ORIGINAL RESEARCH article

Front. Built Environ.

Sec. Earthquake Engineering

Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1594375

This article is part of the Research TopicNHERI 2015-2025: A Decade of Discovery in Natural Hazards EngineeringView all 12 articles

A Decade of DesignSafe: Enabling Open Science in Natural Hazards

Provisionally accepted
Ellen M. RathjeEllen M. Rathje1*Krishna KumarKrishna Kumar1Maria EstevaMaria Esteva2Scott J BrandenbergScott J Brandenberg3Tim CockerillTim Cockerill2Clint DawsonClint Dawson1Jamie Ellen PadgettJamie Ellen Padgett4Jean-Paul PinelliJean-Paul Pinelli5Dan StanzioneDan Stanzione2
  • 1The University of Texas at Austin, Austin, United States
  • 2Texas Advanced Computing Center, The University of Texas at Austin, Austin, Texas, United States
  • 3University of California, Los Angeles, Los Angeles, California, United States
  • 4Rice University, Houston, Texas, United States
  • 5Florida Institute of Technology, Melbourne, Florida, United States

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DesignSafe (www.designsafe-ci.org) is the leading cyberinfrastructure for engineering and social science research related to natural hazards. It provides tools for managing, analyzing, and sharing data, helping researchers study how natural hazards affect both physical infrastructure and communities. DesignSafe connects curated datasets from academic experimental facilities and field reconnaissance teams to researchers focused on data analysis, computation, and numerical simulation. The platform provides researchers with petabyte-scale storage and hundreds of millions of computing hours mediated by intuitive interfaces that lower the bar of entry to advanced computational capabilities. By enabling sophisticated simulations and data-driven workflows previously unattainable with desktop computers or small clusters/servers and enabling streamlined data curation, publication, and dissemination, DesignSafe empowers researchers to accelerate discoveries and helps them amplify the impact of their work.

Keywords: cyberinfrastructure, natural hazards, Data, HPC, Jupyter

Received: 16 Mar 2025; Accepted: 26 May 2025.

Copyright: © 2025 Rathje, Kumar, Esteva, Brandenberg, Cockerill, Dawson, Padgett, Pinelli and Stanzione. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Ellen M. Rathje, The University of Texas at Austin, Austin, United States

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material