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

Authors

GE PENG, The University of Alabama in Huntsville
CARLO LACAGNINA, Barcelona Supercomputing Center
ROBERT R. DOWNS, Columbia University
ANETTE GANSKE, Leibniz Information Centre for Science and Technology
HAMPAPURAM K. RAMAPRIYAN, Science Systems and Applications, Inc.
IVANA IVÁNOVÁ, Curtin University
LESLEY WYBORN, Australian National University
DAVE JONES, StormCenter Communications | GeoCollaborate
LUCY BASTIN, Aston University
CHUNG-LIN SHIE, University of Maryland at Baltimore County
DAVID F. MORONI, California Institute of Technology

Document Type

Article

Date of this Version

3-31-2022

Citation

Peng, G, Lacagnina, C, Downs, RR, Ganske, A, Ramapriyan, HK, Ivánová, I, Wyborn, L, Jones, D, Bastin, L, Shie, C-L and Moroni, DF. 2022. Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets. Data Science Journal, 21: 8, pp. 1–20. DOI: https://doi. org/10.5334/dsj-2022-008

Comments

Open access.

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.