The study of the human brain with neuroimaging technologies is at the cusp of an exciting era of Big Data. Many data collection projects, such as the NIH-funded Human Connectome Project, have made large, high- quality datasets of human neuroimaging data freely available to researchers. These large data sets promise to provide important new insights about human brain structure and function, and to provide us the clues needed to address a variety of neurological and psychiatric disorders. However, neuroscience researchers still face substantial challenges in capitalizing on these data, because these Big Data require a different set of technical and theoretical tools than those that are required for analyzing traditional experimental data. These skills and ideas, collectively referred to as Data Science, include knowledge in computer science and software engineering, databases, machine learning and statistics, and data visualization. The Summer Institute in Data Science for Neuroimaging will combine instruction by experts in data science methodology and by leading neuroimaging researchers that are applying data science to answer scienti?c ques- tions about the human brain. In addition to lectures on the theoretical background of data science methodology and its application to neuroimaging, the course will emphasize experiential hands-on training in problem-solving tutorials, as well as project-based learning, in which the students will create small projects based on openly available datasets.
Summer Institute in Neuroimaging and Data Science: Project Narrative The Summer Institute in Neuroimaging and Data Science will provide training in modern data science tools and methods, such as programming, data management, machine learning and data visualization. Through lectures, hands-on training sessions and team projects, it will empower scientists from a variety of backgrounds in the use of these tools in research on the human brain and on neurological and psychiatric brain disorders.
| Huppenkothen, Daniela; Arendt, Anthony; Hogg, David W et al. (2018) Hack weeks as a model for data science education and collaboration. Proc Natl Acad Sci U S A 115:8872-8877 |
