Research Data Management Plans (DMPs) Best Practices Training Course

Research Data Management Plans (DMPs) Best Practices Training Course

Introduction

In today’s data-driven research environment, effective Research Data Management Plans (DMPs) are essential for ensuring data integrity, reproducibility, long-term storage, and ethical compliance. Research Data Management Plans (DMPs) Best Practices Training Course is designed to equip researchers, data stewards, project managers, and institutional leaders with the knowledge and tools to create and implement robust, FAIR-compliant (Findable, Accessible, Interoperable, Reusable) data management plans. Whether dealing with sensitive data, big data, or collaborative multi-institutional projects, this course emphasizes best practices, policies, and tools that enhance the lifecycle of research data from creation to archiving.

As funding agencies and institutions increasingly mandate DMPs, mastering their creation has become a critical research skill. This course covers the practical, technical, ethical, and legal dimensions of managing research data responsibly. With hands-on activities, case studies from various disciplines, and expert guidance, participants will learn how to design DMPs that align with funding body requirements, institutional policies, and global data-sharing standards.

Course Objectives

By the end of the course, participants will be able to:

1. Understand the principles and importance of research data management.
2. Develop a complete data management plan (DMP) aligned with best practices.
3. Apply FAIR data principles in creating DMPs.
4. Identify and categorize types of research data and metadata standards.
5. Use DMP tools and templates such as DMPTool and DMPonline.
6. Address data privacy, ethics, and legal compliance in data sharing.
7. Plan for secure storage, backup, and data preservation strategies.
8. Evaluate and select repositories for long-term data archiving.
9. Integrate DMPs into grant proposals and institutional protocols.
10. Collaborate on multi-institutional DMPs and manage stakeholder roles.
11. Conduct risk assessments for data loss, misuse, or legal exposure.
12. Monitor and review DMPs throughout the research lifecycle.
13. Communicate data sharing strategies to funders and collaborators.

Target Audiences

1. Academic researchers and principal investigators
2. Research data stewards and managers
3. University research administrators
4. Postgraduate and PhD students
5. Institutional compliance officers
6. IT specialists managing research infrastructure
7. Policy makers in research funding bodies
8. Librarians and digital archivists

Course Duration: 5 days

Course Modules

Module 1: Introduction to Research Data Management (RDM)

• Definition and scope of RDM
• Importance of RDM in scholarly communication
• Lifecycle of research data
• Overview of institutional and funder mandates
• Common challenges in RDM
• Case Study: Failed publication due to poor data management

Module 2: Components of an Effective DMP

• Structure and sections of a DMP
• Common funder requirements (NSF, NIH, Horizon Europe)
• Key considerations: documentation, formats, access
• Aligning DMPs with project goals
• Real-life DMP samples
• Case Study: NIH-funded project with exemplary DMP integration

Module 3: Applying FAIR Data Principles

• What are FAIR data principles?
• Making data findable with metadata and DOIs
• Ensuring data accessibility and licensing
• Interoperability and machine-readability
• Enhancing data reuse with proper documentation
• Case Study: Cross-institutional collaboration using FAIR data

Module 4: Legal, Ethical, and Privacy Considerations

• Data protection laws (e.g., GDPR, HIPAA)
• Managing sensitive and personal data
• Informed consent and data anonymization
• Intellectual property and data ownership
• Ethics approval process and documentation
• Case Study: Human-subject data in a multi-country study

Module 5: Tools and Technologies for DMPs

• Overview of DMPTool, DMPonline, ARGOS
• Institutional platforms and integration with grant systems
• Automating DMP updates and tracking
• Metadata standards (Dublin Core, DataCite, etc.)
• Interfacing with data repositories and catalogs
• Case Study: Using DMPTool for NSF grant success

Module 6: Data Storage, Backup, and Preservation

• Short-term vs long-term storage solutions
• Cloud storage vs institutional repositories
• Version control and data integrity
• Backup scheduling and redundancy protocols
• Data preservation formats and timelines
• Case Study: Data recovery in environmental science research

Module 7: Sharing, Reusing, and Archiving Data

• Data licensing (CC0, CC-BY, etc.)
• Choosing appropriate data repositories (Zenodo, Dryad, etc.)
• Embargo periods and controlled access
• Citing data and assigning DOIs
• Encouraging data reuse and impact tracking
• Case Study: Open-access dataset with high citation impact

Module 8: Monitoring, Reviewing, and Updating DMPs

• Importance of DMP reviews
• Integrating DMP monitoring into project management
• Feedback loops between teams and institutions
• Adapting DMPs to evolving research or funding conditions
• Reporting and audit trails
• Case Study: Adaptive DMP in longitudinal medical study

Training Methodology

• Interactive lectures with practical examples
• Hands-on exercises using real DMP templates and tools
• Group work and peer review of DMP drafts
• Case-based learning across disciplines
• Facilitated Q&A with research data management experts
• Guided walk-through of DMP submission to funding platforms

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.