Redirection¶

This page exists for redirection purposes only.

You will be redirected to your target page in a few seconds.

If you continue to see this page, you’ve ended up in here accidentally and redirection failed – sorry about this.

Here are some links that may take you to where you need to go:

ABCD: An introduction to DataLad for the ABCD ReproNim course week 8b
about: What you really need to know
containers: Computational reproducibility with software containers
dl-for-ml: Reproducible machine learning analyses: DataLad as DVC
extensions: DataLad extensions
GIN: Dataset hosting on GIN
gobig: Use GitHub for sharing content
LFS: Go big or go home
HCP-dataset: Scaling up: Managing 80TB and 15 million files from the HCP release
install: Installation and configuration
reproducible-paper: Writing a reproducible paper
RIA: Remote Indexed Archives for dataset storage and backup
yoda: YODA: Best practices for data analyses in a dataset
OHBM2020: OHBM 2020 Open Science Room: Reproducible Research Objects with DataLad
OHBM2020poster: Handbook Poster from the 2020 (virtual) OHBM
ml-usecase: DataLad for reproducible machine-learning analyses
openneuro: OpenNeuro Quickstart Guide: Accessing OpenNeuro datasets via DataLad
FZJmlcode: An introduction to DataLad with a focus on ML
MPIBerlin: An introduction to DataLad at the MPI Berlin

Alternatively, try searching in the “Quick Search” at the left-hand side, or scan the handbook’s front page at handbook.datalad.org for directions.