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Considerations and challenges for FAIR in large and heteregeneous sets of personalized large-scale models

Difficulty level

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 the remaining barriers. This lecture covers how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.

Topics covered in this lesson
  • Data-driven personalized models: Workflow for big data
  • Challenges to FAIR in personalized models
  • FAIR practices throughout the workflow
  • Improving FAIR practices & scaling up to the cloud
  • Remaining challenges to FAIR in personalized models