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).
Highlights
From about the turn of the millennium, it has become apparent that the rapid acceleration in the production of research data has not been matched by an equivalent acceleration in our access to all that data [1]
We propose to take the work on persistent identifiers a major step forward by encapsulating sufficient information about a dataset into a FAIR Digital Object (FDO)
We briefly discuss some of the current limitations that potentially impede the amplification of actionable knowledge production from the vast volume of newly produced and available data, and discuss the scientific value of FAIR Digital Objects as a unified data organisation model, in particular for data science across the boundaries of domains and disciplines
Summary
From about the turn of the millennium, it has become apparent that the rapid acceleration in the production of research data has not been matched by an equivalent acceleration in our access to all that data [1]. With “data” we do not just refer to published data, but to any data that has been created in research labs and lifted from its original workspace to a domain where it can be managed and shared. Created data and collected data reside in temporary workspaces. Most of the data to be shared with others in internal or external workflows will move to the registered data domain in which rapidly increasing amounts of data are being amassed and managed for reuse. A small fraction of those data will be formally documented and published so that it can be properly cited based on metadata and according to publishers’ requirements.
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