Alternative data datasets

A adityaag2k

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 Amit-Vikramaditya

SEC-EDGAR filings, part f (copy) An unmodified copy of part of TeraflopAI/SEC-EDGAR. I keep it here as staging data for language-model pretraining. Source: TeraflopAI/SEC-EDGAR, at commit 43de32c Files: 587 files, 45.6 GB, in the source's form-type folders: 8-K (217), S-1 (216), 4 (76), 20-F (40), S-8 (25), 3 (6), 5 (4), plus the source's LICENSE file and two chart images from its top folder. Lice

B baridhi

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.

B BEE-spoke-data

dataset_info: - config_name: default features: - name: filename dtype: string - name: cik dtype: string - name: year dtype: string - name: text dtype: string splits: - name: train num_bytes: 32401429243 num_examples: 176289 - name: validation num_bytes: 4043644079 num_examples: 22050 - name: test num_bytes: 4066220856 num_examples: 22036 download_size: 17807481889 dataset_size: 40511294178 - confi

B botmartzai

FDA Form 483 Clinical Trial Protocol Violation Bench High-Consequence Enterprise Evaluation Benchmark Official Maintainer: Botmartz AI Research (botmartz.com)Research Publication: botmartz.com/insightsPrimary Metric: Adverse Deviation Extraction F1 Problem Overview In high-consequence enterprise applications within BioTech & Pharma, standard out-of-the-box LLMs and naive dense vector RAG fail on m

C chenmingxuan

Chinese patents paired with their abstractive summaries in Mandarin, a few thousand records in JSON (Apache 2.0). A window into the pace and direction of patenting inside the Chinese technology base.

C Coder-Dragon

Records of initial public offerings launched on Indian markets between 2006 and 2025, with fields covering open and close dates, listing date, face value, issue price and size, lot size, first-day price, total shares offered and their allocation across anchor, NII, QIB and retail categories, minimum investment, and subscription figures for each investor class.

C cyrilzakka

A tabular extract of clinical study descriptions drawn from ClinicalTrials.gov on 5 February 2025, emphasizing eligibility, design, and objective fields for healthcare and machine learning research.

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.

D dvdmrs09

A large granted-patent text collection from the USPTO, tens of millions of records in Parquet (Apache 2.0). Broad coverage of claims and descriptions for IP-intensity and technology-trend measurement.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_anchor_dagger1.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_cheat.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_dagger1.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_dagger12.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_dagger1_cheat.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_dagger1_cheat5.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_dagger2.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_dart.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_dart_full-xl.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_dart_full-xxl.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_dart_full_anchor-xl.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_dart_xl.

E edgarcancinoe

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 20, "features": { "observation.images.image": { "dtype": "video", "shape": [ 256, 256, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height":… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/water_pour_shift.

I INPI-France

View source information and access options.

I INPI-France

French patents published in 2024, split into chunks at the `<p>` XML tag level and saved in Parquet for streaming and distributed processing. The records come from public French patent filings (A1, 2024) and were independently extracted, structured, and segmented using access to PI API and FTP sources.

I INPI-France

A cleaned collection of French patent documents published from 1981 through 2026, each row in the Parquet file representing one complete A1 publication parsed from the original XML. It was assembled independently by a contributor with direct access to the public patent source and is optimized for distributed loading.

J Jeremydh911

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.

K kapilrao

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.

P PersonaAIDev

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 50, "total_frames": 29874, "total_tasks": 1, "total_videos": 100, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:50" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the f

P PersonaAIDev

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 50, "total_frames": 44746, "total_tasks": 1, "total_videos": 100, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:50" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the f

P PersonaAIDev

This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 20, "total_frames": 11940, "total_tasks": 1, "total_videos": 40, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:20" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the fu

P PhysiQuanty

Patent_FR_US_Merge_Radix_65536 is a Hugging Face dataset by publisher PhysiQuanty that merges French and US patent documents into a single text corpus. It is distributed in Parquet format and is tagged with an Apache-2.0 license, though the README provides no further description.

S seanm924

Citation This dataset builds on edgar-corpus.

T TeraflopAI

Text of SEC filings pulled from EDGAR spanning millions of records in English (Apache 2.0): 10-K, 10-Q, 8-K, annual reports and exhibits, tagged for text-generation and classification. A practical base corpus for earnings- and filings-driven signals, from risk factors and MD&A to management commentary.

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

V vibrantlabsai

View source information and access options.

Z ZipLime

ZipLime US Insider Trading Disclosures (PIT) [!CAUTION] Use knowledge_date, not transaction_date, when backtesting. transaction_date says when a trade occurred; knowledge_date says when the filing became observable through EDGAR. Using the former as the signal date introduces look-ahead bias. visible = trades.filter(pl.col("knowledge_date") <= simulation_time) This dataset normalizes corporate-ins

Z ZipLime

US Short Interest This dataset publishes no short-interest data, and that is the finding, not a bug. Every US source of equity short positioning was audited on 13 September 2026 before any of it was collected. Two forbid the publication pattern and the third does not exist yet. What is published here is the audit itself — which clause, on which page, read on which day — plus the adapters that will