cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17603/ds2-hj0p-bp40
title: Automated Neighborhood Characteristics for Community Resilience Planning

description: Sociodemographic characteristics, such as race and ethnicity, have been shown to have a strong correlation with community resilience. Evidence suggests that neighborhoods with higher concentrations of Black and Hispanic households experience increased negative impacts and slower recoveries. While post disaster field researchers attempt to collect information on household demographic characteristics, this data may not be available for many households not present during survey data collection, especially households that have been dislocated from their homes for extended periods of time. Census block group data may provide a proxy for the missing household characteristics. This project provides example code that automates the process to obtain, clean, and explore census block group data. The data outputs include csv files required to run a post disaster population dislocation model currently in use by IN-CORE. This project applies the code to six community testbeds (Seaside, OR; Joplin, MO; Galveston, TX; Lumberton, NC; Memphis MSA; and Mobile, AL) to illustrate the generalizability of the code. The code utilizes Census API and may be modified to identify some of the socio-demographic and socio-economic characteristics of a neighborhood's social vulnerability.
DOI: 10.17603/ds2-nay0-j518
title: COVID-19 & Social Determinants of Health Data Collection Instrument Repository

description: Following the emergence of COVID-19 in the United States, public health researchers quickly mobilized to study the social determinants of health associated with COVID-19 outcomes and experiences of Americans. Through working groups, listservs, and ad hoc meetings initially very focused on food insecurity during the pandemic, an interest in reducing the duplication of efforts and coordinating on data collection emerged. To facilitate coordination and collaboration, the COVID-19 & Social Determinants of Health Data Collection Instrument Repository was established. The repository includes a summary of survey instruments, contact information for researchers, and a link to the full instrument. As the pandemic has pressed on, the repository has been disseminated to several research and practice networks and has been used to inform protocol development on COVID-19 research. This repository is a resource for researchers developing data collection instruments and protocols or looking for collaborators to examine critical COVID-19 health issues across time, place, and population.

These instruments have been shared by researchers studying COVID-19 and social determinants in good faith to promote coordinated, collaborative, efficient, and high impact work to address issues related to food in this event. Please use this repository to review measures, connect with others working in your area, and to coordinate, collaborate, and replicate measures for comparisons across time, population, and place. If you are planning to use measures from a shared data collection tool, please reach out to the contact person to let them know, ask how they’d like to be recognized, and to open the lines of communication to shared analyses or other collaboration.
DOI: 10.17603/ds2-sp0b-6016
title: Computing Cumulative Absolute Fractional Response (CARα)
title: in Fractional Order Intensity Measure for High-Confidence Probabilistic Seismic Demand Modeling of Structures

description: Main File for computing Cumulative Absolute Fractional Response (CARα)
description: In performance-based earthquake engineering, probabilistic seismic demand models of structures are essential components that provide probabilistic estimates of earthquake-induced demands as a function of a variable(s) called the ground motion intensity measure (IM). Uncertainties in these models are often dependent on the IM used. In other words, an appropriate IM for response prediction play a pivotal role toward reliable probabilistic seismic demand modeling of structures and subsequently, risk and resilience quantification. To this end, a series of novel fractional order (FO) IMs are proposed to release the constraints of traditional integer order IMs such as peak ground acceleration (PGA), velocity (PGV), and displacement (PGD), etc., to yield improved demand models. The developed FO IMs include peak ground response (PGRα), cumulative absolute response (CARα) and its modified version (CAR5α), spectral acceleration at a period of interest for a fractionally damped single degree of freedom (SDF) system (Sad-Tnα) and for a conventional SDF system with fractional response (Sar-Tnα), spectrum intensity for a fractionally damped SDF system (SIdα), as well as for a conventional SDF system with fractional response (SIrα). Superiorities of the developed FO IMs to the traditional IMs are successfully demonstrated using a coupled soil-bridge models considering liquefaction and laterally spreading effects. This demonstration motivates further investigation of these FO IMs for different types of structures or infrastructure systems where introduction of fractional order intensity measures may similarly help to improve performance predictions. Accordingly, seismic risk estimates founded upon the use of these novel IMs have the potential to enhance the confidence and accuracy in future studies.
DOI: 10.17603/ds2-ygx8-eh29
title: Approaches to Sampling—CONVERGE Extreme Events Research Check Sheets Series

description: This publication summarizes some of the most commonly used sampling methods in social science hazards and disaster research. Specifically, it offers definitions for and examples of convenience, purposeful, total population, and representative sampling. This information can help researchers decide how to select participants based on their study’s aims, the population under study, and the setting they are entering. About the CONVERGE Extreme Events Research Check Sheets Series: The National Science Foundation-supported CONVERGE facility at the Natural Hazards Center at the University of Colorado Boulder has developed a series of short, graphical check sheets that are meant to be used as researchers design their studies, prepare to enter the field, conduct field research, and exit the field. The series offers best practices for extreme events research and includes check sheets that are free to the research community. More information is available at: https://converge.colorado.edu/resources/check-sheets.
DOI: 10.17603/ds2-yq3g-2n89
title: South America
title: in Rock Avalanche-Generated Mass Flow Observations

description: GIS mapping of the total area impacted primarily by coarse, rocky debris from rock avalanches (defined as Zone 1 deposits), as well as the total area impacted by sediment mass flows resulting from the rock avalanche events (defined as Zone 2 deposits). The data collected utilized published mapping, aerial and satellite imagery, topographic data, and terrain analysis.
description: Collection of rock avalanche case histories where the fragmented rock material overran sediments, including cases where the sediment was mobilized into a mass flow during the event. Impact areas have been mapped and basic descriptive attributes  were collected for each case. Events in this dataset include prehistoric events (maximum age approximately 8300 years before present) up to modern events. The events included in this dataset have been described previously in literature, but not combined into a single dataset with consistent descriptions of the coarse, rocky deposits and the sediment mass flows (if present). The data was used in the development of empirical relationships to estimate the runout distance and impact area for both the coarse, rocky debris and sediment mass flows stochastically for potential future events. The event description methodology, the data format and bibliography for case histories are described in the "Rock avalanche impact area data description and sources" file included. This methodology and data format are proposed as a template for the description of an expanded global dataset of events to be compiled. Spatial data is provided as shapefiles, which can be viewed within DesignSafe using the QGIS application within the Workspace.
DOI: 10.17603/ds2-0gw4-qa85
title: Ratio of oscillator duration over excitation duration
title: in Non-parametric model for oscillator duration based on the KiK-net recordings

description: Based on random vibration formulations, Pseudo Spectral Acceleration of ground motions can be estimated using Fourier amplitude and oscillator duration. There are several predictive models for the duration of excitation in literature; however, oscillator duration is different from the duration of excitation due to the response of the oscillator. This folder presents a non-parametric predictive model to predict the ratio of the oscillator duration over the excitation duration for three tectonic regimes. The oscillator duration is estimated based on Boore and Thomspon's (2015) formulation. The excitation duration is defined as Ds5-75. The ratios are calculated based on KiK-net recordings downloaded from https://www.kyoshin.bosai.go.jp/kyoshin/quake/index_en.html. The input parameters for the model are magnitude, the closest distance to the rupture, the top 30-meter time-averaged shear wave velocity of the site.  Reference: Boore, D. M., & Thompson, E. M. (2015). Revisions to some parameters used in stochastic‐method simulations of ground motion. Bulletin of the Seismological Society of America, 105(2A), 1029-1041.
description: The ratio of oscillator duration overexcitation duration for each ground motion is calculated.
DOI: 10.17603/ds2-1edp-9782
title: Parametric Studies for Seismic Risk of Tall and Slender Vertical Pressure Vessels

description: Pressure vessels are a critical element in the natural gas industry and are considered sensitive equipment because of their hazardous products. Therefore, they must be designed against any low-probability, high consequence event that may occur over the lifetime of the vessel plant, but minimal research on their seismic effects has been conducted. The purpose of this project is to examine the seismic risk analysis of tall and slender vertical pressure vessels and their connected pipe with developed simplified models based on natural frequency changes. This project takes into account many vertical pressure vessel design characteristics, as well as the connected pipe system for the parametric study. The data can be reused to analyze a variety of vertical pressure vessel configurations, and to develop fragility curves for the occurrence of specific limit states and relative consequences to further estimate seismic risk. Going forward, this project serves to provide the earthquake engineering community with a simplified technique for pressure vessel seismic risk analysis.
DOI: 10.17603/ds2-1vwk-rp47
title: Overlapping Tornado and Flash Flood Warnings for the US
title: in Compound Wind and Water Hazards Embedded in Landfalling Hurricanes and Continental Convection

description: This project was designed to better understand how different experts and publics attended to, experienced, and made decisions about compound wind and water hazards before and during hurricanes. We collected two sets of data: Twitter data that captures mention of wind and water threats over the lifecycle of Hurricanes Florence and Harvey and interviews with National Weather Service Forecast meteorologists who issued warnings for wind and water threats before and during the storms. This project is unique in its triangulation of data from social media, which function as a digital ethnography of both different experts' and publics' reported experiences with compound wind and water threats, overlapping warning data collected for each storm, and from semi-structured interviews with five NWS Weather Forecast Offices a year after the storm. The audience for this data includes those desire to understand multiple perspectives on compound hurricane hazards and who conduct qualitative and social media analysis.
description: This is a record of all National Weather Service (NWS) tornado and flash flood warnings that intersected over some
common area (i.e., at least partially collocated) and were initially issued within  30 minutes of one another from 2008 through 2020. The area contained in each polygon in  the above .shp is the region that was common to both warnings. The analysis is based upon storm-based warnings only, and no county based warnings were used. Initial warning dataset before processing was obtained from the Iowa Environmental Mesonet GIS Archive: https://mesonet.agron.iastate.edu/request/gis/watchwarn.phtml
DOI: 10.17603/ds2-1ztt-6y49
title: Community disaster preparedness to earthquakes in Washington State: Survey instruments in Arabic
title: in Community Disaster Preparedness to Earthquakes

description: The rediscovery of large earthquakes, specifically magnitude-nine (M9) earthquakes, in the 1980s spurred interest in seismicity research from an interdisciplinary perspective. Researchers from the University of Washington"s "The M9 Project" conducted a study to assess individual- and community-level factors related to disaster preparedness and resilience to earthquakes. The study included: a paper mail survey sent to a random sample of Seattle households (N=1,340) stratified by zip codes selected for their contrasting demographic characteristics, and a convenience sample from the Vietnamese (N=54) Health Board in Seattle and King County. Participants were randomly selected to receive a survey instrument on either individual or community disaster preparedness; these surveys differed only in the formulation of a small number of questions about emergency preparations. The survey instruments were originally written in English, then adapted into Arabic, French, Spanish, and Vietnamese to suit the languages used in immigrant and refugee communities in the United States. The instruments used to collect the data are in this published project. The data will be archived at a later date.
description: This collection includes two survey instruments in Arabic.The individual disaster preparedness (IDP) survey measured respondents’ reports of their own disaster preparedness whereas the community disaster preparedness (CDP) survey measured their reports of community-level disaster preparedness. The surveys are apart of a larger research project called the “Community disaster preparedness to earthquakes in Washington State”.
DOI: 10.17603/ds2-503c-ye80
title: Wave 3: Charleston, SC 2021 Survey
title: in Longitudinal Study of Small Business Resilience to Natural Hazards and COVID-19

description: Between 2019 and 2021, four surveys were deployed to study small business natural hazard preparedness and resilience. Wave 1 focused on Charleston, South Carolina. Wave 2a focused on all coastal North Carolina and South Carolina counties. Wave 2b was a national survey with an abbreviated version of Wave 2a. Wave 3 focused again on Charleston, South Carolina.

This longitudinal study informs our understanding of hazard-experienced small business preparation and response before, during, and after the first year of the COVID-19 pandemic.
description: Online follow up survey for Charleston respondents Wave 1 and 2 seeking to understand COVID-19 recovery and future hurricane preparation.
DOI: 10.17603/ds2-8teg-ew03
title: Wave Energy Dissipation and Transmission across the Emerald Tutu Mats

description: The experiments ran at O.H. Hinsdale Wave Research Laboratory aim to explore the parameter-based behavior of the Emerald Tutu mats. Ultimately, the data obtained will be utilized to validate a numerical model. This model could be used to simulate the real-world effects of the Emerald Tutu mats. There is much data on the dissipative effects of submerged vegetation, but little laboratory or field experimentation exists for floating vegetation. The data collected could be used to help fill this knowledge gap, providing a wave height evolution for regular waves subjected to the Emerald Tutu mats. The primary audience for this manuscript would be members of the coastal engineering community or those interested in green solutions to coastal resilience.
DOI: 10.17603/ds2-ngdh-af80
title: Active-Source and Passive-Wavefield 3-Component Nodal Station Measurements at the Garner Valley Downhole Array
title: in Active-Source and Passive-Wavefield 3-Component Nodal Station Measurements at the Garner Valley Downhole Array

description: Active-source data acquisition included 66 vibroseis and 209 instrumented sledge hammer source locations. Multiple source impacts were recorded at each source location to enable stacking of the recorded signal. The source impacts at each source location have been aligned using cross-correlation, but to provide the most flexibility are provided unstacked (i.e., the signals from each source impact are provided separately). The active-source recordings are provided in terms of both raw, uncorrected units of counts and corrected, engineering units of meters per second. For each source impact, the force output from the vibroseis or instrumented sledge hammer was recorded and is provided in both raw counts and engineering units of kilonewtons. The passive-wavefield data includes 28 hours of ambient noise recorded over two night-time deployments. The passive-wavefield data is provided in raw counts, however, the instrument response files are provided should instrument correction be required in the future. The dataset can be used for active-source and passive-wavefield three-dimensional imaging, as well as other subsurface characterization techniques which include: horizontal-to-vertical spectral ratios, multichannel analysis of surface waves, and microtremor array measurements.
description: Between October 6th and October 10th 2019, 196, 3-component nodal stations were deployed at the Garner Valley Downhole Array (GVDA) site located in southern California. The 196 stations were deployed on a 14 by 14 grid with a 5 m spacing, producing a 65 m x 65 m array. The array was used to acquire active-source data generated by a vibroseis truck and an instrumented sledge hammer and passive-wavefield data from ambient noise.
DOI: 10.17603/ds2-11x5-7170
title: Leveraging Machine Learning for Dynamic System Modeling - Application to Shake Table Data
title: in Leveraging Machine Learning for Dynamic System Modeling

description: Machine learning stands at the forefront of technological progress, proving invaluable across various disciplines and is expanding in the realm of natural hazard analysis and modeling. This project serves as a comprehensive case study and workflow, showcasing the adept application of machine learning algorithms for precise regression analysis on experimental data. In the domain of structural engineering, where reliability is paramount, physics-based models offer the most robust foundation for integrating machine learning methodologies. The initial approach employs the widely recognized linear regression algorithm, tailored specifically for this structural engineering application. We've implemented specialized tools that guide the development of features, ensuring both model stability and accurate fitting of nonlinear terms. The second set of algorithms forms a deep neural network providing increased model accuracy at the expense of model interpretability. Further work is in place for expanding these to a physics informed neural network to increase interpretability and generalization. The dataset underpinning this case study comprises dynamic tests conducted at the UCSD Caltrans Seismic Response Modification Device Testing Facility. While some functionalities are tailored to this specific dataset, our notebooks provide clear instructions for customization, allowing seamless adaptation to any regression analysis dataset featuring input-output pairs.
description: Machine learning stands at the forefront of technological progress, proving invaluable across various disciplines and is expanding in the realm of natural hazard analysis and modeling. This project serves as a comprehensive case study and workflow, showcasing the adept application of machine learning algorithms for precise regression analysis on experimental data. In the domain of structural engineering, where reliability is paramount, physics-based models offer the most robust foundation for integrating machine learning methodologies.
The initial approach employs the widely recognized linear regression algorithm, tailored specifically for this structural engineering application. We've implemented specialized tools that guide the development of features, ensuring both model stability and accurate fitting of nonlinear terms. The second set of algorithms forms a deep neural network providing increased model accuracy at the expense of model interpretability. Further work is in place for expanding these to a physics informed neural network to increase interpretability and generalization. The dataset underpinning this case study comprises dynamic tests conducted at the UCSD Caltrans Seismic Response Modification Device Testing Facility. While some functionalities are tailored to this specific dataset, our notebooks provide clear instructions for customization, allowing seamless adaptation to any regression analysis dataset featuring input-output pairs.
DOI: 10.17603/ds2-8aac-gq61
title: KBO02: Load controlled axial cyclic testing on snakeskin-inspired pies
title: in Multiscale investigation of load transfer directionality of bioinspired surfaces under monotonic and cyclic loading

description: This project addresses the effects of soil-structure behavior at the interface when the structural element is asymmetrically structured. This texturing can be applied both to planar applications, such as geosynthetics, or pile shafts. The texturing induces a directional dependence in terms of interface friction angle, which leads to differences in shear resistances which may be outside the bounds expected from many many formulations of pile shaft resistance.
description: 4 piles of various shaft geometries were installed in-flight under 30g centrifugal acceleration, and then subjected to load controlled cycles, with mean and average cyclic loads. Each pile had internal strain gauges and a load cell at the pile head The strain gauge data may be used to evaluate load distribution along the pile shaft throughout cycling. In addition, 4 miniature CPT soundings were performed to assess model variability.
DOI: 10.17603/ds2-8b5b-jx16
title: Tonga Tsunami Event Briefing
title: in StEER - 14-15 January 2022 Tonga Volcanic Eruption and Tsunami

description: This Event Briefing is intended  to: 1) provide details of the 14-15 January, 2022 Tonga Tsunami, 2) summarize damage to structures and other community impacts, and 3) identify key lessons learned.
description: This project encompasses only an Event Briefing, an abbreviated StEER response mechanism that shares StEER’s impressions of the event and implications for natural hazard research and practice. Information provided herein was gathered from various websites, news channels, and relevant social media posts. Therefore, this briefing does not include insights from detailed field investigations.