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A hot 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 its value systems and incentive mechanisms, the essence of science remains unchanged: the pursuit, systematization, and sharing of knowledge and legacy norms. Therefore, it can provide feasible paths for solving bottlenecks in scientific development (such as knowledge silos). As one of the main channels for knowledge production, science has promoted the development and innovation of human society, making it fairer and freer.

The protection of knowledge ownership makes researchers reluctant to share their data and use it as a future competitive advantage, leading to duplication in data production and waste in scientific investment. Ultimately, the hierarchical structure of governance restricts the diversification and openness of scientific development. In a letter to the journal Nature, the DeSci movement was proposed, which garnered widespread attention from 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. Meanwhile, to maintain competitive advantages and effectively control researchers' activities, centralized scientific systems artificially construct semantic barriers, hindering interdisciplinary communication and collaboration.

Hybrid workshops (Dms) were held on March 21 and 30, 2022, by Wang Feiyue and AI experts from IEEE Intelligent Systems to address various issues of DeSci and its impact on the future evolution of scientific activities and operations. The emergence of Web3 or Web3.0, as well as decentralized autonomous organizations (DAOs), is breaking the oligopoly and establishing new paradigms for scientific development. As a proponent of COs and a major advocate for SeiTS, Wang Feiyue organized multiple discussions on distributed networks based on blockchain, cryptocurrency, and DAOs at the IEEE Intelligent Transportation Systems Conference (IEEE ITSC 2022). From the perspective of network ownership, Web3 is distinctly different from Web1 and Web2.

DeSci is a self-organizing system of human-machine hybrid governance without a centralized hierarchy, controlled by smart contracts running on it. These scientific workshops prompted academia and industry to deeply reflect on related issues and further substantively advance the development of DeSci. However, to date, there is still no recognized concept of DeSci. Therefore, this section discusses its concept from multiple angles and defines DeSci. The DeSci movement aims to increase scientific funding, release scientific knowledge from information silos, and eliminate reliance on profit-driven 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 is proposed as a concept emerging with the continuous improvement of blockchain, Web3, and DAO infrastructures, and academic research on DeSci is still limited.

Moreover, current practices of DeSci are limited to decentralized funding. These efforts represent a developing paradigm dedicated to addressing the issues present 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. Section 2 introduces the concept and characteristics of DeSci, noting the complementarity between CeSci and DeSci, which relates to the general principles of complementarity. Section 3 presents a six-layer reference model for DeSci and discusses the components within each layer in detail. Section 4 introduces typical applications. Section 5 discusses research challenges and future trends. Section 6 summarizes this 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 prioritization mechanisms through bottom-up interactions. Knowledge management: In CeSci, knowledge is managed in a more precise and measurable way, with its value owned by third parties. In DeSci, knowledge is managed by the producers who share ownership and value. DeSci was initially proposed by Etzrodt from the perspective of journals, but it 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 the scientific system highly uncertain and uncontrollable. In DeSci, smart contracts are used to simplify the complexity of human-in-the-loop interactions, turning incredible games into credible cooperative commitments. Moreover, distributed networks are established for data collection, model building, and knowledge generation, making collaboration more agile, concentrated, and convergent under the incentives of specific issues and cognitive research progress. DeSci strongly relies on the collective behavior and alliances of creative individuals, and deep insights into relevant human cognitive mechanisms are crucial for both DeSci and cognitive science. Furthermore, ownership and corresponding returns are held by DeSci researchers, 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 the following three aspects. Firstly, 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. Secondly, 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. Thirdly, 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 employs the latest digital tools to fund, organize, train, plan, coordinate, allocate supply and demand activities, and network community resources.

DeSci re-incentivizes the scientific ecosystem through token systems 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.

1. Data layer: It provides blockchain data blocks and related technologies, including encryption, timestamps, hash algorithms, and Merkle trees. In the DeSci system, each computing node that wins 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 allows for precise tracking and localization of data.

2. Network layer: It specifies the mechanisms for distributed networks, data forwarding, and identity verification. Most DeSci application scenarios consist of many distributed, autonomous, and dynamic decision nodes. Thus, the described system can be modeled as a peer-to-peer (P2P) network. Peers are participants with equal power and capabilities, with no central coordinators or hierarchies. Their 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.

3. Governance layer: Governance strategies in DAOs are mainly defined by various voting mechanisms. Current governance strategies include direct voting, representative voting, secondary voting, belief voting, and power mechanisms. Among these, direct voting is the most commonly used voting method in DAO 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.

4. Incentive layer: The incentive level is motivated through financialized technologies and tools. It mainly includes token systems and incentive strategies.

5. Organizational layer: 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 integrate monetary capital while meeting the funding needs of early-stage projects.

6. Application level: This level includes the 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, and conferences. 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, authorization 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 applications in specific fields. In this section, we will discuss some typical application scenarios of DeSci.

A. Decentralized funding: 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.

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 ongoing funding for already launched public goods.

B. Decentralized scientific market: 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 reproducibility. 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 through DeSciMart. DeSciMart not only returns ownership of knowledge to researchers but also expands the actual value of knowledge, allowing researchers to continuously benefit.

C. Other potential scenarios: Currently, other typical practices of DeSci mainly include applied scientific research and major issues related to human social welfare.

1. 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 have continuously improved the speed of drug innovation, drug development has become increasingly slow and expensive. This is referred to as the Moore's Law of biopharmaceuticals and the valley of death phenomenon. This phenomenon is related to the failure of coordination of 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 mechanisms in biotechnology, for example, VitaDA0 focuses on early preclinical drug development for longevity drugs, PsyDA0 funds research at the intersection of psychedelics and mental health, and Lab DA0 provides decentralized service research.

2. Climate: Climate issues relate to the welfare and development of human society. 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 towards intermediaries. Through blockchain technology and DAOs, many urgent issues in carbon trading markets can be addressed. For example, K1 ima DAO incentivizes and promotes climate action by distributing rewards through carbon-powered algorithmic digital currencies. Organizations like akerDA0, dClimate, and ReFi DA0 adopt DAO structures to address the negative impacts of climate change. Additionally, environmental DAOs are proposing to fund young scholars to conduct research on environmental issues through decentralized funding.

3. 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 above 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 DA0 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-focused protocol to incentivize open peer reviews on Ethereum.

V. Future Research Directions

A. Major challenges: DeSci is still in its early stages. In addition to facing governance dilemmas of DAOs and Web3.0, it also faces the following challenges.

1. Scale issues: 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 only provides better funding for 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, constructing 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.

2. Balancing participant quality: The core mission of both 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 with 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 involvement. This capability is still relatively scarce in the current research system.

3. System suboptimal cycle issues: An ideal DeSci system is an autonomous system controlled by humans and machines. However, to date, 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-closure and collusion governance issues. In decentralized governance systems, collusion is harder to resolve. Currently, there are no effective solutions aside from privacy voting based on zero-knowledge proofs.

4. Lack of accountability mechanisms: Accountability mechanisms are unique governance issues in decentralized systems. Centralized systems bind accountability mechanisms with residual value claims; 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 lack of accountability mechanisms is likely to lead to undelivered research results, fragile trustworthy networks, and even irresponsible behavior against social ethics.

5. Cooperation between DeSci and CeSci: DeSci and CeSci have important complementary aspects that can be leveraged. DeSci needs the support of existing social institutions. While DeSci expands organizational management and operational models, it is not stable. The stable operation of the scientific system heavily 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.

B. Research directions: Firstly, DeSci is a typical complex system characterized by social and engineering complexities. The challenges faced by DeSci are difficult to resolve through empirical knowledge. The parallel intelligent theory benefiting from advances in cognitive science in higher-level cognition and conscious human behavior provides a feasible framework and technology for addressing operational and governance issues in DeSci. We refer to this as the parallel DeSci system, which includes the actual DeSci system and one or more corresponding simulation DeSci systems. In parallel DeSci based on artificial system computational experiments (ACP), the simulation system is used to simulate one or more simulation DeSci systems corresponding to the real-world DeSci system. Then, diverse computational experiments can be designed and conducted to evaluate and verify specific behaviors, mechanisms, and strategies involved in the DeSci system. Parallel execution is used to achieve decision optimization and parallel tuning of DeSci governance. The core advantage of parallel DeSci governance lies in its ability to effectively learn and train, experiment and evaluate, and manage and control the actual DeSci governance system.

Secondly, decentralized funding establishes a new funding paradigm that introduces social funding into the scientific system using financial mechanisms and tools. Currently, mainstream funding mechanisms are still characterized by discontinuous funding secondary funding. Science, as a public good, is difficult to commercialize. Discontinuous funding cannot effectively promote the long-term development of the scientific system. How to establish a sustainable DeSci system that transforms limited games into infinite games is worth further exploration.

Finally, 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 introduces traditional limited partners, while Open Law proposes limited liability autonomous organizations to commercialize DAO law. 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.

V. Conclusion: DeSci is a new scientific paradigm that has emerged with the development of Web3 and DAOs, as well as operational infrastructures. 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 provides applications for 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, and pointing out the main challenges and future research directions. This paper contributes to providing valuable guidance and support for its future research and industrial applications.