Today, the SETI Institute announced the second round of grants it will fund with its Support Technology, Research, Innovation, Development, and Education (STRIDE) program. The SETI Institute established the STRIDE fund to support SETI Institute researchers and EOC (Education, Outreach, and Communications) professionals in developing innovative research and education proposals. After funding the first round with $500K, this year’s program will award $1M to fund 10 projects. Advance understanding of the impact of stellar winds and magnetic fields on atmospheric loss in exoplanets and their implications for planetary habitability.

"This year’s STRIDE selections showcase ambitious, cutting-edge work across astrobiology, intelligence, planetary science, AI, and public engagement,” said Nathalie Cabrol, Director of the Carl Sagan Center at the SETI Institute. “These projects push the boundaries of how we explore life, intelligence, habitability, and our place in the universe while fostering innovation that can shape future scientific breakthroughs."

Gladstone Institutes has received an initial award of $2 million from the National Institute of Allergy and Infectious Diseases (NIAID), with additional funding of up to a total of $10 million available over the proposed 5-year project period. This grant will establish the Center for PhAIge Therapy, a research center that will develop new phage-based treatments for antibiotic-resistant bacterial infections.

Researchers at the University of Virginia have developed a new way to identify scientific breakthroughs more accurately. In a study published in Science Advances, data science Ph.D. candidate Munjung Kim and collaborators introduce the Embedding Disruptiveness Measure (EDM), a citation-based metric designed to improve on the widely used Consolidation-Disruption (CD) Index.

The CD Index was created to distinguish truly disruptive research from highly cited but incremental work. However, the method can misclassify papers when multiple teams make similar discoveries at the same time. EDM uses machine-learning techniques inspired by neural language models to analyze how papers connect to both prior and subsequent research, making the measure more robust to citation patterns and better able to identify simultaneous breakthroughs.

The researchers found that EDM more reliably captures transformative scientific contributions, providing a stronger foundation for studies of innovation, scientific discovery, and research impact.