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Dataset artefacts can partially drive the measured decline in disruption

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TL;DR - This Nature paper examines whether dataset artefacts partly explain the measured decline in disruptive research. Only the title is provided, so the specific methods and findings are unavailable.

  • Focuses on potential measurement bias in disruption metrics.
  • Suggests the observed decline is at least partially attributable to dataset construction or quality.
  • Matters for interpreting claims that scientific innovation is becoming less disruptive.

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Dataset artefacts can partially drive the measured decline in disruption

Nature Vincent Holst, Andres Algaba, Floriano Tori, Sylvia Wenmackers, Vincent Ginis 2026-08-12 doi:10.1038/s41586-026-10787-y
Public signals OpenAlex citations 1
Providers: Hugging Face · N/A OpenAlex · Citations 1 Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-12 14:26:52.672762 UTC

TL;DR - This Nature paper examines whether dataset artefacts partly explain the measured decline in disruptive research. Only the title is provided, so the specific methods and findings are unavailable.

  • Focuses on potential measurement bias in disruption metrics.
  • Suggests the observed decline is at least partially attributable to dataset construction or quality.
  • Matters for interpreting claims that scientific innovation is becoming less disruptive.
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