Course Overview

Research Data Management Systems is a comprehensive training program designed to equip researchers, data managers, academic professionals, monitoring and evaluation specialists, and research institutions with the skills required to effectively manage, store, organize, secure, share, and preserve research data throughout the research lifecycle. As the volume, complexity, and importance of research data continue to grow across scientific, social science, health, environmental, agricultural, and development sectors, organizations increasingly require robust Research Data Management (RDM), data governance, research data systems, data stewardship, data quality management, open science, research compliance, and data preservation practices to maximize the value and integrity of research outputs.

The training explores modern research data management frameworks and technologies used by universities, research institutes, government agencies, NGOs, healthcare organizations, and international development programs. Participants will learn how to develop data management plans, establish research data workflows, implement metadata standards, manage research repositories, ensure data quality, and comply with institutional, donor, and regulatory requirements. The course combines theoretical foundations with practical applications using real-world research scenarios and data management systems.

Participants will gain hands-on experience in research data organization, documentation, storage, sharing, security, archiving, and governance. The course examines how effective data management systems support research reproducibility, collaboration, transparency, knowledge sharing, and long-term preservation of valuable datasets. Through practical exercises and relevant case studies, participants will develop confidence in implementing data management strategies that improve research efficiency, integrity, and impact.

The training further addresses emerging trends in research data management, including cloud-based repositories, FAIR data principles (Findable, Accessible, Interoperable, and Reusable), open data initiatives, artificial intelligence for data management, research information systems, digital preservation technologies, cybersecurity, and collaborative research platforms. Participants will develop competencies required to design and manage sustainable research data ecosystems that support innovation, compliance, and evidence-based research.

Course Objectives

1.      Understand the principles and importance of research data management systems.

2.      Develop effective research data management plans and workflows.

3.      Implement data governance, stewardship, and quality assurance practices.

4.      Organize, document, and manage research datasets efficiently.

5.      Apply metadata standards and data documentation techniques.

6.      Ensure research data security, privacy, and regulatory compliance.

7.      Utilize research repositories and data sharing platforms effectively.

8.      Support research reproducibility and open science initiatives.

9.      Develop strategies for long-term data preservation and archiving.

10.  Apply emerging technologies and best practices in research data management.

Organizational Benefits

1.      Improved research data quality, integrity, and reliability.

2.      Enhanced compliance with donor, institutional, and regulatory requirements.

3.      Better collaboration and knowledge sharing among researchers.

4.      Increased research efficiency and productivity.

5.      Improved data accessibility and reusability.

6.      Enhanced protection of valuable research assets.

7.      Stronger support for open science and research transparency.

8.      Reduced risks associated with data loss and security breaches.

9.      Improved research reporting and accountability.

10.  Increased institutional capacity for managing complex research projects.

Target Participants

·         Researchers and principal investigators

·         Research data managers and coordinators

·         Academic staff and university researchers

·         Monitoring and Evaluation (M&E) professionals

·         Data analysts and statisticians

·         Research assistants and project officers

·         Librarians and information management professionals

·         Health, agricultural, environmental, and social science researchers

·         Government and policy research officers

·         NGO and development organization staff

·         IT professionals supporting research systems

·         Graduate and postgraduate students involved in research projects

Course Outline

Module 1: Foundations of Research Data Management Systems

1.      Introduction to research data management concepts

2.      Research data lifecycle and management frameworks

3.      Types of research data and data ecosystems

4.      Roles and responsibilities in research data management

5.      Principles of data stewardship and governance

6.      Research data policies and compliance requirements

Case Study:
Developing a research data management framework for a multi-institutional research project.

Module 2: Data Planning, Organization, and Documentation

1.      Creating research data management plans (DMPs)

2.      Data organization and file management strategies

3.      Metadata standards and documentation practices

4.      Data naming conventions and version control

5.      Data quality assurance and validation procedures

6.      Research workflow management and standard operating procedures

Case Study:
Designing a comprehensive data management plan for a large-scale public health research study.

Module 3: Research Data Storage, Security, and Compliance

1.      Data storage solutions and infrastructure options

2.      Cloud-based and institutional data repositories

3.      Data security and access control mechanisms

4.      Privacy, confidentiality, and ethical considerations

5.      Regulatory compliance and data protection requirements

6.      Backup, recovery, and disaster management strategies

Case Study:
Implementing secure data storage and access protocols for sensitive research datasets.

Module 4: Data Sharing, Collaboration, and Open Science

1.      Principles of open science and open data

2.      Data sharing policies and best practices

3.      Research collaboration platforms and tools

4.      FAIR data principles and implementation

5.      Intellectual property and data ownership considerations

6.      Publishing and disseminating research data

Case Study:
Establishing a collaborative data-sharing platform for international research partners.

Module 5: Research Repositories, Preservation, and Archiving

1.      Research data repositories and digital archives

2.      Long-term preservation strategies and standards

3.      Data curation and lifecycle management

4.      Repository selection and management practices

5.      Data reuse and secondary analysis opportunities

6.      Measuring research data impact and utilization

Case Study:
Developing a digital repository and preservation strategy for institutional research outputs.

Module 6: Advanced Research Data Systems and Future Trends

1.      Artificial intelligence and automation in data management

2.      Research information management systems (RIMS)

3.      Big data and high-performance research computing environments

4.      Blockchain applications in research data integrity

5.      Emerging technologies in digital research infrastructure

6.      Future trends in research data management and open science

Case Study:
Designing an integrated research data management system that combines data governance, metadata standards, secure storage, FAIR data principles, repository management, AI-powered data discovery, and long-term preservation strategies to enhance research quality, collaboration, compliance, and scientific impact.

 

 

 

Essential Information

 

1. Our courses are customizable to suit the specific needs of participants.
2. Participants are required to have proficiency in the English language.
3. Our training sessions feature comprehensive guidance through presentations, practical exercises, web-based tutorials, and collaborative group activities. Our facilitators boast extensive expertise, each with over a decade of experience.
4. Upon fulfilling the training requirements, participants will receive a prestigious Global King Project Management certificate.
5. Training sessions are conducted at various Global King Project Management Centers, including locations in Nairobi, Mombasa, Kigali, Dubai, Lagos, and others.
6. Organizations sending more than two participants from the same entity are eligible for a generous 20% discount.
7. The duration of our courses is adaptable, and the curriculum can be adjusted to accommodate any number of days.
8. To ensure seamless preparation, payment is expected before the commencement of training, facilitated through the Global King Project Management account.
9. For inquiries, reach out to us via email at training@globalkingprojectmanagement.org or by phone at +254 114 830 889.
10. Additional amenities such as tablets and laptops are available upon request for an extra fee. The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a certificate of successful completion. Participants are responsible for arranging and covering their travel expenses, including airport transfers, visa applications, dinners, health insurance, and any other personal expenses.