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International Community Guidelines for Sharing and Reusing Quality Information of Individual Earth Science Datasets
  • +7
  • Carlo Lacagnina,
  • Ge Peng,
  • Ivana Ivanova,
  • Robert Downs,
  • Hampapuram Ramapriyan,
  • David Moroni,
  • Yaxing Wei,
  • Lesley Wyborn,
  • Dave Jones,
  • Anette Ganske
Carlo Lacagnina
Barcelona Supercomputing Center

Corresponding Author:[email protected]

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Ge Peng
NC State University and NOAA’s National Centers for Environmental Information (NCEI)
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Ivana Ivanova
Curtin University
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Robert Downs
Columbia University of New York
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Hampapuram Ramapriyan
NASA Goddard Space Flight Cent
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David Moroni
NASA Jet Propulsion Laboratory
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Yaxing Wei
Oak Ridge National Laboratory
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Lesley Wyborn
Australian National University
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Dave Jones
StormCenter Communications
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Anette Ganske
Technische Informationsbibliothek (TIB)
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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 introduces international community 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. Supportive case studies demonstrate the applicability of the proposed guidelines.