Data Literacy: How to Make Your Experiments Robust and Reproducible provides an overview of basic concepts and skills in handling data, which are common to diverse areas of science. Readers will get a good grasp of the steps involved in carrying out a scientific study and will understand some of the factors that make a study robust and reproducible.The book covers several major modules such as experimental design, data cleansing and preparation, statistical analysis, data management, and reporting. No specialized knowledge of statistics or computer programming is needed to fully understand the concepts presented.
This book is a valuable source for biomedical and health sciences graduate students andresearchers, in general, who are interested in handling data to make their research reproducibleand more efficient.
1. Introduction - p. xi 2. Acknowledgments - p. xiii 3. Why This Book? - p. xv 4. Introduction - p. xix 5. Part A. Designing Your Experiment - p. xix 6. Chapter 1. Reproducibility and Robustness - p. 3 7. Chapter 2. Choosing a Research Problem - p. 17 8. Introduction - p. 31 9. Chapter 3. Basics of Data and Data Distributions - p. 33 10. Chapter 4. Experimental Design: Measures, Validity, Sampling, Bias, Randomization, Power - p. 47 11. Introduction - p. 63 12. Chapter 5. Experimental Design: Design Strategies and Controls - p. 65 13. Chapter 6. Power Estimation - p. 87 14. Introduction - p. 95 15. Part B. Getting a “Feel” for Your Data - p. 95 16. Chapter 7. The Data Cleansing and Analysis Pipeline - p. 99 17. Chapter 8. Topics to Consider When Analyzing Data - p. 113 18. Introduction - p. 123 19. Part C. Statistics (Without Much Math!) - p. 123 20. Chapter 9. Null Hypothesis Statistical Testing and the t-Test - p. 127 21. Chapter 10. The “New Statistics” and Bayesian Inference - p. 137 22. Chapter 11. ANOVA - p. 149 23. Chapter 12. Nonparametric Tests - p. 157 24. Chapter 13. Correlation and Other Concepts You Should Know - p. 169 25. Part D. Make Your Data Go Farther - p. 185 26. Chapter 14. How to Record and Report Your Experiments - p. 189 27. Introduction - p. 209 28. Chapter 15. Data Sharing and Reuse - p. 211 29. Introduction - p. 227 30. Chapter 16. The Revolution in Scientific Publishing - p. 229 31. Introduction - p. 249 32. Postscript: Beyond Data Literacy - p. 251 33. Learned Concepts - p. 253 34. Index - p. 255
Data Literacy: How to Make Your Experiments Robust and Reproducible provides an overview of basic concepts and skills in handling data, which are common to diverse areas of science. Readers will get a good grasp of the steps involved in carrying out a scientific study and will understand some of the factors that make a study robust and reproducible.The book covers several major modules such as experimental design, data cleansing and preparation, statistical analysis, data management, and reporting. No specialized knowledge of statistics or computer programming is needed to fully understand the concepts presented.
This book is a valuable source for biomedical and health sciences graduate students andresearchers, in general, who are interested in handling data to make their research reproducibleand more efficient.
1. Introduction - p. xi 2. Acknowledgments - p. xiii 3. Why This Book? - p. xv 4. Introduction - p. xix 5. Part A. Designing Your Experiment - p. xix 6. Chapter 1. Reproducibility and Robustness - p. 3 7. Chapter 2. Choosing a Research Problem - p. 17 8. Introduction - p. 31 9. Chapter 3. Basics of Data and Data Distributions - p. 33 10. Chapter 4. Experimental Design: Measures, Validity, Sampling, Bias, Randomization, Power - p. 47 11. Introduction - p. 63 12. Chapter 5. Experimental Design: Design Strategies and Controls - p. 65 13. Chapter 6. Power Estimation - p. 87 14. Introduction - p. 95 15. Part B. Getting a “Feel” for Your Data - p. 95 16. Chapter 7. The Data Cleansing and Analysis Pipeline - p. 99 17. Chapter 8. Topics to Consider When Analyzing Data - p. 113 18. Introduction - p. 123 19. Part C. Statistics (Without Much Math!) - p. 123 20. Chapter 9. Null Hypothesis Statistical Testing and the t-Test - p. 127 21. Chapter 10. The “New Statistics” and Bayesian Inference - p. 137 22. Chapter 11. ANOVA - p. 149 23. Chapter 12. Nonparametric Tests - p. 157 24. Chapter 13. Correlation and Other Concepts You Should Know - p. 169 25. Part D. Make Your Data Go Farther - p. 185 26. Chapter 14. How to Record and Report Your Experiments - p. 189 27. Introduction - p. 209 28. Chapter 15. Data Sharing and Reuse - p. 211 29. Introduction - p. 227 30. Chapter 16. The Revolution in Scientific Publishing - p. 229 31. Introduction - p. 249 32. Postscript: Beyond Data Literacy - p. 251 33. Learned Concepts - p. 253 34. Index - p. 255
