## Summary

Within a research project, individual pieces of data or documentation are referred to as Data Assets. These assets evolve throughout the research process, for example, when raw data is cleaned or transformed into a new version. Properly tracking and managing these assets is essential for maintaining data integrity.

The final collection of all data assets forms the Data Package, which includes:

- Collected, analysed, and processed data
- Metadata that describes the data
- Research protocols and guidelines
- Information on data collection and potential future use

A well-organized data package is key to effective archiving and future reuse. To streamline data management, data assets can be categorized into four groups: Raw Data, Processed Data, Analyzed Data, and Other Data. Proper organization of these assets ensures clarity, reproducibility, and long-term usability of research data.

#### Task 1:

Think about the data assets you will collect for your research project.

1. List all the data assets you will create and collect for your project (once again, the more detail the better!).
2. Categorise them into raw, processed and analysed data. Think of the data assets which will transform throughout your project and the ones which will remain consistent.

Having a complete list of data assets helps in the later stages of the data management plan and gives you an overview of your data throughout the entire research lifecycle.