Dynaword: From One-shot to Continuously Developed Datasets

Kenneth Enevoldsen, Kristian Nørgaard Jensen, Jan Kostkan, Balázs Szabó, Márton Kardos, Kirten Vad, Andrea Blasi, Gianluca Barmina, Jacob Nielsen, Rasmus Larsen, Peter Vahlstrup, Per Dalum-Møldrup, Desmond Elliott, Lukas Galke Poech, Peter Schneider-Kamp, Kristoffer Nielbo

Dynaword: From One-shot to Continuously Developed Datasets: 10 upvotes on Hugging Face Daily Papers, #9 of 24 papers on 2025-08-05. Day-by-day upvote history. It lost 5 votes when the Hub removed votes in bulk.

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.

Paper page on Hugging Face · arXiv

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