Acaduneia: How Academics are Using Dune

Let’s take a look at what academia has been writing, examine their conclusions, the Dune data they use, and think about how we can expand on their work as a community.

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Dune Wizards are a diverse group. They come from all backgrounds, geographies, and skill levels - and do their magic everywhere from spare bedrooms to the halls of institutional power.

Some are first-time analysts, taking their first steps into data through Dune. Others have vast data science experience and use Dune as their go-to tool for checking the chain.

Others are some of the top academic & research institutions in the world!

Over the past few months, we’ve seen several interesting papers come out of the academic and think tank sphere, from well-known institutions like:

  • Stanford
  • Cornell
  • UPenn
  • LBS
  • The IMF
  • Bank of Canada

And many more……

In this article, we’re going to take a look at 5 of the most interesting recent papers and see what these academics have been up to.

We’ll cover their methods, the conclusions they drew, and the Dune data they used -  before considering a few ideas for expanding on their work as a community.

The papers cover the following:

  • Scalability and fragmentation in crypto payment systems.
  • NFT product/market fit and pricing theories.
  • Crypto standards development.
  • Slippage reduction in AMM trading.
  • The (inevitable?) centralization of governance tokens.

It’s all here, let’s dive in……

Scalability issues lead to fragmentation | Bank for International Settlements


  • Frederic Boissay, Giulio Cornelli, Sebastian Doerr, and Jon Frost



Key quotes

“The fragmentation of the crypto landscape stands in stark contrast to traditional (payment) networks, which benefit from strong network effects. In the traditional system, the more users flock to a particular platform, the more attractive it becomes for new users to join that platform, creating a virtuous circle. This drives costs down, improves service quality, and promotes financial inclusion (BIS (2021)).”
“To maintain a system of decentralized consensus on a blockchain, self-interested validators need to be rewarded for recording transactions. Achieving sufficiently high rewards requires the maximum number of transactions per block to be limited. As transactions near this limit, congestion increases the cost of transactions exponentially. While congestion and the associated high fees are needed to incentivize validators, users are induced to seek out alternative chains. This leads to a system of parallel blockchains that cannot harness network effects, raising concerns about the governance and safety of the entire system. “
“Fragmentation means that crypto cannot fulfill the social role of money. Ultimately, money is a coordination device that facilitates economic exchange. It can only do so if there are network effects: as more users use one type of money, it becomes more attractive for others to use it.”


The Bank for International Settlements (BIS) is a central-bank-owned international financial institution to foster “international monetary and financial cooperation” and serves as a bank for central banks.

Thus it’s no surprise to find they’re interested in the monetary properties and potentials of crypto assets!

In BIS Bulletin No 56, their authors explore the relationship between blockchain security requirements, scalability, and how these affect “crypto as currency.”

Their analysis:

  1. Blockchains rely on native token prices to ensure security.
  2. High token prices drive new entrants to alternative chains, leading to fragmentation.
  3. Thus, as a blockchain grows it experiences anti-network effects (more users = pressure to shrink the network)
  4. Money is a social coordination tool, and traditional payment methods and networks grow through network effects - thus “there is more promise in innovations that build on trust in sovereign currencies.”

Dune data referenced

@rchen8’s Total DeFi users over time

Using this query, the authors compared the number of DeFi users with $ETH prices:


Further analysis ideas

  1. How can we better understand users' flow from one chain to another based on chain usage and native token price?
  2. What metrics do we have and need to measure the monetary qualities of crypto assets?
  3. What does the industry need to have better interoperability? What data can we surface to support those efforts?

NFTs have achieved some product/market fit | Universite du Luxembourg


  • Roman Kräussl, Universite du Luxembourg - Department of Finance; Hoover Institution, Stanford University



Key quotes

“...the number of NFTs traded at the end of 2021 underwent a sharp increase until the peak at the end of January 2022… the bubble burst causing a strong slowdown in trade on all markets. However, this was not the case for active portfolios, which recorded slight growth even at the worst time for the NFT market. This might imply that investors have no intention of leaving NFT platforms even in times of recession, as the utility they receive from these tokens is not simply linked to a monetary profit factor, but rather to a satisfaction factor in the use of the NFT itself.”
“Figure 4 distinguishes the NFT market into the most popular categories. Such a grouping is very useful for analyzing how investors behave within each category, studying the distribution of active crypto wallets and the amount traded. In the following, we distinguish five groups: gaming, collectibles, utilities, art, and metaverse.”
“...we can also group the characteristics of NFTs according to the guarantees they offer to investors. We can identify two families of features. First, there are ownership-related features: All the metadata that make up a NFT, as well as its ownership, can always be verified and can never be changed once the transaction has taken place. Furthermore, the system makes sure that these data are up-to-date and user-friendly, which means that the data is verifiable, tamper-resistant, and usable. Second, there are trading related features, implying that every NFT can be arbitrarily traded in one atomic, consistent, isolated and durable (ACID) transaction.”


Focusing on pricing determinants, current applications, and future opportunities, this meta-analysis of the NFT market aims to develop a framework to understand NFT price formation and shed light on the value-creation determinants of NFTs

It does this by first tracing the history and motivations behind the creation of NFTs as a kind of token before assessing the magnitude of their market impact thus far and finally reviewing different methods of analyzing NFT investment choices.

What did they find (with a little help from Dune community data)?

  • NFTs share the investment profile of physical collectibles, namely a high yield with a high-risk profile.
  • NFT prices move with BTC and ETH, in part because many people move their crypto wealth into NFTs - to speculate on an investment, to express their passion for a particular creator or collection, and to become members of the virtual communities associated with certain NFTs.
  • Because NFT markets are even newer than cryptocurrencies, they’re even earlier on the price discovery curve and thus very inefficient markets; we don’t have a definitive NFT pricing model as we do for fine art.

They also highlight two challenges to the further growth of the NFT market:

  1. The oracle problem: there have been relatively few attempts to link NFT pricing to real-world data like real-time currency exchange rates to better price NFTs, or to check the delivery status of a physical product that comes with an NFT purchase to enable an extra level of buyer protection.
  2. The interconnection problem: though token bridges have somewhat solved the cross-chain transfer challenge for fungible tokens, the lack of cross-chain transferability for NFTs means the markets on different chains have to, at least in part, be considered separately when thinking about pricing.

Dune data referenced

@cryptok’s Gen. 0 CryptoKitties Sales

This graph compares the sales quantity with the average USD price of Gen 0 CryptoKitties from 2018-2022.


This chart shows the USD value of sales for NBA Top Shots and Gen 0 CryptoKitties from July 2020 through Feb 2022.


The OG dune charts

@rantum’s NFT Market Overview

This graph shows the number of NFTs traded compared to the number of active wallets from March 2021 to 2022, normalized to the unity.


This figure compares the number of Google searches for “NFT” and the number of unique active wallets on Ethereum from March 2021 to 2022.

The OG dune charts

Further analysis ideas

  1. What data and metrics can we use to understand and define the key properties of each main category of NFTs?
  2. How about the pricing models discussed? What dashboards can be created to compare different collections to themselves or others through the lenses of hedonic regression models, repeat sales regressions, vector autoregressive models, machine learning, and wavelet models?
  3. How can we combine Dune blockchain data with other sources to tell a complete picture (e.g. the Google Trends to Unique NFT Wallets graph)?

Crypto Standards could solve the policymaking puzzle | University of Pennsylvania


  • Sarah Hammer, The Wharton School of the University of Pennsylvania
  • Brett Hemenway Falk, University of Pennsylvania - Department of Computer and Information Science



Key quotes

“In the international context, standards become even more important. As activities move globally, commonly accepted standards are essential to achieving public policy goals of consumer protection, financial stability, interoperability, security, and responsible technological innovation. Moreover, the global landscape continues to undergo rapid transformation, with changes in business and political climates, competition, the development of economies, and rapid technological advancement. International standard setting is also important for individual companies wishing to operate in different jurisdictions and needing to comply with the requirements of each jurisdiction.”
“Implementing a standard for stablecoin reserve reporting on blockchain (on-chain) would dramatically increase transparency in this asset class, and it would provide more information to stablecoin users to make more informed decisions. A standard for reserve reporting could require that smart contracts have methods to retrieve and store information about stablecoin reserve composition and categorization, as well as an attestation from their individual auditing firm.”
“In this respect, non-profit working groups like the Internet Engineering Task Force (IETF) which maintains the HTTP standard, or the World Wide Web Consortium (W3C) which maintains the HTML standard, are critical for developing and maintaining standards that are up-to-date and trusted by the community.”


This paper from two University of Pennsylvania researchers adds to the discussion about how policymakers can effectively work within the cryptocurrency space, given the lack of involvement by traditional intermediaries or centralized entities.

Because of this, the authors propose establishing a set of “Crypto Standards” and related industry organizations, similar to the World Wide Web Consortium (W3C) which maintains the HTML standard, or Financial Industry Regulatory Authority (FINRA), an independent nongovernmental organization that creates and enforces rules for brokers and dealers.

They suggest a set of Crypto Standards can have the following benefits:

  • They can reduce development costs by allowing multiple organizations to reuse (standardized) smart contract code.
  • They enable interoperability (see the 60k+ tokens that can be swapped on Uniswap thanks to ERC-20)
  • They can be made global, similar to the Unicode character encoding standards, to facilitate international trade and cooperation.

The limitations they identified:

  • Since they’re voluntary, industry-wide input is needed for their development and formalization so the industry itself has a vested interest in compliance.
  • Since they don’t have the force of law, the potential repercussions of parties not adhering to standards have to be considered.
  • Standards implemented on-chain cannot verify the content of the data put on-chain via oracles - so issues around accuracy and fraud will still exist.
  • On-chain standards can’t control off-chain activities, so compliance and enforcement will have to be handled somehow.

Overall they conclude:

“Crypto Standards offer many benefits, including progress towards public policy goals of consumer protection and financial stability, as well as tools to promote interoperability, security, and responsible technological innovation.”

Dune data referenced

@brett’s Distinct Uniswap V2 Tokens

One of the researchers built a beautifully simple query to determine how many tokens had been listed on proverbial favorite DEX Uniswap.

@niftytable’s NFT contracts traded on OpenSea

Likewise, the authors used @niftytable’s Query to get a fast and accurate count of the number of unique NFTs on OpenSea.

Further analysis ideas

  1. How widely used are OpenZeppelin’s “reference implementations?” Can their adoption rate be analyzed to examine the potential opportunities and challenges in creating blockchain standards?
  2. More and more centralized exchanges are putting proof of reserves on-chain - what metrics need to be monitored to contextualize this information and provide early warnings if a CEX may be in trouble?
  3. What are the key oracles relied on by the crypto industry? How can we see the extent to which certain oracles are referenced?

You may be able to avoid slippage | Cornell University




Key quotes

“Traditional market makers encounter and manage several sources of risk, like inventory risk and the risk of adverse selection (Aldridge, 2013). To manage the risks, market makers use information exogenous and endogenous to the markets. For example, each subsequent trade reveals new information about the market participants’ beliefs. A good market maker incorporates trading data into his market risk assessments.”
“The traditional market making was profitable in the times of consolidated markets each with a high volume of orders. Crypto markets have always been “thin”: the orders are few and far between. To contain the market-making costs and improve efficiency, crypto market-making has been fully automated from the beginning. Originally due to Hanson (2003), the Automated Market Makers (AMMs) use scoring rules to continuously revise the prices and the associated market probabilities of different events”

“The desirable parameters of the curves often are:

  1. Positivity in the quantities of both instruments to ensure that positive quantities are traded.
  2. Convexity, to ensure that the prices rise when liquidity falls.
  3. Asymptotic properties with respect to both x and y axes to ensure support for potentially infinite liquidity.

However, as shown in Figure 4, some AMM curves may be parameterized to violate convexity, asymptotic characteristics, and possibly even positivity. As Figure 4 shows, the curves also differ in their curvature. While LMSR is not shown in Figure 4, its curve lies in between constant product and LS-LMSR.”


This paper aims to explore market making and slippage in blockchain exchanges in three steps:

  1. A review of the key differences between Automated Market Making (AMM), which is the most popular format for Decentralized Exchanges, and traditional limit-order book-based market making.
  2. A step-by-step analysis of crypto-token price formation.
  3. A proposal of one solution for ex-ante slippage estimation that can be used to better trade on AMMs.

After some introduction to blockchain, the paper provides an overview of market making you may find insightful (there are a few different formulas by which Automated Market Making can be done, not just the Constant Product Market-Making method developed by Hayden Adams and Uniswap).

It then gets into some fairly deep math that can be used for approximating and parameterizing crypto pricing curves from exchange pricing data.

Could someone use Dune’s API to build a model for themselves?

Dune data referenced

@hagaetc’s Monthly DEX Volume By Project

The author references this Query from the (in?)famous Dex Metrics Dashboard to illustrate the volume of DEX transactions as well as the dominance of Uniswap in the sector.

She also compares order book to AMM DEX volume:

The OG Dune chart

Further analysis ideas

  1. Can you identify trades with positive and negative slippage and compare by tokens traded, DEXs, and time?
  2. How do trading volumes relate to Liquidity Pool sizes? Do some pools have much more liquidity than they need to minimize slippage given their average volume?
  3. What can we infer about Orderbook vs AMM trade models based on the usage of each in different protocols?

Governance centralizes because tokens are tradeable | Universite du Luxembourg




Key quotes

“Because holders rarely use these to cast votes, Barbereau et al. denote a common theme and propose a purposefully descriptive theory of voting rights tokens as justification for concentration: they are tradable assets on cryptocurrency markets. This description may not seem surprising against the consideration that wealth in the token economy is concentrated (c.f. concentration in Bitcoin and Ethereum), and so are capital markets more broadly. Indeed, the common feature of tradability appears to justify, on an intuitive level, the expectations that “wealth trickles up in free-market economies.”
“Our findings are consistent with Barbereau et al. timocratic description as the ability to trade voting rights tokens appears to be one of the causes of concentration (RQ1). Amid all three simulation sets with high, medium, and low trading probabilities, the scenarios tend towards concentration (RQ2). The concentration of wealth in the long term as observed in our constructed ABM aligns with findings on the concentration of wealth in Bitcoin and Ethereum, and general understandings of the concentration of wealth and inequalities.”
“Hence, the possibility to transfer tokens must be addressed. In practice, this can be achieved through a new class of tokens described as soulbound. The introduced definition refers to “accounts, or wallets, that hold publicly visible, non-transferable (but possibly revocable-by-the-issuer) tokens.” In other words, the (albeit pseudonymous) identity of a holder is encrypted into a Soulbound Token (SBT) that is linked to the respective wallet. The opportunities for on-chain governance are promising: They mitigate Sybil attacks, they (could) grant more voting power to reputable holders, they enable “proofs-of-personhood", they allow correlations between SBTs which support particular causes and prevent a “tyranny of the majority.”


This paper charges straight at one of the biggest claims and challenges of the crypto asset space - that governance tokens can be used to distribute decision-making control beyond the grasp of a monarchic ruler or the hands of a small oligarchic cabal.

It argues that even “fair launch” projects like Yearn Finance end up as oligarchies over time because of the token’s tradeability.

To examine the hypothesis, the authors used Agent-based Modeling to simulate and analyze the concentration of voting rights using different trading modalities.

They specifically sought to answer two questions:

  1. Does trading behavior affect voting rights token distributions over time?
  2. Do alternative, ’fair launch’ token allocations affect voting rights token distributions over time?

Their work specifically adds the following to the previous research they reference:

  • An agent-based model for the analysis of token distributions under various market conditions reflective of trading.
  • Simulation results showing how over time, regardless of initial token allocation, concentration is imminent.
  • Extended understandings of tokenomics to formerly include token allocations as part of governance parameters.

Though they conclude the fair launch allocation pioneered with $YFI did not effectively combat governance token concentration, they acknowledge a few limitations:

  1. Their clearing mechanism lacks a formal price clearing method, and they didn’t implement limit orders.
  2. In their model, the decision-making of an individual agent does not depend on past decisions or those of other agents.
  3. They rely heavily on the Fear Greed Index as a proxy for market conditions instead of more granular indicators like specific asset prices or social media data.

Dune data referenced

The authors used Dune to pull data about YFI holders to include in their analysis.

“To generate data for our model, we used Dune to extract the addresses that hold YFI from Ethereum’s public ledger.

Then, we organized the data such that we could determine how many tokens are owned daily by each address. Finally, we excluded a number of address ’types’ from the dataset: smart contracts (since they never utilized their voting rights, despite holding YFI [5]); addresses holding YFI valued less than $1 (since these rarely vote or trade their tokens owing to Ethereum’s gas fees being significantly higher than the token’s value), and; addresses used to burn tokens (e.g., 0x000. . . 0000) (since no one controls them and YFI is effectively taken out of circulation).”


Further analysis ideas

  1. How do the different distribution methods outlined here compare to each other in the real world? (Schedule-based; Pre-mined, scheduled distribution; Pre-mined, one-off distribution; Discretionary)
  2. What about the allocation or incentive systems? (See Table 7.)
  3. How do Voting Escrow (“ve”) tokens like veCRV, which are less liquid tokens used for voting, compare to non-escrowed, highly liquid governance tokens?

The research must flow

What an interesting research area! This is just a small sample of more than 20 research papers that have come across the Dune desk in recent months. There's so much interesting work being done, and so much more for all of us to explore too. 

The great thing about blockchain data though, is that you don’t need to be a PHd candidate at a prestigious institution to access and crunch it with professional tools. All you need is some SQL chops and a Dune account.

Anon Wizards can put out analysis just as good as Ivy League Professors!

It’s great to see these institutions really diving deep into the frontiers of finance, and we’re looking forward to more papers. 

Make no mistake about it - what’s happening here on the frontiers of finance is very interesting to those who walk the halls of higher institutions - but learning, understanding and analyzing it isn’t limited to those halls anymore........

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