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 aims 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

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

• Production system:
  
  • CORE-CLIMAX production system maturity matrix (EUMETSAT 2013)
    
  • 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)
  
  

FAIR Data Principles

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

Organizational Challenges & 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)
  
• 
  
  

---

Return to Pre-ESIP Workshop: About
Return to Information Quality Cluster Homepage