Alternative data datasets
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What companies are doing, buying, complying with and investing in, from the topics they hire for. 9.4k topics across 14 keyword types rolled up per company: one row per company and topic with confidence and first/last-seen dates, so you can reach out while the need is still open.
Every job posting collected since 2021: who is hiring, for what, where and at what salary. One row per posting with title, full description, location, salary and company; closed postings ship as their own file with the day each one went offline. Companies file included for firmographic joins.
Which of 33k technologies each company runs, inferred from what they hire for. One row per company and technology with confidence score, number of postings mentioning it and first/last-seen dates; technologies catalog and companies file included.
Parsed eligibility criteria from 13,229 ClinicalTrials.gov studies representing the candidate pool gathered by two first-stage retrievers during TREC Clinical Trials 2021–2023, formatted as typed entity-relation graphs for reranking models.
The clinical-trials-patient-graphs dataset is a small tabular collection of patient-level clinical records distributed in Parquet format by the 2001jdev publisher. It organizes entities and relations extracted from trial data into structured rows spanning 2021 through 2023.
This dataset is a parsed snapshot of ClinicalTrials.gov records, distributed in Parquet format on the Hugging Face Hub under an unspecified license. It contains roughly 52,000 trial entries drawn from public registry filings.
clinical-trials-trec-qrels is a tabular relevance-judgment file distributed by the 2001jdev user on the Hugging Face Hub. It maps clinical-trial topic identifiers to NCT registry IDs with graded relevance scores.
The clinical-trials-trec-topics dataset is a small tabular collection of clinical-trial topic records distributed in Parquet format on the Hugging Face Hub. It appears to be derived from TREC clinical-trial retrieval topics, with rows partitioned by year.
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 sharded Parquet archive of NSE equities and index prices from India spanning 2000 to 2026, covering more than 2,500 tickers and organized into roughly 1.5 GB files for efficient streaming.
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.
US patent full text assembled by the Allen Institute from the USPTO, millions of records under ODC-BY: claims, abstracts, descriptions and metadata, in Parquet. A broad base for measuring IP intensity, technology landscapes and the direction of R&D spending.
A static tabular extension of the Bose345/sp500_earnings_transcripts collection covering the same 2005 to 2025 calendar of S&P 500 earnings events, where each row represents a single company-quarter call identified by a stable episode_id and bundles the full transcript along with related SEC press materials and pre-event inputs suitable for supervised learning or reinforcement-style experimentation.
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.
This dataset is a filtered version of https://huggingface.co/datasets/vincha77/filtered_yelp_restaurant_reviews
S&P 500 earnings-call transcripts in Parquet under MIT, tens of thousands of calls. Independently compiled and comparable to other public transcript corpora for sentiment and tone work.
A reference collection drawn from the U.S. Patent Phrase to Phrase Matching Kaggle competition, containing additional details that are still required for full documentation.
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.
DAPFAM patent documents, a curated sample of US patents in Parquet with text for retrieval and classification tasks (CC BY-NC-SA 4.0). Scopus of claim text for building patent-similarity and technology-mapping models.
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.
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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.
DerivedFunction01's sec-filings-snippets is a public-records dataset of short text excerpts drawn from U.S. SEC filings, released on Hugging Face in Parquet format. It is sized for language-model fill-mask work and is not positioned as a commercial alternative-data product.
airline-disruption-data is a tabular dataset of U.S. domestic flight records published on the datasets hub, tagged as covering between 10 million and 100 million rows in Parquet format. The schema describes per-flight scheduling, delay, cancellation, and routing fields.
The airline-disruption-data-phase1b dataset is a tabular release hosted by publisher Dev123Hug456Face that compiles scheduled and actual flight-level operations data with delay, cancellation and diversion flags. It is distributed as a single Parquet split of roughly 12 million rows.
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":…
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
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.
An Electric Sheep Africa compilation drawn from MDPA provides 10 rows tracking hotel room occupancy rates for large hotels in Mauritius across 2019 to 2023, distributed as Parquet with Hugging Face metadata and source lineage.
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.
A Parquet dataset of 60 records providing monthly hotel room occupancy rates across one African country for 2019 to 2023, adapted from MDPA by Electric Sheep Africa and distributed with metadata and source provenance.
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.