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

• URL: http://arxiv.org/abs/2608.26519v2
• Date: Fri, 28 Aug 2026 02:44:26 GMT
• Title: Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science
• Abstract summary: Report synthesizes insights from the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing.<n>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.
• Score: 26.803440837891984
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists [47.41269933143946]
  We introduce OmniScientist, a framework that encodes the underlying mechanisms of human research into the AI scientific workflow.<n> OmniScientist achieves end-to-end automation across data foundation, literature review, research ideation, experiment automation, scientific writing, and peer review.<n>This infrastructure empowers agents to not only comprehend and leverage human knowledge systems but also to collaborate and co-evolve.
  arXiv  Detail & Related papers  (2025-11-21T03:55:19Z)
• Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science [10.378736202765776]
  Report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing.<n>Report presents a vision of next-generation ecosystems for scientific computing where AI, software, hardware, and human expertise are interwoven.
  arXiv  Detail & Related papers  (2025-10-03T18:22:47Z)
• From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery [108.1082357960201]
  Agentic AI shows capabilities in hypothesis generation, experimental design, execution, analysis, and iterative refinement.<n>This survey provides a domain-oriented review of autonomous scientific discovery across life sciences, chemistry, materials science, and physics.
  arXiv  Detail & Related papers  (2025-08-18T05:25:54Z)
• From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery [67.07598263346591]
  Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery.<n>This survey systematically charts this burgeoning field, placing a central focus on the changing roles and escalating capabilities of LLMs in science.
  arXiv  Detail & Related papers  (2025-05-19T15:41:32Z)
• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
  A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.<n>Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
  arXiv  Detail & Related papers  (2025-02-07T18:26:45Z)
• Infrastructure Engineering: A Still Missing, Undervalued Role in the Research Ecosystem [0.0]
  Research has become increasingly reliant on software. The need for such a role is not just ideal, but essential for the continued success of science. In this article we will highlight the importance of this missing layer, providing examples of how a missing role of infrastructure engineer has led to inefficiencies.
  arXiv  Detail & Related papers  (2024-05-17T00:15:43Z)
• A cast of thousands: How the IDEAS Productivity project has advanced software productivity and sustainability [1.3083336716269756]
  Concerns are growing about the productivity of the developers of scientific software. Members of the IDEAS project serve as catalysts to address these challenges. This paper discusses how these synergistic activities are advancing scientific discovery-mitigating technical risks.
  arXiv  Detail & Related papers  (2023-11-03T16:14:17Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• AI for Science: An Emerging Agenda [30.260160661295682]
  This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling" The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains. Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers.
  arXiv  Detail & Related papers  (2023-03-07T20:21:43Z)
• Coordinated Science Laboratory 70th Anniversary Symposium: The Future of Computing [80.72844751804166]
  In 2021, the Coordinated Science Laboratory CSL hosted the Future of Computing Symposium to celebrate its 70th anniversary. We summarize the major technological points, insights, and directions that speakers brought forward during the symposium. Participants discussed topics related to new computing paradigms, technologies, algorithms, behaviors, and research challenges to be expected in the future.
  arXiv  Detail & Related papers  (2022-10-04T17:32:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.