Redirection¶

This page exists for redirection purposes only.

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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
book-intro-v1: Datalad — An Introduction to Research Data Management
cheatsheet: DataLad cheat sheet
clone-priority: Prioritizing subdataset clone locations
containers: Computational reproducibility with software containers
dataladdening: Transitioning existing projects into DataLad
dl-for-ml: Reproducible machine learning analyses: DataLad as DVC
dgpa: An introduction to DataLad at the ReproNim DGPA workshop
extensions: DataLad extensions
filenaming: How to name a file: Interoperability considerations
GIN: Walk-through: Dataset hosting on GIN
gobig: Go big or go home
LFS: Walk-through: Git LFS as a special remote on GitHub
HCP-dataset: Scaling up: Managing 80TB and 15 million files from the HCP release
install: Installation and configuration
osoh: An introduction to DataLad at the Open Science Office Hour
reproducible-paper: Writing a reproducible paper
RIA: Remote indexed archives for dataset storage and backup
runhpc: DataLad-centric analysis with job scheduling and parallel computing
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
Yale: An introduction to DataLad for Yale

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.