This module explores the value of effective research data management and the role it plays in supporting high-quality, transparent, and reusable research. You will examine the benefits of planning and managing data throughout a project, learn about shared responsibilities within research teams, and discover best practices for creating data management plans. The module also introduces the FAIR and CARE principles, helping you develop responsible approaches to organizing, sharing, and stewarding research data.

This module focuses on putting data management principles into practice through effective planning and organization. You will explore tools and resources that support the creation of robust data management plans, learn strategies for managing data storage, backups, and ethical considerations, and develop practical skills in file organization, version control, and collaborative record-keeping. By the end of the module, you will be better prepared to manage research data efficiently and consistently throughout your project.

This module explores the practical and ethical considerations involved in storing, backing up, and protecting research data throughout its lifecycle. You will learn how to select appropriate storage solutions, implement effective backup strategies, maintain data integrity, and apply techniques such as anonymization and encryption to safeguard sensitive information. The module also examines how responsible data protection practices support ethical research and ensure the long-term security and reliability of your data.

This module explores the benefits, responsibilities, and practical considerations of sharing research data. You will examine how data sharing supports transparency, collaboration, and research impact, while learning how to protect sensitive information and comply with legal and ethical requirements. The module also introduces key concepts such as open access, licensing, and copyright, helping you share data confidently and responsibly while maintaining appropriate control over its use.

This module introduces the concept of messy data and explores the common issues that can arise when collecting, recording, storing, and managing research data. You will learn how to recognize problems in both quantitative and qualitative datasets, identify common data errors, and understand the importance of cleaning and organizing data early in the research process to support accurate analysis, reliable results, and efficient data management.

This module explores the importance of clean, well-structured data and the impact that poor data quality can have on research outcomes. You will learn how messy data can complicate analysis, reduce reliability, and limit the usability of datasets, while gaining an appreciation of the FAIR principles and the role of data cleaning in producing accurate, sustainable, and reusable research data. The module also highlights the value of data cleaning as a practical and highly transferable research skill.

This module introduces the fundamentals of cleaning quantitative data using spreadsheet software. You will explore common spreadsheet tools, learn practical techniques for identifying and correcting data issues, and develop skills for maintaining clean, well-structured datasets. The module also covers key spreadsheet concepts, data formatting best practices, and quality control processes that help ensure data remain accurate, consistent, and ready for analysis.

This module introduces the importance of research planning as a foundation for effective data collection, analysis, and decision-making. You will learn how to develop clear and focused research questions, select appropriate research methods, and identify potential challenges before they arise. By planning your research carefully from the outset, you can improve the quality of your study, avoid common pitfalls, and make more informed choices throughout the research process.

This module explores the key stages of research design and data collection, highlighting how careful planning and methodological choices can improve the quality and reliability of research outcomes. Through practical examples and real-world scenarios, you will learn how to select appropriate data collection methods, recognize common challenges, and implement strategies that help you gather robust, high-quality data while avoiding avoidable errors in the research process.

This module explores common mistakes and challenges that can arise during data analysis and their impact on research quality and credibility. You will learn how to identify potential sources of error, apply practical strategies to detect and prevent mistakes, and develop a more critical and systematic approach to analyzing data. By understanding how errors can influence findings and interpretations, you will be better equipped to produce accurate, reliable, and trustworthy research outcomes.

This module focuses on the final stage of the research process: interpreting and presenting data accurately and effectively. You will explore common pitfalls in data interpretation, including the misuse of statistical methods, weak analytical models, and misleading visualizations. The module will help you develop the skills needed to critically evaluate findings, communicate results clearly, and present data in ways that support valid, reliable, and meaningful conclusions.

In addition to the glossary you’ll find woven throughout the course, you can find the full glossary collated in one place here.