The reuse of data should use the concepts and techniques for knowledge transfer

Today data are often provided by simply telling the person (or institute) what the data are. A receiver of data can ask for a clarification. In response the receiver gets a definition of the data. Today most definitions consist of the complete set of necessary characteristics.  

This shows that today there is little or no attention for the understanding of the data by the receiver. If the focus was on the understanding by the receiver, techniques for knowledge transfer would have been used. To give you an idea about these techniques, the receiver of the data would have been provided with:
  • the goal of the data: what do the data allow someone to do?
  • the trigger that leads to creating the data
  • the conditions under which the data are created
  • the target audience: for whom are the data meant?
  • the criteria, when are the data considered to be of sufficient quality?
  • examples and non-examples of the data
  • exercises that teach the receiver of the data how to create these data himself
  • tests that show the receiver whether he has understood the data
  • etc. (advance organizers, pictures, internships, ....)
When we do not consider data as a reflection of the real world, when we consider data as knowledge, than we should use techniques for knowledge transfer in order to be able to (re)use these data in an effective and correct manner.