Improving Deep Neural Network Training With Batch Size And Learning Rate Optimization For Head And Neck Tumor Segmentation On 2d And 3d Medical Images

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Reproducibility and Replicability in Science

Reproducibility and Replicability in Science
Author :
Publisher : National Academies Press
Total Pages : 257
Release :
ISBN-10 : 9780309486163
ISBN-13 : 0309486165
Rating : 4/5 (165 Downloads)

Book Synopsis Reproducibility and Replicability in Science by : National Academies of Sciences, Engineering, and Medicine

Download or read book Reproducibility and Replicability in Science written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-10-20 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the pathways by which the scientific community confirms the validity of a new scientific 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 fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.


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