Not Ready for Convergence in Data Infrastructures

Abstract

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.

Canonical Workflows to Make Data FAIR

“…However, they will take time. This impression is supported by a recent study that offers insights into the work and plans of about 75 current research infrastructures initiatives [1]. We refer to two paradoxes mentioned in that study to be addressed by the concept of Canonical Workflow Frameworks for Research (CWFR):…”

“…There are also examples of well-used comprehensive workflow systems created to be used by laypersons [10] and ready-to-use systems generated with the help of Galaxy [11], for example. The above cited report [1], which is based on a broad analysis, clearly indicates however, that despite these exceptions more comprehensive workflows tackling the possible recurrences are widely lacking. The situation in industry is different, since recurring patterns in industry in general are much more rigid.…”

“…The increasing volumes of data on the one hand and the request for proper documentation to enable repeatability and reproducibility on the other requir e a change in the practices of data science laboratories [1] and a change in the role of researchers. The FAIR principles [2,3,4,5] have been developed as guidelines for changing data practices.…”

Comments to Jean-Claude Burgelman's article Politics and Open Science: How the European Open Science Cloud Became Reality (the Untold Story)

“…The landscape is highly complex and competitive, and until now researchers did not really play a primary role in these debates. In fact, deep insights indicate that researchers in the labs did not care yet about EOSC [9].…”

Revolutions Take Time

“…The starting point for EOSC was the agreement that it should be based on the FAIR principles and its architecture needs to be distributed. This led to an "epidemic" increase of projects that claimed to be FAIR compliant without proving correctness, i.e., the FAIR message was excellent to increase awareness about the proper treatment of data, but until now it had little impact on practices [22]. Due to the European construction, EC and member states had first to construct a governance system for EOSC and to not lose time, some working groups were initiated, which brought together national delegates making it impossible to separate technical and political motivations.…”