Blockchain and scientific data governance

Scientific data represents a comprehensive collection of information generated through various activities, including basic research, applied research, and experimental development. Its significance lies in its high value density, interdisciplinary nature, widespread distribution in terms of supply and demand, and its adherence to stringent ethical standards. As a result, scientific data plays a crucial role as a foundational strategic resource for driving national innovation and development. In line with developed countries and regions like Europe and the United States, China has also taken steps to establish laws and regulations governing the management of scientific data. In 2018, China introduced the "Measures for the Management of Scientific Data" to enhance and standardize the collection, protection, and sharing of scientific data. However, the governance of scientific data encounters common issues and challenges, including the absence of operational guidelines, inadequate budget allocation, lack of incentive mechanisms, and a shortage of long-term open-sharing platforms for data. In this regard, blockchain technology, as an emerging solution, has shown tremendous potential in addressing these challenges. With its inherent characteristics of data transparency, immutability, non-falsifiability, and traceability, blockchain can effectively tackle issues related to data ownership, circulation, and privacy protection in scientific data governance. By leveraging cryptographic techniques, multi-chain systems, and cross-chain interoperability, blockchain can enable secure storage and hierarchical classification management of scientific data, ensuring its integrity and facilitating trusted data sharing among stakeholders. In this paper, we introduce the potential of blockchain technology in governing scientific data from five perspectives. (1) Data security: Multiple technologies, such as cryptography, multi-chain, cross-chain, etc., can be used to achieve secure storage and hierarchical classification management of data. (2) Data trust and ownership: We can leverage the inherent characteristics of blockchain, such as immutability and traceability, to establish data provenance and ownership through trusted sources, trusted timestamps, trusted data circulation, and trusted ownership. (3) Data authenticity and quality: A blockchain-based trusted digital identity system can be used to ensure data authenticity from the source. Smart contracts on the blockchain can be designed for automated compliance checks, thereby enhancing data quality. (4) Data circulation and preservation: The decentralized and distributed nature of blockchain, with its multi-center or even decentralized architecture, provides high robustness. The high reliability and stability of on-chain data facilitate long-term and stable storage of scientific data records. (5) Data sharing and utilization: Blockchain can play a role in breaking down data barriers and stimulating enthusiasm for data sharing. It enables seamless data exchange and promotes active data sharing and utilization. Furthermore, we present several practical examples showcasing the implementation of blockchain-enabled data governance in areas such as digital government and the digital economy. These examples serve to demonstrate the real-world applications and benefits of utilizing blockchain technology in governing and managing data effectively. Finally, to fully leverage the empowering role of blockchain in the governance of scientific data, we propose the following suggestions: (1) Strengthen technological research. Nationwide research efforts should be coordinated to explore how blockchain can empower the governance of scientific data. (2) Enhance platform development. National strategic scientific and technological resources, such as national laboratories and key laboratories, need to undertake comprehensive research on the theoretical, technical, and systemic aspects of utilizing blockchain for the governance of scientific data. (3) Foster talent cultivation. A specialized workforce in scientific data governance with interdisciplinary expertise and a high level of academic and technical proficiency should be built.