Programme Thesis
The Science of Science: Discovery, Communication and Impact program funds research that advances the understanding of the scientific enterprise itself—how discoveries are made, communicated, and translated into societal impact. It exists to support interdisciplinary, data-driven studies that improve the efficiency, equity, and effectiveness of the scientific process, often leveraging computational methods and novel theoretical frameworks.
Selection Criteria
Intellectual Merit: potential to advance knowledge in the science of science (e.g., models of discovery, collaboration dynamics, innovation diffusion). Broader Impacts: benefits to society, including training, diversity, and dissemination. PI qualifications and track record (publications, endorsements, prior funding). Feasibility and clarity of research plan, including data management and budget justification. For early-career/LMIC applicants: evidence of independent research capability and potential for future contributions.
Past Winners / Cohort Profiles
Typical awardees include early-career researchers (postdocs, assistant professors) and established PIs from US universities, often with backgrounds in computational social science, network science, scientometrics, or behavioral economics. Named examples from NSF SoS programs include projects on 'predicting scientific impact using machine learning' or 'team science and innovation.' Independent researchers and LMIC-based applicants are rare but possible if the work is compelling and well-framed.
Ideal Candidate Fingerprint
A US-based early-career researcher (postdoc or faculty) with a strong publication record in scientometrics, computational social science, or network science, proposing a data-driven project that uses large-scale bibliometric or behavioral data to test theories of scientific discovery. The applicant should have clear institutional support, a track record of interdisciplinary collaboration, and a plan for broader impacts (e.g., open science tools, training underrepresented groups).
Recommended Framing
Eniola should position his CCT model as a case study in scientific discovery—using Bayesian validation and computational platforms (IMPRINT, TOPOLOGIX) to demonstrate how novel theoretical frameworks emerge and are tested. Emphasize his independent research trajectory, endorsements from leading neuroscientists (Berridge, Gershman, Daw), and the potential for his work to inform the science of scientific discovery by modeling how reward-memory encoding can be prevented, thereby contributing to both addiction neuroscience and the meta-science of hypothesis testing and replication.
Watch Out
Eligibility: NSF grants typically require US institutional affiliation; as an independent researcher in Nigeria without a US co-PI or host institution, Eniola may not be eligible. The program may prioritize US-based PIs. Additionally, the deadline may have passed (page shows session expiration, no deadline listed). Competitive disadvantage: lack of PhD or current graduate enrollment, and no prior NSF funding history. Strong LMIC angle may not offset institutional requirement.