The introduction of a new technology or innovation is often accompanied by “ups and downs” in its fortunes. Gartner Inc. defined a so-called hype cycle to describe a general pattern that many innovations experience: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. This article will compare the ongoing introduction of Open Science (OS) with the hype cycle model and speculate on the relevance of that model to OS. Lest the title of this article mislead the reader, be assured that the author believes that OS should happen and that it will happen. However, I also believe that the path to OS will be longer than many of us had hoped. I will give a brief history of the today's “semi-open” science, define what I mean by OS, define the hype cycle and where OS is now on that cycle, and finally speculate what it will take to traverse the cycle and rise to its plateau of productivity (as described by Gartner).

Much research is dependent on Information and Communication Technologies (ICT). Researchers in different research domains have set up their own ICT systems (data labs) to support their research, from data collection (observation, experiment, simulation) through analysis (analytics, visualisation) to publication. However, too frequently the Digital Objects (DOs) upon which the research results are based are not curated and thus neither available for reproduction of the research nor utilization for other (e.g., multidisciplinary) research purposes. The key to curation is rich metadata recording not only a description of the DO and the conditions of its use but also the provenance – the trail of actions performed on the DO along the research workflow. There are increasing real-world requirements for multidisciplinary research. With DOs in domain-specific ICT systems (silos), commonly with inadequate metadata, such research is hindered. Despite wide agreement on principles for achieving FAIR (findable, accessible, interoperable, and reusable) utilization of research data, current practices fall short. FAIR DOs offer a way forward. The paradoxes, barriers and possible solutions are examined. The key is persuading the researcher to adopt best practices which implies decreasing the cost (easy to use autonomic tools) and increasing the benefit (incentives such as acknowledgement and citation) while maintaining researcher independence and flexibility.

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from existing implementations, or when they spot a gap, accept the challenge to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle.