The organization, names and labels of variables contribute significantly to making your data files understandable and comprehensible. This is not only important when you share your data with others, or collaborate on data, but also for yourself. Abstract filenames without labels make it difficult overtime to find out what that particular variable stood for.
In general, there are three strategies for naming variables:
Labels of variables give a short description of the variable. In many cases, a clear label is indispensable for understanding the variable
Sometimes it is necessary to assign labels to the values of variables. This is not necessary for continuous variables such as age, height, or weight because these values speak for themselves. However, this is not the case for nominal and ordinal variables. A nominal variable such as gender has two values and is usually represented as an 0 or 1 in the data file. In that case, it is necessary to assign labels to these values ('0=man; 1=woman'), so that you and any re-users of the data know which value represents which gender. The same goes for ordinal variables with, for example, an “agree/disagree” scale from 1 to 5. By assigning labels to the values it becomes clear that 1 stands for 'completely disagree' and 5 stands for 'completely agree'.