All scientific disciplines are conceptually connected by the hierarchy of levels in nature (Anderson 1972). At each level, one observes emergent properties which can be characterized by the slogan “More is different.” as coined by Anderson in his seminal paper. This is also known as “The whole is more than the sum of its parts”. As an example, we might think of water molecules which are neither wet nor fluid before condensation. Typically, the organization of matter at a higher level is connected to a phase transition which breaks some of the original Hamiltonian symmetries.
Organization of complex systems at higher levels does not necessarily mean that more complex structures arise at the new level. On the contrary, a coarse grained description at the macro scale has quite often higher causal powers as a more detailed system characterization at the micro scale. This is the idea of causal emergence as introduced quite recently (Hoel 2017).
This idea has been extended (Ellis and Koppel 2019) describing a delicate balance of upward and downward causation from the micro to the macro level and back in a feedback loop resulting in causal closure. For example, changes in protein configuration in a stochastic environment enable logical cascades of system regulation feeding back on the macro molecular scale.
It has been argued that nature exists at a self-organized critical point to harness fluctuations in evolution (Gleiser et al. 2ooo). Intra and inter level coarse graining is thus reminiscent of renormalization group flow in interaction parameter space leading to universality classes characterized by the same fixpoint. Since experimental data are created by dynamic processes classified by the symmetry of their respective universality class, one expects to find a finite set of spatio-temporal network structures considering all possible data. This is the theoretical foundation to strive for a minimally heterogeneous research data management by considering universality classes of data structures derived from system universality.