Dynaword: From One-shot to Continuously Developed Datasets
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.
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Kenneth Enevoldsen
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2025-08-06T19:20:08.726Z
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Kenneth C. Enevoldsen
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Kristian Nørgaard Jensen
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Kristian Nørgaard Jensen
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Jan Kostkan
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Balázs Szabó
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Márton Kardos
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Kirten Vad
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Andrea Blasi Núñez
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Gianluca Barmina
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Gianluca Barmina
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Jacob Nielsen
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Jacob Nielsen
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Rasmus Larsen
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Peter Vahlstrup
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Per Møldrup Dalum
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2025-08-06T19:20:04.100Z
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Per Dalum-Møldrup
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perdalum
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Desmond Elliott
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Lukas Galke
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2025-08-13T07:28:24.591Z
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Lukas Galke
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lgalke
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Peter Schneider-Kamp
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Kristoffer Nielbo
Dynaword: From One-shot to Continuously Developed Datasets
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.
A framework called Dynaword and its implementation Danish Dynaword enable community-driven, open, and continuously updated large-scale natural language datasets.
Dynaword Danish Dynaword