Alternative data datasets
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Mechanical Utility Patents — 100 HTML Records A curated collection of 100 distinct granted U.S. utility patents from 100 patent families, saved as HTML for mechanical-engineering document analysis. Published by cmuchancel. Source disclosure: the patent text, bibliographic metadata, and drawings were obtained from Google Patents. Original PDFs and drawing assets are hosted on patentimages.storage.g
Metadata for hundreds of thousands of clinical trials in English, French and Spanish, structured in Parquet with study-level fields such as phase, condition, sponsor and status (Apache 2.0). Trial starts and phase transitions lead clinical development, so this works as an early indicator of biotech pipeline activity.
german-job-postings is a publicly hosted corpus of roughly 70,584 normalized German-language vacancies drawn from the Bundesagentur für Arbeit Jobbörse API, each row tagged with the KldB-2010 occupation code and machine-assigned ESCO occupation and skill labels. The dataset is distributed under CC-BY-4.0 and is intended as an open alternative to the predominantly English/US job-posting resources available to machine-learning practitioners.
Two Parquet files hold global patent publication records paired with quality indicators for Chinese invention patent families from 2003 to 2019, with each row representing a focal family and carrying cumulative measures such as semantic knowledge recombination, strict historical semantic novelty, and IPC-based robustness.
🧬 Dataset Summary Patent-linked nucleotide sequence records focused RNAi therapeutics patent portfolio. Each record pairs a raw nucleotide sequence with its source patent number, publication date, and global patent family coverage. 🚀 Key Features Patent Linkage: Each sequence is tied to a patent number (pn) and publication date (pbdt), enabling sequence-to-IP traceability. Global Family Coverage:
SEC 10-K Filings Embeddings Chunked text from 49 SEC 10-K annual reports for seven large US companies, with a 1536-dimensional dense embedding per chunk. Built for the Pinecone 101 workshop as a ready-to-import vector dataset. Records 15,244 chunks Filings 49 (10-K) Companies AAPL, AMZN, GOOGL, META, MSFT, NVDA, TSLA Fiscal years 2018–2025 (7 per company) Vector 1536-dim float32, intended for cosi