This is part 2 of the blog series on Science Token Engineering. If you haven’t already, read part 1 of this series. By now, we have established the major limitations of current science value flow: flow linearity, centralization of value, and dependence on centralized agencies for this value flow to even exist. Today, let’s take a look at an alternative system which solves all of these issues.
One project in the Web3 space that has completely transformed a traditional value flow is Ocean Protocol. Very briefly, Ocean Protocol allows you to take full ownership of your data and sell it as an asset in a decentralized marketplace so that other people can run compute jobs on your data without actually seeing it. There’s more to Ocean Protocol than that, but it is this specific feature that incentivizes complete data sharing, as you can get value for your data without actually losing the intellectual property those data hold.
The data economy spans a large number of fields, including science, hence the obvious question: Can we apply the concept of a decentralized market to scientific research? In this post, we’ll take a closer look at a DeSci ecosystem centered around decentralized science marketplaces and describe how this system improves on the inefficiencies of academia described in Part 1 of this series, namely, linearity of the value flow, centralization of value, and an overall dependence on centralized agencies.
Let’s assume we can, and that such a marketplace exists. Essentially, a decentralized science marketplace (or DeSciMart for short) replaces traditional centralized knowledge curators such as scientific journals, and since each seller can choose the level of privacy on the assets they’re selling, a DeSciMart incentivizes complete sharing of new scientific knowledge. In other words, researchers suddenly have a space where they can share all the data they collect, all the new algorithms or programs they develop, and choose the licenses they attribute to the research papers they publish. What’s more, researchers can also now reap the benefits of their knowledge assets over time using a DeSciMart. For instance, imagine a research project that publishes a private dataset and a research paper to the decentralized marketplace. Not only do the researchers have complete ownership of that research paper, but also if a new researcher wants to use the existing dataset in their own research, they can pay to run compute jobs on it, meaning that a research project conducted years ago can be rewarded continuously based on its actual value within the wider scientific community.
A DeSciMart offers a clear solution to the centralization of the final value from scientific research (the end of the value flow in Part 1), but how do we solve the initial centralization of funding? At the heart of the Web3 movement are DAOs (Decentralized Autonomous Organizations), which are entirely community run enterprises with a common goal, distributed governance, and so much more. DAOs share a number of characteristics with traditional companies, e.g. a treasury that is curated by members of the respective group. Similarly to how a centralized agency provides funding for research grants, a decentralized organization can fulfill this role, only with more flexibility in the design of its funding mechanisms. This is actually not a brand new concept as decentralized funding has been playing a huge part in the development of many projects.
Putting everything together, a possible value flow for decentralize science might look something like this:
Figure 1. Schema of the Web3 profit sharing model
Let’s take a look at how this model solves the problems with the current status quo of scientific research.
As in the traditional system, value in DeSci starts in some treasury. Researchers then apply for grants and the best proposals (note: we’ll discuss the notion of a best proposal in a different post) get funded. This funding is once again used to buy the necessary resources for the research project (data, equipment, etc.). However this time, instead of using funding to publish a limited subset of the knowledge that is then submitted to a centralized scientific journal, all knowledge assets are published to the decentralized knowledge market. The flow linearity is broken in this model in two ways. First, since researchers retain ownership of anything they publish, they will receive continuous rewards as other members of their community pay to gain access to their knowledge assets, thus value flows back to the researchers and is not locked within a centralized entity. Second, the decentralized knowledge market can collect transaction fees which are fed back into the DAO treasury, thus increasing the sustainability of the system. This model is largely influenced by the Web3 Sustainability Loop proposed by Trent McConaghy and its primary mechanism for achieving a fair value distribution is a circular flow of value.
Inevitably, as more people adopt the Web3 model for funding, conducting, and sharing scientific achievement, the DAO Treasury and the Knowledge Market will store more and more value; however, don’t confuse value with centralization. The DAO Treasury is, by its definition, community-run, therefore there is no centralized entity that can operate without a standardized decision-making process that includes everyone in the community. Similarly, the Knowledge Market doesn’t belong to anyone in particular. Yes, some specific people have worked to implement it and perhaps set the transaction fees (although these can be set dynamically by an algorithm), but once the market is deployed, there is no off-switch, and there is no key that gives any potentially malicious actor access to its contents.
The aforementioned points should be enough to convince you of the basic idea behind an open science ecosystem; however, it’s a good idea to also discuss the limitations of this model, which are partly tied to the dependence on centralized agencies. Part 1 of this blog series outlined the problems of current science value flow. Part 2 showed how we can improve the systems that are currently in place to solve these problems. While it would be truly incredible if migration to a new system was entirely independent of its previous versions, that is often not realistic and sometimes not even desirable. A gradual shift towards the Web3 science ecosystem will require support from the centralized agencies that can identify the greater good that open science can bring into the world. Furthermore, reaching the point of sustainability will require a tremendous reallocation of value currently locked in the centralized agencies discussed previously. Consequently, the Web3 open science model is not entirely free from the dependence on centralized agencies, but it does give us the ability to design the ecosystem so as to reach sustainability in a minimal period of time.
In this post, we outlined an alternative model for scientific value flow in which profit is shared fairly between researchers contributing to the expansion of human knowledge. This model solves the flow linearity problem and the centralization of value problem outlined in Part 1 of this series, but is limited by the transition period that any change of systems entails.
So what’s next? The model described in this post provides a high-level overview of what we want to focus on; however, it is very broad in the sense that if you took away the words “science,” “knowledge,” and “researcher,” you can describe almost any field’s Web3 ecosystem. With this in mind, we can develop a higher resolution model that specifically considers the role of science in society and tries to develop the right mechanisms to incentivize the maximization of scientific value. See you in Part 3!
Building a new open science ecosystem that solves the problems outlined above is not going to happen overnight. If you want to join this exciting space, check out OpSci, say hello on Discord, and consider applying for an Open Web Fellowship.