While working on your thesis, you need to document what happens to your data, that is, you describe what you do with the data.
Imagine that someone else takes over your data and is supposed to do the same thing you did. They cannot know what you intended when you started processing your data. You may have calibrated an instrument to measure your data, arranged the data in a certain way, or written a field diary about the origin of the data. All these notes, quality checks, device settings, and readme files are documentation. Especially if you intend to share your data at the end of your project, or if you are writing your thesis as part of a larger research project, it is of utmost importance that you document carefully, otherwise no one will be able to understand your data when you yourself are no longer in the picture. Solid documentation also provides the opportunity to verify and check your results! You can think of documentation as the recipe that makes it possible for yourself and others to repeat the study and arrive at the same results.
Even if you don't plan to share your data, it's important to document it to some extent. Writing a thesis can take months. At the end of the process, will you remember what you thought and did at the beginning? If for some reason you have to take a break from your thesis for several weeks or even months, will you remember when you resume your work how you thought about your data? If you document what you do with your data, you know your thought process from start to finish, and don't have to guess.
One manner of documentation is to organize your data files in a systematic way. Use version control to keep track of older and newer files, create logical folder structures for different data, tag files to make them easier to find.
An important aspect of data management is organizing files. The video presents various aspects of file organization (naming, folder structure, and version control) as well as tips and best practices.
CC BY UGent Open Science
You need to create metadata, which means “data about data,” about your data. This means that you write out the information needed to understand and interpret the data: for example, the origin of the data, authors, time, place, methods, and subject words. The idea here is that your research should be reproducible, which means another researcher should be able to perform the same analysis and arrive at the same answer. For this to be successful, the researcher must understand everything that has been done with the data, which means the data must be documented systematically and in a comprehensible way. You also create metadata for yourself; so that you yourself remember the circumstances of how, when, and where your data was created.