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Bayesian deep learning for error estimation in the analysis of anomalous diffusion

Henrik Seckler, Ralf Metzler · Nature Communications · 2022

DOI 10.1038/s41467-022-34305-6

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  • Metadata only — full assessment not yet run.

The Paper Scorecard

  • Standingclear
    • No retraction on record (OpenAlex metadata as of 2026-07-03).
  • Designjournal article
    • Journal article (Nature Communications). Study design detail comes from the methodology read, not metadata.
  • Executionnot assessed
    • Metadata only — full assessment not yet run.
  • Corroborationcited
    • Cited by 89 works (OpenAlex citation count).
  • Provenanceidentified
    • Published in Nature Communications.
    • Publisher: Nature Portfolio.
    • ISSN 2041-1723.
    • The journal is listed in DOAJ.
    • Open-access status: gold.
Assessed by Epistry · metadata as of July 3, 2026
Bayesian deep learning for error estimation in the analysis of anomal… — Epistry