Alternative data datasets
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The job-postings-english-clean dataset, published by 2024-mcm-everitt-ryan, is an English-language corpus of online job postings distributed as a single Parquet train split of roughly 1.76 million rows. It is tagged as US-region data and is intended for text-based machine learning workflows.
The job-postings-raw dataset is a large-scale tabular collection of scraped job postings distributed in Parquet format. It aggregates records across multiple sources and locales, with US coverage indicated in the dataset metadata.
A copyright-clean text collection released by the ALEA Institute as part of the KL3M Data Project, intended for training language models; full documentation is available via the linked paper and GitHub repository.
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-10-q dataset, published by the ALEA Institute, is a Parquet-formatted index of U.S. Securities and Exchange Commission Form 10-Q filings. It is distributed on the Hugging Face Hub with roughly 495,607 rows in a single 'train' split.
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.
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Dataset overview This is a dataset that contains restaurant reviews gathered in 2019 using a webscraping tool in Python. Reviews on restaurant visits and restaurant features were collected for Dutch restaurants. The dataset is formatted using the 🤗DatasetDict format and contains the following indices: train, 116693 records test, 14587 records validation, 14587 records The dataset holds both inform
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
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.
The TREC Clinical Trials collections released in 2021, 2022, and 2023 are available through the TREC homepage, with corresponding papers for each year. Artur Guimarães ([email protected]) serves as the dataset curator, while inquiries regarding the original authors can be directed separately. The dataset addresses the task of linking patient profiles with appropriate clinical trials, containing entries formatted as JSON objects with fields including query identifiers, disease labels, and accompanying clinical text.
An aggregation of 1.3 million U.S. patent records each accompanied by a human-written abstractive summary, sorted into nine Cooperative Patent Classification groupings spanning human necessities, chemistry, textiles, and related domains.
The awinml/earnings_calls_transcripts dataset on the Hugging Face Hub packages a small collection of earnings-call transcript segments formatted as chat-style messages for fine-tuning language models. It is distributed as a parquet file and is tagged for text modality with libraries including datasets, pandas, mlcroissant, and polars.
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
CAR-T clinical trials registered worldwide Registered clinical trials of CAR-T cell therapies, from 1999 to 20 October 2026. The records come from the WHO International Clinical Trials Registry Platform (ICTRP) and ClinicalTrials.gov, and are grouped into unique trials. Each trial is described by its countries, World Bank income group, phase, status and sponsor type (academic, public, mixed, for-p
A sample of US patent titles, abstracts and CPC classification labels built for multi-class patent classification, tens of thousands of text records in Parquet. Useful as training material for mapping innovation activity onto technology categories over time.
clinical-trials-v2 is a Hugging Face dataset published by chemNLP containing processed ClinicalTrials.gov records stored in Parquet. It exposes three columns, filename, xml, and text, drawn from the official clinical-study XML schema.
GenAI-job-postings-Dataset is a small, US-focused text corpus of generative-AI and machine-learning job postings distributed in Parquet format. The dataset is published on the Hugging Face Hub under an unstated license and comprises a single train split of roughly 120 rows.
A collection of restaurant reviews was assembled in 2019 through Python-based web scraping focused on Dutch establishments, capturing both visit experiences and feature-related information. It is organized in the DatasetDict format with three splits: 116,693 training records, 14,587 test records, and 14,587 validation records.
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.
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.
SEC Filings Structured Signals A structured dataset derived from real SEC EDGAR filings, containing information extracted from corporate disclosures submitted to the U.S. Securities and Exchange Commission. The dataset transforms filing text into structured signals covering financial risks, hedging strategies, litigation, supply-chain dependencies, causal relationships, entity relations, and manag
Dattito's clinical-trials-data is a Parquet-format dataset of roughly 24 million clinical trial records sourced from public trial registries. It aggregates identifiers, conditions, and standardized medical terminology into a single table for large-scale analysis.
An apache-2.0 multilingual corpus tailored for fine-tuning models that must answer technical airline-domain queries drawn from aviation source documents.
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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 (502,924 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
Company Earnings Results 2019–2026: findings, method and data card Fliar Research · released October 2026 · licence CC BY-NC 4.0 How this dataset was acquired. Every row comes from the Radar API, Fliar's company-data API and MCP server. Radar reads business news continuously and extracts each reported earnings result into structured fields: company, period, fiscal year, currency, revenue, net inco
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.
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.
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.
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.