Stephen M. Fiore

Nicholas A. Coles, Joao Francisco Goes Braga Takayanagi, Stephen M. Fiore et al.

Urgent societal events demand scientific responses that are both rapid and impactful. Through an adversarial collaboration, we connected bibliometric databases to evaluate the speed and impact of over 2 million scientific publications in the three years following 48 urgent societal events. A pilot analysis of three cases -- the 2022 release of ChatGPT, the 2019 COVID-19 pandemic, and the 2001 World Trade Center attacks -- yielded unexpected patterns: larger teams were not only more impactful but also quicker to publish. More precisely, increases in team size were associated with (a) initial increases, but eventual diminishing returns in academic citations, (b) curvilinear returns in news and policy document citations, and (c) curvilinear returns in terms of how quickly papers were published. In other words, there are points where further increases in team sizes are either marginally helpful (diminishing returns) or counterproductive (curvilinear returns). To evaluate robustness, we pre-registered a broader test covering 45 additional events spanning two decades.

Lois Curfman McInnes, Dorian Arnold, Prasanna Balaprakash et al.

This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science, which convened more than 40 experts from national laboratories, academia, industry, and community organizations to chart a path toward more powerful, sustainable, and collaborative scientific software ecosystems. To address urgent challenges at the intersection of high-performance computing (HPC), AI, and scientific software, participants envisioned agile, robust ecosystems built through socio-technical co-design--the intentional integration of social and technical components as interdependent parts of a unified strategy. This approach combines advances in AI, HPC, and software with new models for cross-disciplinary collaboration, training, and workforce development. Key recommendations include building modular, trustworthy AI-enabled scientific software systems; enabling scientific teams to integrate AI systems into their workflows while preserving human creativity, trust, and scientific rigor; and creating innovative training pipelines that keep pace with rapid technological change. Pilot projects were identified as near-term catalysts, with initial priorities focused on hybrid AI/HPC infrastructure, cross-disciplinary collaboration and pedagogy, responsible AI guidelines, and prototyping of public-private partnerships. This report presents a vision of next-generation ecosystems for scientific computing where AI, software, hardware, and human expertise are interwoven to drive discovery, expand access, strengthen the workforce, and accelerate scientific progress.

Samaneh Saadat, Olivia B. Newton, Gita Sukthankar et al.

Our goal is to understand the characteristics of high-performing teams on GitHub. Towards this end, we collect data from software repositories and evaluate teams by examining differences in productivity. Our study focuses on the team formation phase, the first six months after repository creation. To better understand team activity, we clustered repositories based on the proportion of their work activities and discovered three work styles in teams: toilers, communicators, and collaborators. Based on our results, we contend that early activities in software development repositories on GitHub establish coordination processes that enable effective collaborations over time.