Why Your Data Silos Are Killing DeSci's Potential

Siloed data and opaque methodologies make scientific claims unverifiable, destroying trust. Blockchain's inherent audit trail is wasted when the core data lives off-chain in proprietary formats.

• ~70% of researchers fail to reproduce another scientist's experiments.
• $28B+ annually wasted on irreproducible preclinical research.
• Without a universal data ledger, peer review is a faith-based exercise.

Valuable datasets cannot be programmatically queried or combined, preventing emergent insights. Each lab's database is a walled garden, forcing manual, one-off analysis instead of automated, cross-disciplinary discovery.

• Months of delay to negotiate data-sharing agreements between institutions.
• Zero composability between bioinformatics tools like Galaxy and clinical trial data from platforms like VitaDAO.
• Missed correlations between genomic data and real-world health outcomes.

Traditional academic incentives prioritize publication over data sharing, creating permanent silos. Token-based models for data contribution and citation, like those envisioned by Ocean Protocol, remain theoretical without foundational data liquidity.

• >80% of research data is lost within two decades.
• Researchers hoard data for future papers, not for community utility.
• No native mechanism to reward data providers, only paper authors.

Publishing a paper's hash on-chain doesn't make the underlying data or analysis reproducible. Silos persist, turning blockchains into expensive notaries for the same broken system.

• 90%+ of published data remains inaccessible or in proprietary formats.
• Verification costs shift from peer review to manual, off-chain audits.
• Creates a false sense of decentralization while centralizing trust in data gatekeepers.

Treat research outputs—datasets, code, models—as composable, on-chain assets with embedded execution logic. Inspired by Ocean Protocol's data tokens and Molecule's IP-NFTs.

• Data becomes a liquid asset that can be staked, fractionalized, and used in DeFi primitives.
• Automatic royalty streams are encoded into the asset, creating sustainable funding loops.
• Enables trust-minimized computational workflows where algorithms pull directly from verified sources.

DAOs for science are too slow. The endgame is AROs: smart contract systems that autonomously fund, execute, and validate research based on on-chain performance metrics.

• Terraform Labs' Warp protocol for smart contract automation provides a blueprint for orchestration.
• Retroactive funding models like those piloted by Optimism can reward verified, reproducible results.
• Shifts the incentive from publishing papers to producing verifiable, composable knowledge assets.

Sensitive data (e.g., genomic, clinical) can't be fully public. ZKPs enable researchers to prove they ran a valid analysis on private data without exposing the raw inputs.

• Aztec Network's private smart contracts and zkSNARKs enable this for general computation.
• Preserves patient/data subject privacy while allowing the scientific method to operate.
• Creates a verifiable audit trail of computation that is itself a composable asset for meta-studies.

Individual datasets are low value. Their connections are high value. On-chain knowledge graphs (like The Graph for DeSci) map relationships between PDAs, papers, and authors.

• Enables discovery of latent connections across disparate fields, accelerating interdisciplinary breakthroughs.
• Graph queries become public goods, funded by protocols like Gitcoin Grants.
• Creates a positive feedback loop: more composable data → richer graph → higher utility → more data published on-chain.

Composability requires trust. Implement a crypto-native peer review via staking mechanisms where credibility is a stakeable, liquid asset.

• Researchers stake on their results' reproducibility; successful replication earns rewards, failures are slashed.
• Adopted from oracle systems like Chainlink and consensus mechanisms.
• Credibility scores become portable, reducing redundancy and creating a meritocratic reputation layer for global science.