FAIR data, FAIR Africa, FAIR world : internationalism of the health data space / edited by Mirjam Van Reisen, Samson Yohannes Amare, Lauren Maxwell & Munyardadzi Mawere.

Format:

Book

Contributor:

Reisen, Mirjam van, editor.

Amare, Samson Yohannes, editor.

Maxwell, Lauren, editor.

Mawere, Munyaradzi, editor.

Language:

English

Subjects (All):

International relations.

Medical informatics--Africa.

Medical informatics.

Public health--Africa--Data processing.

Public health.

Physical Description:

1 online resource (745 pages)

Place of Publication:

Bamenda, Cameroon : Langaa RPCIG, [2025]

Summary:

FAIR Data FAIR Africa FAIR World: The Internationalisation of the Health Data Space is a pivotal work that highlights the transformative power of FAIR (Findable, Accessible, Interoperable, Reusable) data principles in global health.

Contents:

Front cover

Copyright

Title page

Editorial Note and Disclaimer

Contents

Foreword

Acronyms

1 | FAIR Data, FAIR Africa, FAIR World: The Internationalisation of the Health Data Space

Abstract

From digital colonialism to data sovereignty

The building blocks of knowledge: Semantic data

Solid Pods, edge computing and trusted sources of truth

Autonomy, FAIR data and the choreography of access

The African Health Data Space

VODAN: The Value-driven Ownership of Data and Accessibility Network

FAIR Implementation Network: GO TRAIN, GO BUILD and GO CHANGE

FAIR Data, FAIR Africa, FAIR World: The internationalisation of the Health Data Space

Introduction to the chapters

Conclusion

References

2 | Bridging Borders with FAIR Data: Transforming Digital Ecosystems for Maternal Health and Public Health Surveillance in Africa

Introduction

Methods

Study location

Study timeline

Co-design approach

Reusing existing tooling for open science

Data selection

Regulatory and privacy preservation concerns

Relevance of the study

Analytical framework

Workflow specifications and requirements

Platform development

Findings

Facility-specific platform development

Figure 1. VODAN Africa architecture for FAIR infrastructure in health facilities

Templates for controlled vocabulary, semantic data model and linkage

Figure 2. New vocabulary creation through CEDAR

Repository as knowledge graphs

Access and reuse of FAIR data

Figure 3. Platform development and FAIRification approaches followed in creating the VODAN Africa platform

Integration and development of platform components

Bulk upload

Figure 4. CSV file BulkUpload process of VODAN platform.

Figure 5. Tool to automatically load prepared templates from the central CEDAR platform onto the local installation

Data visualisation component

Figure 6. Screenshot from the external dashboard - the VODAN community dashboard

Remote queries component

Terminology service

Extract transform load component

Federated learning infrastructure

HMIS linkage component

Figure 7. VODAN platform to DHIS2 interoperability workflow

DHIS2 JSON template data from the data storage component: Document database and triple store

Varying modus operandi for deployment

Discussion

Development of FAIR Supporting Resources

Engineering ethnography

Theoretical insights for further research

Design of the overall platform based on data visiting

Table 1. A comparison of data sharing and data visiting

Engineering of tools and standardisation of common data models

Conditions for deployment in varying context situations

3 | Introducing Data Sovereignty Over Patient Data: Patient Data Ownership in Residence of Health Facilities in Kenya

Theoretical considerations

Sovereignty of digital data by countries, communities and entities

FAIR-OLR: An operational definition of data sovereignty

Context: The legal embedding in Kenya

Research approach

Implementation of the study: The VODAN research group

Preparation for implementation of the study

Study period

Legal and ethical considerations

Data collection

Data analysis

The legacy of DHIS2 and KHIS

Implementation of the FAIR-OLR infrastructure

Figure 1. VODAN-Architecture for a One-Data Entry Machine-Actionable Semantic Curation for a Multiple Functionalities Architecture Based on Queries through Data-Visiting of Federated Local Depositories in AllegroGraph.

Technical assessment of the deployment of the FAIR-OLRsystem

Control over the data

Control over data visiting

Feasibility in low-resource settings

Conclusions by the technical team

Perceptions of results from health facilities

Reconsidering DHIS2

Lack of local data storage of data generated in the health facility

Difficulties in using the data generated at the health facility within the health facility

Lack of patient-centred data curation

Bias of data use at higher echelons of the health system

Concerns about data quality

Lack of recognition of data concerning patients as data subjects

Towards an African Health Data Space

Figure 2. Patient Data reposited in Health Facilities, visitable under permission for computations carried out in the Health Data Space

Figure 3. VODAN architecture of a Federated Health Data Space

4 | GO TRAIN: A Protocol for Metadata Creation for the FAIRification of Patient Data Health Records

Methodology

Location of the study

Study design

Reasoning for the focus on patient data for FAIRification

Timeline of the study

Input by experts

GO FAIR Foundation

Data Stewardship Wizard (DSW)

CEDAR (Center for Expanded Data Annotation and Retrieval)

Comparing Go FAIR Foundation, DSW and CEDAR

Table 1. Differences between GO FAIR Foundation, DSW and CEDAR

Data collection and analysis

Purpose of the analysis

Theoretical framework: FAIRification of data

Table 2. Categories of FAIR principles

Figure 1. FAIRification process

Table 3. Comparing terminology, vocabulary, ontology and semantic data

(i) FAIRification for data federation: Proof of concept

Figure 2. Steps towards Proof of Concept of patient data in federated data-stores.

Design of a first protocol to produce FAIR granular patient data

(ii) Evaluation: Lacking ownership over data handling

Positive requirements for FAIRification of data-handling

Towards a standard for health data handling: 'Ownership'

(iii) A decision on a first reference architecture of VODAN

R1: One-time FAIR patient data production

R2: Multi-pronged functionality

R3: Functionality for patient's health and wellbeing

R4: Visualisation of the data in the health facility

R5: Visualisation of the data in the VODAN community

R6: Repository in a triple store

R7: FAIR Data Point

R8: Bulk upload

R9: DHIS2 compliance

R10: Federation of the CEDAR Workbench

Figure 3. VODAN Architecture of Patient data Handling in the Health Facilities (2021)

Handling of multiple and parallel data catalogues in a health facility

(iv) Design of a De Novo FAIR protocol through CEDAR

Creation of a template for data input structuring

Figure 5. De NOVO FAIRification process supported by the CEDAR Workbench

Creation of semantic data and selection of available ontologies

Figure 6. BioPortal mini-service in the metadata back-end functionality of FAIR data handing template creation

Creation of new metadata

Figure 7. Creation of new ontologies

(v) Training of the protocol and certification requirements

(vi) Evaluation of the FAIR data production

1. Integration with existing resources

2. Integration of FHIR

3. Lack of relevance of the DHIS2 reporting format

Feasibility and relevance of identified requirements

R6: Repository in a triple store.

R7: FAIR Data Point

Relevant and feasible requirements (R1-R8)

Relevance of GO TRAIN in Achieving R1-R8

Key contributions of GO TRAIN to each requirement

R3: Patient health and wellbeing functionality

R4 &amp

R5: Data visualisation at health facilities and within the VODAN community

R6, R7, R8: Enhancing FAIR maturity

Sustainability through GO TRAIN

Contrast with GO CHANGE for R9 and R10

5 | De Novo FAIRification: A Literature Review

Research design

Relevance

Ethical, regulatory, and data management considerations

Theoretical framework

Related literature

Figure 1. The difference between post-hoc FAIRification and De Novo FAIRification

Figure 2. Overview of the method

Results

Figure 3. Academic searching result of the De Novo FAIRification

Figure 4. Complete literature review result of the De Novo FAIRification

Figure 5. Result of progressive literature review extension

6 | Federating Tools for FAIR Patient Data: Strengthening Maternal Health and Infectious Disease Surveillance from Clinics to Global Systems

Context and location of the study

Ethical and regulatory considerations

Requirements and specifications of the software functionalities

Reference architecture and engineering teams

Figure 1. Reference architecture for the data-handling process

Role of the engineering teams

The CEDAR Workbench

Metadata and template creation

Integration with controlled vocabularies.

FAIRification background services.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Other Format:

Print version:

ISBN:

9789956003709

9956003700

OCLC:

1517038952