As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and remaining barriers.
FAIR Approaches for Computational Neuroscience
Course Features
Lectures
Videos
Slides
Lessons of this Course
1
1
Duration:
8:47
Speaker:
This lecture introduces the FAIR principles, how they relate to the field of computational neuroscience, and the resources available.
2
2
Duration:
13:16
Speaker:
This lecture discusses how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.
3
3
Duration:
15:14
Speaker:
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.
4
4
Duration:
14:19
Speaker:
This lecture focuses on the structured validation process within computational neuroscience, including the tools, services, and methods involved in simulation and analysis.
