Good research data management is important for making research transparent, reproducible, and reusable. In my project, different types of data may be generated over time, including raw data, processed data, protocols, analysis files, and figures. Without clear organization and documentation, it can become difficult to understand or verify the results later.

This module helped me understand that a Data Management Plan can be useful for deciding how data will be collected, stored, backed up, documented, and shared. For my own research practice, I would like to improve file naming, folder organization, metadata documentation, and the separation of raw and processed data. I also think the Data Management Plan should be updated regularly, because research projects often change over time.