Large-scale datasets are foundational for research and development in natural language processing. However, current approaches face three key challenges: (1) reliance on ambiguously licensed sources restricting use, sharing, and derivative works; (2) static dataset releases that prevent community contributions and diminish longevity; and (3) quality assurance processes restricted to publishing teams rather than leveraging community expertise. To address these limitations, we introduce two contributions: the Dynaword approach and Danish Dynaword. The Dynaword approach is a framework for creating large-scale, open datasets that can be continuously updated through community collaboration. Danish Dynaword is a concrete implementation that validates this approach and demonstrates its potential. Danish Dynaword contains over four times as many tokens as comparable releases, is exclusively openly licensed, and has received multiple contributions across industry and research. The repository includes light-weight tests to ensure data formatting, quality, and documentation, establishing a sustainable framework for ongoing community contributions and dataset evolution.
@article{arxiv.2508.02271,
title = {Dynaword: From One-shot to Continuously Developed Datasets},
author = {Kenneth Enevoldsen and Kristian Nørgaard Jensen and Jan Kostkan and Balázs Szabó and Márton Kardos and Kirten Vad and Johan Heinsen and Andrea Blasi Núñez and Gianluca Barmina and Jacob Nielsen and Rasmus Larsen and Peter Vahlstrup and Per Møldrup Dalum and Desmond Elliott and Lukas Galke and Peter Schneider-Kamp and Kristoffer Nielbo},
journal= {arXiv preprint arXiv:2508.02271},
year = {2025}
}