Skip to content

Latest commit

 

History

History
231 lines (162 loc) · 5.77 KB

README.rst

File metadata and controls

231 lines (162 loc) · 5.77 KB
Raw
fair-software.nl recommendations Badges
1. Code repository GitHub Badge
2. License License Badge
3. Community Registry PyPI Badge
4. Enable Citation Zenodo Badge
Other best practices  
Continuous integration Python Build Python Publish
Documentation Documentation Status
Anaconda package Anaconda Package Anaconda Downloads

fairly

A package to create, publish and clone research datasets.

License: MIT

Installation

fairly requires Python 3.8 or later, and ruamel.yaml version 0.17.26 or later. It can be installed directly from PYPI or Conda-Forge.

# Using pip
pip install fairly
# using anaconda or miniconda
conda install conda-forge::fairly

Installing from source

  1. Clone or download the source code:

    git clone https://github.com/ITC-CRIB/fairly.git

  2. Go to the root directory:

    cd fairly/

  3. Compile and install using pip:

    pip install .

Usage

Basic example to create a local research dataset and deposit it to a repository:

import fairly

# Initialize a local dataset
dataset = fairly.init_dataset('/path/dataset')

# Set metadata
dataset.metadata['license'] = 'MIT'
dataset.set_metadata(
    title='My dataset',
    keywords=['FAIR', 'research', 'data'],
    authors=[
        '0000-0002-0156-185X',
        {'name': 'John', 'surname': 'Doe'}
    ]
)

# Add data files
dataset.includes.extend([
    'README.txt',
    '*.csv',
    'train/*.jpg'
])

# Save dataset
dataset.save()

# Upload to a data repository
remote_dataset = dataset.upload('zenodo')

Basic example to access a remote dataset and store it locally:

import fairly

# Open a remote dataset
dataset = fairly.dataset('doi:10.4121/21588096.v1')

# Get dataset information
dataset.id
>>> {'id': '21588096', 'version': '1'}

dataset.url
>>> 'https://data.4tu.nl/articles/dataset/.../21588096/1'

dataset.size
>>> 33339

len(dataset.files)
>>> 6

dataset.metadata
>>> Metadata({'keywords': ['Earthquakes', 'precursor', ...], ...})

# Update metadata
dataset.metadata['keywords'] = ['Landslides', 'precursor']
dataset.save_metadata()

# Store dataset to a local directory (i.e. clone dataset)
local_dataset = dataset.store('/path/dataset')

Currently, the package supports the following research data management platforms:

All research data repositories based on the listed platforms are supported.

For more details and examples, consult the package documentation.

Testing

Unit tests can be run by using pytest command in the root directory.

Contributions

Read the guidelines to know how you can be part of this open source project.

JupyterLab Extension

An extension for JupyerLab is being developed in a different repository.

Citation

Please cite this software using as follows:

Girgin, S., Garcia Alvarez, M., & Urra Llanusa, J., fairly: a package to create, publish and clone research datasets [Computer software]

Acknowledgements

This research is funded by the Dutch Research Council (NWO) Open Science Fund, File No. 203.001.114.

Project members: