Data Literacy: How To Make Your Experiments Robust And Reproducible by Neil SmalheiserData Literacy: How To Make Your Experiments Robust And Reproducible by Neil Smalheiser

Data Literacy: How To Make Your Experiments Robust And Reproducible

byNeil Smalheiser

Paperback | September 11, 2017

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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 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 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
  • Can be used for self-studying or as an optional book for more technical courses
  • Brings an interdisciplinary approach which may be applied across different areas of sciences
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 m...
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Title:Data Literacy: How To Make Your Experiments Robust And ReproducibleFormat:PaperbackDimensions:282 pages, 8.75 × 6.35 × 0.68 inPublished:September 11, 2017Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0128113065

ISBN - 13:9780128113066

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Table of Contents

Part A: Experimental Design 1. Most published findings are false! 2. How to identify a promising research problem? 3. Experimental designs: measures, validity, randomization 4. Experimental design: Sampling, bias, hypotheses 5. Positive and negative controls

Part B: Getting a feel for your data 6. Refresher on basic concepts of probability and statistics 7. Data cleansing 8. Case studies of data cleansing 9. Hypothesis testing 10. The new statistics 11. ANOVA. 12. Nonparametric tests 13. Other statistical concepts you should know

Part C: Data Management 14. Recording and reporting experiments 15. Data sharing and re-use 16. Publishing