This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

Introduction to the principal of anatomical organization of neural systems in the human brain and spinal cord that mediate sensation, integrate signals, and motivate behavior.

This lecture focuses on the comprehension of nociception and pain sensation. It highlights how the somatosensory system and different molecular partners are involved in nociception and how nociception and pain sensation are studied in rodents and humans and the development of pain therapy.

Introductory presentation on how data science can help with scientific reproducibility.

The tutorial is intended primarily for beginners, but it will also beneficial to experimentalists who understand electroencephalography and event related techniques, but need additional knowledge in annotation, standardization, long-term storage and publication of data.

Introduction to the first phases of EEG/ERP data lifecycle

A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Tutorial on collaborating with Git and GitHub. This tutorial was part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Introduction to reproducible research. The lecture provides an overview of the core skills and practical solutions required to practice reproducible research. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

The FOSTER portal has produced a number of guides to help implement Open Science practices in daily workflows, including The Open Science Training Handbook.  It provides many basic definitions, concepts, and principles that are key components of open science, as well as general guidance for developing and implementing these practices in one's own research environments.

Topics include:

• Open Concepts and Principles
• Open Research Data and Materials
• Open Research Software and Open Source
• Reproducible Research and Data Analysis
• Open Access to Published Research Results
• Open Licensing and File Formats
• Collaborative Platforms
• Open Peer Review, Metrics and Evaluation
• Open Science Policies
• Citizen Science
• Open Educational Resources
• Open Advocacy