Originally published in 2006, the second edition of The Design of Experiments in Neuroscience continues to be an excellent and eminently readable guideline for students beginning their scientific careers. Although all of the examples are specific to neuroscience, this slender volume offers valuable illumination on core practices, principles, and experimental approaches pertinent for all new researchers. Chapter topics cover recognizing pseudoscience, ethics, how to critically read journal articles, how to pick an experimental question, basic research design, controlling variables, and tips for becoming an independent investigator. Each of the eight chapters provides descriptive figures and extra information boxes, questions to check reader comprehension, additional thought questions, further reading suggestions, and Web resources. The six appendixes are as valuable as the main text, including information on working with data, writing research papers, a sample paper, questions and exercises for review, a glossary, and answers to chapter questions. Neuroscientist Harrington (Smith College) has created a wonderful resource that should be a must read for every neuroscientist in training, if not all novice scientists. Summing Up: Highly recommended. Upper-division undergraduates and graduate students. Upper-division Undergraduates; Graduate Students. Reviewed by C. L. Iwema.

Using engaging prose, Mary E. Harrington introduces neuroscience students to the principles of scientific research including selecting a topic, designing an experiment, analyzing data, and presenting research. This new third edition updates and clarifies the book's wealth of examples while maintaining the clear and effective practical advice of the previous editions. New and expanded topics in this edition include techniques such as optogenetics and conditional transgenes as well as a discussion of rigor and reproducibility in neuroscience research. Extended coverage of descriptive and inferential statistics arms readers with the analytical tools needed to interpret data. Throughout, practical guidelines are provided on avoiding experimental design problems, presenting research including creating posters and giving talks, and using a '12-step guide' to reading scientific journal articles.

Modern neuroscience research is inherently multidisciplinary, with a wide variety of cutting edge new techniques to explore multiple levels of investigation. This Third Edition of Guide to Research Techniques in Neuroscience provides a comprehensive overview of classical and cutting edge methods including their utility, limitations, and how data are presented in the literature. This book can be used as an introduction to neuroscience techniques for anyone new to the field or as a reference for any neuroscientist while reading papers or attending talks. Nearly 200 updated full-color illustrations to clearly convey the theory and practice of neuroscience methods Expands on techniques from previous editions and covers many new techniques including in vivo calcium imaging, fiber photometry, RNA-Seq, brain spheroids, CRISPR-Cas9 genome editing, and more Clear, straightforward explanations of each technique for anyone new to the field A broad scope of methods, from noninvasive brain imaging in human subjects, to electrophysiology in animal models, to recombinant DNA technology in test tubes, to transfection of neurons in cell culture Detailed recommendations on where to find protocols and other resources for specific techniques "Walk-through" boxes that guide readers through experiments step-by-step

Designing EEG Experiments for Studying the Brain: Design Code and Example Datasets details the design of various brain experiments using electroencephalogram (EEG). Providing guidelines for designing an EEG experiment, it is primarily for researchers who want to venture into this field by designing their own experiments as well as those who are excited about neuroscience and want to explore various applications related to the brain. The first chapter describes how to design an EEG experiment and details the various parameters that should be considered for success, while remaining chapters provide experiment design for a number of neurological applications, both clinical and behavioral. As each chapter is accompanied with experiment design codes and example datasets, those interested can quickly design their own experiments or use the current design for their own purposes. Helpful appendices provide various forms for one’s experiment including recruitment forms, feedback forms, ethics forms, and recommendations for related hardware equipment and software for data acquisition, processing, and analysis. Written to assist neuroscientists in experiment designs using EEG Presents a step-by-step approach to designing both clinical and behavioral EEG experiments Includes experiment design codes and example datasets Provides inclusion and exclusion criteria to help correctly identify experiment subjects and the minimum number of samples Includes appendices that provide recruitment forms, ethics forms, and various subjective tests associated with each of the chapters

Scientists planning experiments in medical and behavioral research will find this handbook and dictionary an invaluable desk reference tool. Also recommended as a textbook for students of Experimental Design or accompanying courses in Statistics. Principles of experimental design are introduced, techniques of experimental design are described, and advantages and disadvantages of often used designs are discussed. This two-part volume, a handbook of experimental design and a dictionary providing short explanations for many terms related to experimental design, contains information that will not quickly become outdated.

Specifically intended for lab-based biomedical researchers, this practical guide shows how to design experiments that are reproducible, with low bias, high precision, and widely applicable results. With specific examples from research using both cell cultures and model organisms, it explores key ideas in experimental design, assesses common designs, and shows how to plan a successful experiment. It demonstrates how to control biological and technical factors that can introduce bias or add noise, and covers rarely discussed topics such as graphical data exploration, choosing outcome variables, data quality control checks, and data pre-processing. It also shows how to use R for analysis, and is designed for those with no prior experience. An accompanying website (https://stanlazic.github.io/EDLB.html) includes all R code, data sets, and the labstats R package. This is an ideal guide for anyone conducting lab-based biological research, from students to principle investigators working in either academia or industry.