FAIR Dataset Quality Information

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This is the document for community guidelines of consistently curating and representing dataset quality information, in line with the FAIR principles.


This document provides resources for developing community guidelines for consistently curating and representing dataset quality information and captures the outcomes. The guidelines aim to help curate dataset quality information that is findable and accessible, machine- and human-readable, interoperable, and reusable.

The target community is any entity that produces, publishes, manages, or uses digital Earth Science datasets or products. However, the guidelines will be general enough to be applicable to digital datasets of other disciplines.


Case Statement

  • Case statement for developing community guidelines for consistently curating and representing dataset quality information (Peng et al. 2020)

Multi-dimensions of Data and Information Quality:

Existing Fitness for Purpose Assessment approaches Through the Full Life Cycle of Earth Science Datasets:

  • Scientific quality:
    • NASA Technical Readiness Levels for Operations (Mankins 2009)
    • NOAA STAR Data Product Algorithm Maturity Matrix (Zhou, Divakarla & Liu 2016)
    • Perspectives of Data Uncertainty (Moroni et al. 2019)
    • OGC UncertML (Williams et al. 2009)
    • Operational Readiness Levels For Disaster Operations (ESIP Disasters Cluster 2018)

Dataset-level metadata quality:

Portfolio Management and Repository Certifications

FAIR Data Principles

Organizational Challenges and Approaches

Data Quality Management Framework

Intended Users

  • Data producers, publishers, providers, and service providers for improved data sharing and reuse;
  • Data quality management professionals for improved data quality and usability;
  • Entities or organizations that manage and steward Earth science datasets during any stage of their full life cycle for improved enterprise data management and stewardship;
  • End users who integrate various datasets and associated quality information for improved interoperability and reusability.


  • Data can refer to anything that is collected, observed, or derived and used as a basis for reasoning, discussion, or calculation. Data can be either structured or unstructured, and can be represented in quantitative, qualitative, or physical forms.
  • Scientific or research data is defined as: the recorded factual material commonly accepted in the scientific community as necessary to validate research findings.
  • Digital data, distinguished from physical records, such as paper weather reports, are represented in discrete numerical form that can be used by a computer or electronic device.
  • Data product refers to “a product that facilitates an end goal through the use of data,” usually with a well-thought out algorithm or approach (Patil 2012). Data products tend to be structured and can be raw measurements or scientific products derived from raw measurements or other products. Products can also be statistical or numerical model outputs, including analyses, reanalyses, predictions, or projections. Earth Science data products may be further categorized based on their processing levels.
  • Dataset is an identifiable collection of physical records, a digital rendition of factual materials, or a product of a given version of an algorithm/model. A dataset may contain one or many physical samples or data files in an identical format, having the same geophysical variable(s) and product specification(s), such as the geospatial location or spatial grid. Dataset and data product may be used interchangeably.
  • Dataset quality includes quality of both data and associated information such as data quality descriptive information such as those captured in documents, e.g., papers or reports, and data quality metadata that is captured in a metadata record, throughout the entire life cycle of a dataset.
  • Information is considered as data being processed, organized, structured, or presented in a given context, while knowledge is gained from an understanding of the significance of information (Mosely et al. 2009, available at: https://technicspub.com/dmbok). Data and information may overlap and may be used interchangeably.
  • Knowledge is an abstract concept, defined as a familiarity, awareness, or understanding of someone or something, gained through education, experience, or association. It can refer to a theoretical or practical understanding of a subject.
  • Maturity model refers to a maturity reference or assessment model with desired evolution in discrete stages from a certain aspect or perspective of dataset quality.


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