Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets

Ge Peng*, Carlo Lacagnina, Robert R. Downs, Anette Ganske, Hampapuram Ramapriyan, Ivana Ivánová, Lesley Wyborn, David Jones, Lucy Bastin, Chung Lin Shie, David Moroni

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re)use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the
    guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity.
    Original languageEnglish
    Article number8
    Number of pages20
    JournalData Science Journal
    Issue number1
    Publication statusPublished - 31 Mar 2022

    Bibliographical note

    © 2022 The Author(s). This is an
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    Commons Attribution 4.0
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