Answers are grounded in this paper and cite their sources.
This paper discusses current open data science and archive principles and issues and their applicability to speech and oral archives. Firstly, a definition of speech and oral archives is provided: they represent a rather tricky object of study not only in language related studies but also in the social sciences and humanities. Secondly, we introduce the FAIR principles for data science, which are now progressively being adopted in the humanities and in the domain of Language Resources, in connection with research infrastructures such as CLARIN and DARIAH. Finally we discuss some issues that may slow down the applicability adoption of FAIR principles for speech and oral archives, as well as current initiatives that may help in solving them.
No takes yet. Share an insight, caveat, or question.
Calamai et al. (2018) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: