Skip to main content

MDAnalysisData

Build Status codecov docs PRs welcome Anaconda-Server Badge DOI

Access to data for workshops and extended tests of MDAnalysis.

Data sets are stored at external stable URLs (e.g., on figshare, zenodo, or DataDryad) and this package provides a simple interface to download, cache, and access data sets.

Installation

To use, install the package

pip install --upgrade MDAnalysisData

or install with conda

conda install --channel conda-forge mdanalysisdata

Accessing data sets

Import the datasets and access your data set of choice:

from MDAnalysisData import datasets

adk = datasets.fetch_adk_equilibrium()

The returned object contains attributes with the paths to topology and trajectory files so that you can use it directly with, for instance, MDAnalysis:

import MDAnalysis as mda
u = mda.Universe(adk.topology, adk.trajectory)

The metadata object also contains a DESCR attribute with a description of the data set, including relevant citations:

print(adk.DESCR)

Managing data

Data are locally stored in the data directory ~/MDAnalysis_data (i.e., in the user's home directory). This location can be changed by setting the environment variable MDANALYSIS_DATA, for instance

export MDANALYSIS_DATA=/tmp/MDAnalysis_data

The location of the data directory can be obtained with

MDAnalysisData.base.get_data_home()

If the data directory is removed then data are downloaded again. Data file integrity is checked with a SHA256 checksum when the file is downloaded.

The data directory can we wiped with the function

MDAnalysisData.base.clear_data_home()

Contributing new datasets

Please add new datasets to MDAnalysisData. See Contributing new datasets for details, but in short:

  1. raise an issue in the issue tracker describing what you want to add; this issue will become the focal point for discussions where the developers can easily give advice
  2. deposit data in an archive under an Open Data compatible license (CC0 or CC-BY preferred)
  3. write accessor code in MDAnalysisData

Credits

This package is modelled after sklearn.datasets. It uses code from sklearn.datasets (under the BSD 3-clause license).

No data are included; please see the DESCR attribute for each data set for authorship, citation, and license information for the data.

Release files for MDAnalysisData 0.9.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for MDAnalysisData 0.9.1
File Size Uploaded
mdanalysisdata-0.9.1.tar.gz 25.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for MDAnalysisData 0.9.1
File Interpreter ABI Platform
mdanalysisdata-0.9.1-py3-none-any.whl Python 3 none any Details

Total release size: 62.8 kB

Release files / mdanalysisdata-0.9.1.tar.gz

Download URL mdanalysisdata-0.9.1.tar.gz
Size 25.2 kB
Tags Source
SHA-256 checksum
How to use checksums
1958636ab982e86bde5358e3cad29a484aa64fafbb320a8d4064b6538aa38fc1
BLAKE2b-256 checksum
How to use checksums
8737a21f5c48265658883ba6fb8df4deff4339d2aeba0758139f4c5fbe6f880b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Apr 3, 2026.

Transparency log

Release files / mdanalysisdata-0.9.1-py3-none-any.whl

Download URL mdanalysisdata-0.9.1-py3-none-any.whl
Size 37.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
7080298c48eb8ded6d90e1a123f5afdefa7a0cb73b4bf1ca26ea70ab4f3691cd
BLAKE2b-256 checksum
How to use checksums
99ebcb5b73e67bcd5d2f27eefb8250749b5dd054f85d2210d5916c94648ee309
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Apr 3, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.9.1 This release

2 release files

0.9.0

2 release files

0.8.1

2 release files

0.8.0

2 release files

0.7.0

2 release files

0.6.0

2 release files

0.5.0

2 release files

0.4.0

2 release files

0.3.0

2 release files

0.2.2

2 release files

0.2.1

2 release files

0.2.0

2 release files

0.1.1

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page