Building an Open, AI-Ready Astrobiology Data Ecosystem for NASA’s Next Scientific Era

NASA Astrobiology Program: The Astrobiology Data Ecosystem, Open Science, and the AI Era

Astrobiology is a uniquely interdisciplinary field focused on studying habitable planets, life, ecosystems, and civilizations across vast scales of time and space. This complexity demands integration across physical, chemical, biological, and social sciences. Artificial intelligence (AI), particularly machine learning (ML), offers powerful tools to identify and model complex, non-linear relationships within diverse datasets, making it highly relevant to astrobiology research.

Recent AI/ML applications in astrobiology include identifying minerals linked to habitability, classifying exoplanet transit signals, and distinguishing biogenic from abiogenic organic compounds in various spectral data. The greatest potential lies in using multi-modal data—such as imagery, spectroscopy, mass spectrometry, isotopic analysis, and metagenomics—to better understand the boundary between life and non-life and the processes behind life’s emergence.

To fully utilize AI/ML, astrobiology requires comprehensive, cross-compatible datasets. This need aligns with the broader scientific shift toward open science principles, like NASA’s Open Science Initiative. Open data sharing enhances reproducibility, reduces redundant effort, increases historical and future data value, and lowers barriers to innovation across disciplines.

Challenges and Recommendations for the Astrobiology Data Ecosystem

1. Finding Existing Data: Labeling, Indexing, and Search

While relevant data exist across multiple repositories—such as RRUFF, USGS Spectral Library, and NASA’s Planetary Data System—locating and extracting it can be time-consuming. Inconsistent annotations, undocumented processing steps, and incompatible APIs hinder effective data use, limiting the assembly of multi-modal datasets for AI/ML analysis.

Many subfields use their own ontologies, but lack of unified standards complicates data integration. To address this, a cross-disciplinary working group should develop an astrobiology-wide ontology and API standards covering diverse systems and measurement techniques. Text-based AI tools can assist by analyzing literature and databases to identify commonalities.

Supporting this, new funding opportunities could focus on standardizing and unifying existing data, improving APIs, and preparing future datasets for AI readiness. This would ensure data are more accessible and usable for the community.

2. Addressing Data Gaps: Breadth and Coverage

Differences in sample handling and measurement techniques between living and non-living systems limit comparability. For example, geological samples are often processed differently than biological samples, hindering cross-project analysis. Field expeditions rarely collect the same comprehensive sets of biological, chemical, and physical data.

Published protocols often lack sufficient detail, making replication difficult. Important metadata—such as raw instrument files, intermediate analysis steps, and exact processing workflows—are frequently missing from data management plans.

Recommendations include establishing minimum metadata standards and context measurements for all astrobiology fieldwork and analyses. Creating a shared “library” of core field instruments and a detailed protocol repository would promote consistency and reproducibility. Repositories must also support physical samples, raw data, and processing documentation.

3. Improving Access to Unique Samples and Instruments

Access to specialized instruments and flight-analog tools is often limited due to mission priorities and concerns about contamination. This restricts research opportunities and slows progress. Additionally, archiving options for valuable field samples and derived materials are insufficient, risking loss of irreplaceable data.

One solution is requiring community-access versions of instruments developed with mission funding. These versions would be more affordable and focus on data equivalence rather than miniaturization or hardening. Alternatively, detailed documentation enabling others to replicate instruments with off-the-shelf components should be encouraged.

Maintaining a public registry of field samples with standardized storage protocols would protect valuable resources and increase their scientific impact by enabling broader research use.

4. Lowering Barriers: Streamlining Data Management and Support

Many researchers default to generic repositories due to lack of guidance, resulting in poorly documented data that is hard to find or reuse. Preparing data for open access requires significant effort, which is often underappreciated compared to publishing papers.

To improve this, providing a curated list of astrobiology-specific repositories and archives would help researchers choose appropriate platforms. Certified repositories compatible with a common ontology and metadata standards would further ease integration.

Greater recognition and publicity for data releases could incentivize better data sharing. Ideally, program staff data scientists would assist principal investigators with data management plan implementation and data upload processes.

Conclusion

The integration of AI/ML into astrobiology depends on coordinated efforts to unify, expand, and properly manage diverse datasets. Investing in standards, community resources, and researcher support will enable the field to generate more comprehensive, accessible data. This foundation is essential for advancing our understanding of life’s origins and existence beyond Earth.

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