Validation Framework
Last updated
Last updated
The Seekers data validation system is an open-source framework designed to ensure the integrity, originality, and reliability of token data submitted to the platform. The process evaluates contributions through four distinct criteria to guarantee that only high-quality, trustworthy data is accepted. Below is a detailed breakdown of each criterion, ensuring no information from the original description is omitted.
The Seekers Network validation process is open-source and can be freely accessed, explored, or forked. Click for details.
The validation process assesses submissions based on the following four key criteria:
Our validation process ensures that each submitted token address is fresh to its blockchain and hasn’t been submitted by the same user before. We assign a uniqueness score that rises with the number of distinct entries in a submission. This system rewards contributors for sharing original data and reduces score on duplicate submissions of the same token address for the same blockchain.
To maintain data integrity, all submissions must align with our schema. Any deviation lowers the authenticity score. We prioritize submissions made via our UI or website, as they’re more likely to be genuine, while entries from other sources receive a reduced score. Additionally, we track internal submissions for market cap changes, flagging any that stray more than 5% from the expected value.
This criterion determines whether the wallet submitting the data holds the token in question. If ownership is verified, the submission earns a full score of 1.0. However, if the token is not owned by the submitting wallet but the data is entered through the platform’s user interface, a score of 0.95 is awarded. This score reflects a minor deduction of 0.05—acknowledging the user’s effort while slightly penalizing the lack of ownership
The quality of the submitted token data is assessed based on its associated risk score. Data with lower risk scores is deemed safer and receives higher quality ratings, indicating it is of superior quality. Conversely, data with higher risk scores is assigned lower quality marks, ensuring that the platform prioritizes safer, more reliable submissions.