Public Datasets for
Artificial Intelligence
Not all data is created equal. This white paper benchmarks 27 major public datasets across 8 categories Vision, NLP, Audio, Video, Tabular, Knowledge Graphs, Code, and Multimodal scoring each against annotation quality, licensing, GDPR compliance, and real-world adoption.
A practical guide built for data scientists, ML engineers, and decision-makers who can’t afford to start from scratch every project.
From Wikidata and MS COCO to ROOTS and HumanEval, every dataset was evaluated on 8 weighted criteria and scored out of 100 with concrete stack recommendations per use case, from sovereign French AI to LLM evaluation pipelines.
The context
The landscape of public datasets is vast and fragmented. Hundreds of sources exist across all modalities with widely varying quality, licence conditions, and GDPR implications. Without a clear evaluation framework, teams risk wasting weeks on inadequate data, or worse, deploying models trained on data that violates licensing terms.
The challenge
AI teams needed a single, structured reference covering the most critical public datasets across every major modality. The goal: reduce selection time, avoid legal blind spots, identify GDPR-safe sources for EU deployment, and match each dataset to its optimal use case — from LLM pre-training to code generation benchmarking.
