cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17603/ds27q4w
title: IFSTTAR1, IFSTTAR2 - IFSTTAR Experiments, in PRJ-1843: LEAP-UCD-2017
title: in LEAP-UCD-2017 Experiments (Liquefaction Experiments and Analysis Projects)

description: Twenty-four centrifuge model tests of liquefaction and lateral spreading, performed as part of a round robin test program, are shared and compared in this archive. Please see the general report section of the published project for an overview comparison and background of all of the experiments. One document in the report (with “ReadMe” in the file name) describes the organization of the data archive. The comparisons presented in the general report section will serve as an index to help the users find individual experiments of interest.  This data from 24 model tests is published as nine separate experiments in this archive (one experiment per centrifuge facility). Each "experiment" includes two or three model tests and each model test includes between one and three destructive shaking events.
All of the tests modeled a 4 m thick deposit of Ottawa F-65 sand with a 5-degree surface slope in a rigid box. The tests covered a range of ground motion intensities and a range of relative densities to define the median response and the sensitivity of the response to relative density and shaking intensity. The nine centrifuge facilities involved in this test program included Cambridge University (UK), Ehime University (Japan), IFSTTAR (France), NCU (Taiwan), KAIST (Korea), Kyoto University (Japan), RPI (USA), UC Davis (USA), and Zhejiang University (China).
description: Lateral spreading of a submerged slope for LEAP-UCD-2017
DOI: 10.17603/ds2-0h6g-en94
title: Natural Hazards Research Summit 2024: Mitigation Household Decision-Making: An Application of the Precaution Adoption Process Model to the SYR Program

description: The residential sector accounts for the majority of expected annual economic losses from hurricane winds and flooding. Building codes and land use policies are effective ways to reduce future damage for new construction but existing homes typically must be voluntarily strengthened. Homeowners can play a critical role in increasing regional resilience and reducing damage and loss through their decisions to retrofit (i.e., physically strengthen) their homes; however, Javeline and Kijewski-Correa found that homeowners typically do not voluntarily strengthen their homes. Disaster mitigation incentive programs, such as the North Carolina Insurance Underwriting Association’s (NCIUA) Strengthen Your Roof (SYR) Program, attempt to motivate homeowners to implement structural mitigations by offering grants to upgrade their roofs to Insurance Institute for Business and Home Safety (IBHS) approved FORTIFIED Roof standards. Previous studies that explored homeowners’ implementation of hurricane-related retrofits and the effectiveness of incentive programs only considered whether a homeowner retrofitted their home or not and did not consider the process – possibly unfolding over time – by which a homeowner arrived at that decision. Stock et al. examined household engagement in protective action from the lens of the Precaution Adoption Process Model (PAPM) theory. This study expands upon the work of Stock et al. by utilizing PAPM theory to break down a homeowner’s progression through the SYR grant program into a series of stages. This work presents a unique opportunity to collaborate with practitioners such as NCIUA who possess first-hand knowledge of increasing regional hurricane risk and the implications for the future of U.S. coastal communities. The partnership between the research team and NCIUA facilitates the development of a rich dataset that combines real-time institutional data from the SYR program with survey data, providing a multi-faceted view of homeowner mitigation decision-making behavior. This study contributes to the existing body of literature by offering a novel approach to modeling household mitigation decision-making as a process rather than an event that occurs at one point in time, and that thus better reflects how households make decisions to retrofit their homes and the various drivers that move them along the process. Furthermore, the insight gained from these models can be pivotal in shaping future mitigation programs and policy decisions to improve adoption rates of retrofit incentives in coastal communities facing heightened risk due to more frequent and severe storms in the future.
DOI: 10.17603/ds2-29f9-9085
title: Network resilience trajectories (small scale simulation)
title: in Webinar material: Leveraging HPC Resources at DesignSafe-CI and Interoperability with IN-CORE for Resilience Assessment

description: A transportation network is modeled leveraging information from the PRJ-3939 project. Earthquake simulations, fragility functions definition, mapping functions, and bridge damage analysis are performed using the pyincore library. The results of the damage probabilities are later used for analyzing the recovery trajectory of the transportation network. The recovery trajectories are presented using different numbers of simulations (small-scale).
description: The recent decade has seen a rise in community resilience modeling, including a quest to model infrastructure resilience (its exposure, damage, and restoration) under extreme events. These efforts entail measuring, visualizing, and probing alternatives to support mitigation, recovery, and resilience-enhancing interventions. However, the practice demands developing different input sub-models, considering various layers of uncertainty, and integrating these for the final assessment. 

In this project, we present how the resources of the DesignSafe Cyberinfrastructure (DesignSafe-CI) can support such efforts. We present different tools that can be leveraged from DesignSafe directly or through its interoperability with other platforms, such as the Interdependent Networked Community Resilience Modeling Environment (IN-CORE). We present illustrative examples of how to leverage publicly available data in DesignSafe-CI and models within the IN-CORE platform to create an infrastructure resilience assessment pipeline. These examples are developed and analyzed using JupyterLab in DesignSafe. Furthermore, we present how JupyterLab HPC in DesignSafe-CI enhances the modeling and testing capabilities as the analysis of larger infrastructure systems (e.g., detailed transportation networks) becomes feasible. While the illustrative example uses earthquakes as the hazard type, the leveraged tools, platforms, and shared codes can be adapted to multiple hazards. 

This project is created to share the material presented on December 11 (2024) in the DesignSafe Webinar "Resilience Assessment of Community Infrastructure: Leveraging HPC Resources at DesignSafe-CI and Interoperability with IN-CORE".  You can find the recording of the webinar at the following link: https://youtu.be/Bdb8s4Rc4h4?feature=shared
DOI: 10.17603/ds2-3qst-mb28
title: tsunami runup response function
title: in Tsunami Run-up Response Function Model

description: We developed a data-driven model that can rapidly predict alongshore tsunami runup distribution from earthquake information.

(1) TRRF0122v3.py : This is the main code of TRRF. This code will work in Python 3 environment. (cf. TRRF0122v2.py will work in Python 2 environment.)

(2) 725 cases.txt : This is the extracted tsunami run-up data of 725 scenarios.

(3) validation9para.txt : This is the validation data of 100 random scenarios that were never used in developing TRRF.

(4) NB01.txt - NB99.txt : These are normalized bathymetry run-up data. These are saved not to repeat pre-processing several times. If these files are not exist, TRRF0122v3.py will create these files automatically.
description: We developed a data-driven model to rapidly predict alongshore tsunami runup distribution from 9 earthquake fault parameters. This model can be used for forecasting and hazard assessment.
DOI: 10.17603/ds2-65t4-1z29
title: Research Experiences for Undergraduates (REU), NHERI 2022: Deriving Soil Constitutive Model using Artificial Intelligence (AI) on Undrained Soil Conditions.

description: Soil constitutive models are one of the most researched topics in soil mechanics. The advancements in the field of artificial intelligence have opened up new avenues to solve complex problems. This research sought to apply artificial intelligence to the problem of soil constitutive models. Using genetic programming, this research developed a method for deriving soil constitutive models. We derived the modified cam-clay using Symbolic Regression based on synthetic soil tests in undrained conditions. This project is intended for geotechnical engineers and engineering researchers who want to study physical systems through a mathematical model. Symbolic regression can be used in many fields, from physics to medicine.
DOI: 10.17603/ds2-7h5e-4f26
title: RAPID/Collaborative Research   Examining Household Movements and Evacuation Decision-Making in a Compounding Risk Event

description: Description of Project and Data: This study examines evacuation and sheltering-in-place decisions of households before and after two simultaneous disasters: Hurricane Ida and COVID-19 pandemic. Building upon the previous work examining protective action decision-making during disasters, and through the extension of the PADM framework to account for the presence of simultaneous risks, this research advances understanding and offers new insights pertaining to household decision-making in dual risk environments. The research team developed a questionnaire that was administered by Qualtrics eight months following Hurricane Ida making landfall in Southeastern Louisiana. Surveys were administered via internet and phone to six parishes in southeast Louisiana: Orleans, Jefferson, St. Charles, St. John the Baptist, Terrebonne, and St. Bernard Parish. Data was collected beginning April 29th, 2022, and concluding June 1, 2022.  

The survey questionnaire consisted of four sections: (1) questions inquiring about hurricane and COVID-19 risk perceptions, (2) questions pertaining to what protective actions were undertaken (e.g. evacuate shelter-in-place), along with additional information about the timing of decisions and evacuation destinations (3) questions asking about the extent of damage sustained to their properties as well as their experiences with secondary hazards stemming from the hurricane and, later, Tropical Storm Nicholas, and (4) questions about their household characteristics, vaccination status, and previous evacuation and sheltering-in-place experiences.

This project also examines evacuation and recovery patterns during compound risk events. The data here includes home locations of mobile phone users estimated from their trajectories from location-based services (LBS) geolocation data, computed for each half-day (12 hours). Using these time-varying home locations, their status of evacuation from and return to home is determined. This project is important in big passively-collected data-based studies as it introduces concepts of distance-based clustering, home detection, and large-scale high-resolution GPS data analysis for decision-making, primarily for academics but also to some extent to disaster modelers and policymakers. The data files are stored in the Apache Parquet format which can be accessed via tools like Python, R, MySQL, SQLite, etc. The description of the fields in these files is provided in ReadMe.txt.
DOI: 10.17603/ds2-894m-0r89
title: Natural Hazards Research Summit 2024: Landslide Exposure in the United States

description: This project quantifies the distribution of landslide exposure in the conterminous United States. The data can be used for case-study analysis to narrow the social vulnerability implications of those locations. This is the first to identify regional trends of landslides in terms of social vulnerabilities and human settlements. This project is relevant to engineers, natural hazard researchers, social scientists, and public policy analysts.
DOI: 10.17603/ds2-8r3q-zx71
title: Research Experiences for Undergraduates (REU), NHERI 2023: Influence of Different Building Damage Prediction Models on Regional-scale Seismic Risk Estimates

description: The goal of this study is to evaluate two building damage prediction models, namely, the HAZUS and MDOF-Lu models, that have not been previously compared, to understand and inform users how they differ and may influence civil infrastructure seismic risk assessments. This study focuses on generating a scenario of an earthquake rupture along the San Andreas Fault and evaluating the resulting damages of 5000 buildings in the San Francisco Bay Area.  The evaluation of these models is based on comparing the outputs of engineering demand parameters, damager measures, and decision variables. The data in this study can continue to be evaluated locally or similarly replicated using the SimCenter Regional Resilience Determination Tool (R2D).
DOI: 10.17603/ds2-e4j9-x720
title: Online Newspaper Data Retrieval and Cleaning

description: This project focused on systematically extracting and refining a comprehensive collection of newspaper articles on the Indian River Linking Project (INRLP) from major Indian English-language newspapers indexed in the LexisNexis API between 2004 and 2021. The INRLP seeks to transfer water from so-called “surplus” areas that are flood-prone to “deficit” areas that are otherwise drought-prone. A similar protocol and code structure can be used to retrieve and clean digital newspapers available in the LexisNexis API for any disaster context. The structure of our protocol can be broken down into 6 stages, which can be found in the data retrieval and data cleaning writeups:
Stage 1: Choosing Keywords 
Stage 2: Authentication, Search, and Retrieval of Results 
Stage 3: Collecting and Parsing Results
Stage 4: Removing duplicates that are exact character matches 
Stage 5: Grouping duplicates with similar content 
Stage 6: Removing duplicates with similar content 
This project aims to provide a foundation for retrieving and analyzing several decades' worth of media coverage through a combination of automated and manual methods. As news and other textual outlets are increasingly being "born digital," there are opportunities for media analyses that break barriers to data accessibility and bolster interdisciplinary research.
DOI: 10.17603/ds2-g5c3-d085
title: ISROC Dataset for: Catastrophic Wave Erosion, Bristol Channel, United Kingdom: Impact of Tsunami?
title: in Inundation Signatures on Rocky Coastlines (ISROC) Global Coastal Boulder Deposits Datasets Version 1

description: This document contains standardized data as an Excel and NC file and explanatory notes for "Catastrophic Wave Erosion, Bristol Channel, United Kingdom: Impact of Tsunami." The project includes 9 coastal boulder deposits from a historic tsunami in the United Kingdom. The boulders can help determine some of the characteristics of the historical tsunami, like wave height and flow velocity.
description: This project is a collection of datasets on studies about coastal boulder deposits worldwide. Each document is a unique dataset that contains data from a published study. The document's name reflects the name of the associated paper, and all authors of papers are given credit in the respective document. Each dataset's data is standardized into an ISROC-formatted Excel and NC File. Also, every dataset has explanatory notes, which contain a data dictionary explaining data parameters and appropriate units, Some datasets have the raw data given to ISROC by the paper's authors. By creating a database in the public domain, ISROC hopes to promote academic collaboration in the field. ISROC, an NSF-funded research network, funds this project.
DOI: 10.17603/ds2-sc8t-0e90
title: Axial Pull-out Pile Tests
title: in Development of an Innovative Ultra High Performance Concrete Foundation System with Bio-inspired Surfaces to Support Renewable Offshore Wind Turbines

description: The planned offshore wind energy (OWE) production in the U.S. has been growing rapidly with targets of 30 GW by 2030 and 110 GW by 2050 and strong support from coastal states. Achieving these targets requires cost-effective and innovative components including foundations, which could cost 14% to 34% of the overall project cost. Offshore wind turbines (OWTs) are commonly supported on large-diameter steel foundations (piles, anchors or suction caissons). Currently the U.S. does not have the capability to fabricate large diameter steel foundations supporting OWTs and instead relies on foundations fabricated abroad. Therefore, a U.S. manufactured foundation system to support renewable OWE infrastructure is required to meet the growing demand. The goal of this project was to develop a domestically fabricated, bio-inspired surface optimized Ultra High-Performance Concrete (UHPC) foundation system to support the growing renewable OWE infrastructure. This project represented the first phase which was focused on material properties, structural capacity of designed UHPC cross-sections, and the effects of surface elements on the soil-pile interaction when subjected to axial loading. The soil-pile interaction was investigated using plates with different designs of surface elements, small-scale piles and larger-scale pile tests subjected to axial loading.
description: Axial Pull-out Pile Tests were conducted on three piles to study the effect of the surface characteristics on soil-pile interaction under monotonic loading conditions.
DOI: 10.17603/ds2-t2nf-m009
title: Full-scale WOW PV Testing
title: in Hybrid Experimental-Numerical Methodology and Field Calibration for Characterization of Peak Wind Effects on Low-Rise Buildings and Their Appurtenances

description: This dataset contains pressure, acceleration, and force data, collected in the FIU Wall of Wind while testing a full-scale PV array. 
description: The accurate estimation of wind loading on rooftop photovoltaic (PV) systems is an ongoing challenge with the aim of improving the resilience of these systems to extreme wind events. Previous studies have proven that PV systems are flexible structures susceptible to wind induced vibrations that lead to the amplification of the wind loading. The purpose of this research is to improve the estimation of peak wind loading by demonstrating the effectiveness of the previously developed advanced PTS method in predicting the total wind loading, which includes background and resonant components. Experimental tests on the full-scale PV array model of the Hogue Technology Center (HTC) at Central Washington University (CWU) building were performed at the NSF-NHERI Wall of Wind (WOW) Experimental Facility (EF) at Florida International University (FIU). This study results in a validated and improved method for the estimation of peak wind effects on low-rise buildings and their non-structural components.
DOI: 10.17603/ds2-yw91-cc74
title: Strategies to Build Community and Develop a Culture around Data Publication and Reuse in Natural Hazards Engineering and the Social Sciences

description: This publication includes the abstract and presentation to the Open Repositories Conference 2022. To remain relevant and expand community, open data repositories must engage users, increase data publications and promote their reuse, and improve services. With these goals, in collaboration with groups within the Network of Natural Hazards Research Infrastructure (NHERI). DesignSafe has developed interconnected metrics and data promotion strategies. These include (1) in-depth citation analysis, (2) interviews with users, (3) publication of data stories, (4) yearly data awards and naming data ambassadors, (5) publish-your-data workshops, and (6) the analysis of metrics to assess growth and pace of data publication and usage. These activities support a more robust understanding of the user community and their behaviors towards publication and reuse across different disciplines. Their results allow focusing educational activities and prioritizing development regarding data access and usability. In addition, they enable development of realistic publication and reuse benchmarks and to target gaps in our program. They are also used to report to funders and communicate with other stakeholders. Importantly, they are helping to drive a cultural shift in natural hazards engineering and social sciences toward data. This presentation describes the results of these strategies to identify what works and what needs to be improved. The presentation is of interest to repository administrators and curators to learn about different components of a multi-pronged data promotion and evaluation strategy. Data producers and consumers can hear about the role of repositories and their research networks promoting research outputs and building data culture.
DOI: 10.17603/ds2-z2pc-4j24
title: Multimedia for Researchers: Techniques and Ethics—CONVERGE Extreme Events Research Check Sheets Series

description: This check sheet describes the benefits of using multimedia—for example, video, audio, photography, and animation—to augment research findings. Ethical considerations, including guidance about how best to share multimedia with the press, are discussed in detail. 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/ds2-44b4-hf65
title: Natural Hazards Research Summit 2024: Engineering Design of Emergent Vegetation to Mitigate Wave Overtopping

description: This poster describes three studies that support the implementation of Nature-based Solutions (NbS), specifically emergent vegetation, for flood risk reduction. NbS are landscape features used for flood risk management. They are increasingly popular due to the additional benefits they confer beyond flood protection, including increased habitat and carbon storage. However, widespread implementation of these systems is lacking. Ostrow et al. (2022) surveyed 32 stakeholders to learn more about barriers to implementation of NbS. We found that in the engineering sector, the most common barrier mentioned was the lack of engineering design standards. To support engineering design standard development, the research group has since investigated performance of emergent vegetation for wave overtopping mitigation. Libby et al. (in review) conducted a large-scale experiment in the O.H. Hinsdale Wave Research Laboratory Large Wave Flume. We compared the overtopping of configurations with wall alone, wall with mangroves, wall with revetment, and wall with both mangroves and revetment. We found that the mangroves reduced overtopping by an amount comparable to the reduction by the revetment. Furthermore, we found that the green and gray components of the hybrid system acted independently. Ostrow and Cox (in review) proposed a performance-based design methodology for design with emergent vegetation. The methodology considered growth and mortality processes, and uncertainties in the system. To demonstrate the methodology, we considered an idealized embayment inspired by Southern Florida, and considered the overtopping onto a coastal road. We found that performance of the mangroves generally improves with time as the mangroves develop. The audience of this poster is natural hazards researchers, and data from the overtopping experiments will be posted on Design Safe.