Towards Metadata Completeness in National ETD Portals for Improved Discoverability

Towards Metadata Completeness in National ETD Portals for Improved Discoverability

Chisale, Adrian

Phiri, Lighton

Zambia has 61 registered Higher Education Institutions (HEIs) with the Higher Education Authority. In order to facilitate easy access to Electronic Theses and Dissertations (ETDs), works are underway to implement a National ETD portal. The diversity of the ETD source IRs poses a challenge in ensuring that ETD metadata is of high quality, with respect to the completeness, adversely affecting discoverability of ETD digital objects. This paper outlines a study conducted to identify HEIs with functional IRs; empirically assess the relative quality of ETD metadata from HEI IRs and investigate potential ways of identifying sources of missing metadata elements. Questionnaires were distributed to 61 HEIs in order to identify HEIs with functional and interoperable IRs. In addition, ETD metadata from HEIs with functional IRs was harvested using the Open Archives Initiative for Metadata Harvesting protocol and subsequently analyzed in order to assess the metadata completeness. Finally, a combination of document analysis of policy documents and, additionally, content analysis of randomly sampled ETD manuscripts from HEIs with functional IRs was conducted in order to identify potential sources of missing metadata. Out of 61 HEIs, only 10 (16.1%) of HEIs in Zambia had implemented functional IRs. The analysis of ETD metadata indicates that there is generally non-compliance of ETD metadata to the ETD-MS metadata standard, the de facto metadata schema for ETDs developed by the Networked Digital Library of Theses and Dissertations. In order to identify reliable sources of missing metadata elements, content analysis of policy documents was performed, alongside an analysis of randomly sampled ETD bitstreams. Potential sources of missing metadata from the ETD bitstream make it possible for automated extraction techniques to be employed to automatically generate missing metadata elements.

2023