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Reproducibility : Principles, Problems, Practices, and Prospects.

By: Contributor(s): Publisher: New York : John Wiley & Sons, Incorporated, 2016Copyright date: ©2016Edition: 1st edDescription: 1 online resource (589 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781118865231
Subject(s): Genre/Form: Additional physical formats: Print version:: Reproducibility : Principles, Problems, Practices, and ProspectsDDC classification:
  • 001.42
LOC classification:
  • Q175.32.O27 -- .R477 2016eb
Online resources:
Contents:
Cover -- Title Page -- Copyright -- Contents -- Contributors -- Introduction -- Part I: Contextual Backgrounds -- Chapter 1 Reproducibility, Objectivity, Invariance -- 1.1 Introduction -- 1.2 Reproducibility in the Empirical Sciences -- 1.3 Objectivity -- 1.4 Invariance and Symmetry -- 1.5 Summary -- References -- Chapter 2 Reproducibility between Production and Prognosis -- 2.1 Preliminary Remarks: Three Myths -- 2.1.1 The Myth of the "Two Cultures" -- 2.1.2 The Myth of Knowledge as a Purely Mental Product -- 2.1.3 The Myth of a Unified Science -- 2.2 How Does Reproducibility Connect with Production? -- 2.2.1 Knowledge Production -- 2.2.2 Manufacture and Industrial Production -- 2.2.3 Repetition and Mass Production -- 2.3 How Does Production Connect with Continuity? -- 2.3.1 "Natura Non Facit Saltus" -- 2.3.2 Increasing Knowledge by Ignorance -- 2.3.3 Deficiency and Innovation -- 2.4 How Does Continuity Connect with Scientific Rationality? -- 2.4.1 The Myth of the Two Cultures Revisited -- 2.4.2 Three Types of Theories -- 2.4.3 The "Covering-Law" Model -- 2.5 How Does Scientific Rationality Connect with Prognosis? -- 2.5.1 Symmetry of Explanation and Prognosis -- 2.5.2 Nature Does Not Have a Future -- 2.5.3 Three Types of Prognosis -- 2.6 How Do Prediction and Prognosis Connect with Reproducibility? -- 2.6.1 A-Series and B-Series -- 2.6.2 The World as a System of "Merton-Sensible" Systems -- 2.6.3 Knowledge Technology -- 2.7 Concluding Remarks -- References -- Chapter 3 Stability and Replication of Experimental Results: A Historical Perspective -- 3.1 Experiments and Their Reproduction in the Development of Science -- 3.2 Repetition of Experiments -- 3.3 The Power of Replicability -- 3.4 Cases of Failed Replication -- 3.5 Doing Science without Replication and Replicability -- 3.6 What Can We Learn from History? -- Acknowledgments -- References.
Chapter 4 Reproducibility of Experiments: Experimenters' Regress, Statistical Uncertainty Principle, and the Replication Imperative -- 4.1 Introduction -- 4.2 The Experimenter's Regress -- 4.3 The Statistical Uncertainty Principle -- 4.3.1 Some Selected Examples -- 4.3.2 Quantum Analogies -- 4.3.3 Meta-Analysis -- 4.4 The Replication Imperative -- 4.4.1 Physics as a Social System -- 4.4.2 Actors and Analysts -- References -- Part II: Statistical Issues -- Chapter 5 Statistical Issues in Reproducibility -- 5.1 Introduction -- 5.2 A Random Sample -- 5.2.1 Simple Inference for a Random Sample -- 5.2.2 The Variance of a Mean -- 5.2.3 General Parameters and Reproducibility -- 5.2.4 Reproducibility of Test Results and the Significance Controversy -- 5.3 Structures of Variation -- 5.3.1 Hierarchical Levels of Variation -- 5.3.2 Serial and Spatial Correlations -- 5.3.3 Consequences for Reproducibility and Experimental Design -- 5.4 Regression Models -- 5.4.1 The Structure of Models -- 5.4.2 Incorporating Reproducibility and Data Challenge -- 5.5 Model Development and Selection Bias -- 5.5.1 Multiple Comparisons and Multiple Testing -- 5.5.2 Consequences for Model Development -- 5.5.3 Internal Replication -- 5.5.4 Publication and Selection Bias -- 5.6 Big and High-Dimensional Data -- 5.7 Bayesian Statistics -- 5.8 Conclusions -- 5.8.1 Successful Replication -- 5.8.2 Validation and Generalization -- 5.8.3 Scope of Reproducibility -- Acknowledgments -- References -- Chapter 6 Model Selection, Data Distributions, and Reproducibility -- 6.1 Introduction -- 6.2 Bayesian Model Selection and Relation to Minimum Description Length -- 6.2.1 Bayesian Inference and Bayesian Model Selection (BMS) -- 6.2.2 Occam's Razor and BMS -- 6.2.3 An Equivalent Characterization of BMS and Bayes Factor -- 6.2.4 Minimum Description Length and Normalized Maximum Likelihood.
6.3 Extending BMS (and NML#): BMS* -- 6.4 Replication Variance and Reproducibility -- 6.4.1 Within- and Between-Setting Replication Variance and the True State of the World -- 6.4.2 Reproducibility -- 6.4.3 A Toy Example -- 6.5 Final Remark -- References -- Chapter 7 Reproducibility from the Perspective of Meta-Analysis -- 7.1 Introduction -- 7.2 Basics of Meta-Analysis -- 7.2.1 Conceptual Preliminaries -- 7.2.2 Systematic Reviews -- 7.2.3 Fixed-Effects and Random-Effects Meta-Analysis -- 7.2.4 Biases in Meta-Analysis -- 7.3 Meta-Analysis of Mind-Matter Experiments: A Case Study -- 7.3.1 Statistical Modeling -- 7.3.2 Analysis of the R&N Data -- 7.4 Summary -- References -- Chapter 8 Why Are There So Many Clustering Algorithms, and How Valid Are Their Results? -- 8.1 Introduction -- 8.1.1 Data Mining and Knowledge Discovery -- 8.1.2 Choices and Assumptions -- 8.2 Supervised and Unsupervised Learning -- 8.3 Cluster Validity as Easiness in Classification -- 8.3.1 Instance Easiness for Supervised Learning -- 8.3.2 Clustering-Quality Measures Based on Supervised Learning -- 8.3.3 Using the Clustering-Quality Measures mp and mc -- 8.4 Applying Clustering-Quality Measures to Data -- 8.4.1 Clustering Based on Prediction Strength -- 8.4.2 Studies with Synthetic Data -- 8.4.3 Studies with Empirical Data -- 8.5 Other Clustering Models -- 8.5.1 Hierarchical Clustering -- 8.5.2 Fuzzy Clustering -- 8.6 Summary -- References -- Part III: Physical Sciences -- Chapter 9 Facilitating Reproducibility in ScientificComputing: Principles and Practice -- 9.1 Introduction -- 9.2 A Culture of Reproducibility -- 9.2.1. Documenting the Workflow -- 9.2.2 Tools to Aid in Documenting Workflow and Managing Data -- 9.2.3 Other Cultural Changes -- 9.3 Statistical Overfitting -- 9.3.1 A Hands-on Demonstration of Backtest Overfitting -- 9.3.2 Why the Silence?.
9.4 Performance Reporting in High-Performance Computing -- 9.4.1 A 1992 Perspective -- 9.4.2 Fast Forward to 2014: New Ways of Bad Practice -- 9.5 Numerical Reproducibility -- 9.5.1 Floating-Point Arithmetic -- 9.5.2 Numerical Reproducibility Problems in Real Applications -- 9.5.3 High-Precision Arithmetic and Numerical Reproducibility -- 9.5.4 Computations Requiring Extra Precision -- 9.6 High-Precision Arithmetic in Experimental Mathematics and Mathematical Physics -- 9.6.1 The BBP Formula for π -- 9.6.2 Ising Integrals -- 9.7 Reproducibility in Symbolic Computing -- 9.8 Why Should We Trust the Results of Computation? -- 9.9 Conclusions -- References -- Chapter 10 Methodological Issues in the Study of Complex Systems -- 10.1 Introduction -- 10.2 Definitions of Complexity -- 10.3 Complexity and Meaning -- 10.4 Beyond Stationarity and Ergodicity -- 10.5 Conclusions -- Acknowledgments -- References -- Chapter 11 Rare and Extreme Events -- 11.1 Introduction -- 11.1.1 What Are Extreme Events? -- 11.1.2 Reproducibility of Extreme Events -- 11.2 Statistics of Extremes -- 11.3 Predictions of Extreme Events -- 11.4 Evolving Systems Exposed to Extreme Events -- 11.5 Conclusions -- Acknowledgments -- References -- Chapter 12 Science under Societal Scrutiny: Reproducibility in Climate Science -- 12.1 Reproducibility Challenges for Climate Science -- 12.2 Reproducibility in Observational Climate Science -- 12.3 Reproducibility in Climate Modeling -- 12.4 Reproducibility in Paleoclimatology -- 12.5 Conclusions and Recommendations -- References -- Part IV: Life Sciences -- Chapter 13 From Mice to Men: Translation from Bench to Bedside -- 13.1 The Drug Development Process -- 13.2 Contributions of Animals to Medical Progress -- 13.2.1 Louis Pasteur and Vaccine Development against Anthrax and Rabies -- 13.2.2 Paul Ehrlich and the Magic Bullet against Syphilis.
13.2.3 Christiaan Eijkman and Frederick Gowland Hopkins and the Discovery of Vitamins -- 13.2.4 Alexander Fleming, Howard Walter Florey, and Ernst Boris Chain and the Discovery and Development of Penicillin -- 13.3 Translation Challenges in Different Fields of Research -- 13.3.1 Vaccines against Human Immunodeficiency Virus (HIV) -- 13.3.2 Acute Stroke Research -- 13.3.3 Anti-Angiogenic Drugs in Cancer Research -- 13.3.4 Amyotrophic Lateral Sclerosis (ALS) -- 13.3.5 Microglia -- 13.4 Increasing Translational Success: Summary and Conclusions -- References -- Chapter 14 A Continuum of Reproducible Researchin Drug Development -- 14.1 Introduction -- 14.2 The Strategy of the Magic Bullet -- 14.3 Specialists and Generalists -- 14.4 From Single-Target to Multi-Target Drugs -- 14.5 Conclusions -- References -- Chapter 15 Randomness as a Building Block for Reproducibility in Local Cortical Networks -- 15.1 Introduction -- 15.2 Spike Trains and Reproducibility -- 15.3 Spike Trains -- 15.3.1 Some Technical Background: Poisson Spike Trains and Coefficient of Variation -- 15.3.2 A Simple Model: A Counter -- 15.3.3 Low Rates: Membrane Leakage -- 15.3.4 Stochastic Synapses -- 15.3.5 Balanced Excitation and Inhibition -- 15.4 Neuronal Populations -- 15.5 Summary -- References -- Chapter 16 Neural Reuse and In-Principle Limitations on Reproducibility in Cognitive Neuroscience -- 16.1 Introduction -- 16.2 The Erosion of Modular Thinking -- 16.3 Intrinsic Limits on Reproducibility -- 16.4 Going Forward -- References -- Chapter 17 On the Difference between Persons and Things - Reproducibility in Social Contexts -- 17.1 The Problem of Other Minds and Its Evolutionary Dimension -- 17.2 Understanding the Inner Experience of Others -- 17.3 Identifying the Neural Mechanisms of Understanding Others -- 17.4 Abduction of the Functional Roles of Neural Networks.
17.5 Psychopathology of the Inner Experience of Others.
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Cover -- Title Page -- Copyright -- Contents -- Contributors -- Introduction -- Part I: Contextual Backgrounds -- Chapter 1 Reproducibility, Objectivity, Invariance -- 1.1 Introduction -- 1.2 Reproducibility in the Empirical Sciences -- 1.3 Objectivity -- 1.4 Invariance and Symmetry -- 1.5 Summary -- References -- Chapter 2 Reproducibility between Production and Prognosis -- 2.1 Preliminary Remarks: Three Myths -- 2.1.1 The Myth of the "Two Cultures" -- 2.1.2 The Myth of Knowledge as a Purely Mental Product -- 2.1.3 The Myth of a Unified Science -- 2.2 How Does Reproducibility Connect with Production? -- 2.2.1 Knowledge Production -- 2.2.2 Manufacture and Industrial Production -- 2.2.3 Repetition and Mass Production -- 2.3 How Does Production Connect with Continuity? -- 2.3.1 "Natura Non Facit Saltus" -- 2.3.2 Increasing Knowledge by Ignorance -- 2.3.3 Deficiency and Innovation -- 2.4 How Does Continuity Connect with Scientific Rationality? -- 2.4.1 The Myth of the Two Cultures Revisited -- 2.4.2 Three Types of Theories -- 2.4.3 The "Covering-Law" Model -- 2.5 How Does Scientific Rationality Connect with Prognosis? -- 2.5.1 Symmetry of Explanation and Prognosis -- 2.5.2 Nature Does Not Have a Future -- 2.5.3 Three Types of Prognosis -- 2.6 How Do Prediction and Prognosis Connect with Reproducibility? -- 2.6.1 A-Series and B-Series -- 2.6.2 The World as a System of "Merton-Sensible" Systems -- 2.6.3 Knowledge Technology -- 2.7 Concluding Remarks -- References -- Chapter 3 Stability and Replication of Experimental Results: A Historical Perspective -- 3.1 Experiments and Their Reproduction in the Development of Science -- 3.2 Repetition of Experiments -- 3.3 The Power of Replicability -- 3.4 Cases of Failed Replication -- 3.5 Doing Science without Replication and Replicability -- 3.6 What Can We Learn from History? -- Acknowledgments -- References.

Chapter 4 Reproducibility of Experiments: Experimenters' Regress, Statistical Uncertainty Principle, and the Replication Imperative -- 4.1 Introduction -- 4.2 The Experimenter's Regress -- 4.3 The Statistical Uncertainty Principle -- 4.3.1 Some Selected Examples -- 4.3.2 Quantum Analogies -- 4.3.3 Meta-Analysis -- 4.4 The Replication Imperative -- 4.4.1 Physics as a Social System -- 4.4.2 Actors and Analysts -- References -- Part II: Statistical Issues -- Chapter 5 Statistical Issues in Reproducibility -- 5.1 Introduction -- 5.2 A Random Sample -- 5.2.1 Simple Inference for a Random Sample -- 5.2.2 The Variance of a Mean -- 5.2.3 General Parameters and Reproducibility -- 5.2.4 Reproducibility of Test Results and the Significance Controversy -- 5.3 Structures of Variation -- 5.3.1 Hierarchical Levels of Variation -- 5.3.2 Serial and Spatial Correlations -- 5.3.3 Consequences for Reproducibility and Experimental Design -- 5.4 Regression Models -- 5.4.1 The Structure of Models -- 5.4.2 Incorporating Reproducibility and Data Challenge -- 5.5 Model Development and Selection Bias -- 5.5.1 Multiple Comparisons and Multiple Testing -- 5.5.2 Consequences for Model Development -- 5.5.3 Internal Replication -- 5.5.4 Publication and Selection Bias -- 5.6 Big and High-Dimensional Data -- 5.7 Bayesian Statistics -- 5.8 Conclusions -- 5.8.1 Successful Replication -- 5.8.2 Validation and Generalization -- 5.8.3 Scope of Reproducibility -- Acknowledgments -- References -- Chapter 6 Model Selection, Data Distributions, and Reproducibility -- 6.1 Introduction -- 6.2 Bayesian Model Selection and Relation to Minimum Description Length -- 6.2.1 Bayesian Inference and Bayesian Model Selection (BMS) -- 6.2.2 Occam's Razor and BMS -- 6.2.3 An Equivalent Characterization of BMS and Bayes Factor -- 6.2.4 Minimum Description Length and Normalized Maximum Likelihood.

6.3 Extending BMS (and NML#): BMS* -- 6.4 Replication Variance and Reproducibility -- 6.4.1 Within- and Between-Setting Replication Variance and the True State of the World -- 6.4.2 Reproducibility -- 6.4.3 A Toy Example -- 6.5 Final Remark -- References -- Chapter 7 Reproducibility from the Perspective of Meta-Analysis -- 7.1 Introduction -- 7.2 Basics of Meta-Analysis -- 7.2.1 Conceptual Preliminaries -- 7.2.2 Systematic Reviews -- 7.2.3 Fixed-Effects and Random-Effects Meta-Analysis -- 7.2.4 Biases in Meta-Analysis -- 7.3 Meta-Analysis of Mind-Matter Experiments: A Case Study -- 7.3.1 Statistical Modeling -- 7.3.2 Analysis of the R&N Data -- 7.4 Summary -- References -- Chapter 8 Why Are There So Many Clustering Algorithms, and How Valid Are Their Results? -- 8.1 Introduction -- 8.1.1 Data Mining and Knowledge Discovery -- 8.1.2 Choices and Assumptions -- 8.2 Supervised and Unsupervised Learning -- 8.3 Cluster Validity as Easiness in Classification -- 8.3.1 Instance Easiness for Supervised Learning -- 8.3.2 Clustering-Quality Measures Based on Supervised Learning -- 8.3.3 Using the Clustering-Quality Measures mp and mc -- 8.4 Applying Clustering-Quality Measures to Data -- 8.4.1 Clustering Based on Prediction Strength -- 8.4.2 Studies with Synthetic Data -- 8.4.3 Studies with Empirical Data -- 8.5 Other Clustering Models -- 8.5.1 Hierarchical Clustering -- 8.5.2 Fuzzy Clustering -- 8.6 Summary -- References -- Part III: Physical Sciences -- Chapter 9 Facilitating Reproducibility in ScientificComputing: Principles and Practice -- 9.1 Introduction -- 9.2 A Culture of Reproducibility -- 9.2.1. Documenting the Workflow -- 9.2.2 Tools to Aid in Documenting Workflow and Managing Data -- 9.2.3 Other Cultural Changes -- 9.3 Statistical Overfitting -- 9.3.1 A Hands-on Demonstration of Backtest Overfitting -- 9.3.2 Why the Silence?.

9.4 Performance Reporting in High-Performance Computing -- 9.4.1 A 1992 Perspective -- 9.4.2 Fast Forward to 2014: New Ways of Bad Practice -- 9.5 Numerical Reproducibility -- 9.5.1 Floating-Point Arithmetic -- 9.5.2 Numerical Reproducibility Problems in Real Applications -- 9.5.3 High-Precision Arithmetic and Numerical Reproducibility -- 9.5.4 Computations Requiring Extra Precision -- 9.6 High-Precision Arithmetic in Experimental Mathematics and Mathematical Physics -- 9.6.1 The BBP Formula for π -- 9.6.2 Ising Integrals -- 9.7 Reproducibility in Symbolic Computing -- 9.8 Why Should We Trust the Results of Computation? -- 9.9 Conclusions -- References -- Chapter 10 Methodological Issues in the Study of Complex Systems -- 10.1 Introduction -- 10.2 Definitions of Complexity -- 10.3 Complexity and Meaning -- 10.4 Beyond Stationarity and Ergodicity -- 10.5 Conclusions -- Acknowledgments -- References -- Chapter 11 Rare and Extreme Events -- 11.1 Introduction -- 11.1.1 What Are Extreme Events? -- 11.1.2 Reproducibility of Extreme Events -- 11.2 Statistics of Extremes -- 11.3 Predictions of Extreme Events -- 11.4 Evolving Systems Exposed to Extreme Events -- 11.5 Conclusions -- Acknowledgments -- References -- Chapter 12 Science under Societal Scrutiny: Reproducibility in Climate Science -- 12.1 Reproducibility Challenges for Climate Science -- 12.2 Reproducibility in Observational Climate Science -- 12.3 Reproducibility in Climate Modeling -- 12.4 Reproducibility in Paleoclimatology -- 12.5 Conclusions and Recommendations -- References -- Part IV: Life Sciences -- Chapter 13 From Mice to Men: Translation from Bench to Bedside -- 13.1 The Drug Development Process -- 13.2 Contributions of Animals to Medical Progress -- 13.2.1 Louis Pasteur and Vaccine Development against Anthrax and Rabies -- 13.2.2 Paul Ehrlich and the Magic Bullet against Syphilis.

13.2.3 Christiaan Eijkman and Frederick Gowland Hopkins and the Discovery of Vitamins -- 13.2.4 Alexander Fleming, Howard Walter Florey, and Ernst Boris Chain and the Discovery and Development of Penicillin -- 13.3 Translation Challenges in Different Fields of Research -- 13.3.1 Vaccines against Human Immunodeficiency Virus (HIV) -- 13.3.2 Acute Stroke Research -- 13.3.3 Anti-Angiogenic Drugs in Cancer Research -- 13.3.4 Amyotrophic Lateral Sclerosis (ALS) -- 13.3.5 Microglia -- 13.4 Increasing Translational Success: Summary and Conclusions -- References -- Chapter 14 A Continuum of Reproducible Researchin Drug Development -- 14.1 Introduction -- 14.2 The Strategy of the Magic Bullet -- 14.3 Specialists and Generalists -- 14.4 From Single-Target to Multi-Target Drugs -- 14.5 Conclusions -- References -- Chapter 15 Randomness as a Building Block for Reproducibility in Local Cortical Networks -- 15.1 Introduction -- 15.2 Spike Trains and Reproducibility -- 15.3 Spike Trains -- 15.3.1 Some Technical Background: Poisson Spike Trains and Coefficient of Variation -- 15.3.2 A Simple Model: A Counter -- 15.3.3 Low Rates: Membrane Leakage -- 15.3.4 Stochastic Synapses -- 15.3.5 Balanced Excitation and Inhibition -- 15.4 Neuronal Populations -- 15.5 Summary -- References -- Chapter 16 Neural Reuse and In-Principle Limitations on Reproducibility in Cognitive Neuroscience -- 16.1 Introduction -- 16.2 The Erosion of Modular Thinking -- 16.3 Intrinsic Limits on Reproducibility -- 16.4 Going Forward -- References -- Chapter 17 On the Difference between Persons and Things - Reproducibility in Social Contexts -- 17.1 The Problem of Other Minds and Its Evolutionary Dimension -- 17.2 Understanding the Inner Experience of Others -- 17.3 Identifying the Neural Mechanisms of Understanding Others -- 17.4 Abduction of the Functional Roles of Neural Networks.

17.5 Psychopathology of the Inner Experience of Others.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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