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This is part 2 of the Science Token Engineering blog series. If you haven’t read Part 1, click here DAOrayaki | Problems Solved by Token Engineering in Science. So far, we have identified the main limitations of the current scientific value stream: linearity of flow, centralization of value, and even the reliance on centralized institutions for this value stream. Today, let’s take a look at alternative systems that solve all of these problems.
A project in the Web3 space that has revolutionized traditional value streams is Ocean Protocol. In short, Ocean Protocol allows you to fully own your data and sell it as an asset on a decentralized marketplace so that others can run computational jobs on your data without actually seeing it. Ocean Protocol is much more than that, but it’s this particular feature that motivates complete data sharing, because you can get the value of the data without really losing the intellectual property that the data holds.
The data economy spans many fields, including science, so an obvious question is: Can we apply the concept of a decentralized marketplace to scientific research? In this post, we will take a closer look at the DeSci ecosystem centered on the decentralized scientific marketplace and describe how the system can improve the inefficiencies of academia described in Part 1 of this series, namely the linearity of the value stream, value centralization, and overall reliance on centralized institutions.
Decentralized scientific marketplace
Let’s assume we can, and such a market exists. Essentially, a decentralized science marketplace (or DeSciMart for short) replaces traditional centralized knowledge managers like scientific journals, and since each seller can choose the privacy level of the assets they sell, DeSciMart encourages complete Share new scientific knowledge. In other words, researchers suddenly have a space to share all the data they collect, any new algorithms or programs they develop, and choose the licenses for the research papers they publish. What’s more, researchers can also use DeSciMart to benefit from their intellectual assets over time. For example, imagine a research project publishing private datasets and research papers to a decentralized marketplace. Not only do researchers have full ownership of the research paper, but if new researchers want to use an existing dataset in their own research, they can pay to run computational work on it, meaning research projects carried out a few years ago can be Its practical value in the wider scientific community continues to be rewarded.
DeSciMart provides a clear solution to the centralization of the final value of scientific research (the end point of the value stream in Part 1), but how do we solve the initial centralization of funds? At the heart of the Web3 movement are DAOs (Decentralized Autonomous Organizations), which are entirely community-run businesses with shared goals, distributed governance, and more. DAOs share many features with traditional corporations, such as vaults managed by members of their respective groups. Similar to how centralized institutions fund research grants, decentralized organizations can play this role, only with more flexibility in the design of their funding mechanisms. This is actually not a completely new concept, as decentralized fundraising has played a huge role in the development of many projects.
Putting everything together, a possible value stream for decentralized science might look like this:
Figure 1. Architecture of the Web3 Profit Sharing Model
Let’s see how this model addresses the current state of scientific research
1. Flow Linearity
Like traditional systems, DeSci’s value starts with some vaults. Researchers then apply for grants, and the best proposals (note: we will discuss the concept of best proposals in a separate post) are funded. This funding is again used to purchase the necessary resources (data, equipment, etc.) for the research project. This time, however, instead of using funds to publish a limited subset of knowledge for submission to a centralized scientific journal, all knowledge assets are published to a decentralized knowledge marketplace. This model breaks flow linearity in two ways. First, as researchers retain ownership of whatever they publish, they will receive ongoing rewards as other members of their community pay for their intellectual assets, so value flows back to the researcher, rather than being locked in a centralized in the entity. Second, a decentralized knowledge market can collect transaction fees and feed back into the DAO treasury, thereby increasing the sustainability of the system. This model is largely influenced by the Web3 Sustainability Loop proposed by Trent McConaghy, whose main mechanism to achieve fair value distribution is the circulation of value.
2. Value Centralization
DAO repositories and knowledge marketplaces will store more and more value as more people adopt the Web3 model to fund, conduct, and share scientific results; however, don’t confuse value with centralization. By its definition, DAO vaults are run by the community, so there is no centralized entity that can function without a standardized decision-making process that includes everyone in the community. Likewise, the knowledge market does not belong to any particular person. Yes, some specific people have worked hard to make it happen, and transaction fees may be set (although these can be set dynamically by the algorithm), but once the market is deployed, there is no off-switch and will not be given to anyone with potentially malicious behavior Permission to access its content.
3. Reliance on centralized institutions
The above points should be enough to convince you of the basic idea behind an open science ecosystem; it’s also a good idea to discuss the limitations of this model, which are partly related to the reliance on centralized institutions. Part 1 of this blog series provides an overview of current scientific value stream issues. Section 2 shows how we can improve existing systems to address these issues. It would be really incredible to migrate to a new system completely independent of its previous version, but this is often not practical and sometimes even desirable. The gradual transition to a Web3 science ecosystem requires the support of centralized institutions that can identify the greater benefits that open science can bring to the world. Furthermore, achieving sustainability would require a massive reallocation of the value currently locked in the centralized institutions discussed earlier. So the Web3 open science model is not completely free from reliance on centralized institutions, but it does allow us to design ecosystems that are sustainable in the shortest possible time.
In this article, we outline an alternative model of the scientific value stream in which profits are equitably distributed among researchers who contribute to the expansion of human knowledge. This model addresses the flow linearity and value centralization issues outlined in Part 1 of this series, but is limited by the transition period required for system changes.
So what’s next? The model described in this post provides a high-level overview of the problem we want to focus on; however, it is very broad in the sense that the words “science,” “knowledge,” and “researcher” are removed. The Web3 ecosystem can be described in almost any area. With this in mind, we can develop a more precise model that specifically considers the role of science in society and try to develop the right mechanisms to incentivize the maximization of scientific value. See you in part 3!
Posted by:CoinYuppie，Reprinted with attribution to:https://coinyuppie.com/decentralized-scientific-markets-and-profit-sharing/
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