This course provides a comprehensive understanding of data management practices essential for research. It covers the lifecycle of data, from planning and collection to storage, sharing, and long-term preservation. Participants will learn how to organize, document, and secure research data to ensure integrity and reproducibility. The course also emphasizes compliance with data management policies, ethical considerations, and the use of appropriate tools for effective data management in research.
Course Duration
5 Days
Who Should Attend
- Researchers and academics involved in data-driven research projects.
- Graduate students and postdoctoral researchers seeking to enhance their data management skills.
- Data managers and research coordinators responsible for overseeing research data.
- IT professionals supporting research data management.
- Anyone interested in improving their understanding of research data management practices.
Course Objectives
By the end of this course, participants will be able to:
- Understand the importance of data management in research
- Develop a robust data management plan
- Implement effective data organization and documentation strategies
- Ensure data quality, integrity, and security
- Utilize appropriate data storage and backup solutions
- Comply with relevant data management and ethical guidelines
- Collaborate effectively within research teams on data management
- Prepare research data for sharing and preservation
Course Outline:
Module 1: Foundations of Data Management
- Introduction to data management concepts and principles
- The research data lifecycle
- Data management planning
- Data governance and stewardship
Module 2: Data Collection and Organization
- Data collection methods and instruments
- Data cleaning and preprocessing
- Data coding and categorization
- Data version control and management
Module 3: Data Storage and Backup
- Data storage options (local, cloud, hybrid)
- Data backup strategies and disaster recovery
- Data security and privacy measures
- Data access control and permissions
Module 4: Data Analysis and Visualization
- Data exploration and analysis techniques
- Data visualization tools and best practices
- Data interpretation and reporting
- Research data reproducibility
Module 5: Data Sharing and Preservation
- Data sharing principles and practices
- Data citation and metadata
- Data archiving and long-term preservation
- Legal and ethical considerations for data sharing
