The COVID-19 pandemic demonstrated both the remarkable potential of modern vaccinology and the limits of our preparedness. While vaccines were developed at unprecedented speed, success depended on decades of prior investment, extensive trial and error, and an urgent mobilization that may not be easily replicated in the future (1). Vaccine design, testing, and manufacturing remain resourceintensive and operationally complex. On average it costs over 800 million and takes 10 years to bring a new vaccine to market (2). As the world faces the growing threat of infectious diseases that are emerging and re-emerging, naturally occurring and deliberately engineered, greater coordination across experimental, computational, and policy domains is required. This Special Issue was conceived to explore how advances across diverse disciplines might collectively improve how vaccine technologies are assessed, compared, and advanced under time pressure. This Special Issue brings together an interdisciplinary collection of research spanning experimental, computational, and theoretical approaches to vaccine development. Collectively, these works advance the overarching goal of enhancing comparability, reproducibility, and transparency across the vaccine research field. The issue spans diverse pathogens -including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (4-6), Ebola virus (EBOV) (7), Burkholderia pseudomallei (8) (9) (10) (11), Yersinia pestis (12), Mycobacterium avium subspecies paratuberculosis (13) -and a broad range of vaccine platforms including mRNA (7), DNA (7, 9), recombinant vesicular stomatitis virus (rVSV) vectored (7), yeast expression systems (14), nanoparticle formulations (12, 15), protein subunit vaccines (4, 8), glycoconjugates (16), and live attenuated vaccines (8). Computational contributions include mechanistic modeling (6), artificial intelligence (AI) -assisted data extraction (17), in silico multi-epitope vaccine design (13), and strategies for data integration and standardization (18). Experimental papers present optimized protocols for detecting SARS-CoV-2 Tcell responses (5), high-throughput PepSeq methods for antigen discovery and immune profiling (10), and human in vitro immunization assay platforms that integrate innate and adaptive immunity (19). Across these manuscripts, a recurring message emerges: progress depends not only on novel technologies, but also on shared experimental practices, transparent reporting, and community standards that enable results to be compared and reused. Several reviews and methodological studies in this collection highlight persistent barriers to computational integration including inconsistent metadata standards, heterogeneous datasets, and the limited availability of negative results that constrain the effective application of machine learning (ML) in vaccinology (17, 18). The consensus mathematical model of antibody dynamics directly addresses several of these challenges by providing a generalizable in-silico platform for cross-pathogen and cross-platform comparisons (6). Methodological studies further emphasize the importance of reporting negative and neutral data, both to understand failure modes and to train more representative computational models. This theme resonates across both experimental and computational contributions in this Special Issue, reflecting a community-wide recognition that transparency, including the publication of negative findings, is fundamental to building cumulative knowledge. Taken together, the contributions in this Special Issue suggest a set of shared commitments that could strengthen vaccine research and preparedness. These commitments span multiple stakeholders: • Researchers: adopting standardized experimental designs where feasible and reporting negative or neutral findings This Special Issue demonstrates the breadth of ongoing innovation in vaccine research while underscoring the need for convergence on shared experimental and reporting practices. Advancing pandemic preparedness will depend not only on continued development of new vaccine technologies, but also on sustained investment in data infrastructure, community standards, and collaboration among experimentalists, computational scientists, funders, and policymakers. The development of the COVID-19 vaccine was an unprecedented global achievement but should also serve as a warning for the need to establish a cohesive, collaborative response in anticipation of the next potential pandemic-level event. The individual tasks involved in the design, development, assessment, approval, and distribution of vaccines need to be accelerated, but with a systematic and standardized approach so the global health community can adopt a proactive, rather than reactive, response. This Special Issue collectively describes the start of integrated efforts to leverage both published and experimental vaccine study data, assessed under tightly standardized methodology, to support the rapid and systematic assessment of vaccine technologies. AI and ML offer a powerful, but still under-realized, opportunity to accelerate vaccine research by enabling the integration, comparison, and interpretation of heterogeneous experimental data at scale (20) (21) (22). The performance and generalizability of these methods remain fundamentally constrained by fragmented data ecosystems, inconsistent metadata standards, limited access to raw experimental results, and the systematic absence of negative or neutral outcomes from the published record. Without coordinated efforts to standardize, curate, and share vaccine study data across platforms and institutions, AI risks amplifying existing biases rather than delivering transformative insight. Addressing these limitations will require sustained community investment in interoperable data infrastructures, transparent reporting practices, and data sharing. To translate these priorities into action, we propose the following next steps for the field: • Standardize metadata frameworks across vaccine studies to improve cross-study analysis. • Invest in shared data infrastructures that enable secure and responsible data exchange and cross-institution collaboration. • Adopt transparent reporting practices that include comprehensive documentation of experimental design, reagent sourcing, data pre-processing procedures, and analytical workflows. • Actively publish or deposit negative and non-positive findings in accessible repositories to reduce publication bias, improve analytical robustness, and build a more representative evidence base. • Partner across research groups to responsibly integrate well-annotated datasets, includingthose not yet publicly available. Collective commitment to these priorities will be necessary to build a more transparent, representative, and durable foundation for AI-enabled vaccinology.
Kubicek-Sutherland et al. (Tue,) studied this question.