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E-raamat: Data Literacy: How to Make Your Experiments Robust and Reproducible

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(Associate Professor, Department of Psychiatry and Psychiatric Institute, University of Illinois School of Medicine, USA)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 05-Sep-2017
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128113073
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Data Literacy: How to Make Your Experiments Robust and ReproducibleSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 05-Sep-2017
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128113073
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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 gain a better 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 and researchers in general who are interested in handling data to make their research reproducible and more efficient.

  • Presents the content in an informal tone and with many examples taken from the daily routine at laboratories
  • Provides exercises for self-study, and is an optional book for more technical courses
  • Brings an interdisciplinary approach which may be applied across different areas of sciences

Muu info

An overview of basic concepts and skills in handling data that is ideal for diverse areas of science
What Is Data Literacy? ix
Acknowledgments xiii
Why This Book? xv
A Designing Your Experiment
1 Reproducibility and Robustness
Basic Terms and Concepts
3(5)
Reproducibility and Robustness Tales to Give You Nightmares
8(6)
The Way Forward
14(1)
References
14(3)
2 Choosing a Research Problem Introduction
17(16)
Scientific Styles and Mind-Sets
18(1)
Programmatic Science Versus Lily-Pad Science
19(1)
Criteria (and Myths) in Choosing a Research Problem
20(3)
Strong Inference
23(1)
Designing Studies as Court Trials
23(6)
References
29(4)
3 Basics of Data and Data Distributions
Introduction
33(1)
Averages
33(2)
Variability
35(2)
The Bell-Shaped ("Normal") Curve
37(1)
Normalization
38(1)
Distribution Shape
38(1)
A Peek Ahead at Sampling, Effect Sizes, and Statistical Significance
39(2)
Other Important Curves and Distributions
41(2)
Probabilities That Involve Discrete Counts
43(1)
Conditional Probabilities
43(2)
Are Most Published Scientific Findings False?
45(1)
Reference
46(1)
4 Experimental Design: Measures, Validity, Sampling, Bias, Randomization, Power Measures
47(18)
Validity
53(3)
Sampling and Randomization
56(4)
Sources of Bias in Experiments
60(1)
Power Estimation
61(1)
References
62(3)
5 Experimental Design: Design Strategies and Controls
"A Feeling for the Organism"
65(1)
Building an Experimental Series in Layers
66(3)
Specific Design Strategies
69(3)
Controls
72(4)
Specific, Nonspecific, and Background Effects
76(5)
Simple Versus Complex Experimental Designs
81(1)
How Many Times Should One Repeat an Experiment Before Publishing?
82(1)
Some Common Pitfalls to Avoid
82(1)
What to Do When the Unexpected Happens During an Experiment?
83(1)
Should Experimental Design Be Centered Around the Null Hypothesis?
83(2)
References
85(2)
6 Power Estimation
Introduction
87(1)
What Is Power Estimation?
87(1)
The Nuts and Bolts
88(2)
A Closer Look at Fig. 6.1 and the Parameters That Go Into Power Estimation
90(1)
How to Increase the Power of an Experiment
90(1)
What Is the Power of Published Experiments in the Literature?
91(1)
The Hidden Dangers of Carrying Out Underpowered Experiments
91(1)
The File Drawer Problem in Science and How Adequate Power Helps
92(1)
Why Not Carry Out Power Estimation After the Experiment Is Completed?
93(1)
References
93(6)
B Getting A "Feel" For Your Data
7 The Data Cleansing and Analysis Pipeline
Steps in Data Cleansing
99(8)
Data Normalization
107(3)
A Brief Data Cleansing Checklist
110(1)
References
111(2)
8 Topics to Consider When Analyzing Data
What Is an Experimental Outcome?
113(1)
Why You Need to Present and Examine ALL the Results
114(1)
Data Fishing, p-Hacking, HARKing, and Post Hoc Analyses
114(3)
Problems Associated With Heterogeneity
117(3)
Problems Associated With Nonindependence
120(1)
Even Professionals Make This Mistake Half the Time!
120(1)
In Summary
121(1)
References
121(6)
C Statistics (Without Much Math!)
9 Null Hypothesis Statistical Testing and the t-Test
The Nuts and Bolts of Null Hypothesis Statistical Testing (NHST)
127(3)
What Null Hypothesis Statistical Testing Does and Does Not Do
130(2)
Does It Matter if My Population Is Normally Distributed or Not?
132(3)
Choosing t-Test Parameters
135(1)
A Final Word
135(1)
References
136(1)
10 The "New Statistics" and Bayesian Inference
Statistical Significance Is Not Scientific Significance
137(1)
The Magical Value P = .05
138(1)
How to Move Beyond Null Hypothesis Statistical Testing?
138(1)
Conditional Probabilities
139(2)
Bayes' Rule
141(1)
Bayesian Inference
142(3)
Comparing Null Hypothesis Statistical Testing and Bayesian Inference
145(1)
Systematic Reviews and Metaanalyses
146(2)
References
148(1)
11 Anova
Analysis of Variance (ANOVA)
149(1)
One-Way ANOVA (One Factor or One Treatment)
149(1)
ANOVA Is a Parametric Test
150(2)
Types of ANOVAs
152(1)
The ANOVA Shows Significance; What Next?
153(1)
Correction for Multiple Testing
153(4)
12 Nonparametric Tests
Introduction
157(1)
The Sign Test
158(1)
The Wilcoxon Signed-Rank Test
159(1)
The Mann---Whitney U Test
159(1)
Exact Tests
160(2)
Nonparametric t-Tests
162(1)
Nonparametric ANOVAS
162(1)
Permutation Tests
162(5)
Reference
167(2)
13 Correlation and Other Concepts You Should Know
Linear Correlation and Linear Regression
169(3)
What Correlations Mean and What They Do Not
172(2)
Nonparametric Correlation
174(1)
Multiple Linear Regression Analysis
175(1)
Logistic Regression
176(1)
Machine Learning
177(3)
Some Machine-Learning Methods
180(2)
Big Data
182(1)
Dimensional Reduction
183(2)
References
185(4)
D Make Your Data Go Farther
14 How to Record and Report Your Experiments
Scientists Keep Diaries Too!
189(2)
Who Owns Your Data?
191(1)
Reporting Authorship
192(3)
Reporting Citations
195(1)
Writing the Introduction/Motivation Section
196(1)
Writing the Methods Section
196(5)
Writing the Results
201(5)
Writing the Discussion/Conclusion Sections
206(1)
References
206(5)
15 Data Sharing and Reuse
Data Sharing---When, Why, With Whom
211(1)
Data Sharing Is Good for You (Really)
212(2)
Data Archiving and Sharing Infrastructure
214(1)
Terminologies
215(1)
Ontologies
216(2)
Your Experiment Is Not Just for You! or Is It?
218(1)
What Data to Share?
219(2)
Where to Share Data?
221(1)
Data Repositories and Databases
222(1)
Servers and Workflows
222(2)
A Final Thought
224(1)
References
224(5)
16 The Revolution in Scientific Publishing
Journals as an Ecosystem
229(1)
Peer Review
229(1)
Journals That Publish Primary Research Findings
230(5)
Indexing of Journals
235(1)
One Journal Is a Mega Outlier
236(1)
What Is Open Access?
237(3)
Impact Factors and Other Metrics
240(2)
New Trends in Peer Review
242(1)
The Scientific Article as a Data Object
243(1)
Where Should I Publish My Paper?
244(2)
Is There an Ideal Publishing Portfolio?
246(1)
References
247(4)
Postscript: Beyond Data Literacy 251(4)
Index 255
Dr. Neil Smalheiser has over 30 years of experience pursuing basic wet-lab research in neuroscience, most recently studying synaptic plasticity and the genomics of small RNAs. He has also directed multi-disciplinary, multi-institutional consortia dedicated to text mining and bioinformatics research, which have created new theoretical models, databases, open source software, and web-based services. Regardless of the subject matter, one common thread in his research is to link and synthesize different datasets, approaches and apparently disparate scientific problems to form new concepts and paradigms. Another common thread is to identify scientific frontier areas that have fundamental and strategic importance, yet are currently under-studied, particularly because they fall between the cracks” of existing disciplines. This book is based on lecture notes that Dr. Smalheiser prepared for a course he created, Data Literacy for Neuroscientists”, given to undergraduate and graduate students.
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