Ready, Set, GO FAIR: Accelerating Convergence to an Internet of FAIR Data and Services © Erik Schultes Leiden University Medical Centre GO FAIR International Support and Coordination Office Poortgebouw N-01, Rijnsburgerweg 10, 2333 AA Leiden The Netherlands erik.schultes@go-fair.org © George Strawn Board Director Board on Research Data and Information (BRDI) US National Academies of Sciences, Engineering, and Medicine USA gstrawn@nas.edu © Barend Mons Leiden University Medical Centre GO FAIR International Support and Coordination Office Poortgebouw N-01, Rijnsburgerweg 10, 2333 AA Leiden The Netherlands barend.mons@go-fair.org Abstract. As Moore’s Law and associated technical advances continue to bulldoze their way through society, both exciting possibilities and severe challenges emerge. The upside is the explosive growth of data and compute resources that promise revolutionary modes of discovery and innovation not only within traditional knowledge disciplines, but especially between them. The challenge, however, is to build the large-scale, widely accessible, and automated infrastructures that will be necessary for navigating and managing the unprecedented complexity of exponentially increasing quantities of distributed and heterogenous data. This will require innovations in both the technical and social domains. Inspired by the successful development of the Internet and leveraging the FAIR Principles (for making data Findable, Accessible, Interoperable and Reusable by machines) the GO FAIR initiative works with voluntary stakeholders to accelerate convergence on minimal standards and working implementations leading to an Internet of FAIR Data and Services (IFDS). Keywords: analytics and data management, data intensive domains, digital libraries, FAIR Data, GO FAIR Initiative, Internet of FAIR Data and Services (IFDS). 1 Introduction Existing data stewardship practices are highly inefficient. Numerous studies indicate that data scientists both in academia and industry spend 70-80% of their time on mundane, manual procedures to locate, access, and format data for reuse [1,2]. Methodological legacies inherited from a pre-digital era (e.g., poor capture of metadata, broken links to various research assets) and outdated professional incentives (e.g., only rewarding publication of research articles rather than also datasets and other research outputs) contribute to massive data loss and a well-documented reproducibility crisis [3-5]. Coupled with the exponential increases in data volumes (driven by, among other things, high through-put instrumentation and IoT data streams) the urgency for automated, commonly usable data infrastructures (i.e., an Internet for Machines) is increasingly recognised by numerous national and international organisations, science funders and industry [6-11]. Despite the urgent need, building a generalised, ubiquitous, data infrastructure that is widely used by diverse stakeholders is an inherently distributed and difficult process to direct. Knowing this to be the case, the GO FAIR initiative was Proceedings of the XX International Conference “Data Analytics and Management in Data Intensive Domains” (DAMDID/RCDL’2018), Moscow, Russia, October 9-12, 2018 19