This lesson discusses FAIR principles and methods currently in development for assessing FAIRness.

This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

In this lesson, while learning about the need for increased large-scale collaborative science that is transparent in nature, users also are given a tutorial on using Synapse for facilitating reusable and reproducible research. 

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

This lesson gives an introductory presentation on how data science can help with scientific reproducibility.

This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.

The state of the field regarding the diagnosis and treatment of major depressive disorder (MDD) is discussed. Current challenges and opportunities facing the research and clinical communities are outlined, including appropriate quantitative and qualitative analyses of the heterogeneity of biological, social, and psychiatric factors which may contribute to MDD.

This lesson delves into the opportunities and challenges of telepsychiatry. While novel digital approaches to clinical research and care have the potential to improve and accelerate patient outcomes, researchers and care providers must consider new population factors, such as digital disparity. 

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

This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein. 

This lecture provides an 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, highlighting how the somatosensory system and different molecular partners are involved in nociception.

This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below. 

This lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling. 

In this lesson, you will learn about one particular aspect of decision making: reaction times. In other words, how long does it take to take a decision based on a stream of information arriving continuously over time?

This tutorial covers the fundamentals of collaborating with Git and GitHub.

This lecture covers the biomedical researcher's perspective on FAIR data sharing and the importance of finding better ways to manage large datasets.

This lecture covers multiple aspects of FAIR neuroscience data: what makes it unique, the challenges to making it FAIR, the importance of overcoming these challenges, and how data governance comes into play.

This lecture covers the processes, benefits, and challenges involved in designing, collecting, and sharing FAIR neuroscience datasets.

This lecture covers the benefits and difficulties involved when re-using open datasets, and how metadata is important to the process.