Decentralized Science (DeSci)

Decentralized science, also known as DeSci, is an emerging concept that aims to make the scientific process more open, transparent, collaborative, and accessible using blockchain technology.

At its core, DeSci envisions creating decentralized platforms and protocols to facilitate more open and trustworthy sharing of scientific data, knowledge, and discoveries. This could significantly change how science is conducted, shared, funded, and rewarded.

Key Aspects of Decentralized Science

There are several key aspects that define the DeSci approach:

Open Science - DeSci aims to make all aspects of the scientific process more openly available. This includes open datasets, open-access publications, open peer review, and open-source code/models. Blockchain provides immutable records to timestamp scientific discoveries.

Decentralized Platforms - Rather than relying on centralized publishers and institutions to control science, DeSci builds decentralized community-owned platforms for sharing studies, data, peer review, and discussion. This removes centralized points of control or failure.

Token incentives - DeSci projects often incorporate digital tokens to incentivize desired behaviors like data contributions, peer reviewing articles, replicating studies, catching errors, and providing computing resources. This "tokenomics" approach aims to realign incentives.

Transparent Processes - Blockchain also enables building transparent reputation systems, attribution systems to track provenance of ideas/data, and validation of research outputs through timestamping. This increases trust and accountability.

Accessibility & Inclusion - By reducing reliance on institutions and publishers to control access, DeSci has potential to greatly expand global participation in the scientific process and allow more diverse voices to contribute.

The Promise of Decentralized Science

Proponents argue that decentralized platforms could address some major shortcomings and biases in how science is currently conducted and published. For example:

• Remove single points of control/failure around scientific publishing by central authorities
• Accelerate discovery by enabling more open collaboration
• Create built-in reproducibility with transparent data and analysis
• Expand who can participate meaningfully in the research process
• Develop richer incentive structures to encourage behaviors that actively advance science
• Prevent fraudulent research by enabling better scrutiny, reproducibility checks
• Help route funding directly to scientists rather than publishers/institutions

Use of Blockchains

Public blockchains like Ethereum provide a critical base layer infrastructure for DeSci. Their decentralized and cryptographically secure ledgers enable timestamping events, building reputation systems, ensuring attribution, and integrating token incentives and governance in a transparent manner.

However, public blockchains face scaling issues. So 2nd layer solutions are also important for decentralizing storage and computing in a scalable way for scientific use cases. These include IPFS, Filecoin, Ocean Protocol, and others. Sidechains are another approach.

Overall there is significant R&D underway around optimal blockchain architecture and tokenomic designs to securely support global scale DeSci platforms one day.

Key DeSci Application Areas

DeSci design patterns are being explored across a vast array of scientific domains, including:

- Open Access Scientific Publishing: Decentralizing scholarly publication through community-owned journals, transparent peer review, and open access models. (e.g. SciPost, P restricting access.
- Open Data Platforms: Making scientific datasets collectively available on decentralized storage/compute platforms like IPFS and Ocean Protocol instead of siloed privately.
- Funding & Grants: Distributed grant programs that allow backers to directly fund scientists and projects. Alleviates reliance on central funding authorities.
- Reputation Systems: Blockchains can securely track credentials, contributions, peer reviews, and milestones across one's scientific career in a decentralized way not dependent on institutions.
- Attribution & Provenance: Immutable ledgers enable reliable tracking of who originally contributed ideas, data, code over time to ensure proper attribution.
- Computational Power: Democratizing access to AI computing power for researchers through decentralized markets and protocols. (e.g. SingularityNET)
- Scientific Collaboration: Secure environments for scientists to jointly collaborate around publishing findings, data, models etc. (e.g. Scieur)
- Knowledge Graphs: Decentralized semantic webs of interlinked scientific concepts to evolve shared understanding.
- Token Incentive Models: Well designed token models aligned with decentralization principles can help advance behaviors that actively further science.

Challenges Facing Decentralized Science

While the promise of DeSci is vast, introducing blockchain and decentralization into long established scientific processes also poses challenges, including:

• Technological maturity - Most DeSci platforms are early stage and blockchain tech is still maturing. Scaling for global science collaboration is not yet proven.
• Adoption incentives - Scientists must see clear career benefit to adopting new decentralized platforms over established publishers and institutions.
• Validating truth - Reaching consensus on scientific truth is not the same as reaching consensus about tokens or ledger state. Reliable assessment mechanisms required.
• Sybil Attacks - Pseudonymous participation in reputation systems opens attack vectors that could compromise integrity of systems. Robust governance needed.
• Sustainability - Economic sustainability is a challenge for many open platforms. Ongoing funding required despite removing paywalls & subscriptions. New revenue models may emerge.
• Regulatory uncertainty - Integrating token models into scientific funding and publishing introduces legal uncertainties that will take time to resolve across jurisdictions.

While decentralized systems aim to be more resilient, elements of centralization will remain pragmatic stepping stones. Striking the right balance is key to adoption.

Overall the DeSci vision is bold and ambitious. It promises a more participatory, inclusive and transparent scientific process. But rearchitecting long established scientific traditions will understandably take time and continued technological evolution.

The Pioneers of Decentralized Science

Despite the early stage, teams spanning blockchain, open science advocates, publishers, and academics have already begun pioneering concepts and infrastructure for decentralized science.

Some early movers include:

- SciPost: An open access science publication built on decentralization principles with transparent peer review. SciPost has an ecosystem of journals across physics, mathematics and more that maintain rigorous peer review while publishing on open access terms funded through a consortium model.
- Ocean Protocol: A decentralized data/services exchange platform to facilitate open sharing of datasets, algorithms, compute resources etc. Uses crypto economic incentives around data tokens that represent ownership/provenance.
- AM Ifrica: African Mathematics Institute ofre diverse communities can proactively participate in advancing mathematics. Has an ecosystem of tools around transparent publishing, funding, mentorship and open source education platforms tailored to African mathematicians.
-SingularityNET: A decentralized AI platform where AIs can cooperate and interact via smart contracts. Working to decentralize access to AI tools and services for scientific domains.
- Scieur: Building collaborative intelligent scientific ecosystems on the blockchain. Scieur enables interlinked smart contracts representing concepts, claims, models, research objects. This constitutes dynamic
- Curia: Enabling decentralized prediction markets around scientific issues to collectively source conditional probability estimates on unresolved questions. Useful for gathering consensus estimates.
- dRand: A network of nodes producing publicly verifiable random numbers to support randomness in sampling, experiments and simulations. Randomness is a critical primitive across science.

While most DeSci models remain in early stages today, proof of concepts are being built across scientific domains. The foundations for a more open, trustworthy and inclusive scientific system are taking shape. Through ongoing adoption, refinement and maturation of platforms, data/incentive protocols, open standards and policies, the vision of decentralized science can gradually materialize over the next decade.

Conclusion

Decentralized science opens compelling possibilities - a more transparent, collaborative, open and incentivized scientific process that progresses truth faster through well aligned collective action.

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