Difference between revisions of "FAIR Dataset Quality Information"

From Earth Science Information Partners (ESIP)
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<big>'''Portfolio Management and Repository Certifications ''' </big><br>  
 
<big>'''Portfolio Management and Repository Certifications ''' </big><br>  
* NGDA Data Lifecycle Maturity Model (LMM)[http://commons.esipfed.org/sites/default/files/2014_FGDC_BaselineAssessment_AGUPoster_PeltzLewisBlakeColemanJohnstonDeLoatch.pdf  
+
* NGDA Data Lifecycle Maturity Model (LMM)  
 +
[http://commons.esipfed.org/sites/default/files/2014_FGDC_BaselineAssessment_AGUPoster_PeltzLewisBlakeColemanJohnstonDeLoatch.pdf  
 
(Peltz-Lewis et al. 2014)] <br>
 
(Peltz-Lewis et al. 2014)] <br>
 
* WDS-DSA-RDA core trustworthy data repository requirements [https://doi.org/10.5281/zenodo.168411 (Edmunds et al. 2016; updated 2019)]<br>
 
* WDS-DSA-RDA core trustworthy data repository requirements [https://doi.org/10.5281/zenodo.168411 (Edmunds et al. 2016; updated 2019)]<br>
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<big>'''Data Quality Management Framework ''' </big><br>  
 
<big>'''Data Quality Management Framework ''' </big><br>  
 
* High Quality Global Data Management Framework for Climate Data (HQ-GDMFC) [https://library.wmo.int/doc_num.php?explnum_id=10197 (WMO 2019)]<br>  
 
* High Quality Global Data Management Framework for Climate Data (HQ-GDMFC) [https://library.wmo.int/doc_num.php?explnum_id=10197 (WMO 2019)]<br>  
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* Implementation of a Data Management Quality Management Framework at the Marine Institute, Ireland [https://link.springer.com/article/10.1007/s12145-019-00432-w (Leadbetter et al. 2019)] <br>
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* The Data Quality Challenge. Recommendations for Sustainable Research in the Digital Turn [http://www.rfii.de/download/the-data-quality-challenge-february-2020/ (Rat für Informationsinfrastrukturen 2020)]<br>
 
* Conceptual Enterprise Framework for Managing Scientific Data Stewardship [https://datascience.codata.org/articles/10.5334/dsj-2018-015/ (Peng et al 2018)]<br>  
 
* Conceptual Enterprise Framework for Managing Scientific Data Stewardship [https://datascience.codata.org/articles/10.5334/dsj-2018-015/ (Peng et al 2018)]<br>  
 
* <br> <br>
 
* <br> <br>

Revision as of 11:54, June 5, 2020

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.

Resources

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:

  • Completeness: NCEI Collection-Level Metadata Rubric Tool
  • FAIR metadata checklist – NCEAS MetaDIG
  • Metadata checklist of LTER network data management system (O’Brien et al. 2016)

Portfolio Management and Repository Certifications

  • NGDA Data Lifecycle Maturity Model (LMM)
[http://commons.esipfed.org/sites/default/files/2014_FGDC_BaselineAssessment_AGUPoster_PeltzLewisBlakeColemanJohnstonDeLoatch.pdf 

(Peltz-Lewis et al. 2014)]

FAIR Data Principles

Organizational Challenges & Approaches

Data Quality Management Framework

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