## What is a data asset?

When we think of research data, we often only consider numerical data; however, this does not truly reflect the diversity of research data. Research data includes all information (physical and digital) collected, observed, generated, or created for the purpose of analysis to produce and validate original research results. Administrative documentation such as a key file, informed consent and interview guides should also be recognized as important elements of research data.

The term 'Data Asset' refers to an individual piece of data or documentation within a project. This can be a CSV file containing your raw data, the transcripts of an interview, or the results of an EEG scan. These data assets may transform throughout your project. For example, after you have cleaned a data set, you will then have a new data asset. This is considered a new data asset and should be treated as one. As you progress through your project, you will create and collect various data assets.

The accumulation of all these data assets creates a 'Data package'. This is the final product of your research data, it should be a collection of the data you collected, analysed and processed; the contextual information that describes the data (metadata), the guidelines and protocols you followed, information on how the data was collected and what it can be used for in the future and any other information which would be relevant to understand, test and, reuse your dataset. When it comes to archiving, this will be important.

Think back to task 1 of the previous chapter, can you think of the data assets which will be created during each task?

Throughout the lifetime of a project, many data assets will be collected and created, the organisation of these data assets is the key to good data management. You can categorize your data assets into four groups:

1. Raw Data
2. Processed Data
3. Analysed Data
4. Other Data