Computational reproducibility—the ability to accurately reproduce outcomes from data sets using the same code and software—will be an increasingly important factor in future scientific studies according to a new paper released in the Ecological Society of America’s journal Ecological Applications.
Authors Stephen M. Powers and Stephanie E. Hampton, researchers in environmental science at Washington State University, highlight the importance of adapting to, providing, and using data sets that are open to and usable by the public and investigators in ecology and other field research.
“Increasingly, peers and the public want more transparency,” Powers explains.
Ecologists, finding themselves in an inherently field-oriented science, have long faced the challenge that it is impossible to perfectly repeat observational studies of the natural world—weather conditions vary, populations change over time, and many other conditions in field work are not reproducible. The paper argues that ecologists should focus more on data sharing and transparency in the future in order to increase scientific reproducibility.
An investigator may spend considerable time, effort, and cost attempting to generate results of someone else’s study from scratch. When both data and code used to obtain statistics and results are published, the investigator saves on these efforts, and can even improve or modify the original author’s computer code. Essentially, sharing this information means less time is wasted for reviewers, editors, and authors alike.
It’s not only scientists that benefit from reproducibility and transparency; “In natural resource management and similar policy issues, high transparency is essential to maintain public trust,” says Hampton, who is also director for the Division of Environmental Biology (DEB) at the National Science Foundation (NSF). Being open about data and code from the beginning of a project can help scientists minimize post-publication work to share or clarify the products or to answer questions about contentious results from outside audiences.
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