Alternative data datasets
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An open, layout-preserving conversion of U.S. SEC EDGAR filings into MultiMarkdown format covers approximately 3.4 million documents submitted between January 2022 and June 2025, designed for long-context language modeling, financial reasoning, and document analysis.
EDGAR_FILINGS_DATASET_2016_2021 is a Hugging Face mirror of parsed SEC EDGAR filings spanning 2016 through 2021, distributed as a single train split of about 6 million rows in Parquet format. It is published by anonymous-md with an unstated license.
EDGAR_FILINGS_DATASET_2022_2026H1 is a Hugging Face dataset that compiles parsed SEC EDGAR filings into a single tabular corpus. It covers roughly 2022 through the first half of 2026 and is distributed as Parquet with about 1.7 million records.
A reconstructed EDGAR corpus of company filings in English (Apache 2.0): a large sample of annual and quarterly reports and exhibits, hundreds of thousands of records. Comparable to other public SEC text sets and a solid starting point for filings-based research.
edgar_xbrl_companyfacts is a Hugging Face dataset published by DenyTranDFW that aggregates U.S. SEC EDGAR XBRL company-facts filings into a single Parquet file. The training split contains roughly 125 million rows of structured financial facts tagged by reporting period and unit.
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.
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This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 30, "features": { "action": { "dtype": "float32", "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ], "shape": [ 6… See the full description on the dataset page: https://huggingface.co/datasets/Edgarium/rangement_pq_20
This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 30, "features": { "action": { "dtype": "float32", "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ], "shape": [ 6… See the full description on the dataset page: https://huggingface.co/datasets/Edgarium/rangement_pq_20
This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v3.0", "fps": 30, "features": { "action": { "dtype": "float32", "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ], "shape": [ 6… See the full description on the dataset page: https://huggingface.co/datasets/Edgarium/rangement_pq_20
This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v2.1", "robot_type": "so101_follower", "total_episodes": 50, "total_frames": 10333, "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":…
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This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v2.1", "robot_type": "so101_follower", "total_episodes": 709, "total_frames": 187905, "total_tasks": 1, "total_videos": 1418, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:709" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path
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This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v2.1", "robot_type": "so101_follower", "total_episodes": 161, "total_frames": 42852, "total_tasks": 1, "total_videos": 322, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:161" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":
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This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v2.1", "robot_type": "so101_follower", "total_episodes": 101, "total_frames": 26027, "total_tasks": 1, "total_videos": 202, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:101" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":
This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v2.1", "robot_type": "so101_follower", "total_episodes": 51, "total_frames": 14615, "total_tasks": 1, "total_videos": 102, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:51" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":…
This dataset was created using LeRobot. Dataset Structure meta/info.json: { "codebase_version": "v2.1", "robot_type": "so101_follower", "total_episodes": 417, "total_frames": 74912, "total_tasks": 1, "total_videos": 834, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:417" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":
A corpus of SEC EDGAR filings in English (Apache 2.0), roughly a few hundred thousand company documents including annual and quarterly reports and exhibits. The filings text is organized for model pretraining and retrieval, but the same documents are the raw material for filings-based and disclosure signals.
Quarterly 10-Q filings parsed from EDGAR into JSON, millions of records in English (MIT). The quarterly companion to the 10-K corpus for tracking sequential changes in operating performance and risk factors.
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A JSONL collection of detokenized EDGAR agreement records, where a single entry may contain multiple agreements and no source-level cleaning has been applied.
Citation This dataset builds on edgar-corpus.
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 component of the ALEA Institute's KL3M Data Project supplying training material cleared of copyright concerns. The dataset card is currently a placeholder, directing readers to the GitHub repo and project paper for full documentation.
The KL3M Data Project from the ALEA Institute supplies copyright-clean training material, and the dataset listed here is one component pending further documentation on its dedicated page.
Material contracts and agreements extracted from EDGAR filings by the KL3M project: debt, M&A, employment and license agreements, millions of documents in Parquet. Contract language is a niche but real input for M&A, financing and litigation-event signals.
The kl3m-index-edgar-filings dataset, published by ALEA Institute, is a Parquet-format index of SEC EDGAR filings distributed via the Hub datasets library. It contains roughly 20 million tabular records spanning the 10M–100M size category, with an unstated license.
The KL3M Index of EDGAR 10-K Filings is a tabular index published by the Alea Institute that catalogs SEC EDGAR filings with associated issuer metadata. It is distributed in Parquet format and is tagged for use with pandas, polars, and the mlcroissant dataset library.
The kl3m-index-edgar-filings-8-k dataset is an indexed catalog of Form 8-K filings from the SEC's EDGAR system, published by the ALEA Institute. It provides structured metadata for over 1.8 million corporate event reports distributed in Parquet format.
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.
List of CMBS deals with publicly available Schedule AL data in SEC.gov EDGAR
sec-edgar-rpt is a Hugging Face dataset published by codewithdark that aggregates SEC EDGAR filings, packaged as a single text column with a train split of roughly 3.9 million rows. It is distributed under an unstated license and is catalogued for text and machine-learning workflows.
Includes all company names and cik keys from the SEC database.
Structured numeric records parsed from corporate filings such as 10-K, 10-Q, and 8-K submissions to the U.S. SEC. The figures are packaged inside a compressed DuckDB instance made available through the open-source Datapond registry for fast querying.
Data Analytics and Data Visualisation for beginners
A consolidated, ready-to-use repository of 13F institutional holdings drawn from SEC EDGAR filings, capturing the most recent quarterly disclosures of more than 13,000 investment managers including hedge funds, mutual fund complexes, pension funds, banks, and family offices, with each record summarizing total assets under management and individual positions.
Structured line items from SEC 10-K and 10-Q filings between 2009 and 2023, plus company facts, fundamentals extracted straight from EDGAR.
Historical Data from SEC Company Filings
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.
10-K filings for S&P 500 companies retrieved from EDGAR, stored as Parquet with the filing text attached (MIT). A focused corpus for the large-cap annual disclosure cycle: risk factors, MD&A and segment detail for every S&P 500 name.
S&P 500 Corporate Filings (SEC EDGAR 10-K & 8-K Archive) Overview This dataset contains clean, structured public data exported directly from production runs of Apify actors. It serves as a benchmark and sample for lead qualification, market intelligence, research, and machine learning pipelines. Source Actor: captainhandsome/sec-edgar-filings-search Dataset Page: Public sample and schema Preconfig
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.
EDGAR filings of companies
Complete filing metadata and URLs for 82,000+ annual report filings (July-Dec)
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Index of filings submitted to the Securities & Exchange Commission since 1993
Catalog of SEC filings dating back to 1993, providing a navigational guide to the EDGAR archive.
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Complete textual SEC 10-K annual filings from 2021, suited for studying corporate disclosures and natural language processing tasks.
Extracted 10k fillings from SEC Edgar for the fiscal year 2021
Search filings on SEC EDGAR using ticker symbols, CIK identifiers, SIC industry codes, or free-text queries, returning a single structured row per filing that includes company classification, period dates, direct links to source documents, and optional XBRL financial data. The dataset contains 2,691 rows across 27 fields, supported by 64 collector runs, with the latest observation dated 2026-08-04, and covers 771 entities, browsable at https://reapx.dev/data/sec-edgar-scraper/.
Public access to SEC filings and related submissions/XBRL data APIs. Filing forms differ in scope, structure and reporting delay; amendments and publication timestamps matter for historical research.
Parquet and csv files associating CIK (Central Index Key) with filing entities
JSON data file for SEC conformed company name, CIK, ticker, exchange association
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.