Reproducibility

Author : National Academies of Sciences, Engineering, and Medicine,Policy and Global Affairs,Committee on Science, Engineering, Medicine, and Public Policy,Board on Research Data and Information,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Division on Earth and Life Studies,Nuclear and Radiation Studies Board,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Board on Behavioral, Cognitive, and Sensory Sciences,Committee on Reproducibility and Replicability in Science
Publisher : National Academies Press
Page : 257 pages
File Size : 53,8 Mb
Release : 2019-10-20
Category : Science
ISBN : 9780309486163

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Reproducibility and Replicability in Science by National Academies of Sciences, Engineering, and Medicine,Policy and Global Affairs,Committee on Science, Engineering, Medicine, and Public Policy,Board on Research Data and Information,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Division on Earth and Life Studies,Nuclear and Radiation Studies Board,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Board on Behavioral, Cognitive, and Sensory Sciences,Committee on Reproducibility and Replicability in Science Pdf

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 : National Academies of Sciences, Engineering, and Medicine,Health and Medicine Division,Board on Health Care Services,Board on Health Sciences Policy,Roundtable on Genomics and Precision Health,National Cancer Policy Forum,Forum on Neuroscience and Nervous System Disorders,Forum on Drug Discovery, Development, and Translation
Publisher : National Academies Press
Page : 143 pages
File Size : 52,5 Mb
Release : 2020-04-28
Category : Medical
ISBN : 9780309664066

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Enhancing Scientific Reproducibility in Biomedical Research Through Transparent Reporting by National Academies of Sciences, Engineering, and Medicine,Health and Medicine Division,Board on Health Care Services,Board on Health Sciences Policy,Roundtable on Genomics and Precision Health,National Cancer Policy Forum,Forum on Neuroscience and Nervous System Disorders,Forum on Drug Discovery, Development, and Translation Pdf

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.

Author : Stefano Gaburro,York Winter,Jeansok J. Kim,Maarten Loos,Oliver Stiedl
Publisher : Frontiers Media SA
Page : 328 pages
File Size : 44,5 Mb
Release : 2022-07-25
Category : Science
ISBN : 9782889765980

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Home Cage-based Phenotyping in Rodents: Innovation, Standardization, Reproducibility and Translational Improvement by Stefano Gaburro,York Winter,Jeansok J. Kim,Maarten Loos,Oliver Stiedl Pdf

Author : Bertrand Kerautret,Miguel Colom,Pascal Monasse
Publisher : Springer
Page : 179 pages
File Size : 46,6 Mb
Release : 2017-04-04
Category : Computers
ISBN : 9783319564142

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Reproducible Research in Pattern Recognition by Bertrand Kerautret,Miguel Colom,Pascal Monasse Pdf

This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Reproducible Research in Pattern Recognition, RRPR 2016, held in Cancún, Mexico, in December 2016. The 12 revised full papers, among them 2 invited talks, presented were carefully reviewed and selected from 16 submissions. They focus on pattern recognition algorithms; reproducible research frameworks; reproducible research results, previous works on reproducible research.

Author : Justin Kitzes,Daniel Turek,Fatma Deniz
Publisher : Univ of California Press
Page : 364 pages
File Size : 48,5 Mb
Release : 2018
Category : Science
ISBN : 9780520294752

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The Practice of Reproducible Research by Justin Kitzes,Daniel Turek,Fatma Deniz Pdf

The Practice of Reproducible Research presents concrete examples of how researchers in the data-intensive sciences are working to improve the reproducibility of their research projects. In each of the thirty-one case studies in this volume, the author or team describes the workflow that they used to complete a real-world research project. Authors highlight how they utilized particular tools, ideas, and practices to support reproducibility, emphasizing the very practical how, rather than the why or what, of conducting reproducible research. Part 1 provides an accessible introduction to reproducible research, a basic reproducible research project template, and a synthesis of lessons learned from across the thirty-one case studies. Parts 2 and 3 focus on the case studies themselves. The Practice of Reproducible Research is an invaluable resource for students and researchers who wish to better understand the practice of data-intensive sciences and learn how to make their own research more reproducible.

Author : R. Barker Bausell
Publisher : Oxford University Press
Page : 128 pages
File Size : 44,6 Mb
Release : 2021-01-26
Category : Psychology
ISBN : 9780197536544

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The Problem with Science by R. Barker Bausell Pdf

Recent events have vividly underscored the societal importance of science, yet the majority of the public are unaware that a large proportion of published scientific results are simply wrong. The Problem with Science is an exploration of the manifestations and causes of this scientific crisis, accompanied by a description of the very promising corrective initiatives largely developed over the past decade to stem the spate of irreproducible results that have come to characterize many of our sciences. More importantly, Dr. R. Barker Bausell has designed it to provide guidance to practicing and aspiring scientists regarding how (a) to change the way in which science has come to be both conducted and reported in order to avoid producing false positive, irreproducible results in their own work and (b) 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. There is a need for change in the scientific culture itself. A culture which prioritizes conducting research correctly in order to get things right rather than simply getting it published.

Author : Jeffrey S. Racine
Publisher : Oxford University Press
Page : 352 pages
File Size : 54,9 Mb
Release : 2018-12-24
Category : Business & Economics
ISBN : 9780190900670

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Reproducible Econometrics Using R by Jeffrey S. Racine Pdf

Across the social sciences there has been increasing focus on reproducibility, i.e., the ability to examine a study's data and methods to ensure accuracy by reproducing the study. Reproducible Econometrics Using R combines an overview of key issues and methods with an introduction to how to use them using open source software (R) and recently developed tools (R Markdown and bookdown) that allow the reader to engage in reproducible econometric research. Jeffrey S. Racine provides a step-by-step approach, and covers five sets of topics, i) linear time series models, ii) robust inference, iii) robust estimation, iv) model uncertainty, and v) advanced topics. The time series material highlights the difference between time-series analysis, which focuses on forecasting, versus cross-sectional analysis, where the focus is typically on model parameters that have economic interpretations. For the time series material, the reader begins with a discussion of random walks, white noise, and non-stationarity. The reader is next exposed to the pitfalls of using standard inferential procedures that are popular in cross sectional settings when modelling time series data, and is introduced to alternative procedures that form the basis for linear time series analysis. For the robust inference material, the reader is introduced to the potential advantages of bootstrapping and the Jackknifing versus the use of asymptotic theory, and a range of numerical approaches are presented. For the robust estimation material, the reader is presented with a discussion of issues surrounding outliers in data and methods for addressing their presence. Finally, the model uncertainly material outlines two dominant approaches for dealing with model uncertainty, namely model selection and model averaging. Throughout the book there is an emphasis on the benefits of using R and other open source tools for ensuring reproducibility. The advanced material covers machine learning methods (support vector machines that are useful for classification) and nonparametric kernel regression which provides the reader with more advanced methods for confronting model uncertainty. The book is well suited for advanced undergraduate and graduate students alike. Assignments, exams, slides, and a solution manual are available for instructors.

Author : Victoria Stodden,Friedrich Leisch,Roger D. Peng
Publisher : CRC Press
Page : 450 pages
File Size : 53,9 Mb
Release : 2018-12-14
Category : Mathematics
ISBN : 9781315360393

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Implementing Reproducible Research by Victoria Stodden,Friedrich Leisch,Roger D. Peng Pdf

In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden. Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. It describes: Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron system Open source practices, good programming practices, trends in open science, and the role of cloud computing in reproducible research Software and methodological platforms, including open source software packages, RunMyCode platform, and open access journals Each part presents contributions from leaders who have developed software and other products that have advanced the field. Supplementary material is available at www.ImplementingRR.org.

Author : Islem Rekik,Ehsan Adeli,Sang Hyun Park,Julia Schnabel
Publisher : Springer Nature
Page : 292 pages
File Size : 41,7 Mb
Release : 2021-09-27
Category : Computers
ISBN : 9783030876029

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Predictive Intelligence in Medicine by Islem Rekik,Ehsan Adeli,Sang Hyun Park,Julia Schnabel Pdf

This book constitutes the proceedings of the 4th International Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in October 2021.* The 25 papers presented in this volume were carefully reviewed and selected for inclusion in this book. The contributions describe new cutting-edge predictive models and methods that solve challenging problems in the medical field for a high-precision predictive medicine. *The workshop was held virtually.

Author : Christopher Gandrud
Publisher : CRC Press
Page : 299 pages
File Size : 40,8 Mb
Release : 2020-02-21
Category : Business & Economics
ISBN : 9780429629594

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Reproducible Research with R and RStudio by Christopher Gandrud Pdf

Praise for previous editions: "Gandrud has written a great outline of how a fully reproducible research project should look from start to finish, with brief explanations of each tool that he uses along the way... Advanced undergraduate students in mathematics, statistics, and similar fields as well as students just beginning their graduate studies would benefit the most from reading this book. Many more experienced R users or second-year graduate students might find themselves thinking, ‘I wish I’d read this book at the start of my studies, when I was first learning R!’...This book could be used as the main text for a class on reproducible research ..." (The American Statistician) Reproducible Research with R and R Studio, Third Edition brings together the skills and tools needed for doing and presenting computational research. Using straightforward examples, the book takes you through an entire reproducible research workflow. This practical workflow enables you to gather and analyze data as well as dynamically present results in print and on the web. Supplementary materials and example are available on the author’s website. New to the Third Edition Updated package recommendations, examples, URLs, and removed technologies no longer in regular use. More advanced R Markdown (and less LaTeX) in discussions of markup languages and examples. Stronger focus on reproducible working directory tools. Updated discussion of cloud storage services and persistent reproducible material citation. Added discussion of Jupyter notebooks and reproducible practices in industry. Examples of data manipulation with Tidyverse tibbles (in addition to standard data frames) and pivot_longer() and pivot_wider() functions for pivoting data. Features Incorporates the most important advances that have been developed since the editions were published Describes a complete reproducible research workflow, from data gathering to the presentation of results Shows how to automatically generate tables and figures using R Includes instructions on formatting a presentation document via markup languages Discusses cloud storage and versioning services, particularly Github Explains how to use Unix-like shell programs for working with large research projects

Author : Garret Christensen,Jeremy Freese,Edward Miguel
Publisher : University of California Press
Page : 266 pages
File Size : 48,9 Mb
Release : 2019-07-23
Category : Social Science
ISBN : 9780520296930

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Transparent and Reproducible Social Science Research by Garret Christensen,Jeremy Freese,Edward Miguel Pdf

Recently, social science has had numerous episodes of influential research that was found invalid when placed under rigorous scrutiny. The growing sense that many published results are potentially erroneous has made those conducting social science research more determined to ensure the underlying research is sound. Transparent and Reproducible Social Science Research is the first book to summarize and synthesize new approaches to combat false positives and non-reproducible findings in social science research, document the underlying problems in research practices, and teach a new generation of students and scholars how to overcome them. Understanding that social science research has real consequences for individuals when used by professionals in public policy, health, law enforcement, and other fields, the book crystallizes new insights, practices, and methods that help ensure greater research transparency, openness, and reproducibility. Readers are guided through well-known problems and are encouraged to work through new solutions and practices to improve the openness of their research. Created with both experienced and novice researchers in mind, Transparent and Reproducible Social Science Research serves as an indispensable resource for the production of high quality social science research.

Author : Krzysztof Ziarek
Publisher : Northwestern University Press
Page : 363 pages
File Size : 54,6 Mb
Release : 2001-08-30
Category : Art
ISBN : 9780810118362

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The Historicity of Experience by Krzysztof Ziarek Pdf

In this groundbreaking volume, Krzysztof Ziarek rethinks modern experience by bringing together philosophical critiques of modernity and avant-garde poetry. Ziarek explores, through selective readings of avant-garde poetry, the key aspects of the radical critique of experience: technology, everydayness, event, and sexual difference. To that extent, The Historicity of Experience is less a book about the avant-garde than a critique of experience through the avant-garde. Ziarek reads the avant-garde in dialogue with the work of some of the major critics of modernity (Martin Heidegger, Walter Benjamin, Jean-François Lyotard, and Luce Irigaray) to show how avant-garde experiments bear critically on the issue of modern experience and its technological organization. The four poets Ziarek considers—Gertrude Stein, Velimir Khlebnikov, Miron Biaoszewski, and Susan Howe—demonstrate the broad reach of and variety of forms taken by the avant-garde revision of experience and aesthetics. Moreover, this quartet illustrates how the main operative concepts and strategies of the avant-garde underpinned the practices of canonical writers. A profound philosophical meditation on language, modernity, and the everyday, The Historicity of Experience offers a fundamental reconceptualization of the avant-garde in relation to experience.

Author : Valeria V. Krzhizhanovskaya,Gábor Závodszky,Michael H. Lees,Jack J. Dongarra,Peter M. A. Sloot,Sérgio Brissos,João Teixeira
Publisher : Springer Nature
Page : 726 pages
File Size : 46,9 Mb
Release : 2020-06-18
Category : Computers
ISBN : 9783030503710

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Computational Science – ICCS 2020 by Valeria V. Krzhizhanovskaya,Gábor Závodszky,Michael H. Lees,Jack J. Dongarra,Peter M. A. Sloot,Sérgio Brissos,João Teixeira Pdf

The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Agent-Based Simulations, Adaptive Algorithms and Solvers; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Biomedical and Bioinformatics Challenges for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health; Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems; Computer Graphics, Image Processing and Artificial Intelligence Part VI: Data Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; Meshfree Methods in Computational Sciences; Multiscale Modelling and Simulation; Quantum Computing Workshop Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainties; Teaching Computational Science; UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic.

Author : Anthony Fischetti
Publisher : Packt Publishing Ltd
Page : 570 pages
File Size : 49,5 Mb
Release : 2018-03-28
Category : Computers
ISBN : 9781788397339

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Data Analysis with R, Second Edition by Anthony Fischetti Pdf

Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use. Key Features Analyze your data using R – the most powerful statistical programming language Learn how to implement applied statistics using practical use-cases Use popular R packages to work with unstructured and structured data Book Description Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst. What you will learn Gain a thorough understanding of statistical reasoning and sampling theory Employ hypothesis testing to draw inferences from your data Learn Bayesian methods for estimating parameters Train regression, classification, and time series models Handle missing data gracefully using multiple imputation Identify and manage problematic data points Learn how to scale your analyses to larger data with Rcpp, data.table, dplyr, and parallelization Put best practices into effect to make your job easier and facilitate reproducibility Who this book is for Budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in R will find this book to be useful. No prior exposure to data analysis is needed, although a fundamental understanding of the R programming language is required to get the best out of this book.

Author : Kristoffer Bjärkefur,Luíza Cardoso de Andrade,Benjamin Daniels,Maria Ruth Jones
Publisher : World Bank Publications
Page : 388 pages
File Size : 50,5 Mb
Release : 2021-07-16
Category : Business & Economics
ISBN : 9781464816956

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Development Research in Practice by Kristoffer Bjärkefur,Luíza Cardoso de Andrade,Benjamin Daniels,Maria Ruth Jones Pdf

Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University