Introduction

Decentralized Science (DeSci) is reshaping how the world funds, executes, and disseminates scientific research. By leveraging blockchains and privacy enhancing technologies (PETs), DeSci promises to minimize gatekeeping by major publishers or institutions, democratize access to data, and expedite the rate of real-world implementation. Yet, even as DeSci projects have raised millions in funding and seen significant user adoption, they have faced a persistent barrier.

Nearly all medical, genetic, and financial data is highly sensitive, raising privacy and ethical concerns for DeSci projects. Without robust privacy-preserving solutions, DeSci will remain limited to a narrow set of nonconfidential use cases. Nillion, a secure computation network that decentralizes trust for high-value, sensitive, and private data, offers a pathway to resolve these concerns. Under a new integration with Monad, a high-performance EVM-compatible blockchain, Nillion aims to catalyze the next wave of DeSci growth by giving DeSci teams a full-stack solution to onchain settlement, governance, and privacy-preserving offchain computation. We have discussed Monad previously in various reports (1, 2).

The DeSci Market Opportunity

Addressing Traditional Research Bottlenecks

The traditional scientific landscape is characterized by protracted funding cycles, siloed data repositories, and opaque peer-review mechanisms–where some academics allege it can be utilized as a mechanism to protect incumbents. Scientists claim that they are spending more time applying for research grants than actually doing science, indicating that there is a greater problem of mismatched incentives among the scientific research institutions of the world. Moreover, top-tier journal paywalls, expensive publication fees, and protracted editorial reviews restrict the free flow of knowledge.

DeSci–an umbrella term for the integration of blockchains, PETs, smart contracts, and token-driven funding mechanisms into research practices–directly addresses these challenges. Rather than relying on slow and potentially biased institutional disbursements of funding, DeSci projects employ direct DAO grants or quadratic funding to reduce bureaucratic overhead. Researchers submit proposals, and token holders can vote on allocations. This process democratizes capital distribution, ensuring that valuable, but neglected, research can secure funding.

Beyond funding, DeSci integrates tokenized incentives to reward contributions from parties that centralized academic systems overlook. Historically, peer-review systems are often volunteers who do not receive recognition or compensation, which can lead to poor quality control. DeSci platforms, on the other hand, can pay individuals with tokens to review experiments, in turn creating a marketplace for peer reviewers. Biased or poor reviews will be posted on immutable blockchain ledgers, creating a reputation dynamic which should improve accountability and encourage good behavior. Finally, DeSci encourages collaborative data ownership by letting researchers issue tokens or NFTs which back the intellectual property (IP) of the DAO, whether that be a patent or a dataset. This aligns the self-interest of data contributors, researchers, and token holders who funded the project, and creates a mechanism to return funding to the DAO if the experiment achieves real-world impact.

Market Overview

DeSci aspires to disrupt or improve multiple sectors, including research funding, health data management, scientific publishing, clinical trials, research data markets, and early-stage research. These sectors have applications spanning multiple high-impact industries, including pharmaceuticals, healthcare diagnostics, biotech, and more. The global research and development (R&D) industry–counting public, private, and philanthropic channels–was estimated at roughly $2.5 trillion annually in 2022, with expectations to continue growing.

By introducing more transparent funding models and open-access data platforms, DeSci could capture a slice of these expenditures and even grow the total size of the market by enabling previously pass-over projects to be funded. Total R&D spending is a broad metric, though. To better understand the revenue streams of DeSci, we can look at the sector as it stands today. DeSci protocols primarily generate revenue through two channels:

IP Licensing: Once a discovery is patented, token holders can vote to license the patent to pharmaceutical manufacturers, biotech companies, or other potential buyers.

Data Marketplaces: Platforms allow aggregated patient or research data to be sold to interested parties, with fees flowing directly back to the data providers or DAOs that generated the data.

Thus, pharmaceutical, biotech, and general healthcare IP licensing, along with healthcare data, could provide a good measure of the market size for DeSci in the short to medium term. With the pharmaceutical industry representing around 25% of all patent licensing revenue, which is projected to be around $150B across all industries, that puts the total market size at approximately $37.5B annualized, while growing at 7% a year. The global addressable market size for healthcare data and analytics is currently around $61B, and expected to grow at 19% per year.

Estimates for the current size of the DeSci market remain fluid given the early-stage nature of the sector. With that said, the total market cap of post-TGE DeSci projects is approximately $850M, with one fund predicting 10 DeSci projects to exceed a $100M valuation by the end of 2025 led by overall sector growth. While revenues are small to non-existent at the moment, the potential for growth is undoubtedly large.

Lack of Privacy has Hindered DeSci Growth

As of 2025, over 85 DeSci projects exist, covering domains from open-data repositories, decentralized peer-review systems, IP licensing DAOs, and open-source research platforms for global collaboration. Despite a few instances of early successes, the DeSci ecosystem has been hamstrung by the lack of privacy solutions. Current projects are handling non-sensitive data only to avoid stringent privacy regulations, limiting the ability to move into fields like human genomic research, advanced drug trials, or patient registries. In turn, almost all DeSci projects at the moment are focused solely on democratizing funding.

Real breakthroughs in personalized medicine, genomic analyses, or cross-institutional data sharing require confidentiality. The public nature of blockchains means that any data posted onchain is transparent for anyone to see. This is a deal-breaker for pharmaceutical firms, hospitals, government agencies, and other privacy-critical industries.

If DeSci platforms integrated PETs, they could safely incorporate data from areas that are historically high value, such as pharmaceutical clinical trials that utilize medical data or genealogical labs that utilize genome data. A privacy-first approach would vastly expand DeSci’s currently marginal role. Democratizing research funding is only a small component of the DeSci market. Anything that includes biomedical data, clinical trial results, or health records require privacy. Should robust privacy preserving mechanisms see adoption, DeSci could plausibly grow from a niche sector to gaining material market share in the context of global R&D.

Nillion: The Blind Computer for DeSci 2.0

Nillion Enables Privacy-Preserving Computation

Nillion is a secure computation network that decentralizes trust for high value data in the same way that blockchains decentralized transactions. Rather than storing or processing raw information, Nillion splits data into multiple fragments and processes them collectively in a distributed network. This approach uses multiparty computation (MPC), homomorphic encryption (HE) and other privacy-enabling technologies (PETs) to ensure that no single node can reconstruct the original inputs, thus enabling “blind computation”. You can read more about PETs in our previous Blockworks Research Report.

For DeSci, Nillion solves the following problems:

Nillion’s blind storage enables the secure and private storage of sensitive health data. NilDB utilises MPC-based secret sharing to distribute trust across a range of decentralized nodes in the network, enabling the highest level of cryptographic security when storing sensitive data. Furthermore, data can be queried in this state in fully encrypted form, enabling next-generation privacy-preserving data insights, ensuring true end-to-end privacy even during data processing. 

Nillion’s blind computation ensures that sensitive data is processed without ever being exposed. Different product suites like nilVM, nilDB, and nilAI facilitate data processing from queries, and statistical insights to running LLMs utilizing a mix of cryptography and hardware based data processing. Next to blind computation enabling the benefits of private data processing, it can also guarantee data verifiability, ensuring that the right model has been run and not been tampered with during AI inference, substantially enhancing the given trust assumptions.  

The Nillion network is a credibly neutral data layer, removing conventional data siloes and barriers. Any data stored in the network can benefit from composability and provide the foundation for cross-data analysis, unlocking new value.

Using Nillion to Solve DeSci’s Privacy Issue

As a credibly neutral network, Nillion will enable DeSci projects to make health data composable the same way as DeFi assets on-chain. As sovereign owners of the data, users can grant other third parties permission to ‘use’ this data by processing it without ever revealing the data in plaintext. Hence, new collaborative compute use cases are unlocked, all while maintaining the users’ data value. 

This is in stark contrast to traditional data exchange which requires the user to sell their data and thus its value. Keeping data private while still opening it up for processing ensures the data maintains its value while generating consistent income streams. Thus, Nillion creates new data-driven economies without compromising privacy, turning user data into productive assets. This fosters a new paradigm of data modularity—where data sets can be used as "data legos" that power multiple applications.

DeSci protocols can utilize Nillion’s blind compute engine to solve the long-standing problems around data sensitivity. Not only will this unlock a vast number of new use cases–including anything dealing with personal health data–but proprietary findings do not need to be exposed during peer review or licensing negotiations. Because DeSci is completely transparent, this level of privacy will protect researchers' findings, and could provide the impetus for larger amounts of investment into the DeSci sector.

Due to the open-source nature of DeSci, research or data from one DAO could be licensed or re-used by other projects where synergies exist. Anonymized data sets could be composed, re-shared, or sold to other DeSci apps, while Nillion could validate and verify that all shared data remains private. This model would drastically expand the scope of permissible open research while enabling new revenue streams.

Nillion & Monad: The DeSci Innovation Hub

Monad: The Perfect Base Layer for DeSci Apps

While Nillion provides privacy-preserving functionality, a DeSci application will still need to build its core smart contract infrastructure on a blockchain. Thus, Nillion and Monad enable DeSci apps with a full-stack solution to build privacy-preserving applications. With Nillion integrated into Monad’s ecosystem, applications will unlock critical new capabilities:

Conduct Private Offchain Computations: DeSci apps can store data and run private computations such as AI queries and analyses over large data sets in Nillion, and then publish results or proofs to Monad. 

Onchain verification: DeSci apps can deploy verification contracts on-chain to ensure integrity of hardware-based attestations by ensuring that the hash submitted on-chain matches the approved code. 

Use Monad for Governance and Payments: DeSci use cases will require frequent onchain governance votes, data attestations, or micropayment disbursements from onchain data sales or licensing agreements. DeSci DAOs can manage token or IP-NFT ownership and licensing fees on Monad, with funding inflows or payments settling with near immediate finality at almost zero gas costs.

Scalable Cross-Chain Collaborations: Because many existing DeSci projects rely on Ethereum-based tooling–such as DAO governance frameworks, tokenization standards (ERC-20, ERC-721, ERC-1155), and smart contract libraries and SDKs-it is the perfect base layer for Nillion. Developers can seamlessly adapt existing DAO and DeFi modules to their specific use cases, and leverage battle-tested crosschain infrastructure to connect to other apps.

The combined stack effectively merges a privacy-focused computation layer with a robust settlement chain, unlocking use cases from clinical trial tokenization to real-time health data streaming.

The DeSci Ecosystem Apps

Although early-stage, a DeSci ecosystem is beginning to grow on Monad, powered by Nillion’s blind compute. This report explores four of the pioneering projects building on Nillion—Fulcra, HealthBlocks, MonadicDNA, and Stadium Science—that exemplify the potential of decentralized technologies in healthcare, genomics, and citizen science. By combining cryptographic security with decentralized governance, applications can establish new paradigms for personal data ownership, collaborative research, and user-driven innovation.

Fulcra

Fulcra addresses the fragmentation of personal data across disparate health, fitness, and lifestyle apps. Traditional systems silo user data in centralized, corporate servers, limiting individuals’ ability to share or even access this data to gain insights beyond what the applications will tell them. Fulcra’s platform aggregates data across these platforms into a unified, encrypted vault, thus enabling analysis of the interconnected trends and relationships between the data. Fulcra’s end goal is to use user-generated data as a rich proprietary knowledge repository for AI agents to provide personalized health advice.

Nillion serves as the core storage and computational infrastructure layer for Fulcra, enabling encrypted data to be shared and analyzed without decryption, thus preserving user privacy. Once aggregated, this data can be analyzed by machine learning models to generate new population insights, such as the impact of noise exposure on sleep. Once the relationships between various datasets are established, they can be used by individuals to uncover latent health insights that they might not have found if the relationships between their siloed data were not utilized. Furthermore, the user data secured in the Nillion network can be used by AI agents to provide personalized health advisory.

Fulcra has a strong development team, being created by the founder of HUMAN, a bot defense platform that has been installed on over 3 billion devices and reached unicorn status. Various case-studies have been released where users have solved health problems.

HealthBlocks

HealthBlocks addresses the problem of fragmented health data by enabling users to consolidate information from IoT health devices—such as wearables, smart scales, and glucose monitors—directly into their accounts, where they maintain full ownership and control. 

The platform adds value by gamifying healthy behavior, providing preventative health support through data-driven insights, and enabling secure, privacy-preserving data exchange. Unlike centralized fitness apps that often monetize user data without consent, HealthBlocks leverages Nillion’s secure storage and computation technology, allowing third parties like research institutions or insurers to derive insights without accessing raw user data. For instance, a pharmaceutical company could analyze exercise patterns across the platform to inform drug development while preserving user privacy. By empowering users to personalize inputs for tailored outputs, HealthBlocks not only boosts engagement—as evidenced by a 15% increase in user steps during the first month post-launch—but also addresses broader aspects of well-being, including nutrition, aging, and stress. Additionally, it provides STEM professionals with access to one of the largest and most diverse consumer health datasets, enabling them to conduct research, train AI/ML models, and deliver personalized health services.

HealthBlocks is decentralizing and revolutionizing the health data landscape—unlocking its potential while keeping control firmly in the hands of the user.

Monadic DNA

Monadic DNA is creating an open and secure ecosystem to unlock the latent value of genomic data. Existing providers such as 23andMe take control away from users, store data insecurely, and fail to keep up with the latest possibilities in genetic research, biohacking, and medicine. Nillion’s blind computing technology allows Monadic DNA to store user data in encrypted form over credibly neutral decentralized infrastructure. In turn, Monadic DNA’s Ikai protocol will allow users to get sequenced anonymously, store their data securely, and then monetize it without the need for decryption. Users will have full sovereignty over their data and a host of ecosystem apps to access new traits and insights.

The most prominent unlock will be combining genetic traits with wellness data to give users more refined insights. Health and fitness apps can combine their data from sensors, monitors, scanners, etc with salient genetic datapoints to provide more tailored experiences. Similarly, other kinds of data composition will provide enhanced experiences for skincare, personalized medicine, lifestyle, etc. Users will be able to open up long tail use cases such as rare disease diagnosis and research by combining their datasets under encryption. 

Nillion’s AI solutions will allow Monadic to perform complex computations on aggregate genomic data without sacrificing user privacy, as well. Ultimately, Nillion’s data access controls will allow the Ikai DAO to effectively steward, secure and monetize user data with consent and auditability.

Stadium Science

Stadium Science transforms everyday users into contributors of scientific discovery by crowdsourcing data from wearables and smart devices. While the initial area of focus is sleep metrics, the application could theoretically be expanded to any field of health data. Users earn tokens by contributing validated data to studies, while Nillion’s blind computation ensures user privacy. For example, users could upload health data stored securely and privately in the Nillion network while facilitating analysis on the data across users in encrypted form.

The platform will also host prediction markets where users can bet on various outcomes, such as how sleep metrics vary by location or time. Prediction markets incentivize accurate reporting of user metrics, to ensure that study results can not be gamed. For instance, a recent challenge was completed where users compared hair growth rates under different treatments. Using Nillion, Stadium Science could integrate user data from other Nillion-powered applications, unlocking new scientific discoveries across various health and wellness fields. 

Risks

The growth of the DeSci industry could face hurdles from regulatory frameworks and entrenched incumbents. Governments and regulatory bodies, such as the FDA or GDPR, have rules which impact entities who handle sensitive health and genomic data. Global or cross-boarder DeSci protocols could experience greater headwinds due to having to deal with multiple regulatory jurisdictions. Incumbent institutions–pharmaceutical giants, academic publishers, and centralized healthcare providers–have vested interests in maintaining control over data IP, and funding pipelines as well. These players may resist DeSci’s disruption by lobbying for restrictive policies. However, PETs could also unlock a new form of best practice in data security and privacy for global companies to adopt, benefiting from privacy-by design and data minimization.

As an emerging field in 2025, DeSci is still in its infancy, and its ambitious vision will likely face execution challenges. The sector will rely heavily on DAOs, and this could introduce volatility from lack of tooling, or governance disputes. DAOs traditionally rely on widespread participation, and it typically takes time to build up an ecosystem of users who contribute their time to the DAO. Although the future looks incredibly promising, expectations should be in line with the fact that we are very early in the adoption cycle of DeSci.

Conclusion

Nillion’s Monad Integration is poised to catalyze the next phase of DeSci’s evolution by eliminating key privacy bottlenecks. This synergy allows researchers, institutions, and DAOs to exchange sensitive data and insights securely while managing governance and payments onchain. Although early-stage, this integration may usher in the beginning of broader DeSci adoption, potentially transforming how critical research is funded, executed, and commercialized.

 

This research report has been funded by Nillion. By providing this disclosure, we aim to ensure that the research reported in this document is conducted with objectivity and transparency. Blockworks Research makes the following disclosures: 1) Research Funding: The research reported in this document has been funded by Nillion. The sponsor may have input on the content of the report, but Blockworks Research maintains editorial control over the final report to retain data accuracy and objectivity. All published reports by Blockworks Research are reviewed by internal independent parties to prevent bias. 2) Researchers submit financial conflict of interest (FCOI) disclosures on a monthly basis that are reviewed by appropriate internal parties. Readers are advised to conduct their own independent research and seek the advice of a qualified financial advisor before making any investment decisions.