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Author: Sharon Crook
Publisher: Frontiers Media SA
Published: 2020-07-09
Total Pages: 279
ISBN-13: 2889638383
DOWNLOAD EBOOKDownload or read book Reproducibility and Rigour in Computational Neuroscience written by Sharon Crook and published by Frontiers Media SA. This book was released on 2020-07-09 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
Published: 2019-10-20
Total Pages: 257
ISBN-13: 0309486165
DOWNLOAD EBOOKOne 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.
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.
Author: Felix Schürmann
Publisher: Frontiers Media SA
Published: 2023-04-26
Total Pages: 431
ISBN-13: 2832521657
DOWNLOAD EBOOKDownload or read book Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute written by Felix Schürmann and published by Frontiers Media SA. This book was released on 2023-04-26 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author:
Publisher: Academic Press
Published: 2023-11-24
Total Pages: 504
ISBN-13: 0128172193
DOWNLOAD EBOOKRigor and Reproducibility in Genetics and Genomics: Peer-reviewed, Published, Cited provides a full methodological and statistical overview for researchers, clinicians, students, and post-doctoral fellows conducting genetic and genomic research. Here, active geneticists, clinicians, and bioinformaticists offer practical solutions for a variety of challenges associated with several modern approaches in genetics and genomics, including genotyping, gene expression analysis, epigenetic analysis, GWAS, EWAS, genomic sequencing, and gene editing. Emphasis is placed on rigor and reproducibility throughout, with each section containing laboratory case-studies and classroom activities covering step-by-step protocols, best practices, and common pitfalls. Specific genetic and genomic technologies discussed include microarray analysis, DNA-seq, RNA-seq, Chip-Seq, methyl-seq, CRISPR gene editing, and CRISPR-based genetic analysis. Training exercises, supporting data, and in-depth discussions of rigor, reproducibility, and ethics in research together deliver a solid foundation in research standards for the next generation of genetic and genomic scientists. Provides practical approaches and step-by-step protocols to strengthen genetic and genomic research conducted in the laboratory or classroom Presents illustrative case studies and training exercises, discussing common pitfalls and solutions for genotyping, gene expression analysis, epigenetic analysis, GWAS, genomic sequencing, and gene editing, among other genetic and genomic approaches Examines best practices for microarray analysis, DNA-seq, RNA-seq, gene expression validation, Chip-Seq, methyl-seq, CRISPR gene editing, and CRISPR-based genetic analysis Written to provide trainees and educators with highly applicable tools and strategies to learn or refine a method toward identifying meaningful results with high confidence in their reproducibility
Download or read book Rigor and Reproducibility in Genetics and Genomics written by and published by Academic Press. This book was released on 2023-11-24 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rigor and Reproducibility in Genetics and Genomics: Peer-reviewed, Published, Cited provides a full methodological and statistical overview for researchers, clinicians, students, and post-doctoral fellows conducting genetic and genomic research. Here, active geneticists, clinicians, and bioinformaticists offer practical solutions for a variety of challenges associated with several modern approaches in genetics and genomics, including genotyping, gene expression analysis, epigenetic analysis, GWAS, EWAS, genomic sequencing, and gene editing. Emphasis is placed on rigor and reproducibility throughout, with each section containing laboratory case-studies and classroom activities covering step-by-step protocols, best practices, and common pitfalls. Specific genetic and genomic technologies discussed include microarray analysis, DNA-seq, RNA-seq, Chip-Seq, methyl-seq, CRISPR gene editing, and CRISPR-based genetic analysis. Training exercises, supporting data, and in-depth discussions of rigor, reproducibility, and ethics in research together deliver a solid foundation in research standards for the next generation of genetic and genomic scientists. Provides practical approaches and step-by-step protocols to strengthen genetic and genomic research conducted in the laboratory or classroom Presents illustrative case studies and training exercises, discussing common pitfalls and solutions for genotyping, gene expression analysis, epigenetic analysis, GWAS, genomic sequencing, and gene editing, among other genetic and genomic approaches Examines best practices for microarray analysis, DNA-seq, RNA-seq, gene expression validation, Chip-Seq, methyl-seq, CRISPR gene editing, and CRISPR-based genetic analysis Written to provide trainees and educators with highly applicable tools and strategies to learn or refine a method toward identifying meaningful results with high confidence in their reproducibility
Author: Ivan Soltesz
Publisher: Academic Press
Published: 2011-09-02
Total Pages: 624
ISBN-13: 9780080559537
DOWNLOAD EBOOKEpilepsy is a neurological disorder that affects millions of patients worldwide and arises from the concurrent action of multiple pathophysiological processes. The power of mathematical analysis and computational modeling is increasingly utilized in basic and clinical epilepsy research to better understand the relative importance of the multi-faceted, seizure-related changes taking place in the brain during an epileptic seizure. This groundbreaking book is designed to synthesize the current ideas and future directions of the emerging discipline of computational epilepsy research. Chapters address relevant basic questions (e.g., neuronal gain control) as well as long-standing, critically important clinical challenges (e.g., seizure prediction). Computational Neuroscience in Epilepsy should be of high interest to a wide range of readers, including undergraduate and graduate students, postdoctoral fellows and faculty working in the fields of basic or clinical neuroscience, epilepsy research, computational modeling and bioengineering. Covers a wide range of topics from molecular to seizure predictions and brain implants to control seizures Contributors are top experts at the forefront of computational epilepsy research Chapter contents are highly relevant to both basic and clinical epilepsy researchers
Download or read book Computational Neuroscience in Epilepsy written by Ivan Soltesz and published by Academic Press. This book was released on 2011-09-02 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Epilepsy is a neurological disorder that affects millions of patients worldwide and arises from the concurrent action of multiple pathophysiological processes. The power of mathematical analysis and computational modeling is increasingly utilized in basic and clinical epilepsy research to better understand the relative importance of the multi-faceted, seizure-related changes taking place in the brain during an epileptic seizure. This groundbreaking book is designed to synthesize the current ideas and future directions of the emerging discipline of computational epilepsy research. Chapters address relevant basic questions (e.g., neuronal gain control) as well as long-standing, critically important clinical challenges (e.g., seizure prediction). Computational Neuroscience in Epilepsy should be of high interest to a wide range of readers, including undergraduate and graduate students, postdoctoral fellows and faculty working in the fields of basic or clinical neuroscience, epilepsy research, computational modeling and bioengineering. Covers a wide range of topics from molecular to seizure predictions and brain implants to control seizures Contributors are top experts at the forefront of computational epilepsy research Chapter contents are highly relevant to both basic and clinical epilepsy researchers
Author: Robert E. Kass
Publisher: Springer
Published: 2014-07-08
Total Pages: 663
ISBN-13: 1461496020
DOWNLOAD EBOOKContinual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.
Download or read book Analysis of Neural Data written by Robert E. Kass and published by Springer. This book was released on 2014-07-08 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.
Author: R. Barker Bausell
Publisher: Oxford University Press, USA
Published: 2021
Total Pages: 297
ISBN-13: 0197536530
DOWNLOAD EBOOK"This book tells the story of how a cadre of dedicated, iconoclastic scientists raised the awareness of a long recognized preference for publishing positive, eye catching, but irreproducible results to the status of a genuine scientific crisis. Most famously encapsulated in 2005 by John Ioannidis' iconic title: "Why Most Published Research Findings are False," awareness of the seriousness of the crisis itself was in full bloom sometime around 2011-2012 when a veritable flood of supporting empirical and methodological work began appearing in the scientific literature detailing both the extent of the crisis and how it could be ameliorated. Perhaps most importantly of all, a number of mass replications of large sets of (a) published psychology experiments (100 in all) by the Open Science Collaboration, (b) preclinical cancer experiments (53) which a large pharmaceutical company considered sufficiently promising to pursue if the original results were reproducible, and (c) 67 similarly promising studies upon which an even larger pharmaceutical company decided to replicate prior to initiating the expense and time consuming developmental process. Shockingly, less than 50% of these 220 study results could be replicated, thereby providing unwelcomed evidence that Ioannidis' projections (and others performed later) were not simply pejorative flights of fantasy but possibly underestimates of the actual crisis at hand. Fortunately a plethora of practical, procedural behaviors accompanied these demonstrations which were quite capable of greatly reducing the prevalence of future irreproducible results. Therefore the primary purpose of this book is to provide guidance to practicing and aspiring scientists regarding how (a) to change the way in which science has historically been both conducted and reported in order to avoid producing false positive, irreproducible results in their own work and (b) ultimately to change those institutional practices (primarily but not exclusively involving the traditional journal publishing process and the academic reward system) that have unwittingly contributed to the present crisis. For what is actually needed is nothing less than a change in the scientific culture itself. A culture which will prioritize conducting research correctly in order to get things right rather than simply getting published. Hopefully this book can make a small contribution to that end"--
Download or read book The Problem with Science written by R. Barker Bausell and published by Oxford University Press, USA. This book was released on 2021 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book tells the story of how a cadre of dedicated, iconoclastic scientists raised the awareness of a long recognized preference for publishing positive, eye catching, but irreproducible results to the status of a genuine scientific crisis. Most famously encapsulated in 2005 by John Ioannidis' iconic title: "Why Most Published Research Findings are False," awareness of the seriousness of the crisis itself was in full bloom sometime around 2011-2012 when a veritable flood of supporting empirical and methodological work began appearing in the scientific literature detailing both the extent of the crisis and how it could be ameliorated. Perhaps most importantly of all, a number of mass replications of large sets of (a) published psychology experiments (100 in all) by the Open Science Collaboration, (b) preclinical cancer experiments (53) which a large pharmaceutical company considered sufficiently promising to pursue if the original results were reproducible, and (c) 67 similarly promising studies upon which an even larger pharmaceutical company decided to replicate prior to initiating the expense and time consuming developmental process. Shockingly, less than 50% of these 220 study results could be replicated, thereby providing unwelcomed evidence that Ioannidis' projections (and others performed later) were not simply pejorative flights of fantasy but possibly underestimates of the actual crisis at hand. Fortunately a plethora of practical, procedural behaviors accompanied these demonstrations which were quite capable of greatly reducing the prevalence of future irreproducible results. Therefore the primary purpose of this book is to provide guidance to practicing and aspiring scientists regarding how (a) to change the way in which science has historically been both conducted and reported in order to avoid producing false positive, irreproducible results in their own work and (b) ultimately to change those institutional practices (primarily but not exclusively involving the traditional journal publishing process and the academic reward system) that have unwittingly contributed to the present crisis. For what is actually needed is nothing less than a change in the scientific culture itself. A culture which will prioritize conducting research correctly in order to get things right rather than simply getting published. Hopefully this book can make a small contribution to that end"--
Author: Erwin B. Montgomery
Publisher: Academic Press
Published: 2019-03-14
Total Pages: 356
ISBN-13: 0128176725
DOWNLOAD EBOOKReproducibility in Biomedical Research: Epistemological and Statistical Problems explores the ideas and conundrums inherent in scientific research. It explores factors of reproducibility, including logic, distinguishing productive from unproductive irreproducibility, the scientific method, and the use of statistics. In multiple examples and six detailed case studies, the book demonstrates the misuse of logic resulting in unproductive irreproducibility, allowing researchers to develop their own logic and planning abilities. Biomedical researchers, clinicians, administrators of scientific institutions and funding agencies, journal editors, philosophers of science and medicine will find the arguments and explorations a valuable addition to their libraries. Considers the meaning and purpose of reproducibility to help design research Reviews famous case studies of alleged irreproducibility to determine if these could be reproducible Provides a theoretical aspect to practical issues surrounding research design and conduct
Download or read book Reproducibility in Biomedical Research written by Erwin B. Montgomery and published by Academic Press. This book was released on 2019-03-14 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reproducibility in Biomedical Research: Epistemological and Statistical Problems explores the ideas and conundrums inherent in scientific research. It explores factors of reproducibility, including logic, distinguishing productive from unproductive irreproducibility, the scientific method, and the use of statistics. In multiple examples and six detailed case studies, the book demonstrates the misuse of logic resulting in unproductive irreproducibility, allowing researchers to develop their own logic and planning abilities. Biomedical researchers, clinicians, administrators of scientific institutions and funding agencies, journal editors, philosophers of science and medicine will find the arguments and explorations a valuable addition to their libraries. Considers the meaning and purpose of reproducibility to help design research Reviews famous case studies of alleged irreproducibility to determine if these could be reproducible Provides a theoretical aspect to practical issues surrounding research design and conduct
Author: Institute of Medicine
Publisher: National Academies Press
Published: 2015-08-26
Total Pages: 128
ISBN-13: 0309368774
DOWNLOAD EBOOKFrom its very beginning, neuroscience has been fundamentally interdisciplinary. As a result of rapid technological advances and the advent of large collaborative projects, however, neuroscience is expanding well beyond traditional subdisciplines and intellectual boundaries to rely on expertise from many other fields, such as engineering, computer science, and applied mathematics. This raises important questions about to how to develop and train the next generation of neuroscientists to ensure innovation in research and technology in the neurosciences. In addition, the advent of new types of data and the growing importance of large datasets raise additional questions about how to train students in approaches to data analysis and sharing. These concerns dovetail with the need to teach improved scientific practices ranging from experimental design (e.g., powering of studies and appropriate blinding) to improved sophistication in statistics. Of equal importance is the increasing need not only for basic researchers and teams that will develop the next generation of tools, but also for investigators who are able to bridge the translational gap between basic and clinical neuroscience. Developing a 21st Century Neuroscience Workforce is the summary of a workshop convened by the Institute of Medicine's Forum on Neuroscience and Nervous System Disorders on October 28 and 29,2014, in Washington, DC, to explore future workforce needs and how these needs should inform training programs. Workshop participants considered what new subdisciplines and collaborations might be needed, including an examination of opportunities for cross-training of neuroscience research programs with other areas. In addition, current and new components of training programs were discussed to identify methods for enhancing data handling and analysis capabilities, increasing scientific accuracy, and improving research practices. This report highlights the presentation and discussion of the workshop.
Download or read book Developing a 21st Century Neuroscience Workforce written by Institute of Medicine and published by National Academies Press. This book was released on 2015-08-26 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: From its very beginning, neuroscience has been fundamentally interdisciplinary. As a result of rapid technological advances and the advent of large collaborative projects, however, neuroscience is expanding well beyond traditional subdisciplines and intellectual boundaries to rely on expertise from many other fields, such as engineering, computer science, and applied mathematics. This raises important questions about to how to develop and train the next generation of neuroscientists to ensure innovation in research and technology in the neurosciences. In addition, the advent of new types of data and the growing importance of large datasets raise additional questions about how to train students in approaches to data analysis and sharing. These concerns dovetail with the need to teach improved scientific practices ranging from experimental design (e.g., powering of studies and appropriate blinding) to improved sophistication in statistics. Of equal importance is the increasing need not only for basic researchers and teams that will develop the next generation of tools, but also for investigators who are able to bridge the translational gap between basic and clinical neuroscience. Developing a 21st Century Neuroscience Workforce is the summary of a workshop convened by the Institute of Medicine's Forum on Neuroscience and Nervous System Disorders on October 28 and 29,2014, in Washington, DC, to explore future workforce needs and how these needs should inform training programs. Workshop participants considered what new subdisciplines and collaborations might be needed, including an examination of opportunities for cross-training of neuroscience research programs with other areas. In addition, current and new components of training programs were discussed to identify methods for enhancing data handling and analysis capabilities, increasing scientific accuracy, and improving research practices. This report highlights the presentation and discussion of the workshop.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
Published: 2020-05-28
Total Pages: 143
ISBN-13: 0309663490
DOWNLOAD EBOOKSharing 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.
Download or read book Enhancing Scientific Reproducibility in Biomedical Research Through Transparent Reporting written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2020-05-28 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
