Difference between revisions of "FAIR Dataset Quality Information"
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<big>'''Existing Fitness for Purpose Assessment approaches Through the Full Life Cycle of Earth Science Datasets:''' </big><br> | <big>'''Existing Fitness for Purpose Assessment approaches Through the Full Life Cycle of Earth Science Datasets:''' </big><br> | ||
* Overview of Maturity Models [https://datascience.codata.org/articles/10.5334/dsj-2018-007/ Peng 2018]; [https://www.youtube.com/watch?v=4mmPMYXQg48&list=PLG25fMbdLRa6Y2GLFUKhuuovSTTC2zHAE&index=3&t=4s Peng et al. 2019a: Recording)]<br> | * Overview of Maturity Models [https://datascience.codata.org/articles/10.5334/dsj-2018-007/ Peng 2018]; [https://www.youtube.com/watch?v=4mmPMYXQg48&list=PLG25fMbdLRa6Y2GLFUKhuuovSTTC2zHAE&index=3&t=4s Peng et al. 2019a: Recording)]<br> | ||
| − | * <br> | + | * Measurement system: <br> |
| − | * <br> | + | * Production system: <br> |
| − | * <br> | + | * Scientific quality: <br> |
| − | * <br> | + | * Product quality: <br> |
| − | * <br> <br> | + | * Stewardship quality: <br> |
| + | * Service quality:<br> <br> | ||
<big>'''Dataset-level metadata quality: ''' </big><br> | <big>'''Dataset-level metadata quality: ''' </big><br> | ||
Revision as of 12:42, June 4, 2020
Document
This is the document for community guidelines for 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 - under development
Multi-dimensions of Data and Information Quality:
- Quality Attributes for Data Consumers (Wang and Strong 1996)
- Multi-dimensions of Data and Information Quality (Ramapriyan et al. 2017)
Existing Fitness for Purpose Assessment approaches Through the Full Life Cycle of Earth Science Datasets:
- Overview of Maturity Models Peng 2018; Peng et al. 2019a: Recording)
- Measurement system:
- Production system:
- Scientific quality:
- Product quality:
- Stewardship quality:
- Service quality:
Dataset-level metadata quality:
Portfolio Management and Repository Certifications
FAIR Data Principles
Organizational Challenges & Approaches
Data Quality Management Framework
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