National Academies of Sciences, Engineering, and Medicine 2019 Reproducibility

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National Academies of Sciences, Engineering, and Medicine (2019) Reproducibility and replicability in science. National Academies Press, Washington, DC. https://doi.org/10.17226/25303

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National Academies of Sciences, Engineering, and Medicine (2019) National Academies Press

Abstract: One of the pathways by which scientists confirm the validity of a new finding or discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some argue that the observed inconsistency may be an important precursor to new discovery while others fear it may be a symptom of a lack of rigor in science. When a newly reported scientific study has far-reaching implications for science or a major potential impact on the public, the question of its reliability takes on heightened importance. Concerns over reproducibility and replicability have been expressed in both scientific and popular media.

As these concerns increased in recent years, Congress directed the National Science Foundation (NSF) to contract with the National Academies of Sciences, Engineering, and Medicine to undertake a study to assess reproducibility and replicability in scientific and engineering research and to provide findings and recommendations for improving rigor and transparency in research.

β€’ Bioblast editor: Gnaiger E

Selected quotes

The terms reproducibility and replicability have different meanings and uses across science and engineering, which has led to confusion in collectively understanding problems in reproducibility and replicability. The committee adopted specific definitions for the purpose of this report to clearly differentiate between the terms, which are otherwise interchangeable in everyday discourse.
Reproducibility is obtaining consistent results using the same input data; computational steps, methods, and code; and conditions of analysis. This definition is synonymous with β€œcomputational reproducibility,” and the terms are used interchangeably in this report.
Replicability is obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data. Two studies may be considered to have replicated if they obtain consistent results given the level of uncertainty inherent in the system under study.
In short, reproducibility involves the original data and code; replicability involves new data collection to test for consistency with previous results of a similar stuwith stronger claims reserved for higher expected levels of reproducibility and replicability.dy. These two processes also differ in the type of results that should be expected. In general, when a researcher transparently reports a study and makes available the underlying digital artifacts, such as data and code, the results should be computationally reproducible. In contrast, even when a study was rigorously conducted according to best practices, correctly analyzed, and transparently reported, it may fail to be replicated.
Journals and scientific societies requesting submissions for conferences should disclose their policies relevant to achieving reproducibility and replicability. The strength of the claims made in a journal article or conference submission should reflect the reproducibility and replicability standards to which an article is held, with stronger claims reserved for higher expected levels of reproducibility and replicability.



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