Open science, reproducibility, and transparency in ecology

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.