FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Abstract

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

Canonical Workflows to Make Data FAIR

“…The above indicated paradoxes illustrate the challenges of generating a domain of FAIR compliant Digital Objects (FDO) [9] that are necessary for flourishing data science. The gap between practices on the one hand and workflow technology and FAIR compliance on the other hand is great and begging for new approaches.…”

“…• The best way to achieve FAIRness in workflow technologies is to request that those canonical workflow components that are available to be integrated into workflow solutions should support the concept of FDO [9], meaning that these components should be capable of both consuming and producing FDOs.…”

SWIRRL. Managing Provenance-aware and Reproducible Workspaces

“… https://jupyter.org/hub  https://www.ics-c.epos-eu.org/  https://dev.climate4impact.eu reproducibility via generic systems and protocols, is one of the core aspects of the FDO [3] architecture. These concepts are extremely relevant for the design and future refinements of our Web API, which aims at empowering Virtual Research Environments (VREs) with reproducible interactive services and traceable data management operations.…”

HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction

“…The intention of the FAIR (Findable, Accessible, Interoperable, Reusable) principle by Wilkinson et al [1] was not limited to data, but also targeted other Digital Objects (DO) [2], e.g., algorithms, tools and workflows that lead to data. The FAIR Digital Object (FDO) subsequently introduced by de Smedt et al [3] provides a framework to have transparent, reusable, and reproducible data [4]. The apparent benefit of reproducible science is that it becomes possible to restore results in a critical situation, increase transparency, trust, interest and the number of citations.…”