Who Gets to Discover? Government Funding, Scientific Access, and Capability Formation in Cryo-Electron Microscopy
Year: 2026
Type: preprint
Abstract: Modern scientific research increasingly depends on expensive shared infrastructure that individual laboratories cannot independently purchase, maintain, and continuously upgrade. Cryo-electron microscopy (cryo-EM) provides a substantive example because effective use depends not only on microscope time, but also on specimen preparation, trained personnel, computing, data management, image processing, modelling, validation, and continued technical support. The United States has responded through three National Cryo-EM Service Centres that provide nonprofit researchers with no-cost access to advanced instrumentation, technical assistance, and training. This study asks how governments can allocate access to expensive scientific infrastructure while preserving immediate research quality and expanding long-term scientific capability. It combines research on public science funding, research infrastructure, cumulative advantage, and the capability approach with comparative analysis of the National Center for CryoEM Access and Training (NCCAT), Stanford-SLAC Cryo-EM Center (S2C2), and Pacific Northwest Center for Cryo-EM (PNCC). The analysis distinguishes formal access, immediate output, capability formation, and capability diffusion. Official centre documents show that all three centres already combine scientific-merit review with mechanisms for training, inexperienced users, geographic reach, or applicants lacking local resources. Publicly reported outputs also show substantial scientific production, although differences in reporting practices prevent direct centre rankings or causal claims. The paper develops a portfolio framework that separates minimum scientific viability from subsequent allocation priority and makes trade-offs among scientific value, alternative access, training needs, mission relevance, and capability formation explicit.
Authors: Yoon, Phillip, Paras, William
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7222902226
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