Stage 1: Scoping

The first step to creating a DLP is figuring out the why and what of your data pool. Here’s how to scope your DLP.

Getting started: Add the Moksha testnet to your MetaMask/Rabby to familiarize yourself with the Vana.

Step 1: Review Existing DLPs

Before diving into the scoping, check out on the DLP Leaderboard some successful DLPs already running on Vana. This will give you a sense of their smart contracts, incentives, and validation models. It’s a helpful way to gather ideas on best practices or innovations.

Step 2: Pick a Valuable Data Source

Identify data that can provide unique insights or has high demand in the market. Current popular sources include platforms like Stack Overflow, Telegram, YouTube, Google Drive, or even personal data through GDPR requests.

• Market Size & Value: Consider how valuable and accessible the data is. Medical data, for example, can be extremely valuable but difficult to obtain due to compliance regulations, while meme data is easier to get but might have fewer buyers.

• Compliance and Accessibility: Think about whether your chosen data source involves privacy regulations or ethical concerns. Does it require user consent (like GDPR compliance), or can users freely contribute it?

• Community: Is there a specific community with a shared interest in contributing and using the data? If you have access to a unique dataset or an unfair advantage over traditional data platforms, this can set your DLP apart.

Step 3: Design Tokenomics

Every DLP has its own token, and the tokenomics can be designed in a way that fits your project’s goals. Answer these key questions when creating your token model:

• Fixed or inflationary supply? Will the token supply remain fixed, or will it inflate over time based on the data being contributed?

• Contribution Model: Decide if data contribution will be a one-time event or a continuous streaming process. This will impact your tokenomics and overall pool dynamics.

• Reward Distribution: How will block rewards be distributed? Should early contributors get more rewards or a more even distribution over time?

• Use Cases: The token can have various use cases, including rewarding data contributors, governance rights, and direct sales to data consumers (think AI corps or digital marketing brands). You have flexibility here, so build a system that aligns with your vision. How communications with data consumers will be managed, including pricing negotiations and access control?

Ensure your token has value over time to incentivize users to hold and use it. Typically, the value of the token will grow with the value of the dataset, but consider additional mechanisms to enhance token value.