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AtlasNLP: A Country-Aware Atlas of Dataset Representation in NLP

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and informing AI policy. However, geographic metadata is very rarely available, and country-level representation is often hidden behind broad language-level claims. We introduce AtlasNLP, a country-aware atlas of over 13,000 NLP dataset records across normalized NLP task categories, tracking both the populations represented and where datasets are produced. AtlasNLP includes AtlasNLP-Gold, a human-curated reference set, and AtlasNLP-Core, an ACL-derived

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-08-31T00:48:30.000Z

First collected: 2026-09-21T07:22:03.933Z. This is not the publication date.