Why Data Composability Is the Ultimate MoAT for DeSci Platforms

Legacy journals like Elsevier and Springer Nature act as data custodians, not conduits. Their APIs are rate-limited, expensive, and designed to prevent bulk analysis, creating a ~$10B/year access toll on scientific progress.\n- Data is locked behind PDFs, not structured for computation\n- Reproducibility is impossible without raw datasets\n- Meta-analyses are crippled by manual, error-prone data entry

Modern platforms like ResearchGate and Figshare create new silos. Data uploaded to one cannot be programmatically queried or combined with data from another, defeating the purpose of open science.\n- No universal query layer across repositories\n- Proprietary identity prevents reputation portability\n- Incentives misaligned; platforms capture value, not creators

Composability requires shared, immutable data structures. Projects like Ocean Protocol (data tokens) and IPFS/Filecoin (decentralized storage) provide the base layer.\n- Data NFTs enable provenance and programmable royalties\n- Compute-to-Data frameworks allow analysis without exposing raw data\n- Universal data ledger creates a single source of truth for citations and contributions

Composability's power is unlocked by a financial layer. Borrowing from Uniswap (AMMs) and Aave (money markets), platforms can create liquid markets for datasets, algorithms, and peer review.\n- Dataset AMMs enable instant valuation and trading of research assets\n- Staking-for-Validation creates cryptoeconomic security for data integrity\n- Composable funding via quadratic funding or DAO grants, inspired by Gitcoin

Data is useless without an index. A decentralized query protocol for science, analogous to The Graph for DeFi, would allow anyone to build applications on a unified knowledge graph.\n- Subgraphs for disciplines (genomics, materials science) standardize schemas\n- Federated querying across Arweave, IPFS, and private storage\n- Incentivized curation via token rewards for high-quality data indexing

A composable data layer turns a platform into a protocol. The defensibility shifts from hoarding data to facilitating its most valuable connections—becoming the Ethereum of scientific data.\n- Protocol fees accrue from all data transactions and computations\n- Positive feedback loop: more data attracts more tools, which attracts more data\n- Unstoppable innovation: independent teams build unforeseen applications on your core primitives

Proprietary data formats and closed APIs prevent the cross-pollination of research, stunting the growth of a true knowledge graph. This is the legacy Web2 model, and it's failing science.

• Result: Isolated datasets with <10% of their potential utility realized.
• Consequence: Platforms compete on data hoarding, not innovation, leading to winner-take-most dynamics that stifle the field.

Treat research data as on-chain, composable assets with embedded logic, similar to how Uniswap treats liquidity. This enables trustless, permissionless integration.

• Mechanism: Data is published as a verifiable credential or NFT with a clear licensing schema (e.g., CCO, MIT).
• Outcome: Any other dApp can query, analyze, and build upon this data without gatekeepers, creating exponential composability.

Composability creates a positive feedback loop where data usage directly accrues value back to the source, aligning incentives.

• Step 1: High-quality, composable data gets used more, building protocol-native reputation (e.g., via Ocean Protocol data tokens).
• Step 2: Reputation attracts curation markets and retroactive funding (e.g., Gitcoin Grants, Optimism RetroPGF).
• Step 3: Capital funds more high-quality data production, restarting the cycle.

Raw scientific data is large. The winning stack separates settlement from storage, using specialized layers for each.

• Execution: Ethereum L2s (Arbitrum, Optimism) for lightweight, high-frequency transactions and logic.
• Storage: Data Availability layers (Celestia, EigenDA) or storage rollups (Arweave, Filecoin) for cheap, permanent raw data anchoring.
• Result: ~$0.01 per MB storage cost with Ethereum-level security guarantees.

As with Uniswap (liquidity) and Ethereum (blockspace), the fundamental, reusable infrastructure captures the most value in a composable ecosystem.

• Analogy: Investing in the AWS of DeSci Data, not a single app built on it.
• Metrics to Track: Number of integrated dApps, cross-protocol query volume, and fee revenue from data access.
• Pitfall: Platforms that resist standardization become legacy systems; those that embrace it become the new standard.

Medical and genomic data cannot be fully public. Platforms without a privacy roadmap will be regulated into obsolescence or ignored by serious researchers.

• Solution Path: Integrate Fully Homomorphic Encryption (FHE) or Zero-Knowledge proofs (e.g., zkML) for private computation on encrypted data.
• Players: Watch Fhenix, Inco, Zama. This isn't a feature—it's a regulatory and ethical requirement for scaling.
• Outcome: Enables a $100B+ market in private clinical trial data and personalized medicine.