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10. The Risk of Losing the Thick Description

Data Management Challenges Faced by the Arts and Humanities in the Evolving FAIR Data Ecosystem
Authors: Erzsébet Tóth-Czifra;

10. The Risk of Losing the Thick Description

Abstract

This chapter explores some of the aspects underlying the domain-specific, epistemic processes that pose challenges to the FAIRification of knowledge creation in arts and humanities. Tóth-Czifra argues that the FAIR principles (findability, accessibility, interoperability, and reusability) have been designed according to underlying assumptions about how knowledge creation operates and communicates. This causes issues in productive reuse of digitised cultural heritage resources and legal barriers can prevent institutions from sharing metadata online, which can further skew research towards what is easily available and free to find online. However, standardisation of shared metadata can also have epistemological challenges and affect the systems of discovery and knowledge creation — a price which Tóth-Czifra argues is too high. She argues that in order to be truly reusable, data should achieve autonomy from their curator, and by bringing scholarly communication, data sharing and academic publishing together, we can reach a more sustainable research data management ecosystem. Relying on domain-relevant community standards as well as increasing the social life of data is critical to avoid having deposited datasets being buried in isolated ‘data tombs’.

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Microsoft Academic Graph classification: Thick description Engineering business.industry Data management business Data science The arts Data ecosystem

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    1
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
hybrid
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