FAIR_Data | minima.media

Open Data is no longer sufficient. Digital knowledge resources should now be provided in a 'fair' manner, findable, accessible, interoperable and reusable.

The FAIR Data principles set clear criteria and measures for the sustainable provision of digital knowledge resources. They were published in 2016 and are now a globally recognized standard. And those seeking funding for a project are increasingly being asked to present a concept on how to concretely implement the principles.

The problem: In many areas, there is still a lack of practical technologies and tools to prepare digital content in a fair-compliant manner. This makes it difficult to implement the principles in practice.

Findability

Knowledge resources should be reliably findable through unique and persistent addresses (persistent identifiers) and through rich metadata descriptions.

Accessibility

Knowledge resources should be provided in a way that all content is usable without specialized technologies or tools. Content that cannot be made accessible should be described with metadata. Ideally, they are made accessible through an authentication and authorization procedure.

Interoperability

Knowledge resources should be prepared in a way that they can be interconnected, based on a common and widely applicable language for knowledge representation.

Reusability

Knowledge resources should be shared in a way that they are versatilely reusable. They should contain a unique usage license and detailed provenance information. Their content (data or metadata) should comply with relevant community standards in their field.

8 years of FAIR Data. And a disastrous record

The FAIR Data principles were a long-overdue step in the right direction. But in many areas, there is still a lack of technologies and tools to prepare research results or educational content in a 'fair' manner. Thus, they continue to be shared in formats that do not comply with the FAIR Data principles. A brief look at the relevant archives and repositories paints a sobering picture.

On Zenodo, most data is uploaded as PDFs, a format that is the least interoperable and reusable. In our sample on 8.3.2023, 1,229,369 of the 2,780,012 uploaded documents were simple PDFs (44.22%). Documents in XML format, which would comply with the principles, are at the bottom of the list (19,716 documents), representing only 0.71%.

In dedicated specialist repositories, the situation is no better. On SwissUbase, a new specialist repository for linguists, who are expected to have significant expertise in handling metadata, few research data are deposited with metadata. Searching the archived projects for deposited research data, it turns out that only 463 of the 12,088 projects (as of 17.3.2023) contain any research data. That is only 3.86%. And even among them, it is not certain whether they are enriched with metadata in accordance with the FAIR Data principles.

Interoperability and reusability of digital knowledge resources are certainly high demands that are not easy to meet. But even in terms of findability and accessibility of knowledge resources, the two principles that are relatively easy to fulfill, significant problems are evident.

A study* found that, among more than seven million scientific publications analyzed, more than a quarter of the addresses (DOIs) are not properly archived and preserved. The preservation of knowledge does not seem to keep pace with its increase, and the gap is becoming increasingly evident.

* S. Wild, Millions of research papers at risk of disappearing from the Internet

Interoperability and Reusability

The FAIR Data principles rely on a harmonizing concept. They aim to achieve interoperability and reusability through a uniform language of knowledge representation. However, the reality in research and education is different. It is characterized by a variety of languages, formats, and standards that cannot really be unified.

Do the FAIR Data principles reflect digital reality? Or are they based on an outdated technical concept? Interoperability and reusability can also be achieved in other ways, without having to agree on uniform standards.

We are working on intelligent solutions that enable the integration of research results and educational content from heterogeneous sources.

Metadata Support

A key prerequisite for the sustainable preparation of digital knowledge resources is their careful indexing with structured metadata. This is an extremely demanding task with many challenges – perhaps the main reason why FAIR Data is progressing so slowly.

We are developing guided authoring solutions that significantly facilitate working with metadata. They can be directly integrated into authoring or publishing tools and provide step-by-step guidance in creating research or educational resources that comply with the FAIR principles.

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