Data Literacy

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.

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