RAPTER Data & Collaboration

Strengthening outcome-driven vaccine platform selection through shared evidence and scientific partnership.

Data gaps are a primary source of predictive uncertainty.

RAPTER’s predictive performance depends on the quality, diversity, and transparency of shared immunological data.

Improving Predictive Accuracy Through Comprehensive Evidence

RAPTER integrates curated literature, structured databases, mechanistic modeling, and standardized experiments to support outcome-driven vaccine platform selection.

Model performance depends on the breadth, balance, and quality of available data. Incorporating diverse datasets — including studies with limited or absent protection — is essential for reducing bias, refining uncertainty estimates, and improving decision-relevant predictions.

Priority Data Types

Accounting for both successful and unsuccessful vaccine outcomes strengthens model robustness and minimizes survivorship bias.

We especially encourage submission of:

• Unpublished negative or null results
• Studies demonstrating limited or absent protection
• Comparative platform evaluations
• Endpoint-specific datasets (e.g., severe disease vs. infection)
• Data with well-characterized dosing, route, and adjuvant information
• Immunological measurements with time-course detail

Data Categories

Examples of high-value immunological and clinical datasets include:

• Antibody titers and kinetics
• Neutralization assays
• Epitope mapping
• Opsonophagocytosis assays
• ELISpot assays
• Flow cytometry–based T cell analyses
• T/B cell receptor sequencing
• CyTOF measurements
• Animal challenge studies
• Clinical immunogenicity endpoints
• Immunobridging analyses

Submission Guidance

• Raw data preferred when available
• Summary statistics acceptable
• Include metadata:
  • Pathogen
  • Vaccine platform
  • Antigen
  • Adjuvant
  • Dose
  • Route
  • Timepoints
  • Study model (preclinical/clinical)
• Provide description of experimental design

Secure transfer pathways and confidential discussions are available prior to data sharing.

Collaboration & Attribution

RAPTER operates as a multi-institution consortium spanning national laboratories, academia, and industry.

Data contributors may engage through:

• Collaborative modeling partnerships
• Joint publications
• Experimental validation studies
• Confidential exploratory analysis

Attribution and authorship are discussed prior to data integration.

Get in Touch

For data contribution inquiries and collaboration opportunities:

Contact the Team: rapter@lanl.gov