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Version 1.0 2023-01
Contributors
Challenges for Clinical data and why this guide will help
Who this guide is designed for
Context of this guide: Clinical Data Challenges and Opportunities
Purpose of this guide: From Awareness to Making Pragmatic Choices
Scope of this guide
Clinical study data can be big!
A multiplicity of clinical data types
How the principles FAIR can bring value to Clinical data
Realising value for clinical trial data
Realising value from Real World Data
Clinical study design and FAIR implementation
Clinical study data process through the FAIR lens
Planning and preparation of the study
Approvals, permissions and agreements
Study conduct and data collection
Data curation / harmonisation processes
Analysis of data
Reporting
Data follow-up activities
Common Data Models for clinical data
CDISC
OHDSI
FHIR®
Shared model of clinical study design and outcome
The BRIDG initiative
Semantic integration with Semantic Web standards
Clinical Trial Registries
Background
FAIR assessment of clinical trial registries
Clinical Real World Data
Other registries and efforts
Quality and Governance of Clinical data
Quality of clinical data and metadata
Clinical data governance at the study level
Infrastructure, training and culture for clinical FAIR data
Infrastructure
Education and training
Data-centric culture and the FAIR mindset