Alternative data datasets
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credit_card_transactions is a small tabular dataset published by aegisheld containing anonymized customer-level credit card account and spending summaries. It is distributed as CSV with a single training split of 8,950 rows.
About 1.5 million US patent claims split into training and test partitions in CSV, organized for claim-level classification and summarization work (Apache 2.0). A large, ready-split corpus for IP-claims modeling.
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.
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.
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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
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.
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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.
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.
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.
A small text-classification dataset by ClarusC64 that labels whether borrow-rate, short-interest and price signals cohere into a real short squeeze or diverge into a false signal. It is published on a model hub with an MIT license tag and a 10-row train split.
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.
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.
An apache-2.0 multilingual corpus tailored for fine-tuning models that must answer technical airline-domain queries drawn from aviation source documents.
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.
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.
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 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.
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.
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.
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.
patent_abstract_uspto is a text dataset of U.S. patent abstracts distributed under an MIT license and hosted on the Hugging Face Hub by publisher kaustav16. It contains roughly 9.36 million rows of patent identifiers paired with their corresponding abstract text.
sec_filings is a small text dataset of 494 rows that pairs natural-language queries about U.S. securities filings with retrieved document facts and ground-truth answers. It is distributed under an unstated license on a public dataset hub.
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Global Job Postings – Multi-ATS is a single dated snapshot of 112,816 job listings aggregated from 23 applicant tracking systems and parsed into a 47-column schema by NextGig-Rocks. It is distributed as Parquet via the Hugging Face datasets library and targets labor-market research, not production job boards.
专利无效检索评测数据集 面向专利无效证据检索系统的公开 Benchmark，以权利要求文本为检索输入， 以真实无效案件中使用的证据文献为标准答案。 简体中文 · English 下载公开数据集 数据集简介 本数据集用于评估 AI Agent、检索模型和传统专利检索系统发现无效证据文献的能力。 每条样本包含待分析的权利要求、案件最终结果、证据文献，以及证据角色和法律依据等 结构化标注。 公开版本包含 80 条 Benchmark 样本，CN 与 US 各 40 条，覆盖 IPC 的 A–H 八个部。它既可以衡量系统能否找到至少一篇有效证据，也可以衡量系统对核心文献和 全部证据文献的覆盖程度。 这份数据可以用来评估什么 能力 说明 无效证据检索 给定权利要求文本，系统能否从专利文献中找到真实案件使用的无效证据 核心与完整召回 分别评估主要证据 primary_reference 和全部证据文献
PatSnap Patent OAR Bench A benchmark dataset for patent Office Action Response (OAR) tasks. The test split contains 40 cases: 13 CN, 13 EP, and 14 US. It includes 40 Office Action PDF documents totaling 229 pages, structured case materials, and 19 shared Judge-scored criteria per case (760 entries). Dataset: data/test.parquet, 40 cases in Parquet format. Office Action PDFs: assets/, named OAR-<id>
A daily record of trading activity across multiple major global stock market indices, structured for trend analysis and forecasting work in international equity markets.
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.
The nj-housing-prices-tabular dataset is a small tabular collection of New Jersey residential property listings, published on Hugging Face under an unstated license. It contains roughly 7,000 rows split across train, validation, and test partitions.
clinicaltrials.gov-summary_and_eligibility is a small Parquet mirror of selected ClinicalTrials.gov records, distributed on the Hugging Face Hub under the rjac publisher. It packages study identifiers, recruitment status, titles, brief summaries, and eligibility criteria into a single tidy table of roughly three thousand rows.
An English-language NLP corpus of cleaned earnings call transcripts gathered from public investor-relations pages, sized between 10,000 and 100,000 documents for financial analysis and LLM work.
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Agent Earnings 87 sourced, dated entries on money made with AI agents: agent businesses, marketplace and bounty payouts, x402 pay-per-call sales, sponsor and affiliate income, and other experiments. The set includes flops, one withdrawn figure, one sponsor list price (not revenue), one project count (not a dollar amount), one bounty total (Superteam Earn, $84,137) that the entry marks as claimed b
Earnings Call Transcript Lite is a small public-records dataset of corporate earnings call transcripts paired with short reference summaries. It is distributed as a CSV and is published on the Hugging Face Hub under an unstated license.
tech-job-postings-labeled is a small public dataset of labeled technology job postings published on Hugging Face by SuhaibAtef. It comprises roughly 8,000 records tagged with source labels and is distributed in JSON.
jobpostingsamples is a small sample dataset of US job postings published by talanAI. It is distributed in CSV format and is tagged for use with the Hugging Face datasets, pandas, polars, and mlcroissant libraries.
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10-K annual filings from EDGAR in Parquet, tens of thousands of documents. The standard annual-disclosure corpus for fundamentals, risk-factor and management-discussion analysis.
The linkedin-job-postings dataset, published by xanderios on Hugging Face, contains a single CSV table of U.S. LinkedIn job listings with about 33,000 rows. It is tagged with a MIT license on the platform, though the underlying LinkedIn terms of service place restrictions on redistribution and use.
The 2024 Earnings Call Transcript dataset, published by yeong-hwan on Hugging Face, aggregates quarterly earnings call dialogue for individual stock tickers. It is distributed as a JSON table with 1,904 rows in a single train split.