Abstract
In a digitally collaborative era, preserving both human and machine-generated data—AI models, scientific datasets, and digital records—is crucial for future innovation and historical insight. These ‘digital archives’ reflect collective intelligence. However, popular centralized storage risks data loss, censorship, and privacy breaches. Choosing the right technologies is essential to safeguard and sustain our digital heritage.
The research paper titled “Preserving Digital Archives of Our Intelligence: Decentralized Storage as the Guardian of Human-, Machine-generated data” addresses these real-world challenges (burgeoning data growth, mandated or required retention period, cost, impact of unwanted data loss or exposure). It outlines a ‘decision framework’ with six-key decision parameters to zero-in on the right storage technology while balancing the variability of requirements pertaining to a focused dataset (scientific as well as non-scientific). The framework was applied to outline the future-fit solution for select use cases or datasets namely, healthcare, connected cars and crowdsourced data.
The paper scrutinizes ‘decentralized storage’ from varied perspectives including alignment to data management (or FAIR) principle, to annotate it as a formidable alternative to centralized storage. In healthcare, it can secure patient records and research data, advancing personalized medicine. For connected cars, it can preserve sensor and navigation data, aiding real-time functions like autonomous driving. In crowdsourced platforms, it can ensure data authenticity and integrity, foster trust and continuous innovation in public health, mobility, and space and environmental monitoring.
The paper proposes a collaborative or hybrid-technology play to drive circular economy while acting as guardians of the digital archive of our intelligence.
Keywords: Web3, Blockchain, Decentralized Storage
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