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Data Literacy: How to Make Your Experiments Robust and Reproducible

Autor Neil Smalheiser
en Limba Engleză Paperback – 10 sep 2017
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.


  • 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
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Specificații

ISBN-13: 9780128113066
ISBN-10: 0128113065
Pagini: 282
Dimensiuni: 191 x 235 x 18 mm
Greutate: 0.57 kg
Editura: ELSEVIER SCIENCE

Public țintă

bioinformaticians; biomedical and allied health sciences graduate students; graduate students and educated lay persons who are interested in handling data for research.

Cuprins

Part A: Experimental Design1. “Most published findings are false!”2. How to identify a promising research problem?3. Experimental designs: measures, validity, randomization4. Experimental design: Sampling, bias, hypotheses5. Positive and negative controls
Part B: Getting a “feel” for your data6. Refresher on basic concepts of probability and statistics7. Data cleansing8. Case studies of data cleansing9. Hypothesis testing10. The “new statistics”11. ANOVA. 12. Nonparametric tests13. Other statistical concepts you should know
Part C: Data Management14. Recording and reporting experiments15. Data sharing and re-use16. Publishing