FAQ

Licensing

Datasets you can use commercially, without share-alike

CC0, MIT, Apache 2.0 and plain CC-BY — permissive, commercial-friendly, and without the copyleft obligation that catches people.

Most “open” datasets permit commercial use. Fewer do so without a share-alike clause, and that distinction is the one that determines whether your obligations end at attribution.

Filter the 434 datasets we checked to the clearly permissive licences — CC0, MIT, Apache 2.0, ODC-By and plain CC-BY, with non-commercial, share-alike and no-derivatives variants excluded — and 275 qualify. That number flatters the position. Many of them are collections scraped from the web, where the licence covers the URLs, captions or embeddings the publisher made, not the photographs and videos they point to.

So the 11 below were chosen by hand: sets where the publisher recorded, wrote or generated the content itself, where the content is in the public domain, or where it is public-sector open data. Each still has terms to honour, and attribution is the usual one.

The exclusions matter more than the inclusions. Non-commercial rules a dataset out of a commercial product entirely. Share-alike does not rule it out, but it carries an obligation to license derivative works you share on the same terms — and whether a model trained on share-alike data is a derivative work is genuinely unsettled rather than settled in your favour. Teams that need certainty generally keep share-alike data out of commercial pipelines for that reason alone.

CC-BY's attribution requirement is real but light: credit the source. Keep a record of which datasets you trained on and you can satisfy it. That record is worth having anyway, which is the subject of our provenance post.

A bar chart of dataset size on a log scale, from 1MB to above 1TB, for 30 sources in nine coloured groups: Code (4775 GB: Stack V2, PEPs), Government & Legal (1172 GB: USPTO, CAP, USGPO, UK Hansard, Regulations.gov), Wikis (528 GB), Web (260 GB), Academic Papers (370 GB), Online Forums (165 GB), Public Domain Books (244 GB), Other (29 GB) and Educational Resources (15 GB).
The 30 sources of the Common Pile, 8TB of text that is public domain or openly licensed at the level of the content rather than the compilation, grouped by domain; code and government records dominate. Its authors dropped sources where they could not confirm the licence came from the rights holder. 'Openly licensed' here still includes share-alike. Image (cropped): r-three/common-pile · MIT repository · from “The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text”, arXiv:2506.05209
DatasetModalitySizeLicenceBest for
Common Voice - Audio corpus for speech recognitionAudio42,600 hoursCC0Robust ASR on diverse voice data
LibriSpeechAudio1,000 hoursCC BY 4.0ASR models
Google Speech CommandsAudio105,000 filesCC BY 4.0Lightweight keyword recognition
Civil Comments - Corpus of moderated comments annotated for toxicityText2M commentsCC0Toxicity detection and automated moderation
Dataset Banking77 - Banking Customer Service IntentionsText13,083 examplesCC BY 4.0NLP intent detection in banking
Gutenberg DatasetTextSeveral tens of thousands of booksPublic domainText generation or completion
World Bank Open DataTextSeveral thousand datasetsCC BY 4.0Analysing global economic and social trends
New York Vehicle Collisions (2014-2023)Text2.9 million rows, CSVCC0Analysing accident trends across NYC geographies
PD12M: Public Domain Image CollectionImage12.4M imagesCC0 images + CDLA-Permissive-2.0Similarity search and image matching
EuroSAT DatasetImage27,000 imagesMITAgricultural and environmental land use
PhysicalAI Robotics GraspGen3D57M posesCC BY 4.0AI models for robotic gripping
Licences in this shortlist — computed from the 11 datasets below. Every licence as labelled in this shortlist is permissive.
Look for NC first and SA second. Under a CC licence, everything else is an attribution line in your model card.
№ 01

Common Voice - Audio corpus for speech recognition

Audio
Size
About 42,600 recorded hours (29,300 validated) of MP3 clips with TSV metadata, 295 languages, as of release 27.0 (data to September 2026)
Licence
CC0: Public Domain

A crowdsourced multilingual collection of spoken clips in MP3 with accompanying TSV metadata, each paired with its written text: about 42,600 recorded and 29,300 validated hours across 295 languages in release 27.0, now downloaded through the Mozilla Data Collective. Mozilla's contributor model gives it unusual demographic and accent breadth, and it is released into the public domain.

What to train on it

Training robust ASR on diverse voice data, evaluating speech recognition by demographic and linguistic criteria, and fine-tuning on specific voices or languages.

Source: innovatiana.com

№ 02

LibriSpeech

Audio
Size
Approximately 1,000 hours of audio in FLAC format, with associated transcripts in TXT
Licence
CC BY 4.0

About 1,000 hours of FLAC audio from public-domain audiobook readings with word-for-word TXT transcripts. It remains the default English ASR benchmark, and unusually for this field it is cleared for commercial as well as academic use.

What to train on it

Training ASR models, fine-tuning or evaluating Whisper, Wav2Vec and DeepSpeech, and research on audio segmentation and audio-text alignment.

Source: innovatiana.com

№ 03

Google Speech Commands

Audio
Size
Approximately 105,000 audio files, WAV format
Licence
CC BY 4.0

About 105,000 one-second WAV clips of spoken keywords recorded by many speakers, built for low-latency on-device keyword spotting. The short uniform clips make it the standard benchmark for wake-word and voice-command models.

What to train on it

Training lightweight keyword recognition, developing voice interfaces for IoT and home automation, and analysing short audio signals.

Source: innovatiana.com

№ 04

Civil Comments - Corpus of moderated comments annotated for toxicity

Text
Size
Approximately 2 million text comments in JSON format, with toxicity labels and types of abuse
Licence
CC0-1.0

Roughly two million English public comments in JSON, gathered from a news-site commenting plugin between 2015 and 2017, labelled for overall toxicity and for specific kinds of abuse. Public domain and large.

What to train on it

Training toxicity detection and automated moderation, analysing hostile online interaction dynamics, and testing multi-class and multi-label classification on long texts.

Source: innovatiana.com

№ 05

Dataset Banking77 - Banking Customer Service Intentions

Text
Size
13,083 text examples with intent labels (77 classes)
Licence
CC-BY 4.0

13,083 real banking customer service requests, each labelled with one of 77 fine-grained intent categories. The high class count over short utterances makes it the standard hard intent-classification benchmark.

What to train on it

Training NLP intent detection in banking, improving customer-service chatbots or voice assistants, and analysing user needs and frustrations in detail.

Source: innovatiana.com

№ 06

Gutenberg Dataset

Text
Size
Several tens of thousands of books, TXT and EPUB format
Licence
Public domain, under Project Gutenberg terms and conditions. Verification required for commercial redistributions

Several tens of thousands of public domain literary works in TXT and EPUB from Project Gutenberg, spanning novels, essays, theatre and poetry. Commercial redistribution requires checking Gutenberg's own terms.

What to train on it

Training text generation or completion, linguistic and stylistic analysis across authors, automatic summarisation or literary classification, and studying language change over time.

Source: innovatiana.com

A clean licence on the dataset you downloaded says nothing about the images inside it. Provenance is the chain, and a restriction anywhere in it is a restriction on what you ship.
№ 07

World Bank Open Data

Text
Size
Several thousand datasets in CSV and XLSX formats
Licence
CC BY 4.0 by default for World Bank datasets; some datasets carry their own terms

Several thousand individual datasets in CSV and XLSX covering economic, social and environmental indicators for hundreds of countries across long time series. The standard open reference for cross-country development data.

What to train on it

Analysing global economic and social trends, academic research in economics and social sciences, predictive modelling for public policy, and validating data science models.

Source: innovatiana.com

№ 08

New York Vehicle Collisions (2014-2023)

Text
Size
2.9 million rows, CSV
Licence
CC0: Public Domain

2.9 million CSV rows of police-reported traffic collisions in New York from 2014 to 2023, covering date, time, location, victims, vehicles involved and contributing factors. A decade of consistent municipal reporting.

What to train on it

Analysing accident trends across NYC geographies, training predictive models for collision prevention, and evaluating human, technical and environmental factors.

Source: innovatiana.com

№ 09

PD12M: Public Domain Image Collection

Image
Size
12.4 million public-domain images, JPEG/PNG format, 16-bit half-precision vectors (1152 dimensions) optimised for L2 indexing
Licence
CDLA-Permissive-2.0 for the metadata; the images are CC0 / public domain

About 12.4 million filtered public-domain images in JPEG and PNG, each with EXIF metadata, long descriptions and 1152-dimension half-precision vectors optimised for L2 indexing. Built explicitly to avoid copyright exposure.

What to train on it

Similarity search and image matching in large databases, training and evaluating vision or multimodal models, and building applications on EXIF metadata and embeddings.

Source: innovatiana.com

№ 10

EuroSAT Dataset

Image
Size
27,000 labelled 64x64 pixel images in 10 classes, RGB JPG and 13-band multispectral TIFF
Licence
MIT (the authors' release); Sentinel data under the Copernicus terms

27,000 labelled Sentinel-2 satellite tiles at 10 metre resolution and 64x64 pixels, supplied both as RGB JPG and as multispectral TIFF, sorted into ten land-use and land-cover classes. The multispectral option is what sets it apart from ordinary image sets.

What to train on it

Classifying agricultural and environmental land use, building geospatial change monitoring tools, and benchmarking vision methods on multispectral imagery.

Source: innovatiana.com

№ 11

PhysicalAI Robotics GraspGen

3D
Size
57 million grasp poses in WebDataset format (.tar), JSON for splits
Licence
CC-BY 4.0

57 million simulated grasp poses in WebDataset .tar archives with JSON split files, generated across 8,515 Objaverse XL 3D objects and three gripper types. Published by NVIDIA. It holds grasp poses and object references, not images.

What to train on it

Training AI models for robotic gripping in simulation, testing Sim2Real approaches for 3D object manipulation, and creating benchmarks for different gripper types.

Source: innovatiana.com

A horizontal stacked bar chart with one bar per video data source category. Each bar is split by licence type with a legend naming non-commercial or academic-only, commercial, and unspecified, and the proportions differ sharply between source categories.
How much of each video source is actually licensed for commercial use, against what is academic-only or simply unstated. The unstated band is the one that ends up in products by accident. Image: Data-Provenance-Initiative/Data-Provenance-Collection · Apache-2.0 repository · from “Bridging the Data Provenance Gap Across Text, Speech and Video”, arXiv:2412.17847

How this list was put together

Filtered to licences matching CC0, MIT, Apache 2.0, ODC-By or CC-BY, with non-commercial, share-alike and no-derivatives variants excluded and vague licence wording excluded; 275 of 434 datasets pass. From those, the list keeps only sets whose content the publisher created or owned, public-domain material and public-sector open data, leaving out web-scraped collections whose licence covers the packaging rather than the media.

This is a mechanical filter on the licence as published, not a legal opinion about your specific use. Verify the current licence on the dataset's own page before you rely on it — licences do change, and a dataset's terms at the time we checked are not a guarantee of its terms today.

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