Alternative data datasets

C cyrilzakka

Vector embeddings of clinical-trial records, hundreds of thousands of entries in Parquet (Apache 2.0). Enables similarity and retrieval analysis of trial design, indication crowding and competitive positioning.

S sutro

Vector embeddings of Apple patent text totalling about 4 million entries, stored as parquet shards with a 768-dimensional float64 feature per record.

T Tim-Pinecone

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

J justicedao

The Patent Legal IR GraphRAG release follows the Publicus retrieval format, providing dense corpus shards, BM25 documents with CID-keyed length and entry metadata, and BM25 postings arranged for inverted-index use.

S Sicheng-Chroma

sec-filings is a Hugging Face dataset published by Sicheng-Chroma that distributes pre-chunked U.S. SEC filings paired with dense vector embeddings. It is distributed in optimized Parquet format and is intended for retrieval and similarity research over regulatory disclosures.

T TMSDMAP

Qwen3-4B Patent Embeddings (专利特征向量数据集) 📌 简介 (Introduction) 本数据集包含了海量专利文本的特征向量（Embeddings）。所有特征向量均使用强大的 Qwen3-embedding-4B 模型提取。 数据领域: 专利 (Patents) 特征提取模型: Qwen3-embedding-4B 数据格式: .mmap (Memory-mapped file, numpy-compatible) + .tsv 数据精度: float16 📂 数据结构 (Data Structure) 由于数据量庞大，在生成时使用了 4 张 GPU 进行分布式计算，因此数据被划分为 4 个分片（Shards）：shard_0 到 shard_3。 每个 Shard 目录下包含以下核心文件： embeddings.float16.mmap: 核心的特征向量矩阵