Reproducibility : principles, problems, practices, and prospects / edited by Harald Atmanspacher, Sabine Maasen.

Format:

Book

Contributor:

Atmanspacher, Harald, editor.

Maasen, Sabine, editor.

Series:

THEi Wiley ebooks.

THEi Wiley ebooks

Language:

English

Subjects (All):

Observation (Scientific method).

Science--Methodology.

Science.

Physical Description:

1 online resource (589 pages)

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey : Wiley, 2016.

Language Note:

English

System Details:

Access using campus network via VPN at home (THEi Users Only).

Summary:

A review of the scientific method. In the scientific method, results must be capable of being reproduced to be valid.-- Source other than Library of Congress.

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

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

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

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

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&amp

N Data

7.4 Summary

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

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

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

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

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

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

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

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

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

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.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references at the end of each chapters and index.

Description based on online resource; title from PDF title page (ebrary, viewed June 28, 2016).

ISBN:

9781118864777

1118864778

9781118865231

1118865235

9781118865064

1118865065

OCLC:

951977649