Alternative data datasets
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A registry of ERC-1155 token contracts paired with end-of-day, historical, and live on-chain cryptocurrency market data.
A registry of ERC-20 token contracts accompanied by historical, end-of-day, and real-time on-chain cryptocurrency data.
A registry of on-chain NFT collections supported by historical, end-of-day, and live blockchain cryptocurrency data.
Datamule, Teraflop AI, and Eventual jointly contributed a 590 gigabyte SEC EDGAR collection encompassing about 8 million filings and 43 billion tokens, assembled using the datamule-python library and the official datamule API.
A large-scale release produced jointly by Datamule, Teraflop AI, and Eventual covering roughly 590 GB of SEC EDGAR content. It comprises about eight million filings totaling 43 billion tokens, gathered using the datamule-python package and the official datamule API.
Ethereum blockchain endpoints covering mainnet and testnets: balances, transactions, internal transfers, ERC-20 and NFT interactions, contract ABIs, gas use and labelled major accounts, the usual first stop for ETH chain analysis.
A joint release by Datamule, Teraflop AI, and Eventual provides the SEC-EDGAR dataset, comprising 590 GB of content drawn from all major filings in the SEC EDGAR database and totaling roughly 8 million samples and 43 billion tokens. The data was assembled using the datamule-python library and the official datamule API developed by John Friedman, with datamule serving as a Python package for collecting and manipulating such records.
Datamule partnered with Teraflop AI and Eventual to publish the SEC-EDGAR dataset, which covers all major filings from the SEC EDGAR database and amounts to 590 GB across about 8 million samples and 43 billion tokens. Collection relied on the datamule-python library and John Friedman's official datamule API, tools designed in Python for gathering and handling SEC filing data.
Wallet labeling service that identifies high-performing addresses and monitors their token activity.
Dataset details:- This dataset is the 2nd iteration following bugs in 1st dataset. The initial data suffered with followoing cases:- (i) The failed reference_answers generation(due error totalling 23) primarly because of 2 reasons/exceptions:- (a) There was normal limit(300 in 1st request) and worst case limit(450 in 3rd request) number of tokens for consolidated 4 refernce_answers per chunk and i