cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17603/ds2-vgea-y593
title: ISROC Dataset for: Testing the numerical models for boulder transport through high energy marine wave event: An example from southern Saurashtra, western India
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 "Testing the numerical models for boulder transport through high energy marine wave event: An example from southern Saurashtra, western India." This study focused on 3 coastal boulder deposits in Saurashtra, India, to test equations for different transport models. Due to knowing the origin of these boulders, boulders are an option to determine the validity of the hydrodynamic equations, which tend to overestimate wave heights.
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-q49y-sg35
title: Natural Hazards Research Summit 2024: Large-Scale Testing of Ductile Mass Timber- Concrete Connections

description: The scope of this project is to develop an improved ductile-anchor connection with replaceable threaded rods to be placed at the superstructure-concrete footing interface in mass timber buildings to improve their seismic performance in seismically active regions. Considering that more than 143 million Americans are exposed to potentially damaging earthquakes (as estimated by the United States Geological Survey) it is imperative to focus on the seismic resistance of mass timber structures, as they are recently gaining extreme popularity, and are positively perceived by the public as the buildings of the future. Ideal connections in mass timber structures should develop high stiffness and ductility. However, state-of-the-art connections rely on the plastic deformation of small diameter fasteners, e.g., nails, connecting metal straps to the timber element and may lead to brittle failures, delivering a premature collapse of the structure during seismic events. A more reliable mechanism to develop ductility and meet the seismic demand may be achieved using ductile-anchor concrete connections, which utilize the concept of anchor stretch length to allow for an enhanced structural response in earthquakes.
DOI: 10.17603/ds2-w2x6-nm09
title: CFD Notebooks (Beginner)

description: Hydro-UQ and WE-UQ facilitate natural hazard research enabled by Computational Fluid Dynamics (CFD). These applications require a basic working knowledge of OpenFOAM, including mesh preparation and the generation of boundary conditions. To prepare researchers for these applications, CFD Notebooks is offered as an educational resource to introduce CFD through a series of Jupyter notebooks hosted on DesignSafe. It provides a foundation for all aspects of CFD as implemented in OpenFOAM, from running cases to programming. The notebook format interactively demonstrates running exercises and simulating cases in OpenFOAM without any software installation overhead by leveraging DesignSafe's High-Performance Computing (HPC) platform. The Jupyter notebooks consist of video tutorials, DIY examples, and reference reading materials. Working knowledge gained in this educational resource will build confidence in users, enabling them to easily transition to using these research applications to augment their research.

CFD Notebooks dedicates a Jupyter notebook to each particular topic. Experienced users can directly access topics of interest, while new users will benefit as they progress through the material. The topics covered include: (a) Introduction to OpenFOAM and DesignSafe (b) Different solvers in OpenFOAM and background information on which solver to use (c) Boundary conditions in OpenFOAM (d) Meshing with blockMesh.

Future enhancements to the CFD Notebooks will include: (a) Multiphase flows and VOF (b) Post-processing in OpenFOAM using Markers (c) Finite Volume Method and interpolations (d) Application of wavemaker boundary conditions (e) Tutorials on writing custom boundary conditions (d) Turbulence models defined and how they can be applied (e) Meshing with snappyHexMesh (f) Importing meshes into OpenFOAM
DOI: 10.17603/ds2-k1ec-4e35
title: Machine Learning Modeling
title: in RAPID: Understanding the Interrelationships Among Floods, Building Characteristics, Mold Growth and Occupants’ Asthma Symptoms in the Aftermath of Hurricane Ida

description: Features machine learning models developed to analyze what driving factors impacts mold and asthma after hurricane Ida and Ian. Includes scripts for regression models predicting mold growth and classification models for asthma symptom prediction, alongside detailed descriptions of model configurations, training, validation processes, and hyperparameter tuning.
description: Extensive dampness and mold growth in buildings are some of the most common, yet overlooked indirect impacts of floods, which adversely affect human respiratory health, particularly among asthmatic individuals. There is currently a lack of understanding on interrelationships among flood characteristics and drivers, building and HVAC system properties (e.g., ventilation rates), human behaviors (e.g., time spent in homes) and vulnerability to mold growth (e.g., asthma symptoms) in the built environment, particularly in residential buildings. This project collects data in the aftermath of two recent catastrophic hurricane events - Ida and Ian - from affected residential buildings to study the relationships among flood characteristics, mold growth, building properties, human behavior and human respiratory health. Our interdisciplinary team uses survey questionnaires, laboratory experiments and machine learning modeling to answer the following scientific questions: (1) what flood characteristics and drivers, building and HVAC system properties and human behaviors cause higher levels of mold growth in residential buildings? and (2) how does living in submerged or water-damaged houses after floods affect asthma symptoms among the residents?

The developed empirical database and identified relationships can be used to guide building designers and occupational health scientists to establish resilient indoor environments, provide a foundation to develop flood-induced mold growth and asthma risk models, assist public health officials and emergency managers to have a better understanding of indirect health-related impacts of floods and support the development of timely strategies for disaster management in population centers.
DOI: 10.17603/ds2-pc7z-7227
title: Healthcare network access
title: in Risk Analysis of Healthcare Network in the San Francisco Bay

description: This folder was prepared by L. Ceferino (ceferino@berkeley.edu) and contains data and code to reproduce the results of Ceferino et al. (2025). "Accessing Acute Care Hospitals in the San Francisco Bay after a Major Hayward Earthquake."

This data repo contains three directories.
1. DamageAnalysisResults: contains the analysis results (e.g., simulated ground motion intensities, hospital structural and nonstructural damage, and bridge damage) produced using R2D.
2. NetworkAnalysis: contains input data and a Python code to analyze the accessibility of the hospital network.
3. PostProcessing: contains input data and a Python code to analyze the data produced in DamageAnalysis and NetworkAnalysis, as well as producing all plots and tables presented in the paper.
description: This file models the healthcare network access after earthquakes in the Bay Area.
DOI: 10.17603/ds2-gxwb-fd57
title: Organizational Impact and Recovery Field Guide
title: in Examining Organizational Disruption and Recovery Post Hurricane Harvey in Southeast, Texas

description: To gather more in-depth information about how disasters affect businesses and nonprofits, the National Institute of Standards and Technology (NIST) and the National Oceanic and Atmospheric Administration (NOAA) launched a study of business disruption and recovery related to several recent disasters. The files uploaded in this projec includes the report that outlines the survey instrument used as the basis for the research designed and conducted in partnership between Texas Sea Grant and the Texas A&M University Hazard Reduction and Recovery Center (HRRC), NIST Applied Economics Office (AEO), and the NOAA Regional Integrated Sciences and Assessment (RISA) Program. Addittionally, the project contains the field guide used to administer the survey and collect data and a preliminary findings report.  This research protocol uses a mixed-method research approach to study businesses and nonprofit organizations that were impacted by Hurricane Harvey in 2017 and their long-term recovery process. The approach combines both quantitative and qualitative data collection methods of in-person and telephone surveys to obtain organizational disruption and recovery information from organizational owners and managers.
description: Provides a field guide for administering the organizational impact and recovery survey.
DOI: 10.17603/ds2-mj33-xd45
title: Natural Hazards Research Summit 2024: Predicting Hurricane Damage and Loss to Mobile Manufactured Homes

description: A comprehensive disaster risk assessment involves understanding the loss distribution across different geographical areas and building types including mobile manufactured homes which has its distinct features. This project is focused on understanding the damage and loss risks associated with mobile manufactured homes in the study area. 
This information is important and unique for the multi-stakeholders decision making model which accounts for the roles of building owners, insurers, reinsurers, and government entities and perspectives towards the risks and retrofits opportunities. This project being a part of the Coastal Hazards, Equity, Economic prosperity and Resilience Hub's (CHEER) project is focused on developing a computational tool called Stakeholder-based Tool for the Analysis of Regional Risk (STARR). This tool will assist in hurricane risk management planning for policy makers.
DOI: 10.17603/ds2-r5wm-6b46
title: Structural Design Drawings
title: in Impact of strongback on structure with varying damper and stiffness irregularity arrangements

description: Design drawings for a 4-story model building structure with a removable strongback that was used for a wide-ranging experimental parametric study of strongback performance.
description: This study explored the impacts of damper arrangement and the location of soft story vertical stiffness irregularities on the dynamic behavior and properties of a structure with a strongback. The investigation was carried out through experiments conducted on a small-scale four-story structure that was subjected to ground motion generated by a shake table. Design drawings of the test model are provided here.
DOI: 10.17603/ds2-1g29-sw56
title: NHERI Computational Academy Data Report 2023

description: This report summarizes the 2023 evaluation of the NHERI Computational Academy (NCA) conducted by TACC’s STEM Evaluation Services team. The team examined the implementation, outcomes, and impacts of the NCA using the NHERI Computational Academy survey. The NCA survey gathered information on participants’ background, their satisfaction with the academy, their knowledge gained, whether and how they plan to use what they learned, and their suggestions for improvement. 

According to the NCA survey findings, respondents are highly satisfied with the academy. Participants reported learning about DesignSafe and SimCenter tools, Python, Jupyter Notebooks, machine learning, AI, and other topics. They plan to apply their new knowledge to their own research and recommend the academy to others. Survey respondents highlighted specific learning topics, tools and resources, and mentors as the most useful aspects of the training. They suggested extending the length of the academy, addressing technical issues proactively, creating more personalized training sessions, and improving awareness of DesignSafe/SimCenter resources and workshops. Additionally, respondents expressed interest in future topics such as programming, large-scale flood modeling, GIS and remote sensing, AI, and deep learning.
DOI: 10.17603/ds2-68kx-qm64
title: TEST 16 - RCK16 : 1m radius centrifuge experiment on clayey levee behavior under ground motions
title: in NEES-2012-1161: Levees and Earthquakes: Averting an Impending Disaster - Experiment 16

description: We repeated Experiment 14 (9m radius) on a smaller scale (1m radius).  Goal of Exp 16 was to investigate the change in rate of secondary compression after imposing moderate shear strain amplitudes to the peat foundation.  We selected and scaled two motions specific to that purpose.  The slow data records show that we spun the model at different g-levels and for a long time, in order to pre-consolidate the peat before the shaking.
description: Most of the material for this project is saved under the main project (link:  https://www.designsafe-ci.org/data/browser/public/nees.public/NEES-2012-1161).  See that project for description and details of this research study.  The main project folder includes data from 15 experiments conducted in 2013-2014.  This project includes the data from Experiment No. 16 conducted in 2015.
DOI: 10.17603/ds2-6ptj-4c37
title: M7.2 Nippes Earthquake, Haiti Field Assessment Structural Team Dataset: (FAST-3269)
title: in StEER - 14 August 2021, M7.2 Nippes Earthquake, Haiti

description: A magnitude 7.2 earthquake with epicenter coordinates 18.408°N 73.475°W and a depth of 10 km struck 13 km SSE of Petit Trou de Nippes in Haiti at approximately 8:29 am local time, affecting a wide swath of the Tiburon Peninsula, including the Departments of Nippes, Sud, and Grand’Anse. This earthquake resulted from an oblique reverse motion along the Enriquillo-Plantain Garden fault zone, 75 km west of the 2010 Mw 7.0 earthquake epicenter. In addition to widespread destruction in the Nippes Department, the earthquake affected major city centers on the north and south coasts (Jeremie and Les Cayes, respectively), both still recovering from 2016’s Hurricane Matthew.  

With hundreds of aftershocks in the weeks that followed, collapses and significant damage to the built environment continues to mount. As of August 22, 2021, Haiti's Civil Protection Agency reported 2,207 deaths with 344 still missing. Another 12,268 were injured. Nearly 61,000 homes have been destroyed and more than 76,000 have sustained damages in the three most affected departments. An initial rapid assessment found that 24 health facilities have been affected in the Sud, Nippes and Grand’Anse Departments. Religious institutions, which provide worship, education, and healthcare in many communities, were also significantly impacted. 

With the 14 August 2021 earthquake and its subsequent aftershocks affecting three departments of Haiti, the geographic expanse and intensity of this event are actually greater than the 2010 earthquake, exposing a more diverse cross-section of regional construction practices to strong shaking. This resulted in StEER initiating a Tier 3 response. This project encompasses the products of StEER's response to this event: Preliminary Virtual Reconnaissance Report (PVRR), Early Access Reconnaissance Report (EARR), and Curated Dataset.
description: With three affected departments and damage in urban, peri-urban, and rural areas, speed and 
broad coverage were  prioritized. Field data collectors were assigned geographies where 
performance  assessments  were  to  be  conducted  each  day.  The  objective  was  to  capture  a  
representative sample of different building classes (residential, schools, commercial, government, 
medical/critical facilities) in each affected department. The selection of sites for data collection in 
each department prioritized localities based on their reported damage levels (referenced against 
projected  ground  shaking  levels  from  USGS),  building  density  and  diversity  of  inventory  (for  
greater efficiencies in data collection), and ease of access (localities along primary roads that had 
been  cleared/repaired  since  the  earthquake).
DOI: 10.17603/ds2-zzzr-pr81
title: EERI VERT Phase 1 Kisante Philippine Earthquake

description: The Earthquake Engineering Research Institute (EERI) Virtual Earthquake Reconnaissance Team (VERT) publishes a series of reports for earthquakes that cause significant damage. These reports summarize the damage observed through virtual means on a variety of multi-disciplinary topics. This project is the Phase 1 preliminary report for the Kisante Philippine Earthquake.
DOI: 10.17603/ds2-r0tv-sv29
title: CDS&E: Enabling Time-critical Decision-support for Disaster Response and Structural Engineering through Automated Visual Data Analytics

description: Each extreme natural disaster is an opportunity to assess the performance of our infrastructure under circumstances that cannot entirely be reproduced in the laboratory or through numerical simulation. When performing reconnaissance in the field, engineers record much of the information being gathered in the form of images. When large quantities of images are collected within a short time period and have such a wide variety of content, timely organization and documentation will be important. Clearly, manual sorting and analysis of all these images would be prohibitively tedious and expensive. Thus, to efficiently and thoroughly use these images, engineers need to exploit recent advances in computer vision and machine learning. In this project, we devised a series of automated capabilities, based on computer vision and machine learning, to classify, organize, and document the large volumes of visual data collected during a reconnaissance mission. We have developed a suite of techniques that can significantly enhance current data collection and organization procedures to support reconnaissance missions.
DOI: 10.17603/ds2-sx2h-8s20
title: Geotechnical site characterization at the Drainage Farm Site

description: A surface wave field-testing campaign was conducted at the Drainage Farm Site in Logan, Utah, USA, a property owned by Utah State University (USU). The field testing involved both active-source multichannel analysis of surface waves (MASW) testing and ambient noise (microtremor array measurements: MAM) testing. Structural geology indicates that Southern Cache Valley, encompassing the Drainage Farm Site and located in the northeastern part of the Basin and Range province, is a graben bounded by high-angle normal faults (Williams, 1962). The site is underlain by Paleozoic rocks, which are overlain by Tertiary formations such as the Wasatch and Salt Lake formations, composed of conglomerate, siltstone, and tuffaceous sandstone. In certain areas of Cache Valley, these formations reach thicknesses of up to 2,440 m (Evans et al., 1996). The near-surface geology of the Drainage Farm Site is characterized by sediments from ancient Lake Bonneville, which receded to form the Provo shoreline. These sediments include alluvial, lacustrine, and deltaic deposits (Williams, 1962; Evans et al., 1996). Well logs presented by Williams (1962) reveal alternating layers of silt and clay, sand, and gravel above the Salt Lake formation. Moreover, limited deep well logs from the vicinity of the Drainage Farm Site indicate that rock can be encountered at depths ranging from 176 m to more than 350 m (Perez, 1969).
DOI: 10.17603/ds22402
title: Small centrifuge tests
title: in Effect of prior strain history on cone penetration resistance and cyclic strength of saturated sand: 1-m radius centrifuge tests

description: A set of 1-m radius centrifuge tests on clean, saturated, Ottawa sand models subjected to multiple shaking events.  These tests supplement a set of 9-m radius centrifuge tests.
description: Three centrifuge tests on saturated Ottawa sand models prepared to various relative densities subjected to multiple shaking events
DOI: 10.17603/ds2-b8rx-mq61
title: A dataset of roots and root-reinforced soils characteristics

description: Landslides induced by extreme rainfall events pose a significant threat to critical infrastructure and public safety in vulnerable regions. Vegetation, particularly plant root systems, offers an environmentally sustainable solution to rainfall-triggered landslides by enhancing the mechanical stabilization of soils through increased shear strength and providing ecological benefits. However, predicting the behavior of root-reinforced soils remains challenging due to the complex variability in root characteristics, such as diameter, length, orientation, and root content within the soil. Most existing empirical models oversimplify the intricate interactions between roots, soil, and moisture, thereby limiting their predictive accuracy. To address this limitation, this study develops a new database of root and root-reinforced soil characteristic.
DOI: 10.17603/ds2-xyee-sq19
title: Field testing of the developed 3D FWI method using a mobile shaker at Newberry, FL
title: in Geotechnical Site Characterization with 3-D Seismic Waveform Tomography

description: We have successfully developed an advanced 3D FWI method using surface-based seismic wavefields for geotechnical site characterization. The method is based on the solution of 3D elastic wave equations, and Gauss-Newton inversion approach. The capabilities of the developed 3D FWI method has been tested on both synthetic and several field experimental datasets. The analyzed results from synthetic datasets show great capabilities of the 3D FWI method in characterizing laterally variable low- and high-velocity layers, and in detecting buried voids at various depths. Comparing the conventional FWI analyses based on the classic cross-adjoint inversion, our method based on the Gauss-Newton inversion provides more accurate and higher resolution inverted results. Accurate results are also obtained from field experiments at four different test sites in Florida. Known cavity (culvert), unknown buried voids (pre-collapsed sinkholes) at both shallow depths of 5 m to 10 m and larger depths up to 40 m, and variable soil/rock layers are well characterized and confirmed by the ground truth.
description: Field tests were performed to assess the ability of the developed 3D FWI method in characterizing subsurface features, and in void detection. Data was collected using a mobile shaker  called the Thumper. A controlled source signature was used during the testing procedure. The dataset obtained from this analysis was analyzed and it was revealed that the developed 3D FWI method could successfully detect variable subsurface layering features and voids using a 2D array of sensors on the surface. The data obtained as part of the experiment is stored in data files for each shot location. Each data file contains information gathered by all the channels.