It may seem like the principles page for DeSci comes down a bit heavily on the question of funding. Of course, science has to do with much more than just free access and fair rewards, and so this page explores some alternative ways in which we can frame our endeavours, both as a means of creating richer maps of our shared activities, and as a way of exploring possible bridges between the world of Open Science and “web3”.
As is always the case in Kernel, we will do this by trying to uncover many “better questions” with regard to our work, the ways we describe, and how we are able to share it and include more, and more diverse, perspectives.
In particular, this page investigates how to balance existing and prior work with the new opportunities created by DeSci. In general, there is a gap between activities in DeSci and what is traditionally known as Open Science. There need not ever be complete overlap, but it would be great to extend a hand backwards to more traditional Open Science people and projects, and continue learning together about the best ways to innovate (without requiring permission!).Strengths and Weaknesses¶
Firstly, here are some areas where DeSci is weak and could learn a lot from the work that has already been done in Open Science (before trying to implement anything in a web3 way):
Incentive structures - what has and has not worked?
- What tasks actually require incentives and what “asks” of researchers fall flat over and over and over again?
- It is ironic that we want to re-invent publishing and peer-review, but do not document our own failures and successes well. Too much work in DeSci is done and then lost. Starting simply by creating an open and useful resource for DeSci experiments run so far and their outcomes is a great project!
What are the natural boundaries across the scientific lifecycle? How have scientific products and services been segmented in the past and why? (publishing, funding and grants, library services and preservation, scientific societies, productivity services).
- What works well and what doesn’t?
- What should be changed?
- What should be kept the same?
GTM and onboarding - which types of projects have failed and why?
- How do you innovate in structured systems without falling flat? (On this front, both sides have much to learn from each other)
How might we leverage the full might of the Open Science machine? This includes lobbying (SPARC), policy and government action (OSTP in US, ERC in EU), community action (OpenCon, Open Access week in October), events and conferences (FORCE11), and technical standards development (RDA).
It is the feeling of this guild that many DeSci projects try to lump too many services together because it makes sense from an engineering perspective. There are many moving parts to creating open and distributed scientific endeavours in a sustainable way, which include scientists, institutions, tools, techniques, communities of practice, communities of use or interest, investors, governments and more. Trying to solve all of this at once seems like too large a scope for any single project to take on and be successful.
Here are some areas where Open Science is weak and DeSci can legitimately take the lead without compromising too much:
Long term technical solutions for infrastructure. Web3 directly addresses the core problem Open Science is trying to solve: how to fund public infrastructure for the advancement of collective knowledge sustainably?
Business models, assuming we can make it work without pay to publish or pay to read.
Globally shared library services. Every university library is only focused on their students and staff. This makes no sense: the social structure of science is global. Geneticists at UCLA are not talking to historians at UCLA, they are talking to geneticists all over the world. There is almost no investment in global library services. We should make this a priority and pride ourselves in addressing this shortcoming.
Our twin principles of Fair Reward and Free Access attend to both fair wages and worker rights, as well as rebalancing IP issues with public domain knowledge and appropriate content stewardship. This is a big mess in the current system and it seems unlikely that anyone in the traditional world is going to fix it.
“Science is vision, from a new perspective and the realisation that there are different perspectives. Science reveals a deep perspectival structure of reality.” - Carlo Rovelli
In general, looking at the work in DeSci through three interdependent lenses can prove very revealing:
Endogenous change - let’s work within the system, replication, funding, metrics, etc.
Exogenous change - let's start over, build independent systems, etc.
Citizen Science - include everyone and move outside existing institutional forms.
The majority of DeSci projects seem to fall into these three buckets. As with any arbitrary classification, some projects include multiple pieces that straddle two or even all three of them. As such, we can make our classifications richer by looking horizontally across topics like:
- Resources and Tools/Capacity/Inputs
- Analytics/Insights (decentralised AI, global collaboration, and collective wisdom)
In practice, there are already a lot of "industry maps" and "who's doing what in DeSci" graphs. Taking the first 3 buckets as one axis, and the horizontal cuts as another axis, we can create a much richer notion of the ecosystem that is emerging in DeSci. Of course, the complex interactions of all these things on the landscape is also fascinating to explore in your own time.Curious Questions¶
We offer here a set of questions, through which you can generally figure out what the emphasis is for any given DeSci project. Getting responses to these questions can help you (i) understand what different projects in the DeSci world are up to; (ii) make more informed choices about whether their scope is appropriate and what this implies about the possibilities of success; and (iii) might add value and further their mission
Scenario: “NFT all the papers; authors own their works”
- Q: “What’s the strategy for the existing 300M papers?”
Scenario: “We’re leveraging crypto to determine credit”
- Q: “Why does this need a token? What do understand ‘token’ to mean?”
- Q: “How will you deal with existing metrics and established prestige?”
Scenario: “We’re solving incentive problems”
- Q: “What incentives do younger scholars have? What incentives do established scholars have?”
- Q: “Where will funding come from? What will be the role of existing funders?”
Scenario: “We’re changing the ownership model”
- Q: “Who will have what rights with regard to the content? What about prior content? New content?”
If you consider the scenarios above, we’re looking mostly at questions that examine the meta-question: “How does your solution compare/compete with existing solutions?”. This usually elicits a response that quickly clarifies the most useful frame to apply. If you reflect even further on this, the meta-question is basically a “go to market” concern.
We recognise that a lot of academic science is not necessarily about going to market, for understandable and, in some cases, really good reasons. Science done for curiosity rather than utility is valid and often extremely and unpredictably innovative. However, “the market” is a useful frame for approaching what is inherently undecidable. There is a very good reason we call it “price discovery”: we do not know what the price ought to be, nor do we have a good way of deciding it, until the market operates. The less it is interfered with, the better and more balanced its discoveries.
It is, in this regard, markets are extraordinarily powerful mechanisms for the collection of knowledge and its increasingly wise curation. Figuring out how to apply market-based or -inspired frames to your own discoveries is an opportunity, not something to be dismissed as petty, worldly, or materialistic. Markets help us discover unpredictable answers to undecidable phenomena, and that is a wonderful tool in a scientist’s toolbox.