Alternative data datasets
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ClinicalTrials.gov XML for studies registered between 2018 and 2024, parsed into CSV, hundreds of thousands of records under CC0. A clean, licensed point-in-time history of US clinical-trial registrations.
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.
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":
Ten rows of Parquet data from Electric Sheep Africa tracking room occupancy rates across all hotel categories in Mauritius between 2019 and 2023, sourced from MDPA and structured for analytical use.
Prepared by Electric Sheep Africa, this MDPA-sourced dataset supplies 10 entries of hotel room occupancy rates for Mauritius over 2019-2023, delivered as ML-ready Parquet files accompanied by uniform Hugging Face metadata and source provenance.
A small Parquet-format collection from Electric Sheep Africa reporting room occupancy rates for large hotels in Mauritius from 2019 to 2023, containing ten records drawn from MDPA and packaged with Hugging Face metadata for machine learning workflows.
Assembled by Electric Sheep Africa from MDPA, this dataset provides 60 rows of monthly hotel room occupancy rates for Mauritius spanning 2019-2023, formatted as ML-ready Parquet with consistent Hugging Face metadata and source attribution.
Compiled by Electric Sheep Africa, this MDPA-derived dataset offers 20 records of quarterly hotel room occupancy rates for Mauritius across the 2019-2023 period, distributed in ML-ready Parquet format with standardized Hugging Face metadata and source traceability.
Compiled by Electric Sheep Africa from MDPA, this 20-row dataset records quarterly hotel room occupancy rates in Mauritius for all hotel categories between 2019 and 2023 and is delivered as ML-ready Parquet.
Electric Sheep Africa releases 60 quarterly observations from MDPA on hotel room occupancy for large establishments in Mauritius from 2019 through 2023, formatted as Parquet with consistent metadata.
A dataset encompassing physician services, inpatient and outpatient care, prescriber information, medical equipment, prosthetics, and acute care categories.
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.
99.90% Accuracy · 12 Categories · 100+ File Extensions · Soft-Voting Ensemble
Indeed Job Postings (2026) is a 1,000-row sample of US Indeed listings joined to company firmographics, published by fact-den on Apify. License terms are not stated, and the publisher directs users to a commercial source actor for fresh or filtered data.
A U.S. patent collection with a minimal placeholder card and no additional documentation provided.
Geographic information covering points of interest with ties to categories and brands.
Produces short-, long-term, and momentum indicator values across 26 ESG categories.
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Official Consumer Price Index and inflation series for Iran drawn from two providers: the Statistical Centre of Iran (monthly, from 2011/1390 onward) and the Central Bank of Iran (annual, long historical run from 1936/1315 onward), each with its own series and a combined view covering national-level index and percentage data.
A legacy archive of Iran's consumer price index and inflation series from 1936 to 2022 remains available, though an updated multisource version using current Farmaanaa methodology supersedes it.
A curated collection of earnings call recordings broken into intelligent segments and paired with high-accuracy transcripts produced by Mistral's Voxtral model, intended for ASR benchmarking, transcription quality studies, and audio-to-text alignment research.
FINRA Short Interest, last 12 months Shares sold short for every exchange-listed and OTC security, twice a month, for the last 12 months (522,296 rows): short shares, change from the prior report, average daily volume and days to cover. import pandas as pd df = pd.read_parquet("hf://datasets/FinzData/finra-short-interest/data/short_interest.parquet") df[df.ticker == "GME"] In the full FinzData dat
Indian equity market price history covering NSE-listed stocks and indices from 2000 through 2026, offered in Parquet shards of roughly 1.5GB each for efficient streaming on the Hugging Face hub. The collection draws on more than 2,500 tickers and supports both minute-level and end-of-day intervals.
Longitudinal pricing information at three different levels for benchmarking costs of therapies across regions and disease categories.
S&P 500 earnings-call transcripts preprocessed into optimized Parquet, tens of thousands of calls (MIT). Ready for sentiment, QA and summarization research on the large-cap earnings season.
A sampled extraction of IPO-related tables from SEC filings between 1994 and 2026, delivering raw table HTML with provenance fields and targeting roughly 100 tables per year, with edge years possibly containing fewer valid extractions.
Text of IPO filings, including S-1 and related prospectuses, in JSON: hundreds of thousands of records in English (CC BY 4.0). Pre-IPO disclosure text is one of the few information sources available ahead of a listing.
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.
🇮🇳 Indian Stock Market Data: Minute & Daily (2000 - 2026) 📌 Overview This is a high-performance financial dataset containing the historical price history of 2,500+ NSE Stocks and Indices. The dataset has been sharded and optimized for high-speed training. Instead of thousands of tiny files, it is grouped into large ~1.5GB Parquet shards, making it ideal for fast streaming with the Hugging Face dat
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Nationwide United States coverage of approximately two billion mobile advertising identifiers alongside around 910 million household exposure markers, organized into 295 IAB-aligned branded audience categories.
Earnings call transcripts for S&P 500 and other large U.S. companies covering 2005 through 2025, useful for financial research, natural language processing, and sentiment analysis.
Information organized around three climate-related categories: hazards, prospects, and mitigation impact.
FR-Patent-2025-Claims is a tabular dataset published by INPI-France containing individual patent claims from French patent publications. It is distributed in Parquet format with 102,748 rows in the provided training split.
Raw French patent publications from 2020 through 2026, pulled from original A1 XML records by an independent API/FTP extraction and delivered one-document-per-row in streaming-ready parquet.
Dataset Card for clinicaltrials/2017 The clinicaltrials/2017 dataset, provided by the ir-datasets package. For more information about the dataset, see the documentation. Data This dataset provides: docs (documents, i.e., the corpus); count=241,006 This dataset is used by: clinicaltrials_2017_trec-pm-2017, clinicaltrials_2017_trec-pm-2018 Usage from datasets import load_dataset docs = load_dataset(
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.
A dataset card entry for restaurant reviews that flags the need for additional information beyond what is currently provided.
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.
🇮🇳 Indian Stock Market Data: Minute & Daily (2000 - 2026) 📌 Overview This is a high-performance financial dataset containing the historical price history of 2,500+ NSE Stocks and Indices. The dataset has been sharded and optimized for high-speed training. Instead of thousands of tiny files, it is grouped into large ~1.5GB Parquet shards, making it ideal for fast streaming with the Hugging Face dat
Earnings-call transcripts from an IBM research project, tens of thousands of records with finance tags, in CC0. A filings-adjacent corpus for call-tone and question-and-answer analysis.
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.
The mintic_linkedin-job-postings dataset aggregates LinkedIn job posting text into a single string column with roughly 124,000 rows. It is published on Hugging Face by user jmparejaz under an unstated license.
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
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.