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 provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.

This lesson gives a general introduction to the essentials of navigating through a Bash terminal environment.  The lesson is based on the Software Carpentries "Introduction to the Shell" and was given in the context of the BrainHack School 2020.

This lesson covers Python applications to data analysis, demonstrating why it has become ubiquitous in data science and neuroscience. The lesson was given in the context of the BrainHack School 2020.

This lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity. 

This lecture picks up from the previous lesson, providing an overview of neuroimaging techniques and their clinical applications.

This lesson provides a basic introduction to clinical presentation of schizophrenia, its etiology, and current treatment options.

This lecture focuses on the rationale for employing neuroimaging methods for movement disorders.

This lesson discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases. 

In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience. 

The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.

This lecture covers the description and brief history of data science and its use in neuroinformatics.

This lesson provides an overview of self-supervision as it relates to neural data tasks and the Mine Your Own vieW (MYOW) approach.

An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.