FAIR Dataset Quality Information

Document

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

Overview

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.

Resources

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:

• Quality Attributes for Data Consumers (Wang and Strong 1996)
  
• Multi-dimensions of Earth Science Data and Information Quality (Ramapriyan et al. 2017)
  
• Overview of Data Quality Perspectives and Maturity Models (Peng 2018; Peng et al. 2019a: Recording)
  
  

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

• Measurement systems:
  
  • GAIA-CLIM Measurement Maturity Matrix (Thorne et al 2015)
    

• Production systems:
  
  • CORE-CLIMAX Production System Maturity Matrix (EUMETSAT 2013; Schulz et al. 2015)
    
  • DKRZ Quality Maturity Matrix (Hock et al. 2015)
    
  • QA4ECV (Nightingale et al. 2018)
    

• 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)
    

• Product quality:
  
  • NOAA CDR Product Maturity Matrix (Bates and Privette 2012)
    

• Stewardship quality:
  
  • NCEI/CICS-NC Scientific Data Stewardship Maturity Matrix (Peng et al. 2015)
    
  • CEOS WGISS Data Management and Stewardship Maturity Matrix (WGISS DSIG 2017)
    
  • WMO Stewardship Maturity Matrix for Climate Data (SMM-CD Working Group 2019)
    
  • GEOSS Data Management Principles and Data Sharing Principles (GEO DMP TF 2015; GEO DSWG 2014)
    

• Service quality:
  
  • Level of Services Models: NSIDC (Duerr et al. 2009) and NASA Earth Science Data System
    
  • NCEI Tiered Scientific Data Stewardship Services (Peng et al. 2016)
    
  • GCOS ECV Data and Information Access Matrix
    
  • Global Ocean Observing System (GOOS) framework
    
  • NCEI/ESIP-DSC Data Use and Services Maturity Matrix (Serv-MM Working Group 2018)
    
  • Data Use and Impact (Downs 2019)
    
    

Dataset-level metadata quality:

• Completeness: NCEI Collection-Level Metadata Rubric Tool
  
• FAIR metadata checklist – NCEAS MetaDIG (Habermann 2019 )
  
• Metadata checklist of LTER network data management system (O’Brien et al. 2016)
  
• Data set provenance for science (Hills et al. 2015)
  
• Stewardship quality metadata (Peng et al 2019b)
  
  

Portfolio Management and Repository Certifications

• NGDA Data Lifecycle Maturity Model (LMM) (NGDA 2015; Peltz-Lewis et al. 2014)
  
• WDS-DSA-RDA Core Trustworthy Data Repository Requirements (Edmunds et al. 2016; 2019)
  
• USGS Trusted Data Repository Checklist (Faundeen 2017)
  
• The TRUST Principles for Digital Repositories (Lin et al. 2020)
  
  

FAIR Data Principles

• FAIR Data Principles (Wilkinson et al. 2016)
  
• RDA FAIR Data Maturity Model (RDA FAIR Data Maturity Model WG 2020)
  
  

Organizational Challenges and Approaches

• NASA’s ESDSWG Data Quality Working Group Recommendations (Wei et al. 2019 - recording)
  
• Gaps in Essential Climate Variables Assessment (Nightingale et al. 2019)
  
• FAIR and Data Management for a Multidisciplinary Research Center (Westra and Zhang 2019)
  
  

Data Quality Management Framework

• High Quality Global Data Management Framework for Climate Data (HQ-GDMFC) (WMO 2019)
  
• Implementation of a Data Management Quality Management Framework at the Marine Institute, Ireland (Leadbetter et al. 2019)
  
• The Data Quality Challenge. Recommendations for Sustainable Research in the Digital Turn (Rat für Informationsinfrastrukturen 2020)
  
• Conceptual Enterprise Framework for Managing Scientific Data Stewardship (Peng et al. 2018); (Peng, Privette, & Maycock 2019)
  
  

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.
  
  

Definitions

• 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.
  
• 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.
  
• 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.
  
• Dataset quality includes quality of both data and associated information.
  
• 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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