Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Exploration

2026-06-27

AI Summary

This study addresses the significant challenge posed by the heterogeneity of space biology data, which severely limits the application of artificial intelligence (AI) in space life sciences. To overcome this barrier, the authors propose a three-tiered data restructuring framework—“FAIR → AI-ready → space-ready”—that systematically enhances data usability for AI through standardization, enriched metadata, purpose-built AI interfaces, and a distributed governance architecture. The work innovatively outlines an AI-ready data evolution pathway tailored for deep space exploration and advocates for the establishment of a neutral international coordinating body to ensure the trustworthiness, interoperability, and agent-accessibility of space biology data infrastructures. This approach provides both a technical roadmap and a governance blueprint to support multimodal AI applications in space biology.

Abstract

While AI holds the potential to revolutionize space life sciences, realizing this promise is contingent upon the systematic restructuring of heterogeneous spaceflight biological data into machine-actionable, AI-ready forms. Even though open access principles support human reuse and scientific reproducibility, this does not necessarily enable AI systems to access and analyze such a diverse set of scientific datasets. In addition, the growing array of AI approaches places distinct demands on data structure, metadata, and access interfaces. In order to respond to such growing changes we propose a three-tier approach, proceeding from FAIR to AI-ready to space-ready data. We discuss existing infrastructures and how they can be improved to close the AI access gap. We conclude by proposing a neutral international coordinating body as the governance backbone for the trustworthy, agent-accessible space biology infrastructure that deep space biological research will require.

Problem

• AI-ready data
• space life sciences
• heterogeneous data
• machine-actionable
• deep space exploration

Innovation

• AI-ready data
• FAIR principles
• space biology infrastructure
• machine-actionable data
• international governance