Decentralized Science (DeSci)
Decentralized Science (DeSci) applies blockchain technology, tokenization, and decentralized governance to create alternative models for funding, conducting, and sharing scientific research. The movement aims to address problems in traditional science including limited funding access, slow publication processes, lack of data sharing, and barriers to participation. DeSci projects use tokens to incentivize contributions, smart contracts to manage intellectual property and funding, decentralized autonomous organizations (DAOs) for governance, and blockchain to create transparent, immutable records of research and data. This creates new models where research can be funded by communities, data can be shared while maintaining attribution, and intellectual property can be managed transparently.
The technology explores whether decentralized models could make science more open, accessible, and efficient by removing gatekeepers and creating new incentive structures. DeSci could enable direct funding of research by interested communities, transparent peer review, open data sharing with proper attribution, and new models for intellectual property that balance openness with incentives. Applications include community-funded research projects, open science platforms, decentralized peer review, and new models for research collaboration and data sharing. Various DeSci projects and platforms are being developed and tested.
At TRL 3, decentralized science remains largely experimental, with early projects testing concepts but limited proven success. The technology faces challenges including ensuring research quality without traditional peer review, managing intellectual property in decentralized systems, achieving sustainable funding models, integrating with existing scientific institutions, and ensuring that token incentives align with scientific rigor. However, as blockchain technology matures and dissatisfaction with traditional science grows, DeSci could become more viable. The technology could potentially transform how science is funded and conducted by creating more open, accessible, and community-driven research models, though it requires solving fundamental questions about quality control, incentives, and integration with existing scientific infrastructure, and represents a significant departure from established scientific practices that may face resistance from traditional institutions.
