FAIR Science for Social Machines: Let's Share Metadata Knowlets in the Internet of FAIR Data and Services

In a world awash with fragmented data and tools, the notion of Open Science has been gaining a lot of momentum, but simultaneously, it caused a great deal of anxiety. Some of the anxiety may be related to crumbling kingdoms, but there are also very legitimate concerns, especially about the relative role of machines and algorithms as compared to humans and the combination of both (i.e., social machines). There are also grave concerns about the connotations of the term “open”, but also regarding the unwanted side effects as well as the scalability of the approaches advocated by early adopters of new methodological developments. Many of these concerns are associated with mind-machine interaction and the critical role that computers are now playing in our day to day scientific practice. Here we address a number of these concerns and provide some possible solutions. FAIR (machine-actionable) data and services are obviously at the core of Open Science (or rather FAIR science). The scalable and transparent routing of data, tools and compute (to run the tools on) is a key central feature of the envisioned Internet of FAIR Data and Services (IFDS). Both the European Commission in its Decla