May 16, 2022

Journal Article: “Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets”

The article linked below was published today by Data Science Journal.

Title

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

Authors

Ge Peng
University of Alabama in Huntsville

Carlo Lacagnina
Robert R. Downs
Anette Ganske
Hampapuram K. Ramapriyan
Ivana Ivánová
Lesley Wyborn
Dave Jones
Lucy Bastin
Chung-lin Shie
David F. Moroni

Source

Data Science Journal (21) 1
DOI: 10.5334/dsj-2022-008

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.

Brief description of four quality aspects (i.e., science, product, stewardship and service) throughout a dataset lifecycle, three key stages and a few quality attributes associated with each quality aspect (e.g., define, develop, and validate stages for the science quality aspect). The quality aspects and associated stages are based on Ramapriyan et al. (2017) with the following changes, based on feedback from the ESIP community and the International FAIR Dataset Quality Information (DQI) Community Guidelines Working Group: i) ‘Assess’ replaced by ‘Evaluate’ in the Product aspect; ii) ‘Deliver’ replaced by ‘Release’ in the Product aspect; and iii) ‘Maintain’ replaced by ‘Document’ in the Stewardship aspect. Additionally, completeness of metadata is moved from the Product to Stewardship aspect. Creator: Ge Peng; Contributors to conceptualization: Lesley Wyborn and Robert R. Downs. Source: 10.5334/dsj-2022-008

Direct to Full Text Article

About Gary Price

Gary Price (gprice@mediasourceinc.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. Before launching INFOdocket, Price and Shirl Kennedy were the founders and senior editors at ResourceShelf and DocuTicker for 10 years. From 2006-2009 he was Director of Online Information Services at Ask.com, and is currently a contributing editor at Search Engine Land.

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