Difference between revisions of "FAIR Dataset Quality Information"
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== Document == | == Document == | ||
− | This is the document for community guidelines | + | This is the document for community guidelines of consistently curating and representing dataset quality information, in line with the FAIR principles. |
= Overview = | = Overview = |
Revision as of 12:53, June 4, 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 - 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:
- Completeness: NCEI Collection-Level Metadata Rubric Tool
- FAIR metadata checklist – NCEAS MetaDIG
- Metadata checklist of LTER network data management system
Portfolio Management and Repository Certifications
- NGDA Data Lifecycle Maturity Model (LMM)
- WDS-DSA-RDA core trustworthy data repository requirements
- USGS Trusted Data Repository Checklist
FAIR Data Principles
- FAIR Data Principles
- RDA FAIR Data Maturity Indicators
Organizational Challenges & Approaches
- NASA’s ESDSWG Data Quality Working Group Recommendations
- Gaps in Essential Climate Variables Assessment
- FAIR and Data Management for a Multidisciplinary Research Center
Data Quality Management Framework
- High Quality Global Data Management Framework for Climate Data (HQ-GDMFC)
- Conceptual Enterprise Framework for Managing Scientific Data Stewardship
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