Data Sharing Platform

1. Decentralized Data Sharing Platform

The core architecture of the DataNovaAI platform consists of decentralized data storage, Solana blockchain smart contracts, and distributed computing technologies. All data uploads, downloads, sharing, and transactions are automatically managed through the blockchain, ensuring transparency and immutability.

1.1 Solana Blockchain and Decentralized Storage

In the DataNovaAI platform, data storage and management use a combination of Solana blockchain and decentralized storage solutions, specifically:

• Solana Blockchain: The platform adopts Solana blockchain as its core infrastructure, leveraging its high throughput and low latency to support fast, economical data transfers and smart contract execution. Smart contracts are used for automating the management of data uploads, downloads, validations, and reward mechanisms. Each data upload or download triggers a smart contract to execute relevant operations.

• Decentralized Storage (IPFS): Since blockchain itself is not suitable for storing large data, DataNovaAI uses IPFS (InterPlanetary File System) as the primary decentralized data storage solution. Each dataset uploaded to IPFS generates a unique hash value, which is stored on the Solana blockchain as the identifier for the data. The integrity of the data is ensured by the combination of IPFS and blockchain, with any changes being recorded and traceable immediately.

  Technical Architecture Diagram:

1.2 Smart Contracts and Payment System

The payment system is implemented through smart contracts, with the following specific processes:

• Data Upload: When researchers upload data, the platform calculates upload rewards through smart contracts, with rewards based on data quality and innovativeness.

• Data Use: Researchers must pay tokens when downloading or using data, with payment amounts determined by data usage, type, and user demand.

• Token Incentives: Based on data quality and contributions, the platform provides token rewards to data providers. The reward formula is as follows:

Token Reward Quantity = Base Reward + ( Quality Factor × Usage Frequency Factor ) + ( Innovation Score × Peer Review Factor )

• Where:

  • Base Reward: A fixed baseline reward set by the platform, influenced by data size, type, and upload difficulty.

  • Quality Factor: Adjustments based on data quality (accuracy, completeness).

  • Usage Frequency Factor: Dynamic adjustment based on how often the data is referenced or used.

  • Innovation Score: Scores the novelty and potential impact of the data.

  • Peer Review Factor: Influenced by peer validations or reviews.

Data User Payment Mechanism

Users pay tokens based on the quantity, frequency, and specifics of data access or use. Data usage can be charged through:

• Query Fee: Users pay per query based on data complexity and query frequency.

• Download Fee: Based on dataset size, type, and download frequency.

• Data Analysis Fee: For complex analysis, extra tokens are charged depending on computational resources and analysis complexity.

User Payment Calculation Formula:

Payment Token Quantity = ( Data Size × Access Frequency Coefficient ) + ( Analysis Demand Coefficient × Request Count ) + ( Complexity Penalty × Operation Count )

• Data Size: Token payment baseline set by dataset size or complexity.

• Access Frequency Coefficient: Adjusts fees based on how often the dataset is accessed.

• Analysis Demand Coefficient: Additional costs for sophisticated data analysis or customization.

• Complexity Penalty: Penalty factor for operations on complex data structures.

• Operation Count: Number of operations performed on the data.

1.3 Data Verification and Quality Assurance

To ensure high-quality data on the platform, DataNovaAI introduces decentralized review and verification mechanisms to ensure that uploaded data is real and effective, avoiding low-quality or fraudulent data entry.

Decentralized Review Mechanism

The platform invites experts, peers, and community members to review uploaded data. After each review, the data receives a quality score, which influences the uploader's rewards. Quality scoring standards include:

• Data Accuracy: Whether the data reflects real-world conditions, verified through experiments.

• Data Completeness: Check for missing data, inclusion of necessary metadata.

• Data Innovativeness: Uniqueness and the potential for scientific advancement.

Data Rating System

Through the scoring system for each dataset, users can rate datasets, helping others judge the quality of the data. Ratings consist of:

• Accuracy Score: 1-5, higher indicates more accurate data.

• Completeness Score: 1-5, higher indicates a more complete dataset.

• Innovation Score: 1-5, higher indicates more innovative and valuable data.

• User Rating: Feedback from users after data usage.