Lomen Hub provides access to a growing range of blockchain datasets specifically curated and structured for AI applications and autonomous agents.

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Types of Datasets

Our goal is to offer diverse datasets covering various aspects of blockchain activity. Examples include:

  • Market Trends: Real-time and historical data on token prices, trading volumes, and market sentiment across different exchanges and chains.
  • DeFi Metrics: Comprehensive data on lending protocols, liquidity pools, staking rewards, and other decentralized finance activities.
  • Token Flows: Analysis of token movements between wallets, smart contracts, and exchanges.
  • NFT Activity: Data related to NFT collections, minting events, sales history, and ownership changes.
  • Prediction Market Outcomes: Datasets relevant for training agents to participate in or analyze prediction markets.
  • Transaction Analytics: Detailed information extracted from blockchain transactions, potentially labeled or categorized for specific use cases like sybil detection.

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AI-Ready Formats

Raw blockchain data is often difficult for AI models to consume directly. Lomen Hub transforms data into user-friendly and AI-optimized formats:

  • Structured Data: Tabular formats (like CSV), time-series data suitable for analysis and model training.
  • Queryable Formats: Accessible via familiar interfaces like SQL queries or API responses in formats like JSON.
  • RAG-Friendly: Datasets designed for seamless integration with Retrieval-Augmented Generation (RAG) pipelines used by Large Language Models (LLMs), enabling them to query and reason about live blockchain data.
  • Pre-processed: Data undergoes necessary cleaning, parsing, and indexing to be immediately usable.

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Quality and Reliability

We understand that the reliability of AI depends heavily on the quality of the underlying data. Lomen Hub emphasizes:

  • Verification: Implementing protocols to ensure data accuracy and integrity.
  • Reputation: Utilizing a provider reputation system to highlight trusted and high-quality data sources.
  • Consistency: Ensuring data is consistently structured and maintained across different datasets and chains.