datahugger

Datahugger - Where DOI :open_hands: Data

Datahugger is a tool to download scientific datasets, software, and code from a large number of repositories based on their DOI (wiki) or URL. With Datahugger, you can automate the downloading of data and improve the reproducibility of your research. Datahugger provides a straightforward Python interface as well as an intuitive Command Line Interface (CLI).

Supported repositories

Datahugger offers support for more than 377 generic and specific (scientific) repositories (and more to come!).

We are still expanding Datahugger with support for more repositories. You can help by requesting support for a repository in the issue tracker. Pull Requests are very welcome as well.

Installation

Datahugger requires Python 3.6 or later.

pip install datahugger

Getting started

Datahugger with Python

Load a dataset (or any digital asset) from a repository with the datahugger.get() function. The first argument is the DOI or URL, and the second is the folder name to store the dataset (it will be created if it does not exist).

The following code loads dataset 10.5061/dryad.mj8m0 into the folder data.

import datahugger

# download the dataset to the folder "data"
datahugger.get("10.5061/dryad.mj8m0", "data")

For an example of how this can integrate with your work, see the example workflow notebook or

Datahugger with command line

The command line function datahugger provides an easy interface to download data. The first argument is the DOI or URL, and the second argument is the name of the folder to store the dataset (will be created if it does not exist).

datahugger 10.5061/dryad.mj8m0 data

% datahugger 10.5061/dryad.mj8m0 data
Collecting...
NestTemperatureData.csv            : 100%|████████████████████████████████████████| 607k/607k
README_for_NestTemperatureData.txt : 100%|██████████████████████████████████████| 2.82k/2.82k
ExternalTemps.csv                  : 100%|██████████████████████████████████████| 1.06k/1.06k
README_for_ExternalTemps.txt       : 100%|██████████████████████████████████████| 2.82k/2.82k
InternalEggTempData.csv            : 100%|██████████████████████████████████████████| 664/664
README_for_InternalEggTempData.txt : 100%|██████████████████████████████████████| 2.82k/2.82k
SoilSimulation_Output.csv          : 100%|████████████████████████████████████████| 229M/229M
README_for_SoilSimulation_[...].txt: 100%|██████████████████████████████████████| 2.82k/2.82k
Dataset successfully downloaded.

Tip: On some systems, you have to quote the DOI or URL. For example: datahugger "10.5061/dryad.mj8m0" data.

Tips and tricks

• No need to struggle with DOIs versus DOI URLs. They both work (and more). Example: The values 10.5061/dryad.x3ffbg7m8, doi:10.5061/dryad.x3ffbg7m8, https://doi.org/10.5061/dryad.x3ffbg7m8, and https://datadryad.org/stash/dataset/doi:10.5061/dryad.x3ffbg7m8 all point to the same dataset.
• Do not republish the dataset when uploading your data to a scientific data repository. These storage resources can be used better :)

Contact

Please feel free to reach out with questions, comments, and suggestions. The issue tracker is a good starting point. You can also email me at [email protected].