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Elsevier

Data Literacy

How to Make Your Experiments Robust and Reproducible

  • 1 Edición - 5 de septiembre de 2017
  • Última edición
  • Autor: Neil Smalheiser
  • Idioma: Inglés

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 sc… Leer más

Descripción

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.

Puntos claves

  • 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

De interès para

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

Índice

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

Detalles del producto

  • Edición: 1
  • Última edición
  • Publicado: 11 de septiembre de 2017
  • Idioma: Inglés

Sobre el autor

NS

Neil Smalheiser

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.
Afiliaciones y experiencia
Associate Professor, Department of Psychiatry and Psychiatric Institute, University of Illinois School of Medicine, USA

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