DesignSafe Data Depot Repository Harvested Subcollection

This sub-collection includes a listing and corresponding descriptions of datasets published in the DesignSafe Data Depot Repository (DDR). The repository preserves and provides access to a variety of datasets gathered during experiments, simulations, field reconnaissance missions, surveys, and other research methods and activities that study the impact of natural hazards on people and structures.

Currently, we include field research datasets containing engineering, social science, and interdisciplinary data gathered during pre and post disaster reconnaissance missions to sites hit by disasters, varied survey instruments, and survey and interview data.

The DDR is the open-access data repository component of the Natural Hazards Engineering Research Infrastructure (NHERI), and is supported by the National Science Foundation. Since 2022 DDR is a Core Trust Seal Certified Data Repository.

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381 to 390 of 425 Results

Interviews with NWS Forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode: Pre-interview survey in Interviews with National Weather Service (NWS) forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode Jun 2, 2025 Demuth, Julie; Bostrom, Ann; Harrison, David; McGovern, Amy; Wirz, Christopher; Cains, Mariana, 2024, "Interviews with NWS Forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode: Pre-interview survey in Interviews with National Weather Service (NWS) forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode", https://doi.org/10.17603/DS2-MR3Z-7947, Designsafe-CI This workbook contains a spreadsheet that details the questions and possible responses during the pre-interview survey. For survey questions with the text in brackets, e.g., "[AI/ML / new] guidance", either phrasing was used depending on the assigned treatment for the respondent. If the respondent was assigned to the "AI/ML" version of the survey a... The link on the title will take you to the dataset's permanent location in the DesignSafe Data Depot Repository were the full metadata and data are accessible.
Public Health Disaster Research Award Program Evaluation Survey Data in Public Health Disaster Research Award Program Evaluation Jun 2, 2025 Adams, Rachel; Mordy, Meghan; Peek, Lori, 2023, "Public Health Disaster Research Award Program Evaluation Survey Data in Public Health Disaster Research Award Program Evaluation", https://doi.org/10.17603/DS2-ANA0-KF03, Designsafe-CI This file includes de-identified survey data from the Public Health Disaster Research Award Program evaluation. In 2020, the Natural Hazards Center—with funding from the Centers for Disease Control and Prevention and the National Science Foundation—established the Public Health Disaster Research Award Program. In 2020 and 2021, as part of the newly... The link on the title will take you to the dataset's permanent location in the DesignSafe Data Depot Repository were the full metadata and data are accessible.
Historical Tropical Cyclones in US_1880-2011 in National Full-Scale Testing Infrastructure for Community Hardening in Extreme Wind, Surge, and Wave Events (NICHE): Legacy Field Data Inventory Jun 2, 2025 Lombardo, Frank; Rhee, Daniel; Moon, Sung Min, 2023, "Historical Tropical Cyclones in US_1880-2011 in National Full-Scale Testing Infrastructure for Community Hardening in Extreme Wind, Surge, and Wave Events (NICHE): Legacy Field Data Inventory", https://doi.org/10.17603/DS2-NGT2-CY65, Designsafe-CI Aggregate of tropical cyclones that made landfall in the United States between 1880 and 2011 This project presents a legacy field observation inventory of windstorms (e.g., hurricanes, tropical storms, tornadoes, severe storms) that occurred in the Atlantic Ocean and in the United States from 1880 and 2021. The objective of this project was intende... The link on the title will take you to the dataset's permanent location in the DesignSafe Data Depot Repository were the full metadata and data are accessible.
Field Survey Dataset in Hurricane Beryl: Reconnaissance of Wind and Storm-Surge Damage to Buildings in Houston and Galveston, Texas Jun 2, 2025 Kalliontzis, Dimitrios; Kotzamanis, Vasileios, 2024, "Field Survey Dataset in Hurricane Beryl: Reconnaissance of Wind and Storm-Surge Damage to Buildings in Houston and Galveston, Texas", https://doi.org/10.17603/DS2-BVCB-7Y13, Designsafe-CI Method: Smart phone devices Types of buildings: Mid-to-high rise buildings in downtown Houston and low-rise buildings in residential areas Types of damages: Wind-induced damage to roofs, walls, windows; Storm-induced erosion and scour around piles; No damage was observed to newly constructed buildings. Sites of inspection: Downtown Houston, Northwe... The link on the title will take you to the dataset's permanent location in the DesignSafe Data Depot Repository were the full metadata and data are accessible.
Post Fire Landslide Hazards in GEER - Rapid data acquisition for the investigation of post-fire landslide hazards in the Pacific Northwest Jun 2, 2025 Booth, Adam; Roering, Josh; Wartman, Joseph; Busby, David; RAPID, NHERI; Dedinsky, Karen; Grilliot, Michael; Lyda, Andrew, 2024, "Post Fire Landslide Hazards in GEER - Rapid data acquisition for the investigation of post-fire landslide hazards in the Pacific Northwest", https://doi.org/10.17603/DS2-H5FA-XG57, Designsafe-CI LiDAR and UAS data were captured at Finn Rock; LiDAR and UAS data were captured at Bogus Creek; UAS data were captured at Dodson. LiDAR was collected by means of Terrestrial Laser Scanning (TLS) and UAS data was created through the use of SfM (Structure from Motion) by drones. At Dodson, two debris flow runout tracks and deposits were captured. The... The link on the title will take you to the dataset's permanent location in the DesignSafe Data Depot Repository were the full metadata and data are accessible.
Lidar Scans of Geotechnical Earthquake Damage at Building Sites in Gölbaşı, Adıyaman in GEER Lidar Data from the 2023 Kahramanmaras Earthquake Sequence Reconnaissance Jun 2, 2025 Moug, Diane; Bray, Jonathan; Bassal, Patrick; Kendir, Sena Begum; Sahin, Arda, 2024, "Lidar Scans of Geotechnical Earthquake Damage at Building Sites in Gölbaşı, Adıyaman in GEER Lidar Data from the 2023 Kahramanmaras Earthquake Sequence Reconnaissance", https://doi.org/10.17603/DS2-FCRY-0191, Designsafe-CI This data was gathered during the Geotechnical Extreme Events Reconnaissance (GEER) efforts following the February 6, 2023, Kahramanmaraş earthquake sequence. This dataset is comprised of terrestrial lidar scan point clouds that aim to capture liquefaction-induced building settlement, building-ground interactions, and ground deformations. The objec... The link on the title will take you to the dataset's permanent location in the DesignSafe Data Depot Repository were the full metadata and data are accessible.
2023 Social Science Extreme Events Research (SSEER) Census in Social Science Extreme Events Research (SSEER) Network Data, Survey Instrument, and Annual Census Jun 2, 2025 Peek, Lori; Mark, Brigid, 2024, "2023 Social Science Extreme Events Research (SSEER) Census in Social Science Extreme Events Research (SSEER) Network Data, Survey Instrument, and Annual Census", https://doi.org/10.17603/DS2-S891-5R70, Designsafe-CI The 2023 Social Science Extreme Events Research (SSEER) Census summarizes the results of responses gathered from 1,620 social scientists who responded to the SSEER survey between its release date on July 8, 2018 and December 31, 2023. This report characterizes the diversity, disciplinary skills, and expertise within the research community. It is or... The link on the title will take you to the dataset's permanent location in the DesignSafe Data Depot Repository were the full metadata and data are accessible.
2023 Kahramanmaraş, Türkiye Earthquake Sequence: Observations of Recovery 20 Months After in EERI-SSEER 2023 Kahramanmaras, Turkiye Earthquake Sequence Reconnaissance Jun 2, 2025 Mieler, Mike; Başbuğ, Burçak; Çelik, Süleyman; Comfort, Louise; Demiryurek, Alpay; Hortacsu, Ayse; Loos, Sabine; Molina Hutt, Carlos; Sezen, Halil; Sönmez, Egemen, 2025, "2023 Kahramanmaraş, Türkiye Earthquake Sequence: Observations of Recovery 20 Months After in EERI-SSEER 2023 Kahramanmaras, Turkiye Earthquake Sequence Reconnaissance", https://doi.org/10.17603/DS2-KBP7-DF36, Designsafe-CI This project refers to reconnaissance data collected from the 2023 Kahramanmaras, Turkiye Earthquake Sequence as part of Earthquake Engineering Research Institute Learning From Earthquakes program (EERI LFE) and Social Science Extreme Events Research (SSEER) Network. Data in this repository covers the engineering and social sciences. A mission was... The link on the title will take you to the dataset's permanent location in the DesignSafe Data Depot Repository were the full metadata and data are accessible.
Regional Analyses in RAPID: Understanding the Interrelationships Among Floods, Building Characteristics, Mold Growth and Occupants’ Asthma Symptoms in the Aftermath of Hurricane Ida Jun 2, 2025 Ahmadisharaf, Ebrahim; Pakdehi, Maryam, 2024, "Regional Analyses in RAPID: Understanding the Interrelationships Among Floods, Building Characteristics, Mold Growth and Occupants’ Asthma Symptoms in the Aftermath of Hurricane Ida", https://doi.org/10.17603/DS2-CDG3-NP75, Designsafe-CI Dedicated to analyses that highlight regional differences in data and impacts, as determined through statistical tests. This collection emphasizes the distinct environmental, structural, and health impacts observed in Northeast, Louisiana, and Florida. Extensive dampness and mold growth in buildings are some of the most common, yet overlooked indir... The link on the title will take you to the dataset's permanent location in the DesignSafe Data Depot Repository were the full metadata and data are accessible.
Data Collection Tools for Interviews and Observations in Hurricane Florence Food Environment Study Jun 2, 2025 Clay, Lauren, 2021, "Data Collection Tools for Interviews and Observations in Hurricane Florence Food Environment Study", https://doi.org/10.17603/DS2-7EH2-0J40, Designsafe-CI Interview guides and field observation guides for Hurricane Florence field work. This study explores issues related to food environment disruption following a hurricane event from a systems perspective using a quick response disaster research methodology. Through interviews and observations during the week following Hurricane Florence (2018) and fo... The link on the title will take you to the dataset's permanent location in the DesignSafe Data Depot Repository were the full metadata and data are accessible.

Interviews with NWS Forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode: Pre-interview survey in Interviews with National Weather Service (NWS) forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode

Jun 2, 2025

Demuth, Julie; Bostrom, Ann; Harrison, David; McGovern, Amy; Wirz, Christopher; Cains, Mariana, 2024, "Interviews with NWS Forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode: Pre-interview survey in Interviews with National Weather Service (NWS) forecasters related to severe weather and new artificial intelligence/machine learning (AI/ML) guidance predicting severe hail and storm mode", https://doi.org/10.17603/DS2-MR3Z-7947, Designsafe-CI

This workbook contains a spreadsheet that details the questions and possible responses during the pre-interview survey. For survey questions with the text in brackets, e.g., "[AI/ML / new] guidance", either phrasing was used depending on the assigned treatment for the respondent. If the respondent was assigned to the "AI/ML" version of the survey a...