Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

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Authors: Lois McInnes, USDOE Office of Science, Dorian C. Arnold, Prasanna Balaprakash, Mike Bernhardt, Franck Cappello, Beth Cerny, Deborah DiazGranados, Anshu Dubey, Nichole Etienne, Roscoe Giles, Diego Gómez-Zará
+37 more Denice Hood, Mary Ann Leung, Vanessa López, Olivia B. Newton, Irene Qualters, Keita Teranishi, Stefan Wild, G. S. Allen, Richard Arthur, Alexandra Ballow, Tony Baylis, David E. Bernholdt, Daniel Bielich, Johannah Cohoon, Jeremy Crampton, Charles Ferenbaugh, Stephen M. Fiore, Thomas Hérault, Tanzima Islam, Stephen Jacobsohn, Meifeng Lin, Charles Lively, Satoshi Matsuoka, Staša Milojević, Daniel Nichols, Chris Oehmen, Santiago Tabares, Michael E. Papka, K. Riley, Damian Rouson, Sudip K. Seal, Brittany Segundo, J Shalf, Andrew Siegel, Valerie Taylor, Jim Willenbring, Lou Woodley
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Abstract

Scientific computing is undergoing rapid transformation as advances in artificial intelligence, heterogeneous computing, automation, and data-intensive research reshape not only computational tools but also the institutions, workforce models, and collaborative practices that support scientific discovery. This report synthesizes insights from the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing, the second in a three-year series focused on strengthening scientific computing ecosystems through socio-technical co-design. Workshop discussions identified four interdependent strategic themes: software ecosystems for AI-enabled scientific discovery; trust, validation, and traceability; human-AI teaming and paradigm shifts; and workforce, pedagogy, and governance. The report translates these themes into eight priorities for community action spanning shared research infrastructure, trust and traceability, user experience, human-AI teaming, workforce development, cross-sector coordination, stewardship and sustainability, and evaluation of scientific value. Together, these priorities outline directions for building scientific computing ecosystems that remain trustworthy, sustainable, innovative, and resilient as AI assumes a growing role in scientific work.

Fields of Study & Subjects

Scientific Computing and Data Management Research Data Management Practices Big Data and Business Intelligence

Publication details

Publication date
1 August 2026
Language
EN