IN012-04
Towards Developing Community Guidelines for Sharing and Reuse of Digital Data Quality Information
Tuesday, 8 December 2020: 20:42
Virtual
Ge Peng1, Carlo Lacagnina2, Robert R Downs3, Hampapuram Ramapriyan4, Ivana Ivanova5, David F Moroni6, Gilles Larnicol2, Yaxing Wei7, Lucy Bastin8, Nancy A Ritchey9, Lesley A Wyborn10, Chung-Lin Shie11, Ted Habermann12, Anette Ganske13, Sarah M Champion14, Mingfang Wu15, Irina Bastrakova16, Dave Jones17, Gary Berg-Cross18 and ESIP FAIR-DQI Guidelines Working Group, (1)North Carolina State University, North Carolina Institute for Climate Studies, Raleigh, NC, United States, (2)Barcelona Supercomputing Center, Barcelona, Spain, (3)Columbia University of New York, Center for International Earth Science Information Network (CIESIN), Palisades, NY, United States, (4)NASA Goddard Space Flight Cent, Greenbelt, MD, United States, (5)Curtin University, Perth, Australia, (6)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (7)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (8)Aston University, Birmingham, United Kingdom, (9)National Climatic Data Center, Asheville, NC, United States, (10)Australian National University, Canberra, ACT, Australia, (11)NASA/GSFC, Greenbelt, MD, United States, (12)Metadata Game Changers, Denver, United States, (13)Technische Informationsbibliothek (TIB), Hannover, Germany, (14)North Carolina State University, Cooperative Institute for Satellite Earth System Studies, Leicester, NC, United States, (15)Australian Research Data Commons, Melbourne, Australia, (16)Geoscience Australia, National Location Information, Canberra, ACT, Australia, (17)StormCenter Communications, Halethorpe, MD, United States, (18)Independent Consultant, Potomac, MD, United States
Abstract:
The knowledge of data quality and the quality of the associated information, including metadata, is critical for data use and reuse. Assessment of data and metadata quality is key for ensuring credible available information, establishing a foundation of trust between the data provider and various downstream users, and demonstrating compliance with requirements established by funders and federal policies.
Data quality information should be consistently curated, traceable, and adequately documented to provide sufficient evidence to guide users to address their specific needs. The quality information is especially important for data used to support decisions and policies, and for enabling data to be truly findable, accessible, interoperable, and reusable (FAIR).
Clear documentation of the quality assessment protocols used can promote the reuse of quality assurance practices and thus support the generation of more easily-comparable datasets and quality metrics. To enable interoperability across systems and tools, the data quality information should be machine-actionable. Guidance on the curation of dataset quality information can help to improve the practices of various stakeholders who contribute to the collection, curation, and dissemination of data.
This presentation outlines a global community effort to develop international guidelines to curate data quality information that is consistent with the FAIR principles throughout the entire data life cycle and inheritable by any derivative product.
