The Internet for Social Machines

The first talk of the afternoon panel was held by Barend Mons, co-leader of the Go FAIR initiative and Professor of Bio Semantics at the University of Leiden. He argued that data not only needs to be open, but also has to be stored according to the FAIR principles in order to have real impact, and even save lives. To back his claim, he gave the example of VODAN (Virus Outbreak Data Network) which processes FAIR data points for data-driven research. With this smart algorithm and FAIR (meta-)data, he and his project team were able to build a disease modelling workflow of Covid-19, using all the data, research, and real-life observations that were published worldwide. The model lists all genes, proteins, organs, etc. that are involved in the process and links them to existing drugs that are effective for individual symptoms. Thus, the model can predict which drugs could potentially be effective as a cure at different stages of the disease; however, it is always humans who decide whether or not a specific drug is actually a viable option.

A particular advantage of this algorithm is its ability to detect which part of published papers is repetition and which is new. Nevertheless, the development of this extremely promising model took three months. It could it have been developed much faster if the structure of an «internet of FAIR data» would have already existed and Open Science practices would be more prevalent. Mons therefore strongly urged the participants to be aware of these practices, store their data, and publish according to FAIR standards. He added that good practice also includes having a professional data steward in every research team to ensure that the shared data is reusable for both humans and machines.