Reproducibility in Science: Improving the Standard for Basic and Preclinical Research

By C. Begley, John P. A. Ioannidis. Circulation Research, 2015

902 citations recorded by the source corpus.

Subfields: Biophysics, Public Health, Environmental and Occupational Health, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/49ae9744b2520f0cae3ccd0afeb5da303b2b8654

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Citations in the corpus

On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations (2017, background): “Only repeated examinations of a certain phenomenon provide confidence for the prevalence of some effect (Begley & Ioannidis, 2015; Jasny, Chin, Chong, & Vignieri, 2011; Open_Science_Collaboration & Kappes, 2014).”
Evidence-based policymaking: promise, challenges and opportunities for accounting and financial markets research** (2018, positive): “In recent years, many concerns about the reliability of scientific publications have been raised (Begley 2013, Begley and Ioannidis 2015).”
The ARRIVE guidelines 2.0: updated guidelines for reporting animal research (2020, background): “In recent years, concerns about the reproducibility of research findings have been raised by scientists, funders, research users, and policy makers (Begley & Ioannidis, 2015, Goodman et al. 2016).”
Non-durable consumption and housing net worth in the Great Recession: Evidence from easily accessible data (2016, positive): “This view is in linewith the recent recognition of the importance of replicability in the physical sciences and other social sciences (Begley and Ioannidis, 2015; Munafò et al., 2017; Nosek et al., 2015).”
Reporting guidelines for health care simulation research: extensions to the CONSORT and STROBE statements (2016, background): “Failure to adequately describe the key elements of a research study impairs the efforts of editors, reviewers, and readers to critically appraise strengths and weaknesses [24, 25] or apply and replicate findings [26].”
The Power of Bias in Economics Research (2017, background): “For example, it has beendifficult to reproduce and verify significant bodies of observational and experimental research independently (Ioannidis, 2005a; Begley and Ellis, 2012; Begley and Ioannidis, 2015; Duvendack et al., 2015; Nosek et al., 2015).”
State of the Art: Reproducibility in Artificial Intelligence (2018, result): “This is even the case for results published in the most prestigious journals; even the original researchers cannot reproduce their own results (Aarts et al. 2016; Begley and Ellis 2012; Begley and Ioannidis 2014; Prinz, Schlange, and Asadullah 2011).”
Experimental design and analysis and their reporting II: updated and simplified guidance for authors and peer reviewers (2018, result): “…to be aware that the calculated P value is almost always bigger than it seems (‘less significant’) owing to the false discovery rate which is one reason why some investigators argue that most (rather than just some) research findings are false (Colquhoun 2014; Begley 2013; Begley & Ioannidis 2015).”
The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research* (2020, positive): “Animal research is a good case in point, where poor reporting impacts on the development of therapeutics and irreproducible findings can spawn an entire field of research, or trigger clinical studies, subjecting patients to interventions unlikely to be effective.(2,19,20) In an attempt to improve th”
The Economics of Reproducibility in Preclinical Research (2015, background): “Much has been written about the alarming number of preclinical studies that were later found to be irreproducible [1,2].”
Poor availability of context-specific evidence hampers decision-making in conservation (2020, background): “Decision-makers should rightly be wary of basing decisions on a low number of studies where reproducible effectiveness has not been or cannot be demonstrated - particularly given the current reproducibility crisis (Begley & Ioannidis, 2015; Nosek & Errington, 2017; Open Science Collaboration, 2015).”