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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

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

extensions

DataLad extensions

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

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.