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Assessing the reproducibility of discriminant function analyses.

Rose L Andrew1, Arianne Y K Albert2, Sebastien Renaut3

  • 1School of Environmental and Rural Science, University of New England , Armidale, NSW , Australia ; Biodiversity Research Centre, University of British Columbia , Vancouver, BC , Canada.

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Summary
This summary is machine-generated.

Reproducibility of scientific findings is crucial. This study found that while most Discriminant Function Analyses (DFAs) in organismal biology are reproducible, issues with data curation and metadata still hinder validation and reuse.

Keywords:
Data archivingData curationRepeatabilityStatistics

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Area of Science:

  • Ecology and Evolutionary Biology
  • Quantitative Biology
  • Scientific Reproducibility

Background:

  • Empirical research relies on data, but datasets are often inaccessible, incorrect, or poorly curated.
  • Lack of accessible and well-curated data impedes result validation and data reuse for new hypotheses.
  • Accurate labels and identifiers are essential for dataset usability.

Purpose of the Study:

  • To assess the impact of metadata and data curation issues on the reproducibility of published results.
  • To evaluate the reproducibility of Discriminant Function Analyses (DFAs) in organismal biology across varying dataset ages.

Main Methods:

  • Surveyed 100 papers from organismal biology using Discriminant Function Analysis (DFA).
  • Excluded 14 papers lacking sufficient DFA details or quantitative results.
  • Attempted to reproduce three common DFA summary statistics (variance explained, percentage correctly assigned, largest coefficient) from 71 eligible datasets.

Main Results:

  • 15 of 86 datasets were not confidently relatable to the published analysis due to issues like missing labels or incomplete data.
  • Reproducibility rates for variance explained and percentage correctly assigned were relatively high.
  • Reproducibility of the largest discriminant function coefficient was lower.
  • Overall, 65% (46 of 71) of datasets allowed for complete reproduction of all three summary statistics.

Conclusions:

  • A majority of Discriminant Function Analyses in organismal biology are reproducible.
  • Significant challenges remain regarding data curation and metadata quality, impacting scientific reproducibility.
  • Improved data management practices are necessary to meet scientific and public expectations for reliable research.