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Earn OCEAN rewards by locking OCEAN, curating data, and making predictions. ../.gitbook/assets/cover/data_farming_banner.png 0

What is Data Farming?

Data Farming (DF) is Ocean's incentive program. It rewards OCEAN to participants who lock OCEAN into veOCEAN, curate data, or make predictions -- all in the name of driving data consume volume (DCV). DF is a bit like DeFi liquidity mining, but tuned for DCV.

The DF webapp is where users perform most DF actions.

There is currently 150,000 OCEAN / week available for DF rewards.

DF Streams & Budgets

DF is organized into two streams: Passive DF and Active DF. Each has 50% of the DF budget. Passive DF allows for passive earning potential. Active DF requires more engagement; it has several substreams, each with its own activity.

Here are all streams & substreams. The links lead to dedicated pages.

  1. Passive DF. 75,000 OCEAN per week. Lock OCEAN for veOCEAN; rewards are pro-rata to veOCEAN holdings.
  2. Active DF. 75,000 OCEAN per week. It has these substreams:
    1. Volume DF. Allocate veOCEAN towards data assets with high DCV, ie "curate data". Rewards are a function of DCV and veOCEAN stake.
    2. Challenge DF. Predict future ETH price, one-time weekly. Rewards are a function of accuracy. Runs from DF48 — DF65.
    3. Predictoor DF. Run prediction bots to earn continuously. Starts on DF62.

All streams and substreams repeat weekly that start on Thursdays 00:00 at UTC and end on Wed at 23:59 UTC.

Claiming rewards You can claim rewards** each Thursday, or wait and claim many weeks' of rewards at once.

Active DF's substreams can -- and do -- evolve over time. It's chronicled here.

Reward Schedule & APYs

Reward Schedule. The 150,000 OCEAN / week currently available for DF rewards will increase over time to over 1.1M OCEAN / week.

Yields. Typical APYs are 520%, but may be as high as 45% and as low as 1%. APYs will vary week to week. APY depends on total OCEAN staked, duration of OCEAN lock, DCV, what DF streams you participate in, and other factors.

Further resources