cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17603/ds2-2qc4-fh48
title: 2018 Social Science Extreme Events Research (SSEER) Network
title: in Social Science Extreme Events Research (SSEER) Network Data, Survey Instrument, and Annual Census

description: This dataset includes information regarding 648 social science researchers who signed up for the Social Science Extreme Events Research (SSEER) network between July 8, 2018 and December 31, 2018. Researchers’ information is collected via an online survey that consists of 19 questions and takes approximately 7 minutes to complete. The envisioned audience for this data and other information includes those who are interested in learning more about the composition of the social science hazards and disaster research workforce, the events that they study, and their skills and expertise.
description: This project includes a survey instrument, data, and annual census reports from the National Science Foundation (NSF)-funded Social Science Extreme Events Research (SSEER) network, which is headquartered at the Natural Hazards Engineering Research Infrastructure (NHERI) CONVERGE facility at the Natural Hazards Center at the University of Colorado Boulder. The SSEER network, which was launched in 2018, was formed, in part, to respond to the need for more specific information about the status and expertise of the social science hazards and disaster research workforce. The mission of SSEER is to identify and map social scientists involved in hazards and disaster research in order to highlight their expertise and connect social science researchers to one another, to interdisciplinary teams, and to communities at risk to hazards and affected by disasters. Ultimately, the goals of SSEER are to amplify the contributions of social scientists and to advance the field through expanding the available social science evidence base. To see the SSEER map and to learn more about the SSEER initiative, please visit:  https://converge.colorado.edu/research-networks/sseer. All social and behavioral scientists and those in allied disciplines who study the human, economic, policy, and health dimensions of disasters are invited to join this network via a short online survey. This DesignSafe project includes: (1) the SSEER survey instrument; (2) de-identified data, which is updated annually as new researchers join the SSEER network and returning members update their information; and (3) SSEER annual census reports. These resources are available to all who are interested in learning more about the composition of the social science hazards and disaster workforce. SSEER is part of a larger ecosystem of NSF-funded extreme events research and reconnaissance networks designed to help coordinate disciplinary communities in engineering and the sciences, while also encouraging cross-disciplinary information sharing and interdisciplinary integration. To learn more about the networks and research ecosystem, please visit: https://converge.colorado.edu/research-networks/.
DOI: 10.17603/ds2-3d7a-x318
title: NEESR E-Defense Base Isolation 2013: system identification

description: After participation in testing of a base-isolated structure at E-Defense in 2013, a system identification of the structure model was performed.
DOI: 10.17603/ds2-g9ka-3m60
title: Research Experiences for Undergraduates (REU), NHERI 2024: Dynamic Modeling and Performance Evaluation of a Shape Memory Alloy-Based Friction Damper for Seismic Hazard Mitigation

description: This project focuses on developing a numerical dynamic model of a shape memory alloy (SMA)-based friction damper. The model combines the LuGre friction model and SMA model by identifying and optimizing its parameters to fit the behavior of an experimental damper. The dynamic model can provide insight into the damper's performance in complex hybrid simulations against various natural hazards. This model is unique because it can accurately capture the dynamic behavior of an SMA cable and friction mechanism. The model's application can extend to real-time hybrid testing, combining physical experiments with numerical simulations to enhance its performance in multi-hazard mitigation. This work targets earthquake engineering researchers and the natural hazard research community, providing valuable insights into the capabilities and performance of a numerical model for an SMA-based friction damper.
DOI: 10.17603/ds2-jdgs-1667
title: Event Briefing
title: in StEER - Hurricane Eta

description: This project encompasses only an Event Briefing, an abbreviated StEER response mechanism that briefly summarizes the event and any key lessons that can be learned or reemphasized from Hurricane Eta.
description: This event briefing is intended to: (1) summarize the impacts of Hurricane/Tropical Storm Eta across Central and North America with emphasis on structural losses, (2) document the hazard intensity associated with the storm, and (3) identify any lessons learned from this event. While StEER did reach out to contacts in Central America in attempts to collect field observations, this outreach was unsuccessful. Thus all content herein is derived from public reports from news services, mission agencies and social media.
DOI: 10.17603/ds2-naw6-rx36
title: StEER: Hurricane Milton Annotated Media Repository
title: in StEER - Hurricane Milton

description: This project encompasses the products of StEER's Level 1 response to this event: Preliminary Virtual Reconnaissance Report (PVRR) with corresponding Media Repository. 
description: This annotated media repository is a supplement to StEER’s Preliminary Virtual Reconnaissance Report (PVRR). The repository is a visual catalog of geolocated photographic evidence, organized by different hazard and structural classes, and annotated with team member interpretations and analyses.
DOI: 10.17603/ds2-resg-ah65
title: StEER: Hurricane Milton Preliminary Virtual Reconnaissance Report (PVRR)
title: in StEER - Hurricane Milton

description: This project encompasses the products of StEER's Level 1 response to this event: Preliminary Virtual Reconnaissance Report (PVRR) with corresponding Media Repository. 
description: StEER activated a Level 1 response, forming a Virtual Assessment Structural Team (VAST) on October 10, 2024 to generate this Preliminary Virtual Reconnaissance Report (PVRR). The PVRR: (1) overviews Hurricane Milton, particularly relating to hurricane winds, tornadoes, flooding, and storm surge impacts on the built environment; (2) establishes the regulatory environment and construction practices in the affected area; (3) synthesizes preliminary reports of damage to buildings, infrastructure, and critical facilities; and (4) provides recommendations for continued study of this event by StEER and the wider engineering reconnaissance community, particularly related to the performance of construction designed for hurricanes during tornadoes and the performance of flood control measures under compound flooding.
DOI: 10.17603/ds2-sbcj-nx44
title: PRELIMINARY VIRTUAL RECONNAISSANCE REPORT (PVRR)
title: in AQUILA, MICHOACÁN, MEXICO SEPTEMBER 19, 2022, Mw 7.6 EARTHQUAKE

description: The main event and its aftershocks caused moderate to extensive damage to structures near the epicenter, mainly affecting the states of Colima and Michoacán. Furthermore, reports of some damage were found in cities located farther away from the epicentral region, as far as Mexico City, which is located approximately 453 km from the epicenter of the main event.
There was a total of five fatalities reported as a result of the two events, three were caused by the main event on September 19th, and the other two occurred in Mexico City after the Mw 6.8 aftershock on September 22nd.
description: On September 19th, 2022, a moment magnitude 7.6 earthquake struck the Pacific coast of Mexico in the state of Michoacán between the cities of Manzanillo and Zihuantanejo. This is the third major earthquake that has occurred in Mexico on September 19th in the last 40 years. A large earthquake drill in Mexico City is carried out annually on the anniversary of the great Mw 8.0 September 19th 1985 earthquake. On September 19th 2017 a magnitude 7.1 occurred on the 32nd anniversary of the 1985 earthquake a few hours after the city wide earthquake drill. The earthquake mechanism corresponds to a reverse faulting mechanism characteristic of earthquakes that occur on or near the plate boundary between the Cocos and North American Plates. USGS focal mechanism solutions indicate that rupture occurred on a shallowly dipping (i.e., 18.0°) thrust fault. The rupture of this earthquake is located approximately between the rupture of the great Mw8.0 September 19, 1985 to the South and the rupture of the great Mw8.0 October 9, 1995 to the North. The earthquake produced a small tsunami with a maximum measured wave amplitude of 1.7m in the port of Manzanillo. Three deaths were reported as a result of the main shock, one of them a minor who was severely injured and subsequently died in the hospital as a result of the injuries. Additionally, two other persons in Mexico City died as result of an aftershock on September 22nd: one from injuries to the head when she fell on the stairs and another as a result of a heart attack. Several hundred injuries were reported as a result of the main earthquake.
DOI: 10.17603/ds2-y5mj-y128
title: Buildings
title: in Seaside Testbed Data Inventory for Infrastructure, Population, and Earthquake-Tsunami Hazard

description: This dataset contains building characteristics for Seaside, Oregon.
description: This project contains datasets associated with Seaside, Oregon, a multi-hazard testbed for infrastructure resilience. Seaside is located along the Oregon coast and is subject to both seismic and tsunami hazards associated with the Cascadia Subduction Zone (CSZ). The Seaside testbed was established as a multi-hazard testbed for the NIST Community Resilience Center of Excellence. The size of Seaside lends itself to parcel-level analyses of multi-infrastructure damage, loss, and recovery modeling. 


Datasets are provided for built-, natural-, and social-systems in Seaside. The built environment consists of buildings, electric power, transportation, and water infrastructures. The building inventory was constructed from tax-lot data, Google street view, and a field survey (Park et al., 2017b). The electric power network was constructed from aerial imagery. Bridges in the transportation network are from the National Bridge Inventory and roads are from the TIGER/Line database. The water network is from the city of Seaside public works (Kameshwar et al., 2019). The natural environment (hazards) are the result of a probabilistic seismic-tsunami hazards analysis (PSTHA; Park et al., 2017a). The social systems consist of results from a housing unit allocation (Rosenheim et al., 2019). Researchers, decision makers, and planners, among others, can utilize these datasets to conduct community-scale multi-hazard risk assessment, focusing on application to the coastal urban city of Seaside, Oregon.
DOI: 10.17603/ds2-2s49-5b39
title: DesignSafe Annual Evaluation Report 2022-2023

description: This report summarizes the 2022-23 evaluation of the DesignSafe cyberinfrastructure, which was conducted by TACC’s Expanding Pathways in Computing (EPIC) STEM Evaluation Services. EPIC used a mixed methods approach to assess the implementation, outcomes, and impacts of DesignSafe on the natural hazards community. The evaluation aims to inform project improvements and report on outcomes over the project’s duration. Multiple data sources were used, including platform analytics, user profiles, annual and microsurvey responses, trainee surveys, and user and stakeholder interviews. 

Annual user survey respondents indicated they have high satisfaction with DesignSafe and believe it to be useful to the natural hazards community. Users valued the platform for data reuse, collaboration, and research publication, although they faced challenges such as JupyterHub instability and the complexity of the site. Training participants expressed satisfaction with the trainings and indicated plans to apply their knowledge, with a preference for more in-depth and practical training opportunities. User interviews highlighted differences among high and low activity users, with high activity users taking advantage of a wider array of DesignSafe components (e.g., Jupyter, HPC, Slack, Python, MATLAB, and OpenSees). User interviewees recommended feature improvements, allowing for more customized functions and applications, and more training materials. Stakeholders said a major accomplishment included obtaining CoreTrustSeal certification; and they were pleased with how well DesignSafe promoted collaboration within the community. Stakeholders also recommended enhancements to platform functionality and user engagement. Based on these findings, the evaluators recommend both educating users and potential, developing entry points through use cases and Jupyter Notebook templates, improving site navigability, considering Data Depot requests, providing guidance on usage restrictions, and continuing current efforts to enhance the platform.
DOI: 10.17603/ds2-38r9-mf76
title: Exploring Implementation of Disaster Risk Management Strategies by Public Housing Authorities: A National Survey

description: The United States is experiencing an affordable housing crisis. Simultaneously, disasters are increasing in frequency and intensity partly due to anthropogenic climate change. Disasters exacerbate adverse impacts on extremely low-income renters living in federally-subsidized housing units managed by Public Housing Authorities (PHAs). To date, there is an overall lack of knowledge around what disaster risk management (DRM) strategies are being implemented by PHAs, as well as factors that influence PHA DRM implementation. This data contains results from a national survey administered to PHAs to assess their implementation of DRM strategies. Secondary data is also included in the dataset, including size of PHAs, state governor political affiliation, urban classification status, and Moving to Work status.
DOI: 10.17603/ds2-bcpy-1836
title: Hurricane Wilma 2005
title: in FCMP Ground Level Hurricane Wind Data (1999 - 2008)

description: The Florida Coastal Monitoring Program (FCMP) is a collaborative experimental effort to measure ground level hurricane surface winds. This publication provides data files of ground level wind speed and direction measured along the east coast of Florida during landfall of Hurricanes: Irene 1999,  Gordon 2000, Lili 2002, Charley 2004, Ivan 2004, Jeanne 2004, Dennis 2005, Katrina 2005, Rita 2005, Wilma 2005, Gustav 2008, Hanna 2008 and, Ike 2008. Urbanists, social scientists, structural engineers, and meteorologists, among others, can use these real wind speed measurements and directions in myriad disaster research and planning projects.
description: Portable weather stations were deployed along the coast and/or inland to collect wind observations. This publication provides data files of ground level wind speed and direction measured along the coast during landfall of Hurricane Wilma 2005.
DOI: 10.17603/ds2-d1j1-mz22
title: Finite element modeling of pretensioned girders with end zone made of ultra-high-performance concrete (UHPC) and carbon fiber reinforced polymer (CFRP) bars
title: in Database for UHPC Simulations

description: This dataset is associated with the journal paper entitled “Strategic use of ultra-high-performance concrete and carbon fiber reinforced polymer reinforcement for end zone design of prestressed girders” that can be found here: https://doi.org/10.1177/13694332231213. The paper concerns the use of UHPC to reduce end zone cracking in prestressed concrete girders. The finite element models have been calibrated with UHPC material tests as well as large-scale tests involving prestress transfer in concrete and UHPC beams. A strand-to-UHPC interface bond-slip law has been calibrated and included in the models. The ATENA-GiD software is required to access the finite element model files.
description: This dataset includes the MATLAB scripts used to code the Softened Membrane Model for Ultra-High-Performance Concrete (SMM-UHPC). The SMM-UHPC is an extension of the SMM originally developed to simulate the in-plane shear behavior of reinforced concrete members. The dataset includes input and output data presented in the paper "Softened Membrane Model for Shear of Ultra-High-Performance Concrete (SMM-UHPC)" which was published by the Engineering Structures Journal in 2024 (https://doi.org/10.1016/j.engstruct.2024.117956). Along with the inputs and outputs from the journal paper, a generic SMM-UHPC code is included along with user instructions in a PowerPoint format. The SMM-UHPC scripts included herein can be used to simulate the behavior of UHPC under pure shear or shear combined with axial loads (compression or tension). Users of these scripts are advised that the accuracy of SMM-UHPC depends on using dependable tensile stress-strain data as model input. This data should preferably be obtained by averaging sets of direct tension prism tests per AASHTO T397.
DOI: 10.17603/ds2-gdp4-fc91
title: Hurricane Gustav 2008
title: in FCMP Ground Level Hurricane Wind Data (1999 - 2008)

description: Portable weather stations were deployed along the coast and/or inland to collect wind observations. This publication provides data files of ground level wind speed and direction measured along the coast during landfall of Hurricane Gustav 2008.
description: The Florida Coastal Monitoring Program (FCMP) is a collaborative experimental effort to measure ground level hurricane surface winds. This publication provides data files of ground level wind speed and direction measured along the east coast of Florida during landfall of Hurricanes: Irene 1999,  Gordon 2000, Lili 2002, Charley 2004, Ivan 2004, Jeanne 2004, Dennis 2005, Katrina 2005, Rita 2005, Wilma 2005, Gustav 2008, Hanna 2008 and, Ike 2008. Urbanists, social scientists, structural engineers, and meteorologists, among others, can use these real wind speed measurements and directions in myriad disaster research and planning projects.
DOI: 10.17603/ds2nm22
title: Ridge runnel post storm beach recovery

description: Post-Nor’Easter in situ data collection at Bethany Beach Delaware from February 22-24, 2014. Data were collected at 5 different cross-shore locations and consist of velocity, water depthand sediment concentration. Offshore wave conditions and cross-shore beach profiles were also collected at each low tide.--
DOI: 10.17603/ds2-c74q-dy46
title: Undergraduate Research Experience (REU), NHERI 2019: Using Social Media for Post-Disaster Assessment

description: As social media has progressed, users have increasingly posted during disasters and produced infrastructure damage data. Gathering social media data has its limitations, but leveraging this data can prove incredibly enlightening and useful, due to the wealth of information. This study focuses on Twitter data related to Hurricane Florence, specifically classifying tweet images showing relevant infrastructure damage and identifying local journalist reporting on the hurricane. 
After some pre-processing, the image data was used to train a machine learning algorithm to classify the presence of damage, the type of damage, and the infrastructure damaged, among other labels. Additionally, Twitter user data was used to identify local journalists tweeting during the hurricane by assessing follower count and user bios. The goals are to use the machine learning algorithm to identify damage from social media images and to engage local journalists to contribute more information in future disasters.
DOI: 10.17603/ds2-gndr-1t45
title: Research Experiences for Undergraduates (REU), NHERI 2022: Finite Element Modeling of Cross-Laminated Timber (CLT) Panels for In-plane Bending Applications

description: This study provides a comparative analysis of two different modeling techniques for cross-laminated timber (CLT) under in-plane loading. The results give insight into the behaviors of a CLT panel when compared with an isotropic panel under identical loading conditions. The data from this project can serve as a foundation for future methods of modeling CLT. This work is beneficial to researchers who are interested in learning about in-plane bending properties of CLT.
DOI: 10.17603/ds2-jdvw-5391
title: Research Experiences for Undergraduates (REU), NHERI 2023: Street View Imagery as a Disaster Impact Survey Method: A Case Study of Seattle, WA During and After the COVID-19 Pandemic

description: Street view imagery is useful in a variety of fields beyond navigation and exploration, including the capture of perishable data for natural hazards research. In this study, street view images of Seattle, WA were used to characterize community impact and recovery throughout the COVID-19 pandemic. The number of countable pedestrians in each image was used to quantify human presence in various areas of Seattle as a way to quantify COVID-19 impact and recovery in those places. These pedestrian counts were then manually extracted in a few key locations to investigate differences in recovery among demographics and neighborhoods of Seattle. Data in this project can be used to further explore how communities in Seattle recovered and what factors may have affected recovery rate and quality. This project is unique in that it provides a long-term imagery survey of a broad cross section of Seattle, offering data on gradual and sudden changes in landscape and activity. This study is for anyone interested in how the pandemic affected Seattle communities and those who are interested in the use of street view imagery as an approach to disaster impact surveying and analysis.
DOI: 10.17603/ds2-jfsc-8902
title: Hurricane Irma
title: in Analysis of tweets posted by professional sources of weather forecast and warning information during tropical cyclone events

description: This project collected, filtered, and analyzed tweets from professional sources of meteorological information (including the National Weather Service, broadcast meteorologists, and emergency managers) during several tropical cyclone events. Here, we share documentation for our Twitter data collection, coding, and filtering processes, as applied to these tropical cyclone cases. The documentation shared here allows users to take “big data” from social media and filter it in order to yield smaller, targeted datasets of the most interest to at-risk users during disasters.
description: The documentation provided here describes our Twitter data collection, coding, and filtering schemes used to analyze professional source Twitter activity during Hurricane Irma. The data collection methods described here were valid as of June 2021, when the data were collected, but may have changed since then due to changes in the Twitter API. This analysis focused on understanding the dissemination and retransmission of different kinds of information provided by key professional sources in the Miami and Tampa metropolitan areas.