FAIR Dataset Quality Information

From Earth Science Information Partners (ESIP)


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.

Guidelines Document

The first baseline of the guidelines document has been released after going through all the review comments and suggestions, and addressed them within the scope of the document. The latest version of the guidelines document is maintained at https://doi.org/10.31219/osf.io/xsu4p. See the peer-reviewed paper (Peng et al. 2022) on the guidelines development process.


A complete draft of the guidelines document (v00r05-20210417) is out for community review. The current document can be accessed at https://doi.org/10.31219/osf.io/xsu4p. A Google Form facilitates anonymous comment collection can be accessed here, which will be available until Friday June 4, 2021. Alternatively, you can use this template to capture all your comments and suggestions and send it to Ge Peng at [[1]], Carlo Lacagnina at [[2]], or Ivana Ivánová at [[3]].

Community feedback is important in helping us improve the quality of the document. Please contact us if you have any questions.


Case Statement

  • Case statement for developing community guidelines for consistently curating and representing dataset quality information (Peng et al. 2020a)
  • Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets (Peng et al. 2020b)

Summary Report of the Pre-ESIP Workshop

This workshop summary report (Peng et al. 2020c) provides background for and summarizes main takeaways of a workshop held virtually to kick off the development of community guidelines for consistently curating and representing dataset quality information in a way that is in line with the FAIR principles.

Multi-dimensions of Data and Information Quality:

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

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 are representations of observations, objects, or other entities and can refer to anything that is collected, observed, generated 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, and it can be processed, curated or published by a single agent. It may refer to 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. The general notion of datasets found in the literature currently is characterized by an interrelated family of more specific concepts: grouping, content, relatedness, and purpose (Renear et al 2010). Dataset and data product may be used interchangeably.
  • Dataset quality includes quality of both data and associated information, examples of which are metadata, software, algorithms, and practices or procedures applied to the dataset throughout its entire life cycle. Dataset quality is a multi-dimensional construct perception and/or a judgment of data's fitness or trustworthiness to serve intended research uses in a given context.
  • Dataset quality information includes quality of both data quality descriptive information such as those captured in documents, e.g., papers or reports, and 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, communicated or presented so as to be meaningful to the recipient in a given context.
  • 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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