Digital Objects – FAIR Digital Objects: Which Services Are Required?

Abstract

Some of the early Research Data Alliance working groups reused the notion of digital objects as digital entities described by metadata and referenced by a persistent identifier. In recent times the FAIR principles became a prominent role as framework for the sustainability of scientific data. Both approaches had always machine actionability, the capability of computational systems to use services on data without human intervention, in their focus. The more technical approach of digital objects turned out to provide a complementary view on several aspects of the policy framework of FAIR from a technical perspective. After a deeper analysis and integration of these concepts by a group of European data experts the discussion intensified on so called FAIR digital objects. But they need to be accompanied by services as building blocks for automated processes. We will describe the components of this framework and its potentials here, and also which services inside this framework are required. Necessary Abstractions in the Data DomainSeveral studies in relevant data analytic projects, for instance a survey of RDA Europe (RDA Europe 2019) from 2013, say that up to 80% of the time of experts working with data is wasted with data wrangling (i.e. making data ready for analytics). This suggests that only a high degree of automation based on simple structures can provide an alternative to this highly inefficient and error prone way of data handling.The major obstacle for automation is the heterogeneity and complexity of data and abstraction is a generic way to hide this heterogeneity and complexity by encapsulation and virtualization.By encapsulation details are hidden that are not needed at a specific layer. For instance at the data infrastructure layer there is no difference to be made between data, metadata, software, semantic assertions etc. All can be seen as some kind of data, for example as files in a filesystem, that is copied, changed or deleted. At that layer all operations do not distinguish between metadata and data, whereas on a data management and reuse layer a distinction is necessary and metadata must be used to govern the management operations on data.By virtualization one substitutes objects by their logical representation. The most abstract way of such a logical representation is the pointer that leads to the object, a classical and often used approach in Computer Science, hiding all complexity behind a pure reference to the object. With Virtual Machines for instance as another virtualization example one hides only the hardware, but still exposes most of the internal structure in the logical representation.

Grand Challenges in the Digitalisation of Wind Energy

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Canonical Workflows in Simulation-based Climate Sciences

“…Based on this, we present the CWFR modules we derived from it. We then take a critical look at the use of FAIR Digitial Objects (FDOs) [23,24] in the CWFR process with its possibilities and limitations. Finally, we summarise and give first solutions.…”

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“…Although essential, the methods and tools developed support machine actionability -capability of computational systems to use services on data without human intervention -in their focus (Schwardmann et al, 2020) making difficult their usability and understanding beyond the data scientist communities which limits a lot their use. Implementation of services and tools required to get FAIR compliant data can only be possible if scientists who are leaders of projects and non-experts in data science are able to understand the whole of the FAIRification processes, the stakes and the steps required.…”