This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.
This lesson describes the fundamentals of genomics, from central dogma to design and implementation of GWAS, to the computation, analysis, and interpretation of polygenic risk scores.
This lesson contains the slides (pptx) of a lecture discussing the necessary concepts and tools for taking into account population stratification and admixture in the context of genome-wide association studies (GWAS). The free-access software Tractor and its advantages in GWAS are also discussed.
This lesson is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes.
This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.
This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain.
This lesson briefly goes over the outline of the Neuroscience for Machine Learners course.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
This lesson explores how researchers try to understand neural networks, particularly in the case of observing neural activity.
This lecture will provide an overview of neuroimaging techniques and their clinical applications.
In this lesson, you will learn about how genetics can contribute to our understanding of psychiatric phenotypes.
This lecture gives an introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
This lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.
This tutorial covers the fundamentals of collaborating with Git and GitHub.
This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.
The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.
This lecture covers why data sharing and other collaborative practices are important, how these practices are developed, and the challenges involved in their development and implementation.
This lecture covers the needs and challenges involved in creating a FAIR ecosystem for neuroimaging research.
This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.