Advanced

There is more to DataLad than the Basics. Equipped with the fundamental building blocks and broad DataLad expertise, you can continue to this advanced section to read more on how DataLad can be used, from special case applications to data management at scale.

This part of the book will abandon the DataLad-101 narrative. Consider yourself graduated. There is no need to read the chapters of this book sequentially. Rather, find chapters that match your interest and usecase, and read its sections and associated usecases.

• 1. Advanced options
  • 1.1. How to hide content from DataLad
  • 1.2. DataLad extensions
  • 1.3. Create your own extension
  • 1.4. DataLad’s result hooks
  • 1.5. Configure custom data access
  • 1.6. Remote Indexed Archives for dataset storage and backup
  • 1.7. Prioritizing subdataset clone locations
  • 1.8. Subsample datasets using datalad copy-file
• 2. Go big or go home
  • 2.1. Going big with DataLad
  • 2.2. Calculate in greater numbers
  • 2.3. Fixing up too-large datasets
  • 2.4. Summary
• 3. Computing on clusters
  • 3.1. DataLad on High Throughput or High Performance Compute Clusters
  • 3.2. DataLad-centric analysis with job scheduling and parallel computing
  • 3.3. Walkthrough: Parallel ENKI preprocessing with fMRIprep
• 4. Better late than never
  • 4.1. Transitioning existing projects into DataLad
• 5. Special purpose showrooms
  • 5.1. Reproducible machine learning analyses: DataLad as DVC
• 6. DataLad internals
  • 6.1. DataLad’s internal design
  • 6.2. Contributing to DataLad