Authors: Yi Zhang (PhD, Codatta, @drtwo101), Diana (BNBChain, @dianabnb), Eva (AuraSci, @1vayou), Andrea (OGV, @Andrea_Chang), Lucy (@BoboLucyWisdom)
Editor: Tess Li (Codatta, @li_tess)
Introduction
Decentralized Science (DeSci) is revolutionizing the way we approach scientific research by addressing the limitations of traditional, centralized systems. Historically, great discoveries often emerged from independent scientists working without institutional priorities or corporate funding constraints. Today, research has become heavily reliant on centralized funding sources, which often prioritize commercially favorable outcomes or reinforce institutional biases. DeSci challenges this model by leveraging blockchain and Web3 technologies to decentralize funding, execution, and dissemination, creating a more transparent and inclusive research environment.
This article explores how DeSci empowers independent scientists and communities to reclaim control over scientific exploration. By examining decentralized funding platforms, data collaboration tools, and community-driven governance models, it highlights the transformative potential of this movement. Through decentralized mechanisms, researchers can access support for high-risk, unconventional ideas, foster transparent decision-making, and disseminate findings openly. With the rise of AI, collaborative tools, and Web3, DeSci provides a blueprint for democratizing innovation and accelerating the pursuit of knowledge for the betterment of society.
Why Decentralized Science (DeSci)?
Scientific discovery is the systematic process of acquiring new knowledge through iterative hypothesis testing and experimentation. Inductive reasoning allows researchers to generalize scientific conclusions from specific observations, and principles are developed to confidently predict outcomes.
Scientific research can be decentralized. Decentralization must start with funding, as control over financial resources fundamentally determines the direction of scientific exploration. Historically, many great scientists conducted independent research, funded either by personal means or patrons, allowing them to explore freely, unaffected by centralized authorities or corporate interests. Figures like Galileo Galilei, supported by the Medici family, Isaac Newton, who worked largely independently, and Charles Darwin, who self-funded his work on evolution, exemplify the impact of decentralized research. Their independence led to groundbreaking discoveries that shaped scientific progress, unhindered by institutional restrictions.
Scientific research should be decentralized. In contrast, today’s scientific research is highly centralized. It mainly relies on government grants, partially on corporate funding, and institutional oversight, which often prioritize research topics by a few “managers” and limit the autonomy of scientists. This centralized funding model introduces significant biases — corporate funding favors commercially beneficial outcomes, compromising objectivity (BMJ, 2014). For instance, industry-funded studies in the food sector are 3.2% more likely to report favorable results (Springer, 2021). Government grants, though less prone to commercially related bias, still often prioritize established institutions and famous research groups over truly novel or unconventional ideas. Even agencies like the NIH, which aims to reduce reputational bias, cannot fully eliminate these issues. Political and commercial influences continue to shape research priorities, sidelining high-risk, innovative ideas from emerging researchers.
Scientific research will be re-decentralized. Decentralized funding has gained momentum, with initiatives like BIO Protocol and VitaDAO enabling scientists to receive funding directly from communities. This community-backed model offers a viable alternative to traditional funding. Web3 technologies also improve the liquidity of scientific outputs, reducing financial risks for independent researchers and allowing them to pursue innovative ideas more freely. Decentralized participation and governance are interconnected aspects of DeSci. Platforms like Codatta facilitate collaborative data sourcing, allowing individuals to contribute knowledge in the format of frontier data while sharing both risks and benefits. Decentralized governance exists to provide essential oversight, safeguarding the progress of research. It ensures balanced, community-driven decision-making, reducing biases typically found in centralized systems. Together, these aspects promote a more transparent and inclusive research environment. Decentralized dissemination is also critical for DeSci. Platforms like ResearchHub help address the issues inherent in centralized scientific publication channels, such as high costs, gatekeeping, and lengthy publication delays, by enabling transparent, community-led publication and peer review.
The mission of decentralized science is to empower collaborative knowledge creation, making research accessible and unbiased by leveraging community-driven efforts, blockchain, and open collaboration.
- Discover more truths about the universe without inherent or systematic bias.
- Lower barriers to entry, allowing talented individuals from unconventional backgrounds to contribute.
- Encourage exploration of suppressed or overlooked scientific directions.
DeSci will start with decentralized funding, but it will not end there. Distributed credit for contributions, transparent funding processes, open-source methodologies, broad community participation, and community-led publication are crucial to fostering collaboration and inclusivity throughout the entire research process.
AI for Science: Supercharging Individuals
AI-powered science is revolutionizing research, fundamentally transforming how scientific discoveries are made and workflows are structured (Toner-Rodgers, 2024). Many of the world’s leading scientists have reported significant productivity improvements through AI integration, including a 44% increase in the discovery of new materials and a 39% rise in patent filings (Toner-Rodgers, 2024). These early achievements demonstrate how AI enhances efficiency, especially in fields with complex data and time-consuming experimentation, such as materials science, drug discovery, and biology (Nature, 2023).
AI significantly amplifies individual capabilities, boosting productivity across the entire scientific workflow. During ideation, AI analyzes vast datasets to uncover patterns and generate ideas beyond human reach (AI4Science, 2023). In hypothesis formation, AI refines questions and highlights promising directions. For experimental design, AI optimizes setups, models outcomes, and assists in decision-making. AI-powered robots automate lab tasks, bridging the gap between design and execution, while virtual simulations allow hypothesis testing before physical experimentation (MIT, 2023). Finally, AI helps interpret data, refine results, and iterate conclusions, leading to quicker and more accurate insights (Nature, 2023).
Human researchers play a crucial role in providing creativity, ethical judgment, and intuition — qualities that AI lacks. While AI excels at data processing and optimization, it is human researchers who interpret these findings within a broader context, ensuring scientific rigor and ethical standards are upheld. Together, AI and human researchers form a complementary partnership that pushes scientific boundaries. In this collaboration, AI handles complex data tasks, while humans provide strategic oversight, creativity, and ethical guidance, making the entire research process more effective and innovative.
The compounding effects of human-AI collaboration are reshaping scientific research and driving productivity and innovation at an accelerating pace. Notably, the developers of AlphaFold, a breakthrough in protein structure prediction, were recently awarded the Nobel Prize, highlighting the transformative impact of human-AI collaboration. Human scientists are excellent at evaluating the potential of candidate ideas, effectively filtering out unpromising directions, and ensuring time and resources are well spent. Their heuristics and methodologies can be recorded as domain-specific knowledge enriched with reasoning and context, which can subsequently enhance AI agents in specific fields through post-training techniques such as RAG, prompt engineering, and fine-tuning.
Scientific workflows also involve complex tool usage, often requiring multiple specialized software tools. The logical workflows scientists define — covering inputs, outputs, and goals for each interaction — are nuggets of expert knowledge that can be encoded into AI agents. Projects like TXYZ.ai aim to create general AI-powered tools to assist researchers, integrating these workflows into AI systems to make them more efficient and effective.
As AI continues to accumulate domain-specific knowledge, it will enhance underlying models, enabling related systems to execute more effectively on ever-growing data. This iterative collaboration between humans and AI forms a self-reinforcing cycle — accelerating research progress and continuously pushing the boundaries of human knowledge.
DeSci Landscape: a Lightweight Survey
Decentralized science (DeSci) reshapes the entire research process, from funding to dissemination, by leveraging blockchain and Web3 technologies. This model decentralizes key aspects of research: funding, execution, and dissemination. The attached figure visualizes this process, highlighting each stage’s participants and contributions.
The process begins with funding, where independent scientists develop a research proposal, moving away from traditional centralized funding sources that often favor established players. With DeSci, research proposals are put forward for funding through decentralized backers, where community-driven contributions play a major role. Backers, governed by community-driven decision-making, review these proposals and allocate resources. This decentralized funding mechanism ensures that even high-risk or unconventional ideas can receive support, bypassing institutional gatekeepers.
Once funding is secured, the next phase involves in-process research, encompassing multiple steps — ideation, hypothesis formation, experiment design, data sourcing, and analysis. Unlike traditional centralized institutions, where processes are tightly controlled, DeSci introduces a more collaborative and transparent workflow. Independent scientists, as illustrated in the diagram, ideate and form hypotheses. During data sourcing, external data creators can contribute to the research, with incentives offered to reward high-quality data contributions. Data analysis follows, and outcomes feed into hypothesis testing, allowing for an iterative approach where results are refined, updated, and subjected to repeated evaluation until meaningful conclusions are drawn.
Another critical component is governance and supervision. Decentralized backers oversee and guide the project, providing financial backing and ensuring research integrity and alignment with community values. This model of decentralized governance ensures that power is distributed and all contributions — whether data or expertise — are fairly acknowledged, as illustrated in the Fair Acknowledgement of Contribution stage in the diagram.
The final step is dissemination and impact. Traditional publication, often restricted by paywalls, is replaced by community-driven platforms, ensuring findings are openly accessible. Publications, along with any resulting intellectual property (IP) or products, flow back to DeSci backers and the broader community, where they can be used to generate further impact and receive appropriate financial rewards or credits. This cycle helps to acknowledge contributions and create incentives, further promoting a more collaborative environment for scientific progress.
This workflow significantly improves upon traditional scientific processes by democratizing funding, encouraging cross-disciplinary collaboration, and enabling seamless data sharing. Decentralized oversight minimizes bureaucratic inefficiencies, while credit and reward systems motivate contributors across all stages of research. Ultimately, the approach not only accelerates innovation but also ensures fair recognition and tangible rewards for all stakeholders involved, making it a model for sustainable and impactful scientific advancement.
Survey of DeSci Sub-Sectors
This sector map illustrates the vibrant and diverse ecosystem of DeSci, highlighting key sub-sectors and innovative players reshaping the scientific landscape. Notable projects include BIO Protocol, supported by Binance Labs, and ResearchHub, co-founded by Coinbase’s Brian Armstrong, democratizing access to research funding and publication, respectively. Pump.Science, another standout, has gained momentum with its URO and RIF initiatives.
In the Decentralized Data Sourcing and Collaboration sub-sector, Codatta stands out as a key player, striving to connect, collaborate, and co-train the future of AGI. Platforms like Data Lake and Ocean Protocol also contribute to fostering collaboration and trust in decentralized data sharing. Furthermore, Codatta is an integral part of the AI/DePIN for Science sub-sector, uniting communities to provide data, samples and labels (includes reasoning), for scientific projects to train AI models. Together, these efforts demonstrate how DeSci is transforming science into a transparent, collaborative, and equitable ecosystem for the future.
In summary, decentralized science (DeSci) is transforming the research and permissing sector, expected to revolutionize the way how human civilization moves forward in terms of uncovering the truth about the world around us, inside us, and even beyond the world around us. However, just like the broader Web3 industry, DeSci is still in its early stages. While decentralized funding is gaining traction, and collaborative research shows promise, adoption remains a challenge. Traditional academic systems still hold significant influence, and further work is needed to build trust and scale these new approaches.
The overall maturity of DeSci depends heavily on the progress of the Web3 ecosystem. There is vast potential here, but it will require ongoing technical development, cultural change, and greater acceptance. As both DeSci and Web3 grow, we can expect a more open, collaborative, and effective scientific research landscape.
The Renaissance of Independent Scientists
History shows that many groundbreaking discoveries were made by scientists working independently, outside institutional systems. Innovators like Nikola Tesla, Albert Einstein, and Marie Curie pursued bold ideas, especially during their early careers, with limited institutional support. Nikola Tesla, for instance, began his work on alternating current while relying on his own income and support from individual investors, rather than formal institutions. Albert Einstein formulated his theory of relativity while working at the Swiss patent office, largely disconnected from academic institutions. Marie Curie, early in her career, worked tirelessly with minimal resources and often relied on personal perseverance and donations to advance her groundbreaking research in radioactivity. These pioneers exemplify how innovation thrives when unburdened by institutional constraints. Over time, scientific discovery became centralized due to the need for more extensive resources, but today’s tools are reversing this trend and sparking a revival of independent science.
Modern technologies are empowering individuals to reclaim their role in discovery. AI democratizes data analysis, open-source platforms foster collaboration, and Web3 enables decentralized funding through community-driven networks. Decentralized Autonomous Organizations (DAOs) provide financial and technical support for independent projects, bypassing traditional gatekeepers. Combined with accessible research tools, these advances are creating a new class of “super-individuals” capable of independently tackling bold challenges.
This movement does more than push traditional boundaries; it opens doors to areas that lack mainstream support but could provide significant insights. For example, UAP research, once marginalized, is now gaining legitimacy through decentralized communities that crowdsource resources and data. Similarly, questions about the connection between gravity and electromagnetism are being re-examined, free from institutional biases. With community backing and cutting-edge tools, independent scientists are well-positioned to explore these uncharted territories.
The rise of decentralized science is redefining how discoveries are made by merging technological empowerment with collective action. Individuals and communities now have the tools and opportunities to democratize the future of innovation. It’s time to embrace this movement and unlock the full potential of independent research.
Reference
- BMJ (2014). Bias in industry-funded research. Retrieved from https://www.bmj.com/industry-bias
- Springer (2021). Industry-funded studies in food sector more likely to report favorable results. Retrieved from https://www.springer.com/industry-food-bias
- Toner-Rodgers, A. (2024). Artificial Intelligence, Scientific Discovery, and Product Innovation. MIT Press.
- AI4Science (2023). AI’s Role in Advancing Scientific Research. Retrieved from https://ai4sciencecommunity.github.io/
- Nature (2023). Scientific Discovery in the Age of Artificial Intelligence. Nature Publishing Group.
- MIT (2023). AI’s Impact on Research Workflows. Retrieved from https://mitpress.mit.edu/ai-research
Meet Codatta
Codatta is a permissionless marketplace connecting data creators with demanders to curate valuable data resources, assetified on the XnY network. These assets fuel AI and DeSci projects with a royalty model that enables revenue sharing with creators.
