological endpoints and reference value.</p> <p>The data model of OpenFoodTox has been designed using <a href="https://www.oecd.org/ehs/templates/">OECD Harmonised Templates (OHTs)</a> as a basis to collect and structure the data in a harmonised manner. OpenFoodTox provides open source data for the substance characterisation, EFSA outputs, reference points, reference values and genotoxicity.</p> <p>OpenFoodTox contributes actively to EFSA’s 2020 Science Strategy (EFSA, 2016) and to the aim of widening EFSA’s evidence base and optimising access to its data as a valuable open source toxicological database that can be shared with all scientific advisory bodies and stakeholders with an interest in chemical risk assessment. In addition, OpenFoodTox 2.0 has been submitted to the OECD’s Global Portal to Information on Chemical Substances (<a href="https://www.echemportal.org/">eChemPortal</a>) so that individual substances can be searched as part of the national and international databases. Further description and associated references are described in the EFSA journal editorial (Dorne et al., 2017) and other publications (Dorne et al., 2020).</p> <p> </p> <p><strong>What's new? OpenFoodTox 2.0 </strong></p> <p>The 2023 upgrade to OpenFoodTox 2.0 now contains <strong>physico-chemical properties</strong> and <strong>pharmacokinetic/toxicokinetic</strong> data for more than 850 substances.</p> <p>In order to disseminate OpenFoodTox 2.0 to a wider community, two sets of data can be downloaded:</p> <p><strong>1. </strong>Six individual spreadsheets including a) substance characterisation , b) EFSA outputs, c) reference points, d) reference values, e) genotoxicity and f) phys-chem&toxicokinetics data*.</p> <p><strong>2. </strong>The full database*.</p> <p> </p> <p><strong>OpenFoodTox 2.0 and innovative <em>in silico</em> models</strong></p> <p><em>In silico</em> models using OpenFoodTox 2.0 data have been developed for ecological risk assessment (bees and rainbow trout) and human risk assessment using rat toxicological data (Carnesecchi et al., 2020a; Carnesecchi et al., 2020b; Como et al., 2017; Benefenati et al., 2017; Toporov et al., 2017, Toporov et al., 2018). These <em>in silico </em>models provide alternative means to animal experiments for the hazard identification and characterisation of chemicals, and are becoming of increasing interest in the risk assessment community to deliver the 3Rs (replacement, reduction, refinement) (Hartung, 2004; OECD, 2005), particularly since the banning of animal testing for the approval of cosmetics as consumer products (Regulation (EC) No. 1223/2009, Art.18(2)).</p> <p> </p> <p> </p> <p> </p> <p><strong>Definitions</strong></p> <p><sup>1 </sup>Reference Value: the estimated maximum dose (on a body mass basis) or the concentration of an agent to which an individual may be exposed over a specified period without appreciable risk. Reference values are established by applying an uncertainty factor to the reference point. Examples of reference values in human health include acceptable daily intake (ADI) for food and feed additives, and pesticides, tolerable upper intake levels (UL) for vitamins and minerals, and tolerable daily intake (TDI) for contaminants and food contact materials. For acute effects and operators, the acute reference dose (ARfD) and the acceptable operator exposure level (AOEL). In animal health and the ecological area, these include safe feed concentrations and the Predicted no effect concentration (PNEC) respectively (EFSA Scientific Committee, 2018).</p> <p><sup>2 </sup>Reference Point: defined point on an experimental dose–response relationship for the critical effect. This term is synonymous to Point of departure (USA). Reference points include the lowest or no observed adverse effect level (LOAEL/NOAEL) or benchmark dose lower confidence limit (BDML), used to derive a reference value or Margin of Exposure in human and animal health risk assessment. In the ecological area, these include lethal dose (LD50), effect concentration (EC5/ECx), no (adverse) effect concentration/dose (NOEC/NOAEC/NOAED), no (adverse) effect level (NEL/NOAEL), hazard concentration (HC5/HCx) derived from a Species Sensitivity Distributions (SSD) for the ecosystem (EFSA Scientific Committee,2018).</p> <p><sup>3 </sup>Uncertainty factor: reductive factor by which an observed or estimated no observed adverse effect level or other reference point, such as the benchmark dose or benchmark dose lower confidence limit, is divided to arrive at a reference dose or standard that is considered safe or without appreciable risk (WHO, 2009).</p> <p>*Phys-chem and toxicokinetics data can be merged with the full database using SUB_COM_ID and OP_ID keys as available in "PhysChem_Toxicokinetics_KJ_2023" and "OpenFoodToxTX22809_2023".</p> <p> </p> <p><strong>This version replaces version 5 to include EFSA Opinions, Statements and Conclusions up to Sept 2022</strong></p>
Mass movement assessment: cascade hazards ratings, Andrews Experimental Forest, 1992
Debris flow hazard and susceptibility rating of the Lookout Creek drainage for less than third-order streams, includes susceptibility classification for stream-side landslides and slumps in Lookout Creek. This data is a first estimation, including unknown things such as distribution of thick colluvium along the stream.
Brainport, Highway pilot, driving adaptation at hazards locations
<p><strong>Scenario description</strong>:</p> <p>The driving adaptation car drives around the track in simulated autonomous mode (ACC) and applies ADASINs at Hazards locations.</p> <p><strong>Session description</strong>:</p> <p>25 laps with Jaguar F-Pace on Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Hazardous geological processes occurrence assessment for Transcarpathian region,_Ukraine
<p>Maps of hazardous geological processes specific occurrence by administrative districts for Transcarpathian region were produced by the Institute of Geological Sciences of the National Academy of Sciences of Ukraine based on the processing of materials from such institutions: State Service of Geology and Mineral Resources of Ukraine, Transcarpathian geological and hydrogeological center of the State Enterprise "Zakhidukrgeologiia" of the National Joint Stock Company "Nadra Ukrainy", Berehovo, State Geological Information Archive of Ukraine. In particular, maps of the distribution of hazardous geological processes with a scale of 1:100000 (by V. Barnychka, 1980) and a scale of 1: 200000 (by M. Gabor) for the period 1980-2010 were used, as well as data provided by V. Petryk ("Zakhidukrgeologiia", 1983-2001), and data from information yearbooks on the of hazardous exogenous geological processes activization for Ukraine territory according to monitoring of engineering and geological processes 2015-2018. The ranking principles for Transcarpathian region administrative districts due to the hazardous geological processes occurrence depended on type of process.</p>
Simulated data used in "The importance of censoring in competing risks analysis of the subdistribution hazard"
<p>The simulated data used for analysis in "The importance of censoring in competing risks analysis of the subdistribution hazard". Simulated using the method described in Additional file 1.<br> <br> <strong>Warning: Large file.</strong> Contains 1000 datasets of 300 observations each, for each of 105 parameter combinations (31,500,000 rows). Some programs (e.g. Excel) will not be able to open it in full.</p> <p>csv file with columns:</p> <p><strong>p.comp:</strong> risk of the competing event in exposure group A for this scenario [0 to 0.30 in increments of 0.05]<br> <strong>lnb.cens:</strong> log(hazard ratio) for loss to follow-up in old versus young individuals for this scenario [0 to 1 in increments of 0.25]<br> <strong>lnb.evt:</strong> log(subdistribution hazard ratio) for the event of interest in exposure group B vs group A for this scenario [0, 0.5, 1]<br> <strong>sim:</strong> ID of the simulated dataset for this scenario [1-1000]<br> <strong>exposure:</strong> exposure group (0 = A, 1 = B) of this individual<br> <strong>age:</strong> age group (0 = young, 1 = old) of this individual<br> <strong>time:</strong> time-to-event or censoring for this individual<br> <strong>evtcode:</strong> event type (0 = censoring, 1 = event of interest, 2 = competing event)<br> <strong>censcode:</strong> type of censoring (1 = end-of-study, 2 = loss to follow-up)</p>
Non-Poissonian Forecast and Hazard source files - New Zealand National Seismic Hazard Model 2022
<h3>This repository contains:</h3><ul><li>The forecast's files for the Distributed Seismicity Model of the NZNSHM2022, as well as figures, and the Paraview files to explore them in the software interactively. (https://www.paraview.org/)</li><li>The Openquake source files (https://github.com/gem/oq-engine) to run the NZ-NSHM2022 model using the non-Poisson forecasts as single branches.</li></ul><h3>Installation instructions</h3><p>For reproducibility, this package should install OpenQuake (https://github.com/gem/oq-engine) in its version v3.16.4. However, Openquake should remain backward compatible for the Negative Binomial formulation in the future. To install the version 3.16.4, a virtual environment can be created used Anaconda/Miniconda/Micromamba (the latter is recommended, see installation instructions https://mamba.readthedocs.io/en/latest/installation.html) by using:</p><blockquote><p><i>conda env create -f environment.yml</i></p></blockquote><p>This environment should already contain the Openquake version. If the Openquake software should be installed manually into an environment created by the user:</p><blockquote><p><i>source activate {user_env}</i></p><p><i>git clone https://github.com/gem/oq-engine --depth=1 --branch=v3.16.4</i></p><p><i>cd oq-engine</i></p><p>pip install -e .</p></blockquote><p>For additional information, please see the README.md file, or visit <a href="https://github.com/pabloitu/nz_nshm2022_nonpoisson">https://github.com/pabloitu/nz_nshm2022_nonpoisson</a></p>
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