Being able to visualize the life cycle of your research data and being able to talk about the consequences of choices is the main goal of this toolkit. Generic life cycle models and data management plans can easily be to broad to grasp the complexity of handling data. This can be loosing important part of your data, because you do not have a copy of your raw data, or talking about metadata as a static thing, but metadata is added or removed throughout the entire life cycle depending on the context of the data.
The intention is that you make a print of the toolkit, or use a white board or similar to visualize your data flow.
The toolkit is divided into three parts; data objects, processes and systems. Data objects are the basic puzzle element of the toolkit. An data object can be any kind of data, and you decide the level of abstraciton. Data objects are stored on a system. Processes are used to link data objects together, e.g. creating a copy of a data set. Processes can also be cyclic on a single data object, e.g. modifications to a dataset.