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

arXiv: https://arxiv.org/abs/2606.28856 · HTML: https://mlsanity.com/paper/2606.28856/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 76 references that are in the map.
- First posted on arXiv 27 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [Empowering Biomedical Discovery with AI Agents](https://mlsanity.com/paper/2404.02831/) (2024)
- [Federated Learning Enables Big Data for Rare Cancer Boundary Detection](https://mlsanity.com/paper/2204.10836/) (2022)
- [Croissant: A Metadata Format for ML-Ready Datasets](https://mlsanity.com/paper/2403.19546/) (2024)
- [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023)
- [Biomedical knowledge graph-optimized prompt generation for large language models](https://mlsanity.com/paper/2311.17330/) (2023)
- [Data Readiness for AI: A 360-Degree Survey](https://mlsanity.com/paper/2404.05779/) (2024)
- [SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models](https://mlsanity.com/paper/2503.13503/) (2025)
- [Applying the FAIR Principles to computational workflows](https://mlsanity.com/paper/2410.03490/) (2024)
- [LinkML: An Open Data Modeling Framework](https://mlsanity.com/paper/2511.16935/) (2025)
- [CurateGPT: A flexible language-model assisted biocuration tool](https://mlsanity.com/paper/2411.00046/) (2024)
- [AIDRIN 2.0: A Framework to Assess Data Readiness for AI](https://mlsanity.com/paper/2505.18213/) (2025)

## 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.