/datalad

name: datalad
description: Retrieves, versions, and publishes scientific datasets with DataLad and git-annex, and captures computational provenance with datalad run, rerun, and containers-run. Use when cloning or fetching data from OpenNeuro, DANDI, datasets.datalad.org, or any DataLad dataset; when a file in a dataset reads as a broken symlink or a small pointer instead of real data; when an analysis needs a machine-readable record of how each output was produced so it can be re-executed; or when publishing a dataset to siblings such as a GitHub repository plus a storage remote. Also use to decide between DataLad and plain Git for a data-carrying repository.
compatibility: Requires Python 3.10+, DataLad 1.6.5, Git, and git-annex 10.x. Tested with git-annex 10.20260901 and datalad-container 1.2.6 on macOS ARM64. Containers additionally require Singularity, Apptainer, or Docker. Remote data access needs network access and may need provider credentials. Local filesystem workflows work offline.
license: MIT
allowed-tools: Read Write Edit Bash
metadata:
  version: "1.2"
  last-reviewed: "2026-09-30"
  skill-author: Dylan Pulver

DataLad

Overview

DataLad is a data management layer over Git and git-annex. Git tracks the dataset structure, small text files, and the history. git-annex tracks the *content* of large files, storing each file as a key and keeping the bytes somewhere that is not necessarily the local repository.

A normal clone retrieves Git history and the top-level file listing while leaving annexed bytes unfetched. Installed subdatasets have their own histories; a clone does not automatically populate them. Clone cost depends on Git history and file count, not just the data volume. Retrieve annexed bytes selectively with `datalad get`.

The second thing DataLad adds is provenance. `datalad run` executes a command and commits the result together with a machine-readable record of the command, its inputs, and its outputs. `datalad rerun` reads that record back and re-executes it. This turns "how was this figure produced" from an archaeology problem into a command.

When to use DataLad instead of plain Git

Use DataLad when any of the following holds:

• Files are too large for Git to handle comfortably, or the total exceeds what every

collaborator wants on disk.

• Data lives in more than one place (a lab server, a cluster scratch, S3, a supercomputer)

and you need to know which copies exist.

• The analysis must be re-executable, and a plain commit message is not enough evidence.
• You are consuming published datasets from OpenNeuro, DANDI, or `datasets.datalad.org`,

which are distributed as DataLad datasets.

• The project nests other datasets inside it and you want each one to keep its own

independent history.

Use plain Git when the repository is code and text only, everything fits comfortably in Git, and nobody needs partial checkouts. DataLad on top of a small pure-code repository adds indirection without buying anything.

Installation

# git-annex is NOT written in Python but is available from PyPI if you already
# have git itself installed:
uv pip install git-annex
# You can also install it first from the system
# (Debian/Ubuntu: apt install git-annex; macOS: brew install git-annex;
#  conda-forge: conda install -c conda-forge git-annex)
uv pip install "datalad==1.6.5"
uv pip install "datalad-container==1.2.6"   # only for containers-run

datalad wtf --section dependencies   # confirm git-annex version is visible

The PyPI `git-annex` package supplies platform-specific binaries. The reviewed 10.20260901.post1 wheels cover Linux glibc 2.34+ (x86_64/ARM64), macOS ARM64 14+ and x86_64 15+, and Windows x86_64. Use a system package when no wheel matches. Keep its environment on `PATH` and verify the executable; the wheel does not supply Git itself. Configure Git author name/email before creating or saving a dataset.

`datalad wtf` prints the resolved environment and is the first thing to run when behaviour looks impossible. An old or missing git-annex is behind a large share of confusing errors.

DataLad is MIT licensed; git-annex has a separate AGPL license. Consult the upstream license when redistributing either tool.

The failure that bites first: pointers are not data

After `datalad clone`, annexed files exist as symlinks into `.git/annex/objects/` (or as small pointer files where symlinks are unavailable, such as on Windows or a crippled filesystem). Nothing has downloaded the content yet.

Illustrative remote-data example; inspect the selected revision for the exact path and install NiBabel before the Python read. The refresh tested equivalent local pointer/get behavior without downloading imaging data.

datalad clone https://github.com/OpenNeuroDatasets/ds000001.git
cd ds000001
ls sub-01/anat/            # the file is listed
python -c "import nibabel; nibabel.load('sub-01/anat/sub-01_T1w.nii.gz')"   # fails
datalad get sub-01/anat/sub-01_T1w.nii.gz                                   # now it works

The failure mode to recognise: a tool reports the file as empty, truncated, corrupt, "not a gzip file", or a broken symlink, and the file size on disk is a few hundred bytes. These symptoms can indicate an unfetched annex pointer; confirm with annex status before diagnosing corruption. **Run `datalad get` before reading data, and treat "file exists" as insufficient evidence that its content is present.**

Before an analysis touches a directory, fetch it explicitly:

datalad get sub-01/                  # everything under a path
datalad get -r .                     # everything, including subdatasets
datalad get -n -r .                  # subdataset structure only, no file content

`datalad status --annex availability` checks which content is present locally, and `git annex whereis <path>` reports which repositories hold a given file. `whereis` reads recorded state and does not contact the remotes, so it tells you wh