datamanifest[py]
Keep track of the datasets used in a scientific project. You declare your data
dependencies — URLs, git repositories, checksums, formats — in a
datamanifest.toml file; datamanifest downloads, verifies, extracts and loads
them, and caches your own computed results with the same machinery.
- A transparent, trackable manifest. Every dataset a project depends on —
URLs, DOIs, checksums (content hashes used to verify each file), formats — is
listed in a single
datamanifest.tomlfile (the manifest) that you can read directly and version with git. The format is defined by a language-agnostic spec (implemented in Python and Julia) and can be edited by hand, from code, or through the CLI. - Fetch from a wide range of sources. Direct URLs, Zenodo/figshare and PANGAEA
DOIs, git repos, object stores (
s3://,gs://, …), and bulk imports from pooch, intake or DVC — all checksum-verified, extracted, and adopted in place when already on disk. - Cache your own computed data too. The same tooling backs a
@cacheddecorator that stores your own results with PID-lock, keyed by their inputs, to speed up calculations locally. Caching is a separate, local concern — nothing is fetched — but cached results are managed through the same CLI as datasets. - A CLI for data download, local management and synchronization across
machines. Add and download datasets, inspect and repair what's on disk, move
or centralize where data is stored, and push/pull datasets and cached results
between machines over rsync+ssh — all without touching your analysis code. A
git-ignored state file (
.datamanifest/state.toml) records where each object actually landed on this machine, keeping local location tracking separate from the portable, shareable manifest.
Installation
pip install datamanifestpy
With optional loader backends (csv, parquet, nc, yaml, fsspec, or
all):
pip install "datamanifestpy[all]"
See the installation page for the per-backend details.
Quickstart
datamanifest init # create datamanifest.toml here
datamanifest add https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_annmean_mlo.csv --name co2
datamanifest list # what's tracked, and where it lives
datamanifest path co2 # resolve the on-disk path (for a script)
Then load it from your code:
import datamanifest
db = datamanifest.Database("datamanifest.toml")
df = db.load_dataset("co2") # download on first use, then load
path = db.get_dataset_path("co2") # just the on-disk path
Commit datamanifest.toml — the recipe of what to fetch and how. The data
lives in a machine-wide shared store (deduplicated across your projects) and
the private .datamanifest/ directory stays git-ignored; a collaborator clones
and runs datamanifest download. See the
quickstart for the full
walkthrough.
Documentation
Full documentation lives at https://perrette.github.io/datamanifest/:
- Installation
- Quickstart
- Using it from your code —
load_dataset,@cached, the file-lessDatabase - Use cases — add, repair, store, sync
- CLI reference
- Storage model · Configuration
- Adding datasets · Importing from other tools
- Language bindings · Related projects
From the same author
A few other open-source tools I maintain.
Scientific writing & data
- texmark — write scientific articles in Markdown and convert them to journal-ready LaTeX/PDF.
- papers — command-line BibTeX bibliography and PDF library manager.
Speech to Text (dictate) and Text to Speech (read-aloud) tools
Acknowledgments
datamanifest is a Python port of
awi-esc/DataManifest.jl, written
by the same author (Mahé Perrette). The Python port was implemented with
assistance from Anthropic's Claude.
1 maintainer
Metadata
1 maintainer
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