Metadata uplift, machine learning and sustainable methods for metadata curation
|Coordinator 1||Mr Jon Johnson (CLOSER, UCL Social Research Institute)|
|Coordinator 2||Dr Suparna De (University of Surrey, Department of Computer Science,)|
The establishment of cross-European infrastructures, (European Question Bank, CESSDA Data Catalogue, SSHOC) and standards (DDI, European Language Social Science Thesaurus, Triple) to support FAIR data in the social sciences and humanities, will have a significant impact on the level, quality and interoperability requirements of metadata from studies to support discovery and reuse for both legacy data and future data collections.
Whilst there has been significant progress in the development of technical architectures and the establishment of standards, generating high quality content remains a challenge particularly in ealy capture of lifecycle metadata and the development of suitable training datasets.
Machine learning and allied technologies offer the possibility to assist both studies and infrastructures to uplift existing metadata and provide new automated methods to curate future metadata to sustain FAIR data infrastructures.
In this session we will explore the latest developments in automated and semi-automated metadata curation, to support FAIR data, reuse and interoperability.