Libera Global AI
  • Introduction
  • The Problem: Complex Landscape
  • The Libera Large Vision Model
    • Key Features
    • Data Flow
    • Value Flow
  • Positioning
    • Data Criteria
    • Data Crowdsourcing
  • Integration of AI Agents across Value Chain
  • Token Utility: The Role of $LIBE
  • Inclusive Ecosystem
  • Benefits for Data Buyers
  • Sustainability and Real-World Impact
  • A Future of Data-Driven Retail
  • Team
  • Peer into the Future of Retail Innovation with Libera’s LVM
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  1. Positioning

Data Criteria

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Last updated 3 months ago

Ensuring the quality and accuracy of the data is critical to the success of Libera’s ecosystem. To incentivize high-quality data contributions, Libera has developed a scoring system based on three key criteria:

  • Completeness: Merchants who share comprehensive data receive higher rewards. Completeness is measured based on how frequently and consistently data is shared.

  • Quantity: Larger merchants who contribute more data are rewarded proportionally. This encourages merchants with higher sales volumes to share their data.

  • Quality: Mechanisms are in place to detect and penalize falsified data, ensuring that the dataset remains valuable for buyers.

This incentive structure ensures that both merchants and data buyers benefit from the ecosystem, while maintaining the integrity of the data.