metasyn/CITATION.cff at develop · sodascience/metasyn · GitHub

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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: metasyn
message: >-
  If you use this software, please also cite the associated
  software paper under preferred-citation in this file.
type: software
authors:
  - given-names: Raoul
    family-names: Schram
    email: [email protected]
    affiliation: Utrecht University
    orcid: 'https://orcid.org/0000-0001-6616-230X'
  - given-names: Samuel
    family-names: Spithorst
    email: [email protected]
    orcid: 'https://orcid.org/0009-0000-4140-0658'
    affiliation: Utrecht University
  - given-names: Erik-Jan
    name-particle: van
    family-names: Kesteren
    email: [email protected]
    affiliation: Utrecht University
    orcid: 'https://orcid.org/0000-0003-1548-1663'
identifiers:
  - type: doi
    value: 10.21105/joss.07099
    description: Journal of Open Source Software paper
  - type: doi
    value: 10.5281/zenodo.7696031
    description: Latest archived version of metasyn
repository-code: 'https://github.com/sodascience/metasyn'
url: 'https://metasyn.readthedocs.io/'
repository-artifact: 'https://pypi.org/project/metasyn/'
abstract: >-
  Synthetic data is a promising tool for improving the
  accessibility of datasets which are too sensitive to be
  shared publicly. To this end, we introduce metasyn, a
  Python package for generating synthetic data from tabular
  datasets. Unlike existing synthetic data generation
  software, metasyn is built on a simple generative model
  that omits multivariate information. This choice enables
  transparency and auditability, keeps information leakage
  to a minimum, and enables privacy guarantees through a
  plug-in system. While the analytical validity of the
  generated data is thus intentionally limited, its
  potential uses are broad, including exploratory analyses,
  code development and testing, and external communication
  and teaching.
keywords:
  - synthetic data
  - metadata
  - generative model
  - data science
  - data management
  - machine learning
  - statistics
license: MIT
preferred-citation:
  authors:
    - given-names: Raoul
      family-names: Schram
      email: [email protected]
      affiliation: Utrecht University
      orcid: 'https://orcid.org/0000-0001-6616-230X'
    - given-names: Samuel
      family-names: Spithorst
      email: [email protected]
      orcid: 'https://orcid.org/0009-0000-4140-0658'
      affiliation: Utrecht University
    - given-names: Erik-Jan
      name-particle: van
      family-names: Kesteren
      email: [email protected]
      affiliation: Utrecht University
      orcid: 'https://orcid.org/0000-0003-1548-1663'
  title: "Metasyn: Transparent Generation of Synthetic Tabular Data with Privacy Guarantees"
  journal: "Journal of Open Source Software"
  year: 2025
  doi: 10.21105/joss.07099
  volume: 10
  issue: 105
  pages: 7099
  type: article