FAIR Principles for Research Data Management Training Course

FAIR Principles for Research Data Management Training Course

Introduction

In today’s data-driven research environment, managing sensitive data ethically and effectively is critical. FAIR Principles for Research Data Management Training Course is designed to equip researchers, data stewards, and institutions with the tools and techniques necessary to apply the FAIR (Findable, Accessible, Interoperable, Reusable) principles in managing sensitive research data. With the rise of studies involving vulnerable populations, private information, and culturally sensitive materials, ethical and secure data management must be central to research design, analysis, and sharing processes.

The course combines theory, policy, and practical application, guiding participants through the lifecycle of sensitive data – from ethical planning and consent, through anonymization and secure storage, to sharing under strict compliance frameworks. It covers current best practices, emerging standards, and real-world case studies, helping learners confidently implement FAIR-aligned workflows that prioritize privacy, transparency, compliance, and integrity in sensitive data research.

Course Objectives

1. Understand the FAIR principles in the context of sensitive data.
2. Identify ethical challenges in researching sensitive topics.
3. Apply GDPR-compliant strategies for managing personal data.
4. Implement anonymization and de-identification techniques.
5. Design informed consent processes with FAIR-compliance in mind.
6. Integrate data protection by design methodologies.
7. Develop metadata schemas for restricted access datasets.
8. Manage data sharing policies for controlled access.
9. Align with open science while protecting sensitive content.
10. Establish data stewardship protocols for sensitive research.
11. Evaluate tools for secure data storage and transmission.
12. Use risk assessment frameworks for sensitive datasets.
13. Produce data management plans (DMPs) for ethical approval.

Target Audience

1. Academic researchers working with vulnerable groups
2. Clinical trial investigators and health data managers
3. Social science and humanities researchers
4. Data protection officers (DPOs)
5. Institutional review board (IRB) members
6. Research data management professionals
7. Librarians and digital archivists
8. Policy makers and compliance officers

Course Duration: 5 days

Course Modules

Module 1: Introduction to FAIR Principles and Sensitive Data

• Overview of FAIR data principles
• Defining sensitive and personal data
• Challenges in applying FAIR to restricted datasets
• Importance of transparency and accountability
• Key ethical considerations
• Case Study: FAIRifying sensitive health survey data

Module 2: Ethical Foundations in Sensitive Research

• Principles of research ethics (autonomy, beneficence, etc.)
• Informed consent for complex data uses
• Engaging vulnerable populations
• The role of IRBs and ethical oversight
• Navigating cultural sensitivity in research
• Case Study: Ethics in researching survivors of trauma

Module 3: Legal and Regulatory Compliance (e.g. GDPR)

• Understanding data protection laws (GDPR, HIPAA, etc.)
• Lawful bases for data processing
• Cross-border data transfer restrictions
• Rights of data subjects in research
• Consent versus legitimate interest in research
• Case Study: GDPR challenges in multinational studies

Module 4: Data Anonymization and Risk Management

• Identifying direct and indirect identifiers
• Anonymization vs. pseudonymization
• Re-identification risks and prevention
• Statistical disclosure control methods
• Risk-benefit analysis for data sharing
• Case Study: Anonymizing social media datasets

Module 5: Metadata and Documentation for Sensitive Data

• Creating meaningful metadata for restricted datasets
• Use of persistent identifiers (PIDs)
• Access restrictions and license metadata
• Data dictionaries and codebooks
• Standardizing metadata for interoperability
• Case Study: Metadata for a sensitive refugee database

Module 6: Secure Storage, Access, and Infrastructure

• Encrypted storage best practices
• Tiered access controls and user authentication
• Trusted Research Environments (TREs)
• Institutional policies on storage and backup
• Secure transfer protocols
• Case Study: Implementing a secure enclave for health data

Module 7: Data Sharing and Reuse Strategies

• Controlled access repositories
• Applying FAIR within limits of sensitivity
• Developing data use agreements (DUAs)
• Licensing sensitive datasets
• Data sharing statements in publications
• Case Study: Reuse of anonymized educational records

Module 8: Creating a FAIR-Compliant Data Management Plan (DMP)

• Components of a sensitive-data DMP
• Integrating FAIR principles into DMPs
• Using DMP tools (DMPonline, Argos)
• Embedding lifecycle planning
• Monitoring and updating DMPs
• Case Study: DMP for a longitudinal mental health study

Training Methodology

• Interactive lectures with expert facilitators
• Hands-on workshops for anonymization and metadata creation
• Group exercises to critique and improve DMPs
• Case-based discussions with real-world scenarios
• Guided tool demonstrations (DMP tools, metadata standards)
• Self-assessment quizzes and feedback sessions

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.