> For the complete documentation index, see [llms.txt](https://docs.ecosphereprime.com/ecosphere/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ecosphereprime.com/ecosphere/technology/peer-to-peer-data-sharing/data-sharing.md).

# Data Sharing

### Data Utilization Flow

1. *Requesting Data*
   * *Objective*: Access validated data tokens for training or inference in AI models (or for 3rd party systems.)
   * *Process*:
     * AI developers or users identify the relevant tokens representing the data they wish to use.
     * They initiate a request to access the data associated with these tokens.
2. *Verifying Token Ownership*
   * *Service Used*: Hedera Token Service
   * *Inputs Required*:
     * Token ID(s) of the data assets
     * User's Hedera account ID
   * *On-chain Recording*:
     * Check that the requesting account owns or has rights to the token associated with the data.
     * If ownership is verified, proceed to grant access.
3. *Data Retrieval*
   * *Objective*: Access the actual data files linked to the tokens.
   * *Process*:
     * Once access is granted, the AI model retrieves the data files from the Hedera File Service using the stored file hashes.
     * Access permissions and licensing terms are checked through smart contracts to ensure compliance.
4. *Utilizing Data in AI Models*
   * *Objective*: Use the retrieved data for training or inference in AI models.
   * *Process*:
     * Data is processed and fed into the AI model.
     * The model may produce outputs used for decision-making or further analysis.
5. *Logging Data Usage*
   * *Service Used*: Hedera Consensus Service
   * *Inputs Required*:
     * Token ID(s) used
     * Model parameters
     * Timestamp of usage
   * *On-chain Recording*:
     * Each instance of data usage is logged on the Hedera ledger, capturing which tokens were utilized, model parameters, and time of operation.
     * This log serves as an audit trail for accountability and transparency.
6. *Performance Monitoring and Feedback*
   * *Objective*: Gather insights on the effectiveness of the data used.
   * *Process*:
     * Collect performance metrics from the AI model (e.g., accuracy, efficiency).
     * Provide feedback regarding data relevance and quality.
   * *On-chain Recording*:
     * Record performance metrics and feedback on the Hedera ledger for continuous improvement and data source evaluation.