Programme Thesis
The NSF IDSS program funds the development, scaling, and operation of national-scale data cyberinfrastructure systems and services that enable data- and AI-driven research and education across multiple scientific disciplines. It exists to support foundational, transdisciplinary cyberinfrastructure that broadly benefits the science and engineering community, rather than single-domain projects.
Selection Criteria
- Intellectual Merit: Potential to advance knowledge in data science, AI, and cyberinfrastructure; novelty and feasibility of the proposed system or service.
- Broader Impacts: Demonstrable benefit to multiple scientific communities, education, and society; open science and data sharing principles.
- National Scale: The system must serve a broad, national user base, not just a single discipline or project.
- Technical Readiness: For Category II, evidence that the system is already operational at a smaller scale and ready for transition to national production quality.
- Team and Management: Qualifications of the PI and team, including cyberinfrastructure expertise; clear project management and sustainability plan.
- Data Management Plan: Compliance with NSF data sharing and FAIR principles.
- Budget Justification: Appropriate and justified costs for development, operation, and scaling.
Past Winners / Cohort Profiles
Past IDSS awards include large-scale data repositories, federated data systems, and AI-ready cyberinfrastructure platforms (e.g., NSF's 'DataNet' and 'Cyberinfrastructure for Sustained Scientific Innovation' projects). Typical winners are U.S. universities or research institutions with established cyberinfrastructure teams, often involving multiple PIs from computer science, domain sciences, and data engineering. Named examples are not listed on the page, but archetypes include projects like 'OpenTopography' or 'iDigBio' that transitioned from pilot to national service.
Ideal Candidate Fingerprint
A U.S.-based PI (typically a tenured faculty or senior researcher at a U.S. institution) with a strong track record in cyberinfrastructure development, data science, and AI. The team includes domain scientists, software engineers, and data managers, and the proposed system is already proven at a smaller scale (for Category II) with clear plans for national deployment, sustainability, and broad community adoption.
Recommended Framing
Eniola should not apply directly due to NSF eligibility restrictions (requires U.S. institution). However, if he partners with a U.S.-based PI (e.g., at a university with cyberinfrastructure expertise), he can frame his CCT model and platforms (IMPRINT, TOPOLOGIX, GATE) as a novel, AI-driven national-scale data cyberinfrastructure for addiction research. Emphasize the transdisciplinary impact (neuroscience, pharmacology, data science), open-source tools, and potential to serve multiple research communities, aligning with IDSS's goal of foundational, multi-disciplinary systems.
Watch Out
Eniola is not a U.S. citizen or permanent resident, and is not affiliated with a U.S. institution; NSF grants typically require U.S. institutional eligibility. He has no prior NSF funding or cyberinfrastructure project management experience at national scale. The proposed work is currently at a conceptual/prototype stage, not yet operational at a smaller scale as required for Category II.