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Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
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The Digital Biosphere: Biomimetic Architectures for Machine-Actionable Data Spaces.

Authors: Zhang, Bin;

The Digital Biosphere: Biomimetic Architectures for Machine-Actionable Data Spaces.

Abstract

Abstract The transition from location-centric data storage (URL-based) to object-centric autonomy (PID-based) represents a fundamental paradigm shift in global information infrastructure. This paper proposes the "Digital Biosphere" framework, a theoretical and engineering approach that synthesizes FAIR Digital Objects (FDO) with bacterial evolutionary strategies to construct resilient, self-organizing data ecosystems. Drawing on the principles of Biomimetics and Thermodynamics of Computation, we introduce three novel architectural mechanisms: 1. Digital Metabolism & Logic Core Distillation: We propose that truly autonomous digital entities (Active FDOs) must consume computational resources (CPU cycles) to maintain information entropy and structural integrity. This mechanism is applied to Artificial General Intelligence (AGI) through the Regenerative Logic-Core Protocol (RLCP), which decouples static factual storage (in FDOs) from neural reasoning logic, thereby mitigating the "Memory Wall" and parameter entanglement issues in Large Language Models (LLMs). 2. Digital Horizontal Gene Transfer (HGT): Addressing the interoperability bottleneck, we model metadata mapping as a form of bacterial HGT. This allows FDOs to dynamically acquire semantic "traits" (metadata profiles) from heterogenous domains without requiring rigid, centralized schema unification, enabling rapid "non-vertical" evolution of the data space. 3. Bionic Security & Quorum Sensing: We define a digital immune system based on the Major Histocompatibility Complex (MHC) analog, where FDOs utilize cryptographic signatures and RegisterEvent protocols (mimicking Quorum Sensing) to distinguish "self" from "non-self" processes, ensuring data sovereignty and integrity in open, distributed environments. This work provides the architectural blueprint for the next generation of the Internet of FAIR Data and Services, offering a scalable solution for Global Data Spaces, Digital Product Passports (DPP), and autonomous scientific discovery.Rights & Sovereignty Statement: > This architecture is licensed under CC BY 4.0. The conceptual framework of "Digital Metabolism" and "Digital Apoptosis" as applied to aFDOs remains the intellectual property of Bin Zhang (ORCID/PID). In alignment with the STAP principles, users are encouraged to implement these biomimetic strategies while ensuring data sovereignty and machine-actionable transparency.

Keywords

Digital Product Passport, Horizontal Gene Transfer,, Organoid Intelligence (OI), AGI Architecture, RLCP, digital biosphere, Nagoya Protocol Compliance, FAIR Digital Objects, ecentralized Data Ecosystem, Biomimetics, Horizon Europe, Evo 2, active fdo, IIT 4.0, Web 4.0

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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