One cornerstone of the Science 2.0 approach is the framework for making Big Data manageable. In fields from physics to biology, it's no longer a question of obtaining data, but managing it in ways that are relevant.

It's been problematic in science just as it has been in business and the public sector because relationships between the different parts of a network have been represented as simple links, regardless of how many ways they can actually interact, and that results in a loss of valuable information in science.

The Science 2.0 approach has advocated representing multilayer networks as piles of 'layers' with each one representing a different type of interaction - an example could be social, technological and biological systems. This approach allows a more comprehensive description of different real-world systems like societies, but has the drawback of requiring more complex techniques for data analysis and representation. 

One method, developed by mathematicians at Queen Mary University of London and researchers at Universitat Rovira e Virgili in Tarragona borrows from quantum mechanics' well tested techniques for understanding the difference between two quantum states, and applies them to understanding which relationships in a system are similar enough to be considered redundant. This can drastically reduce the amount of information that has to be displayed and analyzed separately and make it easier to understand. 

The researchers applied their method to several large publicly available data sets about the genetic interactions in a variety of animals, a terrorist network, scientific collaboration systems, worldwide food import-export networks, continental airline networks and the London 

Dr Vincenzo Nicosia, co-author of the paper from Queen Mary School of Mathematics, said, "We've been trying to find ways of simplifying the way big data is represented and processed and we were inspired by the way that the complex relationships in quantum theory are understood. With so much data being gathered by companies and governments nowadays, we hope this method will make it easier to analyze and make sense of it, as well as reducing computing costs by cutting down the amount of processing required to extract useful information."

The new method also reduces computing power needed to process large amounts of multidimensional relational data by providing a simple technique of cutting down redundant layers of information, reducing the amount of data to be processed.