Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Exploration
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 14 of its 76 references that are in the map. First posted on arXiv 27 Jun 2026.
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References · 14
- 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,433
- 2018Datasheets for Datasetscited by 1,377
- 2021Datasets: A Community Library for Natural Language Processingcited by 529
- 2024Empowering Biomedical Discovery with AI Agentscited by 118
- 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
- 2024Croissant: A Metadata Format for ML-Ready Datasetscited by 80
- 2023Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learningcited by 50
- 2023Biomedical knowledge graph-optimized prompt generation for large language modelscited by 31
- 2024Data Readiness for AI: A 360-Degree Surveycited by 15
- 2025SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Modelscited by 15
- 2024Applying the FAIR Principles to computational workflowscited by 10
- 2025LinkML: An Open Data Modeling Frameworkcited by 6
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