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Open science, reproducibility, and transparency in ecology.

Stephen M Powers1, Stephanie E Hampton1

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Ecological Applications : a Publication of the Ecological Society of America
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PubMed
Summary

Ensuring scientific reproducibility in ecology presents unique challenges due to dynamic systems. Sharing coded workflows enhances transparency and allows others to build upon ecological research effectively.

Keywords:
collaborative toolsdata policydata scienceecoinformaticsecosystemenvironmental scienceopen sciencerepeatabilityreplicabilityreproducibletransparentworkflows

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Area of Science:

  • Ecology
  • Environmental Science
  • Computational Science

Background:

  • Reproducibility is fundamental to scientific reliability.
  • Ecological systems are complex, dynamic, and heterogeneous, posing challenges to traditional reproducibility.
  • Computational reproducibility offers a path to ensure reliable ecological research outcomes.

Purpose of the Study:

  • To highlight unique challenges in achieving reproducibility and transparency in ecological research.
  • To provide practical guidelines for enhancing computational reproducibility in ecology.
  • To emphasize the importance of integrity in ecological data and methods.

Main Methods:

  • Discussing the nature of ecological data and its dependence on spatial and temporal contexts.
  • Defining computational reproducibility in the context of data analysis and code sharing.
  • Reviewing current expectations and shifts in scientific transparency.

Main Results:

  • Ecological observations are often context-dependent, making direct reproduction difficult.
  • Computational reproducibility is achievable through shared data and code.
  • Sharing coded workflows increases transparency and facilitates scientific progress.

Conclusions:

  • Ecologists face specific hurdles in ensuring reproducibility and transparency.
  • Adopting computational reproducibility practices is crucial for ecological integrity.
  • Guidelines are needed to support ecologists in maintaining research integrity and environmental data stewardship.