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

Abstract

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.

Publication DOI: https://doi.org/10.5334/dsj-2022-008
Additional Information: © 2022 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/ licenses/by/4.0/.
Publication ISSN: 1683-1470
Last Modified: 15 Jul 2024 08:17
Date Deposited: 07 Mar 2022 14:28
Full Text Link:
Related URLs: https://datasci ... 4/dsj-2022-008/ (Publisher URL)
PURE Output Type: Article
Published Date: 2022-03-31
Accepted Date: 2022-02-28
Authors: Peng, Ge
Lacagnina, Carlo
Downs, Robert R.
Ganske, Anette
Ramapriyan, Hampapuram
Ivánová, Ivana
Wyborn, Lesley
Jones, David
Bastin, Lucy (ORCID Profile 0000-0003-1321-0800)
Shie, Chung Lin
Moroni, David

Download

[img]

Version: Accepted Version

Access Restriction: Restricted to Repository staff only


[img]

Version: Published Version

License: Creative Commons Attribution

| Preview

Export / Share Citation


Statistics

Additional statistics for this record