Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Exploration
Organizations: Blue Marble Space, Seattle, Washington, USA · Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA · Trivedi Institute for Space and Global Biomedicine, University of Pittsburgh, Pittsburgh, PA, USA · Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK · University of Cambridge, Cambridge, UK · Department of Systems Biology, Harvard Medical School, Boston, MA, USA · Amentum, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA · Massachusetts Institute of Technology, Cambridge, USA · Crown Point Technologies, Inc, Columbia, MD, 21046 · BiosView Labs, Dayton, Ohio, USA · Directorate of Human and Robotic Exploration, European Space Agency, Noordwijk, the Netherlands · Texas State University, USA · McGowan Institute for Regenerative Medicine, Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA · Center for Space Biomedicine, Trivedi Institute for Space and Global Biomedicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA · Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA · Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA · Department of Systems and Computational Biomedicine and WorldQuant Initiative, Weill Cornell Medicine, New York, NY, USA · San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, USA · Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, USA · Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden · European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton CB10 1SD, UK
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.