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.
This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.
This lesson discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases.
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.
The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.
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.
This lecture will provide an overview of Addgene, a tool that embraces the FAIR principles developed by members of the INCF Community. This will include an overview of Addgene, their mission, and available resources.
This lecture covers the IBI Data Standards and Sharing Working Group, including its history, aims, and projects.
This session covers the framework of the International Brain Lab (IBL) and the data architecture used for this project.
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.