A popular topic that has emerged with the development of Web3 or Web3.0, decentralized autonomous organizations (DAOs), and operations.
DeSci fundamentally follows an evidence-based systematic approach, applying knowledge and understanding of nature and society, which is different from centralized science (CeSci) and the open science (OS) movement. It reshapes the current scientific system. Over the past two centuries, although the concepts and connotations of science have evolved, including value systems and incentive mechanisms that have altered the basic structure of the current scientific system, its essence has not changed, which is to seek, systematize, and share knowledge. Therefore, it can provide feasible paths for addressing bottlenecks in scientific development (such as knowledge silos). As one of the main channels for knowledge production, science promotes human social development and innovation, making science more equitable and free.
The protection of knowledge ownership makes researchers reluctant to share their data, leading to repeated data production and waste of scientific investment. In a letter to the journal Nature, Sarah Hamburg proposed the DeSci movement, which has garnered widespread attention in academia and industry. The centralized organizational structure creates asymmetries of power, information, and incentives, making it difficult for scientific ideas to evolve rapidly. Wang Feiyue and his colleagues investigated the impact and significance of the autonomous science movement, as well as the potential uses of intelligent technology in DAO-based DeSci organizations and operations. On March 21 and 30, 2022, Wang Feiyue and AI experts from IEEE Intelligent Systems held two decentralized workshops to address various issues related to DeSci. The emergence of Web3 or Web3.0 and decentralized autonomous organizations (DAOs) is breaking the oligopoly and establishing new paradigms for scientific development.
These scientific workshops prompted academia and industry to deeply reflect on related issues and further advance the development of DeSci. However, to date, there is still no recognized concept of DeSci. Therefore, the problems existing in this field provide a possible path, which is decentralized science (DeSci). The DeSci movement aims to increase scientific funding, release scientific knowledge from information silos, and eliminate reliance on profit-seeking intermediaries because DeSci is a complementary concept to CeSci. Before sorting out the concepts and characteristics of DeSci, we first discuss the differences between CeSci and DeSci.
DeSci differs from the OS movement by fundamentally addressing the issues faced by current centralized science (CeSci). CeSci is a scientific activity based on a centralized organizational structure, technical protocols, and management systems, managed and controlled by elites and administrative bodies. DeSci emerged with the continuous improvement of blockchain, Web3, and DAO infrastructure. Apart from the proposals of DeSci concepts, academic research is scarce.
Moreover, the current practices of DeSci are limited to decentralized funding. These efforts represent a development paradigm dedicated to solving the problems existing in CeSci. Both CeSci and DeSci aim at knowledge discovery, management, and automation, but the differences mainly lie in how knowledge is discovered, managed, and automated. Given the lack of a unified technical and analytical framework for DeSci, this paper will provide a comprehensive introduction to the technical foundations and implementations of DeSci.
The remainder of this paper is organized as follows. The second section introduces the concept and characteristics of DeSci, noting that this reflects the complementarity between CeSci and DeSci, related to the general principles of complementarity. The third section presents a six-layer reference model for DeSci and discusses the components within each layer in detail. The fourth section introduces typical applications. The fifth section discusses research challenges and future trends. The sixth section summarizes the paper.
Knowledge discovery: In CeSci, the exploration of knowledge is conducted through methods, paths, and priorities determined by central decision-makers from the top down. In DeSci, these are supported by loosely connected DAOs with different coordination and priority mechanisms through bottom-up interactions. Knowledge management: In CeSci, knowledge is managed in a more precise and measurable way, with its value belonging to third parties. In DeSci, knowledge is managed by the producers, who share knowledge ownership and value. DeSci was initially proposed by Etzrodt from the perspective of journals, but it has rarely attracted public attention. In fact, various scholars and entities have previously proposed similar ideas. For us, the initial idea of DeSci stems from the concept of social movement organizations (SMOs) in social sciences.
Knowledge automation: In CeSci, there are two approaches to achieving knowledge automation: one is to use artificial intelligence to build knowledge bases, and the other is to digitize and model knowledge through cognitive computing and knowledge graph technologies. Both heavily rely on scientific data aggregation and computing centers, making scientific systems highly uncertain and uncontrollable. In DeSci, smart contracts are used to simplify the complexity of human-in-the-loop interactions, turning incredible games into trustworthy cooperative commitments. Moreover, distributed networks are established for data collection, model building, and knowledge generation, making collaboration more agile, centralized, and convergent, thus enhancing the efficiency and effectiveness of scientific systems.
DeSci strongly relies on the collective behavior and alliances of creative individuals, and deep insights into relevant human cognitive mechanisms are important for both DeSci and cognitive science. Additionally, ownership and corresponding returns are held by researchers in DeSci, facilitating the free flow and full utilization of data. Given that Web3 and DAOs have driven the emergence and development of DeSci, its concept is discussed from three aspects: first, from an economic perspective, DeSci uses Web3 technology and open-source financial tools to introduce science and its services as assets into the market, such as tokenization of intellectual property (IP), democratic governance of scientific systems, peer review, and data access. Second, from an organizational structure perspective, DeSci is viewed as a set of bottom-up individual consciousness formation mechanisms. Individuals in DAOs can autonomously understand the world by defining problems, languages, and methods. Third, from a scientific management perspective, DeSci aims to reform the organization of scientific activities, enhancing the ability of science to fulfill its mission.
DeSci innovates the structure, norms, incentives, and value distribution of centralized scientific systems. In our view, DeSci is a new development paradigm built on decentralized technological collaboration and organizational structures, such as Web3 and DAOs. It utilizes the latest digital tools to fund, organize, train, plan, coordinate, dispatch, collect, and allocate resources for supply and demand activities and network communities.
DeSci re-incentivizes the scientific ecosystem through a token system and decentralized power, returning scientific value and ownership to knowledge producers. The protocol layer encapsulates all technical protocols that support DeSci operations and applications, including data layers and network layers. The data layer provides blockchain data blocks and related technologies, including non-repudiation, encryption, timestamps, hash algorithms, and Merkle trees. In the DeSci system, each computing node that achieves consensus will be authorized to create new blocks and store relevant data generated at specific times in the Merkle tree structure. The timestamp indicates the creation time of the block. The structure based on Merkle trees and timestamps are two key innovations. The former helps quickly, efficiently, and securely verify the existence and integrity of blockchain data, while the latter enables precise tracking and localization of data.
The network layer specifies the distributed network, data forwarding, and identity verification mechanisms. Most DeSci application scenarios consist of many distributed, autonomous, and dynamic decision nodes. Therefore, the described system can be modeled as a peer-to-peer (P2P) network. Peers are participants with equal power and capabilities, with no central coordinator or hierarchy. Nodes with different permissions can request necessary data according to the rules of the protocol. Once a new block is created, it is broadcasted to the network and monitored by all nodes.
The governance primarily includes deterministic non-blockchain governance and optimistic non-blockchain governance. Additionally, common governance processes in the DeSci system include proposals, reviews, voting, execution, disputes, and arbitration. The DeSci governance process integrates governance both within and outside the blockchain.
The governance layer includes the governance structure and strategies required for the DeSci system. Currently, governance strategies include direct voting, representative voting, secondary voting, belief voting, and power mechanisms. Among them, direct voting is the most commonly used voting method in DAOs governance, including one person, one vote based on equity, one person, one vote based on minimum thresholds, and mixed voting based on reputation. A representative system is proposed to address the issues of direct voting, such as low participation and not giving more weight to those with more expertise. It mainly includes proxy voting and liquid democracy.
The incentive layer incentivizes the DeSci system through financialized technologies and tools, mainly including token systems and incentive strategies. The token system is the primary means of introducing science as an asset or service into the market. The DeSci token system includes token issuance, distribution, repurchase, etc. Each DeSci can issue its tokens and set elements of the token model, such as type, issuance volume, lock-up period, and distribution model, based on project attributes.
The organizational form of DeSci mainly includes two types: foundation DAOs and community foundations. These two organizational forms relate to the degree of decentralization of DeSci and the integration of good token models with monetary capital while meeting the funding needs of early-stage projects. When building the DeSci system, founders can choose the "DAO" or "exit DAO" model based on the organization's goals. "Exit DAO" is fully decentralized, while "DAO" operates in a centralized or semi-centralized manner initially, gradually decentralizing as the organization develops.
Incentive strategies mainly include internal incentives and external incentives. Internal mechanisms usually refer to reputation mechanisms and honor systems, including functions such as transferable reputation and reputation decay over time. External incentives are usually related to monetary incentives, such as airdrops, staking, liquidity mining, and grants. Internal and external incentives can be expressed through non-fungible tokens (NFTs) and fungible tokens (FTs). Common protocol standards include ERC20, ERC721, and ERC1155. The operational level mainly refers to the operational model of DeSci. Operating on blockchain and smart contracts, DeSci is an autonomous system with virtual and real functions and human-machine interactions. It relies on underlying technical protocols, token economics, and governance strategies.
Multi-party nodes confirm and verify task results, achieving decentralized self-management and control of the DeSci system. DeSci breaks the top-down operational model of the CeSci system, consisting of setting goals, assigning tasks, confirming, and verifying results.
This level includes potential application scenarios and cases of DeSci. Based on the characteristics and functions of DeSci, its potential application cases can be divided into two categories: one is the scientific system itself, such as funding, incentives, authority, peer review, and scientific development; the other is specific application scenarios of the scientific system, such as biotechnology, climate, journals, conferences, etc. DeSci is still in a very early stage. However, there have been many mature explorations of application scenarios based on Web3 and DAOs, such as protocol DAOs, service DAOs, grant DAOs, decentralized finance (DeFi), and collective DAOs, providing guidance for the construction and development of DeSci.
Currently, the main applications of DeSci are research funding, knowledge sharing, and exploring the ownership and value systems of scientific systems, such as decentralized funding, peer review, incentives, and specific field applications. In this section, we will discuss some typical application scenarios of DeSci. Decentralized funding is a new funding model driven by cryptographic technology. Unlike centralized funding from large institutions and foundations, decentralized funding mainly comes from social funding. It is democratically decided and supervised by donors, rather than entrusted to centralized authorities from the bottom up. Decentralized funding has become an important component of the crypto world.
Its typical applications include secondary funding and retrospective public goods funding. Secondary funding matches individual funding with a funding pool, where the amount of matched funding depends on the number of donors. Retrospective public goods funding focuses on providing continuous funding for already launched public goods. The amount and direction of funding are based on predictions of outcomes.
Secondary funding distributes power to participants, helping to diversify projects and avoid whale attacks. This mechanism has become the core fundraising mechanism for public goods, with platforms like Dorallacks and Gitcoin being typical secondary funding platforms. Since the end of 2021, these two platforms have begun funding DeSci and cutting-edge research. The retrospective public goods funding mechanism is currently only used in the optimistic Ethereum ecosystem.
IP-FT is a new mechanism for managing IP ownership using distributed ledger technology (DLT). The goal of IP-FT is to invest in, own, and trade intellectual property in an open and distributed market while protecting privacy and potential related to unregistered intellectual property. Intellectual property includes not only applied and pending patents but also intellectual products, datasets, and research project contracts before patents. IP-FT was proposed by molecules and has been successfully applied in the biotechnology field, such as Vital DAO and PsyDAO. Grant DAOs are the most well-known cases in the decentralized funding ecosystem. Their governance is achieved through non-transferable shares, meaning participation is primarily driven by accumulating social capital rather than profiting from financial returns, such as symbolic appreciation.
The funding mechanisms of grant DAOs are very diverse, such as Moloch DAO, Aave Grants, Uniswap Grants, decentralized funding, and Dora Ventures. Notably, Dora Ventures has adopted an infinite fund model to fund cutting-edge research, shifting funding from a limited game to an infinite game. In addition to the aforementioned funding, the funding sources for DeSci also include project donations, protocol funding allocations, and DeFi donation platforms. These grants indirectly fund the initiative through the funding mechanisms mentioned above.
The DeSci market (DeSciMart) aims to introduce scientific achievements and outputs into the market in a financialized manner, maximizing research efficiency and improving the fairness of value distribution. The decentralized scientific market is expected to address issues such as knowledge silos, inefficient data sharing, and replicability. The CeSci system is a typical linear value flow activity. Researchers receive funding from central institutions, generate new knowledge, and are captured by publishing institutions. The linear scientific value flow forms an oligopoly of intermediary profit institutions. The idea of DeSciMart is inspired by ocean protocols. Each researcher can share data, algorithms, and programs. Over time, researchers can benefit from knowledge assets using DeSciMart. DeSciMart not only returns knowledge ownership to researchers but also expands the actual value of knowledge, allowing researchers to continuously benefit.
Currently, other typical practices of DeSci mainly include applied scientific research and significant issues related to human social welfare. Biotechnology: Biotechnology and pharmaceuticals have traditionally existed in the form of large companies and centralized organizations. Closed-source culture and IP monopolies are its unique characteristics. In this context, although technologies such as combinatorial chemistry and computational drug design continuously improve the speed of drug innovation, drug development has become increasingly slow and expensive. This is referred to as the Moore's Law and valley of death phenomenon in biopharmaceuticals. This phenomenon is related to the failure of coordination between capital and resources. Specifically, centralized funding inefficiencies and mismatches, data monopolies, difficulty in replicating experimental results, and bureaucratic organizational structures hinder new drugs from entering the market and being used by loyalists. DAOs are currently used in the DeSci movement to change the coordination and incentive methods in biotechnology, for example, VitaDAO focuses on early preclinical drug development for longevity drugs, PsyDAO funds research at the intersection of psychedelics and mental health, and Lab DAO provides decentralized service research.
Climate: Climate issues relate to human social welfare and development. Carbon neutrality is the latest effort to address the climate crisis. Carbon trading markets promise to develop the Earth in a renewable way, which is more profitable than simple explosive development. However, carbon trading markets face inactive markets, lack of transparency, and value flows toward intermediaries. Through blockchain technology and DAOs, many urgent issues in carbon trading markets can be addressed. For example, Klima DAO incentivizes and promotes climate action by distributing rewards through carbon-powered algorithmic digital currencies. Organizations like AkerDAO, dClimate, and ReFi DAO adopt DAO structures to address the negative impacts of climate change. Additionally, the Environmental DAO is proposing to fund young scholars to conduct research on environmental issues through decentralized funding.
Scientific publications: Publications are one of the earliest areas of focus in decentralized scientific systems. Scientific journals and their peer reviews are controlled by a few publishing groups, such as Elsevier, Scopus, and Journal Citation Reports (JCR) impact factors. They face issues of fairness, quality, unpaid labor, transparency, and accuracy. The open access movement attempts to provide published research papers for free, but the aforementioned issues remain unresolved. Today, DeSci is leveraging blockchain and DAOs to experiment with new scientific production and dissemination models to address the shortcomings of the current publishing system. It is named decentralized journals. For example, OpenAccess DAO allows everyone to access research articles for free, Scinet provides a public repository for open peer reviews and a reputation network for reviewers, and Ants Review is establishing a privacy-oriented protocol to incentivize open peer reviews on Ethereum.
Future research directions: The main challenges DeSci faces are still in its early stages. In addition to the governance dilemmas of DAOs and Web3.0, it faces the following challenges. First, scalability: DeSci is one of the potential pathways to achieve scientific missions and social value. However, it is still in the process of small-scale experiments, and defining applicable scenarios is a non-negligible issue. The continued operation of CeSci relies on its standardized operating systems, strict accountability mechanisms, and excellent legal standards. Improving efficiency and scaling applications remains a challenge for DAOs. Currently, DeSci is only better at funding technological applications than CeSci.
We need to carefully consider and design the application scenarios of DeSci to avoid falling into the development traps of DAOs. For example, building its organizational structure and management methods based on matters and purposes, defining the goals of DeSci, and designing applications that can make DeSci more efficient and productive than the current CeSci.
Second, balancing participant quality: The core mission of DeSci and CeSci is to ensure the reliability and credibility of scientific research and applications. Due to the open background and nature of DeSci, it will inevitably attract contributors of varying abilities. Improving participant quality should be balanced with establishing a broad, open research community. Therefore, operators must invest significant time and effort in training participants, helping them overcome barriers to long-term participation. This capability is still relatively scarce in the current scientific research system.
Third, the problem of suboptimal cycles in the system: An ideal DeSci system is an autonomous system controlled by humans and machines. However, so far, DeSci is more managed by humans rather than machines. In a limited-scale system, human governance can easily create filter bubbles, meaning that individuals with similar biases influence each other. This will lead to self-closing and collusion governance issues. In decentralized governance systems, collusion is harder to resolve. Currently, there is no effective solution apart from privacy voting based on zero-knowledge proofs.
Fourth, the lack of accountability mechanisms: Accountability mechanisms are unique governance issues in decentralized systems. Centralized systems bind accountability mechanisms to the right to claim residual value; when decisions are correct, contributors are rewarded. Decentralized systems adopt collective decision-making mechanisms. When decisions are wrong, the maximum cost is the loss of stake tokens or reputation, with no penalties. For scientific systems, the absence of accountability mechanisms is likely to lead to undelivered research outcomes, fragile trust networks, and even irresponsible behaviors that violate social ethics.
Fifth, collaboration between DeSci and CeSci: DeSci and CeSci have important complementary aspects that can be leveraged. DeSci needs the support of existing social systems. While DeSci expands organizational management and operational models, it is not stable. The stable operation of the scientific system highly relies on large institutions, especially government funding. Additionally, DeSci still needs to obtain resources from existing social systems and influence the allocation of institutional funding and agenda-setting.
This requires clarifying the responsibilities and applicable scenarios of DeSci, building bridges for cooperation between DeSci and CeSci. The conflicts and collaborations between DeSci and CeSci will further promote the sense of responsibility, reliability, and influence of the DeSci community, thereby increasing trust in DeSci.
Research directions: First, DeSci is a typical complex system characterized by social and engineering complexities. The challenges faced by DeSci are difficult to solve through empirical knowledge. The parallel intelligent theory based on artificial system computational experiments (ACP) benefits from advancements in cognitive science regarding higher-level cognition and conscious human behavior. It provides a feasible framework and technology for addressing operational and governance issues in DeSci, which we refer to as parallel DeSci systems. Parallel DeSci systems include actual DeSci systems and one or more corresponding simulation DeSci systems. In ACP-based parallel DeSci, simulation systems are used to simulate one or more simulation DeSci systems corresponding to real-world DeSci systems. Then, diverse computational experiments can be designed and conducted to evaluate and validate specific behaviors, mechanisms, and strategies involved in the DeSci system. Parallel execution is used to optimize decision-making and parallel tuning for DeSci governance. The core advantage of parallel DeSci governance lies in its ability to effectively achieve learning and training, experimentation and evaluation, as well as management and control of the actual DeSci governance system.
Second, decentralized funding establishes a new funding paradigm that uses financial mechanisms and tools to introduce social funding into the scientific system. Currently, mainstream funding mechanisms are still characterized by discontinuous funding, which is secondary funding. As a public good, science is difficult to commercialize. Discontinuous funding cannot effectively promote the long-term development of the scientific system. How to establish a sustainable funding DeSci system that transforms limited games into infinite games is worth further exploration. The lack of accountability in DeSci is related to the conflict between the organizational structure of DAOs and existing legal systems. Many attempts have been made to establish the legal and organizational structure of DAOs.
For example, Moloch DAO introduced traditional limited partners, while Open Law launched limited liability autonomous organizations to commercialize the law of DAOs. However, current explorations have not resolved the accountability issues following large-scale collective decision-making in DAOs. Further research is needed on obligations, responsibilities, and powers in collective decision-making and democratic governance.
In conclusion, DeSci is a new scientific paradigm that has emerged with the development of Web3 and DAOs, as well as operational infrastructure. It is expected to address bottleneck issues such as knowledge monopolies and information silos in CeSci. The development and maturation of DeSci will drive reforms in education, management, technology, industry, and social systems. At the same time, it also rounds out applications of Web3, DAOs, and Metaverses. Unfortunately, to date, there is still no recognized concept and analytical framework to guide the research and application of DeSci. This paper aims to provide a comprehensive overview and outlook on DeSci by discussing its concepts and characteristics, proposing reference models, analyzing typical applications, pointing out major challenges, and outlining future research directions. This paper contributes to providing valuable guidance and support for its future research and industrial applications.