The Astrobiology Data Ecosystem, Open Science, And The AI Era – NASA-DARES 2025 White Paper

On 24 May, the National Aeronautics and Space Administration (NASA) officially released the white paper ‘Astrobiology Data Ecosystems, Open Science, and the Age of AI’ (NASA-DARES 2025), which provides an in-depth analysis of how to reshape the paradigm of astrobiology research under the dual-wheel drive of artificial intelligence and open science. This document, co-authored by experts from NASA's major research centres and institutions such as the SETI Institute, is both a strategic outlook and a realistic guide.

Astrobiology: an interdisciplinary frontier and hotbed of AI applications

Astrobiology, the science of the origin, evolution and distribution of life in the universe, is unparalleled in its level of interdisciplinarity, involving the physical, chemical, biological, geological, astronomical, and even social sciences. AI technologies, especially machine learning and multimodal modelling capabilities, provide unprecedented tools for understanding complex life phenomena. From mineral spectral analysis, exoplanet identification, to classification of chemical signatures of life traces, AI is widely embedded in this research frontier.

Meanwhile, astrobiology already has many breakthrough applications. In the field of spectral identification, Raman and LIBS spectra can be used to identify the types of minerals related to habitability; in the discovery of exoplanets, the transits of Kepler and TESS can be classified; and in the identification of biomarkers, biotic and abiotic organic compounds can be effectively differentiated by mass spectrometry, XRF spectroscopy and isotopic signatures, which highlight the important scientific value and practical significance of astrobiology. These results demonstrate the important scientific value and practical significance of astrobiology.

Data ecosystems: ‘usable data’ is a prerequisite for AI empowerment

The whitepaper points out that the success of AI relies on high-quality, multimodal, and cross-compatible data. However, the astrobiology community still faces serious challenges such as data silos, inconsistent standards, missing metadata, and the inability to reuse samples. Even studying two rocks that resemble Martian and Earth basalts requires researchers to trawl through multiple databases, deal with files in different formats, and even consult paper appendices.

(1) Data discovery and unification aims to solve the ‘data silo’ problem. The status quo pain points are significant. Cross-platform data retrieval takes days to read a large number of annotations, each discipline has an independent ontology system, such as IUPAC naming, MeSH keywords, etc., and the API standards are incompatible, leading to difficulties in automated data retrieval. The proposed solutions include the establishment of an interdisciplinary expert working group to develop a unified ontology and API standard for astrobiology, the rapid parsing of existing literature and databases with the help of text-based AI tools, and the establishment of a new ROSES grant to support data standardisation and API improvement.

(2) Data gap filling focuses on building a comprehensive measurement system. Currently there are key issues with different treatments and techniques for biotic and abiotic systems, a lack of samples assessed by both biological and physicochemical techniques, and wide variation in the measurement portfolios of field expeditions. Innovative initiatives ensue to develop AI-ready data generation standards, build core equipment ‘libraries’ containing mission simulation instruments, and create detailed Wiki collections of standard protocols.

(3) Unique Resource Access works to break down technical barriers. Resource constraints are evident with limited access to astrobiology flight simulation instruments, lack of guidelines for archiving physical samples at field simulation sites, and large quantities of valuable samples and data sitting unused in the lab. The path to breakthrough is to require instrument development projects to provide a ‘community access’ version, establish publicly available lists and contact information for field sample collections, and develop recommended protocols for long-term storage of different sample types.

(4) Implementation Barrier Lowering Intended to Provide Full Support. Practical difficulties are highlighted by the lack of data management expertise among researchers, the time-consuming preparation of data and documentation of protocols, and the fact that open science contributions are not equally recognised. Support systems include the provision of astrobiology-specific repositories and archive inventories, dedicated data scientists to assist PIs in implementing data management programmes, and equivalent publicity and outreach support for data release.

Open Science: Maximising the value of data for research

The white paper highlights the principle of open science as an institutional guarantee for the deep integration of AI and astrobiology. From FAIR-compliant data sharing, to high-confidence data preservation, to open-source protocol-supported development of a ‘community version of the instrument’, the NASA system puts forward a number of recommendations, including:

In terms of the value of technological innovation, through multi-modal data fusion, combining visible light imaging, reflectance spectroscopy, mass spectrometry, fluorescence spectroscopy and other technologies; using AI to model non-linear relationships, revealing complex dependencies that are difficult to discover by traditional methods; and realizing cross-scalar integration to achieve unified analysis of data from the molecular to the planetary scales, which will bring new technological means and analytical perspectives to scientific research.

At the level of open science promotion, we actively practice the FAIR principle, realise the discoverability, accessibility, interoperability and reusability of data; effectively improve the repeatability of research, reduce unnecessary duplication of work and enhance scientific reproducibility; and lower the barriers to innovation, especially help breakthroughs in interdisciplinary fields, and promote the sharing of scientific research results and innovation.

In terms of practical application prospects, it can provide a data analysis basis for planetary exploration missions such as Mars; optimise the process of life detection and improve the accuracy and efficiency of biomarker identification; establish better criteria for judging habitable environments and help assess the habitability of planets, which is of great practical value to planetary exploration and life research.

DARES 2025: A Key Pillar of NASA's Open Science Initiative

This whitepaper is a core component of NASA's Open Science Roadmap, ‘DARES 2025’, and represents a significant deployment of NASA's efforts to advance Open Science Infrastructure, AI Research Enablement, and Data Democratisation. As the title of the white paper reveals: in the interstellar exploration of the AI era, the openness and interconnectivity of the data ecosystem will be a decisive force for scientific progress.

Link to original white paper (https://www.nasa.gov/wp-content/uploads/2025/05/gentry-d-%E2%80%93-1-rfi.pdf?emrc=c969a1)

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