In doing data science, we focus less on the process of acquiring the data, and more on what we do after it’s collected. That said, it is critically important to understand what your data are — which includes what was being measured, and how it was measured. Often we might also care about why the data were measured, but not necessarily; increasingly, researchers are making data sets openly available (e.g., through public repositories such as OSF.org not only for purposes of transparency, but with the expectation that other researchers might be able to use the data in ways other than originally intended, to generate new insights.
Our approaches to working with the timing of action potentials of single neurons, Morris water maze behavior in rats, human reaction times, and functional MRI data will necessarily be different, according to our understanding of what was measured, the underlying physiological properties, and our goals — the meaning we are trying to derive from the data. At the same time, all of these are ultimately measurements stored in files on a computer, and data science is about learning the core skills that allow you to work with any of these types of data and try to find meaning in them.