Towards Developing Community Guidelines for Sharing and Reusing Quality Information of Earth Science Datasets
- 1Barcelona Supercomputing Center (BSC), Earth Sciences Department, Barcelona, Spain (carlo.lacagnina@bsc.es)
- 2North Carolina State University, North Carolina Institute for Climate Studies, Raleigh, NC, United States
- 3Columbia University of New York, Center for International Earth Science Information Network (CIESIN), Palisades, NY, United States
- 4NASA Goddard Space Flight Cent, Greenbelt, MD, United States
- 5Curtin University, Perth, Australia
- 6NASA Jet Propulsion Laboratory, Pasadena, CA
- 7Oak Ridge National Laboratory, Oak Ridge, TN, United States
- 8Aston University, Birmingham, United Kingdom
- 9National Climatic Data Center, Asheville, NC, United States
- 10Australian National University, Canberra, ACT, Australia
- 11NASA/GSFC, Greenbelt, MD, United States
- 12Metadata Game Changers, Denver, United States
- 13Technische Informationsbibliothek (TIB), Hannover, Germany
- 14Cooperative Institute for Climate and Satellites North Carolina State, Cooperative Institute for Climate and Satellites North Carolina, Leicester, NC, United States
- 15Australian Research Data Commons, Melbourne, Australia
- 16Geoscience Australia, National Location Information, Canberra, ACT, Australia
- 17StormCenter Communications, Halethorpe, MD, United States
- 18Independent Consultant, Potomac, MD, United States
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.
How to cite: Lacagnina, C., Peng, G., Downs, R. R., Ramapriyan, H., Ivanova, I., Moroni, D. F., Wei, Y., Bastin, L., Ritchey, N. A., Larnicol, G., Wyborn, L. A., Shie, C.-L., Habermann, T., Ganske, A., Champion, S. M., Wu, M., Bastrakova, I., Jones, D., and Berg-Cross, G.: Towards Developing Community Guidelines for Sharing and Reusing Quality Information of Earth Science Datasets, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-23, https://doi.org/10.5194/egusphere-egu21-23, 2020.
