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"Sharing knowledge is what drives scientific progress - each new advance or innovation in biomedical research builds on previous observations. However, for experimental findings to be broadly accepted as credible by the scientific community, they must be verified by other researchers. An essential step is for researchers to report their findings in a manner that is understandable to others in the scientific community and provide sufficient information for others to validate the original results and build on them. In recent years, concern has been growing over a number of studies that have failed to replicate previous results and evidence from larger meta-analyses, which have pointed to the lack of reproducibility in biomedical research. On September 25 and 26, 2019, the National Academies of Science, Engineering, and Medicine hosted a public workshop in Washington, DC, to discuss the current state of transparency in the reporting of preclinical biomedical research and to explore opportunities for harmonizing reporting guidelines across journals and funding agencies. Convened jointly by the Forum on Drug Discovery, Development, and Translation; the Forum on Neuroscience and Nervous System Disorders; the National Cancer Policy Forum; and the Roundtable on Genomics and Precision Health, the workshop primarily focused on transparent reporting in preclinical research, but also considered lessons learned and best practices from clinical research reporting. This publication summarizes the presentation and discussion of the workshop."--
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"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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"Social science practitioners have recently witnessed numerous episodes of influential research that fell apart upon close scrutiny. These instances have spurred suspicions that other published results may contain errors or may at least be less robust than they appear. In response, an influential movement has emerged across the social sciences for greater research transparency, openness, and reproducibility. Transparent and Reproducible Social Science Research crystallizes the new insights, practices, and methods of this rising interdisciplinary field"--Provided by publisher.
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Reproducible research. --- Research --- Statistical methods.
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"In 2014 the National Science Foundation (NSF) provided support to the National Academies of Sciences, Engineering, and Medicine for a series of Forums on Open Science in response to a government-wide directive to support increased public access to the results of research funded by the federal government. However, the breadth of the work resulting from the series precluded a focus on any specific topic or discussion about how to improve public access. Thus, the main goal of the Workshop on Transparency and Reproducibility in Federal Statistics was to develop some understanding of what principles and practices are, or would be, supportive of making federal statistics more understandable and reviewable, both by agency staff and the public. This publication summarizes the presentations and discussions from the workshop"--Publisher's description.
Transparency in government --- Reproducible research --- United States --- United States. --- Statistics
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Research. --- Reproducible research --- Electronic data processing --- Data processing
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The 2nd International Workshop on Practical Reproducible Evaluation of Computer Systems (PRECS 2019) is full-day meeting that focuses heavily on practical, actionable aspects of reproducibility in broad areas of computational science and data exploration, with special emphasis on issues in which community collaboration can be essential for adopting novel methodologies, techniques and frameworks aimed at addressing some of the challenges we face today. The workshop brings together researchers and experts to share experiences and advance the state of the art in the reproducible evaluation of computer systems, featuring contributed papers and invited talks.
Computer systems --- High performance computing --- Reproducible research. --- Evaluation
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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact
Science: general issues --- Neurosciences --- reproducible research --- model sharing --- model validation --- replicability --- code generation --- model parameterization --- reproducible research --- model sharing --- model validation --- replicability --- code generation --- model parameterization
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Life sciences. --- Molecular biology. --- Genetics --- Genomics --- Reproducible research. --- Genetic Techniques --- Genetic Research --- Research --- Methodology. --- methods
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