How to Architect a Modular Governance Stack for Research

Traditional monolithic governance contracts bundle voting, treasury management, and execution into a single, hard-to-upgrade system. A modular architecture deconstructs this into independent, interoperable components. Core components include a voting module (e.g., using OpenZeppelin Governor), a proposal module for lifecycle management, and separate execution modules for treasury payouts or smart contract upgrades. This separation, inspired by the "separation of concerns" principle in software engineering, allows each layer to be upgraded or replaced independently without risking the entire system.

The foundation is the governance token and delegation. Tokens like ERC-20Votes or ERC-5805 (from OpenZeppelin) enable snapshot-based voting power. A critical design choice is the voting strategy: will you use token-weighted voting, non-transferable reputation (like ERC-1155), or a hybrid model? For research DAOs, a reputation-based system often aligns incentives better than purely financial weight, rewarding contributors with non-transferable voting power for verified work. The voting module calculates votes and determines if a proposal's quorum and threshold are met.

Proposal management is handled by a separate proposal module. This contract defines what constitutes a valid proposal—its format, required metadata (title, description, IPFS hash), and lifecycle states (Pending, Active, Succeeded, Executed). It interfaces with the voting module to initiate votes and track results. For research grants, proposals might require linking to a research preprint or a detailed budget breakdown on IPFS. Using a standard like EIP-4824 (DAO Proposal Standard) can ensure interoperability with other DAO tooling in the ecosystem.

Execution is the most critical layer for security. Instead of giving the governance contract direct treasury access, proposals specify calls to be executed by a Timelock Controller or a Safe multisig. The Timelock introduces a mandatory delay between a proposal's passage and its execution, providing a final review period. Execution modules are permissioned to only interact with pre-approved target contracts (like a Gnosis Safe or a custom Treasury contract). This pattern minimizes the attack surface; a bug in a proposal module cannot directly drain funds.

To implement this, you would deploy contracts in a specific order: 1) Governance Token, 2) Timelock, 3) Voting Module (configured with token and timelock addresses), 4) Proposal Module (configured with the voting module). A basic Governor contract setup using OpenZeppelin's framework looks like this:

solidityCopy
import "@openzeppelin/contracts/governance/Governor.sol";
import "@openzeppelin/contracts/governance/extensions/GovernorSettings.sol";

contract ResearchGovernor is Governor, GovernorSettings {
    constructor(IVotes _token, TimelockController _timelock)
        Governor("ResearchGovernor")
        GovernorSettings(7200 /* 1 day */, 50400 /* 1 week */, 0)
    {
        // Initialize with token and timelock
    }
    // Override required functions...
}

Real-world examples include Compound's Governor Bravo and Uniswap's governance structure, which separate the Governor, Timelock, and treasury. For a research DAO, you might add a specialized module for peer-review attestations (using EAS or Verax) that must be submitted before a funding proposal can be created. The key is to start with a minimal, audited core (like OpenZeppelin) and only build custom modules for unique requirements. This approach future-proofs your system, allowing you to adopt new voting mechanisms like optimistic governance or fork-based dispute resolution as they mature.

Architecting a modular governance stack requires a shift from monolithic, one-size-fits-all systems to a composable architecture of specialized components. This approach treats governance as a protocol layer where different modules—like proposal creation, voting, delegation, and treasury management—can be mixed, matched, and upgraded independently. The core assumption is that no single governance model (e.g., token-weighted, reputation-based, multisig) is optimal for every community or decision type. By adopting a modular design, research teams can test hypotheses, iterate on mechanisms, and adapt to community needs without forking the entire system.

Your technical foundation must be solid. You should be proficient with smart contract development in Solidity or a similar language, as you'll be implementing or integrating governance primitives. Familiarity with EVM-compatible chains (like Ethereum, Arbitrum, Optimism) or alternative execution environments (like CosmWasm, Solana) is essential, as your stack's modules will deploy as on-chain contracts. You also need experience with development frameworks such as Hardhat or Foundry for testing, and an understanding of upgradeability patterns (like Transparent Proxy or UUPS) to safely evolve modules post-deployment.

A successful modular stack is data-driven. You must integrate indexing and analytics from the start. This means connecting to services like The Graph for subgraphs or using RPC providers with enhanced APIs to track proposal states, voter participation, delegation flows, and treasury activity. The assumption here is that governance research is empirical; you cannot improve a mechanism without measuring its outcomes. Your architecture should emit standardized events and store key state variables in a queryable format to facilitate off-chain analysis and dashboarding.

Security and composability are non-negotiable assumptions. Each module must have clearly defined interfaces and permissions. For example, a voting module should expose a standard function like castVote(uint proposalId, uint support) that can be called by a proposal execution module. Use access control patterns like OpenZeppelin's Ownable or role-based systems to gate critical functions. Furthermore, assume that modules will be used in unexpected combinations; rigorous testing for reentrancy, vote manipulation, and economic attacks is required, as vulnerabilities in one module can compromise the entire stack.

Finally, you must define the research parameters and success metrics upfront. Are you testing the efficiency of quadratic voting versus simple token voting? Measuring the impact of delegation liquidity? Your stack's architecture—what data it captures, how votes are weighted, how proposals are executed—will be dictated by these questions. This guide assumes you are building not just a tool, but an experimental framework where changing a single module (e.g., swapping in a new voting contract) allows you to run a controlled governance experiment with measurable results.

A composable governance stack is built on the principle of separation of concerns. The core on-chain logic—the governance engine—is deployed as a standalone smart contract or protocol. This engine manages the fundamental state and rules: proposal creation, voting power calculation, vote tallying, and execution. Popular base layers for this engine include OpenZeppelin's Governor contracts, Compound's Governor Bravo, and custom implementations using frameworks like Aragon OSx. By decoupling the engine from the front-end, researchers can test different voting mechanisms (e.g., token-weighted, quadratic, conviction voting) without altering the user-facing application.

The middleware and data layer sits between the engine and the interface, handling complex logic and state derivation. This is where research into novel governance models occurs. Components include snapshot providers for off-chain voting (using tools like Snapshot.org), delegate registries for vote delegation, and analytics engines that calculate metrics like voter participation or proposal sentiment. Researchers can plug in different modules—for instance, swapping a simple majority module for a quadratic funding module—to study their effects on proposal outcomes and community engagement in a controlled environment.

The presentation layer consists of the user interfaces and APIs that interact with the stack. For research purposes, this isn't just a dApp front-end; it includes dashboarding tools (like Dune Analytics or Tally), notification systems, and simulation environments. A key architectural pattern is the use of a standardized API, such as the Governor Compatibility interface, which allows any front-end to connect to any compliant governance engine. This enables A/B testing of different UI flows for proposal browsing or voting across the same underlying governance contract.

Security and upgradeability are critical in a research stack. The core engine should be immutable or use a robust timelock-controlled upgrade mechanism (e.g., via a UUPS proxy). Middleware modules, however, can be designed as upgradeable plugins to allow for iterative research. Access control must be meticulously managed, separating proposal execution permissions from parameter configuration rights. Using established libraries like OpenZeppelin's AccessControl for role management minimizes security risks while allowing flexible permission structures for different research phases.

To implement a basic research stack, start by forking a standard Governor contract. Deploy it on a testnet like Sepolia. Then, develop a simple module that logs proposal data to The Graph or another indexing service. Finally, build a lightweight React front-end that uses the Governor's public getter functions to display proposals. This minimal viable stack allows you to study on-chain event flow and voter behavior. The next step is to introduce an off-chain signing module via Snapshot's strategy SDK to compare gasless voting participation rates against on-chain voting.

Aragon OSx is a modular smart contract framework for DAOs. Its core architecture separates governance logic into distinct, upgradeable components called plugins. For a research DAO, the proposal module is the primary plugin where members create, vote on, and execute research initiatives. This modularity allows you to start with a basic voting setup and later integrate specialized modules for budgeting, reputation, or access control without migrating the entire DAO.

To begin, you'll deploy the DAO's core contract and install the proposal plugin. First, ensure you have Node.js and npm installed. Initialize a project and install the Aragon OSx SDK: npm install @aragon/osx. The SDK provides TypeScript/JavaScript utilities for interacting with the protocol. You'll also need an RPC provider URL (e.g., from Alchemy or Infura) for your target network, such as Ethereum mainnet or Polygon.

The deployment script requires a DAOFactory and the plugin's setup data. Here's a simplified example of deploying a DAO with a majority voting plugin:

javascriptCopy
import { Client, Context, ContextPlugin, MajorityVotingBase } from '@aragon/osx-sdk-client';

const context = new Context('https://eth-mainnet.g.alchemy.com/v2/YOUR_KEY');
const client = new Client(context);

const dao = await client.methods.createDao({
  metadataUri: 'ipfs://Qm...', // DAO description
  daoUri: 'ipfs://Qm...', // DAO resources
  subdomain: 'research-dao',
  plugins: [
    {
      pluginSetupRef: await client.methods.getPluginSetup('majority-voting'),
      data: getPluginInstallData(), // Contains voting settings
    },
  ],
});
console.log(`DAO deployed at: ${dao.address}`);

This creates a DAO where proposals pass based on a configurable majority threshold.

The getPluginInstallData() function encodes the plugin's initialization parameters. For a research DAO, key settings include:

• Voting Strategy: Typically token-based (TokenVoting) or address-list based (AddresslistVoting).
• Support Threshold: The percentage of Yes votes required (e.g., 51% for simple majority).
• Minimum Participation: The minimum percentage of total tokens that must vote.
• Voting Duration: The length of the voting period in seconds (e.g., 5 days = 432000 seconds). These parameters are immutable once set, so careful consideration is required for your DAO's research review process.

After deployment, verify the plugin installation by querying the DAO via the SDK: await client.methods.getDao(dao.address). The returned object will list installed plugins and their addresses. You can also interact with the plugin directly using its ABI to create a test proposal. The next step is to integrate this proposal module with a frontend interface using the Aragon SDK or connect it to other modules in your governance stack, such as a treasury manager for funding approved research.

Snapshot is a decentralized voting platform that enables gasless, off-chain governance. It allows token holders to signal their preferences on proposals without paying transaction fees, which is crucial for broad participation. The core innovation is its use of a signed message standard (EIP-712) for votes, which are then verified and tallied off-chain. This makes it ideal for research DAOs or any community where you want to gauge sentiment before committing expensive on-chain execution. Your governance stack's frontend will interact with Snapshot's GraphQL API to fetch spaces, proposals, and voting power data.

To integrate Snapshot, you first need to create a Space. A Space is the central hub for your project's governance on Snapshot. You configure it with your governance token's contract address, network (e.g., Ethereum Mainnet, Arbitrum), and voting strategies. The most common strategy is the erc20-balance-of strategy, which calculates voting power based on a user's token balance at a specific block number. You can also compose multiple strategies for more complex governance models, such as combining token balance with NFT ownership or delegated voting power.

The technical integration involves two main parts: fetching data and submitting votes. Use Snapshot's public GraphQL endpoint (https://hub.snapshot.org/graphql) to query information. For example, to get all proposals for your space, you would query the proposals field with your space name. To cast a vote, your application must prompt the user to sign an EIP-712 typed message containing their choice (For, Against, Abstain) and the proposal hash. Libraries like @snapshot-labs/snapshot.js simplify this process. Here's a basic vote submission snippet:

javascriptCopy
import snapshot from '@snapshot-labs/snapshot.js';
const hub = 'https://hub.snapshot.org';
const vote = await snapshot.utils.vote(hub, wallet, spaceId, proposalId, choice);

A critical architectural decision is determining voting power. Snapshot does not hold tokens; it reads on-chain state at a predefined block. When creating a proposal, you set a snapshot block number. A user's voting power for that proposal is their token balance at that exact block. This prevents manipulation by buying tokens after a proposal is created. Your integration must clearly communicate this to users. For multi-chain governance, you can use Snapshot's multi-chain strategies or deploy a custom strategy that aggregates balances across networks via a subgraph.

Finally, consider the workflow in your modular stack. Typically, a proposal is drafted and posted to Snapshot for a signaling vote. Based on the off-chain result, a trusted entity or a smart contract (like a Zodiac Module) can execute the will of the vote on-chain. This separation of signaling and execution is a key pattern. Always verify proposal and vote data on your frontend by checking the signatures against the Snapshot hub to ensure integrity. This completes the gasless voting layer, providing a low-friction way for your community to participate in governance.

A Gnosis Safe is a smart contract wallet that requires a predefined number of signatures (e.g., 2-of-3, 4-of-7) to execute any transaction. This is the foundational security layer for your governance stack, ensuring no single point of failure for treasury funds. It acts as the executive branch of your DAO, holding assets like ETH, ERC-20 tokens, and NFTs, and only releasing them when a governance-approved proposal is signed by the required signers. This setup is non-custodial and transparent, with all transactions visible on-chain.

To integrate the Safe with your governance framework, you must connect it to your Governor contract. This is typically done by making the Safe a proposal executor. After a proposal passes a vote on Snapshot or an on-chain Governor, the approved transaction data (e.g., recipient address, amount, calldata) is submitted to the Safe for execution by the designated signers. Tools like the Zodiac Module or SafeSnap bridge this gap, allowing off-chain Snapshot votes to securely trigger on-chain Safe transactions once a proposal reaches quorum and passes.

Your signer committee should be a diverse, trusted group that reflects the DAO's stakeholders—core researchers, community representatives, and technical operators. The signature threshold is a critical security parameter. A 2-of-3 setup offers agility for small grants, while a 4-of-7 configuration provides higher security for large treasury movements. Consider using a hardware wallet or wallet-as-a-service solution for signers to enhance key management security beyond basic browser extensions.

For advanced treasury management, you can install Safe Modules. A Zodiac Reality Module can automate payouts for completed bounties by relying on a decentralized oracle. The Safe{Core} Protocol allows for the creation of custom modules, such as a vesting schedule for researcher grants or a streaming payments module for ongoing stipends. These programmable extensions turn the Safe from a simple vault into an active treasury management engine.

Regular operational security is essential. Maintain an up-to-date signer list, removing inactive members and adding new ones via Safe transactions. Use the Safe Transaction Service to track all pending and executed transactions. For maximum transparency, create a public dashboard using the Safe's public API to display treasury balances, transaction history, and signer status, fostering trust within your research community.

SourceCred is an open-source tool that automatically generates a contribution graph from your community's activity. It analyzes data sources like GitHub commits, Discord messages, and forum posts to assign Cred scores—a reputation metric based on the value and impact of contributions. For a research DAO, this means peer reviews, code submissions, and insightful discussions can be algorithmically weighted and recognized, moving beyond simple token-based voting to a meritocratic system.

To integrate SourceCred, you first configure instances and plugins for your data sources. A typical setup for a research collective involves a github plugin for code and paper repositories, a discourse plugin for forum discussions (like a research forum), and potentially a discord plugin for real-time collaboration. Each plugin is configured with specific weights; for example, a merged pull request adding a new research module might be weighted higher than a general comment. The configuration is defined in a sourcecred.json file.

Once configured, you run the SourceCred CLI to load the historical data, generate the graph, and compute Cred scores for all participants. The output is a persistent, verifiable ledger of contributions. Crucially, these scores can be used to mint a Grain token—a periodic distribution of rewards proportional to earned Cred. You can automate this distribution on a weekly or monthly basis using SourceCred's payout scripts, sending ERC-20 tokens (or your DAO's native token) to contributors' Ethereum addresses, directly linking reputation to tangible rewards.

For on-chain governance, you can use a cred-to-vote strategy within a framework like Snapshot or Tally. This allows you to create voting strategies where a member's voting power is a function of their Cred score or Grain balance, rather than just their token holdings. This ensures that governance influence is earned through active, valuable participation in the research process. The entire system is transparent, as the contribution graph and scoring algorithm are public, allowing for community audit and iteration.

A practical implementation involves running SourceCred as a service, perhaps via a GitHub Action or a dedicated server, to continuously update scores. The Cred data can be exposed via an API and displayed in a leaderboard on your DAO's frontend. This visibility reinforces positive contributions and provides clear pathways for new members to understand how to gain reputation. By layering SourceCred into your modular stack, you create a dynamic, activity-based reputation layer that underpins both rewards and governance.

A modular governance stack decouples the core voting mechanism from the execution layer. This separation is critical for research-focused organizations that need to adapt quickly. The core module handles proposal creation, voting weight calculation, and quorum validation using standards like ERC-5805 (Delegation) and ERC-6372 (Clock). Execution modules are then responsible for implementing passed proposals, whether they involve treasury payouts, smart contract upgrades via a UUPS proxy, or cross-chain actions. This architecture allows you to swap execution logic without disrupting the foundational voting system that researchers rely on.

Interoperability is achieved by designing governance modules as portable, standards-compliant components. For on-chain voting, ensure your vote tallying logic can read from multiple token standards (ERC-20, ERC-721) and delegation contracts. For cross-chain governance, architect a hub-and-spoke model where a main governance chain (like Ethereum or Arbitrum) holds the canonical vote tally. Using a generalized message passing layer (e.g., Axelar, Wormhole, or Hyperlane), the result can be executed on auxiliary chains where research data or specialized dApps reside. This allows a single proposal to manage a multi-chain research treasury or deploy code across an ecosystem.

Plan for upgrades by implementing proxy patterns and a clear governance upgrade path. Use a Transparent Proxy or UUPS (EIP-1822) pattern for your core voting contract, delegating logic to an implementation contract that can be replaced. The upgrade proposal itself should be a standard governance action, requiring the same quorum and voting duration as a major treasury spend. This creates a secure, self-governing upgrade mechanism. Always include a timelock contract between the governance module and the executable target, providing a mandatory review period for researchers to audit pending changes before they go live.

Develop a module registry to manage approved execution components. This is a smart contract that maps proposal types (e.g., TREASURY_SEND, CONTRACT_UPGRADE, CROSS_CHAIN_CALL) to the address of the authorized module that handles them. Governance can vote to add or remove modules from this registry. For example, a new DATA_PURCHASE module could be added to facilitate on-chain payments for research datasets. This registry pattern maintains security—only whitelisted modules can be executed—while enabling the stack to evolve. Reference implementations can be found in OpenZeppelin's Governor contracts and Compound's Governor Bravo.

Testing and simulation are non-negotiable. Use a forking test environment with Tenderly or Hardhat's mainnet fork to simulate governance proposals end-to-end before deployment. Test upgrade scenarios, cross-chain message execution failures, and edge cases in delegation. For research DAOs, consider implementing a sandbox environment on a testnet where new modules can be trialed with dummy assets. This minimizes risk when deploying new governance functionality that will control real intellectual property or grant funding. The final architecture should be resilient, upgradeable, and capable of coordinating research efforts across any blockchain.

A modular governance architecture for research separates the core components—proposal lifecycle, voting mechanisms, treasury management, and data analysis—into interoperable layers. This approach, using standards like OpenZeppelin's Governor contracts, allows you to swap modules (e.g., moving from a simple token-weighted vote to a conviction voting model) without a full system overhaul. The key is defining clear interfaces between your Governor contract, the TimelockController for execution, and custom modules for research-specific logic like result verification or grant disbursement.

For implementation, start with a fork of a proven codebase. The OpenZeppelin Governor Wizard provides a solid foundation. Extend it by writing custom modules in Solidity that adhere to the IGovernor interface. For example, a ResearchGrantModule.sol could release funds from a treasury only after a peer-review contract confirms a research milestone. Always test governance logic extensively on a testnet like Sepolia using frameworks like Hardhat or Foundry, simulating proposal creation, voting, and execution cycles.

Your next steps should focus on real-world validation and community building. Deploy your governance contracts to a testnet and run a pilot research proposal with a small group of trusted contributors. Use this to gather data on gas costs, voter participation, and UI/UX friction. Simultaneously, document your architecture and module specifications to attract other builders. Platforms like Commonwealth or Snapshot can be integrated for off-chain signaling and discussion, creating a hybrid governance model that balances decentralization with practical efficiency for collaborative research.