Life science projects are becoming more and more data-intensive, with both data volume and data complexity increasing. This calls for proper data management planning. A research data management plan (DMP) can be compared with the checklist that pilots use before each flight: it makes sure that you do not forget essential steps and that you base important decisions on well-informed choices.
Nowadays, research data sets are often very large. In addition, the data are so rich that researchers can use it for various types of analyses, which each may involve multiple steps. Special tools are required to get an oversight of the data sets and the analysis pipelines.
At the same time, scientists are increasingly willing to make their research data available for research by others. This is also rapidly becoming a condition to obtain research funds. For instance, X-Ray images of the spine that have been collected for a study of the backbone may be re-used to study the state of the aorta. Such data re-use requires well-documented data that can be located by other scientists. In addition, it often requires extensive documentation of the data collection process, with proper registration of all operations. This all makes it important to carefully manage the processes of data acquisition and analysis (i.e., ‘data management’).