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Data literacy : how to make your experiments robust and reproducible /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Academic Press,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Smalheiser, Neil R., |e author. | |
| 245 | 1 | 0 | |a Data literacy : |b how to make your experiments robust and reproducible / |c Neil Smalheiser, MD, PhD, Associate Professor in Psychiatry, Department of Psychiatry and Psychiatric Institute, University of Illinois School of Medicine, USA. |
| 264 | 1 | |a Amsterdam : |b Academic Press, |c 2017. | |
| 264 | 4 | |c �2017 | |
| 300 | |a 1 online resource (xvii, 261 pages) | ||
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| 520 | |a 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 coursesBrings an interdisciplinary approach which may be applied across different areas of sciences. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Reproducibility and robustness -- Choosing a research problem -- Basics of data and data distribution -- Experimental design: measures, validity, sampling, bias, randomization, power -- Experimental design: design strategies and controls -- Power estimation -- The data cleansing and analysis pipeline -- Topics to consider when analyzing data -- Null hypothesis statistical testing and the t-Test -- The "new statistics" and Bayesian inference -- ANOVA -- Non parametric tests -- Correlation and other concepts you should know -- How to record and report your experiments -- Data sharing and reuse -- The revolution in scientific publishing. | |
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| 776 | 0 | 8 | |i Print version: |a Smalheiser, Neil R. |t Data literacy. |d Amsterdam : Academic Press, [2017] |z 9780128113066 |w (OCoLC)974698661 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128113066 |z Texto completo |