## 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.

All of these assets together form a 'Data Package'. This is the final product of your research data. This package should include the data you collected, analysed, and processed, along with the contextual information that describes it (metadata). It should also contain the guidelines and protocols you followed, details about how the data was collected, its potential future used, and any other information needed to understand, verify, or reuse the dataset. Preparing a well-documented data package is especially important when it comes to archiving.

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

Research data is often visualized as numerical data; however, research data is full of variety and can differ immensely. It includes all physical and digital information collected, observed, generated, or created for analysis. Additionally, administrative documents such as key files, informed consent forms, and interview guides are essential components of research data that contribute to the FAIRness of a project.

FAIRness refers to your data being Findable, Accessible, Interoperable, and Reusable. You will often see this term referred to when discussing research data. The goal of FAIR data is to ensure transparency, longevity, and availability of research data. Applying these four principles to your research data increases the usability of your data and ensures it remains valuable now and into the future. Good data management contributes to the FAIRness of your data. For more information on what FAIR means to your research check out this resource.