Training on Data Management for Research

Module 1: Foundations of Data Management

• Core concepts and principles of data management
• The research data lifecycle: creation → analysis → preservation
• Fundamentals of data management planning (DMPs)
• Roles in data governance and stewardship
• Case Study:How a global health research institution improved data reliability through structured Data Management Plans.
• Practical Exercise:Draft a simple Data Management Plan for a sample research project.

Module 2: Data Collection and Organization

• Quantitative and qualitative data collection methods
• Cleaning, validation, and preprocessing techniques
• Coding, categorization, and documentation
• Version control for evolving datasets
• Case Study:Managing large field data collections in multi-location research studies.
• Practical Exercise:Clean and organize a raw dataset; apply version control to track changes.

Module 3: Data Storage and Backup

• Storage solutions: local servers, cloud systems, hybrid models
• Backup strategies and disaster recovery planning
• Data security protocols and privacy safeguards
• Managing access rights and user permissions
• Case Study:How a research organization avoided massive data loss using layered backup systems.
• Practical Exercise:Design a secure storage and backup architecture for a research project.

Module 4: Data Analysis and Visualization

• Exploratory analysis techniques for structured and unstructured data
• Visualization best practices using tools such as Excel, Power BI, R or Python
• Interpreting and presenting research findings
• Ensuring reproducibility and transparency in analysis
• Case Study:Transforming complex datasets into actionable insights in public policy research.
• Practical Exercise:Analyze a provided dataset and build a visualization dashboard.

Module 5: Data Sharing and Preservation

• Principles for ethical and responsible data sharing
• Metadata standards and data citation practices
• Preparing data for archiving and long-term preservation
• Legal and ethical frameworks: consent, IP, licensing
• Case Study:Successful data sharing in collaborative international research initiatives.
• Practical Exercise:Prepare a dataset for public sharing, including metadata, documentation, and preservation format.