Data Validator

Data Validators are essential to the verification and maintenance of high-quality datasets. They review and validate data submissions to ensure accuracy and reliability within the data set.

Validator Responsibilities

• Data Validation and Review: Data Validators assess and confirm the accuracy and quality of data points submitted by Data Scouts. They play a critical role in maintaining the integrity of the dataset by ensuring that only accurate and reliable data is accepted.

• Validator Staking: To participate in the validation process, Data Validators stake $SVN tokens on data points they believe should be accepted. This stake acts as an assertion of the data's accuracy. If they successfully validate the data, they receive rewards. If their validation is incorrect, their stake is burned, incentivizing thorough and accurate work.

Validator Rewards

• Pro-rata Block Rewards: Data Validators receive a pro-rata share of the dataset’s tokens (e.g., $SLEEP) as rewards for correctly validating data. This distribution of block rewards is contingent on the data being accepted into the dataset, ensuring validators are motivated to only approve high-quality data.

• Incentivized Dispute Resolution: If a validator disputes a data point, they must provide a clear reason and stake $SVN tokens. Validators who successfully challenge inaccurate data are rewarded $SVN, encouraging active and accurate participation in the validation process.

To manage disagreements, the system uses escalating stakes and an exponential back-off timing mechanism, which provides extended periods for thorough validation. If the data is ultimately deemed incorrect, validators who supported its accuracy lose their $SVN stakes. These forfeited stakes are then awarded to the validators who correctly challenged the data's validity, compensating them for their role in maintaining the accuracy and integrity of the data pool.

If validators cannot reach a consensus on a data point, a curator intervenes as the final decision-maker to ensure that only accurate data is accepted. This multilayered process helps maintain the integrity and reliability of the data within the pool.

The staking and reward mechanisms are designed to promote accuracy and fairness in data validation. Validators are motivated to carefully assess data points and stake based on their confidence in the data’s validity, ensuring a high level of integrity within the dataset.